esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver
## 6 Discussion of the Ranging Accuracy  
A further investigation determined that the probability distribution after the weight update from Equation (36) shows a very small variance in range, especially for strong GNSS signals and long integration times (e.g., larger than ten milliseconds). This is briefly illustrated in Figure 4. It can be seen that the amplitude of the correlation value has a significant impact on the variance of the probability function. For a typical open sky case at 45 to 50 dB-Hz and integration times of \(\geq\)10 ms, a weighted standard deviation of \(\leq\)0.32 m would be observed. This is caused by the large amplitude of the correlation function and small variations in the range cause already a significant variation in \(P(\tau)\).  
This behavior is expected and consistent with standard tracking theory and for example represented by the Cramer-Rao lower bound (CRLB) of the code pseudorange noise. However, the ranging accuracy described by the CRLB is never reached in practice, as multipath, residual orbit or satellite clock errors or residual ionospheric delays by far exceed the CRLB. Least-squares positioning algorithms or a Kalman filter (both working with code pseudoranges) can cope well with this increased ranging errors as the overall ranging accuracy is assumed to be unknown and is estimated from the code pseudorange residuals. Those algorithms only consider the relative accuracy variations between the different pseudoranges to define the relative weights, but do not account for a common accuracy scale factor.  
However, for DPE, it is expected that this narrow probability distribution of the weights cause problems. First, grid based filters with fixed resolution and extension may not be able to cover the narrow probability distribution appropriately. Second, a residual code delay bias (multipath, orbit, clock or iono) may shift the probability density of one satellite with respect to another. Instead of overlapping the true PVT, the shifted functions may assume very small values (or even be zero due to the finite precision in the computer) and thus eliminate particles at the true PVT. The particle cloud of the particle filter would be severely concentrated around one maximum and it is expected that the filter may also have difficulties to track the PVT solution accurately in case the amplitudes of the different GNSS signals vary in time relative to each other.  
Thus, a trade-off and an adaptive approach for the grid resolution, grid extension and computational power seem to be necessary to guarantee a good handling of the narrow probability functions. One could think of increasing the process noise to cope with the delay biases, but as the process noise is determined by the dynamic model, this is not a viable approach.  
The next chapter proposes a method to overcome these problems by introducing an unknown delay bias as nuisance parameter.
## 4 Discussion  
### Uncertainties of This Study  
With the increasing accessibility of high-quality quantitative remote sensing products for vegetation parameters, remote sensing has become an indispensable tool for monitoring and assessing forest ecological environments. Utilizing the forest health assessment framework depicted in Figure 2, the indicators, including FCI, annual average LAI, annual maximum FVC, and annual cumulative GPP, at a spatial resolution of 500 m were employed to evaluate the extent of forest coverage, forest quality, spatial distribution, and carbon desorption capacity of the forest ecosystem. Notice that land cover conversions from various forest types (EBF, DBF, ENF, DNF, and MF) to non-forest were taken into account for FCI for the years 2010 and 2015 in this study. Previous study has shown that these conversions represent a non-neglectable effect in the Lancang-Mekong region [3]. But, other indicators and their long-term trends were extracted based on the forest cover in 2020. This study did not take into account the conversion from non-forest areas to forest, because it constituted only a relatively minor aspect.  
Furthermore, the EQI calculated from LAI, FVC, and GPP was employed to gauge the overall health of the forest. Considering that the thresholds for LAI, FVC, and GPP were different, all of these three parameters were normalized on a scale ranging from 0 to 1 before calculating the EQI, and then EQI was computed with equal weights [19; 20; 21]. This standardization was based on the maximum and minimum values observed for different forest types within each climate classification. The only concern is the slight variations in the maximum and minimum values for these three parameters across different years.
## 5 Discussion
This paper uses a channel attention module to select important channels from concatenated channels and assign them larger weights in order to improve the performance of the classification model. Finally, the classification module is used for benign-malignant classification. Through our testing, when noisy breast ultrasound images are input into this classification model, its accuracy can reach 89.31% and recall can reach 88.65%, indicating good performance in classifying such images.
## 2 Materials and Methods
### Analysis of Volatile Compounds  
The inoculated cacao beans from each Falcon tube were transferred into 50 mL SPME vials and placed in a water bath at 55 \({}^{\circ}\)C for 30 min. Then, a 50/30 \(\upmu\)m Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) SPME fiber was exposed to the headspace of each sample for 30 additional minutes. The SPME fiber was then injected into a 7890A GC coupled to a 5975C MS detector (Agilent Technologies, Santa Clara, CA, USA), which was equipped with a DB5-MS (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) column. The temperature of the injector and oven were set to 240 \({}^{\circ}\)C and 310 \({}^{\circ}\)C, respectively. Furthermore, helium was utilized as the carrier gas at a constant flow rate of 0.8 mL/min. The MS detector was programmed to electron impact mode and positive polarity. Additionally, the total ion current was registered at a mass range of 40-750 amu. Spectrum data were retrieved using ChemStation E.02.02 (Agilent Technologies, Inc., Santa Clara, CA, USA), and the identity of the volatile compounds was dilucidated by comparing the mass spectra of each compound with the Wiley 9 library and NIST11 databases, and confirmed by comparing the linear retention index of each compound with that of the pure standard using our internal database [3]. Peak area was used to quantify each volatile compound, and aroma descriptions were assigned to each volatile compound using available databases [35; 36] and The Good Scents Company.
## 2 Materials and Methods
### Numerical Procedure  
#### 2.2.1 Physical Model  
The comparative analysis in this study aims to compare the numerical simulations with the experimental results, so as to obtain the law of marine oil leakage and ignition on a typical heated surface in a fishing vessel's engine room more accurately. The diesel engine in the fishing vessel's engine room was equipped with a fuel pump, oil pump, fresh water-cooling pump, oil cooler, fresh water cooler, and expansion joint supply. Meanwhile, the main diesel engine was equipped with a remote instrument, and the front end of the main engine was equipped with a hydraulic pump station (speed-increase gearbox) through a short shaft. Before establishing the geometric model, this research investigated the real operating environment of the fishing vessel's engine room and the equipment with a high-temperature heated surface to obtain accurate geometric parameters. The main engine, generator set, and other auxiliary engines in the engine room were designed according to the continuous power of the diesel engine. The fluid-phase thermodynamic package utilized in this numerical analysis was FLUENT, and an equal-scale scene model of a typical heated-surface accident area of the fishing vessel's engine room was constructed. As shown in Figure 2, the regional scene model includes equipment such as ship diesel engine, generator set, steam boiler, and related piping system. For the ventilation system in the fishing vessel's engine room, the lower outlet and the top outlet of the engine room were set in the numerical model. The vent is the top outlet, located at the top of the entire fishing vessel's engine room, with a radius of 0.4 m. In the numerical model, the air outlet was set at the lower part of zone model, and the diameter was set at 0.3 m. The computational domain size set was 3.0 m (length) \(\times\) 3.0 m (width) \(\times\) 3.5 m (height). The computational domain was mainly set for the area where the oil leaks and the heated surface ignites, which is the main diesel engine and oil supply pipeline. By simplifying the size of the computational domain, the mesh size was set more finely. In ANSYS 19.0, the fishing vessel's engine room model was segmented. Due to the complexity of the interior structure of the engine room, the numerical model was divided into non-structural grids with strong adaptability. In the current model, the minimum grid size was set at 0.05 m, and the total number of grids in the entire computational domain was 252,000.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Density at** & **Flash** & **Kinematic** & & & \\
**15 \({}^{\circ}\)C** & **Point** & **Viscosity** & **Pour Point** & **Water** & & **Cetane** \\
**(kg/m\({}^{3}\))** & **Point** & **(\({}^{\circ}\)C)** & **at 50 \({}^{\circ}\)C** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)/\({}^{\circ}\))** & **Number** \\ \hline Marine & & & & & \\ diesel oil & 991 & 63 & 180 & 24 & 0.5\% & 40–60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main properties of leaked marine oil diesel in this experiment.
## 2 Materials and Methods
### Physical Properties of Prepared Encapsulates  
Physical properties (color changes, water solubility index, water absorption index, moisture, and water activity) were determined by established procedures described previously [12].  
The color measurements of the encapsulates were determined using a colorimeter (CS-10 portable 8 mm, Smart Color Solutions, Haryana, India). The equipment was previously calibrated using white and black calibration plates. The samples were placed in Petri dishes and measured in three parallels to obtain L* (lightness), a* (redness/greenness), b* (yellowness/blueness), C* (chroma), and hue angle (\({}^{\circ}\)h) parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Maltodextrin** & **Gum Arabic** & **Carboxymethylcellulose** & **Abbreviation** \\  & **(MD)** & **(GA)** & **(CMC)** & **Abbreviation** \\ \hline
1. & 6 & 0 & 0 & MD6 \\
2. & 0 & 6 & 0 & GA6 \\
3. & 0 & 0 & 6 & CMC6 \\
4. & 2 & 2 & 2 & MD2 GA2 CMC2 \\
5. & 4 & 2 & 0 & MD4 GA2 \\
6. & 4 & 0 & 2 & MD4 CMC2 \\
7. & 0 & 4 & 2 & GA4 CMC2 \\
8. & 2 & 4 & 0 & MD2 GA4 \\
9. & 2 & 0 & 4 & MD2 CMC4 \\
10. & 0 & 2 & 4 & GA2 CMC4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental plan for encapsulating bioactive compounds from mandarin byproducts.  
AACC Method 88-04 was used to determine the water absorption index (WAI) and water solubility index (WSI) with minimal modifications reported previously [12]. A total of 0.8 g of encapsulates were mixed with 10 mL of water, left standing for 30 min with periodic mixing and centrifuged for 15 min at 5000 rpm. Separated supernatant was dried at 105 \({}^{\circ}\)C until a constant mass was reached. WAI and WSI experiments were done in duplicates, calculated using Equations (1) and (2) expressed as % for WSI and grams of water per gram of encapsulates for WAI.  
\[WAI[g/g]=\frac{\mathit{gell\ mass}}{\mathit{dry\ matter\ mass\ of\ the\ initial\ sample}} \tag{1}\]  
\[WSI[\%]=\frac{\mathit{weight\ of\ dry\ solids\ in\ supernatant}}{\mathit{dry\ weight\ of\ sample}}\ \times\ 100 \tag{2}\]  
The dry matter of the encapsulates was determined using a halogen moisture analyzer (Mettler Toledo HR73, Columbus, OH, USA). A total of 0.13 g of encapsulates was weighed on an aluminum plate and dried at 105 \({}^{\circ}\)C with switch-off criteria 5 (mass loss less than 1 mg in 140 s interval).  
Water activity (a\({}_{\mathrm{w}}\)) was measured using a HygroPalm (Rotronic, Switzerland) at room temperature (22 \({}^{\circ}\)C). Approximately 10 g of the encapsulates was weighed into a special measuring cup and thermostated at room temperature (22 \({}^{\circ}\)C) under a cover for 10 min. Measurements were done in triplicate.
## 5 Case Study: COTS-based Embedded Real-Time Control of a Time Mash Delivery Machine  
Applying the research results on a real system is often a challenge, due to different working conditions than those of an academic environment, and the considerable efforts needed in various engineering aspects (implementation, operation, validation) in order to be successful.  
The feasibility and applicability of the proposed architectural framework to handle the development and implementation of a COTS-based real-time control system application in manufacturing is demonstrated with a case study in the industry: the control of a lime mash/slurry (milk-of-lime) storage and delivery machine (ADAMs Machines Constructions Company, www.adam.com.gr). Lime mash/slurry is hydrated slaked lime that is quicklime reacted with water to form calcium hydroxide. The machine under control (Figure 5) is a system that stores and delivers lime mash volumes with accuracy at the appropriate speed. The lime mash storage tank is a cylindrical silo (10 m\({}^{3}\), \(\sim\)3 tons) equipped with an agitation (mixing) and scrapping system for delivery of lime mash. The machine can be operated in a manual mode (using the joystick panel) or an automatic mode (based on a custom-build 80C188 EB controller) by setting the value of the required lime slurry volume to be dispensed.  
The operation of the machine is based on a hydraulic pump system driven by electro-hydraulic valves (Duplomatic, Pmax 320 Bar, 24 VDC) and a speed regulative three-phase asynchronous motor (11 HP), which in turn drives a gearbox (motor) mechanism that regulates the speed (rotation) of the single-shaft agitation system (paddles shaft) that regulates the actual flow of lime slurry out of the silo (tank). The proportional control valves can adjust the flow rate from 0 to 1500 L/min according to the input voltage (0-10 V) by changing the spool position. In addition to the valve control by the custom-build controller, the valves can be opened and closed for the delivery of lime slurry by the joystick panel. The actual flow volume of lime slurry is measured with an electromagnetic flowmeter (Danfoss Magflo MAG3500/MAG6000) installed in the tank's lime mash outlet. A custom-built controller (based on 80C188 EB microprocessor) developed within the company, monitors and regulates the delivered lime slurry volume according to preset values.
# Effective Blind Frequency Offset Estimation Scheme for BST-OFDM Based HDTV Broadcast Systems
## 3 Conventional Estimation Scheme
Note that the effect of residual STO is cancelled out in the second term of the right-hand side (RHS) in (6) after conjugate operation because the phase rotation due to \(\Delta_{\tau}\) is not a function of time inedex \(l\). Since \(\varphi(k)\) is a linear function of \(\Delta_{c}\) and \(\Delta_{s}\) in (10), the LS estimates of \(\Delta_{c}\) and \(\Delta_{s}\) are computed as follows [17; 18; 19]:  
Figure 1:1 Figure 1:2  
and  
Figure 1:3 Figure 1:4  
where \(N_{f}\) is the number of CPs, \(k_{i}\) is the index of the \(i\)-th CP subcarrier, the last terms of the RHS are the additional interference caused by the use of non-symmetrically distributed CPs, and  
Figure 1:5As reported in the literature [17; 18; 19], the drawback of LS estimation scheme is that its performance is strongly influenced by the noise under low signal-to-noise ratio (SNR). Since the ISDB-T system has no sufficient CPs for synchronization, it gives rise to a significant loss in estimation accuracy of (11) and (12). To overcome such a problem, an information-carrying TMCC signal can be considered as pilot symbols for the synchronization purpose.
## 6 Analysis of Results and Discussion
### Discussion
Similarly, in healthcare, the model can help prevent critical failures in medical devices, ensuring their availability when needed. The scalability of the SIRPM model and its proven results in different contexts make it an ideal solution for businesses seeking to improve operational efficiency and sustainability. Further research into its application in other industries could help identify sector-specific modifications or enhancements that would optimize performance.
# An Adaptive Throughput-First Packet Scheduling Algorithm for DPDK-Based Packet Processing Systems
## 1 Introduction
However, the dynamic nature of the network traffic and skewed flow size make flow level-based packet scheduling algorithms very easy to overload some cores while the other cores have no work to do [6; 25; 26], thus resulting in packet loss and lower throughput. Therefore, a hash-based flow-level packet scheduling algorithm cannot guarantee achieving higher throughput and lower packet loss rates. In this paper, we propose an adaptive throughput-first packet scheduling algorithm for DPDK-based packet processing systems. Throughput-first means throughput is the primary purpose of our proposed method even though in some cases load imbalance will occur. Combining the feature of DPDK burst-oriented packet reception and transmission, we use Subflow as our scheduling unit and adjustment unit. The definition of Subflow is as follows: A set of packets belonging to the same flow (having the same N-tuple) in the burst is defined as a Subflow, whose size is within the range of [1,bsz\(\_\)rd], where bsz\(\_\)rd is the burst size of receiving packets. We use HRW [22] algorithm to guarantee packets belonging to the same Subflow go to the same processing core since packets in the Subflow have the same packet headers and thus have the same packet identifiers used to compute weights of each processing core. By monitoring the occupancy of the core's queue, named the load factor in this paper, the system decides whether the adjustment is needed. It should be noted that the adjustment is only valid for the current burst, not for others. In summary, the main contributions of this work are as follows:  
* We first propose to use Subflow as the scheduling unit, which makes the packet scheduling algorithm maintain the advantages of flow-level packet scheduling algorithms in most cases where the adjustment does not happen. * We first propose to use Subflow as the adjustment unit, which can avoid packet loss as much as possible when the target core may be overloaded. * An adaptive throughput-first packet scheduling algorithm is proposed and demonstrated that can achieve higher throughput and lower packet loss rate compared with RR, CRC32, and HRW. The rest of the paper is organized as follows. We introduce some related work in Section 2. Then, we present the Subflow-based packet scheduling algorithm in Section 3. In Section 4, we evaluate our proposed method. We conclude our paper with a discussion of future work in Section 5.
## 3 Experimental Section
### Analysis of Bubble Effect on UOCC Link  
#### 3.3.1 Influence Analysis of Bubble Density on Different Data  
Due to the different forms of received signals, the influence of bubbles on optical signals in a photo detector (PD)-based underwater wireless optical communication (UWOC) system and an RSE-based UOCC system was also different. We first investigated the effect of different bubble density on bits \"1\" and \"0\". Figure 7a-c shows the influence of bubbles on sending data bit \"1\" in saline water. Figure 7a illustrates the received \"1\" sequence through a saline water link without bubbles. The received signal was approximately constant over a large period of time when it came to low-range and stable underwater links. When the optical signals went through a bubbly salty water link with different concentrations of air bubbles, as shown in Figure 7b,c, the appearance of bubbles led to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multicolumn{3}{c}{**Bubble Type**} \\ \cline{2-4}  & **Density** & **Distance from Camera K/(cm)** & **Position in Image** & **Moving (Manually)** \\ \hline \multirow{8}{*}{Tap water (25 °C)} & Dense & 10 & Center & – \\  & Sparse & 10 & Center & – \\  & Dense & 5 & Center & – \\  & Sparse & 5 & Center & – \\  & Dense & 10 to 5 & Center & \(\surd\) \\  & Dense & 5 & Moving from left to right & \(\surd\) \\  & Dense & 10 & Moving from left to right & \(\surd\) \\ \hline \multirow{8}{*}{Saline water (25 °C) (Salinity: 30\%)} & Dense & 10 & Center & – \\  & Sparse & 10 & Center & – \\ \cline{1-1}  & Sparse & 5 & Center & – \\ \cline{1-1}  & Dense & 10 to 5 & Center & \(\surd\) \\ \cline{1-1}  & Dense & 5 & Moving from left to right & \(\surd\) \\ \cline{1-1}  & Dense & 10 & Moving from left to right & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Underwater environmental parameters of the experiment.  
Figure 5: Schematic diagram of the bubble movement (**a**) Transverse movement. (**b**) Longitudinal movement.  
serious fluctuations of light intensity. This was mainly due to the random distribution of bubbles through the propagation path, causing the light beams to randomly scatter in different directions and deviate from their original path. In addition, the fluctuation appeared more frequently with the increase of bubble density. Figure 7d-f illustrates the received signal \"0\" through a salty water link with and without bubbles. Apparently, the presence or absence of bubbles had no effect on the reception of bit \"0\" in this experiment. It should be noted that this was related to the modulation format. On-off keying (OOK) modulation was used in this study, which means that the \"off\" state of light denoted symbol \"0\"; hence, the camera captured dark streaks at a suitable international organization for standardization (ISO).  
Figure 6: Experimental scene.  
Figure 7: Sending “1” through a salty water link without bubbles (**a**), with sparse bubbles (**b**), and with dense bubbles (**c**). Sending “0” through a salty water link without bubbles (**d**), with sparse bubbles (**e**), and with dense bubbles (**f**).
# Complex Wavefront Shaping through a Multi-Core Fiber
## 2 Phase Distortion Compensation in a Multi-Core Fiber
To characterize the stability of the wavefront shaping through the MCF, the focus is captured by the camera every 10 s for 50 h under lab conditions. The standard deviation of the measured FWHM is only 0.55%, which denotes a robust wavefront shaping in our system. A similar approach was employed for focus scanning [22] and simple wavefront shaping [19] through the MCF. For complex wavefront shaping using SLMs, both the intensity and phase information at the target plane are required to reconstruct the desired light field. However, the phase information of the desired light field is often unknown for a complex light field. Therefore, a method which can calculate the phase directly from the intensity information is rather essential [30]. Figure 1: Sketch of the experimental setup. A microscopic image of the distal facet of the MCF is shown in the middle of the figure. HWP, half-wave plate; PBS, polarizing beamsplitter; BE, beam expander; QWP, quarter-wave plate; L, lenses; SMF, single-mode fiber; SLM, spatial light modulator; SF, spatial filter; BS, beamsplitter; ND, neutral density filters; CAM, CMOS cameras; MO, microscope objectives; MCF, multi-core fiber; PL, polarizer.