esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Scroll Expander  
The isentropic efficiency of the scroll expander is shown in Figure 6. Most data are in the range of 40% to 55% in the range of the pressure ratio around the expander BVR. Therefore, the under-expansion and over-expansion losses may not affect the isentropic efficiency much. However, the isentropic efficiency is slightly reduced in higher expander rotational speeds corresponding to higher pressure ratios, in which the under-expansion losses occur.  
The expander shaft power is presented in Figure 7, showing that the scroll expander produced up to a maximum of 450 W approximately in a range of relatively low shaft speeds. Higher shaft power is produced at higher shaft speeds, while the isentropic efficiency is penalized as mentioned. In addition, the shaft power increases slightly in higher pressure ratios.  
Similar to volumetric pumps, the volumetric efficiency of volumetric expanders is directly associated with the internal leakage. It is well-known that the filling factor (_FF_) strongly depends on the expander rotational speed and weakly on the inlet pressure of the expander. As shown in Figure 8, the same is observed here. Relatively high values of the filling factor at low shaft speeds indicate significant leakage at these working conditions.  
Figure 5: Refrigerant pump volumetric efficiency with the flow rate.  
Figure 6: Scroll expander isentropic efficiency with pressure ratio (PR) (**left**), and with shaft speed (**right**).  
In general, it is expected that the _FF_ is always higher than unity due to the inevitable internal leakage. Indeed, _FF_ < 1 means the measured flow rate is lower than the theoretical flow rate of the expander. However, it is unlikely that a volumetric expander produces power while its cambers are not filled by the gas. This effect was observed in several experimental studies on scroll expanders and is defined as \"under-filling\" of the suction chamber [10; 14]. Nevertheless, the authors believe that _FF_<1 is because the suction density of the expander was calculated using the temperature and pressure measured at the expander suction pipe, while the real gas density that enters the expander chambers may differ considerably due to the pressure drop between the expander suction pipe and the suction port of the scroll revolute. Therefore, a conclusion of under-filling should be deduced with caution.
## 4 Discussion  
### Motion Tracking Verification  
Two types of verification tests were conducted to show the reliability of the presented IMU measurements. The rotation angle of the IMU was tested by a controlled stepper motor, and an average mean absolute error of 1.485 degrees was observed. The three parameters \(\alpha\), \(\beta\), and \(P_{\mathit{wrist}}(z)\) of the presented system were compared with the M system, and the root mean square errors of 5.654\({}^{\circ}\), 4.385\({}^{\circ}\), and 2.434 cm were found, respectively. A mean absolute error of 3.88 cm was reported in [24] in which the wrist positions of elbow flexion-extension (E\({}_{\text{FE}}\)) measured by nine-axis sensors were compared with the optical motion capture (OMC) system. These results show that the measurements obtained both from the presented system and from [24] provide an acceptable motion tracking within an engineering standard. The waveforms between the M system and the presented system, as shown in Figure 11, have similar trends. However, there is a slight offset between the angular value and the vertical axis (z-axis), which might be caused by the inherent sensor configuration error. Because the size of each person's arm is different, when the user wears sensors on his/her arm, the physical offset occurs. The sensor spindle and the arm orientation vector (\(\overrightarrow{v}_{\mathit{upper}}\) and \(\overrightarrow{v}_{\mathit{ forearm}}\)) cannot be perfectly parallel to each other, and the error of this angle will affect the results of the subsequent computation. The muscle changes in the arm during exercise also cause the IMU data worn on the upper arm to drift dynamically. Moreover, it is still not possible to make the plane of arm movement exactly parallel to the lens surface (the M system is based on a two-dimensional plane), and the arm joint marker point of the M system will have uncontrollable dynamic shift during the movement. Therefore, to improve the reliability of the system, we can focus on eliminating the error of the angles \(\alpha\) and \(\beta\). The sensor configuration error can be eliminated by comparing the developed device with more reliable reference system (e.g., Vicon 3D motion capture system) and then calibrating it using computer software to improve the motion tracking algorithm. The experimental results show that there are inherent system errors in both systems. Although the experiments have been designed to minimize the impact of system errors on the experimental results, it is still impossible to completely eliminate the system errors.
## 5 Discussion  
This study, based on dimensional analysis and elastic net regression, developed a prediction model for the cutterhead rotation speed of tunnel boring machines (TBMs). Using real-world engineering data and excavation parameters, the model aims to enhance construction efficiency and safety. The following points are discussed:  
1. In this study, we explored the factors influencing the cutterhead rotation speed of tunnel boring machines (TBMs). By identifying the key parameters affecting cutterhead speed, operators are empowered to quickly diagnose issues and adjust parameters accordingly when problems arise. This ability to quickly pinpoint and address the factors impacting cutterhead performance not only enhances operational safety but also improves overall construction efficiency. For example, when encountering unexpected ground conditions or equipment malfunctions, operators can refer to the model's predictions to identify whether the issue stems from inappropriate rotation speed or other operational parameters. By adjusting the cutterhead speed based on real-time feedback, operators can prevent potential cutter fatigue, minimize rock disturbances, and avoid machine downtime, ultimately optimizing the tunneling process and ensuring timely project completion.
2. High-quality data are essential for accurate predictions. This study utilized numerical filtering, boxplot analysis, and low-pass filtering to effectively reduce noise and outliers, improving data reliability. Future work could further enhance data quality by improving sensor precision and integrating anomaly detection and data imputation techniques.
3. Despite the promising results, the study has the following limitations: (1) The dataset was sourced from a single project, requiring validation under diverse geological conditions. (2) The model's ability to dynamically adjust parameters in real time has not been tested.
4. Despite the encouraging results of this study, future research should focus on the following aspects: (1) Dynamic real-time prediction: Develop a model that can dynamically adjust the cutterhead speed in real time according to tunnel conditions, combined with a real-time monitoring system to cope with unforeseen operational challenges. (2) Predictive maintenance: Integrate machine learning models to predict TBM downtime and maintenance needs, proactively schedule maintenance and reduce downtime by identifying abnormal patterns in the data. (3) Cross-project and geological validation: Apply the model to different projects and geological conditions for validation to enhance the versatility and accuracy of the model.
# Influence of Uneven Subgrade Frost Heave on Deformation and Damage of CRTSIII Slab Track
## 3 Computational Model  
### CRTSIII Slab Track Numerical Model  
From top to bottom, the CRTSIII slab track was composed of a rail, fasteners, a precast slab, self-compacting concrete (including limit lug boss), and a concrete base (including limit groove). The rail was CHN60, and the model adopted was a solid unit. The grid unit type was C3D8R. The fasteners were simulated as spring-damping elements with a spacing of 0.63 m. The static stiffness of a fastener was 35 kN/mm. In the damage plastic model, the precast slab, self-compacting concrete, and concrete base were simulated by solid element and the grid element type was C3D8R. The former adopted C60 concrete, while the latter two adopted C40 concrete. Since the reinforcement mesh affected the damage plastic behavior of the concrete, a two-node truss unit (T3D2) was used to simulate the reinforcement mesh in the three components. The co-node coupling treatment carried out with the concrete and the prestressed ribs in the precast slab were realized by cooling method.  
In terms of model size, the longitudinal length of the CRTSIII slab track was 34 m. Considering the lateral symmetry of a subgrade structure, a 1/2 model was adopted for modeling and the symmetric boundary was set. The half width of the top surface of the subgrade was 6.8 m. According to the calculation method of equivalent bending stiffness in the literature [29], it can be seen that the bending stiffness of the subgrade surface was 12.69 Mpa and that of the whole track was 710.45 Mpa.  
The precast slab was bound to the self-compacting concrete to simulate the strong constraint of the portal reinforcement between the two to form a composite slab. A rubber cushion layer was inserted between the four sides of the limit lug boss and the groove, and in addition to the four sides of the limit lug boss, a geotextile isolation layer was inserted between the self-compacting concrete and the concrete base. The penalty function was used to simulate it. Underneath the concrete base was a subgrade structure. A hard contact was used to simulate the contact relationship between them. Adhesion was not considered, only the supporting effect of the subgrade on the base was considered. The supporting stiffness of the subgrade surface to the track structure was 76 Mpa [30]. The subgrade was fixed at its base. Based on this, the CRTSIII slab track structure was established, as shown in Figure 5, and the simulation model parameters are shown in Table 1.  
Figure 4: Stress/damage factor-inelastic strain relationship: (**a**) C60 uniaxial tensile; (**b**) C60 uniaxial compression.
## 2 Materials and Methods
### Methods of Analysis  
#### 2.2.1 Data Processing  
To be consistent with the time span of IMERG precipitation data, hourly precipitation observations from the ISD, CMA, and USCRN datasets between 2001 and 2020 were selected as validation data. To assess IMERG data quality across different global regions, the precipitation amounts from previous hour to the current hour recorded in Coordinated Universal Time (UTC) were chosen as the observations at this hour. Due to the maintenance of equipment at meteorological stations as well as their spatial distribution and relocation across different regions, a proportion of effective observations throughout the year of no less than 90% was required to ensure data consistency and reduce sampling errors. The IMERG product with a temporal resolution of 0.5 h was added for the hourly retrieval of precipitation data.  
Because the precipitation observations were collected at meteorological stations, and the IMERG products were available at the 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) grid, the gauge observation datasets (i.e., ISD, CMA, and USCRN) were matched with the grid in GPM IMERG based on the station location to avoid the errors introduced by any interpolation. Specifically, if only one station fell into a given 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) grid, the precipitation records from that station were compared with those from the IMERG grid. If a grid contained two or more stations, the average precipitation metrics obtained from all stations within the grid was used as the observation for that grid [12; 47]. The spatial range of IMERG data considered in this study was between 60\({}^{\circ}\)N and 60\({}^{\circ}\)S, and stations beyond these latitudes were not included. A regional analysis among different continents was conducted based on previous studies, as shown in Figure 1[56; 52]. Due to the limited availability of ground station data, the analysis focused on regions with a relatively large number of stations for validation, specifically
# Stability Analysis of GNSS Stations Affected by Samos Earthquake
## 1 Introduction
The North Anatolian Fault (NAF), East Anatolian Fault (EAF), Oludenzic Fault, and Cyprus and Hellenic arcs are the main active faults in Turkey [20]. North-south extension due to the subduction zone in the Hellenic Arc causes Aegean fault systems to fail, like the north-dipping E-W trending fault of the Samos earthquake, which happened on 30 October 2020 [21; 22]. The Mw 7.0 earthquake that happened offshore Samos Island (Greece) hit the eastern Aegean Sea and the vicinity, especially Izmir city (Turkey), and caused loss of lives and a lot of damage. Furthermore, this devastating normal-faulting earthquake led to landslides, tsunamis and surface displacement. Characteristics of the Samos earthquake were analyzed by researchers using seismic data [23; 24], a combination of InSAR and GNSS [25] and joint seismological and geodetic data [21; 26; 27; 28; 29; 30]. Papers that include coseismic surface deformations derived from GNSS data generally used continuous stations [21; 25; 26; 27; 28]. Several research papers contain both survey and continuous GNSS data [29; 30]. Here, I only used continuously operating reference stations to calculate coseismic deformation, and the result is compatible with the literature. In this paper, a network of continuous stations located approximately 200 km around the earthquake epicenter was processed, and the stations that have significant coseismic offsets were identified with ordinary least squares (LS) S-transformation, also called the \"Helmert-transformation\" [7]. There is no need to perform a global test before S-transformation here to see if the network has undergone deformation [3] because an earthquake with a magnitude of Mw 7.0 is enough to slip the Earth's surface by meters. Therefore, S-transformation is used with two approaches, the statistical test approach and the threshold approach, to identify unstable continuous GNSS stations. In this paper, I used the method that was developed by [PERSON] et al. [16] to detect GNSS stations that have significant coordinate deformation in an earthquake's vicinity.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 4 Implementation of the V2x Data Dissemination over diSRsVN and Its Results
### Routing Overhead of Packet Dissemination over 5G V2X Network Protocol  
We perform a process of data dissemination (speed data in RSU) to a cloud application server through the core network; the core network is based on both LTE-V2X and 5G core network.  
Figure 6 shows the routing overhead with varying vehicle densities. The proposed 5G V2X networks' routing overhead increases with traffic density. This is because establishing a cluster head near the source vehicle increases the routing messages while distributing packet responses to the destination node. As a result, the input topology is routed to SD-SmCH flow entries in the distributed small cell RSU controllers, resulting in fewer beacon messages exchanged between the source vehicle and the small cell RSU. However, when compared to the use of the 5G V2X core network, the SDN-based approach, aided by distributed small cell controllers and the central controller, is the foundation of the lower routing overhead. The small cell RSU node communicates with the SDN controller only if the flow entries in the distributed small cell RSU controller do not match the routing rules. The more distributed local controllers use the input topology to construct the route information, the lower the link failure by lowering the routing overhead. The routing  
overhead of the proposed architecture assisted with the 5G core network is 22.15 at the vehicle of 80, whereas it is 24.44 for LTE-V2X communication.
## 3 Results and Discussion
### Screening Analysis (ELISA) Results  
#### 3.1.1 Selectivity and Sensitivity  
The data reported by the kit producer indicated that the T-2/HT-2 kit presented a high selectivity for the target toxins, with specificities corresponding to 100% and \(\sim\)85% for the HT-2 and T-2 toxins, respectively. For the estimation of method selectivity, a total of 20 cereal samples (wheat, maize, composed feed, barley, and oat; four samples each) was analyzed, and the absence of two toxins was verified (concentration lower than limit of detection indicated by the kit producer: 3.0 \(\upmu\) kg\({}^{-1}\), corresponding to 75 \(\upmu\) kg\({}^{-1}\) in matrix). Though four different types of samples were characterized by different compositions in terms of fat, anthocyanin, soluble and non-soluble fiber, as well as other grain constituents that may have interfered with the extraction of mycotoxins, the same procedure of sample preparation could be used. This was in accordance with the procedure declared by the kit manufacturer in a related technical sheet. Thus, these samples were considered \"blank\". The same samples were then fortified at a concentration of 250 \(\upmu\) kg\({}^{-1}\) for the HT-2 toxin and then analyzed in a short time within two analytical sessions. Each sample was analyzed twice, and the mean value of two measurements was used in data processing to report the relative absorbance, B/B\({}_{0}\) (%). The resulting data are presented in Table 2. The statistical evaluation was assessed by variance analysis (one-way ANOVA), [PERSON]'s exact test, and [PERSON]'s \(t\) test (\(p<0.05\)) for paired data between the signals of ten blank samples and related fortified samples. For both sessions, the t critical value (Stat t) was higher than the experimental (one-tail t critical), so the difference between the blank samples and the corresponding fortified samples was verified as significant (\(p<0.05\)). For both sessions, the fortified samples showed B/B\({}_{0}\) (%) values below the trust semi-interval (65.10 and 65.40 for the first and second session, respectively). Thus, all 20 fortified samples were recognized as \"contaminated.\" Hence, the \(\upbeta\) error was satisfied, and the detection limit was assessed at 75 \(\upmu\) kg\({}^{-1}\).
## 3 Analysis of Measured Data Trend Jamming Signals  
Shifting of the reference zero-line of the strain parameter curve is a phenomenon characterized by the change in the output strain signal being non-zero when the change in the input load signal is zero, caused by environmental factors and the test system itself during strain testing. A strain bridge, consisting of a strain gauge sensing element, reaches an equilibrium state in its output when the aircraft is subjected to static conditions on the ground and no external forces are applied. Under these conditions, the data output recorded by the data acquisition system is set as the reference zero-line, which should not present a varying trend. However, as shown in Figure 6, the zero-drift phenomenon of strain as the wing test bridge operates has a very serious effect on the final test results.  
Since the frequency of trend drift changes is relatively low, conventional methods for mitigating trend drift typically employ low-frequency filtering. However, this approach can inadvertently filter out genuine, slowly varying load signals, leading to inaccuracies in the analysis results. Alternative methods such as moving average filtering and least squares trend curve fitting may also be used to address this issue. In this study, the drift trend line is fitted by taking points in sections and subtracting the value of the drift trend line from the measured data in order to reduce the effects of drift on the subsequent analysis. Compared to the sliding average filtering method, this method demonstrates greater efficiency in eliminating drift trends from strain signals, thereby enhancing the reliability of the data.  
If the data measured in this test were calculated directly as actual loads without removing the trend terms, large errors would be introduced. The fatigue test spectrum compiled on this basis would not accurately reflect the true wing loads, considering that it would be impossible to improve the test method or equipment to make measurements again due to the constraints of objective conditions, and existing measured data would have to be used, which would need to be processed accordingly. The process of analyzing and processing the measured data is the process of eliminating the false signal while preserving the true signal; that is, the drift trend distortion caused by the measurement process should be eliminated, while the loading state curve of the aircraft wing in the real working environment should be retained.
## 4 Discussion
### RQ1: Which Electrophysiological Signals May Index Emotional Processing?  
#### 4.1.1 Electrophysiological Measurements and Analysis  
Twenty-eight studies analyzed EEG signals. Each of them executed different conditions for data acquisition. Nonetheless, the most frequent electrode montage was 32 electrodes with a sampling frequency of 500-1000 Hz and a bandwidth of 0.1-30 Hz. Some authors analyzed EEG signals by frequencies to notice increases or decreases between specific conditions, such as [51], where different ER techniques were assessed by comparing power within five frequency bands. Nevertheless, many others considered that low-frequency bands were related to motivational systems and emotional processes [60]. Specifically, theta and beta bands were associated with emotion recognition [39] and ER [56].  
To study emotion recognition and regulation independently, some authors have used ERP. The former was generally examined with early ERP components, around 100-300 ms, and the latter with LPP. This latter one was the most standardized feature to study ER. A decrease in the amplitude of the LPP was a reliable indicator of the regulation of emotional arousal and was sensitive to different ER strategies [34].  
The second most used technique was ECG. Features extracted from ECG for the study of emotions that reported significant results according to statistical analyses were RSA amplitude, R-R interval characteristics such as the mean and inter-beat R-wave interval, and features of high-rate variability in high-frequency range [29; 33; 36; 44; 50; 61; 62]. An increase in the high-frequency component of HRV, which can be derived from the R-R interval, was determined as a characteristic feature of good ER. RSA reflects the parasympathetic activity, which is part of the high-frequency component and is a reliable index of ER. Nevertheless, a trend in electrophysiological responses was not observed in the revised studies. Further research is needed to determine a correlation between extracted features of cardiac activity and emotions if existent.  
Changes in EDA reflect sympathetic activity in the autonomous nervous system. Although EDA is not the primary electrophysiological recording technique for studying the relationship between cognitive emotional behavior and physiological responses, it provides a clue about changes in sympathetic arousal. The SCR was the most analyzed feature in the characterization of emotions. SCR reflects changes in arousal perception [32; 54].  
On the other hand, EOG is still being explored for studying emotions. In particular, the global velocity of eyeball movement was reported in only one study. Quicker eyeball movement in different targets of happiness, disgust, and sadness for participants with bipolar disorder type I was observed, relating this feature as a marker for emotional to monitor therapies. An increase of this feature was linked to the need for motor activity as a strategy to manage aversive conditions [36].