esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Dataset Construction and Analysis
By comparing and analyzing the B-scan image and the corresponding drilled sample core, combined with the experience of experts in the field, the abnormal area, that is the distress, can be analyzed and classified as interlayer debonding (Figure 1(a)), interlayer water seepage (Figure 1(b)) and interlayer loosening (Figure 1(c)). The characteristics of three distresses are described as follows [31; 32]:  
* Interlayer debonding: The term refers to the continuous delamination phenomenon within the pavement interlayer structure, characterized by following and debonding between layers, typically resulting from aging and excessive loading, causing bond separation while maintaining good horizontal continuity. The random samples with interlayer debonding are shown in Figure 1(a). Interlayer debonding is labeled as \"Poor_1\". * Interlayer water seepage: When interlayer debonding already exists and is not promptly maintained, rain and other infiltration may lead to interlayer water seepage. The dielectric constant of water and air is very different, so the polarity reversal of the interlayer water seepage is obvious compared with interlayer debonding. The random samples with interlayer water seepage are shown in Figure 1(b). Interlayer water seepage is labeled as \"Water_1\". * Interlayer loosening: When interlayer water seepage occurs, it is not repaired in time, and the action of load results in interlayer loosening distress. The GPR B-scan images of the distress are characterized by discontinuity and curvature of the event. The random samples with interlayer loosening are shown in Figure 1(c). Interlayer loosening is labeled as \"Loose_1\". The dataset was established after data preprocessing. The number of samples in the training, validation, and test set of the dataset is shown in Table 2.  
Figure 2: Comparison of GPR B-scan images of interlayer distresses with core samples and samples of three types of interlayer distress. Figure 3 indicates that the distribution of interlayer debonding and interlayer water seepage shows little overlap, while the interlayer loosening is wrapped in the middle by the other two distresses, and there is a certain overlap with these two kinds of distresses.
# Using IoT for Cistern and Water Tank Level Monitoring
## 3 Monitoring of Water Architecture  
### Brief System Description  
Traditionally, house water is stored aboveground (in water tanks) or underground (in cisterns). A house has a water tank on the roof, which stores water that flows down by gravity when a faucet is opened in the kitchen, bathrooms, toilet, shower, garden, etc. A cistern is usually located underground at the front of the house. The cistern is supplied with drinking water by pipes installed throughout the city. The water stored in the cistern is pumped to the water tank by a water pump. The water pump operates automatically by floating inside the tank. When the float drops, a switch is turned on, which activates the water pump. The pump begins to raise water into the water tank and stops when the float rises to its maximum level. Figure 1 depicts all the sensors, actuators, microcontrollers, etc., used to monitor water level in cistern and tank systems. Usually, there can be several states concerning the water level in a water tank and a cistern. We illustrate in Figure 2 three possible scenarios: (a) half-full cistern level and half-full water tank level, (b) half-full cistern level and almost empty water tank level, and (c) full cistern level and whole water tank level. In the same way, the water pipe that arrives at the cistern, the tap water pipe (municipal) that comes out of the cistern and goes up to the water tank, and the water line that comes down from the water tank and enters the house for domestic consumption can be seen.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 4 Challenges
### Non-Collaborative Mode between Communication and Localization  
As two key parts of IoSB, communication and localization should be in a close correlation with each other. Some methods to improve positioning through communication between nodes are summarized in [90] (also known as cooperative localization), which turns out to increase the localization accuracy and decrease the time delay [91]. However, most of the current shared bicycles in China like OFO and Mobike employ an absolutely separated scheme, which applies 3 G/4 G cellular network in communication and GPS in positioning, and there seems no correlation between them. As a result, some problems may be caused, such as high power consumption, poor location accuracy in some places without Line-of-Sight (LoS), a high load on base station, etc. Consequently, an improved organization of communication, positioning, and even wireless power charge would attract more attentions to achieve a novel collaborative mode.
## 2 Trajectory Design and Optimization
### Heliocentric Dynamics of the Spacecraft's Center of Mass
[9], the heliocentric spacecraft equations of motion are  
\[\frac{\mathrm{d}p}{\mathrm{d}t}=\frac{2\ p}{1+f\,\cos L+g\,\sin L}\,\sqrt{ \frac{p}{\mu_{\odot}}}\,a_{p_{\mathrm{T}}} \tag{9}\]  
\[\frac{\mathrm{d}f}{\mathrm{d}t}=\sin L\,\sqrt{\frac{p}{\mu_{\odot }}}\,a_{p_{\mathrm{R}}}+\frac{\left(2+f\,\cos L+g\,\sin L\right)\,\sin L+f}{1+f \,\cos L+g\,\sin L}\,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{\mathrm{T}}}+\\ -\frac{g\left(h\,\sin L-k\,\cos L\right)}{1+f\,\cos L+g\,\sin L} \,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{\mathrm{N}}} \tag{10}\]  
\[\frac{\mathrm{d}g}{\mathrm{d}t}=-\cos L\,\sqrt{\frac{p}{\mu_{\odot }}}\,a_{p_{\mathrm{R}}}+\frac{\left(2+f\,\cos L+g\,\sin L\right)\,\sin L+g}{1+ f\,\cos L+g\,\sin L}\,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{\mathrm{T}}}+\\ +\frac{f\left(h\,\sin L-k\,\cos L\right)}{1+f\,\cos L+g\,\sin L} \,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{\mathrm{N}}} \tag{11}\]  
\[\frac{\mathrm{d}h}{\mathrm{d}t}=\frac{\left(1+h^{2}+k^{2}\right)\,\cos L}{2 \left(1+f\,\cos L+g\,\sin L\right)}\,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{ \mathrm{N}}} \tag{12}\]  
\[\frac{\mathrm{d}k}{\mathrm{d}t}=\frac{\left(1+h^{2}+k^{2}\right)\,\sin L}{2 \left(1+f\,\cos L+g\,\sin L\right)}\,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{ \mathrm{N}}} \tag{13}\]  
\[\frac{\mathrm{d}L}{\mathrm{d}t}=\frac{h\,\sin L-k\,\cos L}{1+f\,\cos L+g\,\sin L }\,\sqrt{\frac{p}{\mu_{\odot}}}\,a_{p_{\mathrm{N}}}+\sqrt{\mu_{\odot}\,p} \,\bigg{(}\frac{1+f\,\cos L+g\,\sin L}{p}\bigg{)}^{2} \tag{14}\]where \(\{a_{p_{R}},\,a_{p_{T}},\,a_{p_{N}}\}\) are given by Equation (8) as a function of the performance characteristics (\(a_{c},\,\eta_{n},\) and \(\eta_{m}\)), the dimensionless control term (\(\delta\)), and the solar distance (\(r\)). The latter can be written as a function of the modified equinoctial orbital elements as [20]  
\[r=\frac{p}{1+f\,\cos L+g\,\sin L} \tag{15}\]  
The initial conditions of the differential equations (9)-(14) depend on the characteristics of the spacecraft parking orbit at the initial time instant \(t_{0}=0\). In this regard, as discussed in detail in Section 3, we assume that the shape of the spacecraft parking orbit coincides with that of the Earth's heliocentric orbit. Specifically, the orbital elements of Earth's heliocentric orbit were retrieved from JPL's Horizon system [21] on 1 August 2024. The control law \(\delta=\delta(t)\) is discussed in the next two subsections.
## 3 MIMO Detection
### MMSE Detector  
Among the linear estimators, the MMSE detector achieves improved tradeoff between noise enhancement and interference cancellation by minimizing the estimation error caused by channel noise and interference [16]. Following [42], by assuming that the quantization noise is uncorrelated with the transmitted signal in (8), it can be verified that the MMSE estimator is computed as  
\[\mathbf{W}=\alpha[\alpha^{2}\mathbf{H}\mathbf{P}\mathbf{H}^{H}+\alpha^{2} \sigma_{n}^{2}\mathbf{I}_{N}+\mathbf{R}_{\mathbf{n}_{q}}]^{-1}\mathbf{H} \sqrt{\mathbf{P}}, \tag{10}\]  
where the covariance matrix of the quantization noise is given in (9). In order to reduce complexity and assuming i.i.d. additive quantization noise, we can approximate  
\[\mathbf{R}_{\mathbf{n}_{q}}=\alpha\beta P_{\mathrm{Rx}}\mathbf{I}_{N}, \tag{11}\]  
where \(P_{\mathrm{Rx}}=\frac{1}{N}\text{trace}\left(\mathbf{H}\mathbf{P}\mathbf{H}^ {H}+\sigma_{n}^{2}\mathbf{I}_{N}\right)\) is the average power received on the antennas.
# Design and Implementation of an Autonomous Charging Station for Agricultural Electrical Vehicles
## 2 Materials and Methods
### The Plugs  
The ACS has as a task to pick up the power-outlet from a wall-plug, plug it in the robot, unplug it again from the robot, and plug it again into the wall-plug. The idea as previously described consists in bringing the wall outlet to the robot. Initially the power-outlet is plugged into a dummy type F Schuko wall-plug, which serves only as a holding mechanism and is not powered (Figure 4a).  
On the top of the wall-plug, we installed a squared fiducial marker [31] with a unique identifier. The marker serves as a reference to the ACS for the alignment and plug/unplug sequences as we will see in next section.  
On the AEV side, we use the included AC-DC charging transformer that comes with the vehicle. The Schuko plug of the transformer is hosted in a box mounted on the vehicle. Similarly to the wall-plug, a fiducial marker with a different identifier is used as reference for the vision-based navigation and plug/unplug processes of the ACS. As the AEV is working in the fields, and to protect the marker from dust/mud, which make its detection not possible, we installed the marker on the inner-side of the box lid; this means that when the AEV is driving in the field, the box is closed, and thus the marker protected. When the AEV enters the garage, and requests to charge its batteries, the lid opens, and the marker becomes visible (Figure 4b). The idea is to install a servo-motor and fit it with the lid, but this is considered as a next step as the focus on this work is on the ACS and its core functionalities.  
Figure 4: The plugs. (**a**) The wall-plug that hosts the power-outlet. (**b**) The vehicle plug.  
Figure 3: Global software architecture of the Autonomous Charging Station.  
Please note that on the vehicle plug (Figure 4b) there exists a guiding socket (combination of a cylinder and half cone). This is indeed meant to be used for allowing a small amount of misalignment error during the plug-in process. The reason why it is on the vehicle plug and not on the wall-plug is purely mechanical. The wall-plug is fixed, with no moving parts, thus the marker will not change its position, meaning that there will be no errors when trying to estimate the position of the plug based on the marker. On the other hand, the lid is a moving part, this means that when it opens, there may be tiny misalignment, inducing errors in estimating the position of the vehicle plug, and that is why there is the guiding socket in the vehicle and not on the wall.
## 2 Results  
### Construction of the IUP in C. reinhardtii  
The codon-optimized ScCK and AtIPK genes were separately expressed or fused in a frame with YFP (mVenus) or CFP (mCerulean) to increase the efficiency of the identification of the transformants showing ScCK and/or AtIPK expressions (Figure 2a). Thus, the transformants with a high expression level of the key enzymes of the IUP could be screened according to the fluorescence level. ScCK and AtIPK were designed to contain a short and flexible GSGSGS or GSGS linker peptide at their C-termini to minimize any steric effects the fusion may have. It has been demonstrated that the fusion of consecutive enzymes in a metabolic pathway into a multi-activity protein can be beneficial for the effective substrate utilization and thereby increase product yields [9; 19; 20; 21; 22]. Therefore, we also tested whether the fusion of ScCK and AtIPK into a single polypeptide had a positive impact on the C5 hemiterpene diphosphate production (Figure 2a). As a result, eight versions of constructs were generated. 2 twoconstructs (V1 and V2) only contained the fluorescent proteins. V3 represents ScCK and AtIPK that were separately fused at the C-terminal end of YFP (mVenus) or CFP (mCerulean) (YFP-ScCK and CFP-AtIPK). V4 represents ScCK and AtIPK that were separately fused at the N-terminal end of YFP (mVenus) or CFP (mCerulean) (ScCK-GSGSGS-YFP and AtIPK-GSGSGS-CFP). V5 represents the fusion enzyme ScCK-GSGSGS-AtIPK-GSGSGS-YFP. V6 represents the fusion enzyme AtIPK-GSGSGS-ScCK-GSGSGS-YFP. V7 represents the fusion enzyme ScCK-GSGSGS- YFP-AtIPK. V8 represents the fusion enzyme AtIPK-GSGSGS-CFP-ScCK.  
Figure 1: Metabolic pathway design for the biosynthesis of limonene from isopentenol isomers in engineered _Chlamoomonas reinhardtii_. The fluorescent protein fusions allowed direct screening for expressing strains by using a microplate reader. AtIPK: _Arabidopsis thaliana_ isopentenyl phosphate kinase; CFP: mCerulean3 (cyan) fluorescent protein; DMAPP: dimethylallyl diphosphate; GPP: geranyl diphosphate; GPPs: GPP synthase; IDI: diphosphate isomerase; IP: isopentenyl monophosphate; IPP: isopentenyl pyrophosphate; IUP: isopentenoid utilization pathway; MsLS: _Mentha spicata_ limonene synthase; RFP: mRuby2 (red) fluorescent protein; ScCK: _Saccharomyces cerevisiae_ choline kinase; YFP: mVenus (yellow) fluorescent protein.  
For each gene expression construct, we randomly selected at least 300 transformantsants and used a microplate reader to assess the fluorescence signal of the transformants in the mid-logarithmic phase of the culture. The strains V3-2, V3-10, V4-49, V-68, V5-1, V5-7, V6-1, V6-15, V7-3, V7-28, V8-7 and V8-25, which had higher fluorescence intensities than other strains generated from the same vector (Figure 2b), were chosen for the quantitative analysis of IPP by HPLC-MS/MS.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.3 Standardized Precipitation Index (SPI)  
The SPI is an important indicator used in meteorological and hydrological fields to monitor droughts and precipitation anomalies. Based on the probability distribution of precipitation data, it quantitatively reflects the degree of deviation of precipitation from the long-term average by calculating the standardized value of cumulative precipitation over a certain period. Its specific calculation expression is as follows [53]:  
\[SPI=W-\frac{c_{0}+c_{1}W+c_{2}W^{2}}{1-d_{1}W+d_{2}W^{2}+d_{3}W^{3}} \tag{15}\]  
\[W=\left\{\begin{array}{l}\sqrt{-2\ln P},P\leq 0.5\\ \sqrt{-2\ln(1-P)},P>0.5\end{array}\right. \tag{16}\]  
where \(P\) is the cumulative probability of PRE exceeding the threshold value. \(c_{0}\), \(c_{1}\), \(c_{2}\), \(d_{1}\), \(d_{2}\), and \(d_{3}\) are 2.52, 0.80, 0.01, 1.43, 0.19, and 0.0013, respectively.
# Drag and Attitude Control for the Next Generation Gravity Mission
## 2 NGGM Objectives, Measurement Technique, and Fundamental Observables
### NGGM Fundamental Observables and Measurement Requirements  
With the ll-SST technique, Earth's gravity field is reconstructed from its effect on the CoM-to-CoM distance variation \(\Delta d_{\rm G}\). However, \(\Delta d_{\rm G}\) is not directly measurable. Instead, the overall distance variation \(\Delta d\) can be measured with a suitable metrology, which in the NGGM will consist of a high-performance laser interferometer, such as that embarked on the GFO as a technological demonstrator [26]. This quantity must therefore be complemented by a highly accurate measurement of the non-gravitational accelerations of the satellite's CoM (\(a_{\rm D1}=F_{\rm D1}/\rm m_{\rm i}\), \(\rm m_{\rm i}\) = mass of satellite \(\rm S_{\rm i}\)), from which the component of the differential acceleration along the satellite-to-satellite direction (\(\Delta a_{\rm D}=a_{\rm D1}-a_{\rm D2}\)) and the resulting distance variation \(\Delta d_{\rm D}\) can be obtained and separated from \(\Delta d\), so as to isolate just the effect caused by Earth's gravity alone (\(\Delta d_{\rm G}\)).  
The requirements on the stochastic measurement errors of the two fundamental observables (\(\Delta d\), \(\Delta a_{\rm D}\)) in the measurement bandwidth of the NGGM (from 1 mHz to 100 mHz) are expressed in terms of amplitude spectral density (ASD) and are shown in Figure 3 (for \(d\) = 220 km) and Figure 4. They have been derived as a function of the minimum (threshold) objectives and the more ambitious ones (goal) established by the user's community of the NGGM products, described in Section 2.1, taking also into account the intrinsic limitations of the measurement instruments (laser interferometer and accelerometers).  
Figure 3: Requirements (threshold and goal) on the stochastic measurement error of \(\Delta d\).  
The measurement performances of \(\Delta d\) and \(\Delta d_{\rm D}\) required for the NGGM, even at the threshold level, represent an improvement by more than one order of magnitude over those achieved in the GFO by the K/Ka-band ranging system (primary instrument for the distance measurement) and by its accelerometer [27].  
The combination of the requirements on \(\Delta d_{\rm D}\) and \(\Delta d\) converted to the same units [\(\rm m/s^{2}/\surd\)Hz], by taking its second temporal derivative, shows that the contributions to the performance from the acceleration and from the distance variation measurement errors are approximately comparable at 1 mHz, while the performance limit below 1 mHz is set by the acceleration measurement error and above 1 mHz by the distance variation measurement error (see Figure 5).  
Figure 4: Requirements (threshold and goal) on the stochastic measurement error of \(\Delta d_{\rm D}\).  
Figure 5: Combined requirements on \(\Delta d_{\rm D}\) and on the second temporal derivative of \(\Delta d\).
# Preparation of Hybrid Molybdenum Disulfide/Single Wall Carbon Nanotube-n-Type Silicon Solar Cells
## 3 Results
### Optimisation of the Component Ratio  
A set of devices was made to determine the best ratio SWCNT to MoS2 dispersion. The summary of the results are shown in Figure 4 while the detailed parameters are provided in Table S4 from the representative JV curves given in Figure S6.  
The optical properties of the films (transmittance) did not change greatly up to half mL of MoS2. This is due to the low concentration of the MoS2 dispersion. At the same time, the sheet resistance of the films decreased as adding the extra material to the film helps to bridge any gaps in the SWCNT connections and helps to lower the resistance. Figure 2c,d clearly shows the SWCNTs wrapped around the MoS2 flakes and this will help improve conductivity.  
The overall trend in the solar cell behaviour is provided in Figure 4b. The addition of any amount of MoS2 improves the performance of the cells with a maximum average efficiency observed of 9.23%  
Figure 4: (a) Electrode properties for films of different ratios of SWCNT and MoS2 dispersions; (b) Efficiencies of solar cells with films made with different ratios of SWCNT and MoS2 dispersions.  
for the addition of 700 \(\upmu\)L of MoS\({}_{2}\) dispersion to 300 \(\upmu\)L of SWCNT dispersion. J\({}_{\text{sc}}\) changes very little over all the cells. Given the small changes in transmittance, this is not unexpected. The largest changes are in fill factor and V\({}_{\text{oc}}\). The fill factor increases from 0.53 for the SWCNT only film up to values on the order of 0.7. Adding the extra semiconducting element in the film will provide a pathway for one type of charge carrier. SWCNTs are amipolar and hence can carry both types of charge carriers, which provides opportunities for recombination and hence lower fill factors. The MoS\({}_{2}\) flakes provide a pathway to remove the holes from the nanotubes and hence lower the recombination rate. The increased lifetime of the holes will lead to improved fill factors as observed.  
The increase in V\({}_{\text{oc}}\) observed is slightly more varied but always higher than the SWCNTs only. This indicates that the two materials interact to shift energy levels involved in the transitions central to the operation of the devices. The variability will stem from the random dispersion of the flakes through the SWCNT film. The drop in efficiency for high MoS\({}_{2}\) loadings is due to the slight decreases in transmittance, which lowers the observed J\({}_{\text{sc}}\).
## 2 Study Area and Data Collection  
### Study Area  
The Xiaobeigani Liu reach spans from Longmen (LM) to Tongguan (TG) in the Middle Yellow River, which is located between 110\({}^{\circ}\)15\({}^{\prime}\)-110\({}^{\circ}\)38\({}^{\prime}\) E longitude and 34\({}^{\circ}\)35\({}^{\prime}\)-35\({}^{\circ}\)49\({}^{\prime}\) N latitude (Figure 1a). The main stream in this reach is scattered and changeable, with rapid and frequent channel lateral migration. Bank erosion has often occurred in this reach because the bank soil is easily eroded and there has been a lack of bank revetment works (Figure 1c). The total length of this reach is 132.5 km, with an average channel width of 8.5 km and an area of approximately 1107 km\({}^{2}\). The sedimentation sections in this reach are successively named CS41 to CS68 from the lower to the upper reaches (Figure 1b), and the sections are surveyed twice a year to monitor the channel adjustments. The Xiaobeigani Liu reach is usually divided into the upper, middle, and lower reaches. The upper reach is from LM (CS68) to Miaoqian (CS61), with a length of 42.5 km, and it is characterized by severe channel lateral migration. The composition of the bed material is relatively thick, with the median diameter of bed material (D50) generally being between 0.2 and 0.3 mm. The reach from Miaoqian (CS61) to Jiamakou (CS54) is defined as the middle reach, with a length of 30 km. It is located at the top of the tertiary red soil anticline, so the channel is relatively stable. The lower reach is from Jiamakou (CS54) to TG (CS41), with a total length of 60 km and channel bed slope of 3% [33].  
Figure 1: Overview of the study area: (**a**) Yellow River basin; (**b**) sketch of the Xiaobeigani Liu reach; (**c**) photograph of the river channel.  
The LM station is the main upstream hydrometric station in the Xiaobeiganliu reach, and the flow and sediment regime at this station can represent the incoming flow and sediment conditions. The TG station is the main downstream hydrometric station, and the changes in TG elevation (the corresponding water level of TG station when the discharge equals 1000 m\({}^{3}\)/s) can significantly influence the channel evolution of the Xiaobeiganliu reach. The Sanmenxia Reservoir, located approximately 113.5 km from the TG station, began operating in 1960 for flood control of the Yellow River. The Sanmenxia Reservoir has experienced several operation modes, and the TG elevation has been closely related to the continuous adjustment of the operation modes.
## 3 The Proposed SensorAnt Protocol
### SensorAnt Description  
In our method, SensorAnt will use both on-demand and proactive mechanisms, where the on-demand process will trigger when the sensor node has a data packet to be sent to the sink whereas no information is available in its routing table. In contrast, the proactive starts during the communication period to update the information of WSN and use it later during route maintenance steps. In addition, this operation provides multiple choices of routes to respond with any malfunctioning in WSN during the failure links or fast run-out of sensor's battery. In both mechanisms we apply the route optimizing concept resulting from the ant agent system. The data routing will be updated dynamically during the reinforcement learning process and the failure will be locally fixed. The framework of the proposed SensorAnt protocol is shown in Figure 3. The operations' details of the proposed protocol are described in the following subsections.