esa-sceva/satcom-chunk-collection huggingface dataset:
# No Free Lunch--Characterizing the Performance of 6 TiSCH When Using Different Physical Layers
## 5 Experiment Setup and Methodology
### Methodology  
We run the OpenWSN network once for each of the three PHYs. During the experiments, the floor is mostly unoccupied, so we do not expect WiFi interference beyond the regular 100 ms beaconing interval of the 8 WiFi access points on that floor [1]. Each time, we load the firmware onto the testbed, then switch on the OpenVisualizer software, which connects to all motes over their serial port (over MQTT). In the OpenVisualizer, we then select the mote that we want to play the role of Directed Acyclic Graph (DAG) root. We always select the same mote, shown in Figure 2, which is positioned at the center of the floor in room A. We then let the network run for 90 min, recording all the data generated by the motes.  
Figure 2: Locations of the 42 motes of the OpenTestbed across an office floor at Inria-Paris.  
We developed a custom-built application which runs at the application layer of each mode. That application sends a data packet every minute containing the following fields:  
* a counter, which we use to detect lost data packets,
* the time at which the data packet is generated (Asynchronous Slot Number ASN),
* the DAG rank of the sender,
* the size of the neighbor table of the sender,
* \(T_{on}\) how long the sender's radio has been on since the previous data packet transmission,
* \(T_{TX}\) how long the sender's radio has been on and transmitting since the previous data packet transmission,
* \(T_{total}\) the amount of time since the previous data packet transmission,
* the maximum and minimum number of packets in the packet buffer since the previous packet.  
After the experiment is done, we use that information to compute the following Key Performance Indicators (these KPIs are recommended by Reference [30]):  
* Network Formation Time: how much time elapses between the moment the root is selected and that root has received at least one packet from _each_ mode.
* End-to-End Reliability: what portion of all packet generated in the network are received by the root.
* End-to-End latency: how much time elapses between the generation of the packet at the sender, and reception at the root.
* Radio duty cycle: what portion of the time a node's radio is on; a good indication for its power consumption.  
We display the results in three forms:  
* Time series. We show the mean and the inter-quartile range of the KPI, with a 3 min sliding window and a 1 s time resolution.
* Cumulative Distribution Function (CDF). We show the cumulative distribution of all data samples at steady state (starting 30 min into the experiment), using 100 bins.
* Probability Distribution Function (PDF). We show the probability distribution of all data samples at steady state using 100 bins. This allows for a more detailed assessment of the distribution of each data set.  
Figure 3: Components of the OpenTestbed experiment setup. (**a**) The OpenMote B, (**b**) the OpenTestbox, part of the OpenTestbed.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 1 Introduction  
### Manufacturing in Space  
Manufacturing in space is a scientific field with synonymous use of terms (Table 1). Nevertheless, manufacturing in space is seen as a promising option to produce spare parts on demand in order to reduce launch mass and cost, which is seen as a basic requirement for the exploration of other celestial bodies. In fact, [PERSON] et al. show that the launch mass of traditional spare parts can be reduced by almost 78% usingin-space manufacturing (Figure 1). This, however, is linked to their manufacturability, which illustrates the importance of suitable manufacturing processes for space missions. Moreover, the proportion can be increased significantly if the potential of recycling is also exploited (Figure 1).  
[PERSON] et al. [4] analyzed the cost per kilogram for space launches worldwide since 1960 using data from the Center for Strategic and International Studies. A significant price reduction between Soyuz (1967) and Falcon 9 (2010) is evident, and it is expected that this decrease will continue if we take current developments into account (Figure 2).  
Either way, the transport costs are significant and the payload is limited. Therefore, it is obvious that the space agencies increased their activities on developing in-situ manufacturing solutions to overcome the limitations related to size, launch mass capabilities or logistic mission cost and complexity [13].  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Term** & **Description** & **Source** \\ \hline \multirow{4}{*}{In-space manufacturing (ISM)} & Manufacturing in an intravehicular (crew) & \multirow{4}{*}{[1]} \\  & environment and takes place inside a pressurized & \\  & habitat structure (e.g., International Space Station) & \\  & and is primarily focused on logistics reduction and & \\  & on-demand manufacturing of spares. & \\  & Aims to foster and support the development of & \\  & manufacturing and assembly technologies adequate & \\  & for implementation in space. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Current definition of activities related to manufacturing in space.  
Figure 1: Reduction in spares mass requirements for items manufactured in space [3].
# Differential Learning for Outliers: A Case Study of Water Demand Prediction
## 3 Data Set  
The data used in this paper was collected for a water distribution service area (Figure 1) in central Indiana from 1997 to 2017. Figure 2 shows a box-plot of the water consumption throughout the study period. The bottom line in each box in Figure 2 is the first quartile, the middle line is the median and the top line is the third quartile of the water consumption levels for the corresponding month of the year. The upper and lower whiskers are 1.5\(\times\) (third quartile - first quartile) above the third quartile and below the first quartile, respectively. This figure shows that water consumption is the highest during the summer months of May through September. A large number of deviations from the median are also observed during these months. For the remainder of the year (i.e., January through April and October through December), the consumption is largely constant with limited deviations from the median values.  
The customer count (Figure 3) for the service area is also collected. It is only available on a yearly basis with an average increase of 1.37% from one year to the next over the study period. The customer count reached 324,500 in 2017.  
Figure 1: Water distribution service area and location of weather stations.  
Figure 2: Monthly ranges of water consumption for the period 1997–2017 in million liters.  
In addition to the operational data, three external data sets relevant to the study were collected. The first external data set connects observed water consumptions to the weather conditions. Weather data from seven weather stations within the service area were obtained from the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) [24] database. The seven weather stations are USC00121303, USC00121326, USC00124260, USC00124272, USC00124286, USW00053842 and USW00093819. They were all active during the target time period from 1997-2017 and their geographical locations are overlaid on the service area map in Figure 1. For some stations, weather data were missing for some days (i.e., not recorded for the day). When available, the daily values of minimum temperature, maximum temperature, rainfall, snow and snow depth from each of the stations for each day were extracted. The mean values for each type of weather data were then calculated based on these available values.  
The second external data consisted of the median household income of the service region. It was obtained from the US Census Bureau [25] for the county (i.e., Marion County). As shown in Figure 4, the highest median income was in 2007 and the lowest was in 1997. The median income for 2017 is unavailable at the time of this publication.  
The third external dataset is the calendar with holidays and major observances [26]. This data set includes all the major public holidays, bank holidays and observances (e.g., Halloween) which may not be holidays but are widely celebrated across the US.
## 3 Polymerization Techniques for MIP Preparation
### Free Radical Polymerization (FRP)  
Bulk polymerization is a fast and simple polymerization method that provides pure MIPs with no special instrumentation requirements. It is the most widely used free radical polymerization method for preparing MIPs applied in sample extraction techniques. However, this method requires increased template amount and the prepared MIP bulk should be grinded, sieved and sedimented with the use of a solvent in order to remove the finer particles and obtain particles of the preferred size. This is time consuming and the resulting particles have irregular shape and size, decreased number of imprinted sites and binding capacity, while template bleeding can be observed [36; 37; 38; 39]. Suspension polymerization is a simple, single-step polymerization method where polymerization mixture is suspended in a continuous aqueous, mineral oil or perfluorocarbon liquid phase and provides spherical porous MIP particles. However, the developed MIP particles size ranges between mm-mm with decreased recognition capability, unsuitable for solid-phase extraction applications [38; 39]. Emulsion polymerization takes place in an oil/water diphasic system with the addition of surfactants that prevents diffusion and favors the formation of small, homogeneous emulsion droplets. Emulsion polymerization can provide mono-dispersed MIP particles, in high yield, however surfactant residues can interfere with the imprinted sites, thus reduce binding capacity of the MIPs [37; 38; 39]. Precipitation polymerization is a single-step polymerization method that provides high-quality, spherical MIP particles with homogenous size in good yield. As the polymerization takes place spherical MIP particles precipitate from the reaction solution. However, this method requires increased amount of organic solvent and meticulous control over the polymerization conditions such as solvent polarity, temperature and stirring speed that affect the MIP particle size [36; 37; 38; 40]. MIP monoliths can be prepared inside a confined space such as a chromatographic column by in-situ polymerization. It is a simple, one-stem process and unlike bulk polymerization it does not require grinding and sieving of the prepared MIP bulk [38; 39; 40]. MIPS prepared by bulk, suspension, emulsion and precipitation polymerization are schematically presented in Figure 2.  
Figure 2: Free radical polymerization techniques used for MIP preparation.
## 5 Simulations  
The simulations adopted the Opportunistic Network Environment (ONE) to evaluate the experimental performance of the routing algorithm proposed in this article. To be specific, it was compared with four other algorithms, three of which were the latest routing algorithms, one of which was a traditional algorithm in opportunistic social networks: EIMST(effective information transmission based on socialization nodes) [33], ICMT (Information Cache Management and data Transmission algorithm) [26], FCNS(fuzzy routing-forwarding algorithm) [28], and epidemic [22]. EIMST attempts to achieve an effective information transmission based on socialization nodes. ICMT makes use of memory management and node collaboration to achieve an effective data transmission. However, FCNS is a fuzzy routing-forwarding algorithm exploiting comprehensive node similarity in opportunistic social network.
# Enhancing the Photocatalytic Activity of Immobilized TiO2 Using Laser-Micropatterned Surfaces
## 2 Materials and Methods
### Photocatalytic Device  
Irradiation was performed with a laboratory-constructed \"illumination box\" (Figure S1), equipped with four F15W/T8 black light tubes (Sylvania GTE, Westfield, IN, USA) (Figure S1). The maximum emission of these tubes was around 365 nm, emitting 71.7 \(\mu\)W cm\({}^{-2}\) at a distance of 25 cm.  
Cells containing fortified atrazine solution in contact with under-investigation surfaces were used for photocatalysis experiments. Positioned at a distance of 11 cm from the light tubes, the cells were placed on a magnetic stirrer. Experiments were also performed using bisphenol-a and 2,4,6-trichlorophenol as test pollutants.  
Selection of Photocatalytic Device Setup  
To ensure a valid observation of the photocatalytic degradation of atrazine, a 2 mL volume of the fortified atrazine solution was selected. This volume was chosen to adequately cover the small photocatalytic surface area (1.5 cm \(\times\) 1.5 cm). Simultaneously, an initial atrazine concentration of 30 ug L\({}^{-1}\) (0.139 \(\mu\)mol L\({}^{-1}\)) was utilized, allowing for the measurement of changes in atrazine concentration during the photocatalytic process. The accuracy of measurements at this low concentration level was upheld by employing atrazine-d\({}_{5}\) as an internal standard (IS).  
In addition to tracking the decrease in atrazine concentration, the confirmation of its photocatalytic degradation involved monitoring the formation of photocatalytic transformation products (TPs), namely 2-chloro-4-(isopropylamino)-6-amino-s-triazine (CIAT), 2-chloro-4-amino-6-(ethylamine)-s-triazine (CAET), 2-chloro-4-6-diamino-s-triazine (CAAT), 2-hydroxy-4-(isopropylamino)-6-(ethylamino)-s-triazine (OIET), 2-hydroxy-4.6-diamino-s-triazine (OAAT), 2-hydroxy-4-hydroxy-6-amino-s-triazine (OOAT), and 2.4.6-thihydroxy-s-triazine (OOOT). These TPs are known to be generated during the photocatalysis of atrazine [48].  
Moreover, the photocatalytic activity of the optimum surface (M-Si-solgel) was evaluated using bisphenol-a and 2,4,6-trichlorophenol solutions at a concentration of 30 \(\mu\)g L\({}^{-1}\).
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 4 Results  
### Grid Scale \(\sigma^{\circ}\) Temporal Profile  
Since irrigation and rainfall events are both considered a water supplement and have the same effect on the SAR backscattering coefficients, it was proposed to minimize the irrigation-rainfall ambiguity using the \(\sigma^{\circ}\) values obtained at basin scale (10 km \(\times\) 10 km). Figure 4 shows an example of the temporal behavior of \(\sigma^{0}_{\mathrm{G}}\) SAR values in VV polarization obtained at a 10 km grid cell (red curve) for Montpellier (Figure 4a), Tarbes (Figure 4b) and Catalonia (Figure 4c). Daily precipitation records obtained from the Global Precipitation Mission (GPM) data are added to the figures (blue curve) to help understand the consistency between the grid scale \(\sigma^{0}_{\mathrm{G}}\) values and the rainfall events. The green line shows the SSM estimation at grid scale (for bare soil plots with low vegetation cover) at each SAR date while the dotted black line shows the threshold value fixed for \(SSM_{G}\) at 20 vol.%. Following a rainfall event, the \(\sigma^{0}_{\mathrm{G}}\) value increases (more than 1 dB) due to important precipitation before the SAR acquisition (black dashed circle) accompanied with high SSM estimations (more than 20 vol.%) indicating humid soil conditions at 10 km scale. On the other hand, the absence of precipitation causes a decrease or stability of \(\sigma^{\circ}\) value at grid scale with low soil moisture values (less than 15 vol.%) indicating dry soil conditions (yellow dashed circle). This consistency between rainfall, \(\sigma^{0}_{\mathrm{G}}\) and SSM estimations at grid scale ensures that both \(\sigma^{0}_{\mathrm{G}}\) and SSM estimations at 10 km scale are a good representative for rainfall events and, therefore, could be used to eliminate the uncertainty between rainfall and irrigation.
## 4 GPS L1/L5 Receiver: L5 Pattern Assumption  
Lack of information on the L5 antenna pattern has always been a big struggle for engineers trying to design a spaceborne receiver. The only information known is that the main lobe is wider than the one of the L1 pattern [2]. In this sense it can be approximated from the L1 pattern using the ICD document [4; 14], but on the other side, the side lobes approximation is less accurate. Indeed, while the main lobe has almost the same gain values for different azimuth of the same satellite, and furthermore for different satellites, the side lobes do not. Hence, this makes the assumption of the L5 pattern, especially for side lobes, very important, as it might affect the results. In this context, here, we want to evaluate the effect of assuming the different L5 antenna patterns defined in Section 2.3 on the GNSS availability and the GNSS-based navigation performance.
## 1 Introduction
However, here we are following a slightly different approach: being aware that since small to medium natural targets are usually contaminated by unexpected bright scatterers at different heights, we need to integrate many more pulses per waveform, about the full illumination time, in order to benefit from the improved sampling of FFSAR to discriminate contaminated waveforms. The main objectives of the present study are twofold: (1) to develop an FFSAR processing chain using S6 data designed for monitoring WL\({}_{\text{s}}\)s in small to medium inland water targets, specifically focusing on error reduction over targets with a transect length below 3 km, compared to operational products processed with UFSAR; and (2) to explore the possibility of exploiting across-track measurements to increase the number of measurements per satellite pass. Regarding the latter objective, we aim to investigate the feasibility of utilizing an extended water mask, incorporating both nadir and across-track measurements, which could prove highly advantageous for small to medium targets, provided they remain accurate. This approach could enable an increase in the number of measurements and enhance outlier detection efficiency. The following sections are organized as follows: First, we describe the collection of selected inland water targets and the available data from both in situ stations and S6. In the methodology section, we illustrate the processing chain used to obtain WL\({}_{\text{s}}\)s from the FFSAR processor and the algorithm behind extending water masks. The results section examines the accuracy obtained from this processing chain and evaluates the quality of across-track measurements in comparison to nadir measurements. Additionally, we analyze the narrowest transects among our targets and investigate the correlation in the observed error between two nearby targets, namely Mequinensa-North and Mequinensa-South.
## 3 HTP Tooth Profile Design Using IH Tooth Profile and Cycloidal Curve
### Cycloid Curve Theory  
A cycloidal curve is generated by a circle rolling along a straight line and serves as a key element in efficient gear meshing. An extension of this concept is the epitrochoid curve, which represents the trajectory formed when a circle rolls externally around a fixed circle. Epitrochoid curves are advantageous in gear design due to their ability to increase the proportion of rolling contact, reduce friction on the tooth surface, and enhance durability. As a result, smooth meshing can be achieved even in gear systems with a low tooth count. Furthermore, by introducing an offset or applying \(\pm\) translational motion, optimized meshing can be maintained despite having fewer teeth than conventional cycloidal curves. In other words, modifying the epitrochoid curve allows the design of low-tooth-count gears while preserving efficient meshing characteristics. The general mathematical expression for an epitrochoid curve is given by the following parametric equations:  
\[x(\theta)=(R+r)\cos\theta-d\cos\left(\frac{R+r}{r}\theta\right) \tag{8}\]  
\[y(\theta)=(R+r)\sin\theta-d\sin\left(\frac{R+r}{r}\theta\right) \tag{9}\]  
where \(R\) is the radius of the fixed circle, \(r\) is the radius of the rolling circle, \(d\) is the distance from the center of the rolling circle to the point generating the curve, and \(\theta\) is the rotation angle of the rolling circle. This epitrochoid formulation extends the traditional cycloidal curve, enabling broader applicability in gear design and improving meshing performance--especially in low-tooth-count configurations.  
To analyze the meshing characteristics using cycloidal and epitrochoid curves, the following parametric equations can be used:  
\[x(t)=r(z+1)\cos t-d\cos(z+1)t \tag{10}\]  
\[y(t)=r(z+1)\sin t-d\sin(z+1)t \tag{11}\]  
where \(r\) is the radius of the rolling circle, \(z\) is the number of gear teeth, \(d\) is the distance from the center of the rolling circle to the reference point of the tooth profile, and \(t\) is the parameter (\(0\leq t\leq 2\pi\)). At the meshing pitch point, Equation (10) can be rearranged through a coordinate transformation as follows:  
\[x(t)-rz=rz\cos t-rz+r\cos t-d\cos(z+1)t \tag{12}\]  
The derivative with respect to the x-axis is expressed as follows:  
\[x^{\prime}(t)=r\cos t-d\cos(z+1)t \tag{13}\]  
By applying a coordinate transformation to Equations (12) and (13), the equations can be rewritten with the x and y coordinates switched, yielding the following:  
\[x=r(z+1)\sin t-d\sin(z+1)t \tag{14}\]  
\[y=rz\cos t-rz+r\cos t-d\cos(z+1)t \tag{15}\]  
Substituting \(\theta=(z+1)t\) leads to the following expressions:  
\[x=r(z+1)\sin\left(\frac{\theta}{z+1}\right)-d\sin\theta \tag{16}\]  
\[y=r(z+1)\cos\left(\frac{\theta}{z+1}\right)-rz-d\cos\theta \tag{17}\]
## 5 Results and Analysis
### Quantitative Performance Comparison  
All three models were evaluated using the same test dataset (i.e., 1866 images), and the results were assessed based on F1-score metrics. To ensure statistical robustness, theevaluation was conducted over four separate runs with different random seeds. The primary \(F1\)-score results, reported as mean \(\pm\) standard deviation, are summarized in Table 3. The data clearly indicate that the difference-based model achieved the highest and most stable overall performance with an overall \(F1\)-score (\(F1_{s}\)) of 0.594 \(\pm\) 0.025. In contrast, the Siamese model exhibited significant performance variance, with a high standard deviation of 0.083 for its \(F1_{s}\) score, indicating inconsistent results across different runs. While the overlay model attained the highest localization F1-score (\(F1_{b}\)), its inability to perform damage classification (\(F1_{d}\)) rendered it unsuitable for the primary objective of this study.  
To diagnose the performance behind these F1-scores, a detailed breakdown of precision, recall, and intersection over union (IoU) is presented in Table 4. This diagnostic analysis provides deeper insights into the specific behaviors and limitations of each model.  
For the difference model, the superior overall score is achieved despite a significant weakness in its damage assessment recall (0.445 \(\pm\) 0.051). This indicates that it consistently fails to identify more than half of the actual damaged road areas, highlighting the difficulty of this task due to class imbalance.  
The Siamese model exhibits a different flaw. For damage assessment, it achieves a high precision (0.770 \(\pm\) 0.041) but suffers from an extremely low and inconsistent recall (0.225 \(\pm\) 0.090). This suggests the model is very conservative, making high-confidence predictions on a very small subset of damage while missing the vast majority of it. This trade-off results in a poor overall \(F1_{d}\) score.