esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## 3 Results  
### Open-Sky Conditions  
The test of the ZED-F9P receiver started with measurements under good satellite visibility conditions. The Pt 1 measurement station had no significant obstacles around it. Table 2 contains results for different hardware configurations. The columns contain the averaged coordinates from the five measurement series (MMS), the differences of these coordinates with respect to the reference ones (RD) and their standard deviations, calculated based on the five measurement series (SD).  
Below (Figure 6) there are cumulative graphs, clearly illustrating the results obtained. There are differences from the reference coordinates with standard deviation bars on it. Both receivers meet the requirements in terms of general RTK Network measurement accuracy. It is worth mentioning that the most convergent coordinates to the reference ones were obtained with the u-blox ZED-F9P + Leica AS10 antenna set. On the other hand, the standard deviations present the slightly higher repeatability of the Leica GS18T. The increase in measurement accuracy in the vertical plane of the ZED-F9P receiver after replacing the antenna with the Leica AS10 can also be seen.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Receiver** & & **Coordinate** & **Ref. Difference** & **St. Deviation** \\ \hline \multirow{3}{*}{GS18T} & X [m] & 5,548,070.095 & \(-\)0.010 & 0.008 \\  & Y [m] & 7,422,309.582 & 0.002 & 0.006 \\  & H [m] & 204.027 & 0.019 & 0.014 \\ \hline \multirow{3}{*}{ZED-F9P + AS10} & X [m] & 5,548,070.092 & \(-\)0.007 & 0.008 \\  & Y [m] & 7,422,309.587 & \(-\)0.002 & 0.014 \\  & H [m] & 204.035 & 0.011 & 0.021 \\ \hline \multirow{3}{*}{ZED-F9P + ANN-MB-00} & X [m] & 5,548,070.094 & \(-\)0.009 & 0.010 \\  & Y [m] & 7,422,309.584 & 0.000 & 0.006 \\ \cline{1-1}  & H [m] & 204.019 & 0.027 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurement results on Pt 1.  
Figure 6: Cumulative graphs of measurement results at the Pt 1 station (open sky conditions; differences in relation to the reference points with standard deviations [m]).
###### Abstract  
Existing convolutional neural network (CNN) methods primarily depend on first-order feature modeling, which makes it challenging to effectively capture higher-order features in breast cancer histopathological images. Additionally, due to the limitations of the receptive field, CNNs have difficulty capturing long-range dependencies, thereby limiting the integration of global information. To address this, inspired by the strengths of high-order statistical features and extended receptive fields in visual tasks, this study proposes a novel high-order receptive field network (HoRFNet). Specifically, HoRFNet expands the receptive field and improves the model's contextual awareness of pathological tissue structures by introducing a multi-branch convolutional structure with convolution kernels of varying sizes, along with dilated convolution layers. Additionally, HoRFNet integrates a matrix power normalization strategy in the covariance pooling module to model the global interactions between convolutional features, thereby improving the higher-order representation of complex textures and structural relationships in tissue images. The BreakHis dataset shows that HoRFNet achieves an image level classification accuracy of 99.50% and a patient level classification accuracy of 99.23%, significantly outperforming existing methods and demonstrating its effectiveness.  
breast cancer histopathological image classification; high-order modeling; multi-branch receptive field; covariance pooling Article
## 3 Results and Discussions
### Comparison to the Literature  
Table 4 shows some comparison results of the density, thermal conductivity and compressive strength. The densities of the samples that are found in the present study are in agreement with the cited references.  
If the attention is turned to the thermal properties, it will be seen that the thermal conductivities of the prepared samples are lower than the ones with fly ash [15] and of the samples with pumice [19] (since exclay has a more porous structure); furthermore, the thermal conductivity of the prepared samples are higher than the thermal conductivity of the samples used in the studies numbered [13; 14; 15; 22]. Thermal conductivity is high because the tragacanth resin used in [13] absorbed more water and released the water from  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Materials** & \(\rho\) **(g/cm3)** & \(k\) **(W/mK)** & \(f_{\text{cs}}\) **(MPa)** & **Literature** \\ \hline Cement (450 kg/m3)—exclay (22\%) + sand (20\%) & 1710 & – & 41.27 & [3] \\ Cement (440 kg/m3)—exclay + sand + silica fume & 1.460 & – & 39.5 & [8] \\ Cement + exclay (10\%) + tragacanth(1\%) & 1.056 & 0.263 & 3.97 & [13] \\ Cement (80\%) + EPS (20\%) + tragacanth (1\%) & 1.230 & 0.220 & 10.85 & [14] \\ Cement + fly ash (20\%) + tragacanth (1\%) & 1.151 & 0.315 & 20.4 & [15] \\ Cement (80\%) + pumice (20\%) + tragacanth (1\%) & 1.306 & 0.306 & - & [18] \\ Cement (80\%) + EPS (20\%) + apricot resin (1\%) & 1.291 & 0.322 & 13.05 & [19] \\ Cement + clay + wood pellet (20\%) & 0.870 & 0.160 & 2.35 & [22] \\ Cement + exclay (10\%) + cherry resin (1\%) & 1.278 & 0.351 & 22.31 & \\ Cement + exclay (20\%) + cherry resin (1\%) & 1.081 & 0.291 & 20.13 & \\ Cement + exclay (30\%) + cherry resin (1\%) & 0.848 & 0.251 & 18.22 & Present study \\ Cement + exclay (40\%) + cherry resin (1\%) & 0.789 & 0.228 & 15.14 & \\ Cement + exclay (50\%) + cherry resin (1\%) & 0.603 & 0.208 & 13.73 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison to the literature of physical properties.  
Figure 8: Water absorption ratio of samples versus exclay ratios: (**a**) 2–4 mm, (**b**) 4–6 mm.  
its structure during drying, generating a high number of artificial pores. In study [14], EPS is used along with the tragacanth resin, and the EPS structure has more micropores.  
Now, it is time to compare the compressive strength. Table 4 illustrates that the compressive strength of concretes with exclay aggregates and cherry resin approximated the values reported in [3; 15], but they are better than the values given in [13; 14; 19; 22]. This may be due to the formation of a hard cluster around the exclay during production. Compressive stress is lower than the value reported in [13] because of the resin addition.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 4 Experimental Comparison
### Disturbed Scenario
#### 4.2.3 Comparison before and after Signal Correction  
It is evident that the neural network combining a CNN and LSTM outperforms the general CNN in terms of training effectiveness. Therefore, the noise predicted by the neural network combining a CNN and LSTM is incorporated into the covariance matrix of the tightly coupled Kalman filter, and the navigation performance is compared with the navigation performance before signal correction.  
In the case of strong signal interference, a comparison of the effect before and after navigation using neural-network-assisted tightly coupled Kalman filtering is depicted in Figure 14.  
Figure 12: Training process of the neural network architecture combining CNN and LSTM.  
Figure 13: Training process LOSS RMSE error graph.  
The experimental results are analyzed for 2D and Z-direction errors, and error analysis plots and CDF plots are constructed. As shown in Figure 15. Their specific data are shown in Tables 3 and 4.  
After neural network processing, the localization performance was significantly improved, with a performance improvement of \((0.559872-1.082632)/1.082632=0.4829\).  
From this, it can be concluded that in scenarios where the signal encounters strong interference, relying solely on the Kalman filter algorithm for GNSS/INS fusion navigation will fail to meet the task requirements. However, by incorporating the noise predicted by the neural network into the covariance matrix of the tightly coupled Kalman filter, there is a significant improvement in navigation performance compared with when errors are not corrected.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Value** & **Variance** & **Standard Deviation** \\ \hline Statistics of 2D error & 0.559872 & 0.063042 & 0.251082 \\ \hline The statistics of the & & & \\ Z-direction error & 0.234357 & 0.052803 & 0.229790 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of 2D and Z-direction errors for CNN-LSTM-assisted Kalman Filtering.  
Figure 14: Comparison of the effect before and after navigation using neural-network-assisted tightly coupled Kalman filtering.
###### Abstract
One-dimensional CNN is designed to extract from the input signal for the detection of acceleration data from SHM systems of large-span bridge in China directly. The results show that the method can detect anomalies with high accuracy in the anomaly detection stage [27]. A new CNN framework is proposed. It combines the advantages of CNN and statistical features and can achieve faster and more accurate data anomaly detection than using CNN alone [28]. A method converts time series signals into images to visualize the data and categorizes them into six categories according to the different features of the images. Then, uses the trained neural network to recognize the images and realize the classification of the target data [29]. The method converted the time series data into a 3-channel image consisting of time, spectral, and probability density functions. Then, used the designed CNN to recognize the images, which achieved high overall accuracy and recall [30]. Two-dimensional CNN is used for the recognition of time-frequency domain images. The frequency domain response is obtained from the time domain data by fast Fourier variation, and the approach is validated for a large span bridge in China with real data and good results [31]. When using CNN to solve problems, when one or more of the predicted categories has a very small sample size, it may face the problem of unbalanced categories. The unbalanced distribution of samples will result in categories with small sample sizes containing too few features, and it is difficult to extract patterns from them, and even if a classification model is obtained, it is prone to the problem of overfitting due to over-reliance on a limited number of data samples. When the model is applied to new data, the accuracy and robustness of the model will be poor, and the optimal results cannot be obtained in real time because the model never fully examines the implied classes. Based on this, this paper proposes a data migration method to migrate other bridge data to the target bridge data set. The method mainly consists of three parts. Firstly, the data are visualized and classified according to the image features. Secondly, determine whether the time domain images of other bridge data can be applied to the target bridge, and then select the data to add to the target bridge training set. Finally, the selected pre-trained model is employed in a circularly new training set to obtain an abnormal data detection model with better performance. The model can complete the classification of all categories of abnormal data to solve the problem that the target bridge cannot accurately identify all categories of data due to the lack of data imbalance of a certain category of data.
## 4 Discussion
### Study Limitations  
This study has several limitations that should be acknowledged. First, it is a single-center observational study with a limited sample size, particularly within subgroups such as those with HFrEF, which may reduce statistical power and increase the risk of Type II error. As this was an exploratory study, no formal power calculation was performed. Second, challenges inherent to ultrasonographic interpretation exist: for example, IVC diameter can be elevated in the absence of true congestion due to factors like low body mass index, athletic conditioning, pulmonary hypertension, or severe tricuspid regurgitation. Additionally, other non-congestive factors such as liver disease, anemia, and obesity may also influence VExUS measurements and biomarker levels, potentially confounding their interpretation. Furthermore, while patients with advanced chronic kidney disease (CKD stages 4-5), dialysis dependence, or renal transplant were excluded, individuals with mild-to-moderate CKD (stages 1-3) or subclinical liver dysfunction were included, potentially confounding VExUS interpretation due to altered venous Doppler waveforms independent of volume status.  
Ultrasound examinations were performed using two portable devices (Lumify(r) and Omega(r)), with Lumify(r) generally providing lower image quality. However, scans were conducted by trained sonographers using a standardized VExUS protocol, and reproducibility was substantial (Cohen's kappa 0.80), supporting consistent scoring despite device differences. Some variability related to device differences cannot be excluded.  
Given these factors, the ultrasound findings must be interpreted cautiously within the broader clinical context, underscoring the importance of individualized assessment when guiding decongestive therapy. Future multicenter studies with larger samples are warranted to validate these findings and explore their prognostic significance.
## 2 Materials and Methods
### Statistics  
The normality of the residuals of the JRE differences between the systems was assessed by visually checking the Q-Q plot and histogram of the unstandardized residuals, by determining the skewness and kurtosis and by using the Shapiro-Wilk test.  
Per the DeepLabCut neural network, a training and test pixel error was calculated. For the training pixel error, the locations of the manually labeled anatomical landmarks in frames in the training data set (i.e., 95% of all labeled frames) were compared to the predicted locations of the anatomical landmarks after training. For the test pixel error, the locations of the manually labeled anatomical landmarks in frames in the test dataset (i.e., 5% of all labeled frames, which were not used for training) were compared to the predicted locations of the anatomical landmarks after training.  
To assess differences in systematic JREs between DeepLabCut and VICON, a linear mixed model was used with a dependent variable (hip or knee JRE), fixed variable (measurement system) and random variable (subject ID). The best repeated covariance type was chosen based on Akaike information criteria (AIC).  
In addition, the best absolute error (i.e., minimal JRE across the three repetitions) was determined per subject and compared between VICON and DeepLabCut using a paired t-test in order to validate a clinically applicable outcome measure. Furthermore, the linear correlation between the best absolute errors measured with DeepLabCut and VICON was assessed using a Pearson correlation.  
Hip and knee Bland-Altman plots were created (based on the systematic JREs) with a 95% confidence interval for the mean difference between the systems. The presence of proportional bias was tested by testing the slope of the regression line fitted to the Bland-Altman plot using linear regression analysis.  
Statistical analyses were performed with SPSS (v29) at \(\alpha\) < 0.05.
## 4 Experiments and Results
### Performance Evaluation  
All experiments were performed using Matlab R2016a on a laptop with an Intel CoreTM i7-6500U CPU of 2.50 GHz with 8 GB of RAM. We evaluated the performance of the algorithm on the dataset concerning recognition accuracy and time cost. In addition, we analyzed the factors that might affect the recognition and compared our finger segmentation method with previous ones.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 1 Introduction
Some of these algorithms can even suit a distributed environment [17], but not the MR model. Because the MR model is more limited than general distributed or parallel computation models, the existing algorithms cannot be used in their current form. They must be adjusted and carefully designed for the MR model to be efficient. Our contributions in this paper are:  
1. A novel algorithm for mining closed frequent itemsets in big, distributed data settings, using the Map-Reduce paradigm. Using Map-Reduce makes our algorithm very pragmatic and relatively easy to implement, maintain and execute. In addition, our algorithm does not require a duplication elimination step, which is common to most known algorithms (it makes both the mapper and reducer more complicated, but it gives better performance). 2. A general algorithm for mining incremental frequent itemsets for general distributed environments with additional optimizations of the algorithm. Some of the optimizations are unique for the Map-Reduce environment but can be applied to other similar architectures. 3. We conducted extensive experimental evaluation of our new algorithms and show their behavior under various conditions and their advantages over existing algorithms. The rest of the paper is structured as follows. Section 2 presents the closed frequent itemset mining algorithm. Section 3 discusses the incremental frequent itemset algorithm. Section 5 is the conclusions section. A preliminary short presentation of the algorithms has appeared in [18; 19].
# Ransomware-Resilient Self-Healing XML Documents
###### Abstract  
In recent years, various platforms have witnessed an unprecedented increase in the number of ransomware attacks targeting hospitals, governments, enterprises, and end-users. The purpose of this is to maliciously encrypt documents and files on infected machines, depriving victims of access to their data, whereupon attackers would seek some sort of a ransom in return for restoring access to the legitimate owners; hence the name. This cybersecurity threat would inherently cause substantial financial losses and time wastage for affected organizations and users. A great deal of research has taken place across academia and around the industry to combat this threat and mitigate its danger. These ongoing endeavors have resulted in several detection and prevention schemas. Nonetheless, these approaches do not cover all possible risks of losing data. In this paper, we address this facet and provide an efficient solution that would ensure an efficient recovery of XML documents from ransomware attacks. This paper proposes a self-healing version-aware ransomware recovery (SH-VARR) framework for XML documents. The proposed framework is based on the novel idea of using the link concept to maintain file versions in a distributed manner while applying access-control mechanisms to protect these versions from being encrypted or deleted. The proposed SH-VARR framework is experimentally evaluated in terms of storage overhead, time requirement, CPU utilization, and memory usage. Results show that the snapshot size increases proportionately with the original size; the time required is less than 120 ms for files that are less than 1 MB in size; and the highest CPU utilization occurs when using the bzip2. Moreover, when the zip and gzip are used, the memory usage is almost fixed (around 6.8 KBs). In contrast, it increases to around 28 KBs when the bzip2 is used.  
ransomware; XML documents; secure document engineering self-healing Article
## 1 Introduction  
Crowdsourcing, Volunteered Geographic Information, and Citizen Science [1, 2, 3] are all terms applied to the recent trend in using the crowd to collect data and to carry out simple analytical tasks that can aid scientific research, e.g., making observations of birds, classifying galaxies, or determining the structure of proteins [4, 5, 6]. The potential of using citizens to collect environmental data has been recognized by the US government, the European Union, and numerous space and environmental agencies and has additional value for education, society and policy-making [7].  
Geo-Wiki is one example of an increasing number of crowdsourcing sites. The focus of Geo-Wiki is on the collection of information about land cover using high-resolution satellite imagery from Google Earth. The driver behind the application is the need to improve global land cover datasets derived from remote sensing [8, 9]. Global land cover is an important input for many environmental applications, yet recent studies have shown that there are large spatial disagreements between the main land cover products available [10]. At present, there are around 5000 registered Geo-Wiki volunteers, where the collective contributions have been used to create an improved global map of cropland [11] and to validate existing maps, e.g., evaluation of a map of land availability for biofuels [12].  
Geo-Wiki has evolved over time from a single website focused on land cover to many different branches that have been customized for specific purposes, e.g., a biomass Geo-Wiki for visualization and validation of global biomass datasets; a cities Geo-Wiki for collecting urban morphology for climate modeling, _etc._ A Geo-Wiki mobile application has also been developed for collecting land cover and other thematic information on the ground to improve the amount of _in situ_ data available for land cover calibration and validation. However, the application can be used for other purposes, e.g., by students, to input and share information within an educational context.  
The aim of this paper is to describe the development of a branch of Geo-Wiki called Geography Geo-Wiki, which has been modified specifically to address challenges in teaching an undergraduate curriculum. The role of the Geography Geo-Wiki in teaching Physical Geography and Climatology, in particular, is discussed in the next section. This is followed by an overview of the Geo-Wiki tools developed for the classroom, and details of the assignments that students undertook as part of piloting these tools with undergraduate students. Student feedback from course evaluations is provided and then discussed in terms of how this will inform future developments.
# A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems
## The Proposed Antenna Design
### Microstrip Patch Antenna Height (h)  
Antenna array performance was also optimized by appropriately selecting the patch antenna height \(h\). In fact, it is known that the higher the \(h\), the broader the bandwidth and the larger the gain [24]. However, it is also known that if the patch antenna height meets the following condition [25],  
\[h>\lambda\frac{0.3}{2\pi\sqrt{\varepsilon_{r}}}, \tag{2}\]  
surface wave modes become significant and reduce the broadside gain. For this reason, a trade-off between the gain enhancement and the bandwidth needs to be made. Parametric study on the patch antenna height \(h\) was conducted as shown in Figure 6a (for each value of \(h\), the antenna length \(L\) and the feed probe position \(p\) has been optimized to resonate at the same frequency). As can be seen after Equation (2) is satisfied, the gain starts to decrease slightly, but the bandwidth still continues to widen. We have selected the value \(h=20\) mm as a good compromise between gain and bandwidth (the gain is only \(0.3\) dB less than its maximum value, with an overlapped bandwidth wider than \(18\%\), where the overlapped bandwidth is defined as the spectrum portion in which \(|S_{11}|\leq-10\) dB impedance bandwidth, AR bandwidth, and \(3\) dB gain bandwidth overlap).