esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
NB-IoT is a low-power wide area network (LPWAN) protocol by the Third-Generation Partnership Project (3 GPP), designed to provide wide connectivity to a huge number of IoT devices in massive deployment scenarios and in scenarios that require good radio coverage and lower power consumption, such as smart metering [14; 15]. NB-IoT devices can reach nominal life times of around 10 years with small and infrequent messaging [16], with an overall good power consumption performance compared to other communication technologies, both cellular and not [17]. Cellular technologies such as NB-IoT can be employed to embed additional services that require internet connectivity in home and kitchen appliances, adding negligible power consumption to their normal mode of operation and advancing the machines as a service (MaaS) model [18; 19] and servitization of the products [20]. The framework proposed in this paper includes a smart IoT device making use of the aforementioned MQTT-based command protocol, and a serverless application on the cloud to manage devices and commands received through Amazon Alexa or through a dedicated application. The NB-IoT protocol was tested to determine if it is suitable to dispatch MQTT messages between MQTT clients. Automatic speech recognition (ASR) software already adds a delay to the overall application latency, and the execution of Lambda functions also adds a (minimal) latency, but the message exchange delay might severely degrade the user experience if it hinders the prompt execution of commands. For the demonstration of the proposed framework viability, a smart extractor fan system was designed, prototyped, and tested as a practical use case. An experimental test-bed with a NB-IoT node sending and receiving MQTT messages was therefore set up to evaluate the latency performance of the protocol in this context. This work is organized as follows. Section 2 explains the main technologies employed in the proposed system, and illustrates some recent works in the field of home automation and vocal interfaces for smart homes. Section 3 goes into detail about the proposed system and the NB-IoT evaluation test-bed, while Section 5 gives the results of the evaluation. Lastly, Section 6 draws conclusions about the work and contributions of this paper.
## 2 Topological Graph and Fault Analysis
### Faults Analysis with GSC  
Most of the existing open-circuit fault diagnosis strategies of power converter are only concerning the single open-circuit failure state. However, the larger current or peak of voltage would break down another LGBT module, result in a double open-circuit faults of the LGBT modules, if the system failed to respond in time to cut off the electric energy transmission after the occurrence of single open-circuit fault. Therefore, this paper is considering the single and double LGBT modules open-circuit faults of GSC. Then, there are 21 open-circuit failure states and 1 normal operating state for the 6 IGBT models of GSC. The code of IGBT modules open-circuit fault of GSC is shown in Table 1.  
Where T1, T2, T3, T4, T5 and T6 represent the corresponding IGBT modules in Figure 1. The value of Ti (i = 1, 2,..., 6) are the states of IGBT modules. When the value of Ti is equal to 0, it means that Ti is operating normally at this time. When the value of Ti is equal to 1, it means that Ti is open-circuit fault at this time. The 1 to 22 are the coding number correspond to the each failure state of the IGBT modules. For instance, code 2 corresponds to 000001, which means that the T1 IGBT module is single open-circuit, other IGBT modules are normal at this time. Code 21 corresponds to 110000, which means that the T5 and T6 IGBT modules are double open-circuit, other IGBT modules are normal. According to the Table 1, technicians can clearly locate which IGBT modules in the GSC have open-circuit faults.
## 1 Background  
Dental implants with implant-supported prostheses represent an effective and reliable treatment option for replacing natural teeth [1]. Advances in surgical techniques and implant components have significantly improved success rates, achieving over 90% for up to eight years [2]. However, despite these advancements, mechanical failures and biological complications continue to affect peri-implant soft tissues, presenting ongoing challenges in dental implantology [3; 4].  
Mechanical complications associated with dental implants often include the loosening and fracture of prosthetic or abtment screws, as well as wear and fracture of the prosthesis or other system components [5]. Mechanical complications in dental implants often arise from excessive occlusal forces, material fatigue, or improper prosthetic design. Thesefactors can lead to screw loosening, fractures, and wear of implant components. Additionally, inadequate maintenance or poor alignment of the implant-abutment connection may exacerbate these issues [6]. Although the occurrence of abutment screw fractures is relatively low, ranging from 0.5% to 8%, they are more frequently observed in fixed partial dentures compared to fixed complex prostheses. The extraction of fractured screw fragments remains a significant challenge in clinical practice, primarily depending on the degree of screw fracture and its engagement with the implant threads [2; 3; 4; 5].  
In instances where extraction proves difficult or could potentially harm the internal threads of the implant, the options typically include either leaving the fragment in place or opting for implant removal, both of which carry considerable risks, high morbidity, and uncertain prognosis [6; 7; 8; 9; 10].  
Moreover, removal techniques can generate heat due to the high thermal diffusivity of metals, which can adversely affect the bone-implant interface and compromise osse-sointegration. Fractured screw fragments can be extracted using conventional or drilling techniques. [PERSON] et al. reported that conventional methods, through the use of an explorer and an ultrasound device, showed a success rate of 73.3%, while the mechanical technique achieved a success rate of 96.7% by precisely drilling into and extracting the fragment. Both techniques have a similar impact on implant-abutment connection integrity [7].  
This study aimed to analyze and compare the retrievability of fractured abutment screws and damage to the implant-abutment connections using conventional and drilling techniques. The null hypothesis (H\({}_{0}\)) posits that there is no difference in the retrievability and connection damage between the conventional and drilling techniques for fractured abutment screws.
# Study of Low-Frequency Hydroacoustic Waves' Behavior at the Shelf of Decreasing Depth
## 2 Description of the Experiment
On the shore, the transformed seismoacoustic signals were received by a 52.5 m shore laser strainmeter, which was located at Schultz Cape at a point with coordinates N 42\({}^{\circ}\)34.798\({}^{\prime}\), E 131\({}^{\circ}\)09.400\({}^{\prime}\). The operation process of the laser strainmeter is briefly described in [3]. The part of the 52.5 m laser strainmeter (corner reflector) closest to the water was located at a distance of 120 m from the water edge and at an altitude of 67 m above sea level. The operating arm of the 52.5 m laser strainmeter was oriented at an angle of 180 degrees (1980) to the \"North-South\" line. All the information from the registration system of the laser strainmeter was fed via cable lines to the laboratory building, where it was entered into a specially created database. Subsequently, the obtained experimental data underwent preliminary and final processing depending on its purpose. The methods of interferometry applied in the laser strainmeter allowed the measuring of the change in the intensity of the interference pattern with high accuracy. Theoretically, using interference methods, it was possible to measure the displacement between the plates of the strainmeter with an accuracy of \(10^{-6}\times\lambda\)/2, where \(\lambda\)i is the wavelength of the applied frequency-stabilized laser. In our case, the measuring accuracy of the displacement between the plates was 0.01 nm. The 52.5 m laser strainmeter used was a frequency-stabilized helium-neon laser that provided a long-term frequency stability of up to the eleventh digit. With a measurement accuracy of 0.01 nm, the sensitivity of the 52.5 m laser strainmeter was \(\Delta\)L/L = 0.01 nm/52.5 m = 0.19 \(\times\) 10\({}^{-12}\). The amplitude-frequency characteristics of the 52.5 m laser strainmeter had a linear form in the infrasonic range, and in the sound range were the square of sine with doubled amplitude [5]. When registering a longitudinal-type seismoacoustic wave propagating along the axis of the laser strainmeter at a speed of 2300 m/s and taken in accordance with [3], it registered the wave amplitudes at frequencies of 22 and 33 Hz, equal to 2A\({}_{0}\) and 0.98A\({}_{0}\), respectively, where A\({}_{0}\) is the amplitude of the seismoacoustic wave. This must be taken into account in further calculations, which were carried out using the data from the laser strainmeter.
## 3 Materials and Methods  
### Sensors  
MEMS are low-cost and high-precision inertial sensors that are used in a wide range of industrial applications. These sensors use chip-based technology, specifically a micro-electromechanical system. These sensors are used to detect and measure external stimuli. Whenever an MEMS sensor applies a tilt, the balanced mass causes a difference in electrical potential. This can be measured as a change in capacitance. Then, this signal can be changed to produce a stable output signal in digital form, 4-20 mA or VDC [25; 26]. The parameters of the sensor used by us are given in Table 1. MEMS sensors are used in a variety of fields which include automotive, consumer, industrial, military and biotechnology, space research, and commercial applications which include inkjet printers, accelerometers in modern automobiles, consumer electronics, personal computers, etc. MEMS sensors are also used in the automotive industry to measure acceleration, tilt, vibration, and motion to provide motion detection and stability control capabilities. It enabled us to measure the necessary accelerations in our research.  
Advantages of using MEMS sensors include, for example, low power consumption, low-cost manufacturing, and resistance to shock, radiation, and vibration. We see the investment involved in development as a disadvantage.  
Three-axis accelerometers supplied for the consumer market are typically calibrated by the sensor manufacturer using a six-element linear model comprising a gain and offset in each of the three axes. This factory calibration will change slightly as a result of the thermal stresses during soldering of the accelerometer to the circuit board [25]. An accelerometer is an electromechanical device that measures acceleration forces. These forces may be static, like gravity, or they could be dynamic--caused by moving or vibrating. There are many types of accelerometers and there are many different ways to make an accelerometer. Some accelerometers contain microscopic crystal structures that get stressed by accelerative forces using the piezoelectric effect. Another sensing change is in capacitance. Capacitive sensing has excellent sensitivity [26]. An accelerometer is the primary sensor responsible for measuring inertial acceleration or change in velocity over time and can be found in various types including mechanical accelerometers, quartz accelerometers, and MEMS accelerometers.
## 2 Materials and Methods
### Virtual Screening of Small Inhibitors Targeting Human the HSP47/Collagen Binding Interface  
As the sequence similarity between canine and human HSP47 is 99%, and residues involved in collagen binding are all conserved, the human HSP47 structure was modelled by SWISS-MODEL [26] using the canine HSP47 crystal structure (PDB ID: 3 ZHA) [24] as the template. The triple-helical structure of HSP47-integrin peptide I was built using software FoldX [27] based on the structure of the HSP47-CMP complex (PDB ID: 3 ZHA) and our previously reported integrin peptide I structure (PDB ID: 1Q7D).  
To select potential HSP47 inhibitors, the SPECS database ([[http://www.specs.net](http://www.specs.net)]([http://www.specs.net](http://www.specs.net))) containing structural information for approximately 100,000 chemicals (logP \(\leq\) 5.5 and logS \(\geq\) -5.5) was adopted for virtual screening using software Glide6.9 (www.schrodinger.com). The docking model for HSP47 was prepared using the Protein Preparation and Grid Preparation tools in the Schrodinger Maestro interface. In the grid preparation process, the default settings were adopted for the cutoff, neutralization, scaling, and dimensions of the binding pocket. The standard precision (SP) mode of Glide was used to explore favorable binding poses. Ligand conformation was allowed to be flexible, while the protein was held as a rigid structure during the docking process. After the first round of screening, 10,000 compounds with the highest scores were selected for the second round of screening using the extra precision (XP) mode of Glide, leading to the selection of -2000 molecules. These molecules were filtered using AutoDock4.2 for binding conformation analysis [28]. The binding site on HSP47 was defined around the center of the collagen model peptide in the crystal structure (PDB ID: 3 ZHA) [24], and was covered by preparing a 75 \(\times\) 75 \(\times\) 75 grid box with 0.375A spacing between grid points. The Lamarckian genetic algorithm was applied to obtain the protein-ligand-binding free energies and the spatial conformations of the bound compounds.
# Seismic Design of Steel Moment-Resisting Frames with Damping Systems in Accordance with KBC 2016
## 3 Damping System Design Procedure
### Damping Device Design
#### 3.3.1 Effective Damping of Displacement-Dependent Damping Devices  
The force-displacement relationship of the TADAS system is defined as a bi-linear model with a post-yield stiffness ratio of 0.02. The strength and stiffness characteristics of the TADAS system including deformation capacity are calculated by Equations (7) to (9) using metal plate dimensions and material strengths of which details can be found in [PERSON] et al. [24].  
\[\delta_{y}=\frac{3}{2}\Bigg{(}\frac{\varepsilon_{y}h^{2}}{t}\Bigg{)} \tag{7}\]  
\[V_{y}=\frac{F_{y}bt^{2}}{4h} \tag{8}\]  
\[\delta_{max}=\frac{\varepsilon_{max}h^{2}}{t} \tag{9}\]  
where \(\delta_{y}\), \(V_{y}\) and \(\delta_{max}\) are yield deformation, yield strength and deformation capacity, respectively, and \(b\), \(h\) and t are width, height and thickness of the triangular metal plate, \(\varepsilon_{y}\) is the yield strain of the material, \(\varepsilon_{max}\) is the strain limit of the damping device and can be calculated by the following equation [25; 26; 27; 28].  
\[\varepsilon_{max}=AN_{f}^{-B} \tag{10}\]  
where \(N_{f}\) is the number of deformation cycles and assumed to be 100, \(A\) and \(B\) are constant and assumed to be 0.08 and 0.3, respectively [24]. The peak deformation demand on the damping device is calculated as follows.  
\[\delta_{u}=h_{i}\theta_{i} \tag{11}\]  
Figure 2: Design process of damping device.  
where \(\theta_{i}\) and \(h_{i}\) are the peak story drift angle (rad) and the story height of the i-th story.  
For given peak deformation demands, the effective damping ratio can be calculated by Equations (12) in accordance with ASCE 7-10 based on the energy dissipation due to cyclic deformations illustrated in Figure 3[29].  
\[\beta_{v}=\frac{\sum E_{dj}}{4\pi E_{s}} \tag{12}\]  
where \(E_{s}\) is the strain energy stored in the structure \(E_{dj}\) is the energy dissipated by the j-th damping device. \(E_{s}\) and \(E_{dj}\) are calculated by the following equations.  
\[E_{s}=\frac{1}{2}\sum F_{i}\delta_{i} \tag{13}\]  
\[E_{dj}=4\left(F_{yj}\delta_{uj}-F_{uj}\delta_{yj}\right) \tag{14}\]  
where \(F_{i}\) and \(\delta_{i}\) are the peak lateral force and peak lateral displacement at the i-th story, respectively, \(\delta_{yj}\), \(\delta_{uj}\), \(F_{yj}\) and \(F_{uj}\) are the yield displacement, peak displacement, yield strength and peak force of the i-th damping device.
## 4 System Level Perspectives of JCRS RF Hardware
### System Validation  
Testbeds to validate the use cases also play an important role to identify additional use case bottlenecks. The use of software-defined radio platforms can facilitate the researchers in the following three steps:  
* A use case and new waveform/signal processing validation platform using standard available RF front-end and available antenna structures [94].
* Replacing the antenna with a custom antenna system with enhanced isolation or enhanced bandwidth.
* Replacing the standard front-end with a custom JCRS front-end with enhanced isolated antenna system.
* In addition, there are different demonstrations of JCRS use cases, available in the literature. The authors of [91] considered a JCRS spectral convergence scenario for collision avoidance in an intersection. The urban connected car scenario is used in [95] when the bandwidth was limited due to sharing with communication services. A  
Figure 7: Power link budget. (k—Boltzmann’s Constant, T—Temperature, B—Bandwidth, NF—Noise Figure; NF = 8 dB assumed, so the sensitivity level Psens = kTB + NF = \(-\)86 dBm).  
framework of using 802.11 ad compliant radar is described in [96]. Ref. [97] discussed Wi-Fi sensing use cases in details.
# A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations
###### Abstract  
This study investigates the use of three different quadrature schemes, as well as an iterative quadrature methodology, to analyze vibrations in magneto-electro-thermo-elastic nanobeams. Individual MATLAB programs for each method are developed with the goal of minimizing errors in comparison to accurate findings, as well as determining the execution time for each strategy. This study shows that the Discrete Singular-Convolution Differential Quadrature Method with a Regularized Shannon Kernel (DSCQM-RSK) and specified parameters produces the best accurate and efficient results for this particular situation. A subsequent parametric study is carried out to determine the effect of various factors on the vibrated nanobeam, including boundary conditions, material types, linear and nonlinear elastic foundation properties, nonlocal parameters, length-to-thickness ratios, external electric and magnetic potentials, axial forces, and temperature variations. Important discoveries include insights into the relationship between fundamental frequency, linear elastic foundation features, axial loads, external magnetic fields, temperature fluctuations, and material types. According to this study, these findings could be critical in the development of sophisticated nanostructures made from magneto-electro-thermo-elastic materials for use in a variety of electromechanical applications. This would entail utilizing nanobeams' unique properties in applications such as sensors, resonators, and transducers for nanoelectronics and biology.  
piezomagnetic composites; nonlocal elasticity; Timoshenko beam theory; vibration analysis; nonlinear foundations; PDQM +
Footnote †: journal:
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 3 Trajectory Planning for the MR-SPS Considering Dynamic Constraints
### State Approximation Using Bezier Curve
\begin{array}{c}P_{\gamma,0}=\gamma_{0}\\ P_{\gamma,1}=\gamma_{0}+\Gamma\dot{\gamma}_{0}/n_{\gamma}\\ P_{\gamma,n_{\gamma}}=\gamma_{f}-T\dot{\gamma}_{f}/n_{\gamma}\\ P_{\gamma,n_{\gamma}}=\gamma_{f}\end{array}  \tag{27}\]  
In order to meet the dynamic constraints at every moment, some discrete points can be adopted to calculate the dynamic constraints. The discrete points can be obtained by applying Gaussian Legendre distribution, which is defined as the roots of \(m\)-order Legendre polynomial to be scaled to \([0,1]\) and to be arranged in ascending order as follows: \([\tau]_{m\times 1}=[\tau_{1}\quad\tau_{2}\quad\cdots\quad\tau_{m-1}\quad\tau_{m}]^{T}\). The discrete points of \(\gamma\) and its first and second \(\tau\) -derivatives can be given in compact matrix notation form as follows:  
\[[\gamma]_{m\times 1}=[B_{\gamma}]_{m\times(n_{\gamma}+1)}[P_{\gamma}]_{(n_{ \gamma}+1)\times 1} \tag{28}\]  
\[[\gamma^{\prime}]_{m\times 1}=[B_{\gamma}^{\prime}]_{m\times(n_{\gamma}+1)}[P_{ \gamma}]_{(n_{\gamma}+1)\times 1} \tag{29}\]  
\[[\gamma^{\prime\prime}]_{m\times 1}=[B_{\gamma}^{\prime\prime}]_{m\times(n_{ \gamma}+1)}[P_{\gamma}]_{(n_{\gamma}+1)\times 1} \tag{30}\]  
where \([P_{\gamma}]_{(n_{\gamma}+1)\times 1}=[P_{\gamma,0}\quad P_{\gamma,1}\quad[X_{ \gamma}]_{(n_{\gamma}-3)\times 1}^{T}\quad P_{\gamma,n_{\gamma}-1}\quad P_{ \gamma,n_{\gamma}}]^{T}\) is the Bezier coefficient column matrix and \([X_{\gamma}]=[P_{\gamma,2}\quad\cdots\quad P_{\gamma,n_{\gamma}-1}]^{T}\) is the unknown coefficient column matrix to be optimized. \([B_{\gamma}]_{m\times(n_{\gamma}+1)}\), \([B_{\gamma}^{\prime}]_{m\times(n_{\gamma}+1)}\), and \([B_{\gamma}^{\prime\prime}]_{m\times(n_{\gamma}+1)}\) are Bezier basis function matrix and the first and second \(\tau\)-derivative matrices of the Bezier basis function which can be calculated by substituting \([\tau]_{m\times 1}\) into Equations (21), (24) and (25). Once the order of Bezier curve and the number of discrete points are determined, \([B_{\gamma}]_{m\times(n_{\gamma}+1)}\), \([B_{\gamma}^{\prime}]_{m\times(n_{\gamma}+1)}\), and \([B_{\gamma}^{\prime\prime}]_{m\times(n_{\gamma}+1)}\) are determined, only calculated once and stored. In the process of optimization, they can be used in every iteration without repeated calculation, which reduce the calculation burden of the computer.
## 4 Discussion
### Uncertainty in GPP Modelling

Table 4: The modelled GPP for the main vegetation types in Southeast Asia, from GIMMS NDVI3g (\(\mathrm{GPP}_{\mathrm{NDVI3g}}\)), GIMMS NDVI (\(\mathrm{GPP}_{\mathrm{NDVI3g}}\)) and MOD15A2 (\(\mathrm{GPP}_{\mathrm{MOD15}}\)) based on the GLOPEM-CEVSA model, and the MODIS GPP product (MOD17A2) based on the PSN model. Spatial statistics were calculated using GPP and land cover data shown in Figures 2 and 7.  
the MODIS FPAR to match the GIMMS NDVI3g data, which diminished the advantages of MODIS in spatial resolution. Despite a relatively coarse spatial resolution, GIMMS NDVI3g is the longest continuous temporal record of vegetation dynamic and as such represents a critical data source for discovering the responses of vegetation to climate change over the long time span from the 1980s to the present. The land cover information was another main source of uncertainty that influenced GPP estimates. Retrieved GPP would be substantially affected if land cover were wrongly classified, e.g., misclassification of subtropical evergreen forest as woody savanna would lead to a substantial bias due to differences in parameterization. Therefore, accurate land cover classification is critical for the simulation of GPP at the regional scale. Other factors, e.g., canopy structure, could greatly influence upstream inputs (e.g., LAI retrieval) and finally affect the GPP estimates [25, 83]. In this study, GPP\({}_{\text{NDVI3g}}\) was shown to agree well with the on-ground GPP observations. However, GPP\({}_{\text{NDVI3g}}\) overestimated GPP in absolute values (Figure 6). This discrepancy could be related to the higher data range in the original GIMMS NDVI3g data, which was ultimately transferred to FPAR\({}_{\text{NDVI3g}}\) due to the application of a linear algorithm (Equations (9)-(11)). The newly released GIMMS FPAR3g data could solve this problem, as the GIMMS NDVI3g and MODIS FPAR (MOD15A2) products for the overlapping period 2000-2009 were harmonized using a neural network algorithm [84]. However, in our research we simulated GPP using the independent GIMMS NDVI3g data, in an effort to evaluate the potential of this dataset for GPP modelling.