esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Outlet Shrinkage on Hydraulics in Hyper-Concentrated Sediment-Laden Flow
## 3 Experimental Results  
### Flow Discharge  
The discharge of sediment-laden flow is a key control factor in the design of hydraulic structures in a hyper-concentrated river, and an appropriate flow rate ensures the safety of dams. Figure 4 depicts the relationships of discharges with two concentrations of sediments (0, 150 kg/m\({}^{3}\)) for three overflow shapes. The experimental results indicated that the discharge capacity of sediment-laden flow was basically similar to that of fresh water. The discharge capacity was only related to the upstream water head, regardless of the sediment concentration; this is consistent with the trends of other studies [26; 27; 28]. Therefore, it was only necessary to evaluate the flow rate based on fresh water when designing a hydraulic structure.  
Figure 4: Relationship between the upstream water head and the flow coefficient with different contraction ratios for fresh water and sediment-laden flow with concentrations of 150 kg/m\({}^{3}\).  
The outlet shrinkage of the overflow weir definitely affects the discharge capacity. The discharge capacity of the weir can be expressed by the flow coefficient as  
\[m=\frac{Q}{b\sqrt{2g}H^{3/2}}, \tag{8}\]  
where \(m\) is the flow coefficient of the overflow; \(Q\) is the discharge capacity; \(b\) is the overflow weir width; and \(H\) is the water head of the weir. The relationship between the flow coefficient of the weir and the upstream head for the three contraction ratios is shown in Figure 4. The results indicated that the flow coefficient of different shape of overflow weir generally increased first and then decreased with increasing water head. When the water head was below 10 cm, the coefficient merely increased with the water head and was not related to the contraction ratios--that is, the discharge capacity of the overflow weir was not affected by the width of the outlet. When the water head of the weir was over 10 cm, the flow coefficient gradually decreased with a decreasing contraction ratio. The influence of outlet shrinkage on the discharge capacity of the weir gradually increased. As the decreased contraction ratio gradually reduced the width of the outlet and enlarged the water depth, this affected the discharge capacity of water flow.
## 3 Methodology
### Rate of Change  
The rate of change was calculated by compiling six optical Landsat and Sentinel-2 images from 1972 to 2018 (see first six entries in Table 2). ArcGIS 10.2 was used to generate the normalized difference water index (NDWI) raster for each image using Equation (1) [41]:  
\[\mathrm{NDWI}\;=\frac{\mathrm{Green}\;-\;\mathrm{NIR}}{\mathrm{Green}\;+\; \mathrm{NIR}} \tag{1}\]  
For both Landsat and Sentinel-2 imagery, NIR (Near Infrared) represents bands at \(\sim\)840 nm while Green represents bands at \(\sim\)560 nm [33]. The water threshold for each image was chosen separately based on a visual evaluation. The water values for each image were then converted into a polygon and were overlaid on the current chart. A transect was drawn through the areas containing the largest visual changes in shoreline. The transects were measured from the 1972 shoreline to the 2018 shoreline. An average of the transects was taken and divided by the amount of time that passed, 46 years, to determine the average rate of change per year.
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
###### Abstract  
A total of 40 pyrazine compounds of cocoa and chocolate were discovered through a literature analysis. Data on the alkylpyrazines' antibacterial properties were gathered. It was discovered that 12 substances were effective against bacteria, fungus and oomycetes. Following calculations using PASS and AntiBac-Pred, 15 substances were identified as potentially having the ability to inhibit the growth of the _Picornavirus_, _Yersinia pestis_, Resistant _Mycobacterium tuberculosis_, _Mycobacterium bovis_ BCG (Bacillus Calmette-Guerin), _Micrococcus luteus_ and _Corynebacterium jekcium_. The study of odour qualities led to the identification of the most potent odourants among alkylpyrazines as well as the aroma composition of cocoa and chocolate. Pyrazines have been given GRAS (generally recognized as safe) status by the Flavor and Extract Manufacturers Association (FEMA) for use as flavouring additives in food, demonstrating that this class of chemicals is a secure substitute for managing and combating microbial contamination, which also provides beneficial odour properties to the recipient. A statistical evaluation of the correlation between the odour threshold and the probability of antibacterial activity was performed. A more in-depth investigation of the antimicrobial and olfactory activities of alkylpyrazines is required in the future.  
alkylpyrazine; pyrazine; heterocycles; antimicrobial activity; odour threshold; odour activity value; cocoa; chocolate +
Footnote †: journal: Applied Sciences
## 3 **Emotional and Social Indicator**
### Cohen's d Effect Size and Multiple Comparison Correction  
#### 4.2.1 Cohen's d Effect Size  
Calculate [PERSON]'s d to quantify the differences between the VR-based \"Co-Relay\" system (experimental group) and the web-based Story Fork platform (control group) using the following formula:  
\[[PERSON]^{\prime}s(d):(d=\frac{M_{1}-M_{2}}{SD_{\text{pooled}}}),(SD_{\text{pooled }}=\sqrt{\frac{(n_{1}-1)SD_{1}^{2}+(n_{2}-1)SD_{2}^{2}}{n_{1}+n_{2}-2}}).\]  
The effect size is evaluated based on the following criteria: if d \(<\) 0.2, it is classified as a \"tiny effect\"; if 0.2 \(\leq\) d \(<\) 0.5, it is considered a \"small effect\"; for values between 0.5 and 0.8, it is termed a \"medium effect\"; and if d \(\geq\) 0.8, it is characterized as a \"large effect.\"  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Classification of Indicators** & **Specific Indicators** & **Control Group (M \(\pm\) SD)** & **Experimental Group (M \(\pm\) SD)** & **t-Value** & \(p\)**-Value** & **Significance** \\ \hline Story writes & Mean Score & 5.45 \(\pm\) 0.31 & 6.08 \(\pm\) 0.14 & 1.85 & \(\approx\)0.07 & No significant difference \\ \hline Attentional Focus & Narrative Engagement & 2.17 \(\pm\) 0.23 & 4.51 \(\pm\) 0.29 & 6.32 & \(<\)0.001 & Highly significant difference \\ \hline Narrative Presence & Environment Presence & 3.39 \(\pm\) 0.22 & 6.10 \(\pm\) 0.21 & 8.92 & \(<\)0.001 & Highly significant difference \\ \hline Empathy & Emotional resonance & 2.9 \(\pm\) 0.8 & 5.3 \(\pm\) 1.0 & 1.87 & \(\approx\)0.07 & No significant difference \\ \hline Flow & Flow State Perception & 2.3 \(\pm\) 1.2 & 5.8 \(\pm\) 1.1 & 2.15 & \(<\)0.05 & Significant difference \\ \hline Innovation & Plot Originality Score & 3.1 \(\pm\) 1.0 & 5.7 \(\pm\) 1.2 & 1.66 & \(\approx\)0.10 & No significant difference \\ \hline SUS Score & System Availability & 57.82 \(\pm\) 1.80 & 81.74 \(\pm\) 5.42 & 4.19 & \(<\)0.001 & Highly significant difference \\ \hline NASA-TLX & Cognitive load & 77.43 \(\pm\) 1.85 & 50.97 \(\pm\) 2.44 & 8.64 & \(<\)0.001 & Highly significant difference \\ \hline Teamwork & Collaboration level & 3.50 \(\pm\) 0.32 & 5.67 \(\pm\) 0.68 & 2.89 & \(<\)0.01 & Significantly higher \\ \hline \end{tabular}
\end{table}
Table 7: Standard questionnaire scoring (1–7).  
Figure 13: Bar chart of SUS and NASA-TLX data (with individual group data).
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 3 Background
### Threat Description
#### 3.3.1 Worms  
These can cause greater damage to networks than viruses because, in a small burst of time, a worm attack can automatically infect millions of hosts [31]. Worms autonomously exploit vulnerabilities in the target system, as well as penetrate and replicate them subject to the conditions stated in Section 3.2. A worm propagation model involves the target acquisition, elevation of privilege, and infection phase [32]. Sophisticated worm attacks on both applications and protocols are quickly evolving and resulting in greater vulnerability exploits from minimized low-cost attack efforts [33, 34].
# Virtual Power Plant Optimization in Smart Grids: A Narrative Review
## 3 VPP Concept and Technology
### Energy Forecasting  
The forecasting of energy demand, generation, and energy prices are fundamental inputs of the VPP management and optimization problem [22]. The prediction accuracy impacts the quality of the VPP solution; thus the uncertainty should be considered in the optimization problem formulation [55]. The stochastic nature of renewable energy generation and volatility of energy prices are limiting factors for VPP participation in energy markets [1]. Therefore, it is important to improve the quality of the forecasting models andtechniques to lower as much as possible the impact of these uncertain parameters on the optimization problem [23].  
Nowadays the literature is addressing the forecasting of energy demand, generation, and price using machine learning models [24]. Few approaches can be found in the prediction of the energy flexibility of customers which are relevant for building VPPs and energy communities [25]. Deep learning models are reported to provide good accuracy results for short-term energy use prediction [26]. Most promising in energy forecasting are artificial neural networks using long short-term memory (LTSM), convolutional neural networks (CNN), multi-layer perceptron (MLP), or recurrent neural networks (RNN) to achieve more accurate energy predictions [27]. Lately, ensemble-based approaches are also providing good forecasting results. For example, hybrid energy forecasting models are reported based on CNN and LTSM [28], two-hidden-layer LSTM and two-hidden-layer CNN [29], CNN-LSTM-RNN hybrid networks [30], and LTSM-RNN [31] hybrid models.  
Analyzing the current state of the art here is very difficult in comparing existing models and technologies in terms of performance, prediction accuracy, and impact on the virtual aggregation of energy assets [32]. The quality of the prediction outcome is influenced by factors such as seasonality, social factors, the intrinsic physical parameters of the assets, time step, and prediction interval [23]. There is no consensus on which is the most suitable forecasting model, so the exploration of more than one technique on the data at hand, and the comparative evaluation of these, is recommended for VPP optimization. Forecasting models can have different accuracy considering the data at hand [33]. There may also be differences in the quality of the prediction results for different time intervals and this significantly impacts the VPP operation processes.  
Finally, the prediction processes can be joined with the characteristics of energy assets and communities determined using physics-informed DT models [34]. They are implemented using big data infrastructure to consider the energy meters' data as well as a significant number of energy assets in VPP management and operation. Such solutions consider the social and behavioral characteristics of customers, local communities, etc., that have the potential of improving the prediction accuracy and thus reducing the uncertainty related to VPP optimization [23].
## 4 SRSI for Temporal Profile Characterization
### _Sd-Srsi_
(**d**) Reconstructed temporal intensity profile of the spectral phase in (**c**) obtained by SD-FROG (red solid line) and SD-SRSI (black dotted line) [43]. Figure 10a,b show the original data obtained by SD-SRSI and SD-FROG measurements of a sub-10 fs pulse at 400 nm. Figure 10c,d show the reconstructed spectra and spectral phase of the unknown pulse obtained by SD-SRSI and SD-FROG, respectively. Even a sub-10 fs pulse at 400 nm can be measured using this setup. However, the setup is complex, because three beamsplitters are necessary to split the beam into three beams. We simplified the setup by using simple fixed holes to split the beam into three [59]. Experiments showed that the SD signal has an obvious angular chirp because SD is a non-collinear optical parametric process that needs to satisfy the phase-matching condition. Figure 11 shows a typical angular chirp of an SD signal. If a different part of the SD signal is selected for the reference pulse, its angular chirp makes the spectrum of the reference pulse different, which will introduce error into the measured result. This angular chirp is not easy to remove in the SD process. Compared to the XPW-SRSI method, the SD-SRSI method has the following disadvantages: (1) The SD process requires a phase-matching relationship and a relatively thin nonlinear medium, whereas the XPW process is self-phase-matched; (2) the SD process needs a non-collinear geometry and two incident beams, which makes the system more complicated. In addition, the two beams must be carefully aligned and stably synchronized; (3) because the SD process requires a thin nonlinear medium and a non-collinear geometry, its efficiency is expected to be lower than that of XPW for the same beam and pulse parameters. On the other hand, the SD-SRSI method has the following advantages: (1) The SD-SRSI method is less limited in the spectral range and pulse duration because no polarizer is used. Therefore, it can be used to measure a shorter pulse in a broader spectral range, from UV to middle IR, using one optical setup; (2) by using SD-FROG measurement, it can simultaneously be used to measure pulse duration with a relatively large chirp; this cannot be done using the XPW-SRSI method; (3) because the SD effect appears in all third-order nonlinear media, birefringence does not  
Figure 10: (**a**) Spectra of the compressed pulse at 400 nm (red dotted line), the SD1 signal (blue line), and the interference between them (black line). (**b**) Reconstructed spectrum (black solid line) and spectral phase (blue dotted line) obtained using SD-SRSI. (**c**) Two-dimensional SD-FROG trace of the compressed pulse at 400 nm. (**d**) Reconstructed spectrum (black solid line) and spectral phase (blue dotted line) obtained using SD-FROG [43]. need consideration. An incident pulse with lower energy should operate in a higher third-order nonlinear medium.
## 2 Materials and Methods
### Sample Size Calculation  
A priori power analysis was conducted using G*Power (version 3.1.9.4) to determine the required sample size. Separate analyses were performed for crown and fixed partial dentures using a two-way ANOVA. With a significance level of \(\alpha\) = 0.05, a power of 0.90, and an effect size (\(f\)) of 0.50, the minimum required sample size per group was calculated as 54. Thus, 120 specimens were included in the study (\(n\) = 10 per subgroup).
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 4 Query Processing
### Continuous Spatio-Temporal Query  
A Continuous Spatio-Temporal Query (_CSTQ_) is a query that retrieves \(Traj\)s based on three dimensions: 2-D space and time. Therefore, when \(k\)-CSTQ = \(\{<RQ_{1},IQ_{1},flag_{1}>,<RQ_{2},IQ_{2},flag_{2}>,\ldots,<RQ_{k},IQ_{k}, flag_{k}>\}\) is given on a trajectory set \(T\), the result must include any \(Traj\in T\) such that  
\[\forall i\in[1,2,\ldots,k],\ Traj_{space}\ \textcircled{5}\ RQ_{i}\wedge Traj_{time}\ \textcircled{1}\ IQ_{i}\, \tag{9}\]  
where the operators \(\textcircled{5}\) and \(\textcircled{1}\) are already defined in Equations (7) and (8), respectively.  
As k-CSTQ follows the same processing steps as in Algorithm 2, we will only focus on the important and novel steps. RMOI starts collecting the ReqPids sets by traversing the GlobalRMOI on both spatial and temporal properties. It traverses down the tree if and only if the global node's MBR and interval intersect with the query clause's \(RQ_{i}\) and \(IQ_{i}\), consecutively. After that, it begins the local reduction on all the engaged RDD partitions by traversing all the corresponding LocalRMOI for each _CSTQ's_ clause. This traversing of LocalRMOI based on _CSTQ's_ clauses is conducted on the interval B-tree and the lower part of the STRtree. It starts from the interval B-tree such that, for any interval intersecting with the \(IQ_{i}\), RMOI will traverse the corresponding sub-STRtree on \(RQ_{i}\). The retrieved \(Traj\)s intersects with both \(IQ_{i}\) and \(RQ_{i}\).
# Pullout Response of Ultra-High-Performance Concrete with Twisted Steel Fibers
## 2 Analytical Model  
### Model Assumption  
The twist geometry of twisted fiber and the physicochemical bond between fiber and concrete matrix cause the pullout response complicated. To understand the general pullout behavior, the typical pullout force data for the UHPFRC with twisted steel fibers were reviewed and shown in Figure 1a. The data were collected from the existing experimental studies from [PERSON] and his coworkers [1]; our experimental data will be used to verify the developed analytical model in SectionExperimental and verification. Based on the previous experimental data, we empirically assume that the relationship of pullout force and pullout displacement is a tri-linear response with three stages, as shown in Figure 1b.  
The first stage is the linear debonding stage, where the pullout force is increasing linearly with the pullout displacement. During the pullout process, the shear force increases up to the fiber-matrix bond strength and debonding occurs at the fiber-matrix interface after the shear force exceeds the bond strength. At the end of this elastic stage, the twisted fiber completes the debonding and starts to untwist, entering into a plastic untwisting stage. The pullout force is assumed constant as \(P_{f}\) in this process when the remaining embedment length \(L_{\text{e}}\) is larger than the effective untwisting length \(\Delta l\). Only the friction on the interface of the effective untwisting length is considered. In addition, the steel fiber is assumed as an ideal plastic material and there is no damage in high-strength UHPC matrix during the pull-out process. At the end of this process, the pullout force is dropped to zero with a high dropping rate, entering a dropping stage.
# Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests
## 2 Materials and Methods
### Specim Preparation  
In total, 37 samples were prepared, of which 35 were made with fiber. In addition, two samples without fiber were prepared to determine the experimental noise. The procedure for sample fabrication is detailed below.  
The 35 fiber samples are prepared by extracting a single carbon fiber from the tow. The fiber is arranged in the center of the mold and then taped so that it retains constant tension. The resin is prepared according to the manufacturer's recommendation in a 2:1 ratio. The prepared mixture is degassed by ultrasound for 10 min. The resin is then poured into the mold using a syringe so that no bubbles remain in the preparation. The curing time is 48 h at room temperature and then post-curing is carried out for 16 h at 70 \({}^{\circ}\)C in a muffle oven. The specimens are sanded symmetrically to a thickness of 2 mm and then polished to a mirror finish of 0.3 \(\upmu\)m alumina grain size. The specimen geometry is shown in Figure 1a and the fabrication procedure is schematized in Figure 1b, where each stage up to the final mirror finish is shown. Analogous to the previous procedure, the two samples without fiber are prepared.  
Before testing, the integrity of each sample is verified using a Nikon Optihot2 Pol polarized microscope (Nikon Instrument Inc., New York, NY, USA) with an objective lens 20\(\times\). A correct alignment of the fiber within the matrix and the presence of significant bubbles within the specimen is sought, discarding the samples that do not satisfy the standard.
# Approximate LSTM Computing for Energy-Efficient Speech Recognition
## 5 Simulation and Implementation Results  
### Algorithm-Level Performance  
In order to show the algorithm-level innovations from the proposed similarity-based LSTM operations, we simulated various processing scenarios using the DeepSpeech network on the LibriSpeech dataset [14], which are evaluated based on the tensorflow framework [21] to realize the original LSTM processing [15], the delta-based computation [12], and the proposed multi-mode approach. To evaluate the computational costs quantitatively, for each LSTM processing method, we measured the number of active MAC operations, and the number of external memory accesses, which are known to be the two most energy-consuming workload in LSTM operations [22]. For the cost-reduction techniques, we allowed a 1% accuracy drop from the baseline WER.  
Figure 9 illustrates the simulation results of different LSTM processing approaches, where the number of MAC operations and the number of memory accesses are normalized to those of the baseline bi-directional LSTM processing [15], which fully utilizes LSTM cells for achieving the most accurate WER result. When compared to the baseline network, more precisely, we can save the number of external memory accesses and the number of MAC operations by 55% and 49%, respectively. Note that our algorithm is also superior to the prior delta-based LSTM processing, as depicted in Figure 9. Therefore, the proposed similarity-based LSTM processing provides the most cost-efficient way to handle speech recognition by dynamically adjusting the processing mode.  
Figure 8: Sparsity-aware buffer accessing method for approximate LSTM processing.  
Figure 9: Computing cost reduction by the proposed algorithm-level optimization.