esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Isochoric Freezing on the Quality Characteristics of Raw Bovine Milk
## 3 Results and Discussion
### SPME GC/MS Analysis
However, if compared with RF results, HTST substantially improves preservation of milk flavor. By week 5, changes in HTST's volatile profile were apparent as there was a -50-fold increase in free fatty acid content suggesting milk spoilage. IF treatments resulted in an overall better preservation of raw milk VOC profiles, even after 5 weeks, with no notable changes (\(p>0.05\)) in FFAs and esters when compared to their initial values. Milk aroma profiles were also measured following isochoric freezing at \(-5\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C. Like the HTST findings, two weeks of IF storage resulted in relatively few significant changes in the aroma profile. The most significant change appears to be an increase in aliphatic hydrocarbons. Interestingly, the resulting aroma profiles between IF \(-5\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C were highly similar. Remarkably, 5 weeks of IF storage led to no substantive changes in the milk's volatile profile from their 2-week measurements. Such findings suggest IF effectively preserves milk flavor for time periods much longer than conventional pasteurization techniques. A principal components analysis was performed on the VOCs in order to identify possible relationships between storage conditions. Figure 8 plots the first two principal components (PC1 and PC2). The biplot shows that, apart from ketones, most chemical classes had high loadings in the first principal component. The sum of the first two PCs results in 71.5% of the variance being explained. As expected, isochoric samples are closely grouped to the initial milk sample. Figure 8: Principal component analysis of volatile samples in milk at initial conditions (+), isochoric freezing at \(-5\)\({}^{\circ}\)C (\(\blacksquare\)), isochoric freezing at \(-10\)\({}^{\circ}\)C (\(\blacktriangle\)), pasteurization and subsequent refrigeration (\(\bullet\)), and refrigeration only (\(\boxtimes\)). Numbers adjacent to symbols correspond to number of weeks storage at each condition.
###### Abstract  
The primary function of noise barriers is to shield inhabitants of affected areas from excessive noise generated by road traffic. To enhance the performance of noise barriers while simultaneously adhering to height restrictions, the attachment of structures (caps) of different shapes to the tops of conventional screens can be considered. These caps can significantly impact the diffracted sound energy, thereby increasing the desired global acoustic losses. This work presents a comprehensive study of the acoustic performance of noise barriers with single and double attached caps of different shapes through a calculation of their insertion losses (_IL_). This study comprehensively addresses and compares different types, sizes, combinations, and numbers of noise barrier caps for different scenarios (including sloping and absorbent grounds) and sources (\"car\" and \"ambulance\") for an extended frequency band up to 10 kHz. To the best of the authors' knowledge, this is a range that has not previously been analyzed. A variety of different cap shapes were considered including cylinders, rectangles, trapezoids, and Y/T-shaped forms. To calculate the _IL_, an innovative and fast uniform theory of diffraction (UTD)-based method developed by the authors was applied in all simulations. The results showed that the Y-shaped single and double barrier caps were, in general, the most effective at increasing _IL_ without raising the height of the barrier, thereby successfully managing the aesthetic impact. The results also showed how the consideration of sloping and absorbent floors could also contribute to improved noise abatement.  
traffic noise mitigation; noise barriers; noise barrier caps; sound attenuation; uniform theory of diffraction +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Physicochemical Properties of Cookies
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multicolumn{3}{c}{**CP Replacement Levels**} \\ \cline{2-4}  & **Control (0\%)** & **T1 (2.5\%)** & **T2 (5\%)** & **T3 (10\%)** \\ \hline \multicolumn{4}{c}{Physical-Chemical Properties} \\ \hline L* & \(75.64\pm 3.74\) a  & \(71.37\pm 2.34\) b  & \(68.82\pm 2.45\) c  & \(64.82\pm 2.35\) d  \\ a* & \(10.12\pm 1.15\) a  & \(12.73\pm 1.34\) b  & \(16.08\pm 0.92\) c  & \(15.55\pm 1.46\) c  \\ b* & \(25.23\pm 0.65\) a  & \(27.61\pm 1.23\) b  & \(28.76\pm 1.07\) c  & \(30.27\pm 2.23\) c  \\ Chroma & \(27.18\pm 0.9\) a  & \(30.40\pm 1.3\) b  & \(32.92\pm 0.9\) c  & \(34.03\pm 1.8\) d  \\ \(\Delta\)E & N/A & \(5.55\pm 0.9\) c  & \(9.72\pm 0.7\) b  & \(13.11\pm 0.8\) a  \\ Water activity (Aw) & \(0.18\pm 0.01\) a  & \(0.18\pm 0.01\) a  & \(0.17\pm 0.02\) a  & \(0.17\pm 0.01\) a  \\ Hardness (g force) & \(7576.04\pm 234.65\) a  & \(7054.54\pm 321.49\) b  & \(7173.57\pm 269.87\) b  & \(7238\pm 207.65\) b  \\ \hline \multicolumn{4}{c}{Sensory Characteristics} \\ \hline Color & \(7.07\pm 1.26\) a  & \(6.54\pm 1.87\) ab  & \(6.03\pm 1.62\) b  & \(4.99\pm 1.67\) c  \\ Aroma & \(6.67\pm 1.76\) a  & \(6.15\pm 1.09\) ab  & \(6.05\pm 1.05\) b  & \(4.78\pm 1.67\) c  \\ Flavor & \(6.63\pm 1.65\) a  & \(6.26\pm 1.54\) b  & \(6.09\pm 1.86\) b  & \(4.76\pm 2.05\) c  \\ Texture & \(6.65\pm 1.34\) a  & \(6.32\pm 1.14\) a  & \(6.09\pm 1.76\) ab  & \(4.07\pm 1.16\) c  \\ Overall liking & & & & \\ Before & \(6.12\pm 1.45\) a  & \(5.05\pm 1.54\) a  & \(5.43\pm 1.43\) b  & \(4.05\pm 2.02\) c  \\ After & \(6.23\pm 1.56\) a  & \(5.09\pm 1.32\) a  & \(5.21\pm 1.32\) b  & \(4.01\pm 1.45\) c  \\ \multicolumn{4}{c}{Purchase intent (\%)} \\ Before & \(65.25\) & \(51.50\) & \(42.50\) & \(36.50\) \\ After & \(64.50\) & \(50.50\) & \(45.75\) & \(33.75\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Physical-chemical properties and sensory characteristics of carao honey cookies. In the same way, the cookies with carao honey had reasonably homogeneous behavior in all the analysis dates, with an increase in the green hue value (a) being demonstrated in the cookies with a higher percentage of carao. If the data are analyzed in detail, it can be seen that 10% of carao differs significantly from the other percentages. For the variable b*, which corresponds to blue and yellow, it can be determined that the tendency is towards yellow colors; however, as more carao is added in the formulation, the tendency for yellow decreases, with the highest values being those with 0% carao and the lowest being those with 10% carao. As a result, significant differences in \(\Delta\)E and chroma values were found among the carao honey addition levels, indicating that panelists can effortlessly differentiate the cookie's color. For water activity, carao honey incorporation did not influence the aW values.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 2 Pull-Out Test  
### Test Program  
As shown in Table 1, four pull-out tests were conducted on the T-perfobond connectors and notched T-perfobond connectors. The aims were to obtain the failure mode, ultimate pull-out resistance, and load-separation behavior of the notched T-perfobond connectors and to verify the reliability of the finite-element analysis. Depending on the type of connection, these specimens can be evenly divided into the following two groups: PFR specimens with a flange of the same material at the bottom of the perfobond connector to enhance the pull-out resistance of the specimens and PMFR specimens with a notch at the bottom edge of the perfobond including the flange. The main parameters of the pull-out test specimens are the hole length (\(d_{l}\)), hole height (\(d_{h}\)), rib thickness (\(t_{p}\)), rib length (\(l_{p}\)), the width of the flange of the perfobond connector (\(b_{f}\)), the thickness of the flange (\(t_{f}\)), the length of the flange (\(l_{f}=l_{p}\)), the width of the notch (\(c_{w}\)), and the diameter of the rebar (\(d_{r}\)). P denotes the connecting piece, R represents for the penetrating rebar, F corresponds to the flange, and M represents the notch.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 5 Experimental Results
### Robustness Evaluation
The additional numerical experimental test was performed on a chosen binary watermark image called \"cup\" of size 32 \(\times\) 32, which was embedded in the standard host image \"Lena\" and attacked using different common types of attacks, resulting in a fair comparison between the proposed method and moment-based methods [28; 29; 41; 60; 61]. The extracted watermark within the corresponding values of BER and NC is notated in Table 4.  
Figure 10: Average NC values of the proposed and compared methods for common attacks. Figure 9: Average BER values of the proposed and compared methods for common attacks. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Attacks** & **QEMs [(60)]** & **QPHTs [(61)]** & **QLEMs [(29)]** & **QRSCMs [(28)]** & **MFrEMs [(41)]** & **Proposed Method** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Rotation \\ Angle \\ \end{tabular}  } & \multirow{2}{*}{\(15^{*}\)} & \multirow{2}{*}{\(35^{*}\)} & \multirow{2}{*}{\( \begin{array}{c}\text{Ber}=0.0557\\ \text{Nc}=0.9316\end{array} \)} & \multirow{2}{*}{\( \begin{array}{c}\text{Ber}=0.1533\\ \text{Nc}=0.9741\end{array} \)} & \multirow{2}{*}{\( \begin{array}{c}\text{Ber}=0.0361\\ \text{Nc}=0.9551\end{array} \)} & \multirow{2}{*}{\( \begin{array}{c}\text{Ber}=0.0449\\ \text{Nc}=0.9445\end{array} \)} & \multirow{2}{*}{\( \begin{array}{c}\text{Ber}=0.0176\\ \text{Nc}=0.9782\end{array} \)} & \multirow{2}{*}{\( \begin{array}{c}\text{Ber}=0.0050\\ \text{Nc}=0.9953\end{array} \)} \\ \cline{2-2} \cline{6-8}  & & & & & & & & & \\ \cline{2-2} \cline{6-8}  & & & & & & & & & \\ \cline{  
The simulation results of the proposed method and compared methods [28; 29; 41; 60; 61] of different NC values under various common attacks are also presented in a graphical simulation in Figures 11-13. These results denote that the proposed method exceeded other existing methods and was robust to the various tested attacks. Figure 11: Graphical comparative of NC values under a scaling attack with different parameters. Figure 12: Graphical comparison of NC values using combination attacks with different parameters. Figure 13: Graphical comparison of NC values under common noise and filtering attacks. From these results, we conclude that our method outperformed other existing methods. The watermark could be realized under various attacks suitably, and it seemed close to its original. In addition, the numerical values of BER and NC proved that the robustness of the proposed model surpassed other similar models. We compared our method with more exiting statistical watermarking methods [6; 9; 16; 18; 33] in a comparison of maximum PSNR values of the watermarked images and the robustness of the extracted watermark over NC values under common attacks as shown in Table 5, from these values we can note that our proposed method succeeded in achieving a high robustness level and visual imperceptibility compared with others methods.
## 3 Related Work
### Milp Formulations  
Several competing approaches for the Challenge were based on MILP formulations. Many constraints have indeed a linear formulation in the specification [14]. There were three types of MILP solving: straightforward B&B based on a compact formulation, a Bender's decomposition, and an extended formulation for a Column Generation (CG) approach.  
Several works noticed that scheduling constraints from CT14 to CT21 are modeled efficiently with MILP formulations using time indexed formulation of constraints [25; 26]. Straightforward MILP solving is efficiently modeling only CT14 to CT21 constraints [25]. We note that a preliminary work designed a compact MILP model with an exact description of all the constraints (including the CT6 and CT12 ones) but with too many binary variables to hope for a practical efficiency [37].  
Only one exact method did not aggregate the stochastic scenarios in a Bender's decomposition [26]. Relaxing constraints CT6 and CT12, an MILP formulation is designed with binary variables only for the outage decisions. Bender's master problem concerns the maintenance dates and the refueling quantities, whereas independent Bender's sub-problems are defined for each stochastic scenario with continuous variables for productions and fuel levels. Even with the weekly aggregation of production time steps, limitations in memory usage and computation time do not allow for deploying entirely the Bender's decomposition algorithm. The heuristic of [26] computes the LP relaxation exactly using Bender's decomposition, a cut and branch approach repairs integrity, branching on binary variables without adding new Bender's cuts, and solutions are repaired considering CT6 and CT12 constraints. The resulting matheuristic approach was efficient for the small instances, and difficulties and inefficiencies occur for the real size instances. Knowing that the time step aggregation keeps dual bounds thanks to [17], dual bounds can be computed after the LP relaxation and the cut and branch phase. However, no such dual bounds are reported by [26].  
An exact formulation of CT6 constraints was considered in a CG approach dualizing coupling constraints among units and aggregating the stochastic scenarios to the average one [27]. Production time steps were also aggregated weekly. These two reductions are not prohibitive to compute dual bounds, contrary to a third one: the production domains are discretized to solve CG subproblems by dynamic programming. This is a heuristic reduction of the feasible domain, and it does not provide dual bounds for the original problem. A CG approach was also investigated to complexify the model after the Challenge in [19], with a robust and multi-objective extension searching considering delays in the maintenance operations, as in [38]. In the recent work [19], CG computations are tractable restricting computations to horizons of 2-3 years, instead of the 5-year time horizon of the Challenge.
## 1 Introduction
In particular, the main contribution of this work is the development of a control scheme that exhibits the following features:  
* Addresses, for the first time, output prescribed performance specifications in accordance with input constraints both on amplitude and the rate of the control signals, and guarantees the boundedness of the closed-loop signals;
* Unlike most of the related works, imposes prescribed state constraints, by combining APPC approach and saturating the generated state reference trajectories of flight-path-angle, pitch angle, angle-of-attack and pitch rate in order to navigate the UAV efficiently, avoiding destabilizing phenomena such as stall;
* Is approximation-free, thus does not require either knowledge of the system nonlinearities as in [9; 10; 11; 12; 13; 14; 15; 16; 17; 24] or any disturbance observer as in [25; 26; 27]. Additionally, the gain tuning constitutes a straightforward task in contrast with [7; 8] and the complexity of the resulted robust controller is low, which facilitates the implementation as motion autopilot in small fixed-wing UAVs.
## 2 Materials and Methods
### Chromatographic Analysis  
#### 2.6.1 Sample Preparation and Chromatographic Analysis  
Before the HPLC-DAD (diode array detector) analysis, circular pre-injection filters were used to filter methanolic extracts (0.45 \(\upmu\)m, polytetrafluoroethylene membrane, PTFE). For the absorption of the polyphenic fraction for the vitamin C analysis, a C\({}_{18}\) cartridge was applied for solid phase extraction (Waters, Milford, MA, USA). Then, for dehydroascorbic acid (DHAA) derivatization into the fluorophore 3-(1,2-dihydroxymethyl)fluoro(3,4-b)quinoxaline-1-one (DFQ), 750 \(\upmu\)L of each sample were diluted with \(o\)-phenylenediamine (OPDA) solution (18.8 mmol L\({}^{-1}\)). After 37 min in the dark, samples were ready to be analyzed using HPLC-DAD [21].  
Concerning the chromatographic analysis, an Agilent 1200 High-Performance Liquid Chromatograph combined to an Agilent UV-Vis diode array detector (Agilent Technologies, Santa Clara, CA, USA) was used. To separate the bioactive substances, a Kinetex C18 column (4.6 \(\times\) 150 mm, 5 \(\upmu\)m; Phenomenex, Torrance, CA, USA) was used. The samples were analyzed using five chromatographic methods and different mobile phases were used in order to separate and identify the compounds, while UV spectra were recorded at different wavelengths [22] (the mobile phases are listed in Table S1).  
In order to obtain a phytochemical fingerprint with all the relevant information on the chemical composition, with a proper resolution and a reasonable analysis time, the chromatographic conditions were set. To optimize the molecule separation, different linear gradients in different slopes were used. Indeed, the same chemical class had similar compounds in term of structure. Since most of the compounds were also weakly acidic, we added formic and phosphoric acid to enhance the resolution and eliminate peak tailing. The selection of the wavelengths was essential to develop a reliable fingerprint. As a matter of fact, only the selected wavelengths achieved more specific peaks, as well as a smooth baseline after a full-scan on the chromatogram from 190 to 400 nm.
# Bionic Design of the Vertical Bracket of Wide Angle Auroral Imager by Additive Manufacturing
## 5 Manufacture and Experiments of the Vertical Bracket of Wide Angle Auroral Imager
### Experiments  
To verify the stiffness of the structure and the accuracy of simulation result, 0.2 g sweep tests from 5-2000 Hz in three directions are selected, as shown in Figure 9a. Seven three-way sensors are arranged on the vertical bracket as shown in Figure 9b, including five measurement sensors and two control sensors, to record the response changes during the vibration test. Furthermore, the swept test curves are shown in Figure 9c-e. The comparison between the test results and the simulation results is shown in Table 4. From the first obvious peak of the sweep frequency curve, it can be determined that the three-way eigenfrequencies of the vertical bracket are 307 Hz, 292 Hz, and 736 Hz, respectively. The simulation analysis results in the same working conditions show that the eigenfrequencies in the three directions  
Figure 8: The printed model of the vertical bracket manufactured by SLM.  
Figure 7: Vibration modes of bionic optimized structure. (**a**) X-direction fundamental frequency. (**b**) Y-direction fundamental frequency. (**c**) Z-direction fundamental frequency.  
are 320 Hz, 303 Hz, and 765 Hz, respectively. The error rates of simulation and experimental results in the three directions are 4.2%, 3.7%, and 3.9%, respectively. The maximum error is 4.2%, which indicates that the accuracy of the simulation analysis results is high. The maximum error rate of simulation and experimental results in the three directions is 4.2%. The results of vibration test demonstrate that the stiffness of the structure is adequate. This optimal design scheme is viable and the performance is reliable.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 6 Evaluation of Algorithm Performance
### Cluster Interference  
The most common method used to reduce interference in a cluster is the transmitter-based encoding distribution, which is introduced in [44; 45]. In this method, each cluster has independent spread-spectrum encoding, and a unique spread-spectrum code is adopted for the communication between each node in the cluster and the cluster head. The node in the cluster uses this spread-spectrum code to transmit data to the cluster head, and the cluster head node filters the received signal based on the spread-spectrum code.  
Figure 9: Comparison of total energy consumption of sinks.  
In this paper, cluster interference is defined as the number of average cluster heads within one hop of one sensor node. When a sensor node wants to select a cluster head, the fewer broadcast packets it receives from the neighboring cluster head, the less energy it takes to receive packets. As shown in Figure 11, the average number of cluster heads in one hop using the CNP algorithm is significantly more than that using the PCEB-MS protocol when the number of sinks changes. The number of cluster heads obtained using the CNP algorithm is approximately four when the number of sinks is 12, but this number becomes two when the PCEB-MS protocol is used. Therefore, the probability of cluster interference is reduced by 50%, and, by using the PCEB-MS protocol, fewer sensor nodes are required to decide which cluster they should belong to.
# Qom--A New Hydroologic Prediction Model Enhanced with Multi-Objective Optimization
## 4 Hydrologic Calibration Strategy
### Qom Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Term** & **Description** \\ \hline \(A\) & basin area (km\({}^{2}\)) \\ \(Shape\) & shape coefficient (dimensionless) \(+\) \\ \(CT\) & concentration time (h) \(+\) \\ \(Eq\) & other alternative exponetial base to \(e=2.711828\)\(+\) \\ \(\%p\) & percent of pervious area \\ \(\%i\) & percent of impervious area \\ \(Ro\) & initial surface storage (mm) \\ \(So\) & initial soil storage (mm) \\ \(N\) & data register amount \\ \(Rmax\) & maximum surface storage (mm) \({}^{*}\) \\ \(Smax\) & maximum soil storage (mm) \({}^{*}\) \\ \(VC\) & volumetric conductivity coefficient (mm/h) \({}^{*}\) \\ \(i\) & vector register index \\ \(P\) & vector of precipitation, volume expressed in height (mm) \\ \(\Delta t\) & time step inter registers (s) \\ \(PEVT\) & vector potential evapotranspiration (mm) \\ \(R\) & vector of surface storage (mm) \\ \(EVT\) & vector of evapotranspiration (mm) \\ \(S\) & vector of soil storage (mm) \\ \(D\) & vector of percolated volume (mm) \\ \(z\) & percolation ratio between \(Rmax\) and \(R_{i}\) (dimensionless) \\ \(z_{2}\) & soil moisture ratio between \(Smax\) and \(S_{i}\) (dimensionless) \\ \(Sp\) & potential soil storage (mm) \\ \(\Delta S\) & soil moisture storage between the current state and the previous (mm) \\ \(Dp\) & percolated potential volume (mm) \\ \(CR\) & soil moisture by capillary rise (mm) \\ \(Vi\) & vector of runoff volume of impervious area (mm) \\ \(Vp\) & vector of runoff volume of pervious area (mm) \\ \(Vg\) & vector of percolated volume for losses or deep runoff (mm) \\ \(Ki\) & time delay for emptying reservoir \(Vi\) (h) \(\sharp\) \\ \(Kp\) & time delay for emptying reservoir \(Vp\) (h) \(\sharp\) \\ \(Kg\) & time delay for emptying reservoir \(Vg\) (h) \(\sharp\) \\ \(Qo\) & vector of observed output streamflow (m\({}^{3}\)/s) \\ \(Qe\) & vector of estimated output streamflow (m\({}^{3}\)/s) \\ \(Qs\) & initial superficial streamflow (m\({}^{3}\)/s) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the terms in flowchart of Figure 2 and in Equations.