esa-sceva/satcom-chunk-collection huggingface dataset:
# Photovoltaics on Landmark Buildings with Distinctive Geometries
## 3 Presentation of the Samples
### Umwelt Arena  
Umwelt Arena, \"Environment Arena\" or \"Arena for Sustainability\" is an exhibition centre and the first Swiss competence centre in ecology, commissioned in August 2012 in the town of Spreitenbach, near Zurich (Switzerland, CH). It is designed by the Rene Schmid Architekten office [45]. The building is erected on a \(100\times 60\) m oval plan with a size of a stadium. It aims to help visitors in experiencing and understanding sustainability (Figure 11).  
The main geometric element of the Umwelt Arena is its giant roof consisting of 33 differently oriented flat trapezoidal surfaces, with a slope ranging from 6\({}^{\circ}\) to 62\({}^{\circ}\), giving appearance of a crystal.  
Figure 11: Umwelt Arena (a top and elevation views. Source: Google Earth, Google 2020, GeoBasis-DE/BKG 2020) (**on the top**); Integration and characteristics of PV systems (**on the bottom**).  
Figure 10: La Seine musicale (on top Source of illustrations Google Earth, Google 2020) (**on the top**); Integration and characteristics of PV systems (**on the bottom**).  
The entire 5333 m\({}^{2}\) roof is made of 5239 monocrystalline PV modules, with a total installed power of 736.62 kWp. The building produces 540 MWh per year, twice of its energy demand. It is the largest BIPV and one of the most famous energy plus buildings in Switzerland [46]. The Umwelt Arena received the \"2012 Norman Foster Solar Award\" [47]. Architecturally, the dominant external element is the BIPV roof visible to the visitors from all sides. The architects treat the fifth facade as equally important as all other facades. Choosing the BIPV principle for the roof materialization, they emphasize the institutional orientation towards environment protection and sustainability. This unique landmark building is a good example for other local, smaller and less exposed public and private buildings.
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
## 2 Discussion of Orbital Parameters, Sampling Process, and SpOCK
### Overview of Observation Sampling Process and Instrument Considerations
#### 2.2.2 GNSS-R Instrument Dependencies  
The GNSS-R observation distributions are highly dependent on multiple instrument configuration parameters. The number of viable science observations generated by a given instrument on a single satellite is constrained by two primary instrument capabilities: (a) the processing bandwidth of the instrument and the resulting number of available surface specular points it is capable of processing into science observations and (b) the surface area coverage of the nadir oriented GNSS-R science antennas which are required to capture the reflected signals with sufficient SNR to be usable. Other secondary spacecraft specific system requirements such as satellite down-link capability, attitude knowledge and other aspects are not analyzed here and assumed to be sufficient for the scenarios studied in this analysis.  
Figure 1: General illustration of three GNSS-R reflection specular points captures by a single LEO receiver. Reprinted with permission from ref. [16], Copyright: 2014, IEEE.  
In this analysis, the processing bandwidth of the GNSS-R instrument considers two cases: the existing on-orbit performance of the CYGNSS GNSS-R instrument which is capable of tracking 4 parallel reflections from GPS only [4], and a next Generation GNSS-R instrument (NGRx) which has shown in initial testing to be capable of tracking up to 16 parallel GNSS reflections from both GPS and Galileo [13]. It is possible that at some point in the future instruments will be capable of tracking more parallel reflections. However, we believe that 16 is a realistic estimate of the state-of-the-art GNSS-R instrument capability and grounds this analysis in existing hardware performance.  
The surface coverage of the GNSS-R instrument antennas is also a key element in filtering the actual observations suitable for science applications. As expected, reflections captured in the main high gain lobes of the reflection antennas result in higher SNR observations and generally better retrievals. In this regard, we have extensive data based on the achieved performance of the CYGNSS constellation where it has been observed that ocean wind speed observations can be achieved within the mission error requirements at range corrected gain (RCG) levels of 15 and above [17]. This allows us to assess additional instrument antenna configurations with respect to this threshold and make realistic judgments as to what specular points will result in viable science observations and which specular points to omit due to low quality. In the subsequent analysis, we simulate both the actual CYGNSS antennas as well as theoretical enhanced antennas to assess the performance of different antenna gains and beam widths, as well as at alternative spacecraft altitudes to provide a more complete trade-space with respect to various spacecraft instrument configurations.
## 2 Trichome Morphology in Mint, Basil and Tomato
For example, in the cultivated tomato (_Solanum bycopersicum_), type I trichomes contain mostly acyl glucoses, while type VI trichomes from this species contain terpenoids. Furthermore, the same trichome type can have different content in different tomato species [22]. Trichome type I and IV, which, according to some authors may actually represent the same type, look physically similar to non-glandular trichomes, but they differ by the presence of one or two glandular cells in the tip, which secrete acyl sugars [22]. Type VI glandular trichomes are composed of four secretory cells on a two-celled stalk which secrete metabolites that are stored under a waxy cuticle [22]. In the cultivated tomato, type VI trichomes contain monoterpenes [23, 24] as well as a number of sesquiterpenes [24, 25]. Interestingly, transcript analysis indicated that both type I and IV, as well as type VI, across _Solanum_ species, express many of the genes necessary for acyl sugar, flavonoid and terpenoid production [22]. Type VII glandular trichomes, which are less abundant, consist of a small multicellular glandular head that is situated on a short one-celled stalk [21]. It has been suggested that type VII glandular trichomes of _Solanum habrochaites_ are less involved in the biosynthesis of secondary metabolites but instead may have other functions, for instance, protease inhibitor synthesis and storage of alkaloids (_i.e._, tomatine and dehydrotomatine) and transcripts related to biosynthesis of alkaloids were detected in type VII, but also in type I, IV and VI trichomes of this species [22]. Finally, the presence and density of glandular trichome types differs between _Solanum_ species and/or cultivars [21, 22, 26] (see Table 2 for an overview of trichome morphology across _Solanum_ spp.). In addition to the species, trichome density may also depend on the tissue [25] and environmental conditions [27]. Taken together, it is clear that different trichome types have distinct physiological properties and may have evolved due to different selection pressures. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Species** & **I** & **II** & **III** & **IV** & **V** & **VI** & **VII** & **VIII** \\ \hline _S. habrochaites_ & + & & + & + & + & + & \\ _S. lycopersicum_ & + & & + & + & + & + & + \\ _S. pennellii_ & & & + & + & & & \\ _S. cheesmaniae, S. galapagense_ & & & + & & & & \\ _S. pimpinellifolium_ & & + & + b & + & + & & \\ _S. peruvianum, S. arcanum,_ & + & + a & & + & + & + & \\ _S. cornelionuelleri, S. huylasense_ & & & + & + & + & + \\ _S. chilense_ & & & + & + & & \\ _S. chmielewski_ & & & + & + & & \\ _S. neorickii_ & & & + & & & \\ \hline \end{tabular}  
* Described in the form _glandulosum_[20], formally _S. cornelionuelleri_; b Described in the accession TO-937 [28].  
\end{table} 
Table 2: Distribution of trichome types in the section _Lycopersicon_ of the genus _Solanum_.
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 2 Methods and Materials
### Reed-Solomon Codes (RS Algorithm)
#### 2.1.3 Application of the Reed-Solomon Algorithm for Remote Sensing Data Fusion  
The procedure assumes that parameter \(a\) corresponds to the maximal value of radiometric resolution. It will be value \(a\) = 255 if the greyscale is coded; then the values of \(x\) can range from zero to 255. Again, parameter \(k\) corresponds to spectral resolution, i.e., the number of spectral bands in which the sensor can collect reflected radiance, i.e., for a Landsat ETM+ satellite image there are nine bands, the maximum value of \(k\) = 9 and \(k\in\) <1, 2, 3, 4, 5, 6, 7, 8, 9> (Algorithm 1).
# Abstractive vs. Extractive Summarization: An Experimental Review
## 3 Evaluation
### Experimental Setup and Evaluation Results
#### 3.2.2 Evaluation Results  
Tables 3-8 summarize the evaluation results of our experiments. For this process, we used the test splits of the datasets mentioned in Section 3.2.1. To evaluate the performance of the selected approaches, we compared their machine-generated summary with the corresponding reference one. This was executed by utilizing the metrics presented in Section 2.4. For each metric, we calculated the macro (mean) score of the scores computed on each dataset. For the pretrained models, we assess their performance without further finetuning on the evaluation datasets. Our aim is to measure the impact of different learning parameters and datasets of distinct pretrained models belonging to the same approach. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & **R1** & **R2** & **RL** & **RLSum** & **B1** & **B2** & **SB** \\ \hline \multirow{8}{*}{Extractive} & _PositionRank_ & **0.3341** & **0.1277** & **0.2130** & **0.2819** & **0.2507** & **0.1432** & **0.0805** \\  & _LexRank_ & 0.3245 & 0.1146 & 0.2013 & 0.2665 & 0.2334 & 0.1252 & 0.0693 \\  & _e-LexRank_ & 0.3211 & 0.1171 & 0.1983 & 0.2679 & 0.2323 & 0.1276 & 0.0715 \\  & _TextRank_ & & & & & & & \\  & _(my/TextRank)_ & & & & & & & \\  & _TextRank_ & & & & & & & \\  & _(sum)_ & 0.2827 & 0.0956 & 0.1782 & 0.2339 & 0.1895 & 0.0994 & 0.0544 \\  & _Luth_ & 0.3091 & 0.1160 & 0.1981 & 0.2584 & 0.2149 & 0.1209 & 0.0684 \\  & _TopicRank_ & 0.2987 & 0.1170 & 0.2022 & 0.2712 & 0.2383 & 0.1311 & 0.0728 \\  & _LSA_ & 0.2933 & 0.0909 & 0.1816 & 0.2399 & 0.2161 & 0.1046 & 0.0593 \\ \hline \multirow{8}{*}{Abstractive} & _distilBART-CNN-12-6_ & **0.4292** & **0.2077** & 0.2971 & 0.3646 & **0.3625** & **0.2384** & 0.1420 \\  & _BART-large-CNN_ & 0.4270 & 0.2058 & **0.2977** & **0.3647** & 0.3156 & 0.2052 & **0.1433** \\ \cline{1-1}  & _PEGASUS-\_ & 0.4186 & 0.2025 & 0.2983 & 0.3607 & 0.3259 & 0.2124 & 0.1312 \\ \cline{1-1}  & _BART-large-CNN-\_ & 0.4203 & 0.1945 & 0.2904 & 0.3560 & 0.3081 & 0.1963 & 0.1303 \\ \cline{1-1}  & _PEGASUS-large_ & 0.3300 & 0.1330 & 0.2294 & 0.2803 & 0.2341 & 0.1367 & 0.0807 \\ \cline{1-1}  & _PEGASUS-multi
## 5 Simulink Results and Discussion
### The Results of CDR Movement on the Ground and the Water Surface
Only the control algorithm considering boundary constraints can ensure the yaw angle velocity does not exceed the set boundary. Such as, ASMIBC and IBC. Figure 10:2 Robot heading angle control on the ground. (**a**) The control result of proposed method. (**b**) The other four control method results. The initial desired heading angle of the robot is \(-1\) rad. At 15 s, the desired heading angle is 1 rad. the actual yaw angular velocity exceeds the set boundary by 2.6 rad/s. Since the boundary constraint is considered in the algorithm, traditional IBC can constrain the yaw angular velocity from exceeding the set boundary. However, IBC is a model-based control method, so it cannot solve the problems caused by model uncertainty. Due to the static error of angular velocity control, the actual heading angle cannot follow the desired heading angle. Adaptive compensation and adaptive sliding mode gain are shown in Figure 10:3  
The desired yaw angle is 1 rad/s, and the desired yaw angle is \(-1\) rad/s at 15 s. The yaw angle control of the robot on the water surface is shown in Figure 10:4  
As shown in Figure 18a, the robot can follow the desired yaw angle with our proposed control method, and ASMIBC can also follow the desired yaw angle without the adaptive rate compensation. However, the robot tracking the desired yaw angle with SMC and IBC has a static error of about 0.2 rad, and PID control can track the desired yaw angle, but the tracking error takes a long time to converge. The maximum desired angular velocity of the robot on the water surface is 1.5 rad/s, and a random disturbance with an average value \(-5\) rad/s/s is applied to the angular  
Figure 10:5 Adaptive compensation output and adaptive sliding mode gain. (**a**) Adaptive compensation output of the angular velocity controller. The robot is subjected to lumped disturbance of 5 m/s/s on the water surface, and the designed adaptive rate can obtain the lumped disturbance. (**b**) Adaptive sliding mode gain of the angular velocity controller. Due to adaptive compensation, the sliding mode gain is 0 even if there exists a lumped disturbance. Figure 10:6 Robot heading angle control on the water surface. (**a**) The control result of proposed method. (**b**) The other four control method results. The initial desired heading angle of the robot is 1 rad. At 15 s, the desired heading angle is \(-1\) rad. acceleration of the robot to simulate the water resistance and wind disturbance encountered by the robot when moves on the water surface. Yaw angular velocity control is shown in Figure 10:7  
The controller of the robot adopts the same control parameters on the ground and the water surface. The ASMIBC and ASMIBC with the adaptive compensation proposed by us can ensure the robot to track the desired angular velocity and do not exceed the set boundaries. The adaptive output compensation and adaptive sliding surface have been discussed in the previous results, so they are not presented here.
# Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
## 3 Methodology
### Label Randomization  
Our experiments revealed that detecting small objects at night is highly challenging. This challenge arises from the fact that small object features are easily obscured by nighttime environmental factors such as glare or low illumination. To enable the student network to overcome this obstacle, we enhance the input images and pseudo-labels at multiple scales. \(C_{\text{ilp}}\) represents the original size of the input image. At the start of training, we concentrate on small features and then determine a new scaling ratio \(W_{\text{un}}\) based on the number of network iterations. Additionally, to prevent the student network from overfitting due to prolonged exposure to one scale, we empirically allow the actual scaling ratio to fluctuate within 0.15 of \(W_{\text{un}}\):  
\[C_{\text{student}}=C_{\text{ilp}}+W_{\text{un}}(-0.15,0.15) \tag{11}\]  
Simultaneously, to prevent imbalances caused by excessively small sizes and misleading models due to excessively large sizes, labels with sizes below or above the threshold will be discarded.
## 3 Data and Method
### Detection of LULC Change  
Vector LULC datasets with 1:100,000 scale in the late 1970s, the early 1990s, 2004 and 2012 were rasterized to perform raster-based change analysis with 100 m spatial resolution. Using GIS overlay function, the LULC transition matrix was computed, and change statistic was calculated on a pixel-by-pixel basis between each pair of LULC maps (_i.e._, the late 1970s to the early 1990s, the early 1990s to 2004, and 2004-2012). It not only locates changes but also quantifies the different types of change with \"from-to\" change information [14; 16].
## 3 Parametric Study for Determining the Optimal Sampling Strategy
### Sampling from a Domain
#### 3.2.5 Effect of the Factor of Safety \((fs)\)  
The variation of \(p_{f}\) with respect to \(d_{d}/H\) for different \(FS\) values is shown in Figure 16; the optimal sampling distance was also found to be at x/\(H=0\) for any \(FS\) value. Thus, only this case is presented here. From Figure 16, it is obvious that the failure probability decreases as \(FS\) increases, but what it is not trivial is that the positive effect of targeted field investigation on \(p_{f}\) (that is, decrease in \(p_{f}\)) is greater for greater \(FS\) values.
# High-Fidelity OC-Seislet Stacking Method for Low-SNR Seismic Data
###### Abstract  
Seismic stacking is a core technique in seismic data processing, aimed at enhancing the signal-to-noise ratio (SNR) of data by utilizing seismic data acquisition with multifold geometry. Traditional stacking methods always have certain limitations, such as the reliance on the accuracy of velocity analysis for dip moveout (DMO) in common midpoint (CMP) stacking. The seislet transform, a compression technique tailored to nonstationary seismic data, can compress and stack along the prediction direction of seismic data, which provides a new technical idea for high-fidelity seismic imaging based on the effectiveness of the compression. This paper introduces a high-order OC-seislet stacking method for low-SNR seismic data, capable of achieving the high-fidelity stacking of reflection and diffraction waves simultaneously. With the multi-scale analysis advantages of the seislet transform, this method addresses the dependency of DMO stacking on velocity analysis accuracy. In the frequency-wavenumber-scale domain, the correction compensation of the high-order CDF 9/7 basis function is used to obtain the compression coefficients of the high-order OC-seislet transform. This approach simultaneously stacks frequency-wavenumber information of reflection and diffraction waves with high fidelity while implementing DMO processing. After normalizing the weighting coefficients and applying soft thresholding for denoising, the final result is transformed back to the original time-space domain, yielding high-fidelity stacking sections. The results of applying this method to both synthetic and field data show that, compared with conventional DMO stacking methods, the high-order OC-seislet stacking technique reasonably represents dipping layers and fault amplitudes, and it can achieve a balance of a high SNR and high fidelity in stacked profiles.  
low SNR; DMO stacking; OC-seislet transform; multi-scale analysis; soft thresholding Article
# Fatigue Behaviour of Concrete Using Siderurgical Aggregates
## 3 Results
### Physical and Mechanical Properties of Concrete  
Figure 4 shows the physical properties of the two mixes manufactured with their standard deviations. It can be observed that the bulk density (D\({}_{b}\)) of the SLC mix is 20% higher than that of the LLC mix, due to the high density of the siderurgical aggregates.  
Likewise, the water absorption of LLC mix is higher, but as this parameter depends on the bulk density of the material, it is not the most suitable parameter to compare mixes with different specific weights. Porosity, however, determines the relationship between the  
Figure 3: Preparation of the specimens and experimental setup used.  
volume of accessible pores and the apparent volume and is similar for both mixes, being around 7%, with below 10% being considered a good value [12]. These results show that concrete vibration was extremely effective with the SLC mix, highlighting the importance of this action for any mix with siderurgical aggregates.  
Table 5 shows the compressive strengths developed by the mixes together with their standard deviations. Natural aggregates have a smooth and spherical surface that does not favour an excessively strong bond between paste and aggregate. The SA concretes show an improved ITZ, favoured by the shape of the siderurgical aggregates, which makes the aggregate interlock with the paste and increases the friction between the two [33]. This is reflected in the compressive strength, which is 42% higher in the SLC mix than in the reference mix (LLC), which is due to the replacement of all the coarse aggregate and part of the fine aggregates by siderurgical aggregates.  
The same explanation, although with a lesser effect, can be used to justify the difference in elastic modulus (20% higher in the SLC mix) which is also caused by the high elastic modulus of the siderurgical aggregates themselves. The increase in compressive strength leads to a less-than-proportional increase in tensile strength (16%), which is one of the reasons that increasing strength also increases the brittleness of the cement paste.
## 1 Introduction
### Hidden but Yet Important Advantages of Containerization  
In a typical CubeSat project, the hardware and the test stand where the software and overall integration testing are carried out are very scarce resources. Therefore, there are a few hidden yet important benefits of the use of containerization:* The ability to develop and test the container on a regular PC, rather than CubeSat hardware. It minimizes the need for the test stand availability and minimizes the risk of breaking the working hardware;
* Full isolation from the other developers with different (lower) qualifications in a frame of the particular container scope. This means that software errors and malfunctions in other containers will not destroy your own work;
* Easier profiling of a container performance as the developer may just stop other containers at any given point in time.
# Non-Real-Time Wireless System for Lightning Effect Measurements
## 2 Theory
Due to the specificity of lightning discharges (in particular, the stochastic nature and the random location of the occurrence of the phenomenon), systematic research is significantly difficult or, in some cases, even impossible. Therefore, detailed studies of lightning interactions are usually carried out with the use of specialized impulse generators that form repetitive current or voltage surges. Rogowski coils are used in many research projects, for example: spires of tall buildings and radio masts [28; 29; 31]. For signal transmission, cable connections are mainly used, but also optical fibers [32]. The use of a larger number of coils enables the construction of self-powered measuring systems during an atmospheric discharge [33]. Fiber optic systems are expensive and complex. Changing the place of measurement requires delicacy and caution (the optical fibers are easily damaged) [2; 32]. To measure overvoltage in power distribution lines, ceramic-capacitor insulators are used. They measure overvoltage and send the measurement results via the GSM network. These are permanently installed systems for monitoring line sections particularly exposed to lightning strikes [34]. Another group of measuring devices designed to record lightning currents are non-contact systems. Antennas record the magnitude of the magnetic field at a certain distance from the tested object. The current is proportional to the measured intensity [35]. However, these are systems designed for larger objects and do not allow for precise measurement when there are more than one conductor.