esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Thermal Properties
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**TGA**} & \multicolumn{2}{c}{**DSC**} \\ \hline
**Formulation** & \(\mathbf{T_{onset}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{final}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{peak}}\) (\({}^{\circ}\)C) & \(\mathbf{\Delta m}\) (\%) & \(\mathbf{T_{g}}\) \\ \hline TPS & 156 \(\pm\) 1 & 455 \(\pm\) 3 & 341 \(\pm\) 1 & 80.7 \(\pm\) 1.0 & 92 \(\pm\) 4 \\ TPS1 & 153 \(+\) 1 & 456 \(\pm\) 1 & 343 \(\pm\) 1 & 81.3 \(\pm\) 0.7 & 86 \(\pm\) 1 \\ TPS3 & 152 \(\pm\) 4 & 453 \(\pm\) 1 & 342 \(\pm\) 1 & 82 \(\pm\) 1.0 & 85 \(\pm\) 6 \\ TPS5 & 154 \(\pm\) 1 & 452 \(\pm\) 3 & 343 \(\pm\) 1 & 79.2 \(\pm\) 0.2 & 82 \(\pm\) 9 \\ TPSDH & 179 \(\pm\) 2 & 455 \(\pm\) 1 & 345 \(\pm\) 1 & 80 \(\pm\) 1 & 106 \(\pm\) 1 \\ TPSDH1 & 183 \(\pm\) 1 & 454 \(\pm\) 2 & 346 \(\pm\) 2 & 78 \(\pm\) 2 & 79 \(\pm\) 2 \\ TPSDH3 & 181 \(\pm\) 1 & 449 \(\pm\) 2 & 347 \(\pm\) 1 & 80 \(\pm\) 1 & - \\ TPSDH5 & 182 \(\pm\) 1 & 450 \(\pm\) 1 & 349 \(\pm\) 3 & 79 \(\pm\) 3 & 124 \(\pm\) 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Onset, final, and peak temperatures (T\({}_{\text{onset}}\), T\({}_{\text{final}}\), and T\({}_{\text{deg}}\)), and mass loss at T\({}_{\text{deg}}\) (\(\Delta\)m%) obtained from thermogravimetric analysis (TGA) and glass transition temperature (T\({}_{\text{g}}\)) obtained from differential scanning calorimetry (DSC) for thermoplastic starch (TPS) and TPS modified by dry heating (TPSDH) films with different contents of cellulose microfhers (CMFs). Figure 6: Thermogravimetric analysis (TGA) curves (**a,b**) and first derivative (DTG) curves (**c,d**) curves of thermoplastic starch (TPS) and TPS modified by dry heating (TPSDH) films with different contents of cellulose microfhers (CMFs). -starch matrix. It can also be observed that the gelatinization enthalpy increased with the CMF ratio due to the aforementioned higher presence of starch granules in the films. In the second heating scan, the TPS films also exhibited the glass transition (T\({}_{\text{g}}\)) of the already gelatinized starch, being this transition less appreciable, that is, having a lower change in specific heat (\(\Delta\)C\({}_{\text{p}}\)) for TPSDH samples. The T\({}_{\text{g}}\) values (midpoint) of the TPSDH sample was slightly higher than that of TPS, which is coherent with the stronger interactions promoted in the matrix when starch was DH modified. This result also agrees with the more resistance but less ductile behavior of the films attained during the mechanical characterization. As expected, the addition of CMFs did not induce notable changes in the starch's T\({}_{\text{g}}\), when compared with the corresponding control sample, since the fibers are not miscible with starch. The small variations of T\({}_{\text{g}}\) in the TPS composites with CMFs can be attributed to potential interactions between fibers or their remaining water contents and plasticizer that could modify the plasticization level of starch [31]. In this context, [PERSON] et al. [40] found similar results for corn starch films filled with fibers derived from rice husk.
## 3 Link Level Simulation Model for Interference Evaluation in 5G UDN with LAB
### SIR Dependence on the Angular Separation of Two gNB-UE Links in 5G UDN with LAB  
#### 3.2.1 Simulation Model for SIR Dependence on the Angular Separation  
The model is designed to estimate the SIR dependence for the scenario of two gNB-UE radio links of 5G UDN with one serving gNB, using an AA for directional transmission. The structure of this simulation model is similar to that, described in Section 3.1. Simulation scenario assumes, that one gNB simultaneously serves two UEs by steering two independent ARP beams. The UEs are stationary, and their relative position is determined by two parameters: distance \(d\) from the UE to the gNB and angular separation \(\alpha\) between two UEs. This scenario does not use antenna array at the UE.  
Figure (a)a illustrates the simulation scenario of SIR dependence evaluation on angular separation \(\alpha\) between two UEs of two gNB-UE links in 5G UDN with LAB. Figure (b)b illustrates the map of SIR values in dB: x-axis shows values of distance \(d\) from UE to gNB and y-axis shows values of angular separation \(\alpha\) between two UEs. All dependences are calculated for 4 \(\times\) 4 URA at gNB. Figure (c)c shows the map of SIR values, that exceed a given threshold 15 dB; areas of the SIR map with values, exceeding 15 dB are shaded in green. Figure (d)d shows the dependence of SIR on the values of the angular separation \(\alpha\) for a given range \(d\) from the UE to gNB.  
Figure 3: _Cont._  
To create the AA, gNB and UE parameter structure, we use the createAnt, createNB and createUE functions, as described in Section 3.1.
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
## 2 Materials and Methods
### Methodology  
After the analysis of the current water requirements and a socioeconomic analysis of the municipalities, the water supply lines for the area were conceptualised based on the best available source of water in relation to the quantity and quality of the water.  
In the analysis, two distinct approaches were considered: (1) separate, local supply systems, which implies that each settlement in the municipalities have their own water treatment plant (i.e., local approach); (2) a central supply option for a microregional system, based on a combination of the best available sources in terms of quality and quantity, taking into account only sources realistically close to the distribution system itself (central approach). Fundamentally, the local approach minimises pumping distance but at the expense of multiple WTPs, whereas the central approach would minimise the number of WTPs required but at the expense of significantly longer distribution systems. Note that from a regulators standpoint, an additional advantage of the centralised system is that water quality across the system is considerably simpler to control.  
Figure 3: Municipalities with WTPs that address chemical water quality (marked as green spots) in Vojvodina.  
In order to inform the decision making, an estimate was made of the investment and operational costs for both of these approaches. Investment costs were evaluated based on the costs of the equipment required by the system and its supporting infrastructure. For both the local and centralised approaches, this includes, at the very least, a WTP, water towers, pumping basins, pumping stations, booster pumps, and pipelines for settlements that are not connected to the distribution system.  
The investment costs of the WTP were estimated based on water quality and a defined treatment technology. Table 1 presents seven possible water treatment schemes, as proposed in the Strategy of water supply and water protection in AP Vojvodina [21] which was co-authored by the current authors. Note that in the Serbian regulation for drinking water quality, the total organic matter is controlled by the oxidisability [10]. In general, the \"Treated parameters\" column broadly reflects the variations in water quality found in Vojvodina groundwaters, where no issues with other heavy metals, such as nickel or uranium, have been detected.
## 4 Results and Discussion
### FPR and FNR  
In the context of intrusion detection, the false/missed alarm (FPR or FNR) is an important metric: a high rate of false alarms disturbs the security staff attention and increases the chance of a missed attack. The minimization of such events is an essential field of IDS research [95]. This metric demonstrates (Table 6) that AWSCTD family models have an advantage against the FCN family: the AWSCTD-CNN-GRU model has the best FPR of 0.018 for 1000 system calls sequence, while LSTM-FCN has the best value of 0.027 for the same sequence, i.e., 50% worse.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Count**} & **AWSCTD-CNN-S** & **AWSCTD-CNN-LSTM** & **AWSCTD-CNN-GRU** & **FCN** & **LSTM-FCN** & **GRU-FCN** \\ \hline
10 & 0.930 & 0.923 & 0.928 & 0.932 & 0.934 & 0.933 & 0.929 \\
2  
The static (AWSCTD-CNN-S) and dynamic (AWSCTD-CNN-D) models demonstrate the lowest FNR values for 1000 system calls--0.003 (Table 7). In comparison, the best achieved FNR result by the FCN family model is equal to 0.007 and is almost 60% worse.
## 3 Results
### Monthly and Annual Energy Production  
With the results from several key intermediary modeling steps presented, we conclude the results section with a comparison of the modeled and measured monthly DC energy. In Figure 16, the monthly and yearly errors in energy predictions across all four PV system types are shown. Please note that roughly 75% of the total annual energy is produced between April and August. With just 25% of the annual energy produced between September and March, errors during these months tend to be larger on a percentage scale. An interesting result in Figure 16 is that on a monthly basis, PlantPredict, PVsyst, SAM, and SolarFarmer fluctuate between negative and positive bias relative to the measurements, sometimes with monthly deviations greater than 5%. However, on an annual basis, all tools simulate the four PV systems within 3.5% or less of measurements, and in some cases, the annual error is less than 1%. This is a rather positive result considering the uncertainty of the solar radiation measurements and the electrical monitoring system.  
The accuracy of annual results from PVsyst and SAM shown in Figure 16 are in fact better than those published by [75], which could be because the parameters and loss factors used here were thoroughly calculated rather than using default values. On an annual basis, results from PlantPredict match PVsyst within 0.7% to 2.0%, which is a slightly larger deviation than that presented in [31] for Cadmium Telluride technology, but the larger differences here could be due to the use of different IAM models to describe reflection losses and/or the introduction of \(G_{POA,Ran}\). The annual MoBiDiG results are between 2% and 3% above measurements, which is higher than the \(\pm\)1% published in [14] for most static-tilt configurations, but agreement could improve if, for example, the DeSoto parameter set was modified such that the modeled DC efficiency was lowered at low-light conditions (Figure A1).  
Figure 15: Sensitivity of albedo on the monthly bifacial gain (Equation (1)). All simulations here are performed with the MoBiDiG VF model. The orange curve shows the base case that uses monthly albedo (Figure 11), the green curve shows results using 21% annual average albedo, and the shaded green regions show the range of results obtained using annual albedo values of 18% to 0.24%.  
Finally, a word on the accuracy of bifacial versus monofacial simulations: The difference between the annual energy yield error in bifacial versus monofacial simulations is 1% or less, for five of six software. The outlier is SolarFarmer, which shows larger differences between its bifacial and monofacial simulations, but this is simply due to the overestimate of \(G_{POA,Rear}\) mentioned previously. The annual errors in bifacial and monofacial simulations are no more than 0.5% different when performed in MoBiDiG VF, PlantPredict, and PVsyst. This result is in accord with the results presented in Section 3.1, which showed a deviation of roughly 0.5% between modeled and measured \(G_{POA,Rear}\) values when considering the contribution of \(G_{POA,Front}\).
## 3 Results
### Negative and Positive Influences on IF  
Many factors were mentioned for having a negative or positive influence on IF. These included nutrition, sleep, stress, medical treatment, (physical) overload and underuse. Participants each put emphasis on different factors, and whether or not they incorporated this into their treatment plan.  
Figure 1: Overview of the identified themes and exemplary codes. Note. Codes are exemplary and were selected because they occurred frequently and meaningfully across the dataset.  
* \"We often discuss the patients' sleep quality. Bad sleep quality also weakens the immune system.\" [6]
* \"When the training intensity is too high, for example what you see in elite sports, then there is a chance that your immune functioning decreases.\" [6]  
Most participants believed that physical activity positively influences IF, and many emphasized that they could not explain the mechanism behind this. Several participants said that the right intensity of physical activity can positively influence the immune system while physical overload may have a negative impact.  
* \"Studies have shown that physical activity can improve your immune system, so physical activity is important, but how should you be physically active? Which advices should we give our patients?\" [3]  
Several participants mentioned that physical activity can influence cancer development, and recovery from cancer treatment. For example, physical training could positively influence negative effects of treatment.  
* \"<immunosuppressive effect of treatments> are often an inevitable consequence of treatment, but we can influence it. We cannot prevent it, but physical activity can positively influence these consequences.\" (...) [4]
* \"... The better the physical fitness before surgery, the better the chances that you have some reserve capacity to improve your recovery. I think the immune system is associated with this.\" [9]
## 5 Conclusions  
Strawberry producers, like other farmers, strive for a high yield and aim to offer high quality fruit to the market. Our findings show that fertilisation with N can significantly increase strawberry yield when applied in the required doses within a month before harvesting. Plants produce more fruit but the quality of fertilised fruit is affected. Fertilisation with nitrogen and bolt calcium significantly increased the content of both analysed aldehydes and changed the strawberry aroma by adding leafy notes. Fertilisation also had a negative impact on the desired fruity volatiles meridolol and \(\alpha\)-terpineol. On the other hand, nano-Ca fertilisation especially improved the content of fruity esters and increase the content of important phenolic compounds. In general, these results show that fertilisation with nitrogen increases yield, but can result in fruits with a leafy green flavour, less flowery and fruity notes, and consequently lower fruit aroma quality compared to the non-fertilised control or nano-fertilised strawberries.  
Conceptualization, N.C.W. and D.K.; methodology, N.C.W.; software, N.C.W.; validation, H.B.C. and N.C.W.; formal analysis, N.C.W., J.J. and H.B.C.; investigation, N.C.W. and J.J.; resources, M.H.; data curation, N.C.W. and J.J.; writing--original draft preparation, N.C.W.; writing--review and editing, D.K., R.V. and J.J.; visualization, N.C.W. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Slovenian Research Agency (grants P4-0133 Sustainable agriculture and P4-0013 Horticulture), by the Republic of Slovenia (Ministry of education, science, and sport) and the European Union from Structure and Investment funds (project \"Raziskovalci-2.0-KIS-529016\", N.C.W. (C3330-17-529016).  
The authors acknowledge the support of co-workers at the Agricultural Institute of Slovenia for their help, sharing of opinion and knowledge, and their important contribution to the first results. We would like to thank also our families and friends for the moral support.  
All the authors declare that there is no conflict of interest. The funders had no role in the study's design; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 High-Speed PMSG Characteristics with Inconel 718 Shaft Material
### Mechanical Characteristics  
After considering the electromagnetic characteristics of PMSG with Inconel 718 shaft material, mechanical characteristics analysis was performed using the same method as that for PMSG constructed with STS420-J2 material. As a result of the critical speed analysis of Inconel 718, as shown in Figure 7, the critical speed is about 52,106 rpm, which is more stable than the STS420-J2 material because of its favorable mechanical rigidity. In the case of modal analysis, as shown in Figure 8, when operating at the rated speed, the 1 st  
Figure 5: Leakage magnetic flux of STS420-J2 shaft material.  
Figure 6: Leakage magnetic flux of Inconel 718 shaft material.  
resonant frequency was 1897.4 Hz, the 2 nd resonant frequency was 1931.8 Hz, and the 3 rd resonant frequency was 5550.9 Hz and 5730.9 Hz. Therefore, it was confirmed that there is no problem in the design of the rotating body because the shaft rotating body made of Inconel 718 also has no bending mode intersection at the rated speed.
## 4 Materials and Methods  
### Strains, Media and Growth Conditions  
_Aurantiochytrium limacinum_ MYA-1381 was obtained from the American Type Culture Collection (ATCC). _Escherichia coli_ DH5\(\alpha\) and BL21(DE3) were acquired from Takara (Dalian, China). The plasmids and strains constructed in this work were listed in Table 1. _Aurantiochytrium_ was grown at 30 \({}^{\circ}\)C and 200 rpm in ATCC 790 By+ medium (1 g/L p-tone, 1 g/L yeast extract, 5 g/L glucose in seawater). As for the recombinant _Escherichia coli_ for \(\beta\)-carotene production, bacterial culture was performed as previously described [55]. Single colonies were inoculated into 4 mL Luria-Bertani (LB) medium (10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl) with 34 mg/L chloramphenicol; the stains were cultured at 37 \({}^{\circ}\)C and 200 rpm overnight. Seed culture was then inoculated into fresh LB containing antibiotic with an initial OD\({}_{600}\) of 0.05. The bacteria were incubated at 37 \({}^{\circ}\)C and 200 rpm for 12 h. Cells were collected for further analysis with three biological replicates.  
Figure 7: Simplified pathway model for de novo \(\beta\)-carotene biosynthesis in _Aurantiochytrium_.
# An Efficient Interface for the Integration of IoT Devices with Smart Grids
## 2 Background
### Open Smart Grid Protocol  
OSGP is a reference architecture for communication between devices operating over SGs [24]. The main purpose of this architecture is to provide greater control of electricity consumption and supply between customers and service providers in order to provide useful information to consumers of these services. OSGP is standardized by European Telecommunications Standards Institute (ETSI) and its layers are defined by the ETSI GS OSG 001, ISO/IEC 14908 and ETSI TS 103 908 specifications. OSGP is designed to work on a variety of SG devices. In order to avoid collisions, the protocol uses a master-slave architecture, and the nodes are not able to hear each other. As OSGP does not support overlapping transactions, the procedures must be executed rigorously one at a time.  
OSGP is divided into three main layers: (i) Application (ETSI GS OSG 001) [24]; (ii) Network (ISO/IEC 14908); and (iii) Physical (ETSI TS 103 908). Figure 2 illustrates the protocol stack and the corresponding seven layers of the Open System Interconnection (OSI) model. The protocol is of request/response type [25; 26].  
Figure 1: Simplified layered stack of Constrained Application Protocol (CoAP) (adapted from [23]).
# Assessing the Role of Program Suspend Operation in 3D NAND Flash Based Solid State Drives
## 5 SSD Simulations to Understand the Role of Program Suspend
### The Impact on QoS  
The QoS study can be performed in multiple ways since it depends on the definition of the QoS level and on the latency that is being profiled for reaching that. In this study, we considered, without a lack of generality, a QoS calculated at the 99.99% percentile of the latency Cumulative Distribution Function (CDF) [32] either considering the total transactions sustained by the drive (i.e., total QoS) or only considering the read transactions (i.e., read QoS), which are those benefiting from the program suspension. Figure 12 shows a profile of a latency CDF in which the read, write, and total contributions of the QoS are appreciable.  
Figure 13a shows that the total QoS performed 630 \(\upmu\)s better when the program suspension was exploited in workloads largely dominated by read operations that were prioritized with respect to write. Workloads performed on lower QD values still displayed an advantage even if it became marginal (i.e., a few hundred \(\upmu\)s). Increasing the number of allowable suspensions from 5 to 10 slightly penalized the QoS, but in the high QD scenario there was still a large advantage compared with the no suspensions case. Similar considerations can be extrapolated from the results of the read QoS shown in Figure 13b; however, here, the QoS gain increased up to 1.03 ms, proving that the program suspension is one of the most suitable ways to reduce the latency in read intensive scenarios.  
Figure 11: (**a**) The power consumption of the memory sub-system in the SSD. (**b**) The power consumption of the internal I/O bus in the drive.
## 2 The Lunar South Pole
### Lunar Volatiles
Furthermore, LCROSS conducted indirect measurements of the amount of water ice by impacting the upper stage of the LRO rocket into the floor of Cabeus, a particularly rich-in-water PSR of the south pole; spectra from the generated ejecta plume revealed a value of 5.6 \(\pm\) 2.9 wt% of water but also the presence of methane, carbon monoxide, ammonia and a strong emission of sodium in the moments immediately following the impact [19; 20]. As we will ascertain in the next paragraph, Na emission is a characteristic marker of some lunar south pole spectra and finding a correlation with this emission and water detection from Earth could be very interesting. It is therefore established how water detection is not straightforward but is strongly influenced by diverse variables such as the particle size of water ice [21] and detection techniques; all the collected data furnish a remarkable overview of the lunar environment but they do not collimate in a unique theory. This motivates the need for lunar surface exploration to ground-truth these potential detections. Figure 4: From left to right: location of the permanently shadowed regions [10] and distribution of water-equivalent hydrogen (WEH) around the lunar south pole. The second image also highlights the first eight areas for the abundance of WEH (adapted from [22]); the first one corresponds to the Cabeus crater.