esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background: Models
### Word Embedding
#### 3.1.2 Context-Based Representations (Contextualized Embeddings)  
By capturing only short-range co-occurrence context, non-contextual embedding models are unable to capture deeper contextual relationships. In contrast, context-aware embedding models can compute the embedding for a word based on its context. An example of the most common deep contextualized pre-trained embedding models is BERT. In [PERSON] et al. (2019), the bidirectional encoder representation from transformer (BERT) is introduced as the first deeply bidirectional, unsupervised language representation, which functions as a transformer encoder with multiple layers (performs self-attention in both directions) that conditions both left and right contexts simultaneously. Therefore, BERT generates embeddings that are context-aware.  
Furthermore, to remove the unidirectionality constraint, BERT performs pre-training using an unsupervised prediction task, which includes a masked language model (MLM) in charge of understanding context and then making predictions (of words). Thus, the model can produce a vector representation that can capture the general information of the input text. These semantic representations of each word in the input text can be improved using an attention mechanism when different words in a context show different effects in boosting semantical representation. As a core component of transformer architecture, the attention mechanism's underlying role is to assign less or more weights to different parts of text towards the output (i.e., differentiate the contribution of different parts of the input on the output). Attention can be considered as a function that maps queries and follows key-value and output vector pairs; the scaled dot-product attention formula can be seen in Equation (1).  
\[Attention(Q,K,V)=Softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{1}\]  
Query, key, and value are marked by the letters \(Q\), \(K\), and \(V\), respectively. \(\sqrt{d_{k}}\) represents the dimension of the key vector \(k\) and query vector \(q\). A Softmax activation function is used by attention to normalize the inputs to a value between 0 and 1. Although BERT uses a transformer's encoder, it employs a multi-head attention mechanism, as shown in Equation (2), where each distinct head and its corresponding weight matrices are identified by the subscript \(i\).  
\[MultiHead(Q,K,V)=Concat(head_{1},\dots,head_{h})W^{O} \tag{2}\]  
where each \(head_{i}\) is calculated using the formula below, and \(W^{O}\) represents a weight matrix that was trained along with the model:  
\[head_{i}=Attention(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i}) \tag{3}\]  
Even though BERT contains millions of parameters (i.e., BERT\({}_{base}\) contains 110 million parameters and BERT\({}_{large}\) has 340 million parameters) [(29)], it is relatively inexpensive to apply BERT to downstream tasks if the parameters are fine-tuned using a pre-trained model. In this work, we use BERT\({}_{base}\).
## 3 Results and Discussion
### Effect of Antecedent Saturation
The unsaturated zone of the soil profile for 55% saturation was given in Figure 3 above. The saturation variations for the same soil conditions after the application of 2.5, 5, and 10 mm/h rainfalls at the limit of the failure conditions are also given in Figure 6, for comparison purposes. The saturation variations of each model at their limits of failure differ from each other. The maximum saturation degrees were found to be 85%, 95%, and 100% for the 2.5, 5, and 10 mm/h rainfalls, respectively. The saturation degree of 85%, which is for the 2.5 mm/h rainfall, was observed in a wider depth when compared to other saturation variations of 5 and 10 mm/h. However, the saturation degrees for the 5 and 10 mm/h rainfalls reached higher values (95% and 100%, respectively) but with narrow depths even for shorter durations when compared to the 2.5 mm/h rainfall saturation variations. When the slope reaches the saturation conditions as determined in Figure 6, then a slope failure is likely to be expected. Similar to the aforementioned situations, with the help of in situ measurements, a mutually controlled mechanism can be established between the failure status and saturation conditions for future studies. Figure 6: _Cont._
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 4 Analysis of the GPR-Based Channel Model
### SMC Amplitudes and SINR
The SINR is an appropriate measure to describe the ranging information provided by some SMC, which is an important metric for (multipath-assisted) positioning algorithms (see  
Figure 6: GPR on SMC amplitudes corresponding to: (**a**) LOS link, (**b**) reflection on EPB, (**c**) reflection on WW, (**d**) reflection on the south part of the room containing various materials, e.g., white board, wall, metal door, (**e**) reflection on the north part of the room, containing glass windows and walls, (**f**) double reflection on EPB then WW, (**g**) double reflection on EPB then SW, (**h**) double reflection on EPB then NGW, (**i**) double reflection on WW then EPB, (**j**) double reflection on WW then SW, (**k**) double reflection on SW then NGW, (**k**) double reflection on SW then WW, (**l**) double reflection on SW then WW, (**m**) double reflection on NGW then SW, and (**n**) double reflection on NGW then WW. The regressed amplitude is \(|\mathbf{a}_{k}(\mathbf{p})d_{k}(\mathbf{p})|\). SINR computed from GPR is plotted in purple with axis on the right-hand side. Section 5), while the amplitude translates to communication-performance metrics like channel capacity [53] and bit error rate. Figure 7: _Cont_. From Figure 8, it is seen that the LOS amplitude is dependent on the distance as well as the antenna radiating pattern, while for other SMCs, the amplitudes are also affected by the reflection coefficients of the plane surfaces. Figure 7: Predicted SINR for the whole floor plan for various links: (**a**) LOS, (**b**) reflection on EPB, (**c**) reflection on WW, (**d**) reflection on on south part of the room, (**e**) reflection on the north part of the room, (**f**) double reflection on EPB then WW, (**g**) double reflection on WW then SW, (**h**) double reflection on EPB then NGW, (**i**) double reflection on WW then EPB, (**j**) double reflection on WW then SW, (**k**) double reflection on SW then GW, (**l**) double reflection on SW then WW, (**m**) double reflection on NGW then SW, (**n**) double reflection on NGW then WW. SINR is obtained by applying GPR on the CIRs obtained from measurement at 595 points along the trajectory, as shown in Figure 1. The same dB scale is used in all sub figures. Figure 8: Predicted SMC amplitude \(\alpha_{k}(\mathbf{p})\) for the whole floor plan for various SMCs: **(a)** LOS, **(b)** reflection on EPB, **(c)** reflection on WW, **(d)** reflection on on south part of the room, **(e)** reflection on the north part of the room, **(f)** double reflection on EPB then WW, **(g)** double reflection on WW then EPB, **(h)** double reflection on the SW then NGW. GPR was applied the set of measurement along the trajectory. The same dB scale is used in all sub-figures.
###### Abstract  
This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA-SA) is given and discussed. Experimental simulation studies demonstrate that the GA-SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.  
Earth observation satellite; Task clustering; Mission planning; GA-SA hybrid algorithm +
Footnote †: journal:  
Article
## 2 Methods and Materials
### Optical Calculations  
To quantify the strength of the THz signatures, we calculated the signature absorbance \(\mathrm{A_{S}}\), the frequency width \(\Delta\
u\), and the quality factor \(\mathrm{Q_{S}}\), all from the experimental transmittance data, and as displayed graphically in Figure 3. As often occurs in the spectroscopy of solid matter, the signature of interest is embedded in broadband attenuation arising from some other mechanism, be it absorption or scattering. Our experience is that all biological samples, liquid or solid, display this behavior. Thus, we calculated \(\mathrm{A_{S}}\) through the ratio of the minimum \(\mathrm{T_{min}}\) to maximum \(\mathrm{T_{max}}\) transmittance, \(\mathrm{T_{min}}\) measured at the signature center frequency \(\mathrm{\
u_{0}}\), and \(\mathrm{T_{max}}\) being measured just outside, but not including the broadband attenuation. This leads to the signature absorbance \(\mathrm{A_{S}}\equiv\mathrm{A_{min}}-\mathrm{A_{max}}=-\mathrm{log_{10}}[\mathrm{ T_{min}}/\mathrm{T_{max}}]\). By convention, the signature width is defined by the full width at half-maximum (FWHM),  
Figure 2: (a) Essential chemical and physical properties of four nucleic acid solutions, as provided by New England Biolabs, Inc., Ipswich, MA, USA, and after 50% dilution in the nanochannels. (b) Exact base sequence of the 21-mer fragments of the si-RNA sample including 5′ overhangs on opposite ends (A, C, G, and U stand for adenine, cytosine, guanine, and uracil, respectively).  
\(\Delta\
u\), in the present case defined halfway between T\({}_{\rm min}\) and T\({}_{\rm max}\), as displayed in Figure 3. The signature quality factor is just Q\({}_{\rm S}\) = (\(\
u_{0}/\Delta\
u\)), again following convention.  
Because we are concerned with the signatures of biomolecules in aqueous solutions, the effect of liquid (\"bulk\") water cannot be ignored. As is well known, it displays a strong absorption in the THz region, with absorption coefficient \(\alpha\) well described by the double-Debye model [22]. At room temperature, this model predicts an increasing \(\alpha\) with frequency, and between \(\approx\)150 and 200 cm\({}^{-1}\) from 500 to 1000 GHz--the range of primary interest in the present research. Salinity increases \(\alpha\) somewhat, but not dramatically [23], so \(\alpha\) = 200 cm\({}^{-1}\) is a good rule of thumb in this range. Consistent with electromagnetic wave propagation, the transmittance through our h = 600 nm-height nanochannels should then be given by T = exp(\(-\alpha\cdot\) h) = 0.988, or an absorbance A = \(-\)log\({}_{10}\)(T) = 5.2 \(\times\) 10\({}^{-3}\). As we shall see next, many of the nucleic acid signature absorbances reported here are all much greater than this, making the effect of bulk water absorption insignificant by comparison.
## 5 Results
### Particle Resolution  
Example experimental traces from the two finer scans (100 nm step size) from Section 5.2 (marked points) are shown in Figure 5. A single frequency component is presented when there is a single nanoparticle. There are no low frequency components (2-7 GHz), which would be expected if a few particles were in contact (considered as dumbbells) [34]. Figure 5b,d show 13.7 GHz and 21.3 GHz component given by \(\sim\)250 nm and \(\sim\)150 nm single gold nanoparticles, respectively.  
On the other hand, if several particles are present in the same point spread function, the signals show various frequency components, due to the different sizes of the nanoparticles. Two frequency components are shown in Figure 5f: 9.9 GHz and 17.1 GHz, respectively, from the center of the middle blob in Figure 4h. This indicates that there are two nanoparticles with different sizes: either in contact (dumbbell) or separated. We suppose that they are separated because the low frequency component might match a \(\sim\)300 nm size particle. This is confirmed when one of the frequency components decays moving towards the edges of the blob; therefore, two independent particles with \(\sim\)300 nm and \(\sim\)200 nm particles are presented in our scanned area. The described behaviour shows a method to characterise sub-optical size nanoparticles (Section 5.2), and it might allow high-resolution detection of several nanoparticles inside the point spread function based on their vibrational mode frequencies and their localisation if we find the centroid for the amplitude maps at each particle frequency.  
Figure 4: Sample containing a mixture of gold nanospheres of different sizes, imaged over an area of 50 \(\times\) 50 \(\upmu\)m\({}^{2}\) and two 5 \(\times\) 5 \(\upmu\)m\({}^{2}\) finer scans measuring transmitted light. (**a**) optical picture of the big scanned area; (**b**) ultrasonic picture showing the frequency measured; (**c**) histogram with the size distribution of particles where the arrows show the expected size of nanoparticles (150, 200 and 250 nm diameter); (**d**) ultrasonic picture showing the size of the nanospheres using the mechanical model (Section 2.4) to convert the breathing mode into size in nm; (**e,f**) are the optical and acoustic pictures, respectively, of the solid square area (5 \(\times\) 5 \(\upmu\)m\({}^{2}\)); (**g,h**) are the optical and acoustic pictures, respectively, of the dashed square area (5 \(\times\) 5 \(\upmu\)m\({}^{2}\)).
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results
### Wireless Data Relay by Introducing Additional (Relayer) Drone
#### 3.2.3 Bypassing Relay  
Even in the line-of-sight relay test, the effects of shoreline vegetation on the wireless data relay could not be excluded from consideration. In the bypassing relay test, the line of sight was obstructed by the shoreline vegetation (Figure 19). Irrespective of the short distance to the direct wireless communication (373 m), no data were received without the relayer drone. We tried to locate the relayer drone to bypass the shoreline vegetation. By confirming a connected path formed with a pair of mutually observable drones (Figure 19), the wireless data relay was achieved during each flight (Figure 20). After landing on L3, the relayer drone was able to relay the data on water (Figure 20). The horizontal distance between L3 to the transmitter drone was 181 m. The actual difference in the antenna height was also approximately 4.9 m (1.7 m + 3.2 m altitude difference), but the altitude difference between the transmitter drone and the relayer drone on L3 was not confirmed by the DEM5B data (1.0 m, inordinately high for stable pond water). A slight elevation of the  
Figure 17: Locations of the wireless water quality monitoring system for extending relay test (Ogata Marsh Pond on 19 July 2023). Transmitter, transmitter drone (landed on the water and was fixed by mooring); 1–3, locations where the relayer drone was placed; Receiver, receiver on a tripod.  
shoreline with vegetation and slowly surging wind waves (approximately 0.05 m) observed at the transmitter location confirmed the 181 m successful data relay on the water by the two-ray ground reflection model.  
Figure 19: Locations of wireless water quality monitoring system for bypassing relay test (Ogata Pond on 27 September 2022): Transmitter, transmitter drone (handed on the water and was fixed by mooring from shore); 1–3, locations where the relayer drone was placed; Receiver, receiver on a tripod. Red arrow indicates the wireless communication path blocked by shoreline vegetation.  
Figure 18: Received water quality data from the extending relay test. The horizontal distance between the transmitter drone and the relayer drone at each landing site was the following: L1, 624 m; L2, 500 m; L3, 381 m; L4, 368 m. Each of the complete datasets (pH, EC, DO, and temperature) is surrounded by a dotted line.
## 4 Results
### Paleoenvironmental Characteristics
#### 4.4.3 Paleoredox Environment  
Paleoredox conditions were reconstructed primarily through the analysis of redox-sensitive elements such as V, Ni, Cr, Co, U, and Th. This study comprehensively utilized four ratios of V/(V+Ni), Ni/Co, V/Cr, and U/Th to reconstruct the paleoredox conditions of the Qigu Formation. Previous studies have suggested that a V/(V+Ni) ratio greater than 0.77 indicates anoxic conditions, ratios between 0.6 and 0.77 suggest dysoxic conditions, and values below 0.6 indicate oxic conditions [84]. The V/(V+Ni) ratios in the Qigu Formation ranged from 0.69 to 0.79, with an average of 0.72 (Table 2; Figure 10C,D), indicating a dysoxic environment, with locally developed weakly anoxic conditions. The U/Th ratio serves as a reliable proxy for determining redox conditions. A U/Th ratio greater than 1.25 indicates anoxic conditions, ratios between 0.75 and 1.25 suggest dysoxic conditions, and ratios below 0.75 are characteristic of oxic conditions [92]. The U/Th ratios in the Qigu Formation ranged from 0.11 to 1.39, with an average of 0.39 (Table 2; Figure 10C,E,F), indicating a predominantly oxic environment. Previous studies have shown that the V/Cr and Ni/Co ratios are reliable proxies for reconstructing redox conditions. V/Cr ratios greater than 4.25, between 2.0 and 4.25, and less than 2.0 correspond to anoxic, dysoxic, and oxic conditions, respectively. Similarly, Ni/Co ratios greater than 7.0, between 5.0 and 7.0, and less than 5.0 correspond to anoxic, dysoxic, and oxic conditions, respectively [93]. The V/Cr value of the Qigu Formation ranged from 1.60 to 2.33, with an average value of 1.89. The Ni/Co value of the Qigu Formation ranged from 2.46 to 5.06, with an average value of 3.78. These geochemical proxies indicate predominantly oxic environments, with localized dysoxic environments (Table 2; Figure 10D-F). In summary, the paleoenvironment of the Qigu Formation can be generally characterized by an arid, dysoxic to oxic freshwater environment.
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Real Data  
#### 3.2.1 Experimental Setup  
Our experimental setup is shown in Figure 13. The KUKA KR 16 L6-2 industrial robot was used to simulate the manipulator that would be used in the application. The manufacturing of the entire target assembly, as described in the methods section, is costly and time-consuming; therefore, it was approximated by a prototype of similar characteristics. The glass beads are made of borosilicate glass (\(n=1.48\)) instead of fused silica (\(n=1.46\)  
Figure 11: Translation (left side) and rotation (right side) error of position estimation over the images of the synthetic dataset using ground truth feature points with added random noise within the interval of \([-0.5,0.5]\) pixels. Data points in the graph represent the average error over the 20 images of the dataset and error bars represent the standard deviation.  
Figure 12: Translation (**left side**) and rotation (**right side**) error of position estimation over the images of the synthetic dataset using ground truth feature points for several added noise levels. Data points in the graph represent the average error over the 20 images of the dataset and error bars represent the standard deviation.  
and the sieve-like holding structure is made of a single 1 mm thick metal piece attached to a prototype of the target.  
We use two Basler acA1920-50 gc industrial machine vision cameras with native resolution of 1920 \(\times\) 1200. The cameras have an RGB Bayer filter to produce color images. To emulate the use of radiation-tolerant (RADTOL) cameras, the images were converted to grayscale in-camera. The original resolution was down-sampled by a factor of three using a bicubic kernel on 4 \(\times\) 4 neighborhoods. The resulting effective resolution (640 \(\times\) 400) is close to that of a RADTOL camera that we considered as a reference, the Visatec RC2 (586 \(\times\) 330), producing a fairly realistic approximation of the image quality of the application. We used lenses with 6 mm focal length that had a horizontal field of view of approximately 82 degrees. The stereo camera pair was attached to a metal plate and had a fixed baseline of 210 mm. A set of radial illuminators (Smart Vision RC130) were mounted as closely as possible to the camera lenses. Each illuminator was constituted of eight high-intensity LEDs that create a near collimated, uniform light pattern that has a diameter of 280 mm at a distance of 500 mm from the lamp. In Figure 14, we show an example image taken in these conditions. For calibration of the cameras, we used a set of 20 images of a 24 \(\times\) 17 checkerboard pattern.
## 4 Experimental Test Results
### Drying Strinkage  
Test results of drying shrinkage are shown in Figure 10a, and their relative drying shrinkage to that of the reference specimen CNC is shown in Figure 10. Finally, at 50 days, the shrinkage strain of the control specimen CNC was 851 \(\upmu\) m/m. Compared to that of the control mix, the drying shrinkage values of the ERA1, ERA2, ERB1, ERB2, and ERR mix were 814 \(\upmu\) m/m, 922 \(\upmu\) mm/mm, 919 \(\upmu\) mm/mm, 1057 \(\upmu\) mm/mm, and 871 \(\upmu\) mm/mm, respectively, indicating roughly a 4% decrease, 8% increase, 8% increase, 8% increase, 24% increase, and 2% increase.  
Using the ACI equation [49], the drying shrinkage difference between the ERA1 and ERA2 mixes can be analyzed. Influencing factors are air content, slump, cement contents, and fine aggregate ratio. Due to the very similar test results for the slump and air content in all mixes, it can be inferred by the ACI 209 equation that combined correction factors from slump and air content do not affect the final drying shrinkage values of all mixes. However, correction factors for fine aggregate ratios of ERA1, ERA2, ERB1, ERB2, and ERR3 dramatically varied at 0.75, 0.84, 0.78, 0.85, and 0.83, respectively. Similarly, cement content changes the correction factor but only slightly. Thus, the drying shrinkage values by the ACI equation were expected to range in the order of ERA1 < ERB1 < ERR3 < ERA2 < ERB2. In a very similar manner, the test results were ERA1 < ERR3 < (ERB1, ERA2) < ERB2. The second best drying shrinkage value from the ERR3 mix may be attributed to be the good quality of the RCAs manufactured from the PC concrete sleepers. Except for the ERB2 mix, the modified EMV mixes are viable against the drying shrinkage, compared to the reference CNC mix.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive White Gaussian Noise \\ BER & Bit Error Rate \\ CIR & Channel Impulse Response \\ CFO & Carrier Frequency Offsets \\ CFR & Channel Frequency Response \\ CP & Cyclic Prefix \\ CSS & Chirp Spread Spectrum \\ DFnT & Discrete Fresnel Transform \\ EDR & Effective Data Rate \\ FDE & Frequency Domain Equalization \\ FFT & Fast Fourier Transform \\ FEC & Forward Error Correcting \\ EFR & Errorless Frame Ratio \\ IDFnT & Inverse Discrete Fresnel Transform \\ IFFT & Inverse Fast Fourier Transform \\ ICI & Inter-Carrier Interference \\ ISI & Inter-Symbol Interference \\ LLR & Logarithm Likelihood Ratio \\ LS & Least Square \\ MMSE & Minimum Mean Square Error \\ MSE & Mean Square Error \\ OCDM & Orthogonal Chirp Division Multiplexing \\ OFDM & Orthogonal Frequency Division Multiplexing \\ PER & Packet Error Ratio \\ PN & Pseudo-Noise \\ PSK & Phase-shift Keying \\ QAM & Quadrature Amplitude Modulation \\ QPSK & Quaternary Phase Shift Keying \\ RMS & Root Mean Square \\ SNR & Signal-to-Noise Ratio \\ UWAC & Underwater Acoustic Communication \\ TDE & Time Domain Equalization \\ TVIR & Time-Varying Impulse Response \\ SISO & Single-Input Single-Output \\ SIMO & Single-Input Multiple-Output \\ SC & Single-Carrier \\ \end{tabular}