esa-sceva/satcom-chunk-collection huggingface dataset:
# New Inorganic Scintillators' Application in the Electromagnetic Calorimetry in High-Energy Physics
###### Abstract  
Scintillation crystals Gd\({}_{3}\)Al\({}_{2}\)Ga\({}_{3}\)O\({}_{12}\) (GAGG) are an excellent candidate for application in ionizing-radiation detectors because of their high radiation resistance, density and light yield. These crystals can be used in combination with lead tungstate (PbWO\({}_{4}\) or PWO) crystals for the development of a new generation of electromagnetic calorimeter with advanced spatial and energy resolutions in a broad energy range. PWO crystals enable the accurate detection of high-energy photons, while GAGG crystals provide the possibility of precisely measuring photon energies, down to a few MeV. Different options for a composite electromagnetic calorimeter based on PWO and GAGG crystals are considered to optimize spatial and energy resolutions in a broad energy range (from 1 MeV to 100 GeV). In particular, different lengths of the GAGG section of the calorimeter are considered, from 0.5 to 10 cm. The separation of signals from photons and hadrons is also taken into consideration through the study of shower shape in the calorimeter. The optimization is based on Geant4 simulations, considering light collection as well as the use of different photodetectors and electronic noise. Simulations are verified with light yield measurements of GAGG samples obtained using radioactive sources and test beam measurements of the prototype of the PWO-based Photon Spectrometer of the ALICE experiment at CERN.  
scintillators; light yield; scintillation detector; calorimetry; high resolution; GAGG; PWO +
Footnote †: journal: Physics Letters B
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 2 Chroma-Level Note Tracking and Pitch Mapping-Based Melody Extraction
### Summary  
In this section, the proposed melody extraction method using chroma-level note tracking combined with pitch mapping is presented in detail. More precisely, the harmonic pitch class profile is first computed to obtain the chromagram. Then, chroma-level notes are tracked by dynamic programming and note detection. Next, chroma-level notes are mapped to accurate pitch frequencies by the coarse mapping and fine tuning. Finally, voicing detection is applied at the end to determine the presence of melody. In the proposed method, the note-level chroma mapping avoids the frequency shifting over each note duration, fine tuning allows the method to follow the dynamic changing of each note, and note-level voicing detection improves the robustness against the signal-to-accompaniment ratio at the end of notes.
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 6 Evaluation and Experiments  
Experiments are carried out not only to decide the related threshold but to evaluate the performance of our mechanism. We implement them in two environments: a testbed and a factory. Our test bed consists of 20 nodes with LPC1769 mote and AT86 RF212B radio transceiver as shown in Figure 9a. The power of each node is supplied by two three-voltage batteries. All nodes are centrally scheduled by the gateway displayed in Figure 9b. We integrate the gateway with the Network Manager for convenience. The proposed mechanism works in the MAC layer mostly, including the record of measurement and the calculation of triggering degree. The member function and the corresponding calculation are predefined as a program in all nodes. To facilitate the computation, each nodes only record ten of the best neighbors which have the higher signal strength. An experimental description can be found in the following section and the details of the remaining parameters are listed in Table 1.  
The decision of whether or not to handoff is based on locally available information that node collects at a specific time. Thus the performance of the proposed mechanism is assessed by packet delivery. The number of lost packets and expired packets are counted in the gateway. The lost packets are not received by gateway since they exceed the Max Frame Retries. The arrival time of some packets exceeds the deadline, meaning these packets are expired. To increase the packet delivery, the statistics of _MN_ is separated from the whole network. Thus, RLP-_V_, RLP-_MN_, REP-_V_ and REP-_MN_ are used to denote the ratio of the network's lost packets, _MN_'s lost packets, the network's expired packets and _MN_'s expired packets, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Microcontroller & Cortex-M3 \\ Power of Transmission & 3 dBm \\ Rate of Transmission & 250 kb/s \\ Length of Superframe & 1 s \\ Size of Packet & 40 Bytes \\ Max. Frame Retries & 3 \\ Max. Speed of _MN_ & 5 m/s \\ \(W\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter Details.  
Figure 9: Hardware of WirelessCAN node and Gateway. (**a**) A node; (**b**) A Gateway.
# Wind Speed Spectrum of a Moving Vehicle under Turbulent Crosswinds
## 3 Turbulent Wind Flow Characteristics in Wind Tunnel  
### Wind Tunnel Tests  
The experimental process was conducted at the XNJD-3 wind tunnel located at Southwest Jiaotong University in China, and the wind tunnel is currently the world's largest boundary layer wind tunnel. The atmospheric boundary layer flows were produced using a combination of spires and cubes. The setup covers roughly 25 m of the floor of the wind tunnel.  
One typical form of atmospheric boundary layer flow is simulated in this study. The turbulent wind field was generated using 13 rectangular spires with a distance interval of 0.16 m (Figure 4). The wind characteristics of the simulated flows were measured by a Cobra probe (Turbulent Flow Instrument series 100). The sampling frequency was set to 256 Hz and the sampling time was 180 s. The Cobra Probe was placed in the middle of wind tunnel in the width direction. The mean and turbulent wind characteristics along the horizontal direction were basically consistent.  
Figure 3: Validation of wind speed spectrum of a moving vehicle based on von Karman spectrum [11].
# Reinforced Concrete Slab Optimization with Simulated Annealing
## 2 Structural Model
### Yield Line Method  
The yield line method makes it possible to model the structural behavior of a structure in the ultimate limit state (ULS). To better explain the physics of the problem, it is useful to describe the limit behavior of reinforced concrete (RC) slabs: under increasing load, the structure has linear elastic behavior, followed by non-linear plastic behavior characterized by tensile reinforcement yielding and plasticization of the cross section. In this condition, a redistribution of internal stresses is expected and reinforcement yielding is spread over a wide set of points until a collapse mechanism is reached. Such behavior can be modeled with some simplifications:  
* The materials present a plastic constitutive law.
* Plastic deformations are located only along hinge lines.
* Hinge lines are always straight lines.  
The yield line method can be divided into two distinct steps:  
1. Taking into account the geometrical, constraint and load characteristics of the slab, one or more families of possible failure mechanisms are determined. Each family is defined by a certain number of geometric parameters that can express the ultimate load as a scalar function of the same parameters \(p=p(x_{1},x_{2},\ldots,x_{n})\).
2. Enforcing the kinematic limit analysis theorem, the failure mechanism corresponding to the smallest limit load is identified for each mechanism family. The smallest value among the loads of each family will be the collapse load of the slab.  
The core of the method is the expression of the collapse load as a function of the mechanism parameters \(p=p(x_{1},x_{2},\ldots,x_{n})\). Typically, this expression is non-linear and its minimization is a complex operation.
## 3 Results and Discussion  
### Performance under Extremely High Temperature Condition  
Figures 6-8 present the ambient temperature and air temperature inside the container without PCM, with water as PCM and with OP5E as PCM under extremely high temperature condition, respectively. It can be seen that the temperature-controlled time was less than 1 h (about 54 min) by using the container without PCM. The performance was distinctly improved when incorporating a PCM into the container. However, the most measuring points inside the container showed a temperature at 1-2 \({}^{\circ}\)C for the first 70 h by applying water as PCM. This would cause a subcooling of drugs or food during the transportation. On the other hand, the air temperature inside the container was controlled in the range of 2-8 \({}^{\circ}\)C for 81 h by using OP5E as PCM. It kept at 4-5 \({}^{\circ}\)C for most of the time, which corresponds to the melting/freezing peak temperature of OP5E. In all three cases, the measuring point, which firstly exceeded the maximum temperature of 8 \({}^{\circ}\)C, was one of the points in the corner of the container, namely T1, T2, T3, or T4. The reason was that the tightness between the insulator base and lid is normally worse in the corner than elsewhere. Consequently, the heat transfer was intensified in the corner by the air leakage. Furthermore, it was observed that the air temperature in the container decreased firstly and then went up. The reason was that the container was not pre-cooled before the test because of the limited operation condition. The air in the container had the room temperature at the beginning and was firstly cooled when inserting the frozen PCM panels into the container. The air temperature began to rise when heat was transferred from the ambient to the inside of the container.  
Figure 8: The ambient temperature and air temperature inside the container with OP5E as PCM under extremely high temperature condition.  
Figure 6: The ambient temperature and air temperature inside the container without PCM under extremely high temperature condition. The shaded part means the controlled temperature range of 2 to 8 \({}^{\circ}\)C.  
Figure 7: The ambient temperature and air temperature inside the container with water as PCM under extremely high temperature condition.
## 3 Simulation Results
### Comparison with Machine Learning Tools  
The performance of the proposed method has been compared with two machine learning methods: the artificial neural network (ANN) method [15] and regression tree (RT) method [16]. Note that both the ANN method [15] and RT method [16] use VSM determined by CPFLOW as target output. In other words, both the ANN method [15] and RT method [16] can only give the results that are very close to or equal to those of CPFLOW.  
Table 8 demonstrates the prediction of the VSM via the proposed method, ANN method [15], and RT method [16] under the selected cases listed in Table 1 for Taipower system. On comparing the predicted VSMs for the cases in Table 8, it is clearly seen that the proposed method was able to provide the best results, which were the same as the actual ones.
# A Method for Constructing a Synthesis Health Index for Metro Vehicle Wheelsets
###### Abstract  
This study focuses on the construction of a synthesis health index for metro vehicle wheelsets through theoretical foundation analysis, procedural steps, and practical examples. By analyzing the theoretical foundation, this study explains the advantages of the synthesis health index compared to traditional health indicators. The procedural steps describe the detailed process from data collection, preprocessing, and feature extraction to health index construction. Additionally, practical examples are used to validate the effectiveness and accuracy of this method in real-world applications. The results indicate that this synthesis health index construction method can accurately assess the real-time health status of in-service wheelsets and also reflect and predict the trends of their health status changes.  
Keywords:rail transportation; metro vehicle wheelset health management; data preprocessing; synthesis health index +
Footnote †: journal: Applied sciences
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
###### Abstract
Another LTE-based protocol for machine-to-machine (M2M) communication, LTE-M, was also included in 3 GPP Release 13 and operates at the licensed frequency bands [21]. 3 GPP Release 17 introduced RedCap devices, a new protocol based on the NR architecture that is suited for devices with lesser complexity, such as IoT devices [22]. There have been numerous approaches in order to present a comparison among the different IoT protocols. Some surveys present a comparison focusing on various IoT-based wireless sensor networks (WSN) but lack real-time testing [23]. Studies in [24; 25] show a comparison among the devices regarding various parameters, albeit in a limited environment. All these surveys focus on various metrics and face distinct challenges, but to our best knowledge, not much attention has been given to three essential comparison  
Figure 1: IoT connectivity number forecast (2022 and 2028). matrices simultaneously, power, range, and cost, and the identification of the most suitable IoT protocol for future industrial use. The contributions of this article are the following:  
* We compare and contrast the six most widely used IoT communication protocol standards (Sigfox, LoRa, Z-Wave, NB-IoT, LTE-M, and RedCap). In order to comprehend the fundamental differences between the technologies, we first provide a summary of their network architectures. Next, we compare their various important performance parameters, including power utilization, cost, range, data rate, QoS, and security. We also review them from an application perspective in various industrial settings. * As per our knowledge, we are the first to make a comparative analysis of the NR-based protocol RedCap with ISM-based protocols. * We, therefore, demonstrate that LPWAN protocols are the protocols of the future for the upcoming industrial revolution and offer the optimal application perspective and case studies for the Industrial Internet of Things (IIoT). * Finally, we discuss some of the key challenges present in the current technologies that require attention and are open for future research exploration in IoT correspondence. The rest of the article is illustrated as follows: Section 2 provides a brief summary and contribution to a comparative analysis of the existing literature. Section 3 describes available wireless IoT protocols. Section 4 contains a performance comparison of the targeted IoT protocols. Section 5 discusses the industrial application perspective of the relevant IoT protocols and summarizes them. Section 6 involves the scope for future works described in the surveyed literature. Finally, Section 7 brings a conclusion to the survey carried out in this research.
# Risk Assessment of Compound Dynamic Disaster Based on AHP-EWM
## 4 Risk Classification of Evaluation Indicators
### Mining Technology Factors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Target Layer** & **Primary Index** & **Secondary Index** & \multicolumn{4}{c}{**Evaluation Index Grade Standard**} \\ \cline{3-6}  & & **1** & **1** & **1** & **1** & **1** \\ \hline \multirow{6}{*}{Risk degree of compound dynamic disaster U} & \multirow{6}{*}{Geological Factors U\({}_{1}\)} & Mining depth U\({}_{11}\) & h \(\leq\) 350 m & 350 m & m \(\leq\) 500 m & 500 m \(\leq\) 500 m & h \(>\) 800 m \\  & & Fault scale index U\({}_{22}\) & 0 \(<\) T \(\leq\) 0.3 & 0.3 \(<\) T \(\leq\) 0.6 & 0.6 \(<\) T \(\leq\) 0.9 & T \(>\) 0.9 \\  & & Characteristic parameter of road rock & L\({}_{4}\) \(\leq\) 50 m & 50 m \(<\) L\({}_{4}\) \(\leq\) 70 m & 70 m \(<\) L\({}_{4}\) \(\leq\) 90 m & L\({}_{4}\) \(>\) 90 m \\  & & \multicolumn{4}{c}{Change of scan thickness U\({}_{14}\)} & 0 & 1 & 2 & 3 \\  & & Plane deformation coefficient U\({}_{15}\) & \(\leq\)10\({}^{-5}\) & \(\leq\)10\({}^{-3}\) & \(\leq\)10\({}^{-1}\) & \(>\)10\({}^{-1}\) \\ \hline \multirow{6}{*}{Risk degree of compound dynamic disaster U} & \multirow{6}{*}{Coal property factors U\({}_{2}\)} & Impact energy index U\({}_{21}\) & K\({}_{\rm E}\)\(\leq\) 1.5 & 1.5 \(<\) K\({}_{\rm E}\)\(\leq\) 3.5 & 3.5 \(<\) K\({}_{\rm E}\)\(\leq\) 5 & K\({}_{\rm E}\)\(>\) 5 \\  & & Elastic energy index U\({}_{22}\) & W\({}_{\rm E}\)\(\leq\) 2 & 2 \(>\) W\({}_{\rm E}\)\(\leq\) 3.5 & 3.5 \(<\) K\({}_{\rm E}\)\(\leq\) 5 & W\({}_{\rm E}\)\(>\) 5 \\  & & Dynamic failure time U\({}_{23}\) & 500 DT & 300 \(<\) DT \(\leq\) 500 & 500 DT \(\leq\) 300 & DT \(<\) 500 \\  & & Residual gas content U\({}_{24}\) & 4 \(\leq\) K & 4 \(<\) K \(\leq\) 60 & 6 \(<\) K \(\leq\) 8 & K \(<\) 8 \\  & & Residual gas pressure U\({}_{25}\) & P \(\leq\) 109 MPa & 0.19 \(\leq\) P \(\leq\) 0.4 MPa & 0.4 \(<\) P \(\leq\) 0.60 MPa & P \(>\) 0.60 MPa \\  & & \multicolumn{4}{c}{Frimness coefficient U\({}_{25}\)} & \(f\)\(>\) 2 & 0.15 \(\leq\) 1.6 \(\leq\) 2 & 0.5 \(\leq\) 6 \(\leq\) 1 & \(<\) 6.5 \\  & & Initial speed of gas release U\({}_{27}\) & 0 \(<\) \(\alpha\)p \(\leq\) 11 & 11 \(<\)\(\alpha\)p \(\leq\) 13 & 13 \(<\) \(\alpha\)p \(\leq\) 30 & \(\alpha\)p \(>\) 30 \\ \hline \multirow{6}{*}{Mining technology factors U\({}_{3}\)} & Coefficient of the protective layer U\({}_{31}\) & 0 & 1 & 2 & 3 \\  & Influence of goal U\({}_{22}\) & 0 & 1 & 2 & 3 \\ \cline{1-1}  & & Mining speed U\({}_{33}\) & M \(\leq\) 2m/d & 2 m/d \(\leq\) M \(\leq\) 3.5 & 3.5 m/d \(\leq\) 4.5 m/d \(<\) M \\ \cline{1-1}  & & \multicolumn{4}{c}{Working face length U\({}_{34}\)} & L \(\leq\) 300 m & 150 m \(\leq\) L \(\leq\) 300 m & 100 m \(\leq\) L \(\leq\)150 m & L \(<\) 100 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of evaluation indicators.
# Biometric Authentication and Verification for Medical Cyber Physical Systems
## 6 Device Authentication
### Head Wearable Authentication Devices  
Head wearable devices including Virtual Reality (VR) headsets and smart glasses have become more common in the market for communication and entertainment. This new technology was also adopted by well-known companies such as Samsung, Google, and others, which made it available for users. Recent research of authentication WBANs used the available devices in the market such as Google Glass (GG) [16; 26; 27] in different techniques for authentication. The main focus was to create a software that uses the sensors of the smart glasses for authentication.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Available Techniques** \\ \hline Head Wearable Devices & Head movement \\  & Eye blinking \\  & Skull Frequency response \\ \hline \multirow{3}{*}{Bracelet (Hand) Wearable Devices} & Tomography system \\  & Behavioral biometric \\  & Bioimpedance \\ \hline \multirow{3}{*}{Body Portable Devices} & Accelerometer from gait signal \\  & Fingerprint \\ \hline \multirow{2}{*}{Implantable Medical Devices (IMD)} & Electroencephalography (EEG) \\  & Electrocardiography (ECG) \\ \hline \hline \end{tabular}
\end{table}
Table 3: WBAN authentication categories based on device.  
In some studies, the user will have to wear the GG which will display a series of changing images of numbers and letters in front of one or both eyes. By using the sensors, the device will be able to verify the users from their eye blinking and patterns of head movements [16]. This study used the blinking and head movements as a biometric for user identification and required a new software to be installed on the device for verification.  
Another study uses the GG sensors to create WBANs authentication system by capturing the unique human head-movement patterns while a person listens to an audio stimulus or music and monitoring these movements for authentication (see Figure 2) [26]. The GG will record the data from the sensors and store it to the installed memory as a text file. The text file will be send to a PC to process the collected data using Python Script.  
Another use of GG WBAN authentication involved creating a biometric authentication system using the audio signal inside the human skull to identify the users by recoding it. The users will be required to wear the GG in in a controlled laboratory setting room in order to record the skull audio signal waves. This study showed the range of the recorded audio between 0 kHz to 8 kHz and all the details about it [27].
## 2 Materials and Methods
### Test Design  
The trial was laid out in a completely randomized plot design. A total of 51 plots (2 m \(\times\) 2 m), representing 3 treatments and 17 replicates, were created. The _Zea mays_. L. cv. Jingke 958 variety was chosen as the tested cultivar for all the treatments and was planted by a tube-shaped seeder on 27 April at a depth of 5 cm. The hole of the seeds was 2.5 cm in the direction of the row and was 3.0 cm in the direction of the column, which was reserved for the space of maize growth. The planting density of maize was 60,000 plants \(\text{hm}^{-2}\). Each plot contained 4 rows, with 6 maize plants per row, that is, 4 rows of 6 columns, at a plant-to-plant spacing of 20 cm and row-to-row spacing of 36 cm (Figure 2). The spacing of 0.5 m between each plot was provided for maintaining independence among treatments, and a buffer channel of 1 m width was provided in the neighborhood of experimental fields to avoid edge effects. The experimental field was oriented west to east. The field was rectangular in shape, with 9 m in the north-south direction and 44 m in the east-west direction. Three treatments were established: straw mulching (JG), organic manure mulching (NF) and no mulching (WG; control).  
Figure 1: Location of Naiman desertification research station in China. Notes: the blue region represents Horqin Sandy Land; the yellow region represents the Naiman banner; the red flag represents Naiman desertification research station, CAS.  
In the JG treatment, when the maize seedlings reached a height of 20 cm, crushed maize straw was evenly applied to 100% of the soil surface with 4000 kg \(\mathrm{hm}^{-2}\) (1.6 kg per plot). In the NF treatment, cattle and poultry manure were the sources of organic manure. They were collected from the cattle and poultry farms located in Naiman banner and were uniformly mixed with soil 15 days before applying, making it fully rotten, preventing environmental pollution. Because organic manure that is not fully rotten contains various bacteria, this would cause a high incidence of crop diseases and insect pests, and affect the ecological environment when applied directly to farmland. Physical and chemical properties of the organic manure were, 145.77 g kg\({}^{-1}\) of soil organic carbon, 260.49 g kg\({}^{-1}\) of soil organic matter, 10.25 g kg\({}^{-1}\) of total nitrogen, 8.64 g kg\({}^{-1}\) of total phosphorous, and 11.57 g kg\({}^{-1}\) of total potassium. When the maize seedlings reached a height of 20 cm, organic manure was evenly distributed on 100% of the soil surface with 30,000 kg \(\mathrm{hm}^{-2}\) (12 kg per plot). In the WG (control) treatment, no surface mulding was used during the entire growth period of the maize.
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 4 Experimental Validation and Discussion
### Results and Discussion
#### 4.2.2 The Calculation of \(f_{\rm brb}\)  
First, the signature frequency \(f_{\rm brb}\) of the BRB is calculated. The frequency corresponding to the maximum of the spectrum of the tachometer signal is the running frequency \(f_{r}\) of IM. Consequently, the slip of the IM can be calculated by  
\[s=\frac{f_{e}-f_{r}}{f_{e}} \tag{14}\]  
According to the formula \(f_{\rm brb}=2 sf_{e}\), the signature frequency \(f_{\rm brb}\) can be calculated. The results of slip and signature frequency are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of poles & 2 \\ Number of phases & 3 \\ Number of rotor bars & 32 \\ Number of broken bars & 4 \\ Number of stator slots & 24 \\ Motor power & 0.25 kW \\ Rated voltage & 190/380 V \\ Motor rated speed & 2850 rpm \\ Rated current & 2.0/1.0 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed parameters of the Induction Motor (IM).