esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Characteristics of Urban Rail Train in Multivehicle Marshaling under Traction Conditions
###### Abstract  
In recent years, urban rail transportation has rapidly developed in China and become one of the most important modes of travel. Most existing studies on the dynamic characteristics of urban rail trains have been based on single-section trains, and there have been fewer studies on marshaling urban rail trains that incorporate traction transmission systems. The dynamic performance of each carriage directly affects the operational reliability and even the running safety of urban rail trains. For this reason, in this paper, a marshaling urban rail train model with a traction transmission system was established and its accuracy was validated by field tests. This dynamics model enables the consideration of the coupling interactions between the gear transmission motion, the vertical, the lateral and the longitudinal motions of the vehicle. First, the model accuracy was validated by field tests. Then, the relationship between the motor torque and the running time of the urban rail train under traction conditions was calculated. Finally, the dynamic performance of each car of the marshaling train was studied. The research results show that there is a clear difference between the dynamics of the motor car and the trailer, and that the motor car is significantly inferior to the trailer. Among the four motor cars, the dynamic performances of the first and last moving cars were worse than those of the other motor cars. Among the two trailers, the trailer at the back was worse than the trailer at the front. The traction transmission system has a greater impact on the vertical and lateral vibration of the train bogie frame and wheelset, but the impact on the vibration of the car body is negligible. This paper provides theoretical support for the research one train dynamic performance optimization and operation safety.  
urban rail train; multivehicle marshaling; traction transmission system; traction condition; dynamic characteristics
## 3 OCC Performance Improvement
### MIMO Functionality  
Each pixel in an image sensor acts as a photodetector. Due to the nature of the imaging lens, optical signals coming from different directions are imaged in different locations onto the image sensor, which can be utilized as a multiple optical signals receiver. This particularly facilitates the utilization of multiple-input multiple output (MIMO) functionalities in OCC. An OCC-MIMO system is depicted in Figure 7. As illustrated, an array of LEDs can transmit signals simultaneously. As long as the LEDs are positioned inside the AOV of the camera, the projected LED images can be spatially separated from each other. The pixels in the projected images for each LED can be considered as groups that are then separately extracted using image processing and computer vision algorithms.  
The performance of an OCC system can be improved significantly using MIMO functionalities. MIMO enhances data rates through spatial multiplexing and provides multiple access conveniences [15]. [PERSON] et al. [13] demonstrated that the communication distance can be significantly increased by employing MIMO using RGB LEDs. Furthermore, by introducing spatial redundancy, the BER can be minimized to a great extent.  
Figure 6: An illustration of the minimum LED area required to decode data bits.  
An OCC-MIMO tandem is exploited to reduce interference. The images of the LED transmitter, neighboring light sources, and other noise elements are projected at different pixels that are separated afterward, resulting in an excellent SINR for OCC.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results
### Dryland Areas in China
#### 3.1.3 Region Analysis  
We separate China into seven regions as in the figure and the dryland areas are mainly located in Northwest (NW), North China (NC), Northeast (NE) and Southwest (SW) (Figure 6). From Figure 7, we find that, in all dryland areas, CC contributes the most positive effects around 48.3%. This suggests that the vegetation in Chinese drylands is very sensitive to variations in temperature and precipitation, which is in agreement with [PERSON] (2021) [36]. The CO\({}_{2}\) fertilization effects contribute about 38.6%. There are differences between regions. For NC, the dominant factor is CO\({}_{2}\) accounting for 34.5%. LU and CC contribute almost the same effects (about 28.9% and 27.6%). For SW, CC has the most key role, accounting for about 61.0%. CV has negative effects, here accounting for about 9.2%. For NW, CC also has the most import role in vegetation growth (49.2%). From the figure, we conclude that the change of vegetation growth is affected by anthropogenic activities.  
Figure 5: Map of three dominant factors distribution in Chinese dryland areas. The areas controlled by CO\({}_{2}\) are shown in red. The areas controlled by LU are shown in green. The areas controlled by CC is shown in blue.  
Figure 6: Regional separation for analysis of China. Chinese dryland areas mainly locate in Northwest (NW), North China (NC) and Southwest (SW).
# Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources
## 2 Simulation Model and Study Cases
### Drop Control of the DER Units
In the simulations, two different _Pf_-droopsfor PVs and BESSs with smaller and larger dead-zones were utilized (Figure 2d). The _Pf_-droops smaller dead-zones are used during summer time (PV/BESS _Pf_-droop 1, Figure 2d) when there is typically also less inertia in the power system and need for more rapid response from DER units to support the power system frequency. The used _Pf_-droops are also more sensitive than those required by the grid codes, because the idea is that the PV and BESS units could offer participation in frequency control as a flexibility service, for example, through a flexibility marketplace. The implementation of PV unit's active and reactive power droop control functions in the used PSCAD model is shown in Figure 3 (see also Figures 1 and 2). From Figure 3 it can be seen how the active and reactive power controllers with respective droop functions (_QU_-, _PU_-, and _Pf_-droops) will create the active and reactive power references (_P_ref_, _Q_ref_) which are further processed in order to have current reference values (_I_d_ref_, _I_q_ref) for the current controller. The current controller (Figure 3) then gives the voltage reference values (_V_ref_) for the controlled voltage sources in the PSCAD model. In the simulations it was also found out that the mutual effects of _Pf_- and _PU_-control functions on PV 2 (Figure 1) during over-frequency support participation need additional blocking logic (_PU_-blocking in Figure 3) in the control system. Otherwise, the active power of PV unit PV 2 needs to be curtailed and it cannot participate in frequency control due to mutual interaction of _Pf_- and _PU_-control functionalities. In the PSCAD simulations, the control functionality of BESS units was very similar when compared to the PVs (Figure 3). Figure 2: (**a**) _QU_-droop settings of PV units, (**b**) _QU_-droop settings of BESS units, (**c**) _PU_-droop settings of PV and BESS units, and (**d**) _Pf_-droop settings of BESS units which were used in the PSCAD simulations (see also Figures 1 and 3).
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
## 3 Theoretical Background
### 5G/NR Radio Interface and DMRS Configuration
Therefore, in Section 4, Doppler shifts will be analyzed in detail to see possible negative effects for individual use cases and to determine the possibilities for optimizing the configuration.
## 2 D2D Communication System Model
### Battery Level Model  
This section describes the way battery power level of a device affects its transmission power. We assume that the mobile devices can get information of battery level of other devices in the network. In case of a cellular network, battery level of devices can also be incorporated along with other signalling information, from devices to the BS. The BS then sends the battery information to the source along with other information required for D2D communications. Therefore, the source device is assumed to know the battery power level of devices within its communication range.  
We consider that relay mobile devices in the network do not always have full battery power. The relay devices are modelled to have battery power levels ranging from 10% to 100% at an interval of 10. All relay devices are equally likely to have any of the battery levels.  
We assume that the transmission power of a relay device also depends upon its battery level. The relay device transmits at a power proportional to the social trust (according to Equation (1)) only when its battery level is greater than a battery threshold (\(B_{\textit{fltrs}}\)). Otherwise, the relay device transmits at a low power possible (\(P_{\textit{min}}\)).  
Next, we present the communication link model used in this paper for the throughput calculation.
## 1 Introduction
These energy consumers are then classified into two categories: static and dynamic. Static energy consumers are systems with energy consumption that remains relatively constant over time. For these systems, direct energy monitoring is not necessary, as their consumption can be estimated with reasonable accuracy based on their operating and idle times. On the other hand, dynamic energy consumers, whose energy usage varies depending on the load, require direct energy monitoring to accurately measure and analyze their consumption patterns. This distinction ensures that monitoring resources are allocated effectively, enabling M-ACE to optimize energy usage and improve overall efficiency. Deploying Industry 4.0 technologies to monitor energy consumption in the dynamic energy consumers at M-ACE is highly dependent on the condition of the equipment. For older machinery, retrofitting is essential--not only to modernize the equipment for seamless communication, but also to enable control software upgrades without disrupting production [9]. To assess the impact of such retrofitting, this study focuses on a critical machine suspected to be inefficient due to its age. By equipping it with sensors, valuable insights into its performance were obtained, supporting maintenance planning, quality control, and real-time production monitoring while minimizing operational disruptions. Building on these findings, this paper details the installation of sensors to quantify the machine's energy and air consumption, enhancing its efficiency through data-driven analysis. The proposed data acquisition system serves as a scalable model for similar machines in M-ACE and other industrial settings. In addition, this study aims to address key challenges in industrial energy monitoring. Specifically, we seek to answer the following research questions:  
* How can an IIoT-based monitoring system be effectively implemented in a 24/7 manufacturing environment with minimal disruption? * What are the scalability challenges in deploying real-time energy and air consumption monitoring across multiple machines? * How can collected data be leveraged to optimize energy use, detect inefficiencies, and support cost-saving strategies in manufacturing? By addressing these questions, this research contributes to the development of scalable, data-driven industrial energy monitoring solutions that enhance efficiency and sustainability. This article is structured as follows: Section 2 presents a literature review of relevant research on IoT-based energy monitoring solutions, an overview of the machine, the challenges faced at M-ACE, and the implementation of the IoT-based energy and air consumption monitoring system. Section 3 outlines the proposed solution, covering sensor selection, installation, network integration, and the monitoring dashboard. Finally, Section 4 discusses the outcomes of the implementation, highlighting its impact and effectiveness.
## 1 Introduction
### Research Contribution  
By studying and analyzing the latest research and identifying their shortcomings, this research offers the following original contributions:  
* Introduction of Advanced Feature Extraction:
* Utilizes wavelet transform analysis to derive novel features from the differential current, significantly enhancing fault detection accuracy.
* Integration with Deep Learning for Real-Time Application
* Implements long short-term memory (LSTM) networks for training, thereby boosting the system's capability to accurately identify internal transformer faults in real time.
* Comprehensive Fault Detection:
* Integrates differential current amplitude and bias current in the detection process, resulting in a more robust and reliable fault detection mechanism.
* Consideration of CT Saturation and Measurement Uncertainty
* Addresses the challenges posed by CT saturation and measurement uncertainties, often overlooked in traditional methods.
* Improved Relay Operations
* Mitigates issues caused by even harmonics and DC components during CT saturation, thereby reducing the likelihood of incorrect relay activations.
* Enhanced Reliability and Security
* Demonstrates superior performance in detecting internal faults in power transformers, even under challenging conditions such as CT saturation.
## 3 Results and Discussion
### Prediction of Antibiotics Removal Efficiency in Absence of Br\({}^{-}\)In Synthetic Water
#### 3.3.2 Removal of FF and OA by Ozonation and Bromination  
For determining the removal efficiency of antibiotics by ozone only, ozonation was performed in a pH 8 buffer with the target antibiotic, without the presence of bromide ion (Figure 5). Less than 5% FF was removed because the reaction rate of FF with ozone is low, i.e., 3.2 M\({}^{-1}\)s\({}^{-1}\). Thus, the direct removal of FF by ozone is negligible. In the case of OA, however, almost all OA was removed within 90 s by ozonation, owing to the high reaction rate of OA with ozone (2400 M\({}^{-1}\)s\({}^{-1}\)). For the removal of OA and FF through ozonation, the experimental values matched well with the predicted values. To  
Figure 4: Concentration of bromine formed at synthetic seawater with/without target compound. ([O\({}_{3}\)]\({}_{0}\) = 1 mg/L, [Br\({}^{-}\)]\({}_{0}\) = 60 mg/L, [Antibiotics]\({}_{0}\) = 1 uM, Temp. = 20 °C, pH 8).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Pharmaceutical**} & \multirow{2}{*}{**pKa**} & **Ozone** & **Bromine** \\ \cline{3-4}  & & **Kapp** & **Kapp** \\ \hline Florfenicol (FF) & 9.0 & 3.2 & 3.5 \\ Oxolinic acid (OA) & 6.8 & \(2.4\times 10^{3}\) & \(4.0\times 10^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of apparent rate constants at pH 8.  
assess the effectiveness of antibiotic removal through bromination, 3 mg/L and 2.6 mg/L of bromine were injected into water containing FF and OA, respectively.  
Figure 6 shows the removal of FF and OA by bromination at pH 8. Like the case of ozonation, FF exhibits little removal within 20 min owing to the low reaction rate of FF with bromine (3.5 M\({}^{-1}\)s\({}^{-1}\)). However, approximately 40% of OA was removed within 90 s of bromination at \(4.0\times 10^{2}\) M\({}^{-1}\)s\({}^{-1}\). The predictions of FF and OA removal by bromination matched well with the experimental values. The effectiveness of both ozone and bromine for FF and OA removal could be predicted using the reaction rate.  
Figure 6: Removal of (**a**) FF and (**b**) OA, by bromination. Symbol: experimental values; dotted line: prediction values ([Bromine]\({}_{0}\) = (a) 3 and (b) 2.6 mg/L, [Antibiotics] = 1 uM, Temp. = 20 °C, pH 8).
## 3 Results and Discussions
### Emission Characteristics
#### 3.2.3 NOx Emissions  
Figure 10 depicts the NOx emissions for all test fuels according to various engine loads. It can be seen that the NOx emissions are significantly reduced with the addition of palm oil at each engine load. At 30 Nm, the NOx emissions of BP10, BP30 and BP50 decrease by 2.44%, 10.13% and 13.88% compared with that of BP 0, respectively. At 60 Nm, the NOx emissions of BP10, BP30 and BP50 decrease by 5.44%, 14.85% and 19.61% compared with that of BP0, respectively. At 90 Nm, the NOx emissions of BP10, BP30 and BP50 decrease by 4.99%, 6.13% and 14.76% compared with that of BP0, respectively. In addition, the NOx of all fuels at 60 Nm and 90 Nm increase by 86.29% and 178.85% respectively compared with that at 30 Nm. According to the NOx generation mechanism, NOx emissions are divided into thermal NOx, prompt NOx and fuel NOx. Generally, a large amount of NOx emissions is generated when the temperature in the cylinder is higher than 1500 \({}^{\circ}\)C [32]. In addition, oxygen concentration and residence time are factors affecting NOx emissions. Many researchers have reported that most biodiesel has a high cetane number and oxygen content, which makes the premixed phase closer to the stoichiometric conditions, resulting in the increase of the cylinder temperature, thereby increasing NOx emissions [16]. Nevertheless, the NOx emissions decrease significantly with the increase of palm oil percentage in blend. This is because palm oil has a lower cetane number and calorific value than diesel, and its viscosity is nearly 16 times higher than diesel (see in Table 1). These characteristics hinder the excessive rise of temperature in the cylinder, thus curbing the generation rate of NOx emissions. Moreover, the significant increase of NOx emissions caused by the increase of engine load is mainly due to the rise of temperature in the cylinder and the larger flame area generated when more fuel is supplied. This is consistent with the study results from [PERSON] et al. [33].  
Figure 10: NOx emissions for all test fuels according to various engine loads.
## 4 Discussion
[47], who investigated the effect of radiofrequency EMF on testicular structure and sperm quality in rats, as well as by [PERSON] et al. [48], who subjected mouse genitals to electromagnetic fields throughout their entire lifespans. In contrast, [PERSON] et al. [39] treated rats with a 900 MHz field for 28 days (for an hour a day), reporting a significantly higher percentage of abnormal sperm. [PERSON] et al. [49] showed that increasing exposure time to mobile phones in men increases the number of morphologically abnormal sperm in their semen. It is well known that radiofrequency electromagnetic fields can affect animal behavior [50]. In the current study, the elevated plus maze test showed statistically significant shorter time spent in open arms, fewer mictions and defecations, and lower locomotor activity in both open and closed arms in animals belonging to the experimental group compared with the controls. As in the above-described test, lower locomotor activity was observed in EMF-treated rats at both the central and wall parts of the maze during the open field test. The experimental rats also spent more time at the walls than in the central part of the field and performed fewer hind leg stands. During an open field test, [PERSON] et al. [23] also showed significantly reduced locomotor activity in rats exposed to EMF at frequencies used by mobile communications (840 MHz). In contrast, [PERSON] et al. [28] did not observe any significant differences in the behavior of rats exposed to a 50 Hz field during an elevated plus maze test. Decreased motor activity may indicate anxiety disorders, possibly related to a stress response [51]. Similar results were obtained by [PERSON] et al. [52], who studied the effects of electromagnetic fields (frequency, 50 Hz) on stress levels in female rats. They established that corticosterone concentrations and anxiety behaviors increase in animals subjected to EMF. The correlations we calculated seem to confirm this phenomenon, especially between stress hormone concentrations and the motor activity of rats in the open arms and central part of the open field maze.
## 3 Results  
### Overall Characteristics of the Compressor  
The overall characteristics of the tested compressor with different stator hub clearances are shown in Figure 3, where the stage static pressure rise coefficient \(C_{ps}\) and the torque efficiency are expressed as functions of the mass flow coefficient \(\varphi\). As can be seen, for the static pressure rise coefficient \(C_{ps}\), when a small stator hub clearance (0.25\(\tau\)) was introduced the value at large mass flow rate conditions (\(\varphi>0.54\)) almost did not change, but at small mass flow rate conditions (\(\varphi<0.54\)) it increased to some extent; as the stator hub clearance increased to 0.50\(\tau\), the value at large mass flow rate conditions (\(\varphi>0.57\)) changed little, and at small mass flow rate conditions (\(\varphi<0.57\)) it increased continually. When the stator hub clearance increased to 1.00\(\tau\), however, the value at all conditions decreased but it was still higher than that of 0.00\(\tau\) and 0.25\(\tau\) at small mass flow rate conditions (\(\varphi<0.54\)). For the torque efficiency, when stator hub clearance was introduced the value at large mass flow rate conditions decreased and at small mass flow rate conditions increased, for all three clearance cases; like the static pressure rise coefficient \(C_{ps}\), at large mass flow rate conditions the value of the torque efficiency changed little from 0.25\(\tau\) to 0.50\(\tau\), and decreased slightly from 0.50\(\tau\) to 1.00\(\tau\); at small mass flow rate conditions, the value of the torque efficiency increased enormously from 0.25\(\tau\) to 0.50\(\tau\), but decreased a little from 0.50\(\tau\) to 1.00\(\tau\).  
From above, when stator hub clearances were introduced, there existed an optimum stator hub clearance that made the compressor performance optimal, and this optimum stator hub clearance  
Figure 3: Characteristics of the tested compressor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clearance**Conditions** & **DE (\(\varphi\) = 0.6)** & **PP (\(\varphi\) = 0.55)** & **P1 (\(\varphi\) = 0.525)** & **P2 (\(\varphi\) = 0.5)** & **NS (\(\varphi\) = 0.48)** \\ \hline
**0.00\(\tau\)** & 5h and Oil [1] & 5h and Oil & Oil & Oil & 5h and Oil \\
**0.25\(\tau\)** & 5h and Oil & 5h and Oil & - & - & 5h and Oil \\
**0.50\(\tau\)** & 5h and Oil & 5h and Oil & - & - & 5h and Oil \\
**1.00\(\tau\)** & 5h and Oil & 5h and Oil & - & - & 5h and Oil \\ \hline \hline \end{tabular}  
* \"5h\" represents five-hole probe measurement, “Oil” represents oil-flow visualization test.  
\end{table}
Table 2: The investigated flow conditions by the oil-flow visualization and five-hole probe.  
should between 0.50\(\tau\) and 1.00\(\tau\) (much closer to 0.50\(\tau\)). In addition, it can be seen that at 0.00\(\tau\), both the operating line of the stage static pressure rise coefficient \(C_{ps}\) and the torque efficiency turn abruptly around \(\varphi=0.54\), but this phenomenon disappears as long as the stator hub clearance is introduced, i.e., the operating line becomes smoother near \(\varphi=0.54\).