esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Compensation Schemes  
This method is the improvement of the T-L compensation model based on fluxgate magnetometer information. Therefore, traditional calibration flights are still applicable to solve the compensation coefficients. The INS/GPS data and the fluxgate magnetometer data are independent, so it can also use only the INS/GPS data to complete the magnetic compensation. After the calibration flight, the calibration flight data are processed, and finally, 36 compensation coefficients of the conjunctive compensation model are obtained. The specific process is as follows:  
1. Obtain the original magnetic data from the OPM and preprocess the magnetic data, mainly filtering;
2. Bring the longitude, latitude, and altitude data in GPS into the IGRF and the XYZ three components of the geomagnetic field in the geodetic coordinate system are calculated by IGRF. Bring the angle of roll, pitch, and yaw in INS into the rotation matrix and the XYZ three components of the geomagnetic field in the aircraft coordinate system are calculated by (17)-(19);
3. The same filtering process is used for OPM data, fluxgate magnetometer data, and XYZ three-component data calculated through INS/GPS data, and the conjunctive compensation matrix is constructed in (23) and (24);
4. Calculate the 36 compensation coefficients by the least square algorithm (LS);
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 4 Alginate Modification and Coupling Strategies
### Hydroxyl Groups
#### 4.2.1 Acetylation and Esterification of Hydroxyl Groups  
Among the earliest attempts of alginate modifications, investigators have introduced the synthesis of acetylated alginate derivatives. Pioneer researchers conducted a series of acetylation reactions. [PERSON] et al. performed acetylation of alginic acid yarn, where the hydroxyl group was first made available by the swelling water process [74]. The relatively mild method was reported to overcome the degradation that occurred during the previous procedure. Alginic acid has been swollen with acetone instead of water before it was reacted with ketene [75]. [PERSON] et al. reported a more detailed analysis of the acetyl substitution of alginates [76]. The acetylation is also reported to increase the swelling ability of alginate calcium gels and decrease their viscosity with the increasing acetylation degree [77]. More recently, acetylation has been applied to tetrabutylammonium (TBA)-alginate in an organic solvent, leading to a maximum degree of acetylation [78].  
The esterification can be applied to modify both the carboxylic acid (half modified) and the hydroxyl group of alginates. In this part, we discuss the ester alginates formation through the hydroxyl group. Alginate has been hydrophobically modified with the dodecyl chain at 12% mol/mol saccharide units [79]. The alginate acetate was synthesized in dimethyl sulfoxide, using a tetrabutylammonium (TBA)-alginate as the substrate [78]. The study hypothesized that the 1,3 axial interactions between C\({}_{2}\)-OH groups with C\({}_{4}\) protons or C\({}_{3}\)-OH groups with C\({}_{5}\) protons within the alginate chain structure are responsible for the acetylation [42].  
This modification is a carboxyl group chemoselective reaction (Figure 5), making it suitable to synthesize benzyl, butyl, ethyl, and methyl alginates. This alginate carboxylate ester is inorganic solvent-soluble and might be ideal for preparing a solid dispersion drug delivery matrix. All in all, the modification strategy in organic reagents would extend the possibility of new pathways for alginate functionalization.  
Additionally, ester formation can also be explored with an epoxide ring-opening mechanism to prepare hydrophobically modified alginate [80]. The condensation of sodium  
Figure 4: The crosslinking formation by Ugi multicomponent reaction for alginate [71].  
Figure 5: Alginate esterification of TBA alginate using alkyl halides [42].  
alginate with dodecyl glycidyl ether (DGE), at pH 9 and 80 \({}^{\circ}\)C, produces a self-assembled amphiphilic alginate derivative. These polymers have been studied as water-insoluble substance carriers [81]. This could increase clofazimine and Ambhoricicin B apparent water solubilities and exhibit a sustained-release effect. In a subsequent report, an improved synthesized method has been reported with the addition of sodium dodecyl sulfate (SDS) as a surfactant to increase polymer production yields [82].
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Strain Sensors
#### 3.2.1 Flex Sensor  
Flex sensors are an interesting choice for sitting posture recognition due to their ability to detect bending and flexing in various materials. They are typically constructed by printing a conductive material on top of a flexible substrate, such as plastic. When the sensor is bent, this conductive substance creates a resistive element that modifies its resistance. The conductive substance extends when the sensor is bent, changing the resistance. The resistance changes more when the sensor is bent further. The flex sensors are normally embedded in the seat or backrest to monitor how these parts bend as a person sits. They are robust enough for long-term usage in settings where posture monitoring is required constantly, such as office chairs or ergonomic evaluations, because they are made to survive frequent bending. Also, they are relatively inexpensive compared to other sensors such as strain gauges or depth cameras, as well as consume less power. However, these sensors have a limited measurement range and nonlinear output, complicating data processing. Also, they have limited precision and are susceptible to wear and tear. [PERSON] [80] proposed a system that used six flex sensors installed on chairs to classify seven different  
Figure 6: Resolution of sensor array vs. postures recognized.  
postures by employing ANN, achieving 97.78% accuracy with a floating-point evaluation and 97.43% accuracy with the 9-bit fixed-point implementation.
###### Abstract
Human images include (**a**) vegetation type (miombo woodland), time-of-day (daylight), distance (<5 m), the colour of clothing (green), orientation towards the CT (yes), height (1.9 m), and occlusion (68–100%); (**b**) Vegetation type (miombo woodland), time-of-day (dusk), distance (15–30 m), height (variations) and occlusion (68–100%); (**c**) Vegetation type (riverine forest), time-of-day (daylight), distance (10 m), the colour of clothing (green), orientation towards the CT (no), height (1.7 m), and occlusion (68–100%). Wildlife image examples include (**d**) Species (_Philantomba monticola_), vegetation type (miombo woodland), time-of-day (daylight), distance (0–4.9 m), orientation towards the CT (no), and occlusion (68–100%); (**e**) Species (_Crocuta crocuta_), vegetation type (miombo woodland), time-of-day (dusk), distance (5–9.9 m), orientation towards the CT (no), and occlusion (34–67%); (**f**) Species (_Tragelaphus sylvaticus_), vegetation type (riverine forest), time-of-day (daylight), distance (>10 m), orientation towards the CT (no), and occlusion (68–100%). To test our hypotheses and meet the study's aims, we have structured this article as follows: in Section 2, we provide a detailed description of the experimental setup and analytical processes. Section 3 provides quantitative data results, presenting the degree of influence of the analysed factors and comparing the performance of both ML and analyst methods. This finally leads to Section 4, which reviews the impact of the tested factors, concluding with several recommendations and discussing the potential of ML to detect and correctly classify targets within CT images irrespective of such influencing factors.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
### New Contribution  
The present study's new contributions are as follows: Firstly, the effect of switching from a group-delay to a single-delay scenario in terms of reconstruction accuracy is hardly evaluated in the existing literature. A detailed comparison of the error and the achievable OSR are presented in Section 4.  
Figure 1: Schematic example for real-time resampling with one sample delay.  
Secondly, a new resampling filter based on Kriging interpolation is introduced. Standard Kriging applications take statistical properties from the input signal using the variogram model for setting the Kriging parameters. Instead, we have tuned the variogram parameters to achieve a frequency response close to the desired brick-wall characteristics (Section 3.1). Accurate resampling was achieved with constant variogram parameters for a wide range of input signals. Furthermore, Kriging interpolation has the advantage of inherently scaling to non-uniform inputs without any changes in the algorithm. The new method provides low-latency resampling of uniform and non-uniform signals with a single-sample delay. Kriging required an OSR of 1.34 to achieve a signal-to-noise ratio (SNR) of 60 dB to reconstruct a jitter test signal in the group-delay scenario. The single-delay scenario was more challenging and required almost double the OSR.  
Thirdly, an extended Lagrange approach, which was originally developed by [PERSON] [3] for recurrent signals, was tested for the non-periodic case. Although perfect reconstruction can no longer be provided if the restriction to periodic signals is disregarded, it is far more accurate than the simple Lagrange method, as shown by simulations (Section 4).
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 3 Dynamic-Weight Parallel Instance and Skeleton Network
### Skeleton-Detection Branch  
The target of our skeleton-detection branch is to extract the skeleton of the railway. This paper uses our previous work AdaLSN [24] as the base architecture for this branch. AdaLSN consists of two components, the backbone and the linear span pyramid (LSP). The LSP is constructed by attaching the linear span unit (LSU) to the convolutional layer of the backbone in five stages, which can be viewed as a side-out branch. The key innovation in AdaLSN is the search for four classes of architecture encoding, including the connection between the backbone and LSUs, the inner edges and operators in each LSU, the connection between LSUs in the LSP, and the connection between loss and the LSU. The focus of this  
Figure 3: Backbone of our network.  
paper is not on searching for the best network architecture through a genetic algorithm; thus, one fixed architecture is utilized.  
AdaLSN uses Inception-v3 [48] as the best backbone. In this paper, a new backbone composed of ELAN is used, which is the same as the backbone of the instance-segmentation branch, to connect the two parallel branches.  
This paper uses a Dice loss layer [28] instead of the binary cross-entropy loss, which is utilized in the original AdaLSN architecture and may result in a sizeable loss at the start of our training process. Dice is suitable for situations where there is a serious imbalance between positive and negative samples, as is the case in our skeleton-detection task. The skeleton pixels, which are positive samples, only represent a small fraction of the whole image compared to the background pixels, which are negative samples. Therefore, the Dice loss is much more appropriate for our mission. The training loss function is defined as follows:  
\[L_{unit}=\sum_{i=1}^{5}L_{i} \tag{2a}\] \[L_{sklt}=L_{unit}+L_{fuse} \tag{2b}\]  
where \(L_{unit}\) is the sum of loss of every LSU; \(L_{i}\) is the loss of each LSU; \(L_{sklt}\) is the loss for the whole skeleton-detection network; and \(L_{fuse}\) is the loss of the fused LSU, whose input is the output from all the LSUs.  
During the inference process, the original method in AdaLSN limits the fused output of the LSP to the range of 0 to 1 using the sigmoid function, but it changes the shape of results significantly. To obtain better skeleton results against complex backgrounds, this paper designs an extension threshold function to be added after the sigmoid function, defined as:  
\[p_{th}=\begin{cases}1,&if\;\;p_{sig}>=\theta\\ 0,&if\;\;p_{sig}<\theta\end{cases} \tag{3}\]  
where \(p_{th}\) is the value of each pixel after our threshold function, \(p_{sig}\) is the value of each pixel after the sigmoid function, and \(\theta\) is the threshold filtering the pixels. All the pixels with a value less than \(\theta\) are changed to zero, and the others are changed to one.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 4 Proposed Method
### Location Estimation
#### 4.3.1 Closeness  
The quality of location estimation in MSL\({}^{*}\) can be measured using closeness [16] equation as follows:  
\[\textit{closeness}_{p}=\ \frac{\sum_{i=1}^{n}w_{i}\sqrt{\left(x_{i}-x\right)^{2 }+\left(y_{i}-y\right)^{2}}}{N} \tag{3}\]  
where \(N\) is the number of samples for node \(p\), \(\left(x_{i},y_{i}\right)\) represents the coordinate of the \(i\)-th sample (\(i\) = 1,2,...,\(n\)), \(w_{i}\) represents the weight of the \(i\)-th samples, and (\(x\),\(y\)) is the estimated location for node \(p\). Closeness is measured when samples for the node in the current time are available, else, the closeness value is set to \(\infty\). The closeness value of seed nodes is fixed to 0 while for others the value should be greater than 0. This indicates that the accuracy of the localization is high when the closeness value is small. The closeness value and location data of each node will be updated during the localization process once the location data and the closeness value of the seed nodes are broadcasted to their neighbors.
## 4 ALS Data Visualization, Analysis and Quality/Accuracy Assessment
### Quality and Accuracy Assessment
The latter is of paramount importance, because multipath or interruption of signals due to canopy cover can degrade the quality of the observations by orders of magnitude [73, 74]. A single ALS return might be of less precision and accuracy with regard to a single well collected total station measurement, however the strength of ALS observations comes from their great numbers; averaging data from several returns increases the precision and accuracy of single ALS returns.
## 4 Review of Current Methodologies against the Background of Calibration Challenges  
With the new challenges resulting from the expected changes by the introduction of a new EU7 standard, a review of the approaches used to support calibration and validation process becomes necessary. The methods currently in use have been implemented to address the changes brought about by the introduction of RDE in EU6d-temp. With the focus of ensuring emission compliance within all possible operation conditions during current RDE boundaries and reducing the required amount of prototype vehicles, several solutions have been introduced, as shown in Figure 7.  
Specific requirements for an efficient EU7 validation methodology can be derived from the previous chapters:  
* Sufficient flexibility toward the measurement of additional pollutant emissions
* Appropriate measurement accuracy in a suitable test environment
* Ability to adapt new/modified RDE boundary conditions
* Consideration of representative driver-vehicle-interaction (\"normal use\")
* Possibility to emulate all influencing ambient conditions  
While not directly resulting from expected regulative changes, further demands for a successful and resource-saving calibration process are:  
* Product or vehicle-specific problem identification and consideration
* Examination of the entire relevant operating area for weak spots within the application
* Reproducibility to validate the success of applicative changes
* Efficient usage of available measurement data  
Figure 7: Overview of current RDE validation methodologies.  
Evaluating these requirements, the so far established RDE validation methodologies will be discussed:  
* Worst-case cycles: \(\bigcirc\) (Fleet) generic cycles--often characterized by high driving dynamics \(\bigcirc\) Pollutant specific created cycles based on simulation and/or Design of Experiment (DoE)
* Real driving route testing--measurements with PEMS on-road or as replay on test bed facilities
* Synthetically designed cycles based on (regionally) relevant driving behavior
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication
### Metal-Assisted Chemical Etching (MACE)
#### 2.1.4 Aluminum-Assisted Chemical Etching  
Al-assisted chemical etching was investigated in several studies by [PERSON] et al. for the optimization of BSi surface properties [35]. In their first study, they manipulated the surface morphology of nanoporous BSi by varying the thickness of the Al catalyst and the concentrations of HF and H\({}_{2}\)O\({}_{2}\) in deionized water. The study revealed that a catalyst thickness of 24 nm, combined with a specific composition of HF-H\({}_{2}\)O\({}_{2}\)-H\({}_{2}\)O, had a considerable impact on the morphology of the nanopores, resulting in a minimum broadband average reflection (Ravg) of 5.7%. This finding is critical for optimizing the performance of PV cells and photosensors. In a subsequent study, [PERSON] et al. further explored the Al-assisted chemical etching technique, observing how the thickness of the Al layer affected nanopore formation [36]. With a 12 nm thickness, a decrease in pore depth and lower surface coverage is noted, leading to a higher Ravg of 11.9% within the 300-1100 nm wavelength range. However, this setup also produced the highest broadband light absorption, indicating a trade-off between pore coverage and absorption efficiency. Ultimately, they investigated the impact of annealing temperature on BSi properties [37]. Annealing at 400 \({}^{\circ}\)C led to the deepest and most extensively covered nanopores, producing the minimal broadband reflection in the 300-1100 nm range. This treatment achieved an average enhancement in absorption of 1.61, significantly surpassing that of a planar c-Si reference, thereby illustrating the potential of thermal treatment to further enhance the light absorption properties of BSi. The aforementioned studies illustrate the effects of process variables on BSi's structural and optical characteristics, which significantly contribute to the development of efficient optical detection mechanisms.
# Nanofiber-Based Materials for Persistent Organic Pollutants in Water Remediation by Adsorption
###### Abstract  
Fresh water is one of the most precious resources for our society. As a cause of oxygen depletion, organic pollutants released into water streams from industrial discharges, fertilizers, pesticides, detergents or consumed medicines can raise toxicological concerns due to their long-range transportability, bio-accumulation and degradation into carcinogenic compounds. The Stockholm Convention has named 21 persistent organic pollutants (POP) so far. As opposed to other separation techniques, adsorption, typically performed with activated carbons, offers opportunities to combine low operation costs with high performance as well as fast kinetics of capture if custom-designed with the right choice of adsorbent structure and surface chemistry. Nanofibers possess a higher surface to volume ratio compared to commercial macro-adsorbents, and a higher stability in water than other adsorptive nanostructures, such as loose nanoparticles. This paper highlights the potential of nanofibers in organic pollutant adsorption and thus provides an up-to-date overview of their employment for the treatment of wastewater contaminated by disinfectants and pesticides, which is benchmarked with other reported adsorptive structures. The discussion further investigates the impact of adsorbent pore geometry and surface chemistry on the resulting adsorption performance against specific organic molecules. Finally, insight into the physicochemical properties required for an adsorbent against a targeted pollutant is provided.  
nanofibers; adsorption; organic pollution; persistent organic pollutant POP; adsorption mechanisms; adsorbent characteristics; surface chemistry; pesticides; disinfectants; phenol +
Footnote †: journal: Applied Physics Letters
###### Abstract  
Digital systems require sample and hold (S&H) systems to perform the conversion from analog to digital and vice versa. Besides the standard zero and first order holds, we find in the literature other versions, namely the fractional and exponential order holds, involving parameters that can be tuned to produce a superior performance. This paper reviews the fundamental concepts associated with the S&H and proposes a new fractional version. The systems are modeled both in the time and Laplace domains. The new S&H stemming from fractional calculus generalizes these devices. The different S&H systems are compared in the frequency domain and their relationships visualized by means of hierarchical clustering and multidimensional scaling representations. The novel strategy allows a better understanding of the possibilities and limitations of S&H systems.  
sample and hold; fractional calculus; hierarchical clustering; multidimensional scaling; distances 1
# Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code
## 4 Irregular Channel Code for Iterative Source Channel Decoding
### EXIT Chart Analysis for Iterative Source Channel Decoding  
Generally, the EXIT chart of a serially concatenated system has two EXIT curves, i.e., the EXIT curve of the inner decoder and the inverse EXIT curve of the outer decoder. The EXIT curve exploits the mutual information (MI) between the data bits at the transmitter and the LLRs at the receiver to describe the input-output relation of each constituent decoder [32].  
For our JSCC system, illustrated in Figure 1, the irregular channel decoder and the SBSD serve as the inner decoder and the outer decoder, respectively. The EXIT curves of the channel decoder, \(\Gamma_{M}\), and the inverse EXIT curve of the SBSD, \(\Gamma_{D}\), can be expressed as:  
\[I_{CD}^{e}\;=\;\Gamma_{CD}(I_{CD}^{a}|L_{M}(\mathbf{v})) \tag{10}\]  
and  
\[I_{SD}^{e}\;=\;\Gamma_{SD}(I_{SD}^{a}|P_{S}) \tag{11}\]  
where \(I_{CD}^{a}\) is the mutual information between bits \(\mathbf{d}\) and LLRs \(L_{CD}^{a}(\mathbf{d})\), \(I_{CD}^{e}\) is the mutual information between bits \(\mathbf{d}\) and LLRs \(L_{CD}^{e}(\mathbf{d})\), \(I_{SD}^{a}\) is the mutual information between bits \(\mathbf{b}\) and LLRs \(L_{SD}^{a}(\mathbf{b})\), and \(I_{SD}^{e}\) is the mutual information between bits \(\mathbf{b}\) and LLRs \(L_{SD}^{e}(\mathbf{b})\). Meanwhile, the EXIT curve of the channel decoder depends on the posterior LLRs of demodulator, \(L_{M}(\mathbf{v})\), and the EXIT curve of the SBSD is also affected by the source statistic, \(P_{5}\). These EXIT curves can be obtained through the Monte Carlo simulation.  
The design of the encoders is based on the property of the EXIT chart, i.e., the system can achieve an infinitesimally low BER by iterative decoding only if there is an open tunnel between the EXIT curves [29]. Thus, the encoders should be designed in such a way that their EXIT curves satisfy \(\Gamma_{CD}(I)>\Gamma_{SD}^{-1}(I)\) for \(I\in[0,1)\) at a low SNR to make the system achieve a near-capacity performance. An effective way to design very well-matching encoders is to apply the concept of irregular codes [23].  
The irregular source block codes are not fit for our scheme. In the irregular block codes, high-rate codes have to be used together with low-rate codes. High-rate block codes naturally have a lower minimum Hamming distance and, therefore, a lower symbol error-correction capability. In order to ensure the error-correction capability, we still adopt regular source block code in our scheme. Furthermore, the well-matching EXIT curves are attained by designing an irregular inner channel code for the given source code.