esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Direct Model
#### 2.2.2 Atmospheric Model  
The total optical thickness \(TOT(h)\), in the lidar equation sense as defined by Measures [44], is the sum of half the forest optical thickness (\(FOT\)) and the total atmospheric optical thickness (\(\tau\)).  
\[\left\{\begin{array}{c}TOT(h)=\frac{FOT(h)}{2}+\tau(h)\\ \tau(h)=MOT(h)+AOT(h)+COT(h)\end{array}\right. \tag{4}\]Considering  
\[X(h)=\int_{h}^{Zp}\alpha_{X}(z)\cdot dz,\,\text{with}\,\,X=FOT,MOT,AOT,or\,\,COT \tag{5}\]  
where _MOT_ is the molecule optical thickness, _AOT_ is the aerosol optical thickness, and _COT_ is the cloud optical thickness. \(\alpha_{X}\) is the extinction coefficient associated to each component \(X\). _FOT_ will be discussed in Section 2.3.  
Two atmospheric conditions are used in the model. Under the ideal atmospheric condition, there is no cloud (_COT\({}_{0}\)_ = 0), and typical medium values of _AOT_ and _MOT_ are considered (_AOT\({}_{0}\)_ and _MOT\({}_{0}\)_), which are given in Table 1 for 2 wavelengths (355 and 1064 nm). However, realistic spaceborne observations are often performed in presence of clouds and aerosol plumes, which decreases the atmospheric transmission of laser beams. Hence, realistic atmospheric conditions are also used to improve the simulations. MODIS observations of _COT_ and _AOT_ are used in our model, which are considered to be representative. The AOTs are derived from the MODIS daily aerosol product MOD04 at the horizontal resolution of 10 km [46]. As _AOTs_ are only given at 470, 550, and 660 nm, the Angstrom Exponent coefficients are used to estimate the _AOTs_ at 355 and 1064 nm [47]. The COTs are derived from the MODIS daily cloud product MOD06 at the 1 km horizontal resolution [46], whereas the corresponding positions are derived from the MODIS geolocation product MOD03 [46].
## 4 Examples
### Example 3  
The Fredholm integral equation of the second kind  
\[u(x)=f(x)+\frac{1}{2}\int_{0}^{1}e^{-|x-t|}u(t)dt,\quad 0\leq x\leq 1, \tag{32}\]  
appears in the one-dimensional transport process [111] and the bending analysis of nonlocal integral models of Euler-Bernoulli nanobeams [112; 113; 114].  
This equation, by removing the modulus in the integrand, can be converted to a Volterra-Fredholm integral equation of convolution type, namely  
\[u(x) = f(x)+\frac{1}{2}\biggl{[}\int_{0}^{x}e^{-(x-t)}u(t)dt+\int_{x}^{1 }e^{x-t}u(t)dt\biggr{]}\] \[= f(x)+\frac{1}{2}\biggl{[}\int_{0}^{x}e^{-(x-t)}u(t)dt-\int_{0}^{ x}e^{x-t}u(t)dt+\int_{0}^{1}e^{x-t}u(t)dt\biggr{]}\] \[= f(x)+\frac{1}{2}\biggl{[}\int_{0}^{x}e^{-(x-t)}u(t)dt-\int_{0}^{ x}e^{x-t}u(t)dt\biggr{]}+\frac{1}{2}\int_{0}^{1}e^{x-t}u(t)dt\] \[= f(x)+\frac{1}{2}\biggl{[}\int_{0}^{x}e^{-(x-t)}u(t)dt-\int_{0}^{ x}e^{x-t}u(t)dt\biggr{]}+\frac{1}{2}e^{x}\int_{0}^{1}e^{-t}u(t)dt\]  
or  
\[u(x)-\frac{1}{2}\biggl{[}\int_{0}^{x}e^{-(x-t)}u(t)dt-\int_{0}^{x}e^{x-t}u(t) dt\biggr{]}-\frac{1}{2}e^{x}\int_{0}^{1}e^{-t}u(t)dt=f(x). \tag{33}\]  
This linear CVFIE can now be solved by the direct operator method presented in previous sections. In particular, comparing (33) with (12) in Theorem 1 it is natural to take \(n=2\), \(m=1\), \(b=1\) and  
\[\mathcal{K}u(x)=\frac{1}{2}\biggl{[}\int_{0}^{x}e^{-(x-t)}u(t)dt-\int_{0}^{x} e^{x-t}u(t)dt\biggr{]},\]  
which is a convolution Volterra integral operator of the form (23) with  
\[k_{1}(x)=\frac{1}{2}e^{-x},\quad k_{2}(x)=-\frac{1}{2}e^{x}. \tag{34}\]  
Additionally, we take  
\[g_{1}(x)=\frac{1}{2}e^{x},\quad h_{1}(t)=e^{-t},\quad\bar{\varphi}_{1}(t,u(t) )=u(t), \tag{35}\]  
and let  
\[f(x)=\frac{1}{2}\Bigl{(}e^{-x}+e^{-(1-x)}\Bigr{)}, \tag{36}\]  
as in [111].  
The functions \(k_{1}(x)\), \(k_{2}(x)\), \(g_{1}(x)\) and \(f(x)\) are continuous and of exponential order, and therefore the Volterra integral operator of the second kind \(\mathcal{J}\) is invertable. By entering the functions in (34)-(36) in the Algorithm 1 and executing, we get the exact solution of the CVFIE (33), viz.  
\[u(x)=1,\]  
which is the solution of the given Fredholm integral Equation (32) with \(f(x)\) as in (36).  
In Table 1, we give the exact solution corresponding to three other types of the input function \(f(x)\).
## 2 Materials and Methods  
### Study Population and Variable Selection  
The Korean Renal Cancer Study group (KRoCS) metastatic RCC (mRCC) database was retrospectively collected from 11 domestic hospitals between February 1994 and March 2018. Several studies have been conducted using the well-established KRoCS database [15; 16]. The KRoCS mRCC database contains patient demographics, clinical data, pathology data, systemic therapy types, etc., and it is possible to observe metastasis through continuous follow-up. The study protocol was approved by the Institutional Review Board of the Catholic University of Korea, which waived the requirement for informed consent (IRB No. KC16 MGGT0157).  
Metastases are classified as synchronous (occurring within three months) or metachronous (occurring after three months). It is necessary to distinguish between the two types of metastases because they have characteristics that lead to differences in prognosis [17]. However, because the sample size of patients with synchronous BM was insufficient, we included patients only with metachronous metastases in our analysis. The process of selecting patients for the study is illustrated in Figure 1.  
In this study, we selected eight variables based on previous studies [2; 7; 13; 14] and consultations with clinicians. The final variables were age, sex, smoking, Eastern Cooperative Oncology Group performance status (ECOG PS), pathological tumor stage (pT), Fuhrman nuclear grade, Heng risk group, and lung metastasis.
# Facile Fabrication of 3D Hierarchically Porous Carbon Foam as Supercapacitor Electrode Material
###### Abstract  
A hierarchically porous 3D starch-derived carbon foam (SCF) with a high specific surface area (up to 1693 m\({}^{2}\cdot\)g\({}^{-1}\)) was first prepared by a facile solvothermal treatment, in which Na\({}_{2}\)CO\({}_{3}\) is used as both the template and activating agent. The hierarchically porous structure and high specific area endow the SCF with favorable electrochemical properties such as a high specific capacitance of 179.6 F\(\cdot\)g\({}^{-1}\) at 0.5 A\(\cdot\)g\({}^{-1}\) and a great rate capability and cycling stability, which suggest that the material can be a promising candidate for energy storage applications.  
Keywords:starch; carbon foam; Na\({}_{2}\)CO\({}_{3}\) self-sacrificing template; hierarchically porous; supercapacitor
## 2 Materials and Methods
### Data Description
#### 2.1.1 Data from Baseline and Periodic Clinical Evaluation  
A plethora of data were collected during baseline and at 3 months and 6 months after the start of the intervention. The data were categorized into three main categories:  
1. _Cardiovascular Risk Profile_: These markers were collected through blood sampling and anthropometric measurements. The Framingham cardiovascular risk score was calculated as described in [32].
2. _Health-Related Physical Fitness_: These data represent the findings from a maximally graded cardiopulmonary exercise test (CPET) on a bicycle, along with muscle strength testing including maximal isometric and isokinetic quadriceps strength, handgrip strength, and a 30 s sit-to-stand test.
3. _Psychological wellbeing and intervention effectiveness_: This subjective information was collected using standardized questionnaires assessing QoL [33], physical activity behavior [34; 35; 36], smoking, alcohol consumption [37], diet [38], stress [39], medication adherence [40], mental wellbeing [41], social support [42], self-efficacy [43], and perceived health status [44; 45].  
In total, 59 features were measured at baseline and 6-month follow-up, while 52 of these features were collected at 3-month follow-up. A detailed overview of the collected data has been published previously [16; 46].
## 3 Simulation Results and Analysis
Similar to the results without correlation (Figure 4), the use of LDPC codes corresponds to a performance improvement for all different receiver types, of the  
nel correlation 0.3 between adjacent antenna elements. Since MIMO multi-layer transmission was employed (spatial multiplexing), the number of transmitting antennas corresponds to the number of parallel flows of data, while the number of receiving antennas provides diversity. Therefore, 4 \(\times\) 256 MIMO has a diversity eight times higher than 4 \(\times\) 32. This translates into an improvement of performance for all receiver types. In the scenario of 4 \(\times\) 32 MIMO, we observe that the MRC performance tends to degrade above 25 dB, while the ZF and IB-DFE performances are very close to the MFB. Nevertheless, with 4 \(\times\) 256 MIMO, the MRC performs very close to the MFB and similar to the ZF and IB-DFE (these curves are almost superimposed). This occurs because the level of residual interference mitigated by the iterative receiver of the MRC is more accurately estimated and canceled due to the higher level of diversity provided by the 4 \(\times\) 256 MIMO. The simulation time of 4 \(\times\) 256 MIMO and LDPC codes is around 48 h, while 4 \(\times\) 32 MIMO and LDPC codes it corresponds to around 36 h.
## 2 (Com)2 INet: Communication and Computation Integrated Network Architecture
### Key Features of (Com)2 INet Computational Paradigm  
_Service-centric paradigm._ In the (Com)2 INet computational paradigm, services become the core components and driving force of the network. The traditional network architecture is centered on data transmission. In (Com)2 INet architecture, communication and computation are deeply integrated, and services need determine the allocation and scheduling of multi-dimensional resources. With the help of NFV technology, computing services are translated into corresponding service chains (SCs), and end-to-end services are realized by sequentially connecting the required network functions to satisfy the diverse requirements of communication and computation [28].  
_Serverless-based model._ Network functions can be chained in an event-driven manner, typically initialized within milliseconds [29]. This on-demand chaining model enables the system to orchestrate different locations and differentiated resources in real time to achieve the optimal combination of user experience, resource utilization, and network efficiency. In addition, the model can easily update and replace specific network functions to adapt to new services or improve algorithm performance, enabling an efficient, intelligent, and elastic management paradigm.  
_Regionless-based model._ In (Com)2 INet architecture, heterogeneous network components are identified by names of network functions instead of IP addresses. (Com)2 INet jointly considers heterogeneous resources from both the communication and computation perspectives, and schedules customized services to target network components for computing and analysis along optimal paths in a decentralized manner, achieving collaborative scheduling within the WAN. Regionless-based model solves the \"IP bottleneck\" problem and enables the application of new network architecture and service-centric paradigm [30].  
In this paper, we propose a new computing paradigm, named (Com)\({}^{2}\)INet, which jointly schedules heterogeneous resources from both communication and computation perspectives to solve the dilemma that emerging applications require more computing power while available computing power is ubiquitous but not easy to use.
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
## 3 Methods  
### System Design  
To detect snoring when a person is sleeping, a wearable device was designed using Arduino Nicla Voice [13]. The device contains built-in microphone to receive snoring sound and provide input to TinyML model that was built. The main features of the device are given in Table 1:  
The device is powered by a 3.7 V Li-po battery, as illustrated in Figure 1.  
The system works by detecting the sound of snoring using a wearable sensor. If snoring is detected by the TinyML model running on the wearable device, it sends a signal to the nearby gateway device which in turn generates an alarm. The gateway also stores the snore detection data with timestamps locally and further sends it to a cloud server for permanent storage and analysis. In this way, the system not only detects and alerts users to snoring but also keeps track of historical data which may be shared with medical practitioners for further analysis and diagnosis.  
\begin{table}
\begin{tabular}{c c} \hline
**Microprocessor** & Syntiant® NDP120 Neural Decision ProcessorTM (NDP) (Syntiant Corp., Irvine, CA, USA) \\ \hline
**Microcontroller** & nRF52832 64 MHz (Arm Cortex M4) (Nordic Semiconductor ASA, Trondheim, Norway) \\ \hline
**Sensor** & Microphone IM69D130 (Infineon Technologies AG, Neubiberg, Germany) \\ \hline
**Power** & 3.7 V Li-po battery \\ \hline
**Memory** & 512 KB Flash, 64 KB SRAM 16 MB SPI Flash, 48 KB SRAM \\ \hline
**Connectivity** & Bluetooth® Low Energy (ANNA-B112) \\ \hline \end{tabular}
\end{table}
Table 1: Device configuration.  
As shown in Figure 1, the device can be comfortably worn on the wrist by the user during sleep and is used to detect snoring and send alerts to the user. The system architecture is shown in Figure 2.
# A Survey on Fault Diagnosis of Rolling Bearings
## 2 Background, Taxonomy, and Scope  
### Fault Forms/Types of Rolling Bearing  
Rolling bearings have several types, but their basic structures remain the same. Typically, a rolling bearing consists of four parts: the inner ring, the outer ring, the rolling element, and the cage. There are four types of corresponding faults, i.e., the inner ring fault, the outer ring fault, the rolling element fault, and the cage fault. A rolling bearing may fail due to internal and/or external problems/factors. Nowadays, bearing failures are mainly caused by external factors, including improper assembly, oil lubrication failure, pollution corrosion, and overloading. Rolling bearing faults often have the following forms [3]:  
(1) Fatigue  
Rolling bearings operate with great periodic contact stress between the rolling element and the inner/outer ring surface, causing the contact surface (generally the track surface) to fatigue and crack, which gradually extends to the raceway surface. Fatigue causes the bearing surface material to fall off and form pits. In severe cases, the material on the surface may fall off in large areas. Fatigue pitting and fatigue peeling are commonly used terms for describing fatigue.  
(2) Wear  
The failures of the rolling bearing sealing system cause bearing wear. When the sealing system fails, foreign matter will enter the bearing, resulting in abnormal friction between the inner ring/outer ring and the rolling elements. Additionally, improper lubrication will further aggravate wear, resulting in continuous material loss, increased surface roughness, increased clearance between bearings, and decreased running accuracy.  
(3) Deformation  
Deformation means that the bearing surface has undergone plastic deformation, or more specifically, a permanent indentation will appear on the bearing surface if the load borne by the bearing exceeds the yield strength limit of the material. Incorrect assembly methods and foreign matter appearance are the main reasons for the bearing deformation.  
(4) Corrosion  
Corrosion of rolling bearings occurs when chemical reactions occur on their surface. The first one is the oxidation reaction between the water in the lubricating oil and the bearing surface. The second one is fretting friction between components that leads to the oxidation of surface materials. The last one is abnormal current/voltage that causes local overheating of the bearing, resulting in welding of the element contact surface.  
(5) Fracture  
Rolling bearing fractures are the damage caused by local stresses exceeding the material's tensile strength limit. Generally, the crack propagates over time and penetrates part of the bearing component, causing complete separation of the material and fracture of the bearing. In addition, violent loading and unloading can also lead to bearing fracture.
## 2 Materials and Methods
### Cell Lines and Transfection  
CHO-K1 cells (CCL61, American Type Culture Collection, Manassas, VA, USA) were cultured in Dulbecco's modified Eagle medium (DMEM, Lonza Group Ltd., Basel, Switzerland) supplemented with 10% fetal bovine serum (Hyclone FBS, GE Healthcare, Boston, MA, USA), 1% penicillin/streptomycin (Lonza Group Ltd., Basel, Switzerland) and 0.07 g/L L-Proline (Sigma Aldrich, Saint Louis, MO, USA) in a humidified atmosphere at 5% CO\({}_{2}\). The day before transient transfection, cells were seeded at an appropriate density to reach 50-80% confluency at the time of transfection on CELLBIND plates (Corning Incorporated, Kennebunk, ME, USA) or chambers slides (Ibidi GmbH, Grafelfing, Germany). The plasmid vectors (pcDNA 3.1, Thermo Fisher Scientific, Rockford, IL, USA) containing \(F7\) cDNA coding for wild-type (wt) or p.A354V-p.P464 Hfs FVII were generated as previously described [13; 28]. The empty vector without insert was used as negative control in the experiments. Plasmid DNA was introduced into the cells by lipid transfection (Lipofectamine LTX, Thermo Fisher Scientific, Rockford, IL, USA) according to the manufacturer's protocol. CHO-K1 cells with stable expression of rFVIIwt or rFVII-354V-464 Hfs were generated as previously described [24].
## 5 Performance Evaluation
### Simulation Setup
#### 5.1.2 Satellite-to-Ground Channel  
The influence of clouds and atmosphere needs to be strongly considered in low earth orbit satellite communication scenarios. As stated in Section 3.1, the fading of the satellite-to-ground channel mainly originates from FSL and rain loss, However, to model the realism of the scenario, we also take into account losses such as atmospheric noise, flicker loss, and losses caused by terrain.  
After considering the cloud cover and atmospheric environment modeling, we specified the coding approach. We adopted AR4 JA as the channel encoding method, with code length \(k=1024\) and code rates of 50%, 66%, and 80%. After repeating Monte Carlo simula  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Value** \\ \hline Orbit Planes & 72 \\ Satellite Per Orbit & 22 \\ Inclination & 53\({}^{\circ}\) \\ Altitude & 550 km \\ Transmitter EIRP & 12.88 dBW/MHz \\ Receiver G/T & 13.7 dB/K \\ Ground Communication Range Radius & 573.5 km \\ Farthest Connection Distance & 794.6 km \\ Downlink Speed & 50 Mbps \\ Modulation Method & QPSK \\ Channel Encoding Method & AR4 JA \\ Frequency & 12.0 GHz \\ Data Rate & 50\%, 66\%, 80\% \\ Date & 8 and 9 December 2020 \\ Data Interval & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters in the scenario simulation.  
Figure 4: The scene is modeled by Starlink, where the lines represent satellite-to-ground connections.  
tions 10 million times at different SNRs, we obtain their PER and BER curves, as shown in Figure 2. Quadrature phase-shift keying (QPSK) modulation is adopted as the modulation method. According to users' actual download speed test nowadays, the maximum is 116 Mbps, so we take 100 Mbps as the downlink speed. In the simulation, we assume that the satellite's location and the channel quality are derived once per second.
## 3 NLOS Ranging Error and Correction Methods of UWB and Wi-Fi RTT Signals  
In typical indoor positioning environments, UWB and Wi-Fi both suffer largely from NLOS and multipath effects. NLOS signals may cause significant ranging errors, and NLOS identification and correction have been widely recognized as research topics.
## 1 Introduction  
The Japanese writing system stands as one of the most intricate in the world, composed of three distinct character sets: hiragana, katakana, and kanji. Hiragana and katakana each include 46 basic characters, serving different linguistic functions--hiragana for native Japanese words and grammatical particles and katakana for foreign loanwords. However, it is kanji, adapted from Chinese, that introduces the greatest complexity. With tens of thousands of kanji in existence, even native speakers rely on the standardized Joyo kanji list, consisting of 2136 characters, deemed as sufficient for reading newspapers, legal documents,and other official materials. Additionally, the Jinmeiyo kanji list, with 863 characters, is designated for use in proper names. Despite these efforts to systematize the writing system, the sheer volume and complexity of kanji continue to pose challenges, particularly for non-native speakers striving to engage with Japanese culture and literature. As technology advances, artificial intelligence has made remarkable strides in tackling tasks that once required human cognition. From image recognition to natural language processing, AI systems now handle many functions previously thought to be the exclusive domain of humans. However, certain challenges remain, particularly when it comes to nuanced, highly variable tasks. Handwritten kanji recognition is one such problem that has yet to be solved with a high degree of accuracy. Although printed kanji recognition has mostly been mastered, the transition to handwritten forms introduces a range of complexities that continue to challenge modern machine-learning models. The difficulties arise from the inherent variability in human handwriting. Kanji characters are composed of radicals formed by specific strokes, and although there are established rules for stroke order and composition, these are frequently disregarded in everyday writing. This leads to subtle or sometimes significant variations in the final form of the character. Strokes that should connect may be slightly separated, merged unintentionally, or even cross each other in ways that deviate from standard forms. Additionally, writers often use shorthand, omitting or simplifying radicals, which further complicates the recognition process. The variability does not end there. Differences in handwriting styles mean that stroke thickness, pressure, and continuity can vary significantly, influenced by writing speed or the physical characteristics of the writing instrument. Radicals within a kanji character may appear distorted--misaligned, resized, or tilted--affecting the overall structure and making the kanji difficult to recognize. These inconsistencies create a significant challenge for machine-learning models tasked with accurate handwritten kanji recognition. This paper addresses this complex problem by implementing a Preact ResNet-18 model tailored for handwritten kanji recognition.
# Joint Power and Time Allocation in Hybrid NoMA/OMA IoT Networks for Two-Way Communications
## 1 Introduction
The proposed two-way communications with scalable TDD and hybrid NoMA/OMA are distinguished from both the equal-length UL and DL two-way transmission schemes and the one-way hybrid NoMA/OMA schemes mentioned above, and can well address the asymmetric quality of services (QoS) requirement and the unbalanced receivers processing capability between UL and DL.