esa-sceva/satcom-chunk-collection huggingface dataset:
# BEVCorner: Enhancing Bird's-Eye View Object Detection with Monocular Features via Depth Fusion
## 4 Experiments
### Ablation Study
#### 4.2.4 Runtime and Efficiency  
It is evident that BEVCorner performs slower than the Baseline BEVDepth [6], as the framework is extended from it. The evaluation was conducted on the NuScenes [5] Eval dataset using two Quadro RTX 6000 GPUs, each equipped with 24 GB of memory. A comparison of the resources used is provided in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline
**Configuration** & **mAP (\%) \(\uparrow\)** & **mATE \(\downarrow\)** & **mASE \(\downarrow\)** & **mAOE \(\downarrow\)** & **mAVE \(\downarrow\)** & **mAAE \(\downarrow\)** & **NDS(\%) \(\uparrow\)** \\ \hline \multicolumn{8}{c}{Baseline: BEVDepth [2]} \\ \hline Vanilla: non EMA + non CBGS + 1 key & 33.13 & 0.7009 & **0.2796** & **0.536** & **0.5533** & **0.2273** & 43.59 \\ \hline \multicolumn{8}{c}{BEVCorner (Ours)} \\ \hline direct\_replacement (fixed) & 55.79 & 0.4461 & 0.295 & 0.7014 & 0.8257 & 0.2566 & 52.65 \\ \hline weighted\_fusion (fixed) & 54.84 & 0.4705 & 0.3035 & 0.8963 & 1.079 & 0.2971 & 47.75 \\ \hline weighted\_fusion (geometry) & 43.11 & 0.5622 & 0.2972 & 0.6121 & 0.6978 & 0.2484 & 47.38 \\ \hline weighted\_fusion (learned) & 55.84 & 0.4466 & 0.2935 & 0.6796 & 0.8 & 0.2561 & 53.16 \\ \hline roi\_refinement (fixed) & 55.81 & 0.4463 & 0.2947 & 0.691 & 0.7998 & 0.259 & 53 \\ \hline roi\_refinement (geometry) & 45.88 & 0.536 & 0.2952 & 0.6245 & 0.7284 & 0.2625 & 48.47 \\ \hline roi\_refinement (learned) & **55.07** & **0.45** & 0.2919 & 0.6627 & 0.7727 & 0.2561 & **53.21** \\  & **+21.94** & **-0.25** & +0.01 & +0.13 & +0.22 & +0.03 & **+9.62** \\ \hline hard\_combine (fixed) & 23.32 & 0.737 & 0.2945 & 0.578 & 0.7504 & 0.2391 & 35.67 \\ \hline \end{tabular} Note: \(\uparrow\) indicates that higher values are better, while \(\downarrow\) indicates that lower values are better.  
\end{table}
Table 4: Upper bound performance of BEVCorner
## 3 Results
### Results of Phytochemical Analyses of Aqueous Extracts
#### 3.1.3 Wormwood Herbs (A. _absinthium_ L.) Aqueous Extracts  
The results of phytochemical analysis of wormwood herb (_A. absinthium_ L.) aqueous extracts (Table 3) showed almost the same order of compounds in the extracts with content higher than 1 mg L\({}^{-1}\) as in aqueous extracts of black cumin, with the exception of gallic acid content. This means that the highest content of epicatechin was also detected in aqueous extracts of wormwood herbs, followed by esculetin, cinnamic acid, quinic acid, kaempfer and finally gallic acid. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Extract Concentration**} \\ \cline{2-4}
**Compound** & **2.5\%** & **5\%** & **10\%** \\ \cline{2-4}  & & **mg L\({}^{-1}\)** & \\ \hline Epigallocatechin gallate & 0.06 \(\pm\) 0.01 & 0.10 \(\pm\) 0.01 & 0.11 \(\pm\) 0.02 \\ Quercetin 3-\(O\)-rhammoside & 0.55 \(\pm\) 0.03 & 0.02 \(\pm\) 0.00 & 0.05 \(\pm\) 0.02 \\ Epicatechin gallate & 0.89 \(\pm\) 0.02 & 0.73 \(\pm\) 0.02 & 0.02 \(\pm\) 0.01 \\ Catechin & 0.18 \(\pm\) 0.01 & 0.05 \(\pm\) 0.01 & 0.06 \(\pm\) 0.02 \\ Epicatechin & 6.70 \(\pm\) 0.02 & 12.35 \(\pm\) 0.06 & 0.05 \(\pm\) 0.01 \\ Kaempfer & 0.91 \(\pm\) 0.02 & 0.81 \(\pm\) 0.03 & 0.79 \(\pm\) 0.04 \\ Naringenin & 0.32 \(\pm\) 0.03 & 0.19 \(\pm\) 0.02 & 0.07 \(\pm\) 0.02 \\ Symrignic acid & 0.96 \(\pm\) 0.01 & 0.31 \(\pm\) 0.03 & 0.41 \(\pm\) 0.02 \\ Ferrulic acid & 0.09 \(\pm\) 0.02 & 0.36 \(\pm\) 0.05 & 0.65 \(\pm\) 0.04 \\ Scopoletin & 0.19 \(\pm\) 0.01 & 0.20 \(\pm\) 0.01 & 0.13 \(\pm\) 0.03 \\ Quinic acid & 0.83 \(\pm\) 0.02 & 17.10 \(\pm\) 0.01 & 130.52 \(\pm\) 4.57 \\ Caffeic acid & 2.32 \(\pm\) 0.03 & 1.22 \(\pm\) 0.04 & 0.75 \(\pm\) 0.04 \\ Esculetin & 9.44 \(\pm\) 0.05 & 4.49 \(\pm\) 0.01 & 2.68 \(\pm\) 0.20 \\ Gallic acid & 0.55 \(\pm\) 0.01 & 0.50 \(\pm\) 0.02 & 0.53 \(\pm\) 0.06 \\ \(p\)-Coumaric acid & 0.07 \(\pm\) 0.01 & 0.07 \(\pm\) 0.01 & 0.20 \(\pm\) 0.05 \\ Umbelliferone & 0.25 \(\pm\) 0.02 & 0.14 \(\pm\) 0.01 & 0.71 \(\pm\) 0.10 \\ Gentisic acid & 0.05 \(\pm\) 0.01 & 0.04 \(\pm\) 0.01 & 0.11 \(\pm\) 0.03 \\ Protectable acid & 0.07 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 & 0.09 \(\pm\) 0.01 \\ Cinnamic acid & 7.12 \(\pm\) 0.04 & 7.78 \(\pm\) 0.02 & 4.84 \(\pm\) 0.19 \\ \(p\)-Hydroxypbenzoic acid & 0.96 \(\pm\) 0.03 & 0.82 \(\pm\) 0.04 & 0.87 \(\pm\) 0.09 \\ \hline \hline \end{tabular} Results are expressed as mean \(\pm\) standard deviation (SD).  
\end{table} 
Table 1: Phytochemical composition of chia (_S. hispanica_ L.) aqueous extracts of different concentrations.
# Automatic Tumor Identification from Scans of Histopathological Tissues
###### Abstract  
Latest progress in development of artificial intelligence (AI), especially machine learning (ML), allows to develop automated technologies that can eliminate or at least reduce human errors in analyzing health data. Due to the ethics of usage of AI in pathology and laboratory medicine, to the present day, pathologists analyze slides of histopathologic tissues that are stained with hematoxylin and eosin under the microscope; by law it cannot be substituted and must go under visual observation, as pathologists are fully accountable for the result. However, a profuse number of automated systems could solve complex problems that require an extremely fast response, accuracy, or take place on tasks that require both a fast and accurate response at the same time. Such systems that are based on ML algorithms can be adapted to work with medical imaging data, for instance whole slide images (WSIs) that allow clinicians to review a much larger number of health cases in a shorter time and give the ability to identify the preliminary stages of cancer or other diseases improving health monitoring strategies. Moreover, the increased opportunity to forecast and take control of the spread of global diseases could help to create a preliminary analysis and viable solutions. Accurate identification of a tumor, especially at an early stage, requires extensive expert knowledge, so often the cancerous tissue is identified only after experiencing its side effects. The main goal of our study was to expand the ability to find more accurate ML methods and techniques that can lead to detecting tumor damaged tissues in histopathological WSIs. According to the experiments that we conducted, there was a 1% AUC difference between the training and test datasets. Over several training iterations, the U-Net model was able to reduce the model size by almost twice while also improving accuracy from 0.95491 to 0.95515 AUC. Convolutional models worked well on groups of different sizes when properly trained. With the TTA (test time augmentation) method the result improved to 0.96870, and with the addition of the multi-model ensemble, it improved to 0.96977. We found out that flaws in the models can be found and fixed by using specialized analysis techniques. A correction of the image processing parameters was sufficient to raise the AUC by almost 0.3%. The result of the individual model increased to 0.96664 AUC (a more than 1% better result than the previous best model) after additional training data preparation. This is an arduous task due to certain factors: using such systems' applications globally needs to achieve maximum accuracy and improvement in the ethics of AI usage in medicine; furthermore if hospitals could give scientific inquiry validation, while retaining patient data anonymity with clinical information that could be systemically analyzed and improved by scientists, thereby proving AI benefits.  
machine learning; automatization; histopathology; medical imaging 2023
## 3 Results and Discussion
### Model Evaluation and Performance  
The study used several evaluation metrics (accuracy, recall or sensitivity, specificity, F-score and AUC-ROC) to determine the discriminative ability of the predictive models. To identify the most efficient model for our one-year prediction, we built multiple predictive models using different subsets and compared their predictive power. To boost the model's performance, statistical relationships between variables were taken into account during imputation, and extensive data wrangling and feature selection techniques were used before loading the data into the models. All models were developed using the same predictors. Tables 4 and 5 showed the performance comparison of random forest, logistic regression, and XGBoost at various subsets of predictors trained without and with creatinine, respectively. Without the creatinine data (Table 4), the XGBoost model provided the highest AUC value in all the subsets (A, B and C), followed by the random forest and the logistic regression model. The training and testing accuracy, sensitivity, recall (specificity), and F-score values of the XGBoost model are all greater than the three competing models. Moreover, the discriminative ability of XGBoost at various predictor groups was superior when trained with subset C (Age + Gender + PAI), generating an AUC-ROC of 0.93. The subsets B and C are also comparatively good with an AUC-ROC of 0.90 and 0.92, respectively (Table 4). The ROC and precision-recall curves of the three classification models for the one-year CKD prediction at the different predictive subgroups are shown in Figure 3. It also shows that, when compared to the two comparison models, the XGBoost classifier has the strongest predictive performance and is a promising approach for predicting CKD in one year. As previously mentioned, age and gender are important predictors of CKD and have great influences on the models. Interacting age and gender with the metabolic indices provide stability and enhancement to the modes.
## 4 Simulation Results
### Simulation of the Estimated SNR  
We considered a LEO satellite with a circular Keplerian motion trajectory of an orbital height of 1000 km and an inclination angle of 96\({}^{\circ}\). The trajectory is on the same vertical plane as the cloud attenuation field of interest. There are 91 receivers evenly placed at the ground level, with any two adjacent receivers being 1 km apart. The first and the last receivers are located at \(x=-5\) km and \(x=85\) km in Figure 3a, respectively. With the assumption that the minimum elevation angle for the receivers is 40\({}^{\circ}\), it can be calculated that the overpass time is approximately 289 s. We use 400 signal samples  
Figure 3: Panel (**a**) is the reference attenuation field. Panel (**b**) shows the retrieved attenuation field after the first least-squares. Panel (**c**) is the retrieved field by averaging the third and fourth iterations of least-squares. Panel (**d**) is produced by processing the result in panel (**b**) using the piecewise linear (PL) function. Panel (**e**) is the retrieved attenuation field with enlarged basis functions.  
(\(M=400\)) during the satellite overpass time for each receiver. The samples are taken at fixed time intervals, i.e., the overpass time is evenly divided by the 400 samples.  
The observation window for each sample is chosen as 0.05 s. According to previous results [6], the error induced by satellite movement in the observation window is significantly lower than the Cramer-Rao Lower Bound (CRLB) of the SNR estimation. Consequently, the path-integrated cloud attenuation \(A_{I}\) and the free space loss \(A_{F}\) are calculated only for the starting point of the observation window and assumed to remain the same within the window. The estimated SNR \(\hat{\rho}\) for each sample of each receiver is simulated in the following steps:  
* For each receiver, preset the constant \(C\) along a sine curve between 214 dB and 216 dB to simulate the differences across different receivers.
* Calculate the path-integrated attenuation \(A_{I}(k)\) and the free space loss \(A_{F}(k)\) using Equations (12) and (14), respectively.
* The sky noise temperature \(T_{sky}(k)\) is computed according to the cloud attenuation from nine different directions and a simplified antenna beam pattern (Equations (10) and (11) with \(t_{m}=275\) K, see [6] for details). Noise figure \(F_{n}(k)\) is then calculated using Equation (8) with \(T_{sys}=150\) K.
* The true SNR is then calculated using Equation (9).
* We assume a Binary Phase Shift Keying (BPSK) modulated signal with a bit rate of 10 Mbps and the estimated SNR \(\hat{\rho}\) is generated by adding the true SNR to a Gaussian random variable with zero mean and variance being equal to the CRLB.
###### Abstract  
Sentiment analysis of domain-specific short messages (DSSMs) raises challenges due to their peculiar nature, which can often include field-specific terminology, jargon, and abbreviations. In this paper, we investigate the distinctive characteristics of user-generated content across multiple domains, with DSSMs serving as the central point. With cross-domain models on the rise, we examine the capability of the models to accurately interpret hidden meanings embedded in domain-specific terminology. For our investigation, we utilize three different community platform datasets: a Jira dataset for DSSMs as it contains particular vocabulary related to software engineering, a Twitter dataset for domain-independent short messages (DISMs) because it holds everyday speech type of language, and a Reddit dataset as an intermediary case. Through machine learning techniques, we thus explore whether software engineering short messages exhibit notable differences compared to regular messages. For this, we utilized the cross-domain knowledge transfer approach and RoBERTa sentiment analysis technique to prove the existence of efficient models in addressing DSSMs challenges across multiple domains. Our study reveals that DSSMs are semantically different from DISMs due to F1 score differences generated by the models.  
sentiment analysis; short messages; cross-domain modeling; RoBERTa transformer Article
## 2 Materials and Methods
### Data Overview
#### 2.1.2 Remote Sensing Products Utilized  
[PERSON] et al. [16] provided the Caltech SIF product covering the period from April 2009 to June 2020. In this study, we specifically utilized the SIF data for the entire year of 2019. The Caltech SIF dataset includes instantaneous and daily mean SIF values for three spectral bands, corresponding to wavelengths of 757 nm, 771 nm, and 740 nm, respectively. To maintain consistency, we adopted the same naming convention.  
For comparison with the Caltech SIF product, we applied a quality screening strategy similar to that outlined by [PERSON] et al. [16]. The quality factor for retrieval results was generated based on the criteria presented in Table 1.  
The criteria include continuum radiation levels to filter scenes with extreme brightness, a \(\chi^{2}\) threshold to exclude poorly modeled spectra, O\({}_{2}\) and CO\({}_{2}\) thresholds to eliminate cloudy scenarios, and a solar zenith angle (@SZA) threshold to restrict retrievals during extreme solar zenith angles. Both the \"best\" and \"good\" quality combinations were used for comparison, and the resulting SIF intensity maps were generated.  
We validated our retrieval results and the Caltech SIF product using Terra MODIS MOD17A2 HGF Version 6.1 GPP at a resolution of 500 m and Terra MODIS MOD13C2 Version 6.1 VI (EVI and NDVI) at a spatial resolution of 0.05\({}^{\circ}\). The GPP product is a cumulative 8-day composite based on the radiation use efficiency concept, using an equal-area sinusoidal projection. We projected it into 0.1\({}^{\circ}\) and 2\({}^{\circ}\) spatial resolutions for monthly and annual GPP comparisons with the retrieval results and Caltech SIF product. The VI is a monthly gridded product with latitude and longitude coordinates, and monthly and annual products at both spatial resolutions can be directly obtained by averaging. Considering the influence of sub-pixel heterogeneity on vegetation index retrievals, only results from uniform surfaces were retained when generating SIF products at 0.1\({}^{\circ}\) and 2\({}^{\circ}\) spatial resolutions [11].
## Data
### Micrometeorological Data  
A standard EC setup was used to collect micrometeorological data. Carbon and water fluxes were calculated with a time step of 30 minutes according to EUROFLUX methodology [30].  
The CO\({}_{2}\) fluxes were corrected and filtered following [30] in order to assess the quality of measured data and to discard the half-hourly data not fulfilling the hypothesis necessary for the application of the EC technique (i.e. steady state and integral turbulence characteristics of the vertical wind [31]). Data were corrected for storage of CO\({}_{2}\) in the air layer below the measuring height [32]. Missing half-hourly data caused by malfunctioning of system, periodical calibration of instruments, u* filtering or data quality check not fulfilled, were filled with the marginal distribution sampling method [33].  
EC measures the half hourly net ecosystem exchange of CO\({}_{2}\) (NEE). Half-hourly GPP can be estimated from NEE via the general equation:  
\[GPP=-NEE+R_{co} \tag{1}\]  
Ecosystem respiration (R\({}_{\text{eco}}\)), was estimated using the partitioning method described in [33]. Both the MDS and the partitioning algorithm were implemented in an online tool [33], widely employed by the Carboeurope-IP project and FLUXNET network for both gap-filling and partitioning [34,35] of fluxes.  
Along with the flux measurements, standard micro-meteorological data were collected continuously. Moreover, PAR transmitted below the canopy at ground level (PAR\({}_{\text{t}}\)) was measured by means of a transect of 3 quantum sensors (LI-190S, LI-COR Inc., Lincoln NE, USA) and used to investigate the radiative regime within the canopy. All meteorological data were stored as half-hourly means on a data logger (DL2 DELTA-T Devices, Burwell, Cambridge, UK).
## 3 Colloidal Silica Transport Through Soil Stratum
### Transport Mechanisms of CS in Liquefiable Sand
#### 3.2.1 Soil Type  
[PERSON] and [PERSON] (2009) designed a series of column tests to investigate the factors that could influence CS transport. The column used was made of a PVC pipe, with an internal diameter of 10 cm and a total length of 90 cm. Nevada and Ottawa sand, with hydraulic conductivities of \(8.9\times 10^{3}\) and \(220\times 10^{3}\) cm/s, respectively, were adopted as the porous media to fill in the column. The inlet water chamber was connected to the bottom end cap to supply steady water flow, as shown in Figure 11. CS grout (pH = 6.8, ionic strength = 0.1 N) was delivered through the valve located 5 cm above the bottom cap. During testing, water and CS were transported through the entire column to the outlet chamber. Nine sampling ports were set to obtain samples in order to study the influencing factors. The results show that the traveling time of the CS grout through Ottawa sand was two orders of magnitude lower than through the Nevada sand. It is worth noting that the hydraulic conductivity of Ottawa sand is two orders of magnitude higher than Nevada sand, which indicates that the hydraulic conductivity of the porous media used is significantly correlated with the delivery rate of CS grout.  
Figure 11: Schematic and photo of the column test [45].
## 2 Literature Review  
### Development of VR Devices and Software  
VR is a 3D virtual world generated through computer simulation. It provides users with multiple sensory simulations and a sense of presence, which enable them to observe the surroundings in 3D space in real time and without limitation [6]. [PERSON] regarded VR as an interface with three features, namely immersion, imagination, and interaction [7]. Immersion refers to a condition in which users are completely immersed in a scenario to complete a task or goal and they feel delighted and satisfied that they have forgotten about the real world. Imagination refers to a condition in which sensory stimuli create a virtual environment and convince users that they exist in the virtual space. Finally, interaction refers to a condition in which users can travel freely and interact with the surroundings in the virtual space [8].  
In VR applications, users can interact with the virtual objects in the scene by using various sensing devices, including 3D glasses and gloves. When users move, the computer can immediately perform complex calculations and present an exact, timely 3D image, thereby creating a sense of presence [9]. VR displays can be employed to experience VR. Many companies have developed and launched VR displays with different features. Currently available professional head-mounted displays include Sony PlayStation VR, HTC Vive, and Oculus Rift, all of which are high-end products that must be attached to VR-compatible computers or equipment with a high computing speed.  
The use of VR and extended reality (XR, representing all technologies that merge the physical and virtual worlds) has been widely investigated across various sectors, notably in Industry 5.0, Architecture, Engineering, and Construction (AEC), and education. [PERSON] et al. (2023) emphasized the role of digital twins (DTs) and XR in accelerating the human-centric Industry 5.0 transformation, highlighting their ability to provide digital representations of physical assets and offer interactive environments for humans and machines [10]. [PERSON] et al. (2020) demonstrated the value of XR in simulating multidimensional digital models of construction projects [11], while [PERSON] (2019) discussed the potential of VR for cognitive design-review and failure modes and effects analysis (FMEA) in industrial engineering [12].  
VR integration with Building Information Modeling (BIM) has been recognized for facilitating project integration [13], while VR-based engineering education was proposed as a method to enhance manufacturing sustainability in Industry 4.0 [14]. [PERSON] et al. (2019) proposed that DTs and VR are key for the design and optimization of cyber-physical production systems within Industry 4.0 [15], and [PERSON] and [PERSON] (2023) explored the intersection of BIM, DTs, and XR for automated construction progress monitoring [16]. In summary, VR and XR technologies hold the potential to transform various areas of our lives, from the design and construction process to human-machine interaction and modern manufacturing practices.