esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Data Generation and Interpolation Analysis  
Evaluating interpolator performance requires sufficient data density to allow the chosen interpolator to learn the observed spatial variations. This condition prevented us from using the collected friction values because they were extremely sparse. To overcome this problem, road surface images were extracted from 18 January 2022, with footage taken at a rate of one image every five seconds. These images were fed into the friction model mentioned above to generate spatially dense friction values.  
Within the friction dataset generated, the Whitemud Drive section was selected as the focus of this study due to the high friction variation present. After extracting data from this section, each friction measurement was averaged with neighboring values (50 m radius) to make the spatial patterns more distinct [17]. The generated friction values and the road section selected for this study are shown in Figure 3.  
The generated data were then separated into training and validation data. This process involved keeping only measurements spaced by a certain distance, i.e., a measure is kept every \"x meters\". The measurements kept were used for training, whereas the measurements removed were reserved for validation. A total of 10 datasets were created in this manner with increasing distance--from 100 m to 1000 m per observation (step size of 100 m)--to evaluate the impact separation distance has on interpolation accuracy. An example of the training data is depicted in Figure 4.  
The function of an interpolator is to estimate unmeasured values using known values. From the perspective of this study, the interpolator is used to estimate the removed friction values using the retained values. In this research, we examine three different friction interpolation methods: kriging, ML, and hybrid models. We have selected two representatives for each category based on their outstanding performance in previous research. Specifically, we have chosen RK and OK for kriging, RF and the RFSI for ML, and RFOK and RFSIOK for hybrid models. Our aim is to identify the optimal interpolator for friction and to determine how effectively ML can interpolate road-related variables in comparison to geostatistical methods.
# Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
## 3 Results
### Symptom Burden--An Underestimated Player  
Side effects of cancer therapy may have an influence on nutritional status. To estimate symptom load, we added a simple symptom burden module to our survey. The frequency of single symptoms related to nutrition was as follows: xerostomia--178/300 (59.33%) patients, loss of appetite--122/300 (40.67%) patients, dysgeusia--93/300 (31.00%) patients, constipation--80/300 (26.67%) patients, meteorism--70/300 (23.33%) patients, nausea--64/300 (21.33%) patients, dysphagia--63/300 (21.00%), diarrhea--49/300 (16.33%) patients, oral mucositis--48/300 (16.00%) patients, abdominal discomfort--38/300 (12.67%) patients, odynophagia--30/300 (10.00%) patients, and emesis--17/300 (5.67%) patients (Figure 2). Symptom burden has an influence on nutritional status and risk of malnutrition: an analysis of symptoms and malnutrition risk revealed that xerostomia in particular is significantly (\(p=0.008\)) associated with being at risk of malnutrition (positive NRS-2002). Patients with dysgeusia showed at least a trend toward a risk of malnutrition (\(p=0.100\)). Other symptoms showed no association with the risk of malnutrition. A detailed overview of subgroup analysis is depicted in Table 4.  
Certain cancer entities are associated with a distinct symptom profile. Comparing patients with hematological neoplasms and solid tumors showed that dysphagia (\(p<0.001\)), odynophagia (\(p=0.010\)), and constipation (\(p=0.032\)) occur significantly more frequently in patients with solid tumors. In contrast, patients with hematological diseases reported diarrhea significantly more frequently (\(p=0.016\)). They also suffered significantly more frequently from nausea (\(p=0.042\)) but not from emesis. There was a trend for patients with hematological neoplasms to report dysgeusia more frequently. Furthermore, patients reported an increased loss of appetite (\(p=0.069\), Table 5). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Nutritional Status**} & **Symptom** & **Symptom** & \multirow{2}{*}{\(p\)-Value} \\  & **Positive** & **Negative** & \\  & **[N]** & **[N]** & \\ \hline \multicolumn{4}{c}{**Mucositis**} \\ \hline
**NRS-2002 positive** & 39 & 170 & \\ \hline
**NRS-2002 negative** & 5 & 25 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Symptom burden in relation to nutritional status. Figure 2: Symptom burden in cancer patients under therapy. Word cloud: size of words corresponds to the frequency of the symptom mentioned.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
###### Abstract  
Within a supply chain organisation, where millions of messages are processed, reliability and performance of message throughput are important. Problems can occur with the ingestion of messages; if they arrive more quickly than they can be processed, they can cause queue congestion. This paper models data interchange (EDI) messages. We sought to understand how best DevOps should model these messages for performance testing and how best to apply smart EDI content awareness that enhance the realms of Ambient Intelligence (Aml) with a Business-to business (B2B) supply chain organisation. We considered key performance indicators (KPI) for over- or under-utilisation of these queueing systems. We modelled message service and inter-arrival times, partitioned data along various axes to facilitate statistical modelling and used continuous parametric and non-parametric techniques. Our results include the best fit for parametric and non-parametric techniques. We noted that a one-size-fits-all model is inappropriate for this heavy-tailed enterprise dataset. Our results showed that parametric distribution models were suitable for modelling the distribution's tail, whilst non-parametric kernel density estimation models were better suited for modelling the head of a distribution. Depending on how we partitioned our data along the axes, our data suffer from quantisation noise.  
EDI; performance; parametric; distribution modelling; KDE; supply chain; quantization; transaction modelling; B2B +
Footnote †: This paper is an extended version of our paper published in 2021 30 th Conference of Open Innovations Association FRUCT.  
Footnote †: These authors contributed equally to this work.
In this article, we propose a new approach to discover the communities in multiplex networks called the Co-regularized Robust Tensor-based Spectral Approach (**Mx-CRTSA**). In particular, we develop an objective function that merges multiple terms, including tensor decomposition, spectral clustering and a centroid-based co-regularization. The goal of the proposed objective function is to optimize a common centroid set of eigenvectors to reveal the communities in the network after recovering a clean representation of the multiplex network. The introduced model is solved through an alternating minimization scheme, where the clean representation is recovered through alternating direction methodsof multipliers and the consensus set of eigenvectors through Tucker decomposition. A summary of the proposed **Mx-CRTSA** is presented in Figure 1. The contributions of **Mx-CRTSA** are outlined as follows:  
1. The proposed **Mx-CRTSA** constructs an adjacency tensor form the multiplex network and minimizes its rank through nuclear norm minimization while pruning the noise by adopting the L\({}_{2,1}\) for error minimization. 2. The proposed **Mx-CRTSA** balances the agreement between the eigenvectors of each layer, i.e., the individual subspace, and a consensus set of eigenvectors, i.e., common or centroid subspace. This is achieved by minimizing the projection distance between the individual and common subspaces. 3. A co-regularization term is considered in the objective function that includes a weighting parameter, which is optimized efficiently along with the consensus subspace through high order orthogonal iteration (HOOI), which can provide a more accurate and robust solution than other matrix factorization methods. With the aid of HOOI, the contribution of the individual subspaces to the common subspace is optimized. 4. To assess the performance, robustness and effectiveness of the proposed **Mx-CRTSA**, multiple experiments are conducted to highlight its potential in clustering multiplex networks. The results indicate its superiority compared to other existing techniques. The structure of this paper is as follows: The background is given in Section 2. The **Mx-CRTSA** approach is presented and solved in Section 3. The results are discussed in Section 4.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 4 Conclusions  
Microwave-generated plasma treatment can be an interesting alternative for inactivation of molds in spices such as black pepper seeds, juniper and allspices berries. The level of bacterial inactivation depended on treatment time, and no molds were found in samples irradiated for 45 and 60 s. However, bacteria were more resistant and were present even after 60 s of plasma treatment. The chemical properties of material exposed to 45 and 60 s plasma application were similar, or even better, when compared to intact material. Moreover, the water activity of such treated material decreased, which should positively influence the shelf-life. However, it should be emphasized that color changes of spices treated for 45 and 60 s were noticeable. Potential reasons for the results may relate to the effects of plasma treatment including generation of reactive chemical compounds and a thermal effect, that can inactivate microorganism and cause other quality changes due to oxidation and thermal degradation. It is worth emphasizing that further research in the field of sensorial properties, or economic analysis, is needed to analyze the feasibility of plasma treatment of spices in industrial conditions.  
Conceptualization, A.W., M.J., and D.W.-R.; methodology, K.R., A.W., M.K., K.K.; formal analysis, A.W., B.H., M.K., K.K. and K.R.; investigation, A.W., B.H., M.K., K.K. and K.R.; resources, M.J.; data curation, A.W., B.H.; writing--original draft preparation, A.W., B.H., M.J., K.K., M.K.; writing--review and editing, A.W., B.H., M.J., K.K., M.K., D.W.-R.; visualization, A.W., B.H., M.K., K.K.; supervision, M.J. and D.W.-R.; project administration, B.H., M.J. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by The National Science Centre, under the program no. 2015/19/B/ST8/02123.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion  
### Analysis of Raw Spectra  
Figure 2 shows the raw spectra obtained with different pretreatment time. It can be seen that the whole spectra show two broad absorption peaks located at 1300-1600 nm and 1860-2200 nm, which are the octave and combined frequency peaks of water absorption, respectively. In the region of the combined frequency peaks, saturation of water absorption can be seen. This is due to the fact that the absorption in these regions is beyond the detection range of the NIR spectrometer [29] and therefore does not need to be investigated further. Therefore, we mainly analyzed the water absorption from 1300 to 1860 nm. The raw spectra from 1300 to 1600 nm belong to the first overtone region of the OH stretching band. This peak identifies many absorption bands containing important structural information. In addition, a small peak is observed at 1750-1860 nm, which indicates the OH stretching and bending vibrational absorption bands and deserves further investigation. These two absorption bands will be the focus of further analysis in this study. As can be seen from the partial enlargement of Figure 3, neither the processing time nor the storage time had an obvious effect on the raw spectra, so further aqueous spectral processing is required. This is the case for the bands between 1300 and 1600 nm, and 1750 and 1860 nm, which will be an important area of future research to compare spectral differences.
## Appendix A Theoretical Overview
### The Long Term Short Term Memory-Based Recurrent Neural Network (LSTM-RNN)  
Although the FFNN model discussed in the previous subsection does not use the information from the previous time steps to learn the equations, this information has significant value in improving the performance of a prediction task. To overcome this problem, RNN is introduced into time series forecasting. The RNN uses the information from the output of the previous step to predict the next time step. However, because this is an iterative process, the retention of information for a long time may lead to a vanishing gradient problem that results in worse predictions. To solve this problem, LSTM-based RNN is used to store the relevant information from the previous time steps and discard any information that is not needed. These tasks are performed using different gate functionalities in the LSTM cell.  
The LSTM was developed in 1997 by [PERSON] and [PERSON] [72]. In the LSTM-RNN network, hidden RNN layers are replaced by the LSTM cells. As shown in Figure 11, each cell consists of various gates that can manage the inputs and outputs, including an input gate, a forget gate, a cell state, and an output gate. The LSTM cells also contain a _tanh_ block, a sigmoid block, and a multiplication function. The input gate is used to store new information in the memory, while the forget gate is responsible for discarding the previously stored information when it is activated. Finally, activation of the output gate allows the propagation of the latest cell output to the ultimate state. The function of the sigmoid block is to generate values between 0 and 1 to indicate what percentage of each component should be allowed. The _tanh_ block creates a new vector to be added to the state, and the cell state is accordingly reorganized depending on the outputs formed by the gates. Mathematically, LSTM cells can be represented by the Equations (A12)-(A16): [73]  
\[f_{t}=\sigma\Big{(}W_{xf}\cdot x_{t}+W_{hf}\cdot h_{t-1}+b_{f}\Big{)},\] (A12)  
\[c_{t}=f_{t}\cdot c_{t-1}+i_{t}\cdot tanh\ (W_{xc}\cdot x_{t}+W_{hc}\cdot h_{t-1}+b_{c}),\] (A13)  
\[i_{t}=\sigma(W_{xi}\cdot x_{t}+W_{hi}\cdot h_{t-1}+b_{i}),\] (A14)  
\[h_{t}=o_{t}\cdot tanh(c_{t}),\] (A15)  
\[o_{t}=\sigma(W_{xo}\cdot x_{t}+W_{ho}\cdot h_{t-1}+b_{o}),\] (A16)  
where \(\sigma\) is the sigmoid, \(i,f
## 3 Results and Discussion
### Method Validation
#### 3.2.2 Specificity  
Specificity is defined as the ability to qualitatively determine the analyte in the presence of other components (degradation products, related substances, or matrix). In practice, the elution times of the analyte and that of possible impurities in the sample should not be identical.  
Under the present conditions, the chromatograms of a blank (MeOH/ACN/FA 0.2%) and a standard solution were compared (Figure 2). Comparing the results, it was found that in the elution time of the five substances, there was no interfering peak (co-elution).
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 9 Evaluation and Analysis
### Limitations
#### 9.5.3 Scalability and Generalization  
In terms of scalability, the current system is designed to handle reference search requests on a limited scale and with pre-defined queries. Although multi-threading support and integration of various APIs have been implemented to improve efficiency, the system has not been fully tested for scenarios with large data scales or high concurrent user loads.  
In terms of generalization, the capability of the system to generalize various disciplines of scientific domains is still limited. Although our proposed system shows promising results in terms of efficiency and accuracy in the computer science domain through five representative queries, the generalization capabilities to other literature domains with different terminology and keywords have not been thoroughly evaluated. Further work is needed to test the adaptability of the system to a broader range of disciplines, a larger number of queries, and more diverse semantic contexts.
## 3 Performance Evaluations
### Fiberless Channel
#### 3.1.1 Communication System  
By employing the spread signal technique using a PN sequence with a chip duration of 2.86 ns, the overall performance of the communication system is evaluated using the BER metric at different values of \(E_{b}/N_{0}\), where \(E_{b}\) is the bit energy and \(N_{0}\) is the noise one-sided power spectral density. For binary BPSK, the BER is given by [2]:  
\[\text{BER}=Q(\sqrt{2E_{b}/N_{0}}) \tag{2}\]  
where \(Q(\cdot)\) is the \(Q\)-function. Figure 5 shows the estimated and theoretical BER values, which are found to be remarkably close, indicating their considerable similarity. The estimated BER results are obtained from a Monte Carlo simulation utilizing the transmission of 1,000,000 information bits. The results presented in Figure 5 highlight the effectiveness of the spread signal technique, utilizing a seven-chip PN sequence, regarding the reliability of performing signaling over AWGN channels.  
Figure 6 shows the results of transmitting a 'Clock' image over an AWGN channel at \(E_{b}/N_{0}=-20\), \(-10\), and 0 dB. Notably, as \(E_{b}/N_{0}\) increases, the quality of the received image improves.  
Figure 5: Theoretical and estimated BER of the communication system.
## 7 Experimental Evaluation
### Streaming ML vs. Rule-Based Predictions  
This section compares the proposed streaming ML methodology against non-ML, rule-based approaches that have been used in the past. In particular, for predicting the _Next Offset_, we implemented a sequential prefetching policy used in commercial storage systems, which detects the presence (or not) of the sequential access pattern for a file and makes the next offset prediction accordingly [73]. For the _Next File Hotness_, we implemented a linear interpolation approach using the most recent file hotness measurements for predicting future file hotness [11].  
Figures 13 and 14 compare the F1 score achieved by the rule-based approaches against streaming ML for predicting the _Next Offset_ and _Next File Hotness_, respectively. For the traces that represent predominantly sequential read workloads, such as Traces 1 and 2, the rule-based approach can make very good predictions and achieve over 90% F1 score. However, there are also several traces with more complex access patterns, for which the rule-based approach performs very poorly, with an F1 score as low as 39%. The average F1 score across all traces is 87% for the rule-based approach as opposed to 98% for the streaming ML approach. For the next file hotness case, the rule-based approach achieves an average F1 score of 70% across all traces, revealing that it is hard to predict the future file hotness based on trends from previous hotness values. On the other hand, streaming ML achieves a consistent performance, with an average F1 score of 97%.  
**Key takeaways:** The streaming ML approach consistently exhibits the highest F1 score for all traces for both targets (97% on average), as well as very robust performance, irrespective of the complexity of the workload.
## 2 Problem Formulations  
### Kinematics Model for NWMRs  
The aim of this paper is to solve the path-following and obstacle avoidance problems for NWMRs by utilizing the RL algorithm. The classical two-wheel differential driving mobile robot is studied, as presented in Figure 1. When the robot moves, its state is given by the three dimensions in set \(\mathbf{q}=[\begin{array}{cc}\tilde{\pi}&\tilde{\eta}&\tilde{\theta}\end{array} ]^{T}\), as the current state in the two-dimensional coordinate plane. The parameters associated with the motion of a mobile robot are the linear and angular velocities \(v\) and \(\omega\), respectively, which are obtained from inputs as \(\mathbf{u}=[\begin{array}{cc}v&\omega\end{array}]^{T}\). The kinematics model of the mobile robot can be described as [29]:  
\[\tilde{\mathbf{x}}=\left[\begin{array}{c}\hat{x}\\ \hat{y}\\ \hat{\theta}\end{array}\right]=\left[\begin{array}{cc}\cos\tilde{\theta}&0 \\ \sin\tilde{\theta}&0\\ 0&1\end{array}\right]\left[\begin{array}{c}v\\ \omega\end{array}\right] \tag{1}\]
## 4 Results
### Results of Applying FFT to Amplitude Image
#### 4.3.3 Amplitude Thermal Equalization Trend Analysis of Sine Wave  
Figure 21 shows the Line 2 profile graph with the FFT algorithm applied to the amplitude image shown in Figure 20. In the case of the specimen Line A profile, it can be seen that the higher the Gaussian weight, the higher the effect of the thermal equalization. This makes it possible to clearly detect thinning defects when applying the FFT algorithm, as shown in Figure 21.  
The detectability of thinning defects due to thermal equalization was evaluated by applying the FFT algorithm to the raw image, phase image, and amplitude image. In the case of the raw images, the thermal equalization had a significant impact but did not significantly contribute to detectability. In the case of the phase images, when the FFT algorithm was applied, noise occurred as the Gaussian weight increased, and detectability significantly decreased. In the case of the amplitude images, when the FFT algorithm was applied, the high-frequency components moved to the side, greatly increasing the detectability. However, as the Gaussian weight increases, the high-frequency data of the defective area decreases, and the detectability significantly decreases, so appropriate settings are required. Therefore, the SNR was evaluated to verify the validation of the FFT algorithm.  
Figure 21: Amplitude profile graph of Line 2 to which FFT of B-type specimen was applied. (**a**) A-type, center; (**b**) A-type, left side; (**c**) A-type, top left; (**d**) B-type, center; (**e**) B-type, left side; (**f**) B-type, top left.  
Figure 20: Amplitude image with FFT applied of B-type specimen. (**a**) center; (**b**) left side; (**c**) top left.
## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.3 PT-Former Model  
The vegetation change dataset is put into the PT-Former model for training and testing. This process does not need to input \(kNDVI\); it only needs to input two remote sensing image datapoints and label datapoints in the dataset. The results of monitoring vegetation changes using the PT-Former model are as follows:  
CPA: 0.6194. It shows that the classification and recognition accuracy of the model for vegetation is low.  
IoU: 0.6157. It shows that the model has some errors in the classification boundary of vegetation and non-vegetation (sandy land). The output of the model is shown in Figure 11.  
The vegetation changes in the whole study area are shown in Figure 12.  
Figure 11: PT-Former model output diagram.  
Figure 10: Random forest model output of the study area.