esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Aggregate Interference from Starlink to NTN  
In this scenario, we considered the aggregate interference level originating from the Starlink constellation, which comprised 1694 interfering satellites. Starlink satellites were active only when located above landmass areas and remained inactive over oceanic regions. It is important to note that, since the NTN satellite utilizes nineteen spot beams, we assumed that only seven beams directly overlap with the Starlink interferers, while the other twelve beams receive out-of-band interference. Figure 11 illustrates the results of the aggregate interference from the Starlink system operating in the 1990-1995 MHz frequency band.  
Figure 10: CDF of single-entry interference from Starlink to the NTN satellite receiver: (**a**) carrier-to-interference reduction; (**b**) throughput loss of the NTN uplink.  
Our findings reveal a significant throughput loss in the case of aggregate interference, amounting to 40%. This throughput loss greatly exceeds the 5% threshold and would lead to a substantial degradation in the quality of service provided by the future NTN systems operating in the n256 band. This would result in a highly inefficient spectrum utilization in the uplink channel of the 1980-2010 MHz band.
# Exopolysaccharides from Cyanobacteria: Strategies for Bioprocess Development
## 3 Cyanobacterial Bioprocess (Cyano-EPS) Development
### Production and Optimization
#### 3.2.1 Culture Medium  
Culture medium optimization has a very important role in the economic feasibility of a bioprocess as well as to highlight and fine-tune the presence of certain nutrients. The combined presence of nitrogen in _Cyanothece_ sp. CCY 0110 was able to improve RPS production (1.8 g/L) [56]. Although the highest levels of EPS on _Nostoc_ sp. BTA97 and _Anabaena_ sp. BTA990 were obtained in the absence of any combined nitrogen source [47]. The most common source of nitrogen used is NaNO\({}_{3}\)[57; 58], though KNO\({}_{3}\)[59; 60] has also shown a positive correlation with EPS production. Interestingly, among the four different sources of nitrogen, urea promoted the highest amount of RPS in _Nostoc flagelliforme_[61]. The absence of inorganic nitrogen, i.e., diazotrophic conditions, was reported to influence positively the production of EPS in certain heterocystous forming [62; 63; 64] and unicellular species [65].  
The inorganic carbon source most commonly used is CO\({}_{2}\) (0.05-10% _v/v_) combined with air and used to control pH on demand. NaHCO\({}_{3}\) was found to positively influence EPS production in _Arthropira platensis_ while at the same time it also promoted biofocculation with almost 90% of harvest efficiency [66]. Some authors have evaluated the effect of using organic carbon sources. The use of glucose to induce mixotrophic growth on _Nostoc flagelliforme_ was not found to increase EPS production [58; 67]. Contrarily, a kinetics study on _Arthrospira platensis_ tested in photoautotrophic, heterotrophic, and mixotrophic mode attained 290 mg/L of EPS in the presence of combined glucose and light [68].  
The species _Cyanothece_ sp. grown at very high salinities (70 g/L NaCl) was able to attain an EPS titer, namely CPS, of 22.34 g/L [33]. The increased EPS production, namely CPS fraction, on the freshwater _Synechococcus_ under different salinities was shown to promote osmotic cellular balance. Cell-cycle phase duration was shown to be increased and thus a slowdown on growth rate was noted at higher salinities. Conversely, salinity stress marked an increase in EPS production [69]. Many authors reported the enhanced production of EPS through NaCl stress [60; 62; 67; 46; 52], though others reported no significant effect [54; 56; 59] or even its absence became beneficial [70].  
Micronutrients such as MgSO\({}_{4}\)[46; 62], K\({}_{2}\)PHO\({}_{4}\)[54; 57], and CaCl\({}_{2}\)[60] have been shown to produce significant effects on EPS, while no effect was found for MgCl\({}_{2}\) or NaH\({}_{2}\)PO\({}_{4}\)[65; 65]. A less common stress applied in _Cyanothece epiphytica_ was the exposure to ozone for 50 s at 0.06 mg/L resulting in a relatively high EPS yield. The EPS produced showed biolubrifient potential, while the effect of ozone did not provoke any damage to cell structure [62].
### Background
The obtained results show the effectiveness of this solution for generating a unique key and a key agreement for the protection of transmitted information.
# Wearables for Industrial Work Safety: A Survey
## 3 State-of-the-Art Techniques in the Field of Industrial Wearables
### Data Collection and Wearable Metrics
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Description** & **Examples** \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Air Quality \\ In- \\ dex (AQI) \\ \end{tabular} } & An index shows the degree of air pollution in a certain area [91]. It is calculated based on measured concentrations of pollutants and government-set limits for those concentrations. The list of measured pollutants can include ozone, carbon monoxide, sulfur dioxide, nitrogen dioxide, dust, etc. The possible values of the index are between 0 to 500. The scale is divided into ranges, usually 5 or 6, each corresponding to a specific air quality rating, from good to hazardous. The influence of high AQI levels (101 and above) on the human body varies depending on the predisposition (great age, heart/lung diseases), and could lead to such diseases as lung cancer, stroke, pneumonia, etc. [91] \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Atmospheric \\ pressure \\ \end{tabular} } & The pressure exerted by the weight of the atmosphere on the surface (of the Earth or another planet) below it [93]. This metric is necessary for jobs in low (pilots) or high (divers) barometric pressure conditions. On average, the measurement range of pressure sensors is from 300 to 1100 hPa with an error of 0.5 hPa. Extra low or extra high atmospheric pressure cause respiratory, heart, neurological changes, barotraumas, decompression illness, etc. [94; 95] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Environment-related metrics.
# Numerical Fit Modeling for Temperature Mitigation in
## 2 Numerical Fit Model for Arid Regions  
A numerical fit model will be created by fitting equations to heating and cooling rates. The heating rate will be fitted to solar insolation. The cooling rate will be fitted to air temperatures drops at night.  
When interaction between the calcite plume and the water clouds becomes insignificant, it simplifies the modeling requirements. The energy budget is simplified to give the incident radiation \(I\).  
\[I=R\left(I\right)+B+L\left(E\right)+S+A \tag{1}\]  
where the atmospheric warming is \(A\), \(R\) is the surface reflectivity, \(B\) is the ground radiation, \(L\) is the latent heat of evaporation, \(E\) is the amount of water evaporated, and \(S\) is the heating of the soil. Cloud feedback normally makes all of these variables complex to analyze. For example, cloud cover reduces the incident radiation by making the surface appear more reflective. If clouds reduce radiation, then they prevent evaporation from occurring at the same rate. Cloud cover makes the atmosphere below more stable, so less heat flux occurs. The advantage of applying simple models to arid regions is that they are often cloudless.  
Other processes also influence the energy budget. For example, other aerosols such as dust affect the energy budget processes [22]. However, these other processes are less complex than those found for water clouds. Dust is often produced by wind inflating it. There is little reason to expect that adding a calcite aerosol would cause wind to significantly increase or decrease. Therefore, calcite production could be considered constant. Another aerosol is black carbon (BC) or soot. The main source of BC is human activity [23]. Since human activity will not significantly change, the amount of BC could be assumed to be constant.  
The calcite is likely to be distributed at aircraft heights of around 2 km. The material will disperse quickly horizontally and vertically. Previous work has shown that the elevation of the material had a low effect on differences in ground temperatures. With the numerical fit model, the effect on the solar radiation reaching the ground is the same regardless of its height. Therefore, height is not a factor in the analysis.
## 2 Creation of Signals Database Measurement Conditions
### Rails  
The wheel signatures are gathered for each type of a rail (**R**) for which system must provide reliable information about train speed. Nowadays 60E1 (UIC 60) is the most popular rail profile not only in Europe. However, for example: 115 RE (according to. AREMA standard in America) or AS68 (Australian Standard) can be found on other continents. In the East, P60, P65 or P68 (Russian Standard) are used. The degree of wear and tear, if not specified, is an individual decision of the axle counter provider. In the case of 60E1 it can reach up to 20 mm of vertical wear (W\({}_{\text{V}}\)) and 22 mm of lateral (W\({}_{\text{L}}\); from class 0 to class 5 of tracks, according to [15], Table 1, Appendix 14). It is also recommended to acquire signals for mixed vertical and lateral wear, as it is more natural. According to [15] in such a case, the maximum, allowed vertical wear shall be decreased by half of the real lateral one. The value of angle \(\alpha\) is equal to \(55^{\circ},60^{\circ}\) or \(65^{\circ}\), depending on the class of track (Figure 4). Maximum wear and tear of the rail shall be defined by the Infrastructure Manager and depends on the category of tracks (main lines, industrial, urban, stabling yard).  
Example of parameters of 60E1 for the main lines (class 0, according to [15], Table 1 Appendix 14) is as follows:  
* a new rail: W\({}_{\text{V}}\) < 5 mm, W\({}_{\text{L}}\) < 5 mm (assumption);
* a maximum vertical wear: W\({}_{\text{V}}\) = 12 mm;
* a maximum lateral wear: W\({}_{\text{L}}\) = 14 mm;
* a mixed wear: W\({}_{\text{V}}\) = 5, W\({}_{\text{L}}\) = 14 mm.  
The combination of rail wear may be reduced in the situation when the position of a sensor (detector head) is adjusted during the periodical maintenance and its relation to the wheel is ensured, independently of the condition of the rail. If the impact of the rail  
Figure 4: Type of rail wear and tear for testing purposes.  
dimensions is negligible (e.g., a detector is sensitive only to the flange of a wheel), the measurement may be limited to the single type of a rail.
###### Abstract  
Switched filter banks find widespread application in frequency-hopping radar systems and communication networks with multiple operating frequencies, especially in situations demanding elevated filter element isolation. In this paper, the design and implementation of a highly isolated switchable narrow-bandpass filter bank architecture using hairpin microstrip topology is presented. The filter bank has four discrete bandpass filters with passbands of 2.0-2.2 GHz, 2.3-2.5 GHz, 3.1-3.3 GHz, and 3.9-4.1 GHz. These filters span the radar S-frequency band (2.0-4.0 GHz). In order to switch between channels with a switching speed of nanoseconds, low-loss and highly isolated SP4T switches are implemented. Advanced design system (ADS) software is used to design the various filter functionalities, and the entire system is tested on a vector network analyzer (VNA). The proposed architecture makes it much easier to put the filter bank into practice and switch it to the desired frequency, which is useful for radar receiver applications.  
bandpass filter; insertion loss; return loss; switched filter bank Article
# Aerial Physical Interaction in Grabbing Conditions with Lightweight and Compliant Dual Arms
## 5 Experimental Results  
### Position Estimation with Zero Deflection/Torque Control  
The goal of this experiment was to evaluate the accuracy of the position estimation provided by the grabbing arm when this was used as position sensor, enabling the zero-torque controller to reduce the reaction wrenches induced over the aerial platform on flight (see Section 4.1). The experiment, shown in the video provided as Supplementary Material, consisted of four phases:  
1. The multirotor takes off.
2. The arms adopt the nominal operation pose given by Equation (15).
3. The aerial robot approaches the contact point using the Opti Track system for navigating.
4. Once the arm grabs the support structure, the zero-torque controller and the estimator are enabled while the multirotor is controlled in position with the Opti Track system.  
Figures 8-10 represent the signals of interest during the grabbing maneuver, that is, the multirotor position (arm estimation and ground truth) relative to the nominal pose, the multirotor orientation, the estimation error, and the joint position and torque in the grabbing arm. In order to appreciate the ability of accommodation of the arm and the accuracy in the estimation, we intentionally deviated the aerial platform in the \(x\)- and \(z\)-axes, with small deviations in the \(y\)-axis. Note that in the experiments the end-effector is supposed to be firmly attached to the grabbing point in such a way that the displacement of the multirotor platform will force the displacement of the grabbing arm. However, the magnetic gripper may slip around the contact area, introducing errors in the position estimation. It was also assumed that the multirotor heading (yaw angle) was almost constant, with the \(Y_{B}\)-axis parallel to the linear structure. Otherwise, additional degrees of freedom should be integrated in the wrist joint to estimate the relative orientation, although this is out of the scope of this work. The influence of these two effects over the positioning accuracy can be observed in Figure 9, where the estimation error increased as the multirotor orientation in the yaw angle changed with respect to its initial value, causing mainly errors in the \(y\)-axis estimation.  
Figure 8: Multirotor displacement in the grabbing maneuver with zero torque control. Nominal pose (black), displacement measured by OptiTrack (magenta), and arm estimation (blue).  
Figure 10: Evolution of the servo position and torque estimation during the grabbing maneuver.  
Figure 9: Position estimation error, using Opti-Track as ground truth. \(x\)-, \(y\)-, and \(z\)-axes error (up) and error norm (middle). The three shaded areas correspond to situations in which the platform is rotated in yaw and the error increased since the magnetic gripper was not firmly attached to the point.
# Preparation and Characterization of Hybrid Nanocomposites for Dental Applications
## 3 Results and Discussion
### Dispersion Characteristics  
The surface modification of nanoparticles by chemical treatments (such as the functionalization by natural polymers or the absorption of silane coupling agents) is a useful method to improve the dispersion stability of nanoparticles in a polymer matrix. Thus, the functionalization of HNTs is extremely important for processing and enhancing the properties of HNTs/polymer nanocomposites. The polymer nanocomposites based on modified nanotubes exhibited improved mechanical and thermal properties, because the functionalization improved the dispersion and stress-strain characteristics [67]. In addition, [PERSON] [64] confirmed that the modification of particles' surfaces using silane coupling agents cause improvement of the compatibility between the particle and polymer surfaces and; thus, the properties of composite materials. In the present study, composites containing 5% pristine HNTs, 5% of gelatin-modified HNTs (HNTs-g), 5% of silane-modified ATH, and 2.5% of gelatin-modified HNTs + 2.5% of silane-modified ATH were observed by SEM (Figure 3). The effect of the modification of nanofillers was depicted. HNTs exhibited a high specific surface area, which was favorable for interfacial interaction with the polymer matrix, thus promoting stronger bonding at the interfaces. One can find that unmodified HNTs seemed to be completely covered by the polymer matrix (Figure 3a). As expected, the chemical modification of HNTs by natural polymer (gelatin) affected the dispersion of nanofiller in the polymer matrix. At the same magnification (20,000\(\times\)), one can find that the morphology of the sample was changed completely, and single nanotubes sticking out from the matrix could be seen (indicated with arrows, Figure 3b). In turn, the SEM image of polymer nanocomposite with 5% of silane-modified ATH revealed a peel-like and a loose microstructure with leaflet pattern. Such magnification (20,000\(\times\)) allows one to observe the close stacking of a layered structure with a smooth surface (marked with a circle, Figure 3c). Moreover, Figure 3d presents the SEM image of the hybrid nanocomposite containing both modified nanofillers: HNTs-g and ATH-sil. At the magnification of 10,000\(\times\), both structures were clearly visible (i.e., well-dispersed HNTs-g along with the layered structure of ATH-sil). Herein, it can be concluded that ATH-sil appeared to be \"more complex\" with the matrix, while the nanotubes, distributed evenly in the matrix, appeared to protrude from one end and the other to be anchored in the polymer. Nevertheless, on the basis of the morphological observations and the obtained proper nanoparticles' dispersion, an improvement in the functional properties (mechanical, biological activity) of the finished product, in the form of a denture, could be expected.
## 3 Results and Discussion
### Flow Characteristics of Various VRFB Flow Frames  
Figure 14 shows the flow distribution for CASE 1. The maximum velocity in the channel was 1.449 m/s. In particular, the flow from the channel inlet did not move upward owing to the influence of the first outlet guide and moved toward the left. Figure 15 shows the velocity distribution at position A for CASE 1. The velocity was the highest at the first outlet of the inlet channel and the lowest at the second outlet because the electrolyte was not discharged smoothly owing to the influence of the first outlet guide; this was in agreement with the previously observed flow characteristics. The maximum velocity into the electrode was 0.091 m/s, and the total area of the outlets was 1/2 of the electrode area. According to this, the variability range coefficient (\(R_{i}\)) decreased from 0.072 to 0.030 m/s at position A. The maximum velocity deviation (\(D_{v}\)) was 19.27%, which was increase as compared with the previous value of 11.89%. This was an improvement over the existing result; however, flow uniformity was still low. Nevertheless, the flow path in the channel was simplified as compared with the existing shape, and the area per channel outlet was doubled, indicating a decrease in the overall pressure drop. However, there was still a problem with an increase in pressure drop due to vena contracta. Table 6 shows the pressure drop in each section. In the electrode, the pressure drop did not change significantly. However, in the channels, the pressure drop decreased up to 93.02% and, in the entire flow frame, the pressure drop decreased 44.89%.  
Figure 13: Two improved models of flow frame of vanadium redox flow battery. (**a**) CASE 1; (**b**) CASE 2.  
Figure 16 shows the flow distribution for CASE 2. The maximum velocity of the channel is 2.289 m/s in the entrance area located at the bottom. In the top section, the flow at the inlet was vertically discharged and distributed to the left and right owing to the elliptical structure. Figure 17 shows the velocity distribution at the position A for CASE 2. The deviation in the flow velocity was significantly  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Conventional Design (Pa)** & **CASE 1(Pa)** & **Difference (\%)** \\ \hline Inlet channel & 44,320 & 3630 & 91.81 \\ Electrode & 96,150 & 95,890 & 0.270 \\ Outlet channel & 45,970 & 3210 & 93.02 \\ Entire region & 186,440 & 102,730 & 44.89 \\ \hline \end{tabular}
\end{table}
Table 6: The difference in pressure drop between conventional design and CASE 1.  
Figure 14: Flow distribution for CASE 1.  
Figure 15: Velocity distribution at the electrode inlet for CASE 1 (location A).
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 3 Two-Stage Quantization Method  
In this section, an optimized linear quantization for a low bit-width network is proposed. This method includes explicitly two stages: clustering and mapping, as shown in Figure 5. The \"Hybrid clustering\" module takes as input for a fine-grained pruned model which is pre-trained by PyTorch or other deep learning frameworks. It uses PSO-K-means to cluster the weights on each layer according to the preset bit-width. The mapping module maps the floating-point weights in each clustered set into one same fixed-point number according to its max and min ranges through the uniform partition algorithm. Details of this method will be explained in the following sub-sections.  
Figure 5: Overview of two-stage quantization. It is composed of a clustering module and a mapping module. It takes as input a DNN model pretrained and pruned by some framework such as Pytorch.
## 4 Experiments
### Compared Techniques  
The real bug benchmarks, Defects4j v1.2 [18] and Defects4j v2.0 [18], which are widely recognized in the APR field, were selected for evaluation. The most used version of Defects4j for benchmarking, v1.2, comprises 393 bugs, 130 of which are single-block bugs. Defects4j v2.0, the latest version, adds 438 errors to v1.2, including 82 single-block errors. CodeTransFix was tested on 130 single-block defects in Defects4j v1.2 and 82 single-block bugs in Defects4j v2.0 since it is intended to fix single-block bugs. Considering the fact that CodeTransFix combines NMT-based techniques and LLMCs, its performance was evaluated by comparing it with open-source, state-of-the-art NMT-based methods and LLMCs for bug fixing. For NMT-based methods, CURE [16], RewardRepair [17], and Recoder [12] were chosen, with other APR methods fixing fewer bugs [4, 11]. For LLMCs,PLBART [8] and CodeT5 [7] were chosen for comparison. Specifically, for PLBART, its two different-sized models were evaluated: PLBART-base (140 M parameters) and PLBART-large (400 M parameters). For the CodeT5 model, two versions were also selected for evaluation: CodeT5-small (60 M parameters) and CodeT5-base (220 M parameters). In addition, to verify the generalization of CodeTransFix on Defects4j v2.0, two APR methods that previously performed optimally on Defects4j v1.2 were selected for further comparative experiments: CodeT5-base, a large language model of code, and Recoder [12], an NMT-based APR method.