esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multiple Nodes Cooperative Transmission  
### Co-Carrier-Based Direct Combination  
Since the equivalent channel gain of the beam-edge user and the beam-center user has a significant difference in LEO SATCOM [15], we propose a co-carrier transmission method shown in Figure 2.  
UTs in the overlapping area can receive multiple signal paths from the \(M\) satellites. To enhance the signal strength, we propose a co-carrier-based DC scheme where UTs receive \(M\) path signals and directly superpose them. In the co-carrier-based scheme, one pair  
Figure 1: System model.  
Figure 2: Co-carrier transmission in LEO constellation.  
of beam-edge and beam-center UTs is allocated with one signal carrier at one time-slot. Multiple UT pairs could be served by allocating more carrier and time-slot resources.  
For UT \(k\) in the overlapping area, the received signal can be expressed as  
\[y_{k}=\sum_{m=1}^{M}\mathbf{h}_{k,m}^{\mathrm{H}}\mathbf{w}_{m}(s_{k}+x_{m})+n, \tag{3}\]  
where \(\mathbf{w}_{m}\) is the beamforming vector of the \(m\)-th satellite. \(s_{k}\) represents the signal for UT \(k\) in the overlapping area, \(x_{m}\) denotes the signal for the beam-center UT under the \(m\)-th satellite. The background noise with power spectral density \(\sigma^{2}\) is denoted as \(n\sim\mathcal{CN}(0,\sigma^{2})\). Note that the effect of Doppler frequency shift could be relieved by applying the satellite ephemeris-based frequency offset pre-compensation scheme.  
Assume \(\mathbb{E}[|s_{k}|^{2}]=P_{k}\) and \(\mathbb{E}[|x_{m}|^{2}]=P_{m}\). To guarantee the fairness for the beam-edge and beam-center UTs, we have \(P_{m}=\alpha P_{k}\), where \(0<\alpha<1\). Since \(s_{k}\) is stronger compared with \(x_{m}\), the useful information could be directly demodulated at UT \(k\) where the SINR can be derived as  
\[SINR_{k,k}=\frac{|\sum_{m=1}^{M}\mathbf{h}_{k,m}^{\mathrm{H}}\mathbf{w}_{m}|^ {2}P_{k}}{\sum_{m=1}^{M}|\mathbf{h}_{k,m}^{\mathrm{H}}\mathbf{w}_{m}|^{2}P_{ m}+\sigma^{2}}. \tag{4}\]  
We apply SIC at UT \(m\), the SINR of \(s_{k}\) can be calculated as  
\[SINR_{m,k}=\frac{|\mathbf{h}_{m,m}^{\mathrm{H}}\mathbf{w}_{m}|^{2}P_{k}}{| \mathbf{h}_{m,m}^{\mathrm{H}}\mathbf{w}_{m}|^{2}P_{m}+\sigma^{2}}. \tag{5}\]  
After processing SIC, the SINR of \(x_{m}\) is  
\[SNR_{m,m}=\frac{|\mathbf{h}_{m,m}^{\mathrm{H}}\mathbf{w}_{m}|^{2}P_{m}}{\sigma ^{2}}. \tag{6}\]  
Thus, the SINR of UT \(k\) satisfies \(SINR_{k}=\min\{SINR_{k,k},SINR_{1,k},\ldots,SINR_{m,k},SINR_{M,k}\}=SINR_{k,k}\).
## 2 Previous Efforts with UAS-Mounted Spectroradiometers for Reflectance Measurements  
Ground based spectroradiometer measurements can be carried out relatively easily following standard protocols for setup, calibration, and data storage. When the instrument is mounted on an aerial platform, the accurate footprint (defined as the sampled Earth surface area from which radiance is received) geolocation on the ground, and the measurement of ambient light changes during the flight become the main challenges, which have been addressed in previous studies from different perspectives:  
1. [PERSON] et al. [38] calculated reflectance based on synchronized measurements from two, one on-board and one on-ground, equally configured and cross calibrated STS micro-spectroradiometers (STS Series Spectrometers, Ocean Optics, Dunedin, Florida, USA) under the same environmental conditions. In this case, the radiometric calibration was performed indirectly from comparisonsbetween STS and an Analytical Spectral Device (ASD) FieldSpec 4 (Analytical Spectral Devices, Inc., Boulder, Colorado, USA) calibrated spectroradiometer. Results proved the high precision of the reasonably priced STS measurements, notwithstanding a second order effect was detected influencing NIR readings. The 338-412 nm stray light interfered with bands within 676-823 nm, especially in the O\({}_{2}\)-A band. Consequently, the authors suggested additional studies for STS-based SIF retrieval. Current investigations are focused on the use of STS micro-spectroradiometers, not for SIF measurements, but for the assessment of reflectance factors as a complementary (canopy scale) data source to leaf level spectral information acquired with an ASD spectroradiometer. The system, from the Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), included a real time optimization of the integration time, seeking to maximize signal independently of target brightness or changes in illumination. This feature was relevant considering the low signal-to-noise ratio of the STS spectrometers, and the variability of surface reflectance factors in heterogeneous Mediterranean tree-grass ecosystems, where bright dry grass is mixed with dark tree canopies during summer [39].
* [PERSON] et al. [40] realized downwelling irradiance measurements with a USB4000 spectroradiometer (Ocean Optics, Dunedin, Florida, USA) through linear interpolation of two measurements of the radiance reflected by a reference tarp, and the use of a second on-ground hand held ASD FieldSpec measuring a Lambertian surface (Spectralon(r)) as reference (both spectroradiometers were synchronized). The robust radiometric and spectral calibration of the instruments, despite not being temperature stabilized (e.g., the STS and the USB4000), permitted accurate radiance measurements especially at O\({}_{2}\)-A. Both approaches present a relative root mean square error lower than 10% compared with ground information.
## 4 Discussion
### Spatial Distribution  
The predicted soil PSFs showed a similar distribution trend in both study areas. The finer soil texture was distributed in the southeast or east, while the coarse soil texture was distributed in the northwest. We compared the spatial distribution of soil fractions (take sand content for example) and some of the important predictors for both regions (Figures S7 and S8) to test if the predicted PSF distribution followed the same spatial pattern as the corresponding important predictors. Since the predicted silt and clay had a similar distribution pattern, though sand was opposite, we took sand fraction as a representative. We found that in the RQ region, even though VRM had the highest variable importance in predicting clayilt, its spatial distribution was not that similar to sand. The most important predictor of siltilt, s2_df_a_9, had a very similar spatial distribution as the sand fraction in the northwest part. However, it did not depict the spatial variance of the sand fraction in the southeastern part. In contrast, some other predictors, such as EVI_1, s1_d_a_1, and s3_d_a_9, showed more a similar spatial distribution as the sand fraction. According to the land use type (Figure S7), coarse soil texture was usually distributed in the alpine desert grassland, where the elevation was relatively low, and the daytime LST (i.e., s3_d_a_9) and the LST difference (i.e., s2_df_a_9) were relatively high. The finer soil texture was usually distributed in the alpine meadow and alpine steppe. While in the WQ region, the s4_df_e_9 and yr_n_i_9 showed a similar spatial distribution pattern as the sand fraction, especially in the northwest part. The coarse soil texture was mainly distributed in the northwest WQ (Figure S8), dominated by alpine steppe and alpine desert grassland. In the northwest WQ, the minimum LST was lower, and the LST difference was larger. It indicated that the predicted distributions of soil PSFs were determined by the compound impact of multiple predictors rather than solely impacted by the most influential predictor.
# Development of Operator Theory in the Capacity Adjustment of Job Shop Manufacturing Systems
## 1 Introduction
### Related Research  
The methods of capacity adjustment can be classified into two types: Labor-oriented approaches and machinery-oriented approaches. Labor-oriented approaches mainly modify capacity through adjusting the working time. In contrast to these, machinery-oriented approaches adjust capacity through the flexibility of machines. Reconfigurable machine tools (RMT), as one advanced piece of technology of Industry 4.0, provide a new opportunity for machinery-oriented capacity adjustment, which cannot be achieved using classical dedicated machine tools (DMT) only. In [12], RMTs were used in harmonizing the throughput-time capacity control approach to plan the delivery dates and analyze the inventory range of each workstation considering reconfiguration delays. The proportional-integral-differential (PID) control method [13; 14] was applied to adjust the capacity, and a mathematical model of job shop systems was developed, including the new application degree of freedom of RMTs. Furthermore, the respective model was continuously extended, including WIP and planned WIP level of each workstation, and a model predictive control (MPC) approach was applied considering time-varying input orders [15]. However, job shop systems are not single-input-single-output (SISO) systems but instead show nonlinear dynamics as well as a multi-input-multi-output (MIMO) structure with strong coupling between the workstations (subsystems). Additionally, these systems also suffer from various disturbances and delays, which are unaccounted for in the literature. Operator-based robust right coprime factorization (RRCF) [16] represents one option to deal with these issues.  
Operator theory is an advanced control theoretical approach to effectively control and analyze the dynamic and stability performances of a class of nonlinear systems [17]. Moreover, it is accessible to ensure the stability of nonlinear systems using the Bezout identity [18]. The robust stability and traceability of the nonlinear affine systems with unknown bounded disturbances were analyzed in [19]. The design of robust and tracking controllers of nonlinear feedback systems was considered in [20; 21; 22; 23]. Additionally, this method has been applied to deal with complex problems, such as disturbances, delays, couplings, and so on. In [24], this method was used to deal with internal and external disturbances to ensure robust stability. [PERSON] et al. [25; 26] analyzed the delays and faults detection in a thermal process control system using operator theory. In [27], a tracking operator was designed to cope with unknown time-varying delays in nonlinear uncertain systems. [PERSON] and [PERSON] [28; 29] solved the coupling problem in MIMO systems and designed robust controllers for MIMO systems. The literature illustrates the effectiveness of the RRCF approach in dealing with nonlinearities, disturbances, delays, and couplings, which occur in job shop systems.
# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
## 2 Characteristics of the Detected Signal in Fracture Zones
### Characteristics of GPR Detection Data in Fracture Zones  
Ground-penetrating radar operates by transmitting high-frequency electromagnetic waves toward a target using a transmitting antenna and capturing the reflected waves via a receiving antenna. During propagation, electromagnetic waves encounter electrical differences in the stratum or target body, resulting in reflection. The working principle of GPR in detecting fracture zones in front of the tunnel face is illustrated in Figure 3. The spatial location of the fracture zone is deduced by analyzing the waveform, amplitude intensity, and phase variation characteristics of the received reflected electromagnetic waves.  
When an electromagnetic wave propagates in a propagation medium, the propagation constant is described by the attenuation coefficient and phase coefficient of the amplitude of the electromagnetic wave, which are expressed as follows:  
\[k=\alpha+j\beta \tag{4}\]  
where \(\alpha\) and \(\beta\) are the attenuation coefficient and phase coefficient, respectively, and are expressed as follows:  
\[\left\{\begin{array}{l}\alpha=\omega\sqrt{\frac{\mu\epsilon}{2}\left[\sqrt{1+ \left(\frac{\sigma}{\omega\epsilon}\right)^{2}}-1\right]}\\ \beta=\omega\sqrt{\frac{\mu\epsilon}{2}\left[\sqrt{1+\left(\frac{\sigma}{ \omega\epsilon}\right)^{2}}+1\right]}\end{array}\right. \tag{5}\]  
where \(\omega\) is the angular frequency of the electromagnetic wave; \(\mu\) is the permeability of the medium; \(\epsilon\) is the dielectric constant of the medium; and \(\sigma\) is the conductivity of the medium.  
\[\gamma=\frac{\sqrt{\epsilon_{1}}-\sqrt{\epsilon_{2}}}{\sqrt{\epsilon_{1}}+ \sqrt{\epsilon_{2}}} \tag{6}\]  
When an electromagnetic wave propagates from the intact surrounding rock to the fracture zone, as indicated by Equation (5), the amplitude of the reflected signal increases  
Figure 3: Principles of GPR for detecting fracture zones.  
due to the disparity between the dielectric constants of the fracture zone and the intact rock mass. Equation (6) states that \(\gamma\) is negative when a wave propagates from a low-dielectric-constant medium to a high-dielectric-constant medium. The reflection coefficient is positive when a wave propagates from a high- to a low-dielectric-constant medium. Therefore, the electromagnetic wave's phase as it enters the fracture zones from the intact surrounding rock is opposite to that as it enters the tunnel face rock from the air.
## 5 Experimental Analysis
### Case Study  
We provide a parsing example to show why GNNSDP benefits from higher-order information. Figure 4 shows the parsing results of the biaffine parser (Figure 3(a)) and GNNSDP(GCN) (Figure 3(b)) for the English sentence (sent_id = 40504035, in OOD of PSD formalism) _\"There is never enough corn, peas or strawberries\"_. Both parsers are trained in the basic embedding settings.  
In the gold annotation, three words _corn_, _pas_, and _strawberries_ are three members of the disjunctive word _or_. In addition, the word _enough_ has three dependency edges labeled _EXT_ with them. In the results of the biaffine parser, only the dependency edge between _enough_ and _corn_ is identified; the remaining two are not. In GNNSDP(GCN), given the initial SDG predicted by the biaffine parser, the words _corn_, _pas_, and _strawberries_ aggregate the higher-order information _or_ and _is_ (first-order), _there_ (second-order), and _ROOT_ (third-order). The word _enough_ aggregates the higher-order information of the _corn_ (first-order), _is_ and _or_ (second-order), _there_ (third-order), and _ROOT_ (fourth-order). The dependent word _enough_ and three head words _corn_, _pas_, and _strawberries_ aggregate information of four common words (_ROOT_, _There_, _is_ and _or_). Therefore, the representations of them with higher-order information bring more evidence into decoders' final decisions. As a result, it is effortless for GNNSDP to identify that there are also two dependency edges labeled _EXT_ between the dependent word _enough_ and the head words _pas_ and _strawberries_.
## 3 Numerical Simulations and Discussion  
### Methodology of Numerical Simulations  
Based on Algorithm I and Algorithm II, programs are developed by Matlab for the numerical simulations. In the simulations, the identified bearing coefficients are compared with their setting values for the validation of the two algorithms. Six computational examples are used. They represent single-span and single-disc rotor (g1.1 and h1.1), single  
Figure 2: Identification procedures of Algorithm I and Algorithm II.  
span and four-disc rotor (g1.4 and h1.4), and four-span and four-disc rotor (g4.4 and h4.4). They are supported by rolling bearings and oil-journal bearings. Their parameters are the same as what is listed in Tables 1-6 in reference [26]. The computational example g1.1, g1.4, g4.4, h1.1 and h1.4 are the same as what is shown in Figures 3 and 4 in reference [26]. The computational example h4.4 is shown in Figure 3.  
Three kinds of numerical simulations are conducted. Firstly, the simulated unbalance responses calculated by CRDAM are fed into Algorithm I and Algorithm II to estimate the bearing coefficients. Secondly, similar identification exercises are performed by contaminating simulated unbalance responses by the set measured error of 5%/5\({}^{\circ}\). The relative error of the unbalance response amplitude is 5% and the absolute error of the unbalance response angle is 5\({}^{\circ}\). Thirdly, the resolution of the vibration displacement sensor is considered in the simulation. Three kinds of typical sensor resolutions (0.1 nm, 1 nm and 1 um) are considered. By limiting the number of digits after the decimal point in the unbalance responses, the resolution of unbalance responses measurement systems are applied. In the simulations, the calculation rotating frequency is from 1 to 2001 Hz and the interval is 2 Hz.  
For simulation validation of Algorithm I, the maximum relative errors of identified main coefficients to the setting values are obtained. For Algorithm II, the maximum relative errors of identified main stiffness and damping coefficients to the setting values and the maximum absolute value of identified cross-coupled stiffness and damping coefficients are obtained in the computational examples g4.4, g1.4 and g1.1. In the computational examples h4.4, h1.4 and h1.1, the maximum relative errors of identified main and cross-coupled coefficients to the setting values are obtained. Moreover, in the third kind of simulation, statistical results of the amount of the frequency point called low error frequency points (LEFPs) are counted. At LEFPs, the relative error of the identified coefficient is less than 10% or the absolute value of the identified coefficient is less than 10.
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
### Problem Statement  
This article addresses the design space exploration problem to specify the memory consumption of a typical (as hypothesized in this article) IP reassembly block, using contemporary data science methods. These methods consist of unveiling a relevant statistical image of the network data, constructing the simulation model equipped with tuning parameters and performing Monte Carlo estimation of key model performance indicators.  
The main hypothesis is that the traffic locality of IP fragments is very high, which translates to the proximity of the packets from the same IP flow. Therefore, the memory requirements of the IP reassembly module should not be measured by the total number of IP connections but by the local traffic behavior. As similar prior experiments (Section 2.3) demonstrate, a flow table of modest size in the order of 1000 entries or even less should be able to process most of the traffic.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
###### Abstract  
Objective: This study aims to critically review and synthesize the existing literature on the use of voice analysis in assessing nasal obstruction, with a particular focus on acoustic parameters. Data sources: PubMed, Scopus, Web of Science, Ovid Medline, and Science Direct. Review methods: A comprehensive literature search was conducted without any restrictions on publication year, employing Boolean search techniques. The selection and review process of the studies followed PRISMA guidelines. The inclusion criteria comprised studies with participants aged 18 years and older who had nasal obstruction evaluated using acoustic voice analysis parameters, along with objective and/or subjective methods for assessing nasal obstruction. Results: Of the 174 abstracts identified, 118 were screened after the removal of duplicates. The full texts of 37 articles were reviewed. Only 10 studies met inclusion criteria. The majority of these studies found no significant correlations between voice parameters and nasal obstruction. Among the various acoustic parameters examined, shimmer was the most consistently affected, with statistically significant changes identified in three independent studies. A smaller number of studies reported notable findings for fundamental frequency (F0) and noise-related measures such as NHR/HNR. Conclusion: This systematic review critically evaluates existing studies on the use of voice analysis for assessing and monitoring nasal obstruction and hyponasality. The current evidence remains limited, as most investigations predominantly focus on glottic sound and dysphonia, with insufficient attention to the influence of the vocal tract, particularly the nasal cavities, on voice production. A notable gap exists in the integration of advanced analytical approaches, such as machine learning, in this field. Future research should focus on the use of advanced analytical approaches to specifically extrapolate the contribution of nasal resonance to voice thus defining the specific parameters in the voice spectrogram that can give precise information on nasal obstruction.  
os 2025 15 8423 2025 20
## 2 Materials and Methods
### Statistical Analysis and Method  
This digital questionnaire was distributed among participants of the European Group of Research in Tolerancing (E-GRT) Biannual Seminar 2019 and other researchers and industrial engineers with an expertise in tolerance analysis and variation management. In total, 43 answers were collected. Information about the background variable responses can be seen in Table 1. For age and years of experience in tolerancing, the respondents could choose between different alternatives presented in the form of intervals. The midpoints of the intervals were used in the calculation of the average values presented in Table 1.  
The answers to the questions posed above were analyzed with the purpose to see the participants' opinions about current focus areas and future needs in research and the industry. To analyze the frequency differences among different answers, a Cochran Q test was employed. This statistical test is a nonparametric test for related categories with binary responses [9]. The related categories are the questions (a)-(r). This test can determine if there is a significant difference between the categories. The test statistic, for binary responses \(Y_{ij}\), \(i=1,\ldots,n\), and \(j=1,\ldots,k\), is:  
\[Q=\frac{(k-1)\Big{(}kC-T^{2}\Big{)}}{kT-R} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Respondents** & **Gender** & **Average Age** & **Average Years of Experience in Tolerancing** & **Continent** & **Affiliation** \\ \hline \hline
43 & Male: 37 & 40 & 9.8 & Europe: 32 & Academia: 26 \\  & Female: 6 & 40 & 9.8 & Asia: 11 & Industry: 17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Background information of respondents.  
The variables are: \(n\): number of respondents, and \(k\): the number of questions.  
\[C=\sum_{j=1}^{k}\left(\sum_{i=1}^{n}Y_{i,j}\right)^{2}\]  
\[T=\sum_{i=1}^{n}\sum_{j=1}^{k}Y_{i,j}\]  
\[R=\sum_{i=1}^{n}\left(\sum_{j=1}^{k}Y_{i,j}\right)^{2}\]  
The \(Q\) statistic follows approximately a \(\chi^{2}\)-distribution with \(k-1\) degrees of freedom. This approximation is considered valid if \(n>4\) and/or \(nk>24\). Here, \(n=43\), and \(k=18\).  
The Cochran \(Q\) test is used to test whether there is a difference between any of the different categories. To point out what categories significantly differ from the other, a minimum required difference (MRD) test is used with Bonferroni adjustments to compensate for repeated tests [9]. The MRD is calculated as:  
\[MRD=z_{adj}\sqrt{2\frac{KT-R}{n^{2}k(k-1)}} \tag{2}\]  
where \(z_{adj}\) is the value of \((1-\alpha_{adj}/2)\) in a standard normal distribution. The MRD is based on pairwise observations.  
When comparing answers to the same question, with filtering based on the affiliation of the respondent, another test needs to be used. In this case, a standard \(z\)-test with pooled variance will be used [10]. This test compares the proportions, \(p_{i}\) and \(p_{j}\), respectively, of positive answers to a certain question and test the hypothesis  
\[H_{0}:p_{i}=p_{j}\]  
for two groups, \(i\) and \(j\).