esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### In Vivo Measurement of the GI of Breads  
The results of GI and satiety for Pita and Tandoori breads are shown in Figures 1 and 2 and Table 3, respectively. It can be seen that GI of Pita and Tandoori is slightly different, particularly for the first 30 min. The results also indicate that after this time, the post-prrandial blood glucose of breads with FI declined more clearly.  
Statistical analysis of data showed that GG and IN independently and in combination significantly (\(p<0.05\)) decreased the post-prrandial blood glucose of Pita bread by 45% and 43%, respectively (Table 3). Moreover, the combination of all the FI reduced the GI by 37%. The GI was statistically reduced (\(p<0.05\)) in GG and IN Tandoori breads (Table 3).  
Figure 1: The glycaemic response for Pita breads.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Bread** \\ **Types** \\ \end{tabular} } & \multirow{2}{*}{**Samples**} & \multicolumn{1}{c}{\multirow{2}{*}{**0**}} & \multicolumn{1}{c}{\multirow{2}{*}{**15**}} & \multicolumn{1}{c}{\multirow{2}{*}{**30**}} & \multicolumn{1}{c}{\multirow{2}{*}{**45**}} & \multicolumn{1}{c}{\multirow{2}{*}{**60**}} & \multicolumn{1}{c}{\multirow{2}{*}{**90**}} & \multicolumn{1}{c}{\multirow{2}{*}{**120**}} & \multicolumn{1}{c}{\multirow{2}{*}{**G AUC**}} & \multicolumn{1}{c}{\multirow{2}{*}{**GI**}} & \multicolumn{1}{c}{\multirow{2}{*}{**S AUC**}} & \multicolumn{1}{c}{\multirow{2}{*}{**SI**}} \\ \cline{3-3} \cline{8-11}  & & \multicolumn{1}{c}{} & & & & & & & & & \% & & \% \\ \hline \multirow{4}{*}{Pita} & Control & 5.0 & 5.8 & 6.9 & 6.7 & 6.6 & 6.2 & 5.8 & 161 + 38 a & 100 & 355 + 110 a & 104 \\ \cline{2-11}  & All & 4.9 & 5.4 & 6.3 & 6.7 & 6.0 & 5.5 & 5.5 & 102 + 34 b & 63 & 418 + 110 a & 122 \\ \cline{2-11}  & IN & 5.0 & 5.5 & 6.7 & 6.9 & 5.9 & 5.8 & 5.4 & 92 + 27 b & 57 & 451 + 66 a & 132 \\ \cline{2-11}  & GG & 4.8 & 5.3 & 6.3 & 5.9 & 5.8 & 5.6 & 5.2 & 89 + 38 b & 55 & 452 + 71 a & 132 \\ \hline \multirow{4}{*}{Tandoori} & Control & 5.8 & 5.7 & 6.6 & 7.7 & 6.9 & 7.5 & 6.9 & 162 + 51 a & 100 & 329 + 100 a & 96 \\ \cline{2-11}  & All & 4.9 & 5.6 & 6.6 & 6.8 & 6.3 & 5.6 & 5.4 & 120 + 37 a & 74 & 420 + 117 a & 123 \\ \cline{2-11}  & IN & 5.2 & 5.3 & 6.5 & 6.7 & 6.3 & 5.9 & 5.6 & 98 + 32 b & 60 & 381 + 86 a & 111 \\ \cline{2-11}  & GG & 5.5 & 5.5 & 6.5 & 6.2 & 5.9 & 5.5 & 5.3 & 92 + 45 b & 57 & 390 + 56 a & 114 \\ \hline \hline \end{tabular}  
* G AUC = area under curve (mmol/L min); GI = glycaemic index; SI = satiety index. Same letters mean no statistical differences (\(p<0.05\)).  
\end{table}
Table 3: Area under curve, glycaemic and satiety indices of Pita and Tandoori breads.  
Figure 2: The glycaemic response for Tandoori breads.
###### Abstract  
Rock mass conditions are extremely sensitive to tunnel boring machine (TBM) tunneling. Therefore, establishing a surrounding rock excavatability (SRE) classification system applicable to TBM tunnels. Accurately and intelligently identifying excavatability grades can also facilitate efficient TBM tunneling and intelligent construction. Specific excavation and penetration rates were used to evaluate SRE. Their correlations with geological and tunneling parameters were explored using the field data from two water conveyance tunnels in China with different lithologies. A high-precision empirical SRE classification system was constructed using TOPSIS for multi-objective decision-making, and it was verified using engineering cases. An intelligent identification model for SRE grades in the stable phase of a TBM excavation cycle was established using 12,382 TBM rock-breaking datasets and deep forest models. Ten characteristic parameters, e.g., total thrust, were selected as model input features. Hyperparameter optimization was achieved using the grid search method. Deep forest was compared with decision tree, random forest, support vector classifier, and deep neural network. The contribution of the model's features was measured using random forest. The main conclusions are as follows: the proposed SRE classification method is feasible and matches well with the actual excavation. In the intelligent identification of SRE classification, the accuracy and F1 scores when using deep forest were 96.33% and 0.9581, respectively. Deep forest exhibited better grade identification performance than the four models. Among the ten input features, penetration is the most important feature for the model's input, while the top shield cylinder rod's chamber pressure is the least important. The findings can provide some references for SRE classification and prediction and intelligent TBM control.  
surrounding rock excavatability classification; tunnel boring machine; water conveyance tunnel; deep forest; machine learning; TOPSIS +
Footnote †: journal: Medical Imaging
# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 3 The Proposed Technique  
In multiview video-plus-depth (MVD) format, depth-image-based rendering (DIBR) is used to generate virtual views at novel viewpoints to support 3D vision in stereoscopic and autostereoscopic displays. The DIBR obtains the virtual view by warping the original left and right views to a virtual viewpoint with the help of the corresponding depth maps. As discussed earlier, when the virtual view is generated its quality may degrade due to several structural or textural distortions introduced during synthesis. The major cause of these distortions is the inaccurate depth. This inaccuracy in the depth estimates and other compression-related artifacts can cause several distortions in the synthesized image,such as ghost artifacts, holes, and blurry regions, as shown in Figure 1. These distortions degrade the image quality and eventually result in poor overall user quality of experience (QoE). Estimating the quality of the synthesized image is therefore important to ensure better QoE. We propose a 3D-IQA metric that attempts to estimate the distortions introduced in synthesized images. Specifically, the proposed metric is a combination of two measures: one estimates the variations in the texture and the other calculates the deterioration in the structures in the image.
## 6 Conclusions  
This paper compares the CLT system for office buildings to similar buildings with RC structures in order to study the energy saving potential for China's five different climate zones. The simulation method, IES analysis, is an effective approach to analyze energy consumption related to office buildings. The simulation makes it possible to evaluate embodied energy on operation phase of buildings and to examine the contribution of different types of energy consumption. Based on the simulation results, the following conclusions can be drawn:  
1. In terms of embodied energy and embodied GHG, CLT has been observed to be better than conventional materials. The CLT system is more energy efficient in winter with reduced heating energy requirements in comparison with RC structures. Overall, the calculated heating energy saving ratio for Harbin, Beijing, Shanghai and Kunming is 11.97%, 22.11%, 30.94%, and 23.30% respectively. Furthermore, more energy could be saved by increasing the thickness of the CLT panels.
2. In comparison with those conventional materials, such as reinforced concrete, brick and steel, CLT has been proved to be a good candidate as alternative material for construction. In view of the potential for the reduction of heating energy requirements by using the CLT system, it is recommended that policy makers encourage the application of CLT in the construction industry in the Severe Cold Region and Cold Regions in China.
3. Inversely, in terms of thermal mass and thermal comfort in the summer, CLT is found to be less attractive than concrete. The CLT system also increases the cooling energy requirementsin summer, with an increased possibility of the occurrence of overheating in CLT office buildings. As a result, CLT may not be such a good alternative building material for regions, namely the Temperate Region and Hot-Summer Warm-Winter Region without considerable heating requirements.  
Conceptualization, H.G. and Y.D.; methodology, H.G.; software, X.C.; validation, X.C. and X.Y.; formal analysis, X.Y.; investigation, X.C.; resources, H.G.; data curation, X.C.; writing--original draft preparation, Y.D.; writing--review and editing, H.G. and X.Y.; visualization, Y.D.; supervision, H.G.; project administration, H.G.; funding acquisition, H.G.  
This research was funded by National Natural Science Foundation of China, grant number 51608144.  
The authors declare no conflict of interest.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 4 Software Design of RFID Reader System  
### Main System Flow Chart  
Throughout the system, ZigBee coordinator forms entire ZigBee communication network, and waits for router nodes and sensor nodes to join it. During network operation RFID reader collects related node information, then sends it to the host computer through ZigBee network, and finally stores related information into the database. The main system flow chart is shown in Figure 6.  
Figure 5: The I/O port circuit between CC2530 and MF RC522.  
In RFID reader/router, RF chip MF RC522 communicates with electronic tags following the communication protocol of ISO/IEC14443A. Through SPI (Serial Peripheral Interface), CC2530 controls communication between MF RC522 and type matched cards within the range of the antenna. It then reads information in card and deals with the information accordingly to make it compliant with the ZigBee protocol packets [15; 16]. It sends the packets to the coordinator in ZigBee network following IEEE 802.15.4. The coordinator connects with the host computer via the UART interface, and ultimately sends the data to host computer for processing. Similarly, the host computer can send control commands to operate the electronic tag, such as modification of the information.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
###### Abstract  
In the actual era of smart homes and smart grids, advanced technological systems that allow the automation of domestic tasks are developing rapidly. There are numerous technologies and applications that can be installed in smart homes today. They enable communication between home appliances and users, and enhance home appliances' automation, monitoring and remote control capabilities. This review article, by introducing the concept of the smart home and the advent of the smart grid, investigates technologies for smart homes. The technical descriptions of the systems are presented and point out advantages and disadvantages of each technology and product today available on the market. Barriers, challenges, benefits and future trends regarding the technologies and the role of users have also been discussed.  
smart home; smart grid; energy management system (EMS); integrated wireless technology (IWT); smart home micro-computers (SHMC); home automation (HA) +
Footnote †: journal: _Energies_
## 4 Discussion
### New Workflow for MZ Delineation in eSSPM  
Based on the results and discussions presented during the development of this work, an improved version of current eSSPM zoning methods was described. This new workflow consists of five straightforward steps: (1) description of cause-effect relationships between georeferenced presence-absence data and mapped environmental predictors (not discussed in this paper), (2) single binary partition of a crop field validated through biologically meaningful CVI, (3) \(n\) complementary partitions of presence-only and absence-only clusters validated through biologically meaningful CVI, (4) suitability-based classification of MZ, and (5) validation of MZ based on SDM background tests.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation  
This section describes the simulation settings and discusses the influence of simulation parameters on the tracking task. The simulation is conducted with various experimental settings (e.g., different network density, the communication range and network topology). The MATLAB(r) (MathWorks, Natick, MA, USA) simulation tool is applied to perform the comparison among our proposed approach CTCI, DCTTP [12] and TCAT [13]. The following subsections respectively describe the parameters, mobility model and energy consumption model used.
## 3 Design and Implementation of the Smart Campus System
### Operational Requirements for Outdoor Applications  
To cover potential smart campus outdoor applications, a set of operational requirements grouped by capabilities was defined, including:  
* Coverage capabilities. The coverage of the smart campus should be maximized. The typically expected coverage should be around 1 km\({}^{2}\) considering both Line-of-Sight (LoS) and No-Line-of-Sight (NLoS) scenarios.
* Robustness capabilities. The system should provide robustness to signal interference and/or loss of network operation. The network should provide redundancy and thus be robust against single points of failure.
* Supported services and applications. The previously mentioned applications (in Section 2.4) should be supported. Quality of Service (QoS) requirements should include support for high-peak rate demand, latency-sensitive traffic, and location-aware IoT applications. A transmission speed of up to 50 Kbps should be expected.
* Deployment features and cost. It should be expected that the deployment will depend largely on low-cost IoT nodes resource-constrained in terms of memory, battery, computing capabilities, and energy consumption.
* Network topology. The network architecture should support Point-To-Multipoint (PMP) and Point-to-Point (PtP) links. The system should be capable of establishing ad-hoc networking for specific scenarios (i.e., by using star or mesh topologies).
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 3 Production of Bioplastics from Volatile Fatty Acids
### Microorganisms Responsible for the Transformation of VFA into PHA
#### 3.1.2 Mechanism of PHA Accumulation. Accumulating Microorganisms
It is crucial to emphasize that PHA polymerase is specific to each bacterial species, thereby determining the type of PHA that is produced (PHB or copolymers such as PHBV). In the case of PHBV, the incorporation of 3-hydroxyvalerate (3 HV) units into the polymeric chain is achieved through the introduction of precursors, such as propionic acid or valeric acid, into the culture medium. With regard to the intracellular accumulation of the polymer, it is synthesized and subsequently accumulates in the cytoplasm of the bacterium in the form of PHA granules. Figure 4: Scl-PHA production pathway using glucose as the carbon source. PhaC is the PHA synthase enzyme in all pathways and catalyzes the polymerization of monomers to PHA polymers. Modified from [PERSON] et al. [138]. The granules are encased in a layer of proteins and phospholipids, which regulate their size and structure [135]. The production of copolymers such as PHBV necessitates the utilization of particular precursors (including propionic acid and valeric acid) within the culture medium. These acids are incorporated into the metabolic pathway as alternative substrates that PHA synthase polymerizes into 3 HV units. The ratio of 3 HB to 3 HV in the final PHBV can be modified by controlling the relative concentration of these acids in the medium, thereby allowing the properties of the bioplastic to be tailored to suit specific applications [135]. The regulation of gene expressions in PHA-producing bacteria is encoded in several genes, including phaA, phaB, and phaC. These genes encode the enzymes \(\beta\)-ketoacyl-CoA thiolase, acetoacetyl-CoA reductase, and PHA polymerase, respectively. These genes are organized in an operon (the pha operon), and their expression is controlled by regulatory factors that respond to the availability of nutrients and carbon sources [135]. Table 2 provides a list of the PHA-producing bacteria, ordered according to the substrate used for synthesis.
# Graphene Passively Q-Switched Nd:YAG Laser by 885 nm Laser Diode Resonant Pumping  
[PERSON]  
1 Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin 300308, China 1  
[PERSON]  
1 Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin 300308, China 1  
[PERSON]  
1 National Key Laboratory of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China 1  
[PERSON]  
1 Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin 300308, China 1  
[PERSON]  
1 Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin 300308, China 1  
[PERSON]  
2 National Key Laboratory of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China 1  
[PERSON]  
2 National Key Laboratory of Tunable Laser Technology, Harbin Institute of Technology, Harbin 150001, China 1  
[PERSON]  
1 Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin 300308, China 1  
Footnote 19 in 2015, for which the output power was 305 mW, with an optical-optical efficiency of 10.2% versus the incident pump power [21]. In all of the above reports, the traditional pump wavelength of 808 nm was employed.  
The end-pumping of an Nd:YAG laser using bonded crystals with different doping concentrations through the use of an 885 nm LD has been reported in 2013, for which the optical-optical efficiency was 23% versus the absorbed pump power in the continuous wave (CW) operation, and the output power was 200 mW in the passively Q-switching operation using the Cr:YAG crystal as the SA [22]. The end-pumping of a passively Q-switched pulse train laser with the composite crystal of YAG-Nd:YAG-Cr:YAG through the use of an 885 nm LD has been reported in 2017, for which the single pulse energy was 239 \(\upmu\)J with a pulse repetition frequency (PRF) of 86.3 kHz [23]. However, the end-resonant-pumping of a passively Q-switched Nd:YAG laser using graphene as the SA through the use of a 885 nm LD, with the advantages of a reduced thermal effect and a higher conversion efficiency, has not been reported yet.  
In this paper, we demonstrated a passively Q-switched Nd:YAG laser resonantly pumped by the 885 nm LD using an SA of graphene. In the CW operation, the absorbed pump power was 11.7 W, the average output power was 1.8 W and the slope efficiency was 51.2%, and the maximum average output power was 639 mW, with a pulse width of 2.06 \(\upmu\)s and a PRF of 102.7 kHz in the passively Q-switching operation.
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
###### Abstract  
In this paper, we propose a preprocessing strategy for denoising of speech data based on speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, it becomes more important as the deep learning-based methods have been developed because they require significant costs while showing high performance in general. The basic idea of the proposed method is using the speech segment detection so as to exclude non-speech segments before denoising. The speech segmentation detection can exclude non-speech segments with a negligible cost, which will be removed in denoising process with a much higher cost, while maintaining the accuracy of denoising. First, we devise a framework to choose the best preprocessing method for denoising based on the speech segment detection for a target environment. For this, we speculate the environments for denoising using different levels of signal-to-noise ratio (SNR) and multiple evaluation metrics. The framework finds the best speech segment detection method tailored to a target environment according to the performance evaluation of speech segment detection methods. Next, we investigate the accuracy of the speech segment detection methods extensively. We conduct the performance evaluation of five speech segment detection methods with different levels of SNRs and evaluation metrics. Especially, we show that we can adjust the accuracy between the precision and recall of each method by controlling a parameter. Finally, we incorporate the best speech segment detection method for a target environment into a denoising process. Through extensive experiments, we show that the accuracy of the proposed scheme is comparable to or even better than that of Wavenet-based denoising, which is one of recent advanced denoising methods based on deep neural networks, in terms of multiple evaluation metrics of denoising, i.e., SNR, STOI, and PESQ, while it can reduce the denoising time of the Wavenet-based denoising by approximately 40-50% according to the used speech segment detection method.  
n acse reduction; speech enhancement; speech processing; machine learning; data pre-processing +
Footnote †: journal: Applied Sciences