esa-sceva/satcom-chunk-collection huggingface dataset:
# Laboratory Evaluation of Eco-Friendly Pervious Concrete Pavement Material Containing Silica Fume
## 3 Results and Discussion
### Freeze-Thaw Cycles  
The relationship between compressive strength and freeze-thaw cycles of pervious concrete is shown in Figure 11. As can be seen in Figure 11, for the control group and silica fume-modified groups, the compressive strengths decreased with the increasing freeze-thaw cycles, which indicated  
Figure 10: Flexural strength of silica fume-modified pervious concrete.  
Figure 9: Compressive Strength of silica fume-modified pervious concrete.  
that the freeze-thaw resistance of pervious concrete gradually degenerates. When the freeze-thaw cycle was the same, the compressive strengths of silica fume-modified pervious concrete were higher than the control group, and the higher the silica fume content, the higher the compressive strength. The compressive strength of control group was reduced to 14.2 MPa after 100 freeze-thaw cycles, while the compressive strength of 9% and 12% silica fume-modified groups were still more than 20 MPa.  
Figure 12 indicates the variation of compressive strength loss versus freeze-thaw cycles of pervious concrete. It can be concluded from Figure 12 that, for all groups, the compressive strength losses gradually increased with the increasing freeze-thaw cycles. Before 25 freeze-thaw cycles, the compressive strength losses of all groups were less than 5%, which met the standard requirement of 20% in the Chinese national standard [34], after 25 freeze-thaw cycles, the compressive strength losses of the control group increased rapidly while the silica fume-modified groups increased relatively slowly. The control group had the highest compressive strength loss at all freeze-thaw cycle levels than the silica fume-modified groups, which indicated that the addition of silica improved the freeze-thaw resistance of pervious concrete. The 12% silica fume-modified group had the minimum compressive strength loss in all groups at each level of freeze-thaw cycles, and the maximum compressive strength loss after 100 freeze-thaw cycles was 9.5%. Figure 6 shows the specimens after 100 freeze-thaw cycles, obvious morphological differences between control group and silica fume-modified groups can be found, concrete spalling appears on the specimen surface of control pervious concrete while the silica fume-modified pervious concrete specimens were nearly intact. This was because the pozzolanic reaction caused by silica fumes improved the interface characteristics and adhesion between cement binder and aggregate, and reduced the concrete deterioration under freeze-thaw cycles. The addition of silica fumes significantly improved the freeze-thaw resistance of pervious concrete.  
Figure 11: Compressive strength of silica fume-modified pervious concrete under freeze-thaw cycles.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Flavonoids
#### 3.1.1 Impact of Structural Modifications on Flavonoid Hydrophilicity and Bioavailability  
Flavonoid hydrophilicity--and thus their bioavailability--depends not only on the native molecular structure but also on applied chemical and biotechnological modifications. Hydroxyl groups (-OH), glycosylation, and structural transformations that increase water solubility play key roles.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 4 Results and Discussion
### Experiment Analysis
#### 4.4.2 Different Sequence Lengths on Prediction Results  
To further understand the prediction performance of the HiP-Transformer model, this experiment groups the business process cases in the testing dataset according to the length of the event sequence and calculates the prediction performance of the HiP-Transformer model on different length case groups.  
Figure 3 shows HiP-Transformer's prediction accuracy for the next activity and the Mean Absolute Error for the remaining time on the four datasets. By plotting the evaluation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **LSTM [10]** & **Accurate-LSTM [23]** & **Transformer [16]** & **HiP-Transformer** \\ \hline BPI2012 & 383.00 & 29.95 & 4.60 & **3.89** \\ BPI2012W & 157.05 & 30.484 & **4.87** & 5.56 \\ BPI2013i & 30.08 & 28.13 & 8.36 & **4.17** \\ BPI2015 & 42.36 & 40.10 & 28.42 & **24.88** \\ BPI2017 & 127.80 & 16.85 & 10.28 & **9.93** \\ Hospital & 28.30 & 38.77 & 24.87 & **14.28** \\ Sepsis & 421.60 & 17.82 & 19.47 & **17.26** \\ \hline Avg. & 170.02 & 28.87 & 14.41 & **11.4** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Remaining execution time prediction effect (MAE: Day).  
results of different length business processes on the two prediction tasks, we found that as the sequence length increases, HiP-Transformer's prediction accuracy for the next activity tends to increase, and the prediction error for the remaining time tends to decrease. We can conclude that the model can solve the problem of decreasing prediction accuracy for long sequence business processes to some extent. The above comparison experiments reveal that the three-layer stacked self-attention model, by virtue of fully mining the internal knowledge of the business process, enables the model to fully model the historical information of the sequence when predicting the subsequent states of longer sequences. Thus, HiP-Transformer can achieve good results in most lengths, and the richer the historical information, the better the prediction results.  
Figure 3: Influence of Prefix Length on Accuracy and MAE of HiP-Transformer.  
Figure 2: Comparison of the Prediction Accuracy of Business Processes under Different Segmentation Strategies.
## 5 Comparative Analysis
### Discussion  
Based on the above analysis, we first conclude that the displacement data at the monitoring points within the deformation area of the Huanglianshu landslide have more complex nonlinear relationships than the displacement data at the monitoring points outside the deformation area. Second, the ARIMA statistical method is effective for predicting short-term stationary time-series displacement data within the deformation area, and the machine learning models are better at handling the nonstationary displacement data outside the deformation area. Third, the traditional machine learning models of the SVR and XGBoost were less effective than the ARIMA model at fitting outlier points in displacement data. Finally, deep learning approaches have better application prospects in slope deformation prediction problems, especially LSTM models with an attention mechanism, as well as transformer models.  
At present, there is almost no research on applying the transformer model to slope deformation predictions. For time-series forecasting problems, the transformer model far surpassed the traditional long- and short-term memory networks in a variety of application scenarios due to its unique network structure. In recent years, with the rapid development of data science, deep learning has made outstanding achievements in various disciplines, which are not limited to the natural language processing (NLP) field. The application of deep learning to solve slope deformation prediction problems will become a powerful solution. In this paper, we established different prediction models based on the monitoring data of the Huanglianshu landslide and the development history of the time-series prediction method. We hope to apply the latest and most applicable computer technologies to slope prevention and early warning work, and to make advanced computer technologies an effective means of solving various geological disasters to compensate for the limitations of traditional physical means.  
In the future, we will first comprehensively consider multiple influencing factors of slope deformation and exploit the existing deep learning approaches to establish a multivariable slope displacement prediction model. Second, more data interpolation methods will be added to the analysis. Finally, at present, spatiotemporal prediction [45; 46] has been widely supported in traffic flow forecasting, that is, forecasting from two aspects, time and space, which is a great inspiration for multimonitoring landslides. Therefore, we will develop a spatiotemporal model for slope deformation prediction according to the development of the spatiotemporal prediction in the transportation field.
## 3 Adaptive Time-Frequency Analysis
### Variational Mode Decomposition and Optimization Models
#### 3.2.3 Variational Mode Extraction
Third, in some cases, the VME algorithm may fall into a local optimum without finding the global optimum, which can result in deviations between the extracted modes and the true signal characteristics. Finally, the VME method may not be suitable for all types of signal processing tasks. In certain specific scenarios, it may be necessary to combine other signal processing methods or adopt other algorithms to achieve better processing results.
# Brake Light Detection Algorithm for Predictive Braking
## 4 Results
### Range Test  
To analyze the applicability in a real-world use case, a range test with two vehicles was performed at 25 m intervals from 25 m to 150 m. The algorithm with preprocessing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Algorithm(s) 1** & **Conditions 2** & **Range (m)** & **Acc. (\%)** \\ \hline [PERSON] [15] & HSV & N & 0–50 & 97 \\ [PERSON] [14] & RGB & N & -3 & 87 \\ [PERSON] [16] & NDM & N, R & 0–38 & 76 \\ [PERSON] [18] & HOG, LAB & D & - & 88 \\ [PERSON] [19] & HOG, CNN & D & - & 89 \\ [PERSON] [20] & HSV, HOG, SVM & D & 5–40 & 89 \\
**This paper** & RFY3 & D, DU, R & 5–100 4 & 82 \\ \hline \hline \end{tabular}  
* RGB = RGB filter/model, NDM = Nakagami-distribution model, HOG = histogram of oriented gradients, LAB = L*a’tB filter, CNN = Convolutional Neural Network classifier, HSV = hue saturation value filter, SVM = Support Vector Machine, RF = Random Forest, and Y3 = Yolov3. 2 D = Day, DU = Dusk, N = Night, and R = Rain. 3 = Not reported. 4 Mostly over the distance of 50 m.  
\end{table}
Table 6: Comparison of the brake light detection algorithms.  
Figure 7: Normalized confusion matrices from the random forest algorithm.  
performed with a high accuracy in standstill situations, recognizing brake light occasions at every 25 m point all the way up to 150 m. An example of the recognition can be seen in both extremes in Figure 8. A brake light detection occurrence is displayed with a yellow triangle featuring an exclamation mark.
## Neural Network Modeling to Predict Shelf Life of Greenhouse Lettuce  
**[PERSON] \({}^{*}\) and [PERSON]**  
Agriculture and Agri-Food Canada, Pacific Agri-Food Research Center, P.O. Box 1000, Agassiz, BC, V0M 1A0, Canada  
E-Mails: [EMAIL_ADDRESS] (W. C. L.); [EMAIL_ADDRESS] (G.S.B.)  
\({}^{*}\) Author to whom correspondence should be addressed; Tel.: +1-604-796-1713;  
Fax: +1-604-796-0359  
_Received: 24 October 2008; in revised form: 7 March 2009 / Accepted: 25 March 2009 / Published: 3 April 2009_  
**Abstract:** Greenhouse-grown butter lettuce (_Lactuca sativa_ L.) can potentially be stored for 21 days at constant 0\({}^{\circ}\)C. When storage temperature was increased to 5\({}^{\circ}\)C or 10\({}^{\circ}\)C, shelf life was shortened to 14 or 10 days, respectively, in our previous observations. Also, commercial shelf life of 7 to 10 days is common, due to postharvest temperature fluctuations. The objective of this study was to establish neural network (NN) models to predict the remaining shelf life (RSL) under fluctuating postharvest temperatures. A box of 12 - 24 lettuce heads constituted a sample unit. The end of the shelf life of each head was determined when it showed initial signs of decay or yellowing. Air temperatures inside a shipping box were recorded. Daily average temperatures in storage and averaged shelf life of each box were used as inputs, and the RSL was modeled as an output. An R\({}^{2}\) of 0.57 could be observed when a simple NN structure was employed. Since the \"future\" (or remaining) storage temperatures were unavailable at the time of making a prediction, a second NN model was introduced to accommodate a range of future temperatures and associated shelf lives. Using such 2-stage NN models, an R\({}^{2}\) of 0.61 could be achieved for predicting RSL. This study indicated that NN modeling has potential for cold chain quality control and shelf life prediction.  
**Keywords:**_Lactuca sativa;_ postharvest; storage temperature; neural network (NN); simple NN; 2-stage NN; regression analysis
###### Abstract  
Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation efficiency. This study will be extended to the integrity monitoring of multisensor systems.  
error overbounding; fault propagation; inertial measurement unit (IMU)/global navigation satellite system (GNSS) integration; integrity; unmanned aerial vehicle (UAV) navigation +
Footnote †: journal: Sensors
## 2 Second-Order Statistical Modeling of Current-Steering Electronic DACs (eDAC)
### All eDAC Currents Are Normalized by the (Mean) Unary Current
\begin{split}\bigg{\langle}\widetilde{I}_{c}^{\text{BIP BiWgt}[ \text{ThWgt}}\bigg{\rangle}&=\frac{1}{2}(2c-C-1)=c-\frac{C+1}{2} \\ \bigg{\langle}\widetilde{\mathbf{J}}^{\text{BIP BiWgt}[\text{ ThWgt}}\bigg{\rangle}&=\frac{1}{2}\{-C+1,-C+3,....C-3,C-1\} \end{split}  \tag{81}\]  
For UNIP BiWgt \(|\) ThWgt eDACs, for which \(\big{\langle}I_{0}^{\text{UNIP}}\big{\rangle}=\frac{1}{2}\big{\langle}I_{u}^{ \text{UNIP}}\big{\rangle}\), we have  
\[J_{c}^{\text{UNIP BiWgt}[\text{ThWgt}}=(c-1)\Big{\langle}I_{0}^{\text{UNIP}} \Big{\rangle}=(c-1)\Big{\langle}I_{u}^{\text{UNIP}}\Big{\rangle}. \tag{82}\]  
The normalized output current levels of UNIP BiWgt \(|\) ThWgt eDACs are simply obtained by dividing by \(\big{\langle}I_{u}^{\text{UNIP}}\big{\rangle}\):  
\[\bigg{\langle}\widetilde{I}_{c}^{\text{UNIP BiWgt}[\text{ThWgt}}\bigg{\rangle}= c-1,\ \ \ \bigg{\langle}\widetilde{\mathbf{J}}^{\text{ UNIP BiWgt}[\text{ThWgt}}\bigg{\rangle}=\{0,1,2,\dots,C-1\} \tag{83}\]  
The results are summarized in the following. **Corollary 1**. : _The UNIP and BIP mean normalized constellations \(\bigg{\langle}\widetilde{\mathbf{J}}^{\text{ UNIP}[\text{BIP}}\bigg{\rangle}\) (be they BiWgt \(|\) ThWgt) are arithmetic sequences with increment 1:_  
\[\bigg{\langle}\widetilde{\widetilde{I}}_{c}^{\text{ BiWgt}[\text{ ThWgt}}\bigg{\rangle}=\bigg{\{}c-\frac{C+1}{2}\text{ for BIP}\\ c-1\text{ for UNIP} \tag{84}\]  
_Equivalently, \(\bigg{\langle}\widetilde{\widetilde{I}}_{c}^{\text{ BIP}}\bigg{\rangle}=c-\frac{C+1}{2},\bigg{\langle}\widetilde{\widetilde{I}}_{c}^{ \text{ UNIP}}\bigg{\rangle}=c-\frac{C+1}{2}.\)_  
The mean normalized full scale (FS) of both is \(C-1\) (with \(C\) the constellation size):  
\[\begin{split}&\
abla\bigg{\langle}\widetilde{I}_{c}^{\text{ UNIP}[\text{BIP}}\bigg{\rangle}=1,\ \ \ \ \
abla\bigg{\langle}\widetilde{\mathbf{J}}^{\text{ UNIP}[\text{BIP}}\bigg{\rangle}=\mathbf{1}_{C-1}^{\forall}\equiv\underbrace{\{1,1, \dots,1\}}_{C-1\text{ ones}},\ \ \ \ \bigg{\langle}\text{FS}_{\widetilde{\mathbf{J}}^{\text{ UNIP}[\text{BIP}}}\bigg{\rangle}=C-1\\ &\
abla\bigg{\langle}I_{c}^{\text{ UNIP}[\text{BIP}}\bigg{\rangle}=\langle I_{u}\rangle,\ \ \ \
abla\big{\langle}\mathbf{I}^{\text{ UNIP}[\text{BIP}}\big{\rangle}=\mathbf{1}_{C-1}^{\forall}\langle I_{u}\rangle,\ \ \ \big{\langle}\text{FS}_{\{\mathbf{J}^{\text{ UNIP}[\text{BIP}}\}}=(C-1)\langle I_{u}\rangle\]The FSs for UNIP vs. BIP are identical; however, the mean MSLs differ by a factor-of-two:  
\[\left\langle\text{MSL}_{-\text{UNIP}}\right\rangle=\left\langle\overset{-\text{ UNIP}}{J}_{\text{C}}\right\rangle=C-1,\hskip 14.226378 pt\left\langle\text{MSL}_{- \text{BIP}}\right\rangle=\left\langle\overset{-\text{UNIP}}{J}_{\text{C}} \right\rangle=\frac{1}{2}(C-1). \tag{86}\]  
**Proof.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
### Contributions  
This paper makes the following contributions:  
* The design of a four-tier framework for QoE-aware content delivery over SD-WAN.
* Performance analysis of various transport protocols for multimedia delivery, through a set of human-paired comparison (HPC) experiments.
* Performance analysis of real-time SD-WAN rerouting under quality degradation in an intent-based network environment.  
The experiments presented in this research aim to answer the following questions:  
1. Will the participants be able to identify quality degradations at the same time as the network condition changes?
2. Does the content of the video affect quality-rating decisions by the participants despite a noticeable quality degradation?
3. When comparing objective analysis to human-perceived quality, would the results be consistent?
4. Which protocols are most suited for unstable or rapidly changing networks?
5. Is there consistency in protocol performances when video content changes, or when different events and scenarios occur?  
For each case, we present an analysis of the experimental results and reflect on how they address the relevant question from the abovementioned list.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### Effect of TCP Congestion Window  
Figure 2 illustrates the effect of TCP congestion window size (bytes) on the packet dropping rate of the FTP server over 2700 s of simulation time. We observe that more TCP segments are dropped out by the Router when the TCP congestion window size is smaller. In contrast, no TCP segments are dropped by the Router when the size of the TCP congestion window is as big as 135,000 bytes.  
Figure 3 illustrates the effect of TCP congestion window size on packet dropping over the first 80 s of simulation time. We observe that a bigger congestion window allows the FTP server to send more TCP segments before waiting for an ACK from the client.  
\begin{table}
\begin{tabular}{c c} \hline
**Scenario** & **Total Ethernet Frame Sent** \\ \hline \(p_{drop}=0\%\) & 6854 \\ \hline \(p_{drop}=0.01\%\) & 6855 \\ \hline \(p_{drop}=0.10\%\) & 6861 \\ \hline \(p_{drop}=1\%\) & 6916 \\ \hline \(p_{drop}=10\%\) & 7946 \\ \hline \end{tabular}
\end{table}
Table 6: FTP Server: Ethernet Frame Sent for Five Selected Scenarios.  
Figure 2: Effect of TCP Congestion Window Size on Packet Dropping.
## 3 Results and Discussion
### Absorption Evaluation Using the In Vitro Dynamic Intestinal Model
Literature data have shown that QCT (Figure 1d) uptake by Caco-2 cells should be approximately 50%, as confirmed by our results obtained using the dynamic system in which 50-60% QRC was absorbed after 4-6 h. These results highlighted its quick intestinal absorption starting from 4 h of contact with cells (Table 1) [37; 57], which reaches a complete metabolization after 24 h and could be ascribed to its strong metabolization into quercetin glucuronide [37; 58]. Nowadays, RU (Figure 1e) is considered the least absorbed flavonoid, notwithstanding the existence of very different bioaccessibility results in the literature, because it particularly depends on the analyzed concentration [37; 59]. Our dynamic set-up confirmed that RU is not absorbed until 6 h when non-absorbed RU% is 90.74 \(\pm\) 4.70, and that its absorption remained constant over time till 24 h when non-absorbed RU% was 86.85 \(\pm\) 8.63. This indicated that RU permeation across the Caco-2 cell monolayer was almost not detected, which be due not only to the lack of Caco-2 cell permeability, but also to an efflux permeability mechanism. In fact, the measurement of apparent RU permeability coefficients in the static Caco-2 cell model has been a well-known indication of non-existent or low permeation across the intestinal barrier with the potential role of PgP, which is an efflux transporter [37; 59].
###### Abstract  
Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.  
active thermography; pulsed thermography; signal processing; data processing: defect detectability +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Literature Review
Several other emerging cooling technologies have been studied recently, such as thermoelectric coolers (TECs) [30], thermo-acoustic refrigerators (TARs) [31], active magnetic regenerators (AMRs) [32], and internal cooling in battery cells [33]. These techniques offer many advantages, but they require much more works before they can be commercially applied in vehicles. Technology readiness level (TRL), designed by NASA in the late 1990s is a useful evaluation towards the market (Table 1). The TRL index can be used as a rough estimation of the required costs, time to market, risks taken, and so on [34]. With the increase of TRL, the technical is more mature and close to mass production. In conclusion, although different thermal management systems have their own advantages and disadvantages, liquid cooling is generally the best solution of thermal management systems. [PERSON] [35] evaluated the battery thermal management system by the analytic hierarchy process (AHP) proposed by [PERSON] [36], a famous American operational research scientist. The results also prove the advantages of a liquid-cooling system comprehensively considering technology, maturity, consumption, cost and other factors. The global market sales data shows the absolute advantages of air-cooling and liquid-cooling systems. The battery pack of HEVs is small and arranged inside the vehicle, and air cooling is basically adopted, such as in the Toyota Prius; the battery pack of EVs and PHEVs is large and arranged outside the vehicle, and liquid-cooling system is basically adopted, such as in the Tesla Model 3. The research object of this paper is a Ni-MH battery pack applied to a hybrid sport utility vehicle (SUV), which has a higher current ratio than Prius and is mounted on the bottom of the chassis to obtain a flat trunk space. In order to achieve better thermal management performance, a liquid-cooling system is selected and applied.