esa-sceva/satcom-chunk-collection huggingface dataset:
# Bit Error Rate Closed-Form Expressions for LoRa Systems under Nakagami and Rice Fading Channels
## 3 Bit Error Probability
### Error Probability for Rayleigh channels
 \tag{37}\]  
Expression (35) is the average bit error rate probability for Rician channels in the integral form. A closed-form expression of (35) can be obtained following the same rationale in the steps of (10)-(13), and substituting (37) so that  
\[P_{e|\
u}=\sum_{q=1}^{2^{SF}-1}\frac{(-1)^{q+1}}{1+q+\frac{2^{ SF}q\Omega}{1+\kappa}}\binom{2^{SF}-1}{q}\times\exp\left[-\frac{\gamma 2^{ SF}q\kappa\Omega}{1+\kappa+q\left(1+\kappa+\gamma 2^{SF}\Omega\right)}\right] \tag{38}\]  
Note, when \(\Omega=1\), we have that  
\[\lim_{\kappa\rightarrow\infty}\left[\exp\left(-\frac{\gamma 2^{ SF}q\kappa\Omega}{1+\kappa+q\left(1+\kappa+\gamma 2^{SF}\Omega\right)}\right) \right]=\exp\left(-\frac{q}{q+1}\gamma\cdot 2^{SF}\right), \tag{39}\]  
and  
\[\lim_{\kappa\rightarrow\infty}\left[\frac{(-1)^{q+1}}{1+q+\frac{2 ^{ SF}q\gamma\Omega}{1+\kappa}}\right]=\frac{(-1)^{q+1}}{q+1} \tag{40}\]  
which agrees with the limit case given in (14) for AWGN scenario when the Rician shape parameter tends to infinity. On the other hand, we have that  
\[\lim_{\kappa\to 0}\left[\exp\left(-\frac{\gamma 2^{ SF}q\kappa\Omega}{1+\kappa+q\left(1+\kappa+\gamma 2^{SF}\Omega\right)}\right) \right]=1, \tag{41}\]  
and  
\[\lim_{\kappa\to 0}\left[\frac{(-1)^{q+1}}{1+q+\frac{2 ^{ SF}q\gamma\Omega}{1+\kappa}}\right]=\frac{(-1)^{q+1}}{1+q+2^{ SF}q\gamma} \tag{42}\]  
which agree with the limit case in the (26) of Rayleigh scenario when the Rician shape parameter tends to zero. The average error bit probability for Rice case is derived such that [12]  
\[P_{b}=\frac{2^{SF-1}}{2^{SF}-1}\sum_{q=1}^{2^{SF}-1}\frac{(-1)^{q+1}}{1+q+\frac{2 ^{SF}q\gamma\Omega}{1+\kappa}}\binom{2^{SF}-1}{q}\times\exp\left[-\frac{\gamma 2 ^{SF}q\kappa\Omega}{1+\kappa+q\left(1+\kappa+\gamma 2^{SF}\Omega\right)}\right]. \tag{43}\]
## 2 Materials and Methods
### Physical Meat Quality Evaluation throughout Ageing  
At each of the aforementioned time points, the relevant steaks were assessed for physical meat quality parameters. Firstly, moisture loss was determined by removing the steak, blotting it dry, and then weighing it to determine the weep loss, as a percentage of the steak's original weight. Thereafter, the pH was measured with a two-point calibrated (using standard buffers of pH 4 and pH 7) Crison pH25 portable pH meter (Crison instruments, Barcelona, Spain) with a glass electrode. Measurements were taken by inserting the electrode at an angle as close to the center of each steak as possible. Between each measurement, the electrode was cleaned by rinsing with distilled water and blotted dry with absorbent paper. The steaks were then allowed to bloom for 30 min, after which color measurements were taken with a calibrated Color-guide 45\({}^{\circ}\)/0\({}^{\circ}\) colorimeter (4 mm aperture, 2\({}^{\circ}\) observer angle; BYK-Gardner GmbH, Gerestried, Germany) at five random positions, and averaged per sample. The International Commission on Illumination (CIE) system was used, where hue angle (color definition) and chroma values (saturation/color intensity) were calculated from L* (lightness), a* (red-green spectrum) and b* (blue-yellow spectrum) values according to the following equations:  
\[\text{Hue angle }(^{\circ})=\text{tan}^{-1}\bigg{(}\frac{\text{b}^{*}}{\text{a}^{* }}\bigg{)} \tag{1}\]  
\[\text{Chroma }(\text{C*})=\sqrt{(\text{a}^{*2}+\text{b}^{*2})} \tag{2}\]  
Subsequently, the cooking loss of each sample was determined by placing a weighed piece of steak inside a thin plastic bag and cooking in a preheated water bath (80 \({}^{\circ}\)C) for a period of 60 min [6]. All steaks from the relevant ageing day were cooked in one batch and each steak was fully submerged for the duration of the cooking period. After 60 min, the cooked steaks were removed from the water bath and excess water was drained before chilling at \(4\pm 1\)\({}^{\circ}\)C for approximately six hours until cooled. Thereafter, the steaks were blotted dry with absorbent paper and weighed, and moisture loss was expressed as a percentage of the initial weight of the uncooked steak [28].  
Following the cooking loss determination, the Warner-Bratzler shear force (WBSF) was measured using six cylindrical core samples (diameter: 1.27 cm) taken from the center of each steak, while excluding visible connective tissue. The cores were placed within the Warner-Bratzler blade such that they were cut at a right angle to the longitudinal axis of the fibers (blade speed: 3.33 mm/s) and the peak force during cutting was recorded in Newtons (N). The mean force was determined from the six samples per steak, with lower shear force values indicating higher tenderness.
## 1 Introduction
Single receiver and double difference integer ambiguity resolution methods are investigated and realized. Figure 1: Appearance and instrument installation position of GRACE-FO satellites. With one year of data, GRACE-FO orbits based on different AR strategies, including FA, SD AR, and DD AR, are studied and evaluated with JPL's orbit, SLR, and KBR measurements. Section 2 provides details of the strategy and data adopted for GRACE-FO POD. The antenna PCV estimation approach and related results are presented in Section 3. Section 4 introduces the GNSS observation model and focuses on the SD and DD AR. POD and PBD results with PCV corrections and different AR methods were analyzed. The necessity of in-flight antenna calibration and ambiguity resolution is verified. Finally, discussions are made, followed by conclusions.
# Decellularized Scaffolds for Skin Repair and Regeneration
## 2 Tissue Sources for Preparation of ECM-Based Biomaterials for Skin Repair
### Acellular Dermal Matrix (ADM)
#### 2.1.1 AlloDermTM RTM (LifeCell Corp., Branchburg, NJ, USA)  
AlloDermTM (LifeCell Corp., Branchburg, New Jersey, USA) is one of the first FDA-approved acellular matrix materials and among the most extensively investigated [25]. It has been used since 1992 to treat burns with successful engraftment of both the AlloDermTM and the subsequent overlying skin graft in many individuals [36]. This product was FDA approved for the replacement of inadequate integument tissue, but the most common cited application has been to cover the skin flaps of donor sites as a sheet graft without an overlying split-thickness graft (Table 1) [37]. AlloDermTM is processed directly from fresh cadaver skin treated with high salt to remove the cellular components. The treated skin is then freeze-dried, leaving an ADM with an intact basement membrane complex (Figure 2A) [25]. AlloDermTM is hydrated before use, typically in an antibiotic saline solution according to the manufacturer's recommendations. The meshing of the ADM at a 1:1 ratio optimizes revascularization and nutrition of the overlying skin graft [25]. Finally, in vitro recellularization of AlloDermTM with allogeneic keratinocytes, adipose-derived stem cells, fibroblasts, and umbilical vein endothelial cells allows for production of biological skin substitutes [25].
## 4 Application Results
### Speed Performance  
Analysis of the speed performance of the system was mandatory, since the system must support the clinician in a fast and timely way. Hence, they have been taken times needed to execute the set of rules and propose a set of possible diagnoses by our system. Times have been measured through the same set of 40 clinical cases in two computers with different specifications. Specifications of these computers and results are summarized in Table 2.  
Results show that our decision support system can propose diagnoses in a mean time shorter than 0.5 s. Only one of the 80 cases computed through the two computers took more time than 1 s. Even though more processor power and RAM memory affect the system performance, all cases can be managed in the order of a few seconds. This means that our system can make a set of proposals in a fast and timely manner to help the clinician in diagnostic decision step.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Actual**} & \multicolumn{3}{c}{**Predicted**} \\ \cline{2-4}  & **Positive** & **Negative** & **Total** \\ \hline Positive & True positive (TP) = 29 & False Negative (TN) = 6 & 35 \\ Negative & False positive (FP) = 5 & True Negative (FN) = 4 & 9 \\ Total & 34 & 10 & 44 \\ \hline \end{tabular}
\end{table}
Table 1: Confusion matrix with the value for every measure.
## 1 Introduction
A wide range of information can be extracted from SAR data [14], the most common of which is the backscatter coefficient. The backscattering intensity of SAR data can be used to obtain information about surface properties and has been widely used in soil science research to obtain information about soil roughness [15], soil moisture [16], and soil salinity [17]. The application of SAR images in mapping soil properties depends on the sensitivity of backscattering intensity to changes in soil moisture and land surface conditions [18]. [PERSON] et al. [19] found a significant correlation between SAR backscatters and various soil properties (including SOC, STN, and sand) during the growing season, and reported that multi-temporal SAR data is useful for predicting soil chemical properties because it can capture soil-vegetation relationships. Similar results have been reported by, for example, [PERSON] et al. [20] and [PERSON] et al. [21]. The soil-vegetation relationships observed in these studies can explain the spatial variation of the soil and thus help remote sensing techniques to predict soil properties [22;23]. Although SAR data is a promising data source for digital soil mapping, few studies have used the backscatter coefficients of SAR data as predictor variables for STN mapping. Arid and semi-arid areas account for about 50% of China's land surface. Due to irrational land use of these areas, as well as a decline in soil fertility and a lack of water resources, desertification in such areas is intensifying. Hence, greater attention should be given to the variation in soil properties here. Representing both arid and semi-arid regions in China, the spatial distribution of soil properties in the Heihe River Basin (HRB) has attracted the attention of researchers in recent years. However, these studies have focused primarily on SOC [24; 25] and soil texture [26], with little information on STN in the area. This study used machine learning algorithms to predict the STN content in the middle reaches of the HRB in Northwest China. The specific objectives were (i) to explore whether adding SAR data improves STN prediction; (ii) to evaluate and compare the performance of RF, BRT, and SVM models; and (iii) to assess the importance of predictor variables in mapping the STN content of the study area. For these purposes, 15 predictor variables (including the climate, the topography, LULC, and remote sensing-derived variables) and 85 soil samples were obtained. The remote sensing images used in this study included optical (Landsat-8) and multi-temporal SAR (Sentinel-1A) data.
## 4 Security Issues and Solutions for CAVs
### Cyber-Attacks on Intra-Vehicle Systems  
#### 4.1.1 Models of the Attacks  
Cyber-attacks indicate that the attacks on the vehicle are launched through physical/wireless connection to the vehicle. Some entry ports on vehicles to the intra-vehicle system to enable a wide range of services exist, including support for self-diagnostics, media play, etc. In the early stage, attacks must have physical connectivity to the vehicle, such as using a USB stick or an OBD-II scanner [2; 6; 27], which limits the implementation of the attacks.  
CVs enable diverse types of wireless connectivity on intra-vehicle systems; thus, cyber-attacks can be implemented remotely. In [2], two vehicles are connected through the wireless communication of two laptops. In the victim vehicle, the laptop running CARSHARK is connected to the vehicle's CAN bus through an OBD-II port; in the attack vehicle, another laptop transmits commands to the victim vehicle to manipulate its body control module, engine control module, etc. Most modern vehicles provide Bluetooth ports or Wi-Fi hotspots to outside devices. Cyber attackers can directly pair with the vehicle or crack a device which has joined the Wi-Fi hotspot. Typically, the range of such cyber-attacks is quite short, approximately tens of meters. Due to the large coverage areas and high-penetration-rate cellular networks, some researchers exploited the cellular networks to perform remote cyber-attacks. In [3], the researchers provided a \"super-remote\" connection (between Pittsburgh and St. Louis) to compromise an unaltered vehicle through a cellular network. Currently, vehicles are gradually installed with VANET communication modules, which allows vehicles to communicate with other vehicles or infrastructure even without a base station. This would provide much convenience for attackers to launch cyber-attacks on inter-vehicle systems in the future.  
Figure 5 presents the general model of cyber-attacks on intra-vehicle systems. Essentially, cyber-attacks on intra-vehicle systems involves manipulating messages on intra-vehicle networks, especially the CAN bus, to control the physical functions of the vehicleremotely. Therefore, the attacker needs to identify the address of the vehicle first and has access to a communication module on the system via physical/wireless connectivity. Then, the attacker needs to compromise the communication module, typically by modifying the files on the module. For certain types of vehicles, the compromised communication module is connected to the CAN bus, which allows an attacker to manipulate a CAN message to affect the control functions of the vehicle. For some types of vehicles, the communication module does not connect to the CAN bus, which will require more efforts of the attacker to compromise another module which has the ability to send messages to the CAN bus. After all these things are achieved, attackers can launch a real cyber-attack on the intra-vehicle system [32]. The remote attack surfaces, internal network structure, and computer-controlled features of several popular vehicles' patterns on the U.S market were investigated.
## 2 Materials and Methods
### Volume and Angle Measurements of Orbital Tissue  
A total of 7 parameter measurements, including orbital volume (OV), extraocular muscle volume (EMV), retroorbital fat volume (RFV), orbital apex volume (OAV), extraocular muscle volume in orbital apex (EMV-OA), the angles between medial rectus and optic nerve (AMR-ON), and the angle between the lateral rectus and optic nerve (ALR-ON) were measured, and 3 ratios were further derived. The extraocular muscles include four muscles as follows: medial rectus, lateral rectus, superior rectus, and inferior rectus. We gathered original CT data in DICOM (Digital Imaging and Communications in Medicine) through the imaging department. Digital data were manipulated using the medical imaging software Mimics Research (19.0 version; Leuven, Belgium).  
The whole orbital content with soft tissue, including retroorbital fat and extraocular muscles, was discriminated by setting specific windows. The specific window thresholds were set at \(-200\) to \(-30\) Hounsfield Units (HU) for fat, \(-30\) to \(+100\) HU for the EM, and \(-200\) to \(+100\) HU for orbital volume, as [PERSON] et al. introduced in 2008 [8]. The orbital boundary reached from anterior to the frontal bone, zygomatic bone, inferior orbital rim, and anterior lacimal crest and posterior to the entrances of the optic nerve in the optic canal, the pterygopalatine fossa, and superior and inferior fissures. Reconstructed 3D models were generated and OVs were calculated automatically. Retroorbital fat was separated from extraorbital fat by manually drawing a line parallel to the anterior orbital margin. The EM tissue was separated from the lacimal gland by manually drawing along the EM margin in consecutive slices. Subsequently, RFV and EMV were calculated (Figure 2A-C).  
To further investigate the optic nerve compression by the EM within the orbital apex, we defined the spatial range of the orbital apex and measured the orbital volume and the volume of the extraocular muscles in the orbital apex (Figure 2D,E). At the horizontal level, the anterior boundaries were parallel to the line of the anterior lacimal crest to the fronto-zygomatic bone starting from the midpoint of the lower trigonometric surface of the temporal side of the spinenoid bone. The posterior boundaries were the same as those of the OV. The OAV and EMV-OA were calculated similarly.  
In the axial plane, the AMR-ON was defined as the angle between medial rectus (MR) and optic nerve (ON), and the ALR-ON as the angle between lateral rectus (LR) and ON. The AMR-ON and ALR-ON were measured in degrees at the intersection automatically by Mimics software (Figure 2F).
## 3 Results and Discussion
### Machine Learning Coupled with Computational Fluid Dynamics (CFD)  
In power plants, the documentation of historical data may be sparsely available; thus, using validated CFD models and generating large datasets for training machine learning models are also used by researchers for model development. The CFD method, commonly performed by using the commercial software ANSYS 2024 or the free software OPENFOAM v11, is capable of generating training data to be used by machine learning models through the development of a physics model. For example, to address the problem of limited understanding of combustion slagging in boiler combustion in fossil fuel power plants, Ref. [52] developed a neural network-based machine learning model by using real data from CEM. Furthermore, the authors proposed a non-dominated sorting genetic algorithm (NSGAII) for multi-objective optimization by combining CFD to improve the efficiency of fossil fuel-based boiler furnace combustion. The authors of Ref. [46] utilized a self-designed deep belief network (DBN) to forecast NOx emissions from coal-fired boilers. Their DBN-based models demonstrated superior prediction accuracy and increased reliability compared to alternative CO2/NOx emission prediction models by achieving an  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **References** & **Characteristics** \\ \hline Reinforcement learning & [35; 36] & Good for real-time prediction of CO2/NOx emissions \\ Forward neural network & [23; 37; 38; 40] & Computationally efficient, straightforward algorithm structure, \\  & & relatively easy to develop model with sufficient input data \\ Long short-term memory neural network & [43] & The potential issue of overfitting, \\  & & Long training time, and computational complexity \\ Support Vector Method & [44; 45; 47] & Good for real-time prediction of CO2/NOx emissions \\ Extreme Learning Method & [48; 49; 50; 51] & Input data quality requirement is higher \\ \hline \hline \end{tabular}
\end{table}
Table 2: Status of a machine learning models by the method used by researchers.  
accuracy of approximately 0.9. The authors mentioned that their input data for training were validated against CFD for data quality examination. Although CFD can be used as an alternative for gathering historical data from power plants for model development, generating CFD data and validating results may take additional computational time.
## 3 Iterative Learning Observer-Based Thruster Fault Reconstruction Approach
### Stability Analysis of the Iterative Learning Observer
** The detailed proof process of Corollary 1 is similar to that of Theorem 1; hence, the proof process is omitted here. \(\square\)  
**Remark 4**. : _Consider the spacecraft formation control systems described by (3) under Assumption 1. Using Theorem 1, the state-estimating and fault-reconstructing errors are uniformly ultimately bounded such that the developed ILO can accurately reconstruct both constant faults and time-varying faults. If \(\mathbf{Q}_{1}=\mathbf{I}_{3}\), then \(\Delta\mathbf{f}(t)=0\) for constant thruster faults according to (12). Therefore, using Corollary 1, the developed ILO could achieve both asymptotic reconstruction and unbiased reconstruction of constant thruster faults._  
Learning Output-Feedback Fault-Tolerant Tracking Control for Spacecraft Formation Configuration Maintenance  
In this section, based on thruster bias faults reconstructed using the designed ILO, an LOF\({}^{2}\)TC approach was developed for spacecraft formation control systems such that on-orbit desired spacecraft formation configuration could be accurately tracked in the presence of thruster faults, space perturbations, and possible uncertainties. The block diagram of the tracking control system is shown in Figure 2.  
Figure 2: The proposed control framework for maintaining the formation configuration.
# Low-Pass Filtering Method for Poisson Data Time Series
## 3 Results
### Testing the Method and the Quasi-Gaussian Filter on Model Normalized Poisson Data
#### 3.1.1 Testing on Model Hourly Data Using Statistical Modeling
Model piecewise constant modulating function \(Y_{M0}(T_{0}n)\ =\ \overline{N}_{M}(T_{0}n)\), represented by a dotted line (index 4), \(m_{0}+dm\ =\ Y_{M0}(T_{0}n)\ =\ 0.4165\) for \(1\leq n<n_{a1}\), \(n_{a2}\leq n<n_{0}\), \(m_{0}\ =\ Y_{M0}(T_{0}n)\ =\ 0.4087\) for \(n_{a1}\leq n\leq n_{a2}\), where the value of \(dm\ =\ 0.833\times 10^{-2}\) corresponded to the predefined 2% decrease. The plots show that the result of the quasi-Gaussian filter application (line 2) is a better approximation to the model piecewise constant modulation (line 4) than the result of a common FIR filtering (line 3). The calculation of approximate estimates of filtering errors for the quasi-Gaussian filter and fir1-filter was performed by calculating the root-mean-square (RMS) errors according to the following formulas for datasets \(Y_{M0}(T_{0}n)\), \(Y_{MG}(T_{0}n)\), \(Y_{MF}(T_{0}n)\):  
\[\sigma_{MG}^{2}\ =\ \frac{1}{n_{0}}\sum_{m=\ 1}^{n_{0}}(Y_{M0}(T_{0}n)-Y_{MG}(T_{0 }n))^{2},\sigma_{MF}^{2}\ =\ \frac{1}{n_{0}}\sum_{m=\ 1}^{n_{0}}(Y_{M0}(T_{0}n)-Y_{MF}(T_{0}n))^{2}. \tag{14}\]  
Figure 3: Fragments of model datasets \(Y_{M}(T_{0}n)\) (line 1), filtering results \(X_{MG}(T_{0}n)\) (line 2), \(X_{MF}(T_{0}n)\) (line 3) and model modulating function \(Y_{M0}(T_{0}n)\) (line 4). Results of a large number of tests performed for (14) showed that the \(\sigma_{MG}\) error values for \(X_{MG}(T_{0}i)\) regarding \(Y_{MG}(T_{0}n)\) are, on average, 15-30% less than the corresponding \(\sigma_{MF}\) error values for \(X_{MF}(T_{0}i)\). An overview of model \(X_{MG}(T_{0}i)\) and \(X_{MF}(T_{0}i)\) (Figure 3) made it possible to ensure that the minimum duration of the interval, within which recognition for the decrease \(d\mu\ =\ 0.02\) can be performed, is 12-24 h.  
The proposed method and the quasi-Gaussian filter provided more noise reduction than a common FIR filter. Consideration of the results of statistical modeling made it possible to draw a conclusion about the efficiency of the quasi-Gaussian filtering method.