esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 5 Physical Layer
### Modem: Receiver Side  
At the receiver side, the modem works under 3 distinct modes: the standby, locking and decoding mode [22]. These operation modes are depicted in Figure 9. The objective of having these three distinct modes is for power saving issues, without loosing any useful received information.  
At the standby mode, the modem is continuously measuring the received signal power, and in case it detects an increase in the signal power, the locking mode is activated. In the locking mode, the signal of interest is detected and synchronized. The signal detection utilizes a maximum likelihood (ML) approach [24]. In this approach, a bank of matched filters is used for a joint estimation of the signal delay and of the Doppler factor. The first OFDM symbol is considered as a pilot, so that the filter bank is composed of distorted versions of this symbol. The selected branch is the one that provides the maximum value. The signal delay and the Doppler factor associated to this branch are chosen. The delay is used for synchronizing the received signal, and the Doppler factor is employed at a later processing stage.  
After the signal be synchronized, the modem goes to the decoding mode, which is responsible for recovering the transmitted signal. Figure 10 shows the block diagram of the receiver when operating at this mode.  
In order to keep only the desired signal, which has a bandwidth of \(B=M\,\Delta f\) and is centered in \(f_{\rm c}\), the received signal passes through a passband filter. This is a raised cosine filter, and in Figure 10 it is denoted as RC filter.  
Notice that the received signal samples, do not correspond to the originally equally spaced transmitted samples [26]. This phenomenon is due to the Doppler effect, which introduces a time-warping effect on the signal, i.e., the signal suffers a compression and/or dilatation in the time domain. For compensating the Doppler effect, the following procedure is performed. Knowing the time instant that each signal sample was received, and the initial Doppler factor (that was obtained at the locking mode), we can recover the  
Figure 10: Modem: receiver operating at decoding mode.  
Figure 9: Receiver modes.  
original transmission time instant. With this information, the received signal is resampled for finding the original equally spaced signal.  
At the next stage, the carrier frequency is removed, and the signal is shifted to baseband. This signal is multiplied by a squared-root raised cosine window [22]. This apodization operation is part of the pulse shaping design [25], which enables a reduction on the power consumption.  
Then this signal passes through a serial to parallel converter. At the S/P converter output, we will have an OFDM block, which contains \((N_{\text{sub}}+K)\) symbols. So, this OFDM block is demapped, leading to approximations of the differential complex modulated symbols. These symbols are demodulated, giving rise to bits. At the next stage, the set of bits is decoded, leading to the estimated transmitted bits.
# A Comprehensive Review on Music Transcription
###### Abstract  
Music transcription is the process of transforming recorded sound of musical performances into symbolic representations such as sheet music or MIDI files. Extensive research and development have been carried out in the field of music transcription and technology. This comprehensive review paper surveys the diverse methodologies, techniques, and advancements that have shaped the landscape of music transcription. The paper outlines the significance of music transcription in preserving, analyzing, and disseminating musical compositions across various genres and cultures. It also provides a historical perspective by tracing the evolution of music transcription from traditional manual methods to modern automated approaches. It also highlights the challenges in transcription posed by complex singing techniques, variations in instrumentation, ambiguity in pitch, tempo changes, rhythm, and dynamics. The review also categorizes four different types of transcription techniques, frame-level, note-level, stream-level, and notation-level, discussing their strengths and limitations. It also encompasses the various research domains of music transcription from general melody extraction to vocal melody, note-level monophonic to polyphonic vocal transcription, single-instrument to multi-instrument transcription, and multi-pitch estimation. The survey further covers a broad spectrum of music transcription applications in music production and creation. It also reviews state-of-the-art open-source as well as commercial music transcription tools for pitch estimation, onset and offset detection, general melody detection, and vocal melody detection. In addition, it also encompasses the currently available python libraries that can be used for music transcription. Furthermore, the review highlights the various open-source benchmark datasets for different areas of music transcription. It also provides a wide range of references supporting the historical context, theoretical frameworks, and foundational concepts to help readers understand the background of music transcription and the context of our paper.  
music transcription; melody transcription; monophonic and polyphonic transcription; deep learning
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 3 The System Model and Problem Statement  
### The System Model  
Consider an envisioned wireless sensor network consisting of sensor nodes that are uniformly and randomly scattered in the surface of an half sphere, which are shown in Figure 2. The radius of the half sphere is \(R\) (m) and the node density is \(\rho\) (m\({}^{-2}\)). Furthermore, nodes do not move after being deployed. In a data gathering round, each sensor node generates a packet, whose payload includes sensed data without error and loss. The sensor nodes consist of two types of nodes, non-collecting and collecting nodes. Each non-collecting sensor node only generates its own data and forwards it to the corresponding collecting nodes by unicast. However, each collecting node not only senses its own data, but also aggregates the data from other nodes and forwards them to the sink through multi-hop wireless communications by broadcast. In the multi-hop transmission, routing is fixed. Besides, time is divided into slots. Each node is assigned the time slot to ensure transmission without collision. The system model assumes that all sensor nodes are synchronized, and the packet loss rate during the wireless channel transmission is \(1-p\) for all links. In order to avoid the information loss caused by packet lost, a retransmission technique is applied.  
Furthermore, there is the following assumption:  
**Assumption 1**.: _Each non-collecting node generates its own data, and forwards it to the corresponding collecting nodes with a small identical transmission range, which is denoted by \(\Re\). In order to forward data in the outer tier to its parent collecting nodes in the inner tier, each collecting node has the same transmission power corresponding to the larger transmission range \(r_{o}\). The nodes' interference radius is \(2r\). The gateway or sink node, which collects information and sends the collected information to the user terminal by Internet or WiFi,  
Figure 2: The envisioned spherical network with sensors deployed in physical objects.  
has one wireless channel. To avoid transmission interference, the wireless channels between two adjacent tiers are independent across links. Moreover, for each collecting node in adjacent inner tier, there is one father collecting node in hotspots, wherein the shortest distance to the sink is less than r. Nodes in hot-spots transmit data directly to the sink._
# Investigation of Gentamicin Release from
## 3 Results and Discussion
### In Vitro Drug Release from G-PDA NPs
The drug release profile for the G-PDA NPs 0.6:1 was almost similar to the G-PDA NPs 1:1 with a higher drug loading amount, except for the amount of drug released during the studied time interval. The cumulative percentage of drug release after 7 days was very limited in pH 7.4, and tended towards the linear even after 40 h. It is speculated that the minor release of gentamicin at pH 7.4 was because of the excellent stability of the PDA NPs in neutral or alkaline conditions. On the other hand, at pH 5 and 3, approximately 20% and 40% of the drug had been released, respectively, showing the long-term release behavior. The concentration of drug releasing from the G-PDANPs 0.6:1 was comparatively one half of the loading amount. In conclusion, the drug release rate of both kinds of particles at different pH values showed an insignificant difference. However, the PDA NPs with less loading amount of  
Figure 2: (**A–C**) are representing non-cumulative gentamicin release at pH 7.4, 5 and 3, respectively; and (**D**) is representing cumulative percent release of gentamicin at all pH conditions from G-PDA 0.6:1. Data were mean \(\pm\) SD (\(n=3\)). gentamicin (approximately 70%) also showed a lower amount of drug release, as shown in Figure 3. On the contrary, the particles with a significantly higher loading efficiency (almost 80%) lead to a two times higher amount of drug release. It might be attributed to the fact of the concentration gradient between the two kinds of particles with different loading amounts of gentamicin. Hence, the work resulted in two different choices in terms of loading percentages and corresponding release rates for implementation in drug delivery systems. It was concluded that by tuning the feeding amount, the loading efficiency can be tuned as well as the amount of drug release, with no effect on the release profile but an effect on the drug dosage amounts.
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 3 Design of the Speed Controller Based on Improved SMC  
This section introduces the design of the SMC-based speed controller, including the design of the traditional speed SMC, the improvement in the velocity SMC, the stability proof of the controller, and the analysis of the new approach law.
# Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation
## 2 Instance Segmentation and Calorie Estimation System Overview  
As shown in Figure 1, the Instance Segmentation and Calorie Estimation System includes the following steps: (1) Image acquisition and processing using a simple monocular camera; (2) Instance segmentation using the Mask R-CNN algorithm; (3) Calorie estimation.  
In step (1), the image is resized to 640 \(\times\) 480 \(\times\) 3, in order to increase transmission speed. In the calibration pixel to real-world block, the contour of the coin is detected. The contour's minimum and maximum pixels on the \(x\)-axis \((min_{x},max_{x})\) and on the \(y\)-axis \((min_{y},max_{y})\) are calculated, separately. Based on the diameter of the coin, the physical sizes of each pixel on the \(x\) and \(y\) axes of the image are calculated as (1) and (2), where \(L_{ppx}\) and \(L_{ppy}\) are the physical sizes corresponding to each pixel on the \(x\) and \(y\) axes. \(D_{c}\) is the diameter of the coin.  
\[L_{ppx}=D_{c}/(max_{x}-min_{x}) \tag{1}\]  
Figure 1: Flowchart of Gimbap Instance Segmentation and Calorie Estimation System.  
\[L_{ppy}=D_{c}/\left(max_{y}-min_{y}\right) \tag{2}\]  
The calibration result is shown in Figure 2. The cropping image is used as a calibration area, to reduce the calculation time. The big green area is the calibration area. The calibration tool, such as a coin, should be in this area. The blue line presents the contour of the coin. Based on this contour, the physical sizes of each pixel on the \(x\) and \(y\) axes are calculated.  
For image data transmission, the network topology user datagram protocol (UDP) is used and set up. Message Queuing Telemetry Transport and Data Distribution Services provide real-time behavior, so they are considered to be used.
## 5 Conclusions  
In this paper, the performance change with the capacity of the desiccant rotor in an HDCS linked to a cogeneration system was predicted with TRNSYS. The capacity of the desiccant rotor was increased to reduce the humidity ratio of the process air to 9.5 g/kg, and subsequently, the thermal comfort conditions of the cooling space were evaluated. Furthermore, the cooling capacity, regeneration heat, and COP were predicted and compared with data from an actual system. The main conclusions are as follows.  
1. When the capacity of the desiccant rotor was increased, the amount of used regeneration heat with respect to the outside air conditions increased 1.7 times. According to this result and the simulations, the thermal environment of the cooling space entered the ASHRAE comfort zone even under outdoor conditions that were outside the thermal comfort zone of the actual system.
2. When the capacity of the desiccant rotor was increased, the simulated cooling capacity improved compared with the cooling capacity of the actual system by 10%. Actual systems have high cooling capacities under low temperature and high humidity conditions. A simulation was conducted to investigate the effect of an increase in the dehumidification capacity on the cooling capacity under hot and humid conditions.
3. Increasing the capacity of the desiccant rotor rendered the COP more sensitive to temperature compared with the COPs of conventional systems. The existing system had a high COP under low temperature and high humidity conditions. However, when the dehumidification capacity increased, the COP was high under high temper  
Figure 12: COP with thermoelectric value (\(\text{COP}_{\text{PURPA}}\)) from the Public Utility Regulatory Policies Act (PURPA) for (**a**) Unit 1 and (**b**) Unit 2. (**c**) Field test data [10] of the COP for the existing system.  
ature and dry conditions. These characteristics are similar to those of a pure desiccant cooling system.
4. Less regeneration heat was required in hot and dry outdoor conditions. While the cooling capacity increased in hot and humid conditions. The variation of the regeneration heat ranged from 0 to 6.14 kW, while the cooling capacity varied from 3.91 to 7.5 kW. Accordingly, if the capacity of the desiccant rotor was sufficient to match the humidity ratio of the process air to 9.5 g/kg, then the COP was determined by the regeneration heat rather than the cooling capacity.
5. In the HDCS, when the capacity of the desiccant rotor was increased to an extent where the humidity ratio
## 1 Introduction
A complete theoretical description of reference surface on realistic surface roughness to derive effective slip length is yet to be published. Surface roughness most commonly refers to the average height and width of the topography relative to a reference plane, and it can be characterized by amplitude roughness and spacing roughness. Studies have reported the effect of amplitude roughness on slip length; however, there is no experimental data on the effect of spacing roughness. Investigating the boundary conditions for the flow of liquids on surfaces with varying spacing roughness can provide a fundamental understanding of the interaction of solid and liquid at interfaces. In this paper, the effect of roughness on effective slip length at the solid-liquid interface was investigated. We reformulate the theory of thin liquid film drainage between sphere and superoleophobic/superoleophilic surfaces, taking into account the roughness effect. A measurement technique is established to obtain effective slip length on roughness-induced surfaces immersed in oil by using AFM and laser confocal scanning microscopy. Due to the wide application of hexadecane and ethylene glycol in the lubrication and antifreeze of MEMS/NEMS, these two kinds of oil were selected. A series of multilayered compositing surfaces with adjusted roughness and the same chemical properties were fabricated, and then were used to investigate the effect of spacing roughness on effective slip length. The results and mechanisms are discussed in this paper.
## 2 Proposed Method
### Simulation of the Full GNSS Constellation Orbit  
At present, GPS and GLONASS are fully global operational GNSS systems, while the European Union's Galileo system and the Chinese BDS are both scheduled to be fully operational by 2020. International GNSS Service (IGS) analysis centers release all the precise ephemeris information for GPS and GLONASS and partial details of the available Galileo and BDS satellite orbits methodically. At this time, one precise orbit file contains at most the coordinates of 31 GPS satellites, 23 GLONASS satellites, 14 BDS satellites and 13 Galileo satellites, for a total of up to 81 navigation satellites. However, the full global BDS constellation will consist of at least 35 satellites, including five geostationary Earth orbit (GEO) and three (inclined geostationary Earth orbit) IGSO satellites. The Galileo navigation system will consist of 24 satellites plus at most six spares by 2020 and these satellites will be deployed in three orbital planes with 56\({}^{\circ}\) inclination, and the ascending nodes will be separated by 120\({}^{\circ}\) in longitude [35]. The IGS orbit files cover the full constellation of GPS and GLONASS, besides all of GEOs and IGSOs of BDS, parts of BDS MEO (Medium Earth Orbit) and parts of GALILEO constellations. Therefore, to form a complete four-system GNSS constellation, in this paper, orbits of the full Galileo and BDS MEO constellations are simulated based on their officially released files. The main parameters are listed in Table 1[36; 37]. The values of altitude, inclination, satellite number, number of orbit planes, and satellite number in each plane are given in the released files. As all the satellites are in near circle orbit, the values of eccentricity and argument of perigee are set to 0.001 and 0\({}^{\circ}\), respectively. The values of longitude of the ascending node are set to 0\({}^{\circ}\), 120\({}^{\circ}\) and 240\({}^{\circ}\) because the ascending nodes will be separated by 120\({}^{\circ}\) in longitude for the two constellations. In addition, for GALILEO every 10 satellites are equally spaced in one orbit plane, while for BDS MEO constellation all 9 satellites are equally spaced in one orbit plane. Consequently, the total number of GNSS satellites used in this simulation ranges up to 120, which means that this number of satellites should be considered.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 4 Experiments
### Expected Battery Life  
Figure 18 compares the battery life expectancy of an NB-IoT device under different optimization settings, based on the 5 Wh battery capacity. These simulations involved transmitting 49 bytes of data every 24 h. After transmission, the UE enters the IDLE state, except in the default RAP scenario, where the UE remains awake until the inactivity timer expires before transitioning to the IDLE state.  
The figure illustrates that the default RAP has the shortest battery lifespan due to its conventional method, which involves multiple steps for establishing a network connection. This approach requires frequent UE wake-ups and consumes significant energy for communication. On the other hand, EDT improves battery life by 1.6 times compared to the default RAP [11]. EDT achieves this by allowing the UE to send data immediately after the initial access attempt, reducing the need for multiple network interactions. The most significant improvement is observed with PURs, where network resources are allocated in advance. This reduces the need for repetitive access procedures and minimizes the active communication time of the UE, resulting in a 2.1-fold increase in battery life compared to the default RAP.  
These optimizations enable extended battery life, which is crucial for the sustainability of UEs in long-term deployments where frequent battery replacements are not practical.  
Figure 17: PUR behavior under different radio coverage with varying packet size.  
The battery life improvements presented in the figure are all related to the 5 Wh battery capacity, emphasizing the importance of efficient network operation strategies to maximize the operational lifespan of IoT devices.
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 2 Materials
### ISMN In-Situ Soil Moisture Network  
The International Soil Moisture Network (ISMN) observations of in situ soil moisture were used for validation. The ISMN provides a database of in situ soil moisture measurements from 74 soil moisture networks worldwide [27]. By selecting observations from 10 July 2021 to 31 October 2022, the data of 260 stations were taken for comparison in this study, as shown in Figure 3. Most of the stations are located in the United States.
## 2 Measurement System Architecture
### Development of Embedded Sensor Hardware  
The measurement system for this study measures the three-axis vibration and gearbox temperature signals and sends these signals to a user's device by wireless communication. Figure 3 shows the general block diagram for the measurement system.  
To ensure that the measurement result is accurate, and the sensor is efficient, a signal conditioner is used to modify the signal, which is sent to a 16-bit analog convertor for digitalizing for the microcontroller. The microcontroller features logic to select a specific amount of data and transmit it using the wireless module.  
* 24 VDC Power Module: Industrial environments use 24 VDC. Without the power management integrated circuit (PMIC), it is impossible to provide adequate power to the microcontroller; however, this module delivers sufficient power for the microcontroller.
* Microcontroller: A STM32 series microcontroller is used, which has an ARM Cortex-M3 core that meets all the necessary requirements for receiving sensor data and hardware communication. There are a total of 47 pins, including 10 analog input pins and 37 digital input and output pins, and the operating voltage is 2 to 3.6 VDC. The maximum clock speed is 72 MHz clock speed, and there are 128 Kbytes of flash memory and 20 Kbytes static random-access memory (SRAM). The microcontroller stores the sensor data values and sends these values to the receiver via a Wi-Fi module.
* Vibration Sensor: Figure 4 shows that the vibration sensor contains an accelerometer to record 3 axis (X, Y and Z) vibration data and a temperature sensor to measure the temperature of the gearbox. An amplifier and analog filter constitute preconditioning circuitry to amplify the acceleration of analog signals and filter them to suit the bandwidth of the sensor. A group of resistors and capacitors eliminate interference and noise.  
Figure 3: Operation flowchart and prototype of the embedded sensor.  
* Wi-Fi Module: The Wi-Fi module includes an MCU and is used as a transmitter to send measurement data. It supports the standard IEEE 802.11 b/g/n protocol and a complete transmission control protocol/internet protocol (TCP/IP) stack. This module allows networking between devices and can be used to construct separate network controllers. The PCB and the monitoring system must be on the same local area network to transfer data.  
The specification of the measurement system is detailed in Table 1. This specification can be changed for different applications, such as monitoring temperature at 1 Hz and vibration at 50-3000 Hz, as required by domain experts.