esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Effects of Hob Temperature on the Rock-Breaking Characteristics of Sandstone Strata
## 1 Introduction
Variations in rock damage patterns during excavation directly impact excavation efficiency, with the direct penetration of cracks between hobs potentially accelerating this process. Due to the significant limitations of indoor experiments, research on the cutting characteristics of tunnel boring machine (TBM) cutters is primarily accomplished using numerical simulation methods. The above-mentioned articles conducted corresponding analyses on rock-breaking characteristics using the finite element method and the discrete element method, providing optimal cutter spacing and cutter forces accordingly. However, according to the actual cutting environment, the aforementioned papers did not address the issue of substantial heat generation during TBM cutter cutting. Considering the influence of heat on rock mechanics, this paper employs the discrete element analysis method to study the cutting temperature of the tunnel boring machine (TBM). It investigates the effects of confining pressure, cutter spacing, and cutter penetration on rock-breaking characteristics under different cutting temperatures. Additionally, it determines the optimal cutter spacing under various cutting temperatures. Based on the distribution of cracks and contact quantities in the particle flow code (PFC), it explores the impact of different failure states on rock-breaking characteristics. Analyzing the influence of cutting temperature on rock-breaking characteristics can provide references for rock-breaking efficiency and cutter wear during excavation. Moreover, different forms of rock failure can offer corresponding conclusions on excavation efficiency.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Evaluation Metrics  
In the following experiments, we use three metrics widely used to evaluate positioning performance, which are mean error (MeanErr), root mean square error (RMSE), and mean square loss function (MSELoss). The three metrics are defined as follows:\[\left\|S_{i}-\hat{S}_{i}\right\|_{2}=\sqrt{(x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y}_ {i})^{2}+(z_{i}-\hat{z}_{i})^{2}} \tag{9}\]  
\[MeanError=\frac{1}{N}\sum_{i=1}^{i=N}\left(\left\|S_{i}-\hat{S}_{i}\right\|_{2}\right) \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{i=N}\left(S_{i}-\hat{S}_{i}\right)^{2}} \tag{11}\]  
\[MSELoss=\frac{1}{N}\sum_{i=1}^{i=N}\left[(x_{i}-\hat{x}_{i})^{2}+(y_{i}-\hat{y} _{i})^{2}+(z_{i}-\hat{z}_{i})^{2}\right] \tag{12}\]  
where \(\hat{x}_{i}\), \(\hat{y}_{i}\), and \(\hat{z}_{i}\) denote the estimated three-dimensional coordinates of the \(i\)-th data, and \(x_{i}\), \(y_{i}\), and \(z_{i}\) represent the actual 3D coordinates of the \(i\)-th data, respectively, so \(\hat{S}_{i}\) and \(S_{i}\) are the estimated position of the \(i\)-th data and the actual position of the \(i\)-th data, respectively. Additionally, \(\left\|\cdot\right\|_{2}\) denotes the Euclidean distance.
# Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
## 3 Methodology
### Deep Convolutional Neural Networks (DCNN) Models
#### 3.1.2 DenseNet  
The DenseNet was suggested by [PERSON] et al. [46] for improving the depth of CNN. This approach was first implemented to address issues when CNNs became more complex in the model size. The authors solved the issue by linking each layer completely to the next, thus assuring maximal information and gradient transfer. One of the key benefits of adopting such a structure is that the DenseNet structure maximizes its capacity by reducing the usage of a deep or broad design via feature recycling. Unlike traditional CNNs, DenseNet does not train duplicate features. Thus, it requires fewer parameters. Moreover, since the structure has relatively thin layers, it only adds a tiny number of new feature maps. Further, the structure depends on each layer having immediate access to the gradients from the loss function and the input image during the training stage.  
It is worth noting that the DenseNet concatenates the layer's return image features with the input feature maps; thus, there is no aggregate between them. The feature maps could be the same dimension to accomplish this combination in any instance. To overcome this issue, DenseBlocks are a concept introduced by DenseNet. DenseBlocks are used to ensure that the size of feature maps stays consistent inside a block while the number of filters varies among them. Layers of a particular sort (called transition layers) are put in the DenseBlocks. Moreover, downsampling is performed using batch normalization, a \(1\times 1\) convolution, and \(2\times 2\) layers in these layers.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Uncertainties Analysis
#### 4.6.2 Other Sources of Errors  
The characteristics of microwave links and monitoring environment will also induce errors and affect the overall measurement accuracy. [PERSON] et al. [76] used seven months of instantaneous signal power collected from about 2000 microwave links in the Netherlands to retrieve rainfall intensity, and the results show that the bias is the relatively high for path lengths less than 2 km during late night, early morning, and colder months. [PERSON] et al. [77] installed three microwave links between two main buildings in Wageningen, with one commercial microwave link at 38 GHz and two research microwave links at 26 GHz and 38 GHz, respectively. The results show that WAA in the presence of fog or dew induces about 3 dB attenuation, and changes in temperature can also cause an attenuation of similar magnitude.  
[PERSON] et al. [75] divided the errors presented in rainfall maps, which were generated from link rainfall depths, into two categories: (1) measurement errors, including sampling interval, received power quantification, wet/dry classification, baseline fluctuation, WAA, DSD, multipath, etc., and (2) interpolation errors, including interpolation algorithm and link space density. An extensive experiment was carried out by comparing rainfall maps created from three sets of link rainfall depths: actual available links, simulated links with the actual network availability, and simulated links with 100% network availability assumed. The results show that the uncertainty is mainly caused by measurement errors. The factors leading to the error of CML-based rainfall measurement are ranked from most important to least important by [PERSON] et al. [1], which are spatial variability of rainfall, zero baseline selection, DSD and WAA, and RSL quantification.
# Constitutive Expression of a Cytotoxic Anticancer Protein in Tumor-Colonizing Bacteria
## 3 Results
### Fate of E. coli Injected into Tumor-Bearing Mice
(**A**) At the indicated times after the injection, tumors, livers, and spleens were extracted to determine bacterial counts (CFU/gram). (**B**) RNA sequencing analysis was performed to determine the expression profile of the rrnB operon (number of reads) (NC_000913.3) in _E. coli_ residing in tumors and livers at 1 and 3 days postinjection (dpi). (**C**) The rrnB P1 promoter sequence with three _Fis_-binding sites (I–III) in the upstream activation region. (**D**) _E. coli_ MG1655 carrying prrnBP1-gfpOVA (EMP4002) was administered intravenously to CT26-grafted mice (\(n=5/\)group, \(1\times 10^{8}\) CFU/mouse). To monitor rrnB P1 promoter activities in vivo, the levels of _gfp_ expression in tumors, livers, and spleens at each time point were determined by quantitative real-time PCR. The _E. coli_ rpoB gene was used as a reference, and data were normalized by the \(2^{-\Delta\Delta\text{Ct}}\) method. All data are expressed as the mean \(\pm\) SD. _p_-values indicate differences between groups (unpaired Student’s _t_-tests; ns: not significant).
# Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs
## 4 Evaluation
### Performance of Aerial Video
When the target is rotated, it is easy to lose the target because the tracking features are occluded. * The promoted SIFT matching method provided sufficient target image features during initialization, and collect the current features of the target in the tracking process to ensure that the target can be quickly retrieved after the target is lost. * The overall performance of the detection tracking framework using the YOLOv3 target detection module and the Tdarknet target tracking module is superior to the detection tracking framework using the Faster R-CNN target detection module and the TVGG target tracking module.
# Does Augmented Reality Affect Sociability, Entertainment, and Learning? A Field Experiment
## 1 Introduction
### This Study  
In this experimental study, we investigated how indoor AR technology influences users' sociability, entertainment, and learning in shared space. To test whether AR technology has an independent effect on the three aforementioned perspectives, we conducted a field experiment in a science center exhibition and designed an experiment comparing AR application participants to non-AR (a mobile application without AR features) and pen-and-paper participants. Based on previous research, AR technology could potentially have effects on sociability and communication with other players [14; 15; 16], as well as entertainment and learning-related consequences [20; 21; 25; 26; 27; 28]. To investigate this further, this study has the following research questions:  
1. Does augmented reality have an effect from the social perspective on (a) perceived sociability or social behavior of (b) leaving messages or (c) talking with others face-to-face?
2. Does augmented reality have an effect from the entertainment perspective on (a) excitement, (b) nervousness, (c) tiredness afterwards, (d) app satisfaction, or (e) exhibition enjoyment?
3. Does augmented reality have an effect from the educational perspective on learning performance?  
More specifically, from the social perspective, we include aspects of perceived sociability and actual social behavior with other people. From the entertainment point of view, we examine perceived satisfaction with the exhibition and application, reported affects of excitement and nervousness during the experiment, and tiredness after the exhibition. Lastly, the educational perspective encompasses learning performance during the experiment. We compared all these aspects among three experimental groups that provided three different stimuli: AR application, an application without AR technology, and without smartphone application using only pen and paper. The experiment environment provides stimuli and opportunities for learning, entertainment, and social behavior through shared social space. Figure 1 shows the detailed study design.  
Figure 1: The study design.
## 3 Optimal Dispatching Model of MGs and ESP
### Optimal Dispatching Model of MG  
As for each MG, the generation strategies of BESS and exchange power can be made by taking the minimum total cost as the objective based on the forecasting power of PV, WT and loads. The optimal dispatching problem of MG \(n\) can be given as:  
\[minC_{n}(\mathbf{x}_{n})=C_{gridn}(x_{gridn})+C_{BESSn}(x_{BESSn}) \tag{11}\]  
\[s.t.\qquad x_{gridn}+x_{BESSn}+P_{n{c}_{n}}-d_{n}-P_{lossn}=x_{n}^{in} \tag{12}\]  
\[max(P_{charge}^{max},Q(S_{OCinit}-S_{OCmax})/\Delta T)\leq\eta x_{BESSn}\leq 0 \tag{13}\]  
where \(\mathbf{x}_{n}=[x_{BESSn},x_{gridn}]\) is a vector composed of charge-discharge power \(x_{BESSn}\) of BESS and the exchange power \(x_{gridn}\) between MG \(n\) and the main grid; the cost of purchasing electricity from the main grid can be calculated as \(C_{gridn}(x_{gridn})=P_{gs}x_{gridn}\); \(P_{gs}\) denotes the selling price of the main grid; \(d_{n}\) is the load demands of MG \(n\); \(x_{n}^{in}\) is the exchange power between MG \(n\) and other MGs in the coalition, whose value is positive when the exchange power is output; \(\eta\) denotes the charge-discharge efficiency of BESS. Equation (12) is the power balance constraint of individual MG. The maximum limitations of the charge-discharge power of BESS are expressed by (13). \(P_{charge}^{max}\) and \(P_{discharge}^{max}\) are the maximum and minimum charge-discharge power; \(S_{OCmax}\) and \(S_{OCmin}\) are the upper and lower bounds of SOC, respectively.  
Note that if there are other generation units (such as CHP) in MGs, the cost of the generation units should be added in (11). Equation (11) corresponds to Scenario 1 in the case study, in which there are only BESS and RESs in MGs. As for Scenario 2, in which the CHP system is considered, we need to add the cost of the CHP system in (11), that is \(C_{n}(\mathbf{x}_{n})=C_{gridn}(x_{gridn})+C_{BESSn}(x_{BESSn})+C_{chpn}(x_{ chpn})\). \(x_{chpn}\) is the output power of CHP in MG \(n\).
# Real-Time Monitoring of Passenger's Psychological Stress
## Materials and Methods
### From Learning to Test Phase  
The previous model is the one described all along Section 3. Following the validation stage, the results of which are displayed in Table 1, the final \(\beta\)weight vector was estimated using the whole learning database. All the required steps for stress estimation (first processing, feature computation and combination within the linear model) were then implemented in a home-made Android application, able to communicate with the E4 wristband in streaming mode. The overall framework (wristband-smartphone-application) was able to record the same user's physiological signals and estimate his stress levels every minute in real time. This setting was put on test during the journey from Grenoble to Brussels. Model outputs are those displayed in Figure 1.  
**Author Contributions:** Conceptualization, C.G. and E.L.; Methodology, O.S. and G.V.; Software, S.O. and A.V.; Investigation, E.L.; Data curation, S.O. and O.S.; Writing--Original draft preparation, G.V.; Writing--Review and editing, C.G., E.L., A.C. and G.V.; Supervision, S.C., A.C. and C.G.; Funding acquisition, C.G. and E.L.  
**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 635867.  
**Conflicts of Interest:** All authors declare having neither financial nor non-financial interest in publishing the previous results or citing any research material. The funders had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Discussion
### Analysis of Implemented Controller  
Based on the results in Table 3, an analysis of accuracy and controller repeatability was performed determining as a result an accuracy of \(\pm\)2.165 cm and a repeatability of 0.973 for vertical movements and an accuracy of \(\pm\)1479 cm and a repeatability of 0.944 for lateral movements. Figure 18 shows a compendium of results (3 tests of the whole dataset from Table 3a are shown); the results of the analysis made by MATLAB are shown when establishing a target point (in blue) for all cases, to evaluate the results and obtain the values of accuracy and repeatability.  
On the other hand, within the work done previously related to jellyfish, the information on controllers is not detailed, while within a few works, there is a PD control system made with arduino to control the jellyfish microvalves developed by [PERSON] in 2018 at Naval Surface Warfare Center Carderock Division.  
Although this work presents the control loop, it does not fully specify the parameters used, or the accuracy and repeatability; this controller also focuses on regulating the tentacles deformation but not on the robot's position control with respect to a trajectory.
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
###### Abstract  
The Belief Propagation (BP) algorithm has the advantages of high-speed decoding and low latency. To improve the block error rate (BLER) performance of the BP-based algorithm, the BP flipping algorithm was proposed. However, the BP flipping algorithm attempts numerous useless flippings for improving the BLER performance. To reduce the number of decoding attempts needed without any loss of BLER performance, in this paper a metric is presented to evaluate the likelihood that the bits would correct the BP flipping decoding. Based on this, a BP-Step-Flipping (BPSF) algorithm is proposed which only traces the unreliable bits in the flip set (FS) to flip and skips over the reliable ones. In addition, a threshold \(\beta\) is applied when the magnitude of the log-likelihood ratio (LLR) is small, and an enhanced BPSF (EBPSF) algorithm is presented to lower the BLER. With the same FS, the proposed algorithm can reduce the average number of iterations efficiently. Numerical results show the average number of iterations for EBPSF-1 decreases by 77.5% when \(N=256\), compared with the BP bit-flip-1 (BPF-1) algorithm at \(E_{b}/N_{0}=1.5\)dB.  
polar code; belief propagation; bit-flipping; stepping Article  
An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy. Entropy202241073.  
[[https://doi.org/10.3390/e24081073](https://doi.org/10.3390/e24081073)]([https://doi.org/10.3390/e24081073](https://doi.org/10.3390/e24081073))
# A Comparative Study of Storage Batteries for Electrical Energy Produced by Photovoltaic Panels
## 1 Introduction  
When considering potential applications of solar energy, both its advantages and disadvantages must be taken into account. The main advantages of solar energy are that it is non-polluting, it has a limitless supply as it is available everywhere, and it is free. Its main disadvantage is that since the solar radiation incident to the Earth surface is variable--being dependent on the day/night cycle, meteorological conditions, and the cycle of seasons--solar energy is dispersed across the globe. Because of this disadvantage, a need has arisen for the installation of solar energy storage equipment within energy systems. Research activity in this domain is currently focused on designing and building high-performing equipment for the storage of solar energy produced by photovoltaic systems.  
This work presents a comparative analysis concerning the control of batteries used for storing such energy. The main contributions of this study are as follows: the development of an innovative model, in the programming environment MATLAB-Simulink (version R2018a), of a system for storing electrical energy supplied by photovoltaic systems; the development of the MPPT algorithm of incremental conductance for controlling a buck DC-DC convertor; the elaboration of a control diagram for an electronic power circuit equipped with IGBT-type transistors for controlling the charging and discharging of batteries storing electrical energy; a comparative analysis of simulation results obtained for two types of batteries; and the construction of an experimental stand for controlling the charging of the batteries supplied by the photovoltaic panels by means of the buck DC-DC convertor. Measurements were taken from the MATLAB-Simulink model. The MPPT algorithm for controlling the buck DC-DC convertor based on the method of incremental conductance was implemented in the model through a MATLAB function, which the author developed in C++. In experiments, the MPPT algorithm for incremental conductance was implemented with the help of a program developed by the author in the programming environment Arduino IDE, which is similar to the program developed in C++.
## 4 Modeling Errors of KANs and Discussion
### Modeling Errors of KAN-C  
The experimental results of KAN-C are shown in Table 7. Its R\({}^{2}\) and Adjusted R\({}^{2}\) on the training set both reach 0.9986, which indicates excellent fitting performance for the training data. Although slightly lower, the R\({}^{2}\) and Adjusted R\({}^{2}\) of the test set remain close to 0.9950, demonstrating good generalization capability.  
The MaxAE of the test set is roughly double that of the training set, indicating larger maximum prediction errors in testing. The gap explains why KAN-C performs better on training data than on test data. The MSE and MAE metrics follow this pattern consistently.  
However, the test set's sMAPE (3.7965) is smaller than the training set's (4.0589). This may be attributed to the larger overall magnitude of flank wear in the test set: according to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Category** & **Error** & **Value** \\ \hline \multirow{8}{*}{KAN-B} & & MSE & 0.0004 \\  & & MAE & 0.0018 \\  & & sMAPE & 1.4544 \\  & & MaxAE & 0.4950 \\  & & R\({}^{2}\) & 0.9990 \\  & & Adjusted R\({}^{2}\) & 0.9990 \\ \cline{2-3}  & & MSE & 0.0004 \\  & & MAE & 0.0020 \\  & & sMAPE & 1.9151 \\  & & MaxAE & 0.4736 \\  & & R\({}^{2}\) & 0.9988 \\  & & Adjusted R\({}^{2}\) & 0.9988 \\ \cline{2-3}  & & Total runtime & 299.5320 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Modeling errors of KAN-B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Category** & **Error** & **Value** \\ \hline \multirow{8}{*}{KAN-C} & & MSE & 0.0005 \\  & & MAE & 0.0062 \\  & & sMAPE & 4.0589 \\  & & MaxAE & 0.3513 \\  & & R\({}^{2}\) & 0.9986 \\  & & Adjusted R\({}^{2}\) & 0.9986 \\ \cline{2-3}  & & MSE & 0.0020 \\  
the sMAPE formula, relative errors are diluted when actual values increase, leading to a numerically smaller sMAPE.
## 1 Introduction
### Related Work  
Deep learning has proven superior for automatically extracting features from complex visual data, making it an essential technology for cultural heritage work, including artifact recognition, heritage restoration, and multimedia cultural data classification [14; 15; 16; 17]. Early applications of deep learning in cultural heritage focused primarily on identifying archeological sites [18; 19; 20]. These studies classified traces of tombs, legacy marks, and topographic visualizations using aerial photographs [14; 15; 21]. Additionally, various studies have presented and classified architectural styles using platforms such as Blocklet [5]. Other systematic reviews have applied deep learning models like VGG, InceptionNet, ResNet, and R-CNN to classify surface damage during regular inspections of architectural cultural heritage sites [7; 8]. Beyond architectural and archeological sites, some studies have applied deep learning to cultural heritage objects that are not entire buildings, archeological sites, or cities [22]. Various spectra have been used for the analysis of cultural heritage materials, and deep learning has been applied to X-ray fluorescence spectroscopy measurements [9]and near-infrared spectral data to classify samples and predict their components or changes in composition [23]. Recently, deep learning has been adopted as a strategy for the digital preservation of intangible and tangible cultural heritage, such as relics and historic sites. This approach has revolutionized how researchers and conservators engage with cultural heritage research [24; 25; 26; 27]. For intangible cultural heritage, deep learning has enabled the documentation of performing arts and dance through the generation of vast amounts of RGB-D and 3D skeleton data. This has led to significant progress in choreography summaries and dance pose recognition using visual sensors, motion capture devices, and machine learning model [28; 29]. As digital cultural heritage research continues to expand, it is expected to cover a wide range of topics and impact multiple areas of preservation and documentation. Various aspects of visual computing related to tangible and intangible cultural heritage preservation and documentation have been explored, and the field is expected to expand into more areas.