esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Local Descriptor for Comparing Renders with Real Images
## 3 Methodology
### Two-Step Training for Feature Extractor  
#### 3.3.1 Bootstrapping  
We started with the VGG16 deep architecture with ImageNet weights for classification training, as detailed in [27] (Table 2, Column D). The filters in the first convolutional layer were modified from \(3\times 3\times 3\) to \(3\times 3\times 1\) and their weights initialized by averaging the 3D filters along the 3 rd dimension [28]. The 1000-way linear+softmax layer was replaced with 2-way linear+softmax layer. The 2 fully connected layers following the convolutional layers were initialized from scratch using Xavier initialization [28] (Figure 3, step 1). In the original architecture, the fully connected layers have 4096 neurons each. For us, they have \(L\) neurons each, with \(L=1024\) when patches are of size \(128\times 128\) and \(L=4096\) when they are \(224\times 224\).  
Figure 2: Proposed method for creating a patch-pair data set given CADs of inspection elements and their environments along with real images with tracked viewpoint. The simple renders (in red) are manually registered with the corresponding real image. Effect of registration is visible inside the pink circle (images after step 2). From each registered image pair, patch pairs are produced by cropping around FAST points detected in the render.  
For bootstrapping, the patch-pair data set described before was organized into 2 classes, one containing meaningful geometric information and the other not (texture patches). Both classes contained patches from both real and synthetic images. A cross-entropy loss (Equation (1)) was minimized to classify these patches. All layers except the first convolutional and the last fully connected layers were \"frozen\" during this stage (Figure 3, step 2). Training was performed for two epochs with minibatch gradient descent using batches of size 128, initial learning rate was 0.005, and dropout was used after fully connected layers for regularization with \(p=0.5\). The softmax-cross-entropy loss (E) that we minimized is:  
\[E=\frac{1}{n}\sum_{i}[y_{i}log(\hat{y_{i}})+(1-y_{i})log(1-\hat{y_{i}})] \tag{1}\]  
where \(n\) is the total number of training examples, \(y_{i}\in\{0,1\}\) is the patch class label of the training example \(i\) (\(y_{i}=0\) means the patch contains interesting geometry, and \(y_{i}=1\) means that it doesn't, and hence, is a texture patch) and \(\hat{y_{i}}=\frac{E^{i}}{e^{\hat{\
u}_{0}}+e^{\hat{\
u}_{1}}}\) is the predicted softmax probability for example \(i\), \(x_{0}\) and \(x_{1}\) being the predicted probabilities for the two patch classes.
## 4 Multiplexing  
Due to the increasing advancement of wireless communication systems, a dependable system that can provide larger channel capacity and data transfer rates for users is essential. MIMO systems achieve this because their multiple antennas on both the transmitter and receiver sides allow for spatial diversity and spatial multiplexing techniques. MIMO is a practical solution to avoid losing data in the channel due to fading and errors, it ensures that the receiver gains more than one version of the data sent and all of them are identical. It enhances the reliability and performance of the system with high spectral efficiency and low energy per bit [105]. The goal of this research is to compare and contrast the capacity of single-input single-output (SISO), single-input multiple-output (SIMO), multiple-input single-output (MISO), and MIMO systems. We shall concentrate on the MIMO system in this research because of its increased capacity and better data transfer rates. These characteristics of MIMO systems makes it ideal for modern communication technologies. Table 4 summarizes the main differences between SISO, SIMO, MISO and MIMO communication models.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 5 Experiment
### Ablation Study  
We performed a detailed ablation study on the validation set to evaluate the impact of each component in the model. Using the MID model as a baseline, the stepwise addition of other components of our system. Firstly, in order to enhance the model's understanding of the relationship between the state characteristics of the predicted vehicles and avoid the inefficiency caused by the separate trajectory prediction of each vehicle in the traditional diffusion model, we integrate an independent Residual denoising module into the model as the backbone network, which is represented by \"MID + Residual\" in Table 3. Secondly, in order to verify the impact of the trajectory Constraint module on the trajectory prediction performance, we construct the traditional denoising module that predicts the trajectory only under the trajectory constraint framework, namely \"MID + Constraint\" in the table. When combining the above modules, we observe that the performance improvement of \(minFDE_{5}\) is more obvious. This shows the constraint role of the module in long-term prediction and its importance in enhancing the performance of the model. Finally, we use the improved Transformer Decoder to integrate the dynamic influence of vehicles on traffic situations with the real-time feedback of static objects on vehicle behavior. In the table \"MID + Decoder\", the introduction of the new decoder also makes an important contribution to further improve the model performance.  
\begin{table}
\begin{tabular}{l|c c c c c} \hline \hline
**Method** & **minADE1** & **minADE5** & **minFDE1** & **minFDE5** & **mAP** \\ \hline Social Gan [4] & 2.081 & 1.10 & 4.10 & 2.213 & 0.06 \\ CS-LSTM [3] & 1.964 & 1.07 & 3.98 & 2.16 & 0.07 \\ STA LSTM [31] & 1.888 & 1.04 & 3.78 & 2.11 & 0.08 \\ Trajection++ [32] & 1.696 & 0.86 & 3.69 & 1.99 & 0.13 \\ VectorNet [33] & 1.95 & 0.91 & 3.91 & 1.74 & 0.14 \\ LaneGCN [34] & 1.69 & 0.83 & 3.83 & 1.79 & 0.10 \\ GANet [24] & 1.58 & 0.73 & 3.78 & 1.77 & 0.15 \\ MultiPath++ [35] & 1.55 & 0.70 & 3.66 & 1.73 & 0.19 \\ DenseTNT [36] & 1.66 & 0.86 & 3.78 & 1.82 & 0.16 \\ JFP [37] & 1.45 & 0.61 & 3.49 & 1.61 & 0.23 \\ MID [16] & 1.58 & 0.65 & 3.51 & 1.70 & 0.18 \\ mmTransformer [38] & 1.52 & 0.61 & 3.33 & 1.66 & 0.19 \\ Ours & 1.46 & 0.57 & 3.27 & 1.54 & 0.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Trajectory prediction results based on NGSIM I-80 dataset.
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 4 Conclusions  
In this paper, we introduced a framework for FR-IQA relying on feature vectors, which were obtained by comparing reference and distorted activation maps by traditional image similarity metrics. Unlike previous CNN-based approaches, our method does not take patches from the distorted-reference image pairs, but instead obtains convolutional activation maps and creates feature vectors from these maps. This way, our method can be easily generalized to any input image resolutions or base CNN architecture. Furthermore, we carried out a detailed parameter study with respect to the applied regression technique and image similarity metric. Moreover, we pointed out that the proposed feature extraction method is effective, since our method is able to reach high PLCC, SROCC, and KROCC values trained only on 5% of KADID-10k images. Our algorithm was compared to 26 other state-of-the-art FR-IQA methods on six benchmark IQA databases, such as KADID-10k, TID2013, VCL-FER, MDID, TID2008, and CSIQ. Our method was able to outperform the state-of-the-art in terms of PLCC, SROCC, and KROCC, as well. The generalization ability of the proposed method was confirmed in cross database tests.  
To facilitate the reproducibility of the presented results, the MATLAB source code of the introduced method and test environments is available at [[https://github.com/Skythianos/FRIQA-ActMapFeat](https://github.com/Skythianos/FRIQA-ActMapFeat)]([https://github.com/Skythianos/FRIQA-ActMapFeat](https://github.com/Skythianos/FRIQA-ActMapFeat)).  
**Funding:** This research received no external funding.  
**Acknowledgments:** The author thanks the anonymous reviewers for their careful reading of the manuscript and their many insightful comments and suggestions.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 3 System Characterization  
This paper explores a scenario that implements massive MIMO communications in a highly frequency selective channel and a Single Carrier modulation in the uplink transmission. In this scenario, the communication is established between a MT equipped with \(P\) single transmitting antennas and a Base Station (BS) equipped with \(R\) receiving antennas, with \(R>>P\) and a perfect synchronization.  
In order to remove the ISI, a cyclic prefix, which is larger than the maximum system delay, is added to each transmitted block and removed in the reception side. The MT \(p^{th}\) transmits a block of \(N\) data symbols \(\{x_{n,p};n=0,1,\ldots,N-1\}\) which is received in the \(r^{th}\) BS antenna as \(\{y_{n}^{(r)};n=0,1,\ldots,N-1\}\) corresponding to a frequency-domain block \(\{Y_{k}^{(r)};n=0,1,\ldots,N-1\}\). The \(\{Y_{k}^{(r)}\}\) block can also be represented in the matrix format \(\mathbf{Y}_{k}^{(r)}=[Y_{k}^{(1)},\cdots,Y_{k}^{(R)}]^{T}\), being given by Equation (1):  
\[\mathbf{Y}_{k}=\mathbf{H}_{k}\mathbf{X}_{k}+\mathbf{N}_{k}, \tag{1}\]  
where the channel matrix \(R\times P\) for the \(k^{th}\) subcarrier is represented by \(H_{k}\), the frequency-domain block of the transmitted block \(x_{n}\) is represented by \(X_{k}\) and \(N_{k}\) represents the channel noise. In Figure 1 the considered scenario is depicted.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**BER** & **Iteration** & **MFB** & **ZF** & **IB-DFE** & **MRC** & **EGC** \\ \hline \(10^{-2}\) & 1 & 4.2 & 4.8 & 4.8 & 9.1 & 10.9 \\ \hline \(10^{-4}\) & 4 & 8.4 & \(-\) & 8.5 & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(E_{b}/N_{0}\) [dB] for the Bit Error Rate (BER) performance of the scenarios presented in [15].  
Figure 1: System scenario.
## 4 Discussion
### Nonvisual Lighting Simulation
[34] give a tolerance of about 15% between lighting simulation and real measurements, although the generalizability is limited). The process also allows for the estimation of the possible role of daylight in a design, although the used _daylight autonomy_ should be used tentatively, as there is no one daylight spectrum, and orientation and sky conditions heavily influence any indoor daylight illumination [35]. The process could be adapted, however, to more complex climate-based lighting design processes [36]. Compared to other research dealing with lighting simulations in the industrial context, our method provides additional information about the work environment. For example,Duplakova et al. [37] described the lighting simulation and lighting optimization for a cutting workplace. They used the free software _Dialux evo_, which is comparable to the _Relux_ software used in this publication. The authors first performed a comparison between several illuminance measurements and the respective simulation results, before optimizing the illumination through simulation. They focused on visual aspects, such as illuminance and glare rating. Our method adds a nonvisual parameter within the same software. The same holds true for a publication of [PERSON] et al. [38] in the context of an aluminum piece production facility. In addition to the prior publication [37], the authors provide a rationalization algorithm for the redesign of the working environment light. The authors also mention the possibility of circadian quantities to assess and improve the health aspects of lighting. Other studies have dealt more in detail with nonvisual lighting simulation. [PERSON] et al. [39] simulated the daylight illumination in a hospital room, which was then translated to a circadian stimulus. He used the _Daysim_ software [40], which allows for a detailed daylight simulation across one year but requires a third-party application for model generation and is limited to daylighting. Compared to our method, the authors did not discuss the influence of spectral transmission or reflectance differences, e.g., due to color [41]. However, the authors investigated several eye-level planes, and their method seems robust for daylighting purposes. [PERSON] [42] used a similar method and software to simulate the daylight availability of indoor scenarios depending on position and viewing direction. As such, it has the same benefits and limitations as [PERSON] et al. [39]. [PERSON] et al. [43] showed a similar approach as the previous two papers but specifically looked at the influence of room surface reflectance on corneal illuminance. Finally, [PERSON] and [PERSON] [44] impressively showed the nonvisual-light-stimulus differences resulting from variations in wall colors and spectral glazing transmittance. They investigated changes in daylighting through both a physical model and a simulation model. For simulation, they used _Rtimocoeros_ software and the above-mentioned _ALFA_ software. See above for a discussion on this type of dedicated software compared to our method.
## 1 Introduction  
Many countries around the world have been transitioning from fossil fuels to more renewable energy sources to mitigate the effects of climate change. Because the current energy grid is built around dispatchable sources of power, the intermittency and variance of many renewable energy sources, such as solar and wind, makes large-scale integration difficult. To meet this challenge, new tools must be developed to aid grid operators and energy utilities in accurately predicting the output of renewable power plants on an hourly or daily basis. Accurate intra-hour forecasts would alert grid operators to when other backup sources of power should be brought online or ramped down. With reliable day-ahead energy forecasts, the sale and purchase of prospective power across the boarders of independent system operators (ISOs) would become easier. One of the goals of the PROGNOSIS project, a research project based at the Cyprus University of Technology, is to develop a \"dynamic data assimilation model for intra-hour forecasting of solar irradiance over a specific area taking into consideration the presence of clouds/aerosols over the area\" [1].  
One PROGNOSIS sub-project is to research the use of artificial neural networks (ANNs) as a method for near-future solar forecasting. While many ANN models for solar forecasting have been developed over the past several years, few use solely endogenous data. Rather, they rely on meteorological data which can be expensive to obtain. This article presents the training, testing, and performance of a single-layer long-short-term memory (LSTM) network for intra-hour solar forecasting of a single PV system in Cyprus. The performance of the single-layer LSTM network is compared to a previously-developed 5-layer LSTM network trained using the same data.
## 3 Results and Discussion
### Changes in Organoleptic Characteristics of Apple before and after Storage in CA
#### 3.2.1 Changes in Apple Peel Color
Studies have shown that during the browning of apples, the values of the lightness index (L*) decreased, and the values of the redness (a*) and yellowness (b*) co-ordinates increased [4; 29]. Determination of the values of apple color co-ordinates C*, L*, a*, and b* is important in assessing changes in fruit quality indices during storage [34]. Apples tend to brown during storage due to aging of the tissues, resulting in the loss of their original color and commercial appearance [4; 53]. The browning of apples is determined by temperature, pH value, changes in the qualitative and quantitative composition of phenolic compounds, and a decrease in the mechanical resistance of apple skin due to damage to the surface of the apples [4]. It has been found that color changes in apple samples can cause changes in apple texture [34], and, thus, it was expedient to perform an analysis of changes in apple peel and flesh firmness under controlled atmosphere conditions.
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
## 4 Discussion
### How to Obtain the Best Guide Tree?  
If we agree that the true tree is the best guide tree, then how do we obtain a guide that is the best approximation of this true tree? In this research, we explore the potential of three-way alignment in improving the accuracy of the guide tree. Our results are consistent with the hypothesis that three-way alignment can produce better guide trees (exhibiting lower RFd with true tree) compared to guide trees from PSA or k-tuple approaches, leading to improved MSAs and the phylogenetic reconstruction based on the MSAs (Tables 1-4). Two lines of evidence were presented to support the conclusion that the guide tree from the three-way alignment (3-WAY) is better than that generated from the most accurate option in MAFFT (L-INS-i, which creates the initial guide tree from local pairwise alignment). First, the guide tree from the 3-WAY approach is closer to the true tree than that from the L-INS-i approach. Second, when the MSA generated from 3-WAY and L-INS-i guide trees were fed to PhyML for phylogenetic reconstruction, the MSA from the 3-WAY guide tree produced PhyML trees closer to the true tree than that from the L-INS-i approach.  
While guide trees based on k-tuple similarities in MAFFT are poor, the guide tree from the L-INS-i option in MAFFT is very good, and three-way alignment may be useful only in the most challenging cases with extremely diverged sequences. Sequences simulated from our half-symmetric and half-asymmetric trees are comparable in divergence to many real homologous amino acid sequences, yet MAFFT performed well with these sequences. Only with the highly diverged sequences simulated from the asymmetric trees did MAFFT experience difficulties in generating quality MSA (Tables 1-4).
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 4 Case Studies
### Sea Surface Temperature  
The customized processing workflow of sea surface temperature also consists of seven steps, three of which are the same as the ones in the wind field processing workflow. These are the clip algorithm, the interpolation algorithm and the mosaic algorithm. The workflow has three inputs that are a file list of Level 2 data of the HY-2A scanning microwave radiometer in HDF5 format, a polygon representing the AOI, and a name of the output thematic map. The outputs of the workflow include a sea surface temperature product file in HDF5 format, a sea surface temperature raster dataset in TIFF format, and a thematic map in JPG format. With similar processing steps, the workflow reads the level-2 HDF  
Figure 5: An Ocean Wind Field thematic map of a single HY-2A swath for 25 March 2015 derived from the HY-2A CAPF. The ocean wind vectors are mapped to a 0.25-degree grid. The lines and arrows show the wind direction; the colors show the wind speed.  
files of the HY-2A scanning microwave radiometer and validates the values. Afterwards, the data are extracted based on the AOI and interpolated into raster datasets. Followed by a gridding algorithm with a 1/4-degree interval, the data can be mapped to a uniform grid. The thematic map is shown in Figure 6.
## 4 Discussion
### Effects of Glucose Addition on RubisCO Activity and cbbL-Containing Bacteria  
The soil RubisCO activity differed significantly among the land types [23]. Its higher values in the RS and RSG pots than in the BS or BSG pots suggested higher C assimilation potential, similar to the reports of [PERSON] et al. (2014), who presented higher RubisCO activity in paddy soils than that in upland soils [25]. The significant effect of glucose addition indicated that the responses of _cbbL_-containing bacteria and RubisCO activity were instantaneous. The reduced RubisCO activities after the glucose addition in RS, BS, and BSG suggested the lessened abundance of autotrophs, while the similar values at the 45 th h of RSG and RS and that of BSG and BS may demonstrate that the effect of glucose degradation was flexible and restorable. [PERSON] et al. (2012) showed that the RubisCO activity increased when rice straw was returned to field, and this study also found extremely significant relations among LFOC, LFON, and RubisCO activity [32]. LFOM probably provides substrate for the production of ribulose-1,5-biphosphate, thus enhancing the RubisCO activities as well as the C-fixation efficiency. The significant power function relationship between RubisCO activities and _cbbL_ gene abundance (\(p\) = 0.889, R\({}^{2}\) = 0.906) suggested that RubisCO came mostly from the expression of the _cbbL_ gene. [PERSON] et al. (2001) showed that RubisCO activities were easily affected by light, temperature, humidity, CO\({}_{2}\) concentration, and pH, among which RubisCO activities were high in a relatively alkaline environment [56]. Our results showed a high number of _cbbL_ gene copies in a faintly acid environment, which was consistent with the results of [PERSON] et al. [23]. The _cbbL_ gene abundance in the wetland pots of this study was significantly higher than for both unplanted soils and paddy rice-upland crop rotation soils, but lower than paddy soils [32].  
Although the microbial composition and abundance were sensitive to temperature, moisture, pH, OC content, etc. [57; 58], we found that the effects of soil property and surroundings on _cbbL_-containing bacteria were negligible in contrast to the effects of root functions. RS and BS showed significantly different compositions of _cbbL_-containing bacteria. [PERSON] et al. (2014) found that _Mycobacterium_ sp., _Rhodgesudomonas_ _plustrials_, and _Bradyrhizobium japonicum_ were the dominant species of _cbbL_-containing bacteria, and we found that _Rhizobiales_ was greatly expressed in this study [25]. As the decomposer of polysaccharide [59], _Burkholderiales_ was also abundant in the _cbbL_-containing bacteria. The significant relationships between the absorbance of certain regions and _cbbL_-containing bacteria or between organic matter and the bacteria may indicate the diverse microbial functions. Further research on the metabolic relationship between _cbbL_-containing bacteria and organic matter fractions is still needed.