esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of the Impacts of Climate Change on Power Systems: The Italian Case Study
## 4 Climate Change Impacts on Demand and Electricity Supply of Present-Day Power Systems
### Hydropower  
In [13], the ensemble mean results projected a marked increase in hydropower production in most countries, except for the southern Europe countries (Greece, Portugal and Spain). For the latter, a decrease that becomes more severe with the global warming scenarios was projected. Whereas for Baltic and Scandinavian countries, an increase in hydroelectric generation with the increase in global warming was projected. This suggests a strong difference between northern and southern Europe countries that should be considered by European energy system policy choices. Focusing on Italy, if the 1.5 \({}^{\circ}\)C warming scenario projects an increase in hydro generation of about almost 5%, with the 2 \({}^{\circ}\)C scenario, this percentage decreases, until it becomes a decrease in production below the 5% (almost 2%) for a 3 \({}^{\circ}\)C warming scenario. Looking at the projections of the individual models, a strong spread between them has been observed, that causes a strong uncertainty in the reliability of the individual models' results. Specifically, for northern European countries, the spread between models is larger than Italy.  
Reference [29] confirms what was stated in [13]. The authors of [29] stated that southern, eastern and central Europe will be affected by a decrease in discharge volumes of rivers, that will negatively impact hydropower generation. Italy will be affected by a reduction in hydropower generation, but in a milder way than the other southern European countries (such as France, Spain, Greece).  
The above results with regard to Italy are consistent with another work, [41], where a reduction in the range of 3.6-5.7% has been projected (considering an RCP 8.5 scenario).
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods
### Mechanical Cell Disruption
#### 2.2.3 High-Pressure Homogenization  
For microalgal cell disruption by high-pressure homogenization on a 200 mL scale, a tabletop high-pressure homogenizer (HPH) (HPL6, Maximator GmbH, Nordhausen, Germany) was used. In this case, 200 mL of each sample was processed at a flow rate of 4 L h\({}^{-1}\) for up to 10 passes at a specified outlet pressure. For homogenization at scales larger than 30 L, an industrial HPH (Ariete NS3015H, GEA Group AG, Dusseldorf, Germany) with an external manually operated diaphragm pump (SartoJet, Sartorius AG, Gottingen, Germany) as the inlet pump was used. In this case, for practical reasons, the biomass was circulated in a tank with a cooling jacket and an external stirrer unit during homogenization. Thus, instead of counting the actual number of passes (N), the time equivalent of passes (N\({}_{\text{E}}\)) was calculated, which was defined as the homogenization time (t\({}_{\text{H}}\)) divided by the total volume (V) and multiplied by the volumetric flow rate (F) as follows:  
\[\text{N}_{\text{E}}=(\text{t}_{\text{H}}\cdot\text{F})\cdot\text{V}^{-1}, \tag{1}\]  
The biomass was processed at a flow rate of 150-200 L h\({}^{-1}\) for up to 5 equivalent passes (N\({}_{\text{E}}\)) at an outlet pressure of 1000 bar. During the homogenization process, the inlet pressure was manually adjusted to 3.5 bar at all times using a diaphragm pump.
## 3 Results and Discussion
### Results of 3D CFD Modeling of Liquid-Gas Ejector
#### 3.1.2 The Effect of Different Parameters on Gas Phase Hold-Up  
The effects of liquid flow rate and separator pressure on gas phase hold-up in ejector are depicted in Figure 5a,b, respectively. These results are obtained by drawing several vertical lines from the beginning (z = 0 mm) to the end of the ejector (z = 512 mm) and averaging the gas hold-up along these lines by Fluent software. It is clearly observed that gas phase hold-up increases by enhancing the liquid flow rate, which results in an increment in the nozzle velocity and more gas entrains, which in turn increases gas hold-up. On the contrary, by increasing the separator pressure, less gas entrains, leading to a decrease in gas phase hold-up. Because our research has numerically investigated the performance of two-phase ejector by CFD method under transient condition, variation of gas phase hold-up with time is shown in Figure 6. The coefficient of mass transfer is proportional to the gas phase hold-up [43], so by increasing the gas phase hold-up over time, the mass transfer rate increases too.  
Figure 5: (**a**) The effects of liquid flow rate (Q\({}_{L}\)) on gas phase hold-up (\(\varepsilon_{S}\)) for nozzle diameter = 6 mm and pressure outlet = 3250 Pag (**b**) The effects of separator pressure (Ps) on gas phase hold-up (\(\varepsilon_{S}\)) at a constant liquid flow rate of 2.85 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s for the air–water system (nozzle diameter = 6 mm).
## 3 Experimental Methods
### Experimental System
#### 3.1.1 Uniaxial Loading System  
An electro-hydraulic testing machine servo-controlled by a microcomputer was applied to conduct the uniaxial compression experiment in this study. It was composed of a control system, a loading system, and an automatic data acquisition system. The maximum loading capacity was 1000 kN. As shown in Figure 4, two rigid steel loading plates were placed between the loading frame and experimental sandwiched coal-rock specimen. The displacement loading mode was adopted with a strain rate of 0.002 mm/s in the present study. Moreover, the load and deformation of the sandwiched coal-rock specimen was monitored continuously over the whole loading process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & **No.** & **Diameter** & **Height** & **Quality** & **Density** & **Initial** & **Composition Forms** \\  & **/mm** & **/mm** & **/g** & **/(kg/m3)** & **/(M\(\Omega\)-m)** & & \\ \hline \multirow{3}{*}{R-C} & 01 & 49.97 & 101.12 & 354.08 & 1.76 & 1.62 & \multirow{3}{*}{Rock element (50–100 mm)} \\  & 02 & 49.95 & 101.14 & 359.55 & 1.81 & 1.60 & \multirow{3}{*}{Coal element (0–50 mm)} \\  & 03 & 49.97 & 100.08 & 350.19 & 1.76 & 1.67 & \\ \hline \multirow{3}{*}{C-R} & 01 & 49.92 & 101.07 & 356.52 & 1.80 & 1.61 & \multirow{3}{*}{Coal element (50–100 mm)} \\  & 02 & 49.89 & 101.10 & 353.12 & 1.78 & 1.63 & \\  & 03 & 49.95 & 101.05 & 350.52 & 1.77 & 1.68 & \\ \hline \multirow{3}{*}{R-C-R} & 01 & 49.88 & 101.13 & 388.53 & 1.97 & 1.36 & \multirow{3}{*}{Rock element (66.7–100 mm)} \\  & 02 & 49.93 & 100.14 & 387.17 & 1.96 & 1.39 & \\  & 03 & 49.90 & 100.11 & 390.82 & 1.98 & 1.40 & \\ \hline \multirow{3}{*}{R-C-R-C-R} & 01 & 49.97 & 101.17 & 375.67 & 1.89 & 1.45 & \multirow{3}{*}{Rock element (80–100 mm)} \\  & 02 & 49.93 & 101.19 & 369.45 & 1.86 & 1.41 & \\ \cline{1-1}  & 03 & 49.90 & 101.20 & 379.02 & 1.92 & 1.49 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters and composition forms of sandwiched coal-rock specimens.
## 3 Wireless Control System Using Smart Sensors
A consequence is that the bandwidth requirements of control loops are also constant at runtime, given that the sizes of the packets to transmit are fixed. When the system operates in dynamic environments with variable workload, the network may be sometimes overloaded, and sometimes underloaded. In overloaded conditions, the QoC may be degraded, as pointed out previously; the system may even become unstable in extreme cases due to severe delay and packet loss problems. In underloaded conditions, on the other hand, the resulting performance may be worse than possible because of low resource utilization. Therefore, new design methods need to be developed to cope with workload variations and to improve the flexibility and resource-efficiency of the system. In this paper, special attention is paid to the impact of workload variations, which are caused by e.g. system reconfiguration and radio interference, on QoC. To address both delay and packet loss problems that may be induced by workload variations, a flexible time-triggered sampling scheme facilitated by the use of smart sensors will be presented in the next section.
## 2 Materials and Methods
### Growth Media and Preculture Cultivation Conditions  
Tryptic soy broth (TSB) media (17 g\(\cdot\)L\({}^{-1}\) tryptone, 5 g\(\cdot\)L\({}^{-1}\) NaCl, 3 g\(\cdot\)L\({}^{-1}\) peptone) was used for the first preculures and with an additional supply of 2% (w-v\({}^{-1}\)) agar for culture plates. The second preculures and bioreactor cultivations were conducted in mineral salt media (MSM) containing 4.62 g\(\cdot\)L\({}^{-1}\) NaH\({}_{2}\)PO\({}_{4}\)\(\cdot\)H\({}_{2}\)O, 5.74 g\(\cdot\)L\({}^{-1}\) Na\({}_{2}\)HPO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O, 0.45 g\(\cdot\)L\({}^{-1}\) K\({}_{2}\)SO\({}_{4}\), 0.04 g\(\cdot\)L\({}^{-1}\) NaOH, 0.80 g\(\cdot\)L\({}^{-1}\) MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, 0.06 g\(\cdot\)L\({}^{-1}\) CaCl\({}_{2}\)\(\cdot\)2H\({}_{2}\)O and 1 mL\(\cdot\)L\({}^{-1}\) trace element solution consisting of 0.48 g\(\cdot\)L\({}^{-1}\) CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O, 2.4 g\(\cdot\)L\({}^{-1}\) ZnS\({}_{0}\)\(\cdot\)7H\({}_{2}\)O, 2.4 g\(\cdot\)L\({}^{-1}\) MnSO\({}_{4}\)\(\cdot\)H\({}_{2}\)O, 15 g\(\cdot\)L\({}^{-1}\) FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O. All cultivation media and plates contained 10 mg\(\cdot\)L\({}^{-1}\) sterile filtered gentamycin sulfate. Rapeseed oil (Edeka Zentrale AG & Co. KG, Germany) was used as the sole carbon source and urea as the sole nitrogen source in the MSM. The explicit amounts are described in the text. All chemicals were purchased from Carl Roth GmbH & Co. KG (Germany) unless stated otherwise.  
_R. eutropha_ H16 was streaked from a cryoculture on a TSB agar plate and incubated for 3-4 days at 30 \({}^{\circ}\)C. A single colony from the plate was used to inoculate the first preculture in 10 mL TSB media in a 125-mL Ultra Yield(tm) Flask (Thomson Instrument Company, USA) sealed with an AirOtop(tm) enhanced flask seal (Thomson Instrument Company, USA). After incubating for 16 h, 2.5 mL were used to inoculate the second preculture (250 mL MSM with 3% (w\(\cdot\)v\({}^{-1}\)) rapeseed oil and 4.5 g\(\cdot\)L\({}^{-1}\) urea) in a 1-L DURAN(r) baffled glass flask with a GL45 thread (DWK Life Sciences GmbH, Germany) sealed with an AirOtop membrane. After 24 h of incubation, the complete second preculture was used to inoculate the main bioreactor culture. The precultures were incubated at 30 \({}^{\circ}\)C and shaken at 200 rpm (first preculture) or 180 rpm (second preculture) in an orbital shaker (Kuhner LT-X incubator, Adolf Kuhner AG, Switzerland, 50 mm amplitude).
## 6 Results and Discussions  
This section presents the closed loop simulation results obtained from the FPGA-based real-time simulator. Three scenarios are presented in this section. Two scenarios involve reference signal tracking and the third scenario involves load switching, both ON and OFF.  
**Scenario I:** While the system is in the islanded mode with both \(V_{dref}\) and \(V_{qref}\) set to zero, \(V_{dref}\) is stepped up to 6 kV and \(V_{qref}\) is stepped up to 4 kV. Figure 8 shows an oscilloscope screen capture of the FPGA-based simulation results corresponding to the d and q components of the load voltage, and the phase a and b voltages of the load. Figures 9 and 10 show the d and q components of the load voltage, and the phase a and b voltages of the load obtained from the Matlab/simpowersystems.  
**Scenario II:** While the system is operating in the islanded mode, in steady state, with \(V_{dref}=6\) kV and \(V_{qref}=4\) kV, \(V_{dref}\) is stepped down to 4 kV and \(V_{qref}\) is stepped down to 2.667 kV. Figure 11 shows an oscilloscope screen capture of the FPGA-based simulation results corresponding to the d and q components of the load voltage, and the phase a and b voltages of the load. Figures 12 and 13 show the d and q components of the load voltage, and the phase a and b voltages of the load obtained from the Matlab/simpowersystems.  
Figure 10: The phase a and b voltages of the load generated by the Matlab/simpowersystems.  
Figure 9: The d and q components of the load voltage generated by the Matlab/simpowersystems.  
Figure 11: Oscilloscope screen capture of the d and q components of the load voltage, and the phase a and b voltages of the load.  
Figure 12: The d and q components of the load voltage generated by the Matlab/ simpowersystems.  
**Scenario III:** This scenario demonstrates the controller responses to load switchings, both ON and OFF. While the system is operating in the islanded mode, in steady state, with \(V_{dref}=6\) kV and \(V_{qref}=4\) kV, a resistive load of 76 \(\Omega\) is switched ON. After the system settles to steady state the resistive load is switched back OFF. Figure 14 shows an oscilloscope screen capture of the FPGA-based simulation results corresponding to the d and q components of the load voltage, and the phase a and b voltages of the load. Figures 15 and 16 show the d and q components of the load voltage, and the phase a and b voltages of the load obtained from the Matlab/simpowersystems.  
Figure 16: The phase a and b voltages of the load generated by the Matlab/ simpowersystems.  
Figure 15: The d and q components of the load voltage generated by the Matlab/simpowersystems.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 7 Virtual Resource Allocation Algorithms  
The paradigm shift with network softwarization leads to the abstraction of network resources and makes it possible to dynamically allocate computation and storage resources. In Section 7.1, we present the selected virtual resource management optimization solutions for converged optical and radio networks. The existing solutions to virtualized resource allocation optimization issues are listed in Table 6. In Section 7.2, we discuss how these solutions can be enriched with network slicing and the use of agents for distributed decision-making.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 3 Design of a Scheme for Urban Dissemination
### Store-Carry-Forward Mechanism
#### 3.2.2. Waiting before Forwarding Again  
When we discussed the case of roadways in [11], we adopted **two measures** about how much to wait before forwarding for a second time or more:  
1. As we do not use beacons in our scheme, we would not rely on receiving them from vehicles that do not appear in each vehicle's neighbor database.
2. The best time to forward again is after the vehicle has moved out of the area that it covered with its first retransmission. In order to suit the speed of not only the relay, which may be slow, but also the nearby vehicles, we opted to use the maximum allowed speed, \(v_{limit}\). Then, the right time to forward again was every \(r/v_{limit}\), being \(r\) our communication range.  
Measure 1 is still applicable to the urban scenario, as our adapted scheme does not relay on a neighbor database. In regard to Measure 2, we are going to try different approaches to finding the best delay between repeated retransmissions from the same vehicle. We have come up with three different alternatives:  
Speed-Adaptive  
A possibility is to modify the equation above in order to adapt the interval to the vehicle's speed:  
\[r/v_{relay} \tag{5}\]  
Fixed Interval  
This would be the most similar option to what we included in the mechanism for roadways because the period is fixed. The delay given by Equation (5) might be too long, as the speed limit in urban areas is significantly lower than in roadways. We are going to test a wide range of values, from short intervals (every 0.5 s) up to the long ones that would result in applying the speed-adaptive method at 5 km/h. This range includes the period used in roadways and the corresponding value to the speed limit in urban areas.  
Map Polling  
Finally, we can make the retransmissions happen when the relay is passing through an intersection. This could help the message spread in other directions than the forwarding vehicle's. Our way to implement this will be via a \"map polling\": the vehicle checks its coordinates in the digital map at a fixed interval. If at the time, it is in an intersection, it forwards. Otherwise, it waits for the next check. This will let us test if we can avoid forwarding at every junction (that will surely happen with a low frequency) or not (by using a short period).  
The reasoning for the speed-adaptive approach brings the case for a **third measure**: not forwarding if the vehicle has not moved since the last retransmission. If the vehicle has remained stopped, probably the vehicles around it are still the same, and the retransmission would be useless.
## 4 Results and Discussion
### The Influence of Temperature on MoS\({}_{2}\) Growth
In contrast, the intermediate product Mo3O9S4 not only fails to directly participate in the MoS2 film growth but also obstructs the diffusion of MoS6, hindering overall film growth, as shown in Figure S8. At low substrate temperatures, the diffusion rate of MoS6 is also low, and Mo3O9S4 severely restricts its diffusion. This results in a thicker film with a substrate surface covered by irregular and fluffy MoS2, which prevents the MoS2 from growing layer by layer. As the substrate temperature increases, although the concentration of Mo3O9S4 rises, the diffusion rate of MoS6 increases and the hindrance caused by Mo3O9S4 decreases. This enables MoS6 to diffuse more effectively to the vicinity of the nucleus, promoting the growth of a well-ordered MoS2 film. It is important to note that the MoS6 precursor undergoes an exothermic reaction on the substrate, forming MoS2 and releasing S4, without involving a transition state. As a result, we believe that temperature has a minimal effect on the growth rate.
## 1 Introduction
### Data Plane  
The data plane, also known as the foundation plane, is comprised of the system gadgets, among which include routers, switches, and passages liable for moving all the data of the clients that circulate through the network. These system gadgets do not extend, and already have a defined and static usefulness; yet they are described by a lot of guidelines provided by the control layer. In this manner, similar equipment could function as a switch or as a firewall, contingent upon what the system supervisor has characterized [1,4].
# Analysis of Potential Markers of Pork Freshness Based on Volatile Organic Compounds
## 4 Conclusions  
This study systematically investigated the spoilage process of pork stored at 6-8 \({}^{\circ}\)C through microbial community analysis and VOC profiling. The results showed that days 3-5 of storage represent a critical period of quality deterioration, accompanied by significant proliferation of spoilage-related bacteria, such as _Pseudomonas_ spp. and _Listeria_ spp., as well as characteristic changes in VOCs. Hexanal was identified as a stable spoilage biomarker consistently present throughout the storage period, confirming its potential as a reliable indicator of meat spoilage. Meanwhile, changes in compounds such as acetoin, 3-methyl-1-butanol, 3-methylbutanal, 1-octen-3-ol, vinyl acetate, 2-methylaziridine, and heptanal suggest their potential as markers for pork spoilage. A strong correlation was observed between the G-TVBN index and storage time (R\({}^{2}\) = 0.976), providing an effective tool for the real-time monitoring of pork freshness.  
This study established a dual monitoring system combining VOCs and the G-TVBN index, offering a novel approach to the real-time tracking of meat spoilage. These findings lay a solid theoretical foundation for the development of intelligent packaging systems based on VOC sensing technologies, with significant implications for enhancing the quality control systems of the meat supply chain.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods14050832/s1](https://www.mdpi.com/article/10.3390/foods14050832/s1)]([https://www.mdpi.com/article/10.3390/foods14050832/s1](https://www.mdpi.com/article/10.3390/foods14050832/s1)), Table S1: Changes in the concentrations of VOCs during pork storage.  
Conceptualization, W.W. and Y.W.; Data curation, Y.W.; Formal analysis, Y.W.; Investigation, J.P.; Methodology, Y.W. and Y.Z.; Project administration, W.W.; Resources, W.W. and H.Z.; Validation, Y.W. and P.W.; Writing--original draft, W.W. and Y.W.; Writing--review and editing, W.W., F.M. and H.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 6: Temporal variation in the cumulative daily concentration of gaseous total volatile basic nitrogen (G-TVBN) during pork storage.  
Funding: This work was financially supported by the National Key R&D Program of China (No. 2022 YFD2100602).  
**Institutional Review Board Statement** Not applicable.  
**Informed Consent Statement** Not applicable.  
**Data Availability Statement** The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest** The authors declare no competing financial interests.
## 4 Results and Discussion
### Comparison of Present Results
#### 4.5.2 Proposed Results with Existing Work  
In the work of [PERSON] and [PERSON] [30], the BER value at \(10^{-3}\) was 12 dB for the Haar wavelet family for the \(2\times 2\) MIMO diversity technique using 16 QAM modulation, whereas our proposed technique using a two-element ULA with half-length spacing had an SNR value of 9 dB for the same BER, wavelet family, and modulation scheme. Therefore, our technique showed an improvement of 3 dB diversity gain. Similarly, for the Daubechie and Bior wavelet families, our work demonstrated a significant improvement of 10 dB gain using 16 QAM modulation at \(10^{-1.6}\) and \(10^{-1.5}\), respectively. Furthermore, in the work of [PERSON] and [PERSON], [31] for the Daubechie wavelet family, the BER value at \(10^{-2}\) was 14 dB using 16 QAM modulation, whereas our proposed technique using two-element ULA with half-length spacing had an SNR value of 4 dB for the same BER, wavelet family, and modulation scheme. Therefore, our technique showed an improvement of 10 dB diversity gain.  
At \(10^{-2}\) of BER, our technique led to the same SNR value as that of [PERSON] and [PERSON], [32] using a 16 QAM modulation scheme. However, the wavelet families were different. They used a DWT with a discrete cosine transform (DCT). In addition at \(10^{-3}\) of BER, the work of [32] showed a better improvement of diversity gain compared to our results due to fact that those researchers used a DCT in addition to a DWT while we used the Daubechie wavelet family. Therefore, based on the comparisons shown in Table 6, our diversity technique led to a significant improvement in terms of BER.