esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Pre-Process Analysis
#### 2.2.3 Photon Background and Offset Determination
If \(s_{\text{ph,tot}}^{2}>1.03\cdot N_{\text{ph,tot}}\) that is, the variance exceeds by more than 3%, signal contamination is assumed and the window size for the background calculation is reduced (use, e.g., Figure 8 for guidance): in the search region before the backscatter signal, the size of the background search range is reduced by a single time slice; for the search region after the signal, the search region is reduced by 20%. This is performed iteratively until the condition is met, or the sample range falls below a minimum number of background time slices of \(n=2/(0.03)^{2}\approx 2000\). In the latter case, the channel is declared unreliable. The amplitude offset is then obtained from the same window. Once the mean background count and its variance are defined, it is straightforward to compute the mean background signal rate and its standard deviation:  
\[\mathcal{R}_{b} :=\mu_{t} \tag{10}\] \[s_{\mathcal{R}_{b}} :=\sqrt{\frac{s_{n}^{2}}{n-1}} \tag{11}\]  
Figure 9: Background calculation for the Raman Channel (387 nm). Same scheme as in Figure 8. On the left side, the LOTR AN signal exhibits a small drop in its electronic baseline right after half the readout range, at a range of about 65,000 m. Only this single LOTR is affected. On the right side, the PC region is shown. The resulting \(<\)1.5% systematic error allowed by the procedure corresponds roughly to the accuracy, with which an absolute calibration of the LIDAR is possible [9; 22]. This procedure allows also to possibly reveal technical issues with LOTR channels, like the amplitude channel of one TR20-16 bit, which exhibits an unexpected electronic amplitude offset that decreases after half the time slices are reached (see Figure 9). In this case, we resort to computing the background only using the time slices before the count jump, at exactly half the time slice range. To avoid any significant leakage of the signal into these samples, we used the corresponding LOTR for a Raman channel.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 4 DR Model Considering Real-Time Tariff
### The Consumer Simulation  
Users will adjust their consumption behavior all the time based on their real-time tariff, which optimizes the load curve. DR combined behavior model based on real-time tariff includes two cases, namely self-elasticity and cross-elasticity, which can be expressed as follows [29]:  
\[D(i)=D_{0}(i)+\sum_{j=1}^{24}E_{d}(i,j).\frac{D_{0}(j)[P(j)-P_{0}(j)+E(j)]}{P_ {0}(j)}\quad(i=1,2,\ldots,24) \tag{15}\]  
where \(P(j)\) is the price at moment \(j\) after implementing the real-time tariff. \(D_{0}(i)\) and \(D_{0}(j)\) are the initial load at moments \(i\) and \(j\). \(D(i)\) is the load after DR at moment \(i\). E is the reward or punishment part for users changing their electricity consumption behavior. \(E_{d}(i,j)\) is the price elasticity of demand coefficient (PED), which reflects the sensitivity of changes in commodity DR to price changes in the market. When \(i=j\) PED is the price self-elasticity of demand coefficient. When \(i\
eq j\), PED is the cross-price elasticity of demand (CPED). CPED represents the sensitivity of a change in the demand for one commodity response to the price of another commodity, which can complement or replace each other. The PED and CPED values refer to the operation experience of foreign power market. It is considered that all the PED values of trough, flat and peak are \(-0.05\), and the CPED values between trough and flat, trough and peak, flat and peak are \(0.01\), \(0.012\) and \(0.016\), respectively. Considering the actual electricity load in agriculture, we chose to only use PED rather than CPED because of its small numerical value.
## 4 Results and Discussion
### Experimental Results
However, cellular traffic data often have complex nonlinear characteristics, which makes it difficult for ARIMA to accurately capture these complex change patterns, especially in mid-term and long-term predictions. The SVR is a machine learning algorithm that can capture nonlinear time features. Nevertheless, in cellular traffic prediction, cellular traffic data usually have a high degree of noise and uncertainty, which makes it difficult for the SVR to accurately model and predict. Secondly, the SVR needs to manually select kernel functions and adjust hyperparameters, which is a challenge for the complexity of cellular traffic data. As a deep learning algorithm, the GRU has produced good results in time-series prediction. But the GRU only processes the time characteristics of cellular network traffic data and does not consider the spatial distribution of cellular traffic; so, the GRU is more suitable for single-region cellular traffic prediction without spatial characteristics. In this scenario, its simple structure and faster running speed are its advantages. In contrast, T-GCN and STA-GCN not only consider the time characteristics of cellular traffic, but also use GCN to process the spatial characteristics of cellular traffic, which greatly improves the prediction accuracy. Nevertheless, T-GCN does not use the attention mechanism module, so that it may repeatedly learn similar spatial features, resulting in a low training efficiency and model performance degradation. STA-GCN only uses the structure of one GLU, which causes the GLU to learn the feature representation of multiple different components at the same time, which reduces the adaptability of the model. The efficacy of DBGSTNN-Att in predicting cellular network traffic was substantiated through numerical comparisons to and the analysis of several models. This is attributed to the attention mechanism of DBGSTNN-Att, which prioritizes the periodic changes in cellular traffic. Furthermore, the use of the dual GLU module enables distinct GLUs to concentrate on learning the distinctive feature representation of various components. As a result, the model's adaptability to different data is enhanced, significantly boosting the accuracy of the prediction.
## 4 Factors Affecting Resistance Evolution
In general, fitness costs tend to increase with the magnitude of resistance, with strains that have higher resistance ratios incurring more fitness costs than strains with lower resistance ratios [34]. Data from strains with field-evolved resistance to Cry3 Bb1 maize indicate that resistance ratios tend to range from 2.5 to 19, which is substantially lower than resistance ratios found for pests targeted by high-dose Bt crops [65; 66; 67]. For example, in cases where a high dose was achieved, such as with Bt cotton that targets pink bollworm _Pectinophora gossypiella_ and Bt maize that targets European corn borer _Ostrinia nubilalis_, resistance ratios were greater than 500 [74; 75]. As such, for western corn rootworm, fitness costs of resistance to Bt maize may often be less than costs associated with Bt crops that are high dose and require pests to have a much higher resistance ratio to survive [23]. Consequently, fitness costs, and the corresponding delay in resistance evolution, are expected to be less for western corn rootworm than for other pests targeted by Bt crops where a high dose is achieved. Furthermore, fitness costs of Bt resistance in western corn rootworm appear insufficient to delay resistance development in the field, at least with the current refuge requirements. Data are currently lacking on the inheritance and fitness costs of resistance to Cry34/35 Ab1, in either laboratory-selected strains or in strains with field-evolved resistance. With the emergence of field-evolved resistance to Cry34/35 Ab1 [49; 50; 51] such data would enable valuable insights into how quickly resistance will develop in the broader agricultural landscape. In the case of resistance to Cry3 Bb1 maize, which in turn confers cross-resistance to mCry3A and eCry3.1 Ab, it appears that several factors contributed to the evolution of resistance. In particular, continuous planting of maize containing the same Bt trait, coupled with limited adult dispersal, likely favored the evolution of resistance. Additionally, it appears that substantial standing genetic variation for resistance, non-recessive inheritance of resistance, and minimal fitness costs of resistance also contributed to resistance development in the field.
## 3 Mathematical Process Modeling
### Diffusion Coefficient  
The diffusion coefficient depends largely on the nature of the particles, the solvent, the temperature, and the viscosity of the solvent. In this case, neither the type of particles nor the solvent is modified during the process. On the other hand, temperature variations are not important with respect to diffusion and, as a consequence, there are also no significant variations in the viscosity of the solution. Considering all these conditions, we can affirm that the diffusion coefficient will remain constant.  
In general, if there is an active flow, as presented in this paper, the diffusion effect is negligible. However, to determine the relationship between the advective and diffusive terms, the Peclet number _Pe_, Equation (4), was estimated:  
\[Pe=\frac{L\times v}{D} \tag{4}\]  
where \(L\) is the distance between concentration measurement points, \(v\) the fluid velocity, and \(D\) (m/s\({}^{2}\)) the diffusion coefficient.  
In this study case, the Comsol CFD software was used to obtain the value of the diffusion coefficient of the particles from the characterization of the tank and different flow rates giving a range of 0.0055 to 0.013 m\({}^{2}\)/s for water flow in the range between 1.5 m\({}^{3}\)/s and 3.7 m\({}^{3}\)/s, respectively. Similar to this strategy, in [15], CFD software was used to obtain the dissemination number of the treated tank.  
For the calculation of the minimum and maximum values of the number of Peclet, according to the extreme values of the velocity, we took the most unfavorable value of the diffusion coefficient (\(D=0.013\) m\({}^{2}\)/s). Considering these conditions, the Peclet number oscillated between 71.92 and 1831.0, both higher than 1. These values suggest that the diffusive term was negligible compared to the effects of advection, and since the diffusion can be neglected for \(Pe>>1\) under these conditions, the effects of advection exceeded those of diffusion in determining the overall mass flow.
# Inverted F Type Antenna Design for the Cattle Activity and
###### Abstract  
In this paper, an inverted F type antenna (IFA) for ZigBee communication of a sensor board has been designed and optimized, and it replaces the chip antenna on an RF (Radio Frequency) module that is not performing well enough for the ZigBee communication. The sensor board detects cattle behavior and identifies the breeding (estrus) period and transmits the data to the main station by the RF (Radio Frequency) module and IFA antenna. The proposed and optimized TRx (transmitting/receiving) IFA antenna of the ZigBee communication module has a return loss of \(-19\) dB and a gain of \(1.6\) dB at \(2.45\) GHz. The size is about \(2.5\times 0.5\) cm in width and vertical length, and the height is \(0.55\) cm. The strength of signals with the chip antenna and the IFA antenna have been measured and compared. There is about a \(20\) dB enhancement with the IFA antenna compared to the chip antenna. The antenna is designed and applied to the RF transmission and reception (TRx) module. This antenna and sensor module can be applied to livestock in general as well as cattle.  
inverted F type antenna; antenna of ZigBee; sensor module antenna +
Footnote †: journal:  
gain, and efficiency) is improved with the additional open stub in the radiating element of the PIFA [10].  
The IFA antenna is designed to be small in size to resonate at 2.45 GHz, the ZigBee frequency, considering the size and space of the communication module case and PCB (Printed Circuit Board) board. The RF sensor module equipped with an IFA antenna can grasp the behavior and habits of livestock by measuring the movement and momentum of livestock through a wireless network with an acceleration or tilt sensor. In addition, the purpose is to identify the breeding (estrus) period from the collected data and to individually tailor breeding and fertilization to livestock.
## 7 New Challenge: Multimodality
### Data Collection  
Multimodal interaction often involves combining data from satellites, radar, LIDAR, cameras, and ultrasonic sensors. While this fusion aims to create a robust perception system, it is not foolproof. Note that these kinds of issues are similar to those encountered on an aircraft so both domains become closer, suggesting adapting the aircraft safety approach for AVs [85]. The following aspects need to be taken into account:  
* Conflicting Inputs: If a satellite says the vehicle is in one location but LIDAR suggests another (e.g., due to a map error or sensor glitch), the system must resolve this discrepancy quickly. Failure to do so could lead to navigation errors.
* Environmental Limitations: Heavy rain, fog, or snow can degrade camera and LIDAR performance, forcing over-reliance on satellite or V2V data, which might also be impaired.
* Processing Demands: Real-time integration of multimodal data requires immense computational power. Any lag or failure in processing could delay critical actions like braking or steering.
# A Procedure to Prepare Sand-Clay Mixture Samples for Soil-Structure Interface Direct Shear Tests
## 5 Assessment of Samples' Repeatability
### Interface Direct Shear Response  
The S1 samples with 75% K clay content and a concrete plate (140 mm \(\times\) 100 mm \(\times\) 11 mm) were used for the interface direct shear test. The samples were sheared with a rate of 0.005 mm/min under three normal stresses, i.e., 50, 100, and 150 kPa, until a critical state (maximum horizontal displacement of 5 mm). This small shear rate ensured a total drained shearing according to [65]. The shear tests were repeated at least one time for each normal stress. Shear stress versus horizontal displacement of the interface direct shear tests are plotted in Figure 11a, which verifies the repeatability of the tests.  
The curves at each normal stress overlap with each other at the beginning phase of the shearing (Figure 11a), which indicates that the samples have good repeatability. The different critical shear strengths are characterized with values of 2.43 kPa, 4.45 kPa, and 0.81 kPa (average value), at the normal stress of 50 kPa, 100 kPa, and 150 kPa. The difference among the critical shear strength is in good agreement with the previous publication [7]. The critical adhesion (\(c\)) and interface friction angles (\(\delta_{\rm cv}\)) of the first and second tests are shown in Figure 11b. Both the first and second tests are characterized with the same critical adhesion of zero (Figure 11b). The critical \(\delta_{\rm cv}\) for the 1 st and 2 nd interface direct shear tests are 23.74\({}^{\circ}\) and 23.98\({}^{\circ}\), respectively, with a small difference of 0.24\({}^{\circ}\). The shear stress curves, critical shear strength, critical adhesion, and interface friction angles of the interface direct shear tests demonstrate great repeatability of the samples generated by the S1 technique.  
It is worth noting that, though there is a sudden shear stress drop on the curve of 150 kPa normal stress (see Figure 11), the overall trend and the critical state are not affected. This collapse is caused by the fact that the soil particles go into the small pits on the concrete surface during shearing, and similar phenomena can be also found in [7; 57].
# A Subjective and Behavioral Assessment of Affordances in Virtual Architectural Walkthroughs
## 4 Results
### From ITC-SOPI  
The results of the questionnaire data were compiled in a mean opinion score (MOS)--average judgment for one scenario over all subjects. A multivariate analysis of covariance (MANCOVA) was run after controlling for the covariate of active run-time. This was done considering the possible effects of active run-time (duration of time spent within the VE) on the four dimensions of the ITC-SOPI. Statistical significance was assumed at \(p=0.05\). There was a statistically significant difference between the two categorical scenarios (PW and IW) on the combined dependent variables of spatial presence (SP), engagement (EN), naturalness (NV) and negative effects (NE) after controlling for active run-time: \(F(4,\,59)=4.662,\,p=0.02\), [PERSON]'s \(\lambda=0.76,\,\eta^{2}p=0.24\). We ran separate ANOVAs for each dependent variable SP, EN, NV and NE. We found significant differences between PW & IW for SP & EN, but no statistically significant difference was found for NV or NE:  
* Spatial presence (SP) : \(F(1,\,62)=43.50,\,p=0.001,\,\eta^{2}p=0.166\).
* Engagement (EN) : \(F(1,\,62)=26.00,\,p=0.017,\,\eta^{2}p=0.089\).
* Naturalness (NV) : \(F(1,\,62)=133.0,\,p=0.279,\,\eta^{2}p=0.019\).
* Negative Effects (NE) : \(F(1,\,62)=75.40,\,p=0.383,\,\eta^{2}p=0.012\).  
Table 1 shows the comparison of the means of the three items under both scenarios. The mean levels in the table indicate higher values for the IW scenario in at least two categories of SP and EN. These differences can be visualized in the min-max plots available for the four variables in Figure 4.  
Figure 4: Minimum and maximum for the four ITC-SOPI dimensions under PW and IW: (**a**) Spatial Presence (SP); (**b**) Engagement (EN); (**c**) Naturalness (NV); (**d**) Negative Effects (NE).
## 5 Discussion
The ideal value of the absolute accuracy is \(\pm\)0.5 LSB. In our instrument, a 10-bit ADC (Atmel Corporation, San Jose, CA, USA, ATmega328P) is utilized with a reference conversion voltage of 5 V and a typical absolute accuracy of \(\pm\)2.2 LSB. This implies a minimum detectable voltage of 4.88 mV, with ideal and typical absolute accuracies of \(\pm\)2.44 mV and \(\pm\)10.74 mV, respectively. As is the case with other deviation metrics, statistical methods can be utilized to reduce these errors. The mean sensitivity of our instrument can be obtained from the absolute values of Table 2, and is equal to 172.67 [mV/pH]. This maps directly to the usable conversion range of the ADC, which is 2.41 V, over the full range of pH values. Therefore, we expect the minimum detectable pH value to be 0.028 units, with ideal and typical absolute accuracies of \(\pm\)0.014 pH units and \(\pm\)0.062 pH units, respectively. From this analysis and the results presented in this work, we can conclude that the current instrument design is ADC-limited. A straightforward improvement in the instrument can be implemented by using ADCs with greater dynamic ranges. For instance, the STM32L041 (STMicroelectronics, Grenoble, Auvergne-Rhone-Alpes, France) incorporates a 12-bit ADC, which would reduce by a four-fold factor the minimum detectable voltage, improving absolute accuracy proportionally. In such a setup, the minimum detectable pH increment would be 0.007 units, with ideal and typical absolute accuracies of \(\pm\)0.0035 pH units and \(\pm\)0.015 pH units, respectively. Global (conventional and _Vertical Farming_) solutions are necessary worldwide. Such systems need to be robust in order to ensure adoption. Even in a controlled laboratory setting mechanical instabilities are readily recognized, refer to Figure 5. Due to the fact that minimal increments in sensor readings convert into significant pH variations, such instabilities impact significantly the recovered values. Our group has recently proposed an artificial intelligence (AI) approach to compensate for these types of scenarios [43; 44].
## 2 EO Satellite and In Situ Observations for Earth Surface
### GOES, METEOSAT and Other Geostationary Satellites

Table 2: Geostationary satellites operated positions as of 2018. Operational position can vary over time. Accurate derivations of land surface temperature and land surface emissivity from satellite measurements are difficult because the two variables are closely coupled. At the very surface, the land surface temperature is characterised by a strong diurnal cycle that is driven by solar radiation and is dependent on the exact sensing depth and thermal properties of soil and vegetation. Geostationary observations with temporal samplings ranging between 30-min (earlier satellites) and 10-min are particularly suitable for monitoring variables with pronounced diurnal cycles, with the exception of the polar regions where revisit times of polar orbiters are generally short. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard MSG takes top-of-atmosphere measurements within 12 bands, every 15-min and with spatial sampling of at least 3 km at nadir. SEVIRI-based retrievals of Land Surface Temperature (LST) can therefore capture the high spatial and temporal variability (see Figure 3, as example), which in turn are closely linked to surface conditions, such as vegetation cover and state, or soil moisture availability. LSTs (or equivalently Sea Surface Temperatures) are usually derived from top-of-atmosphere thermal infrared measurements in the atmospheric window, by correcting for the atmospheric absorption and emission and surface emissivity. Such surface temperature estimates are fairly close to actual observations [47], though they are restricted to clear-sky conditions and to the disk field of view. Multiple geostationary satellites can be combined to extend coverage, maintaining a good temporal sampling. The use of satellite LSTs to understand and improve the modelling of land-surface processes is still largely under-utilised [13; 48]. The LST spatial and temporal variability reflects changes in the surface energy balance. The amplitude and phase of the LST diurnal cycle are strongly linked to net radiation and turbulent energy flux partitioning at the surface [49], and to surface properties such as surface wind, the surface type and vegetation state, soil moisture availability, amongst others.
## 2 Materials and Methods
### Data Analysis  
#### 2.15.1 Analysis of Public Datasets  
Raw RNA-seq data from Erythroid cells (ENCODE mouse E14.5 fetal liver cells ENCSR000 CHE [49], Ter119-positive cells [24; 50], and Ter119-negative cells [24]) were mapped to the mm9 genome using STAR. The transcriptome was assembled by StringTie. Then, we used Gftcompare to select transcripts with class codes of \"x\", \"i\", \"u\", and \"r\". The Bedtools intersect function was used to find the transcript that overlapped with the erythroid enhancer. Coding transcripts were removed by CPC2 [51]. Promoter-Capture Hidata from mouse E14.5 fetal liver cells [52] were analyzed with GenomicInteractions [53] to select eRNAs with long-range interactions (>50 kb). The resulting 791 eRNAs were further filtered for erythroid-specific eRNAs by analyzing ENCODE mouse RNA-seq data with salmon [54] (supplied with a custom reference file, which combined GENCODE M21 and eRNA sequences) and TissueEnrich [55] (see Supplementary Table S2 for the full list of datasets used for analysis). RNA-seq data from BFU, CFU, and Ter119+ cells were analyzed with salmon [54], then DEseq2 [56] was used to find differentially expressed genes.  
RNA-seq data for BFU, CFU, and Ter119+ cells were obtained from GSE52126 [24] and GSE26086 [50]. Promoter-Capture HiC data (\"FLC_promoter_other_significant_interactions.txt\") from [PERSON] et al. [52] were downloaded from ArrayExpress (accession E-MTAB-2414) in 2019. Erythroid-specific enhancers are a union set of enhancers from ENCODE3 mouse E14.5 fetal liver cells [57] and enhancers in MEL, G1E, and G1 ER cells annotated by Enhancer atlas 2.0 [58].  
Mouse E14.5 fetal liver, ESC, and C2C12 cell HiC data were analyzed by Juicer [59] and visualized with Juicebox [60]. The raw Fastq files for E14.5 fetal liver and ESC were downloaded from ArrayExpress (accession E-MTAB-2414 [52]), while the raw Fastq files for C2C12 (Myoblasts) were downloaded from [PERSON], et al. [61] GSE84279.  
ENCODE ChIP-seq data: RAD21 (ENCSR000 ETS), CTCF (ENCSR000 ETQ), and SMC3 (ENCSR000 ETL).