esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Powered Surgical Instruments in Ear Surgery: An Acoustical Blessing or a Curse?
## 5 Generation of Noise and Vibrations by Otological Surgical Instruments and Hearing Loss
### TEES Procedures
#### 5.3.1 Ultrasonic Aspirator Generated Noise Levels  
Our study was designed to confirm that the noise levels generated by an ultrasonic aspirator during powered TEES fall within safe levels and should not induce sensorineural hearing loss. The study was conducted from September 2014 to February 2015 and data was collected during surgery from patients undergoing a powered TEES procedure to remove a cholesteatoma with a total of 14 patients (5 males/9 females) ranging in age from 15 to 76 and a median age of 50. All patients underwent a transcanal atticoantrotomy which was performed using a Sonopet(r) Omni ultrasonic aspirator (model UST-2001 Ultrasonic Surgical Aspirator, Stryker Corporation, Kalamazoo, Michigan, USA) in the LT (longitudinal-torsion) mode at 25 kHz (Figure 3a) and a high-speed drill with a curved burr at 80,000 rpm/1333 Hz (Visao(r) High-Speed Otologic Drill, Medtronic, Shinagawa, Tokyo, Japan) (Figure 7).  
The noise levels were measured at 10 cm (Figure 8a and 70 cm (Figure 8b) from the devices from 0.5 kHz to 16 kHz. These distances were selected because 10 cm is the closest that it was physically possible to measure noise levels generated within the external auditory canal and 70 cm represents the distance to the surgeon's ear. The noise level at 70 cm was measured in our original study because when new powered instruments are introduced, it is important to take into consideration the potential for hearing loss for anyone in the operating room due to long term noise exposure. Furthermore, we only measured up to 16 kHz despite the Sonopet(r) LT mode generating frequencies of up to 25 kHz, because standard noise measurement equipment can only measure up to 16 kHz, which is also close to the maximum auditory threshold of a normal adult. The results for both the Sonopet(r) Omni and the Visao(r) drill at 10 cm were at 85 dB or below with the noise levels lower for the Sonopet(r) Omni up to 2000 Hz and higher for the Sonopet(r) Omni from 4000 Hz to 16,000 Hz. The results for both the Sonopet(r) Omni and the Visao(r) drill at 70 cm were below 70 dB with the noise levels for the Sonopet(r) Omni lower or equal to the Visao(r) drill up to 8000 Hz and higher for the Sonopet(r) Omni from 8000 Hz to 16,000 Hz.
## 4 Discussion
### Limitations and Future Work  
At present, some achievements have been made in the research of NPP estimation model, but there are a few studies on NPP prediction. [PERSON], [PERSON] [22] coupled CASA and CA-Markov model, finding that comparing the estimate and predict values of NPP in 2015, the kappa coefficient reaches 0.8776 which indicates that the coupled model has high accuracy and good applicability for NPP prediction. This study also attempted to use the coupled model (CASA and PLUS model) to predict the values of NPP. NPP was divided into 5 types, 7 types and 10 types as land use types, and the kappa coefficients are 0.584571, 0.524487 and 0.508822, respectively, which only achieved the level of moderate consistency. In the next step, we will try to improve the accuracy of the coupling model or study a better model to predict the value of NPP. Moreover, the influencing factors of NPP are relatively complex, such as the combination of water and heat, the physiological and ecological characteristics of vegetation, and there are also interactions among various climate factors. Therefore, it is required in the following study to take multiple driving factors into account predicting spatiotemporal variations of NPP.  
Our future work plan will also appropriately expand the spatial scale of the research. The research will not only be limited to the better developed Beijing area, but also include the areas with large differences in development, such as Hebei Province and Tianjin city, China.
## 4 Experiment Results and Discussion
### Computational Complexity
#### 4.6.2 Surface Detail Transfer  
Surface detail transfer is defined as the fusion of the disentangled enhanced displacement field of a source object with the deformed implicit field of another target object. In this application, the specified surface details can be transferred and Figure 9 shows examples of surface detail transfer between different objects.
## 4 Results  
The results from the four training and test models are presented in this section. The four training models are specified to account for location information such as the inclusion of delineated boundaries (Models 1 and 3) and the extracted ESFs (Models 2 and 4). Overall, all models exhibit good levels of explanation with model performance ranging between 78.8% and 87.3% (Table 3). The findings reveal the standard Multiple Regression Analysis (MRA) model explains the lowest variation in house prices (78.8%) when incorporating the delineated spatial information. With the inclusion of ESFs, the level of explanation increases by 5.1% demonstrating an Adjusted \(R^{2}\) of 83.8%. The level of explanation further increases when examining the Elastic-net ESF which observes an \(R^{2}\) of 87.3%. The model coefficients reveal, by-and-large, the expected signs, magnitudes and significance (Within this study, for the penalized regressions we report only the values of \(\lambda\)-min (lambda)). For the standard OLS and Elastic-net models, apartments show negative coefficients of 9.1%, however, for the augmented ESF models this effect increases to 13.9%. The same observation is notable for the terrace coefficient which also increases in magnitude  
Figure 3: Spatial filters extracted across the study area geography. by approximately 5.8% between the model specifications. The analysis shows for a unitary increase in one squared metre, price, increases by 0.5%. Similarly, property age (Year built) exhibits a negative coefficient symbolizing that for every one year decrease in property age, price decreases by 0.2%.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 3 The Inverse Model of Radiation Thermometry
### Derivation of the Inverse Model of Radiation Thermometry
#### 3.1.2 Scalar Inverse Model
The temperature measurement can be obtained by inverting the sensor characteristic function \(M_{\text{BBS}}\big{(}T_{\text{obj}}\big{)}\), which is obtained from Equation (13) and displayed in Figure 6. The inverse sensor characteristic function is presented in Figure 11.  
As a scalar simplification, this model is appropriate for grey GBRT and inherently for BBRT, where interactions are not spectrally dependent. The vast majority of instruments in the field of radiation thermometry currently operate according to this model, as can be seen from the scalar input values. Since the settings of the measuring instruments are often unknown, these input parameters are usually estimated, taken from literature or determined experimentally, usually without following the rules of traceability for the particular system of measurement. It is also common procedure to use constant instrumental parameters for all measurements of a given surface, regardless of the temperatures and spectral interactions involved. Due to the omission of processing of the spectral information of the signal, this model and method are considered inaccurate. An improved version of this model is needed to take full advantage of the available accuracy and traceability of radiation thermometry. To do this, the spectral interactions must be accounted for in the inverse model.
# Time Management in Wireless Sensor Networks for Industrial Process Control
## 3 Results
### Transceiver Driver  
To maintain consistency throughout the software project in terms of register access, we have followed the CMSIS guidelines also for the transceiver driver. This meant putting in place the bit definitions for each transceiver register, like the ones available for the microcontroller. As an example, we have listed below a part of the bit definitions for the TRX_CTRL_0 register defining the clock rate for the CLKM pin.  
Functions were then defined and implemented to access the transceiver's features like reading and writing registers, reading frames from and writing frames to the internal FIFO, setting the transceiver in different states (idle, transmission, and reception) and using the internal AES module to encrypt data.
## 3 Proposed Methodology
### Proposed GATransformer
#### 3.2.1 Large Model Architecture (LMA)  
The LMA consists of large-base models with three pre-trained CNN models and a channel attention module designed to mitigate the excessive memory capacity of large networks. The channel attention module, comprising a GAT and Transformer, employs a two-stage attention mechanism. For this work, a set of three pre-trained CNN models is finalized among selected five pre-trained CNN models including InceptionV3 (In) [31], DenseNet121 (De) [32], EfficientNet-B0 (E0) [33], DarkNet53 (Da) [34], and MobileNetV2  
Figure 5: Comparison of the original and SMOTE-generated image; (**a**) original input image; (**b**) matching synthetic image; and (**c**) binary difference map between the original and artificial images.  
(Mo) [35]. The architecture of LMA with channel pruning to obtain a pruned model is shown in Figure 6.  
LMA improves the brain tumor classification feature extraction and representation learning. A model that can capture fine-grained information while keeping global contextual awareness is needed for MRI images' complex spatial and structural patterns. The LMA preserves spatial relationships in MRI images using patch-based embeddings, linear projections, and positional encoding. In addition, the channel pruning approach eliminates redundant computations, making the network more efficient without affecting accuracy despite the high model size. With PViT (pruned vision Transformer), model complexity and computational feasibility are balanced, decreasing redundancy and preserving good feature representation. This method preserves the model's large-scale architecture benefits while optimizing resource use for practical deployment. The model captures detailed tumor characteristics, improves classification performance, and balances model size and efficiency using LMA, making it ideal for high-performance and resource-constrained environments.  
The GAT module processes channel weights as nodes to capture intra-layer relationships and assigns attention scores, identifying significant channels within individual layers. The Transformer module analyzes inter-layer relationships using a multi-head attention mechanism, enabling cross-layer channel attention effects. This progressive channel pruning strategy evaluates layer-by-layer dependencies, gradually removing redundant channels across the entire network while maintaining performance. The pruning process is optimized by quantifying discrepancies using mean squared error (MSE) loss. Compared to traditional manual or single-layer pruning methods, this approach incorporates local (GAT) and global (Transformer) attention mechanisms, achieving efficient model compression without significant performance loss. This strategy reduces model complexity while retaining critical network functionalities.
# Impact of Grounding Modeling on Lightning-Induced Voltages Evaluation in Distribution Lines
## 2 Induced-Lightning Modeling
### Field-to-Line Coupling  
The field-to-line coupling computation is obtained considering the well-known Agrawal model [26], which is here presented in its extended version taking into account the presence of a finite-conducting ground and a multi-conductor line.  
\[\begin{cases}\frac{\partial V_{i}^{s}}{\partial x}(x,t)+\sum\limits_{j=1}^{M} L_{ij}\frac{\partial I_{j}}{\partial t}(x,t)+V_{i}^{S}(x,t)=E_{inc,x,i}(x,t)\\ \frac{\partial I_{i}}{\partial x}(x,t)+\sum\limits_{j=1}^{M}C_{ij}\frac{ \partial V_{i}^{s}}{\partial t}(x,t)=0\end{cases} \tag{1}\]  
with  
\[V_{i}^{S}(x,t)=\int\limits_{0}^{t}\xi_{g}^{i}(t-s)\frac{\partial I_{i}}{ \partial s}(x,s)ds \tag{2}\]  
where \(V_{i}^{s}(x,t)\), \(I_{i}(x,t)\) and \(E_{inc,x,i}(x,t)\) are the scattered voltage, the current and the tangential component of the exciting electric field (computed in the previous subsection) on the \(i\)th conductor at distance \(x\) from the beginning of the line. As expressed in Equation (1), the knowledge of the inductance and capacitance matrices (\(L\) and \(C\)) is required. Please note that \(\xi_{i}^{g}\) is the time-domain expression for the ground impedance [27].  
The total voltage occurring on the \(i\)-th conductor at the point \(x\) can be then expressed as the sum of the scattered voltage and the incident voltage, whose value depends on the vertical electric field (computed in the previous subsection).  
The proposed methodology is adapted to an EMT-type software (in this framework Simulink-Simscape is used), through the finite-difference time-domain (FDTD) technique. In this case, a second-order scheme is adopted with \(dt=10\) ns and \(dx=9\) m, which satisfies the well-known Courant stability condition. Further details can be found in [22].
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
###### Abstract
However, because such estimation relies on seismic data from broadband seismometers and has not been able to address the saturation and clipping-off problems that occur after large earthquakes, alternative methodological approaches are needed to estimate an earthquake's magnitude more accurately in a short space of time. GPS, with its ability to record ground displacement data, has the potential to generate more accurate magnitude estimations without suffering saturation and clipping-off problems [6]. Indeed, some methods for rapid magnitude estimation based on GPS data have been examined in previous research [4; 5; 7; 9; 10; 11; 12; 13; 14]. This study proposes a method for rapidly estimating an earthquake's magnitude based on an inversion of [PERSON]'s model using a genetic algorithm (GA). This method can also estimate other source parameters such as the length, width, rake, and slip parameters of the fault plane. GAs are optimization algorithms that can estimate many parameters to find the optimal value of an objective function [15]. Such algorithms are also known for their robustness in solving nonlinear problems [16; 17]. In research on earthquake problems, GAs have been used to understand focal mechanisms and stress tensor inversion [18; 19], carry out InSAR data inversion to learn about deformation [20], predict earthquakes [21], and select earthquake ground motion [22]. Other evolutionary computation-based algorithms have also been used to estimate the hypocenter location of an earthquake [23], inverse an earthquake's dislocation [24], estimate earthquake magnitudes [25], estimate fault parameters [26; 27], and solve engineering problems related to earthquakes [28]. In this study, the proposed rapid estimation method using GAs was tested on the 2011 Tohoku Oki earthquake data to measure its ability to estimate the earthquake's source parameters and magnitude.
## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Determination of Baseline  
After the classification of dry and wet periods, the wet period attenuation cannot be used directly for rainfall inversion; instead, the baseline attenuation (also known as the zero-level attenuation), which consists mainly of free-space losses and gas attenuation, needs to be removed from the wet period attenuation. Considering the generally short duration of rainfall, a common assumption is that factors affecting baseline attenuation may differ little between the wet period and the preceding dry period. Thus, the baseline can be set as the most recent dry period attenuation prior to rainfall [23] or the average dry period attenuation over a period of time before and after the rainfall event [64] and kept constant during the wet period. However, a constant baseline during the wet period may be impractical. To reflect the variability of the baseline during the wet period, [PERSON] et al. [48] linearly interpolated the dry period attenuation at both ends of the rainfall event to determine the baseline.
## 3 Results and Discussion
### Effect of Foam Content on Compressive Strength  
Figure 5 depicts the change in mean compressive strength with increased foam volume. It also compares the strength gain in 3, 7 and 28 days. The compressive strength of all the samples along with the standard deviation can be found in Appendix A. While comparing GP mix vs. blended mix, it is apparent that GP mixes attained significantly higher compressing strength than blended mixes. At approximately 45% foam volume, the 28-day compressive strength of the GP mix is almost 3.9 times higher than the same of the blended mix. In terms of strength gain, at 3 and 7 days, respectively, the GP mixes attained 45 and 62% of its 28-day strength (at 43% foam content), while these gains are 54 and 76% for the blended mix (at 45% foam content).  
The use of fly ash slows down the strength gain. [PERSON] and [PERSON] reported that a 45% replacement of cement using fly ash (with water to cement ratio of 0.4) reduced the 3-day compressive strength by 58% (from 35 MPa to 14.5 MPa) [39]. This reduction is 29% for 28-day compressive strength. They also found that for the same water-to-cement ratio, a binder with 55% cement, 40% fly ash and 5% silica fume yielded a reduction of 26% at 28-day compressive strength.  
Figure 4: Unstable void network for low-density foam concrete.  
Figure 5: Compressive strength as a function of foam volume.  
While compressive strength reduces with increasing foam volume, the difference in strength between GP and blended mix is found to be converging. This is possibly attributed to the unstable void network in blended mixes at higher foam content, as discussed before. Also, as the foam content increases, the properties of the mix are governed less by the properties of the binder. Therefore, the void network plays a more important role that is possibly responsible for the convergence of strength between blended and GP mixes. The compressive strength requirement for infill, as per Vic Roads Technical note TN007 [40], is 0.5-2 MPa, which was achieved for all the foam volume contents considered in this study, except the 70% foam volume case related to blended mix where the 28-day compressive strength was 0.48 MPa.
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Explanation** \\ \hline
1G & First generation of mobile networks \\ \hline
2G & Second generation of mobile networks \\ \hline
3G & Third generation of mobile networks \\ \hline
3 GPP & Third generation partnership project \\ \hline
4G & Fourth generation of mobile networks \\ \hline
5G & Fifth generation of mobile networks \\ \hline
6G & Sixth generation of mobile networks \\ \hline AI & Artificial intelligence \\ \hline AKA & Authentication and key agreement \\ \hline AP & Access point \\ \hline AR & Augmented reality \\ \hline AS & Angular spread \\ \hline BCDID & Blockchain-based collaborative distributed intrusion detection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acronyms appearing in the article. Figure 1: 6G visions and requirements [2]. \begin{table}
\begin{tabular}{c l} \hline \hline
**Acronym** & **Explanation** \\ \hline BER & Bit error rate \\ \hline BFSK & Binary frequency shift keying \\ \hline BLE & Bluetooth low energy \\ \hline CF-mMIMO & Cell-free massive multiple-input multiple-output \\ \hline CIR & Channel impulse response \\ \hline CoM2M & Collaboration through M2M messaging \\ \hline CPS & Cyber-physical system \\ \hline CRSN & Cognitive radio sensor network \\ \hline DS & Delay spread \\ \hline DL & Deep learning \\ \hline DTS & Dynamic bandwidth throttling \\ \hline eMBB & Enhanced mobile broadband \\ \hline ERLLC & Extremely reliable and low-latency communication \\ \hline eSIM & Embedded subscriber identity module \\ \hline FA & Factory automation \\ \hline FP & Fractional programming \\ \hline GEO & Geostationary earth orbit \\ \hline HAP & High altitude platform \\ \hline HCS & Human-centric services \\ \hline ICS & Industrial control system \\ \hline IIoT & Industrial Internet of Things \\ \hline IMS & Intelligent multi-surface \\ \hline IoD & Internet of Drones \\ \hline IoE & Internet of Everything \\ \hline IoMT & Internet of Medical Things \\ \hline IoRT & Internet of Remote Things \\ \hline IoT & Internet of Things \\ \hline IoV & Internet of Vehicles \\ \hline IP & Internet protocol \\ \hline IRS & Intelligent reflecting surface \\ \hline ISO/OSI & International Organization for Standardization/Open Systems \\ Interconnection \\ \hline IUABP & Intelligent-reflecting surface-user equipment association based on pilots \\ \hline LAP & Low altitude platform \\ \hline LEO & Low earth orbit \\ \hline LOS & Line-of-sight \\ \hline LTE & Long-term evolution \\ \hline M2M & Machine-to-machine \\ \hline MAC & Media access control \\ \hline MEO & Medium earth orbit \\ \hline MIMO & Multiple-input multiple-output \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
The article consists of the following sections: after the introduction, the methodology of the review is described, followed by the results relevant to show 6G support for M2M
## 4 Hardware Architecture
### Power Supply  
The power supply of the MSP consists of a lithium-ion battery. This is a secondary battery and therefore needs to be recharged. This requires an energy source, as a recharging circuit to be powered is needed. The main components of the recharging circuit are a battery gas gauge, a voltage-level translator, and a battery charger. A step-down converter, also called buck chopper, is needed to regulate voltages: a DC/DC switching converter based on ON/OFF regulation.  
* The battery gas gauge is a _Linear Technology(r) LTC2942_ (Milpitas, CA, USA). The component's role consists in measuring board temperature, voltage, and battery charge state. The battery gas gauge is also used to monitor the battery's charge availability. The state of charge (SoC) of the battery is determined by a coulomb counter, which is one of the inner components of the device. This is achieved by measuring the differential potential between the two SENSE pins, which must be lower than 50 mV. An inner analog-to-digital converter is used to measure the temperature, thanks to a temperature sensor on the chip, and to measure the applied voltage [27].
* The voltage-level translator is an _NXP Semiconductors(r) PCA9517 ATP_ (Eindhoven, The Netherlands). This component aims to realize a bidirectional voltage-level translation for the serial data (SDA) and serial clock (SCL) signals between the microprocessor and the battery gas gauge. The peripheral for serial communication is the I2C [28].
* The battery charger is an _Analog Devices(tm) L73650_ (Norwood, MA, USA). The component has a programmable charging current of up to 2 A, which a sense resistor of 20 k\(\Omega\) can reach. Moreover, it can be directly connected to a machine's electrical cabinet, with an input voltage of 24 V. The device has two internal timers: an end-of-cycle time of 3 h and a precondition time of 22.4 min, which is needed in case of generation of battery faults [29].