esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Data Analysis of the Isolated PVPS (Acquisition and Log)
Figure 12 compares the measured and theoretical pump discharge (water flow) for the four representative days. On the clear-sky day (Figure 12a), the measured total pumped volume (726.5406 m\({}^{3}\)) closely matched the theoretical prediction (726.8505 m\({}^{3}\)), producing an equivalent operational time (obtained according to Equation (13), by the division of the measured flow \(Q\) over the theoretical flow \(Q_{0}\)) of 9.4143 h.  
The total volume \(V\) [m\({}^{3}\)] pumped per day was calculated using Equation (11):  
Figure 5:5  
Figure 5:6 Measured and theoretical pump pressure (pumping head) during the 4 representative days: (**a**) clear-sky day, (**b**) day with a big cloud, (**c**) day with many small clouds, and (**d**) fully cloudy day. where \(V\) [m\({}^{3}\)] represents the total volume of water pumped in one day, \(Q\) [m\({}^{3}\)/h] is the current pump discharge (volume flow), \(t_{initial_{-}}\) = 0 s is the starting time and \(t_{final}\) = 24 \(\times\) 3600 s is the ending time. The total theoretical volume \(V_{theoretical}\) [m\({}^{3}\)] that could be pumped per day was calculated using Equation (12):  
Figure 5:7  
where \(V_{theoretical}\) [m\({}^{3}\)] is the total theoretical volume of water pumped in one day, \(Q_{0}\) [m\({}^{3}\)/h] is the rated (nominal) pump discharge (volume flow), \(t_{initial_{-}}\) = 0 s is the starting time and \(t_{final}\) =24 \(\times\) 3600 s is the ending time. The equivalent operating time \(TE_{openting}\) for one day was calculated according to Equation (13):  
Figure 5:8  
where \(Q\) represents the pump discharge (current irrigation water flow rate) of a specific sector and \(Q_{0}\) is rated irrigation water flow rate (service, nominal, flow according to the installation design). For the day partially obstructed by a large cloud (Figure 12b), the measured volume was 594.3717 m\({}^{3}\), approximately 88.33% of the theoretical value (672.8987 m\({}^{3}\)), corresponding to an operational time of 6.6291 h. During the day with multiple small, fast-moving clouds (Figure 12c), the measured volume decreased further to 583.7665 m\({}^{3}\) (77.51% of the theoretical  
Figure 5:9 Measured and theoretical pump flow (discharge) during the 4 representative days: (**a**) clear-sky day, (**b**) day with a big cloud, (**c**) day with many small clouds, and (**d**) fully cloudy day. 753.1772 m\({}^{3}\)), resulting in an operational time of 6.7556 h. This deviation was primarily due to the theoretical calculation neglecting pump inertia and transient flow effects. Lastly, on the fully cloudy day (Figure 12d), only 89.9906 m\({}^{3}\) was pumped, approximately 42.99% of the expected volume (209.343 m\({}^{3}\)), corresponding to an operational time of just 0.9999 h, reflecting significantly limited PV energy availability.
## 3 Results and Discussion
### Oxidation Tests
#### 3.2.2 Storage Stability of Extract-Loaded Emulsions
A possible explanation for this phenomenon is the activation of a different protective mechanism or the migration of some acetone extract components closer to the oil-water interphase, where the oxidation takes place. Suppressing the oxidation of emulsions as a result of modifying the physical location of antioxidants was observed in the case of Toc in O/W emulsion prepared from canola oil [27]. However, in this case, the factor causing this phenomenon was the emulsifier. Previous reports also demonstrated that some additives could improve the antioxidant properties of lipophilic antioxidants in O/W emulsion [13; 47]. Our findings suggest that the emulsion aging process might enhance the protective properties of certain antioxidant compounds.
# Text Semantic Annotation: A Distributed Methodology Based on Community Coherence
## 4 Results
In Figure 4, we observe the peak of F1 score near the 90% mark on the recall axis. Overall, the Clauset-Newman-Moore methodology attains consistently supreme performance, as clearly depicted towards the rightmost part of the recall axis. The nearly impeccable accuracy of our methodology, even at high recall, outlines the value of our approach to the problem, along with the substantially beneficial impact of introducing community coherence as structure interpretation of contextual resemblance in a highly variant collection of documents at a big-data scale. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Recall** & **1.0** & **0.9** & **0.8** & **0.6** & **0.4** & **0.2** \\ \hline Louvain (Precision) & 0.7491 & 0.8294 & 0.8693 & 0.9201 & 0.9643 & 0.9836 \\ Louvain (F1) & 0.8563 & 0.8632 & 0.8330 & 0.7262 & 0.5622 & 0.1809 \\ CNM \({}^{1}\) (Precision) & 0.7554 & 0.8362 & 0.8738 & 0.9337 & 0.9678 & 0.9866 \\ CNM \({}^{1}\) (F1) & 0.8606 & 0.8667 & 0.8351 & 0.7287 & 0.5659 & 0.3325 \\ TAGME (Precision) & 0.6720 & 0.7640 & 0.8242 & 0.9101 & 0.9619 & 0.9832 \\ TAGME (F1) & 0.8038 & 0.8264 & 0.8118 & 0.7222 & 0.5650 & 0.3323 \\ GPC \({}^{2}\) (Precision) & 0.6980 & 0.7946 & 0.8351 & 0.9108 & 0.9540 & 0.9808 \\ GPC \({}^{2}\) (F1) & 0.8221 & 0.8440 & 0.8171 & 0.7232 & 0.5633 & 0.3320 \\ \hline \hline \end{tabular}  
* [PERSON]–Moore, \({}^{2}\) Gaussian Process Regression without Community Coherence.  
\end{table} 
Table 1: Precision, recall and F1 score of Louvain and CNM methodology variants. Figure 4: F1 score of the two community detection methodology variants, the Gaussian Process Classifier and TAGME baseline.
## 2 Results and Discussion
### Phytochemical Investigations
#### 2.1.2 Identification of Volatile Compounds by SPME-GC/MS
Nitriles are among the amino-acid-derivative compounds. In fact, amino acids are the precursors of glucosinolates, a group of sulfur-containing secondary metabolites characterizing _Brassicaceae_ vegetables; from the glucosinolate hydrolysis, through the action of the myxosinase enzyme, isothi cyanates, nitriles, and thiocyanates may arise. In both subspecies of _M. fruciculosa_, only nitriles were detected, with a higher percentage in subsp. _coronopifolia_, where 4-(methylthio)-butanenitrile was the most abundant compound, followed by 4-methylpentanenitrile. 4-(Methylthio)-butanenitrile originates from glucoerucin, which has been identified as the main glucosinolate in _M. fruciculosa_ seeds [11]. The absence of isothi cyanates suggests that in our samples the hydrolysis of glucosinolates is altered in favor of nitriles; many factors can modify the activity of myxosinase, favoring nitrile formation [14], such as the presence of ferrous ions, nitrile-specific proteins, acidic conditions, and, according to [PERSON] and [PERSON] [13], also frozen-thawed processes of the vegetable tissue. The only sulfur compounds detected in the extracts of _M. fruciculosa_ subsp. _fruticulosa_ and subsp. _coronopifolia_ were sulfides (i.e., dimethyl disulfide, dimethyl trisulfide, and dimethyl tetrasulfide). Sulfides are derivative compounds of the S-alk(en)yl-l-cysteine pathway and occur very frequently in the volatile fraction of _Brassicaceae_ family plants, even being the most abundant volatiles in _M. incana_ subsp. _incana_ and _Brassiciana incana_ grown wild in Sicily [2,15] and in different cultivars of _Brassica oleracea_[13]. Benzenoid compounds are derived from t-cinnamic acid with the propyl side chain shortened by two carbon atoms via either a non-\(\beta\)-oxidative or a \(\beta\)-oxidative pathway, and the formation of benzaldehyde and benzoyl-CoA as intermediates for benzoic acid production [16]. Benzaldehyde and benzoic acid were detected only in the _M. fruciculosa_ subsp. _fruticulosa_ extract; moreover, methyl benzoate was also present, being the most abundant compound among esters. Finally, hexahydrofarnesyl acetone was among the most abundant volatiles of the _M. fruciculosa_ subsp. _coronopifolia_ extract; hexahydrofarnesyl acetone, or phyto, is a ketone very common in plants that arises from the oxidative degradation of (_E_)-phytol, a diterpene alcohol that occurs as a side chain of chlorophyll a [17].
## 3 Proposal Architecture
### Remote Monitoring and Alert Interface  
The web monitoring system consists of a front-end application which continually analyses every new entry sent to the database server. The system, in real time, can show a user's heart rate, possible and real falls, and can interpret the information and send notifications to caretakers in case of emergency. Figure 6 illustrates the interface functions.  
The monitoring system functions reactively because it must first analyze the status of the database before it determines if the entry it is receiving is new. If the system does not detect any changes, an inactivity counter initializes and will compare the counter's reported values with previously set limits for inactivity. If the inactivity counter reports data that are below the established limit, it reinitializes. Importantly, if the inactivity counter reports are equal to the already established values, the system sends an alert message to the user to report a possible network error, because the system is not receiving new entries. In this way, the user can more easily determine if any problem exists. If the entry is new, the counter will automatically reset the counter and identify all the information to be displayed on the mobile or web interface.  
Besides the alerts mentioned previously, the system also sends alerts relative to situations that may place the user's physical integrity, making it possible to notify those who are concerned for the patient's wellbeing about a potential fall or a bradycardia or tachycardia event. The alerts are generated upon analyzing any new entries received and recorded in the database. After receiving a new entry, the  
Figure 5: SINK processing algorithm.  
application analyzes it and validates the type of registry (fall or heart rate). Upon determining the type of registry, the alert counter resets, activating the alert mode and displaying the existing alert on the mobile device or Web page. After resetting, if there are no new entries registered in the database, the alert counter increases. Once the counter increases, the application compares the alert counter with the pre-established counter limits. If the entries surpass the alert counter limits, the alert mode is deactivated. This is an important safety measure as it prevents repeated and unnecessary alerts, or prolonged periods of system alerts.
## 1 Introduction
Authors of a previous study [36] account for stochastic estimation of PV and EV parking in the day-ahead method, adding a real time model for predictive control. Energy management of a DC microgrid presents peculiar aspects and functions, as reported by previous authors [37; 38; 39]. In-line with this framework, a general control structure is illustrated in a previous study [40]. In another study [41], an optimization model with voltage network constraints is depicted, whereas semidefinite programming is exploited in a previous study [42] to deal with non-linear features. A simplified linear model of DC microgrid for integration in optimal bidding procedure with risk measures is proposed in another study [43]. In one study [44], a procedure based on fuzzy logic is embedded in real-time power control of a DC microgrid, whereas real time optimal power management of a DC microgrid is adopted in another study [45]. Real-time control of a DC microgrid based on dynamic models is proposed in other studies [46; 47]. Among various approaches investigated in the literature, a limited focus is devoted to specific aspects of DC microgrids when V2G is enabled. In particular, in operation programming, few details are given on economic relations with EVs and on actual operational features of converters. In this paper, a mixed-integer linear procedure for determining optimal operation planning of an electric vehicle supply infrastructure structured as a DC microgrid is proposed. The procedure aims at optimizing daily operational cost, including the viewpoint of a microgrid operator and EV owners, according to predictions of PV production and EV utilization for mobility needs. A set of indicators of EV usage is drawn. The proposed approach is applied to a test DC microgrid on different days of operation and its effectiveness is compared to non-linear formulation solved by means of a genetic algorithm. The main contributions of the proposed approach can be synthesized as follows:  
* A model of degradation costs and capacity for energy storage devices is included;
* Specific features of active power losses in DC microgrids and in converters are taken into account;
* The economic objective involves a microgrid and an EV aggregator;
* An iterative procedure is exploited in order to consider non-linear realistic models and keep linear programming properties;
* Performance indicators related to EV exploitation for electric system integration are adopted to prove procedure validity. The paper is organized as follows. In Section 2 the proposed methodology is illustrated, according to the nomenclature reported in the appendix, along with a reference non-linear approach. In Section 3 the test system is described, while the performed tests and remarkable indices are discussed in Section 4. Conclusions are presented in Section 5.
# Invisible Threats in the Data: A Study on Data Poisoning Attacks in Deep Generative Models
## 3 Research Methodology
### Threat Model
#### 3.1.2 Attacker's Goals  
The attackers need to concentrate on two core objectives during the invisible backdoor attack:  
**Objective 1: The fidelity of the threat model**  
The objective of an invisible backdoor attack is to insert malicious hidden triggers without disrupting the normal functioning of the model. Consequently, the attacker must ensure that the performance of the model on regular tasks remains unaltered following the implantation of the backdoor. By optimizing the attack strategy, the attacker can achieve the implantation of hidden triggers while maintaining the performance of the model, thus enabling the malicious control of the model.  
**Objective 2: The stealthiness of the trigger**  
The stealthiness of the trigger is significantly important when performing invisible backdoor attacks. Triggers may be defined as specific input patterns, specific environmental conditions, or other variations that should be designed to be sufficiently stealthy. This implies that attackers must design backdoor triggers in a manner that renders them challenging for the human eye to discern or for the system to detect. By meticulously analyzingthe model's operational principles and the characteristics of the input data, the attacker can identify and exploit the vulnerabilities and embed the triggers into the model in a manner that is almost undetectable during normal operation.
# Research Progress on the Manufacturing of Screw-Shaped Parts in Screw Compressors
## 2 Common Manufacturing Process for Screw-Shaped Parts
### Additive Manufacturing Process  
Additive manufacturing (AM) is a versatile technology for rough production applied in a number of application areas [50; 51; 52; 53]. Moreover, the parts produced by the AM process usually require subsequent manufacturing steps, including grinding or polishing, to obtain  
Figure 11: Schematic diagram of the equipment for the RTM process [49].  
Figure 10: Sand-coated casting process: (**a**) iron mould without sand coating; (**b**) iron mould with sand coating; (**c**) formed products of screw parts [45].  
higher dimensional and shape accuracy. According to the works of [PERSON] et al. [54], additively manufactured parts, such as heat pipes, thrusters, magnetic shields, etc., can be used in different applications in space. Additionally, AM can be used for prototyping rapidly. [PERSON] et al. [55] used selective laser melting (SLM) to additively manufacture (3D-print) female and male screw rotors made from a TC4 titanium alloy powder. The dimensional and shape accuracy of the produced screw rotors shown in Figure 12 reached a high quality, the same as rotors after coarse grinding. To decrease the size and capital investment, [PERSON] et al. [56] applied fused deposition modelling (additive manufacturing) to produce the blade of a controllable-pitch Archimedean screw (CPAS) for generating hydropower, the process of which is illustrated in Figure 13. The designed CPAS was used for a hydropower generation system. The performance evaluation results show that the power generated was 123 W, with an efficiency of 71%, at a flow rate of 0.04 m\({}^{3}\)/s. Although the additive manufacturing process can realise the accurate forming of various complicated profile screws, its production efficiency and economics need to be improved.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 4 Performance Analysis  
### Simulation Settings  
We considered five kinds of jamming signals in this paper: single-tone jamming, single partial-frequency band-blocking jamming, multiple partial-frequency band-blocking jamming, frequency sweeping jamming, and sweeping collision-blocking jamming, which are denoted as jamming 1 to jamming 5, respectively. Parameters such as the frequency and bandwidth of the jamming signal were randomly selected within the allowable value range. The number of frequencies in the frequency-hopping set of the frequency-hopping signal was 16. The number of hops in each signal was 60. The frequency points of the frequency-hopping sequence were randomly selected. The received signal samples were frequency-hopping signals with or without jamming. The signal-to-jamming ratio was \(-10\) dB to 10 dB with an interval of 2 dB. There were 2200 groups of frequency-hopping received signals under each jamming situation and thus a total of 13,200 groups of received signals. Each received signal was subjected to STFT with a window length of 1024 points, and finally, 13,200 time-frequency images were obtained, and the label corresponding to each image is a frequency-hopping sequence with a length of 60. The ratio of the training set to the validation set in the final generated dataset was 8:2. An Adam optimizer was used in training the network. The mini-batch size was 60. The initial learning rate was 0.001. A total of 6 rounds of training were carried out, and the learning rate of each round of training was reduced to 0.5 times that of the previous round. The physical parameters required are shown in Table 1.
## 3 Results and Discussion
### Mechanism of Enzyme Kinetics  
To understand the mechanism of enzyme inhibition, oxidation of the L-DOPA indicator of diphenolase activity was determined from Lineweaver-Burk and Dixon plots. The Lineweaver-Burk plots, in the presence of most potent drugs, i.e., enoxacin sesquihydrate, ciprofloxacin, ofloxacin, levofloxacin, sparfloxacin, moxifloxacin and gemifloxacin, are shown in Figures 3 and S1-S6, respectively. Additionally, a kinetic study for nonfloxacin was also performed (Figure S7). The plots (1/\(V\) versus 1/[S]) showed straight lines with different slopes. Their analysis revealed that \(V_{\max}\) reduces with \(K_{m}\) shift and increasing drug concentration, which indicated that they inhibit tyrosinase by dual distinct channels  
Figure 2: Study drugs (**a**) ciprofloxacin, (**b**) enoxacin sesquihydrate, (**c**) gemifloxacin, (**d**) levofloxacin, (**e**) moxifloxacin, (**f**) ofloxacin, (**g**) sparfloxacin and (**h**) hydroquinone. Their structures are adopted with permission [8; 53].  
and show a mixed type of inhibition. This revealed that antibiotics can bind with a free enzyme (E), as well as with the enzyme-substrate (ES) complex [54]. The insightful analysis of EI and ESI binding affinities showed their mixed-type mode of inhibition. The secondary replots of slope versus concentration of the test drugs showed the EI dissociation constant (_Ki_) (Figures 3b1 and S1-S7(b2-b8)), while the ESI dissociation constant (_Ki_') was shown by the secondary replots of intercept versus concentration of the test drugs (Figures 3c1 and S1-S7(c2-c8)). Both the Dixon plot (Figures 3d1 and S1-S7(d2-b8)) and secondary replot of the Lineweaver-Burk plot were used to calculate the _Ki_. The _Ki_ values ranged from 0.1 to 0.4 mM (Figures 3b1 and S1-S7(b2-b8)) and the _Ki_' values ranged from 0.8 to 2 mM (Figures 3c1 and S1-S7(c2-c8)). The analysis showed lower dissociation constant values for _Ki_ compared to _Ki_', demonstrating more prosperous binding between the enzyme and antibiotics [55], justifying their preferred competitive mode of inhibition.  
Figure 3: (**a1**) Lineweaver–Burk plot for inhibition of tyrosinase enzyme in the presence of various concentrations of enoxacin sesquihydrate (as indicated) and L-DOPA concentrations (0.0625, 0.125, 0.25, 0.5, 1 mM and 2 mM). (**b1**) The insets represent the plot of the slope from Lineweaver–Burk plot versus inhibitor. (**c1**) The secondary replot of the Lineweaver–Burk plot, \(1/V\) (y-intercept) versus various concentrations of inhibitor. (**d1**) The Dixon plot of the reciprocal of the initial velocities versus various concentrations of the test drug. (**e1**) Relationship between the catalytic activity of tyrosinase and various concentrations of the test drug.
# Study on Siphon Drainage Capacity of Slopes with Long-Horizontal Pipe Sections
## 4 Discussion
### Comprehensive Analysis of Horizontal Section Flow Pattern  
Figure 9 shows the flow patterns obtained for different conditions of siphon drainage, where the apparent velocity of the gas phase and the average flow velocity are obtained from experimental measurements. In these plots, the flow patterns of bubble flow ([DB], [B]), intermittent flow ([I], [S]), and annular flow ([A]) within the horizontal section of each siphon with diameters of 3 mm, 5 mm, 6.5 mm, and 8 mm are shown and plotted simultaneously on the flow pattern diagram in the same flow direction to examine the effect of tube diameter on the flow pattern.  
The crossed line areas on the map show the areas of bubble flow and annular flow. It is worth noting that the tube diameter has almost no effect on the flow pattern in the range of 3 to 5 mm for this experiment. Figure 9 shows the flow pattern distribution for the horizontal flow of gas-liquid two-phase, and the solid line in the figure shows the gas-liquid two-phase flow pattern boundary obtained by [PERSON] et al. [15] regarding the diameter of 4 mm, while the dashed line shows the results of [PERSON] et al. [16]. In the legend, the letter before the short line represents the abbreviation of the flow pattern, and the number after the short line represents the diameter of the tube, where \"B\" refers to bubble flow, \"DS\" refers to dry slug flow, and \"WS\" refers to the wet slug flow. Figure 9 shows that the data obtained by experiment for D-3-5 tubes can fit well with the flow pattern line of [PERSON] et al. while the data for D-6-5-8 tubes do not give a good fit. In general, the effect of  
Figure 8: Siphon lift—flow velocity curve: (**a**) Total length of 30m; (**b**) Total length of 200m.  
Figure 9: The flow pattern of gas-liquid two-phase in a horizontal circular tube.  
tube diameter on the flow pattern is less than that of the data for larger tube diameters in the range of tube diameters tested in this section. According to the test results, the effect brought about by the tube diameter gradually becomes evident when the tube diameter is significantly more than 6 mm. The test results are very similar to the results of flow pattern analysis in the vertical direction by [PERSON] et al., except that the annular flow region plotted in this test is more comprehensive than that in other cases. This fact suggests that the flow direction does not seriously affect the flow pattern in small diameter tubes.  
Analyzed in terms of physical properties, this means that for gas-liquid two-phase flows in circular tubes with diameters no larger than about 5 mm, surface tension has a much more significant effect on the flow pattern than gravity. This argument is also argued in the next section.