esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Shear Test Results and Discussion
### Load and Displacement Resistance  
The effect of steel fibers on the cracking and ultimate shear loads of the anchor was investigated (\(V_{cr}\) and \(V_{u}\), respectively). The linear regression analysis was performed with respect to the steel fiber volume percentages from zero to 1.00%. As shown in Figure 9, \(V_{cr}\) is almost constant in the range of approximately 31.0 to 33.1 MPa, regardless of the increasing fiber volume fractions. However, \(V_{u}\) increases almost linearly from approximately 36.3 kN in the non-reinforced concrete to 52.2 kN in the SFRC with a fiber volume percentage of 1.00%, which is approximately 1.44 times that of the non-reinforced concrete anchor. The slope of the line obtained from the linear regression analysis with fiber volume percentages of zero to 1.00% indicates an increase of approximately 17.6 kN in \(V_{u}\) per 1% increase in the fiber volume percentage.  
Figure 10 shows the displacement at the maximum shear load of the anchor, \(\delta_{c,u}\), with respect to the steel fiber volume fractions. The linear regression line shows that the displacement of the anchor at the maximum load increases in proportion to the steel fiber content. The displacement of the anchor in the SFRC with a fiber volume percentage of 1.00% was approximately 11.4 mm at the maximum load, which is 5.74 times that of the non-reinforced concrete anchor. The slope of the linear line in the range of the fiber volume percentages from zero to 1.00% is approximately 9.92.  
Figure 9: Cracking and ultimate loads measured from the shear tests of anchors with increasing fiber volume percentages from zero to 1.00%.
## 3 Tracking Measurement Overview
### TDRS Measurements
#### 3.2.1 TT&C Measurements  
Carrier tracking is a critical process in satellite operations that involves locating and establishing a connection with a satellite from a ground station. This process is achieved using the principle of phase coherence, which involves creating a two-way-coherent signal.  
In practice, carrier tracking is typically accomplished by synchronizing the downlink communication frequency with a predetermined ratio of the uplink frequency. This ratio ensures that the phases of the uplink and downlink signals are in synchronization, which is crucial for reliable communication.  
To establish a connection with the TDRS, the satellite first searches for and validates the uplink frequency based on the predetermined parameters. Once the uplink frequency is validated, the TT&C subsystem implements commands to set the frequency of the downlink communications, which is related to the uplink frequency through a prespecified ratio. This synchronization of the uplink and downlink frequencies not only enables the ground station to lock onto the satellite quickly but also ensures that the expected downlink frequency is known in advance.  
The TT&C tracking configuration is depicted in Figure 2. In this setup, a tone is modulated onto the uplink frequency, which is then transmitted from the ground station (Node #1) to the TDRS (Node #2). Upon receiving the signal, the TT&C subsystem of the TDRS adds the same tone to the downlink signal and sends it back to the ground station (Node #3). This enables the ground station to determine the round-trip time for the tone to travel from the station to the satellite and back. In general, the ground station located at Node #1 is the same as the one situated at Node #3.  
Using the round-trip time, the command segment can determine the distance between the ground station and the satellite, thereby establishing the range. As such, the actual location of the satellite can be determined. TDRS TT&C tracking events generally occur  
Figure 2: Range-only Telemetry, Tracking, and Command (TT&C) tracking configuration. Node #2 represents the locations of the TDRS in space, whereas Node #1 and Node #3 are identical ground stations used for transmitting and receiving signals in relation to Node #2.  
every hour, with accuracy ranging between 1 and 3 m (a typical residual range of FDF's operational solutions).
## 4 Result and Discussion  
### Pipeline Stratum Model under 3D Effect  
#### 4.1.1 Resistivity Ratio (\(n=R2/R1\))  
To understand the influence of the geological resistivity ratio of the pipeline stratum to the 3D effect, this study set four different kinds of geological resistivity ratio values (\(n=0.01\), \(0.05\), \(0.1\), and \(0.25\)). We fixed the \(R1\) as \(1000\) ohm-m, and set the \(R2\) as \(10\), \(50\), \(100\), and \(250\) ohm-m, respectively, for the discussion on the influence of the resistivity ratio.  
This study analyzed the influence through small pipeline sizes (\(1.5\) m \(\times\) 2 m), and the survey line L1\(\sim\)L5 of the small-scale pipeline was sequentially \(1.5\), \(0\), \(3\), \(6\), and \(9\) m away from the boundary of the pipeline, therefore, we adopted L3 (where the horizontal distance from the pipeline was \(3\) m) to analyze. The result of its resistivity ratio is shown in Figure 5. When the resistivity ratio was less than \(0.05\), the mapping phenomenon of the 3D effect could be clearly observed, and when the resistivity ratio was more than \(0.1\), the 3D effect was gradually nonsignificant.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Influence Factor**} & \multirow{2}{*}{\(\mathbf{n}\)} & \(R1\) & \(R2\) & \multirow{2}{*}{\(dx\) (m)} & \multirow{2}{*}{\(dyp\) (m)} & \multirow{2}{*}{\(dyp\) (m)} & \multirow{2}{*}{\(dyp\) (m)} & \multirow{2}{*}{\(dyp\) (m)} & \multirow{2}{*}{\(L\) (m)} \\  & & & & & & & & \\ \multirow{4}{*}{\
\begin{tabular}{c} \end{tabular} } & 0.05 & 1000 & 50 & 2 & 2 & 2 & 2 & 50 \\  & 0.1 & 0.25 & & 250 & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Boundary effect \\ model \\ \end{tabular} } & \multirow{2}{*}{Medium size} & 0.01 & 1000 & 10 & 2 & 2 & 2 & 2 & 2 & 50 \\  & & & & & 2 & 4 & 4 & 50 \\ \cline{1-1} \cline{2-11}  & Embedding depth & 0.01 & 1000 & 10 & 2 & 2 & 2 & 2 & 50 \\ \cline{1-1} \cline{2-11}  & Influence distance & 0.01 & 1000 & 10 & 2 & 2 & 2 & 2 & 50 \\ \cline{1-1} \cline{2-11}  & Electrode spacing & 0.01 & 1000 & 10 & 4 & 2 & 2 & 2 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The explanatory table of the boundary effect stratum model parameters.  
Figure 5: The resistivity ratio and the 3D effect influence on the pipeline stratum model.
## 3 Antimicrobial Peptides (AMPs)
### Application of AMPs Against Foodborne Pathogens and Persisters
#### 3.2.2 AMP-Based Detection of Foodborne Pathogens and Persisters  
Compared with traditional detective strategies for foodborne pathogens including colony counting, the polymerase chain reaction (PCR) test, immunology-based assays, and test kits, biosensors possess significant advantages such as high sensitivity, efficiency, and convenience [150]. AMPs can also be used to develop biosensors due to their stability, flexibility, low-cost, easy synthesis, and high affinity [151]. Most importantly, compared to commonly used aptamers-based and antibiotics-based biosensors, AMPs-based biosensors can effectively attach on the outer membrane of pathogenic bacteria including persisters, and provide real-time food pathogens detection. Compared to antibody-based sensors, AMP-based biosensors are more cost-effective. It was speculated that the attachment of the immobilized AMPs to the bacteria is achieved via electrical and hydrophobic interaction between AMPs and the bacterial membrane [152], but the interactive mechanism has only been sparsely studied in the biosensing field.  
For AMP-based biosensors, availability and sensitivity of selected AMPs are of great importance. [PERSON] et al. [153] designed a biosensor with magainin I immobilized on microelectrodes to detect Gram-negative pathogens and achieve a limit of detection (LOD) of \(10^{3}\) cfu/mL for _E. coli_. Later, [PERSON] et al. [154] optimized [PERSON]'s module by changing magainin I into Colicin V, thus, reducing the LOD of _E. coli_ to \(10^{2}\) cfu/mL. For Gram-positive pathogens, [PERSON] et al. [155] successfully proposed a biosensor containing immobilized Class IIa bacteriocins leucocin A with a LOD for _L. monocytogenes_ of \(10^{3}\) cfu/mL. A colorimetric biosensor based on HRP-AMPs (AMPs conjugated with horseradish peroxidase as a detection probe) can decrease the LOD for _E. coli_ O157:H7 to 13 cfu/mL [156].  
Moreover, the integration with emerging technologies such as nanomaterials and microfluidics can further improve the efficiency and portability of AMPs-based biosensors [152; 157]. Therefore, AMPs are potential alternatives to develop productive and sensitive biosensors for detecting foodborne pathogens as well as persisters.
## 3 The Implementation of the Novel ARPD-RTK/INS Algorithm  
### Overall Flow Chart of the ARPD-RTK/INS Algorithm  
The flow chart of the proposed ARPD-RTK/INS algorithm that combines GPS/BDS/INS measurements is depicted in Figure 3. The new integrated navigation algorithm also has the INS-aided AR process and the correction step. To start with, the initial states are obtained by the single-epoch RTK algorithm. Then, the new states are derived from INS mechanization. This step corresponds to the \"INS derivation\" in Figure 3. Subsequently, critical AR steps are marked in Figure 3 with blue dashed boxes. There are two significant stages in the spatial searching procedure; the first one is to determine the searching region. We call it \"grid creation\". Since the INS can provide short-term precise position prediction, the searching space is established by a set of grids centered at the estimated position. The total number of grid points is determined by the priori covariance matrix, which is obtained from the INS derivation step. The next major stage in the searching procedure is to identify which candidate grid point in the searching region is the optimal solution. The weight of each grid point depends on the distance between the float ambiguity estimation and its nearest integer vector. We complete the \"weight update\" step using the fractional part of carrier-phase measurements. When searching to find  
Figure 2: The second implementation of tightly-coupled INS/RTK integration using EKF.  
the optimal solution, enough candidate positions are required to avoid missing the maximum weight. As the number of candidates increases, the computational cost increases, too. Thus, our new algorithm makes a compromise between the accuracy and computational cost. The final solution is determined as the nearest integer vector of the weighted ambiguity average of ambiguities, and the state vector is corrected using the carrier ranging information corresponding to this suboptimal solution.  
Since the searching step length should be proportional to the observation wavelength [16], we use wide-lane measurements to complete the AR process, although a longer wavelength is always accompanied by larger noise. If higher precision is expected, another AR process with \(L1\) measurements can be performed on the basis of the wide-lane positioning result. When the GNSS observation is absent, the dead reckoning approach is utilized to enhance the availability for a short time. The specific steps will be described in the following subsections.
# Innovative Approaches for Minimizing Disinfection
## 2 Methodology  
This work aims to examine and collectively present the techniques that can be applied to minimize the formation of DBPs, as reported by relevant research worldwide, as the technology is evolving, including optimizations of procedures and recent innovative approaches.  
The bibliographic study was performed by searching terms such as \"photocatalysis\", \"electrochlorination\", \"ozonation\", \"chloramination\", and \"coagulation\" on Scopus platform terms. The initial search resulted in 9576 articles (Figure 1) from 2020 to 2024 period. Further, only relevant papers were selected by meeting the criteria related to disinfection byproducts in water. Figure 1 shows that the term \"adsorption\" returned the highest number of articles.  
Figure 1: Number of articles covering the years 2020-2024 returned for each searched keyword.  
Advanced oxidation processes (AOPs) and photocatalysis are widely applied, followed by activated carbon removal techniques and alternative disinfection methods. Combinations of methods are also frequently reported, while more recent advances include the use of machine learning approaches to predict/model DBP formation, and to enhance the effectiveness and aid in the selection and application of the above-mentioned methods to obtain DBP-free water.
# L-Band Erbium-Doped Fiber Optimization and Transmission Investigation +
## 2 Fiber Optimization and Verification
### The Structure Optimization of the FM-EDF
Finally, the best solution is obtained: The ratio of the four doping concentrations \(C_{Er,1}\)-\(C_{Er,2}\)-\(C_{Er,3}\)-\(C_{Er,A}\) is 2.2:1:1.6:1.7, and the length of the active fiber is optimized to 10 m. In this case, \(G_{max}\) reaches 38.9 dB, and the \(DMG\) is 0.17 dB at 1600 nm. Furthermore, the performance of the optimized FM-EDF is also evaluated across the L-band; the results are demonstrated in Figure 3b. As shown in Figure 3b, in the wavelength range of 1570 nm to 1620 nm, the minimum modal gain is 27 dB, and the DMG remains below 0.8 dB. The results indicate the exceptional performance of the optimized FM-EDF. The optimized FM-EDF was subsequently manufactured by YOFC. Following the fabricated FM-EDF, the performance of the FM-EDF was experimentally verified to ensure its applicability and effectiveness.
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
## 5 Performance
### Random and Systematic Error Scenarios  
In order to transfer the errors on level 2 products to errors on flux estimates (level 4), global spatial and temporal patterns of both random and systematic errors have been generated for cloud-free observations.  
As explained above, the random error mainly depends on the signal intensity measured by the instrument that depends on the ground reflectance and atmospheric transmission. Thus, the random error maps of level 2 data are obtained as a simple function of surface reflectance and aerosol optical depth, both provided by observations with the Moderate Resolution Imaging Spectrometer (MODIS) which is operated on NASA satellites.  
Through the analysis of the root cause of systematic error, geographical patterns have been attributed to each component of the systematic error (e.g., the error on SSE coming from limited knowledge of the platform exact altitude is expected to lead to orbital effects and thus produce a systematic error pattern linked to latitude). The six following individual patterns were defined: seasonal; latitudinal; proportional to surface pressure; proportional to topography variations, and; proportional to the product of surface albedo and atmospheric transmission. The amplitude of each pattern of systematic error was obtained by analysis of the error budget. By combining the six individual patterns with + or \(-\) signs, we obtained a full set of 32 different mixed scenarios of systematic error global maps. A detailed description of the error patterns on global basis will be given in a dedicated study of a follow-on paper.
## 4 Discussion
### CT Imaging  
The worst-case CT imaging error emerged in the z-axis of the MSCT system and generated the second least error value compared to the other processes. The LD uncertainty of all four CT protocolsranged at an insignificant scale. The evaluation of the hole-to-hole center points of the QA Phantom ensured that the thresholding effects were minimized when estimating the CT imaging error. This is because the center points of the Phantom holes remain unchanged regardless of the thresholding effect on the hole diameter. The homogenous material of the Phantom also complemented the minimization of the thresholding effects to compute the CT imaging error. Moreover, the NLD uncertainty of CT imaging represented noise associated with the hole surfaces that were used to predict the center points. It consisted of components such as the resolution and non-linearity. This estimated error can also be negligible for most cranioxillofacial surgical procedures.  
Though the overall CT imaging error can be negligible, the impact of CT reconstruction kernel, image thickness, and Hounsfield units on the QA Phantom was evaluated quantitatively for practitioners seeking optimal results. Following a conservative conclusion, the J30S kernel with the 0.5 mm image thickness and \(-\)500 HU yielded the least amount of uncertainty for CT imaging. This is because the J70H kernel was more prone to the image noise than the J30S kernel.  
The results indicated that the used MSCT system is a good candidate for AM applications, in particular for the development of an end-use implant, owing to its high accuracy. Though the calibration of the HU number is standardized in MSCT systems [40], a MSCT system from a different vendor does not have identical CT protocols and applied x-ray spectra due to different x-ray tubes, varying beam filtration, beam shaping filters, etc. in different CT scanner models. Furthermore, the soft tissue and bone have HU numbers which are outside of the calibration point of water (especially bone) and typically span a wide HU range even with a fixed scan protocol. To this end, linear and orthogonal errors calculated in this study can vary to a reasonably small extent [69]. Further, the reported error may deviate to a much larger extent when using a different type of CT system, e.g., a CBCT system, because of a lack of standardization in HU number and different CT protocols [40]. Nevertheless, a CBCT system might be an alternative when the manufacturer's recommended protocol is used [70], but more research is needed. In principle, the developed methodology for error analysis can be extended to any type of CT system and its protocols using the tailor-made Phantom.
## 3 The Design of _HeaLow_ and Implementation
### Overall Design  
Figure 1 shows the layout of the proposed system. First, a manager is a worker device that does slightly more work than the other workers. The manger maintains a socket connection to all the other workers, keeps the membership, and provides a communication bridge among workers. The job of the manager is not heavy, and any device can be a manager. In Figure 1, the left side of the diagram shows operations for the worker. Each square box is a significant entity in _HeaLow_, and the arrows among the squares mean the data or information delivered between the entities. An arrow has one of two descriptions.  
The following is how to read the arrow and description notation:  
* A one-sided arrow only has one description, which means the data moving in the description are delivered in the direction of the arrow.  
Figure 1: The overall design of _HeaLow_.  
* A double-ended arrow has one or two descriptions. If it has one description, the same data are delivered both ways, but the ownership is different. Details are provided with dedicated explanations.
* When a double ended arrow has two descriptions above and below it, the above is a description for data from left to right, and below is the opposite.
* When multiple data flows are connected to an entity, the type of arrow line differentiates the types of data. Details are given with dedicated explanations.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 8 Impact on Climate Change Monitoring  
### Impact of Global Mean Sea Level Bias Uncertainty on Trend Detection  
A precise estimation of the inter-satellite bias on the global mean sea level (GMSL) is needed to build consistent data records and estimate long-term trends. The tandem configuration provides an optimal configuration to estimate this bias because the two satellites observe exactly the same oceanic conditions. On the other hand, the switch of SRAL-B between several observation modes (see Figure 2 and Section 5) introduces differences in the time series; although a correction can be applied, this will result in additional uncertainty in the bias estimation. During the Sentinel-3 tandem for climate Study, an uncertainty analysis has been performed to quantify these effects and assess their net impact on the bias uncertainty, see Tables 2 and 3 reports the maximum value on the propagated uncertainty on the GMSL trend for different scenarios:  
Figure 8: Example of SLSTR L1 per-pixel uncertainty validation for the nadir (**left**) and oblique (**right**) views. Blue: histogram of normalized differences. Black: normal distribution (solid) and 3-sigma values (dashed). The comparison reveals a general under-estimation of the uncertainties, while the bimodal distribution on the oblique viewpoints to an instrumental effect not properly accounted for in the uncertainty model. Reprinted from [25].  
Figure 9: **Left**: Histogram of A-B SST differences, per retrieval algorithm (N2-blue, N3-green, D2-red, D3-cyan). **Right**: normalized differences for each retrieval algorithm. All data are for quality level 5. The raw histogram reveals that, in general, the two instruments provide very similar SSTs. The histograms of normalized differences reveal an over-estimation of the N2 retrieval uncertainty, a fair estimation for the N3 and D2 retrievals, and an under-estimation for the D3 algorithm. Reprinted from [25].  
* An \"ideal\" tandem scenario with all cycles performed in SAR open-loop (OL)
* A tandem phase with switches to SAR closed-loop modes
* A tandem phase with additional switches to low-resolution mode (LRM): this is the actual Sentinel-3B scenario
* Estimation from data acquired on the Sentinel-3B final orbit (post-tandem).  
This comparison shows that while the GMSL trend uncertainty would be minimal under an \"ideal\" tandem scenario, operational constraints due to commissioning activities (mode switches) increase the uncertainty to the point where the tandem phase becomes less advantageous than the post-tandem phase for GMSL bias estimation. This finding has important consequences for the planning of future tandem operations, see Section 8.
## 2 Materials and Methods
### System Acquisition
#### 2.2.2 System Calibration  
Following the placement of the calibration piece, the subsequent stage involves camera calibration and the establishment of reference points, which are essential for positioning the precise profiles of the pieces within the visual field, facilitating comparison with ideal profiles referred to as ground truth (GT) \(I_{g,\beta}(x,y)\), as depicted in Figure 8.  
In the system calibration process, crucial steps were undertaken to ensure the acquisition of image profiles with precise co-ordinates of GTs. The calibration procedure is interconnected and includes the following key points. The first step involves camera calibration, encompassing adjusting parameters such as focal length, reference points, depth of field, white balance, lighting, and distortion coefficients (radial and tangential) to ensure the precision of image acquisition, especially considering the distinct material colors of aluminum, nylon, delrin, and steel 1018. After camera calibration, the X and Y axes of the system are set to their respective home positions, establishing a consistent starting point for all subsequent movements and measurements within the system. Further, each reference axis is set to a zero value, creating a baseline for positioning and measurement. Subsequently, the X and Y axes are moved to specific reference point positions defined by the WCS1, WCS2, and WCS3 co-ordinates established in Table 5, ensuring that the profiles align with the desired datum.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Point** & **X (mm)** & **Y (mm)** \\ \hline MCS & 0.0 & 0.0 \\ WCS1 & 107.0 & 112.0 \\ WCS2 & 116.0 & 115.0 \\ WCS3 & 120.0 & 120.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: WCS reference point positions.  
Figure 7: Fixture for calibration piece placement.  
Lastly, the calibration data, including the camera's intrinsic and distortion parameters, is saved for future use in all image analysis software.