esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
It has been demonstrated that it is more difficult to objectively quantify milk spoilage than organoleptically. Some types of milk, with a standard plate count of 10\({}^{6}\) CFU/mL, are perfectly palatable, whereas others have become unpalatable. Further, it is even more difficult to estimate \"keeping quality\", the number of days between manufacture and spoilage [1].  
Keeping quality is essentially determined by the rate of production of off-flavors and off-tastes. As such, it is only indirectly related to the bacterial quality of milk. Thus, a bacterial strain which produces more off-flavors will obviously cause spoilage at a lower count than one which produces lesser off-flavors [2]. In addition, off-flavor production may also vary with temperature, storage time and substrate availability, which itself may be produced by other bacteria [3].  
Pasteurized milk-induced spoilage causes significant economic losses to food industry. [PERSON] and [PERSON] [4] reported that the presence of large populations of _Pseudomonas_ spp. generally resulted in shorter shelf-lives for pasteurized milk than if products were contaminated with other types of organisms [4]. Thus, a mixed culture of bacteria may produce more off-flavors than those containing individual strains.  
Many attempts have been made to correlate the keeping quality of milk with chemical, physical or bacteriological parameters. The most promising methods have been impedance measurement [5], and acetaldehyde estimation in milk headspace [6]. Nonetheless, all of these methods measure chemical changes produced by bacteria rather than their actual numbers and as such might be expected to correlate better with organoleptic quality than would standard plate count or psychotropic count [7].  
Volatile compounds in milk have been extensively studied using different extraction techniques including static headspace, purge and trap, and solvent-assisted flavor evaporation [8; 9; 10]. The headspace solid-phase microextraction (HS-SPME) has been developed. This technique is able to substantially reduce analysis time and sample manipulation steps [11]. Indeed, HS-SPME has been widely used to extract volatile components from dairy foods, such as fluid processed milk, cheese, milk powder and milk chocolate [12]. On the other hand, many studies have reported the off-flavor development in milk at a given storage time, usually at the end of the product shelf-life. However, there is limited information in literature regarding changes in milk flavor compounds, as affected by storage time and distinct temperatures. Thus, the present study aims to identify and to quantify volatile compounds in pasteurized milk, stored at different temperatures and specific time periods, for possibly correlated changes in volatile compounds to sensory and microbiological attributes for keeping milk quality.
## 5 Evaluation Scenario
### Traffic and Network
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter(s)** & **Values** & **Corresponding Time Ranges (h)** \\ \hline \(A_{min}^{a,b}/A_{max}^{a,b}\) & \(A_{min}^{a,b}/A_{max}^{a,b}\)\(\in\)\(\{0.25,0.5,0.75,1.0\}\) & - \\ \hline \(T_{low}\) & \(|T_{low}|\)\(\in\)\(\{0,18,36,54,72,90\}\) & 0–7.5 \\ \(T_{high}\) & \(|T_{high}|\)\(\in\)\(\{126,162,180,198,216\}\) & 10.5–18 \\ \hline \(T_{inc}\) & \(|T_{inc}|\)\(\in\)\(\{0,9,18,27,36,45\}\) & 0–3.75 \\ \(T_{dec}\) & \(|T_{dec}|\)\(\in\)\(\{0,9,18,27,36,45\}\) & 0–3.75 \\ \hline \(k\) & \(k\)\(\in\)\(\{0,1,6,12,24,36,48,72,96,120,144\}\) & 0–12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Varied parameters of the traffic model with \(\Delta t=5\,\mathrm{min}\). reference time zone. We choose Greenwich Mean Time (GMT) as the reference time zone. The TZM \(\Theta\) for the Abilene network is created according to the time zones in which network nodes are located. Five randomly generated SVMs \(\Gamma\) are considered. Eventually, we vary the time displacement parameter \(k\in\{0,1,6,12,24,36,48,72,96,120,144\}\). We assume that each fiber carries \(B=80\) wavelengths and each wavelength channel has capacity \(C=40\) Gbps. We consider a set of routers \(N\) with capacity \(R_{n}\) in the range 640-14720 Gbps [37]. We assume overprovisioning in the SBN design phase and limit maximum utilization of each lightpath to 50% (i.e., \(\delta=0.5\)). Geographical node locations are determined by the corresponding cities (with the two nodes in Atlanta being 2 km away from each other). Lengths of physical links are calculated using the spherical distance between their end-nodes. The maximum lightpath length is set to 3000 km.
# Experimental Research on the Properties of Rock-Filled Concrete
## 3 Experimental Tests for the Properties of Rock-Filled Concrete
### Compressive Test  
Fined cores are needed to obtain good results in compressive tests and considering that some contained large defects cannot be chosen, core specimens should be carefully chosen. Based on \"Test code for hydraulic concrete (SL352-2006)\", core specimens for compressive test should be cut to a height-diameter ratio of 1:1 with dimensions of \(\phi\)88 mm \(\times\) H88 mm. After specimen preparation, there were only 13 specimens that met the test requirements. In accordance with \"test code for hydraulic concrete (SL352-2006)\" (Ministry of Water Resources of the P.R.C. 2006) [30], first, the specimen should  
Figure 9: Histogram on ultrasonic velocity of RFC.  
be aligned with plate center of a universal testing machine, then the test should be started. When the upper plate of the universal testing machine is close to the top surface of the specimen, we adjust the spherical hinge to make the plate contact specimen in a balanced way, then we load the specimen at a uniform rate of 0.5 MPa/s until broken. Experiments show that the average compressive strength of RFC is 22.76 Mpa, with a standard deviation of 4.32 Mpa. Converting this to cube compressive strength (\(f_{\text{cu}}\)) (cube dimension 150 mm \(\times\) 150 mm \(\times\) 150 mm), the average is 22.55 Mpa, with a standard deviation of 4.09 Mpa, which is shown in Figure 10. The cube compressive strengths of specimens are well distributed, and all are more than 15 Mpa. Converting them to standard cube compressive strength (\(f_{\text{cuk}}\)), \(f_{\text{cuk}}=\mu_{f_{\text{cu}}}-1.645\sigma_{f_{\text{cu}}}=15.82\) Mpa,\(f_{\text{cuk}}\) for all specimens are more than 15 Mpa, which meet the design requirements. According to the compressive tests, the compressive strength of SCC is 15.92 Mpa, which also meets the design requirements. Compared with rock block which has an average compressive strength of 51.77 Mpa, the compressive strength of RFC drops dramatically, with a decrease of about 43.5%; and a slight increase compared with SCC, whose average compressive strength is 17.6 Mpa. Therefore, the quality of massive RFC is mainly controlled by the construction technology used and only slightly influenced by SCC and rock block.  
As shown in Figure 10, the compressive strength of some specimens reached 56.36 Mpa, far higher than other specimens. These specimens were taken from borehole No. 3 at the depth of 14.30 m\(\sim\)14.50 m, which porosity, permeability, ultrasonic velocity are 7%, 5.59 Lu, 2707 m/s, respectively. Compared with high compressive strength, those values are relatively low, probably because of the good compaction of this specimen as it is, mainly composed of hard rock block with little rock joints and ITZ. However, the quality of RFC in this part is common. It can thus be seen that the homogeneity of RFC is inferior.
## 4 Empirical Analysis
### Analysis of Alternatives from All Respondents
Among service elements in the category of community, the improved parking environment system (CC\({}_{\text{i}}\): 0.54), smart safety community (CC\({}_{\text{i}}\): 0.39), and AI guide system for village information (CC\({}_{\text{i}}\): 0.20) ranked 7 th, 10 th, and 12 th, near the bottom. The reason for these low ranks is in part because they are indirect forms of support without directly engaging in crime response and prevention. Notably, even though the AI guide system for village information provided a variety of information about crime prevention to residents, it was rated most poorly among other intelligent CPTED technology elements. Although \begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Sub-Category**} & \multirow{2}{*}{**FPIS (d\({}_{\text{i}}^{\star}\))**} & \multirow{2}{*}{**FNIS (d\({}_{\text{i}}^{-}\))**} & \multirow{2}{*}{**CC\({}_{\text{i}}\)**} & \multirow{2}{*}{**Rank**} \\ \cline{3-3} \cline{5-6}  & CCTV system for AI (e.g., high-resolution CCTV systems, Image function improvement) & & & & \\ \cline{2-6} AI and CCTV & AI video analysis for living safety (e.g., detection for littering, drunk people/smoking) & & & & \\ \cline{2-6}  & AI video analysis for crime prevention (e.g., automated tracking, assault detection) & & & & \\ \hline \multirow{6}{*}{IoT and lighting} & IoT automatic situation recognition system (e.g., automatic recognition/warning) & & & & \\ \cline{2-6}  & Intelligent pedestrian environment system (e.g., walking guide lights for pedestrian safety) & & & & \\ \cline{2-6}  & Smart street lights (e.g., scream recognition, emergency bells, voice warnings) & & & & \\ \hline \multirow{6}{*}{Platform} & Crime monitoring system (e.g., crime maps, CCTV analysis, automatic link to 112) & & & & \\ \cline{2-6}  & Mobile crime prevention app (e.g., real-time indoor and outdoor precise locations) & & & & \\ \cline{2-6}  & Real-time control drones/robots (e.g., drones/robots for automatic real-time patrols) & & & & \\ \hline \multirow{6}{*}{Community} & AI guide system for village information (e.g., crime prevention safety zones) & & & & \\ \cline{2-6}  & Improved parking environment system (e.g., shared parking, illegal parking reporting) & & & & \\ \cline{2-6}  & Smart safety community (e.g., Safe return home for women, safety booths) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of fuzzy TOPSIS for alternatives. it was ranked poorly, it is considered necessary to strengthen CPTED systems, such as establishing governance between the public sector and residents as an interactive service element enabling the residents to participate. Real-time control drones/robots (CC\({}_{i}\): 0.33) ranked eleventh, which was also very low. The results may reflect that while the drones/robots support automatic patrol and control in real-time, the cost of investing in the drones/robots remains high; such new technologies are applied on-site in only a few cases and may end up as a one-off measure.
## 4 Blackout Analysis
### Preconditions
#### 4.1.1 Peak Demand and Errors in Renewable Energy Sources (RES) Forecast (PREC_1)  
In the past, blackouts typically occurred at times of high demand (typically in the winter or summer), when the system is stressed from the heavy load and operates near its operating and stability limits. That was the cause of the Greek blackout in July 2004, referred to in Table 1. During the past few years, the great penetration of RES in the European system has increased the jeopardy of a blackout because RES have high variability in their forecast. Therefore, there are times that the European TSOs face a major challenge of a lack or excess of energy deriving from RES. This affects the transmission lines with other TSOs of other countries, disturbing the energy exchange of the energy market. This is depicted in the Area Control Error (ACE) [28] as it is expressed in the following equation:  
\[\mathrm{ACE}=\Delta\mathrm{P}+\mathrm{K}\cdot\Delta\mathrm{f} \tag{1}\]  
where:  
\(\Delta\mathrm{P}\): The Power Control Error of a control area. \(\Delta\mathrm{P}=\mathrm{P}-\mathrm{P}_{0}\), where \(\mathrm{P}\) is the active power flow and \(\mathrm{P}_{0}\) is the control program (sum of all related exchange schedules and the compensation programs).  
\(\Delta\mathrm{f}\): The frequency deviation from 50 Hz.  
K: A control area factor.  
ACE with a positive value means that more power is flowing out of an area than scheduled, which informs the TSO that generation should be ramped down because there is more supply than demand in that area. ACE with a negative value, on the other hand, indicates that more power is entering the area than anticipated and that local production needs to be increased to keep up with demand.
# A New Concept of Instantaneous Whirling Speed for Cracked Rotor's Axis Orbit
## 1 Introduction
[PERSON] [23] proposed a Kurtosis-guided local polynomial differentiator to estimate the instantaneous angular speed and applied it to the health monitoring of planetary gearbox. [PERSON] [24; 25] used instantaneous angular speed for the fault diagnosis of gearbox and multistage gearbox. IAS is also used for the monitoring of bearing defects and gearbox faults in wind turbines [26; 27]. Basic signal processing techniques is also introduced in order to analyze the shape of rotating speed fluctuations [28]. The above reviews show that instantaneous angular speed reflects the characteristics of the rotation speed of the rotor system in a rotation cycle and provides abundant diagnostic information for rotor system faults. To sum up, axis orbits and instantaneous angular speeds are from two different perspectives to provide methods for fault diagnosis. The axis orbit describes the whirling of the rotor system; however, the instantaneous angular speed describes the spinning of the rotor system. The axis orbit contains the amplitude and phase information of the two directions' vibration response, and has important reference values for crack diagnosis. However, the axis orbit can only reflect the characteristics of the rotor in a whole whirling cycle. At the same time, it cannot reflect the instantaneous characteristics of the rotation during one whirling cycle like the instantaneous angular speed. At some special cases, if only the morphological characteristics of the axis orbit are used, it is easy to miss the diagnosis of cracks. Therefore, in this paper, the instantaneous whirling speed of axis orbit within a whirling cycle is defined and studied by a Jeffcott rotor model. Moreover, the relationship between the breathing effect of the crack and the transient variation characteristics of the RWS is studied. Section 2 introduces the dynamic modeling processes. Section 3 introduces the definition of instantaneous whirling speed. Section 4 explains the mechanism of the transient characteristic of whirling speed through the study of the work of the additional stiffness excitation and the conversion relationship between the kinetic energy and the potential energy. In Section 5, the minimum of the relative whirling speed is proposed as a potential monitoring index for crack severity.
# Infrared Small Moving Target Detection via Saliency Histogram and Geometrical Invariability
## 3 Inter-Frame Detection Based on Geometrical Invariability  
### Track Correlation  
Within a short time, the motion of a small moving target can be described with a uniformly accelerated motion model [25], which is applied to implement track correlation and form completed trajectories in our inter-frame detection.  
Assume the space coordinates of all the candidate points in an arbitrary frame compose a set \(\text{P}^{\xi}\)\(=\{\text{P}_{1}^{\xi},\text{P}_{2}^{\xi},P_{3}^{\xi},\dots,\text{P}_{n}^{\xi}\}\), where \(\xi\), represents the frame number and n is the total number of candidate points in that frame. Here, we just take the m-th candidate point \(\text{P}_{\text{m}}^{\xi}\) as an example to explain the process of track correlation.  
First, the velocity \(\text{v}_{\text{m}}^{\xi}\) and acceleration \(\text{a}_{\text{m}}^{\xi}\) of \(\text{P}_{\text{m}}^{\xi}\) are written as  
\[\text{v}_{\text{m}}^{\xi}\text{=}\left(\text{P}_{\text{m}}^{\xi}\text{-P}_{ \text{m}}^{\xi-1}\right)/\Delta\text{t} \tag{12}\]  
\[\text{a}_{\text{m}}^{\xi}\text{=}\left(\text{v}_{\text{m}}^{\xi}\text{-v}_{ \text{m}}^{\xi-1}\right)/\Delta\text{t} \tag{13}\]  
where \(\Delta\text{t}\) means the time interval. For two adjacent frames, \(\Delta\text{t}=1\).  
Next, based on the uniformly accelerated motion model, the estimated position \(\widetilde{\mathrm{P}_{m}^{\xi+1}}\) of \(\mathrm{P}_{m}^{\xi}\) in the \((\xi+1)\)-th frame is predicted as  
\[\widetilde{\mathrm{P}_{m}^{\xi+1}}=\mathrm{P}_{m}^{\xi}+\mathrm{v}_{m}^{\xi} \cdot\Delta t+\frac{1}{2}\mathrm{a}_{m}^{\xi}\Delta t^{2}+\Delta\mathrm{P}^{ \xi+1} \tag{14}\]  
where \(\Delta\mathrm{P}^{\xi+1}\) denotes the displacement of background in the two adjacent frames. In this paper, we only consider the rotation and translation displacement caused by camera motion, and \(\Delta\mathrm{P}^{\xi+1}\) is calculated by an automatic registration method introduced in Ref. [26].  
Furthermore, as is shown in Figure 3, a circular gate whose radius is \(\Re\) (we take \(\Re=5\) empirically) is set at \(\mathrm{P}_{m}^{\xi+1}\). In the \((\xi+1)\)-th frame, all of the candidate points (represented by the light green dots) located inside the gate make up of another set \(\mathrm{Q}_{m}=\{\mathrm{Q}_{1},\mathrm{Q}_{2},\mathrm{Q}_{3},\ldots,\mathrm{ Q}_{1}\}\) where t is the quantity of candidate points inside the gate and the point \(\mathrm{Q}_{*}\) is the maximum bin value in the saliency histogram is selected as the correlated one for \(\mathrm{P}_{m}^{\xi}\) (if there is more than one point located inside the gate). Thus, a complete trajectory of \(\mathrm{P}_{m}^{\xi}\) can be established if the above-discussed track correlation is repeated in every frame.
###### Abstract  
Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT.  
complex background; infrared image; MNSTLA; point target detection Article
# Combined Investigation of Indoor Climate Parameters and Energy Performance of a Winery
## 2 Materials and Methods
### Field Sampling and Analysis for VOCs  
Air samples for VOC analysis were taken using low volume personal pumps (SKC) and pre-conditioned glass tubes filled with Tenax TA (Chrompack, Middelburg, Netherlands) at flow ratios of about 80 mL/min for 30 min. Moreover, duplicate samples were taken and blank tubes were analyzed for quality assurance/quality control purposes. Samples were analyzed using a thermal desorption unit (Gerstel TDS2, Mulheim an der Ruhr, Germany) coupled to a gas chromatograph (Agilent 6890N, Santa Clara, USA), equipped with a mass spectroscopy detector. Tenax tubes were thermally desorbed using Gerstel thermodesorption system TDS2 with autosampler TDSA, in splitless mode using the following programme: initial temperature: 40 \({}^{\circ}\)C, raised to 250 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/min, held at 2.5 min and final temperature of 280 \({}^{\circ}\)C. The transfer temperature to the Cryocooling injection system (CIS) was 280 \({}^{\circ}\)C. The desorbed compounds were trapped at the CIS at \(-\)120 \({}^{\circ}\)C and then underwent rapid heating to 280 \({}^{\circ}\)C to enter the gas chromatography capillary column. The initial oven temperature was kept at 35 \({}^{\circ}\)C for 5 min and then increased at 80 \({}^{\circ}\)C with a rate of 8 \({}^{\circ}\)C/min and kept there for 6 min. After that the temperature increased up to 100 \({}^{\circ}\)C with a rate of 20 \({}^{\circ}\)C/min and kept there for 3 min. The total run time was 30.63 min.
## 2 Methodology
### Data Extraction  
The nonlinear dynamic behavior of the electrocardiogram (ECG) signal is well known, and it was a key feature that is used in this study. Since the CNN system needs feedback in the form of digitized signals, the digitization process was implemented, wherein the data were extracted from ECG and TMT-ECG graphs. The proposed method of digitization was also recommended to overcome various paper degradation problems, in addition to the reasons cited in the introduction.  
According to a previous analysis, the digitization of ECG graphs was applied only for single-lead ECGs, and the majority of cases were treated with only resting condition ECGs. However, TMT-ECG was measured while the patient was running on the treadmill, based on the Bruce protocol. This plays a key role in predicting arrhythmia, CAD, and other heart-related conditions. The proposed approach was designed to digitize both the resting and TMT-ECG conditions (TMT-ECG). This novel approach will include digitized ECG data and features for each of the 12 leads individually.  
The electrical signal of the heart was printed as a waveform (TMT report), which was collected through 10 electrodes and printed as 12-lead heart signals. The ECG and TMT-ECG reports used for data extraction were of an exercised (TMT-ECG) condition, as shown in Figure 3. The \(p\)-value, QRS complex, ST segments, and T value were all parts of the single-lead ECG and 12-lead ECG signals. The digitization of the TMT report, as shown in Figure 4, which involved both the resting (ECG) and TMT-ECG, aided in the study of the entire pattern of heart disease activity. Different stages of data extraction in the study are discussed below.