esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 4 Verification by Simulation Experiment
The reason is analyzed mainly because the radiometric calibration of the traditional calibration method does not take into account the influence of the polarization properties of optical system, and the spectral radiance obtained by the radiometric calibration deviates from that at the entrance pupil, which lead to the great inaccuracy of the reconstruction results of Stokes parameters. In order to further quantitatively analyze the measurement accuracy of the polarization information of the fieldable CDISP, we use the degree of polarization (DOP) [24], given by  
Figure 8: Input values and calibration results of the retardance of the fore-optics. Figure 3.0 Input values and calibration results of \(\varphi_{1}(\sigma,\theta)\)  
\(DOP(\sigma)=\sqrt{S_{1}{}^{2}(\sigma)+S_{2}{}^{2}(\sigma)+S_{3}{}^{2}(\sigma)}/S_{0 }(\sigma)\), to further analyze the reconstruction results, which are shown in Figure 9d. With these two calibration methods, the measurement accuracy of normalized Stokes vectors and DOP obtained from central to edge FOV are shown in Figure 3.1 It can be seen that the measurement accuracy of polarization information obtained by the traditional calibration method is decreased as the FOV increases. It is noteworthy that the measurement accuracy is about the same at different field of view by using the presented calibration method, and they are reduced by approximately an order of magnitude compared with the traditional calibration method. Though the measurement accuracy of \(S_{3}/S_{0}\) by the presented method is less accurate than the traditional method, they are on an order of magnitude and are all very precise. The influence of the polarization properties of an optical system on the radiometric calibration of the fieldable CDISP is effectively compensated. Figure 3.2 Reconstructed results of normalized Stokes parameters and DOP, wherein the solid and broken line respectively represent the presented calibration method and the traditional calibration method, and the different colors represent reconstruction results at different field of view. (**a**) \(S_{1}/S_{0}\); (**b**) \(S_{2}/S_{0}\); (**c**) \(S_{3}/S_{0}\); and (**d**) DOP. Since the CDISP studied in this paper is mainly used for airborne remote sensing, the circular polarization component of the target light is usually small enough to be ignored in practical applications, so we use the linearly polarized beam as the target light to verify the validity of the presented calibration method in simulation experiment.
## 3 Methodology
### Initial LSP Modeling--Random Forest  
Random Forest (RF) was selected due to its proven effectiveness in LSP studies [18; 20; 43]. As the most widely used method in LSP research (approximately 49% [18]), RF serves as an ideal baseline model for investigating the fundamental issue of non-landslide sampling strategies. Using this commonly accepted model allows us to focus on the sampling effects while addressing a universal challenge in LSP modeling.
# A Mobility-Assisted Localization Algorithm for Three-Dimensional Large-Scale UWSNs
## 4 Mobility-Assisted Localization Scheme
### Phase I
The distance between the sensor node \(\left(X_{a_{i}},Y_{a_{i}},Z_{a_{i}}\right)\) and the anchor node \(\left(X_{b_{j}},Y_{b_{j}},Z_{b_{j}}\right)\), expressed as \(r_{a_{i}b_{j}}\), is given as follows:  
\[\left(X_{b_{j}}-X_{a_{i}}\right)^{2}+\left(Y_{b_{j}}-Y_{a_{i}}\right)^{2}+ \left(Z_{b_{j}}-Z_{a_{i}}\right)^{2}=r_{a_{i}b_{j}}^{2} \tag{22}\]  
Let \(\varepsilon_{a_{i}b_{j}}\) be the projection distance error between them. The projection distance obtained by using distance measurement can be expressed as \(d_{a_{i}b_{j}}\) and given as follows:  
\[r_{a_{i}b_{j}}^{2}-\left(Z_{b_{j}}-Z_{a_{i}}\right)^{2}=d_{a_{i}b_{j}}^{2}+ \varepsilon_{a_{i}b_{j}} \tag{23}\]  
Therefore, the relation equation between the sensor node and the anchor node can be obtained, as shown in Equation (24). \[AX=b+\varepsilon \tag{24}\]  
Then the estimated position of the ordinary sensor node is as follows:  
\[X=\underset{X}{\text{argmin}}\ \left\|b-AX\right\|_{2}^{2} \tag{25}\]
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 3 History-Based Prediction Approach
### Basis
```
Input :\(TPg\) (TP msg generated by Routing Layer) Output :\(TPs\) (TP msg to be sent to Network Layer) or \(none\) (if this TP msg can be predicted at destination) \(TPs\gets none\)// assume \(TPs\) can be correctly predicted by all neighbours forall current neighbours of origin nodedo\(TPp\gets PredictNextTP(neighbourHistory_{i})\)// \(TP\) msg predicted by \(neighbour_{i}\)if\(TPp\
eq TPg\)then\(TPs\gets TPg\)// wrong prediction at \(neighbour_{i}\), send \(TPg\) endif Append \(TPg\) to \(neighbourHistory_{i}\) endforallifif\(TPs\
eq none\)then Send \(TPs\) to Network Layer endif return \(TPs\)
```  
**Algorithm 1**Predictor 1 (TP msg sender) at \(origin\) node  
Consequently, if the predictor in Node 2 (i.e., the destination node) does not receive the TP message, it assumes that such a message can be predicted using the historical information. Therefore, it generates the message and passes it to Node 2. Thus, the destination node regularly receives the TP message and processes it, regardless of whether or not it was generated by the source node or local predictor. Working in such a way, this prediction approach becomes transparent for the routing protocols that use it. Algorithm 2 shows the operation of Predictor 2 at the nodes where TP messages would be received or generated by the predictor. Periodically, a new TP message must be received by the destination node. When the time for the next TP message reception is over and no message was received, a new TP message is locally generated using the predictor and the corresponding TP history. Whether the TP message is received or generated, it is appended to the history and injected into the routing layer. Figure 1: System architecture that includes the twin predictors. In the next section we present an example scenario that allows us to illustrate the dynamic of the prediction process and its potential benefits using confidence mechanisms and different sizes for the historical data-window. ```
Output:\(TPr\) (TP msg to be injected into Routing Layer)
1 Wait until a new TP msg should be received
2 ifa new TP is receivedthen
3\(TPr\leftarrow\) TP received from Network Layer
4 else
5\(TPr\gets PredictNextTP(localHistoryFromSender_{i})\)
6 endif
7 Append \(TPr\) to \(localHistoryFromSender_{i}\)
8 Inject \(TPr\) into the Routing Layer
9 returnTPr
```  
**Algorithm 2**Predictor 2 (TP msg receiver) at _neighbour_ nodes
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 3 Visibility-Based Collision Warning System Design
### Headway Distance Prediction Model  
The headway distance between SV and LV are estimated or predicted as follows:  
\[h=H+D_{err}+T_{d}(V_{SV}-V_{LV})\]  
where \(H\) is the headway distance, \(D_{err}\) is the GPS positioning error, \(T_{d}\) is the information delay which includes GPS delay, transmission delay, and computation time of collision detection, \(V_{SV}\) and \(V_{LV}\) are the velocities of SV and LV, respectively. The future headway distance is computed as follows:  
\[D(t+1)=D(t)+\frac{V_{LV}(t+1)-V_{SV}(t+1)}{p}\]  
Figure 3: Core Concept in Visibility-based Collision Warning System (ViCoWS).  
where \(D(t)\) is the space headway in the time slot \(t\) and \(t+1\), respectively, \(V_{LV}(t+1),~{}V_{SV}(t+1)\) are the predicted velocities of LV and SV for the time slot \(t+1\), respectively, \(p\) is the period of one time slot, which is 0.1 s in this work.
## 2 Biomass Feedstocks Processing with Fossil Fuels in a Common Process
### Feedstocks for Co-Processing  
The majority of biomass feedstocks currently used in co-processing are lipid feedstocks of either vegetable oils, fats and greases. Vegetable oils and fats can be sourced directly from oil crops, e.g., rapeseed, soybean oil and palm oil, or they can be waste cooking oils, i.e., vegetable oils which have been used, collected and recycled. Concerns over the sustainability of using new vegetable oils as fuel has put more focus on the use of waste or recycled materials. However, the amount of both waste cooking oil and the waste fraction of animal fats are a relatively limited, leading to searches for other options.  
In the future, feedstocks used for co-processing may come from \"advanced\" biomass feedstocks, e.g., agriculture residues such as straw, bagasse from sugar cane production or the biomass fraction from Municipal Solid Waste (MSW). These materials are first thermally treated to produce crude biomass oils, and then co-processed to upgrade to finished fuels. For instance, crude biomass oils can be produced from lignocellulosic feedstocks using thermochemical technologies such as gasification, pyrolysis, and HydroThermal Liquefaction (HTL). Pyrolysis and gasification require relatively dry feedstock while HTL is able to process \"wet biomass\" or biomass feedstocks with high moisture content into bio-crude. In this paper \"bio-oils\" refer to those produced by pyrolysis of biomass, whilst \"bio-crudes\" come from HTL.  
Another possibility is to use specific co-product streams from the wood or paper industry. Black Liquor from the paper pulping industry can be pre-treated to produce a suitable co-processing feedstock by deoxygenation via hydrotreating. Another proposed method is to remove the lignin from the black liquor in the paper mill, to mix it with diesel fuel, and then to co-process. It is highly likely however that only countries or areas with significant paper processing industries would be able to produce volumes of these materials to allow co-processing to take place, i.e., in Scandinavia.  
In a significant review of refinery co-processing technologies, [PERSON] et al. [12] indicate pyrolysis and HTL as the most promising pathways to produce crude biomass oils for co-processing. The rationale is that gasification (and thus Fischer-Tropsch technologies for biomass) is more challenging with issues such as economies of scale and gas clean up. Indeed, crude biomass oil gasification technologies have been slow to reach commercial maturity due to significant technical challenges, low reliability, high capital costs, and the generally lower oil prices [12]. Therefore, the use of such crude biomass oils remain a longer-term possibility. Table 1 shows the typical composition of various bio-based feedctocks that can be used for co-processing in refineries [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26].
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 4 Our Construction
### Hybrid Encryption
#### 4.1.1 Encrypt  
If user1 (main site) chooses the quantum key encryption scheme, it will be encrypted by the one-time pad encryption algorithm. Once the key amount is insufficient, it will switch to the AES encryption algorithm. If the classical key encryption algorithm is selected, it will be encrypted by AES. User1 transmits the encrypted information to the VOSS; see Algorithm 1 for the specific process.  
```
Input: Video plaintext \(M\) Output: Video ciphertext \(C\)
1 Init \(quantum\_AES\_flag=FALSE\);
2 User1 sets the encryption mode \(quantum\_flag\);
3 Send flag \(Server\leftarrow\)User\(1(quantum\_flag)\);
4 if\(quantum\_flag==TRUE\)then
5 if\(quantum\_AES\_flag==FALSE\)then
6 for\(i=1\) to \(QT_{A1}.len()\)do
7\(C_{i}=En_{xor}(QT_{A1}^{l_{i}},M_{i})\);
8 Send ciphertext \(Server\leftarrow\)User\(1(C_{i})\);
9 if\(\mathit{Insufficient}\)quantum\(\mathit{keys}\)then
10 BREAK;
11
12 end if
13
14 end if
15
16\(quantum\_AES\_flag=TRUE\);
17\(ID_{AES}=i\);
18 Server\(\leftarrow\)User\(1(quantum\_AES\_flag,ID_{AES})\);
19\(C=En_{AES}(QT_{A1}^{l_{i}},M)\);
20
21 end if
22 if\(quantum\_flag==False\)then
23\(C=En_{AES}(ck,M)\);
24
25 end if
26 return\(C\);
```  
**Algorithm 1**Video encryption algorithm
## 3 Results and Discussion
### Simulation Results of Integrated Optimization  
Based on all the simulation results above, considering the economic factors, the best integrated optimization measures for tubular houses in northern Zhejiang should be conducted as follows: CASE-B + CASE-C (30 mm) + CASE-D = CASE-X. The optimal measures were determined by all the simulation results of CASE-B to E. As shown in Figure 10, CASE-B, CASE-C, and CASE-D all provided significant results in terms of energy consumption, while CASE-E provided little reduction, with an energy-saving rate of only 1.6%. According to the CASE-X analysis in Open-Studio, the annual energy consumption of the tubular house was 13,719 kWh in total, and 86.83 kWh/m\({}^{2}\) in the building area, with an energy-saving rate of 47.4% compared with CASE-A. The heat load was 14.52 kWh/m\({}^{2}\), while the cooling load was 20.66 kWh/m\({}^{2}\), and the heat load was more optimized than the cooling load.  
According to the National Bureau of Statistics of China, the consumption of 1 kWh of electrical power requires the burning of 0.404 kg of standard coal, which results in CO\({}_{2}\) emissions of 0.872 kg. The CO\({}_{2}\) emissions corresponding to CASE-A and CASE-X were 18,838.21 kg (21,603 kWh) and 11,962.9 kg (13,719 kWh), respectively, with a total difference of 6875 kg.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 3 Related Work-Coordination Analysis
### Related Work Challenges  
The authors of [18], despite utilizing the k shortest path technique during the link mapping phase of their uncoordinated Greedy-kSP solution, did not present any results regarding the performance variations of the aforementioned algorithm while selecting different k values. Moreover, the study was entirely focused on portraying the algorithm's efficiency in the context of the revenue and cost metrics, forsaking the other available performance indices which could have offered a more complete overview of the capabilities of the involved solution.  
The work presented in [22] represents a robust attempt to introduce a VNE solution supporting two stages coordination by utilizing a range of performance metrics depicting the algorithms' capabilities over time. Nonetheless, the variations in the incoming VNRs' density and intensity were not addressed in a managed way that could produce usable data leading to useful conclusions, instead adopting a random distribution of VN scale.  
Although, the researchers of the single stage coordinated SID solution presented in [27] used an adequate variety of VN sizes between 20 and 40 nodes, the validity of their experimental results relied on only two indices of intensity of the demanded resources. By doing so, they narrowed the value space of received data, stripping their research from possible important conclusions. Furthermore, despite the fact that the evaluation of their results was achieved by utilizing the most prominent of the existing performance metrics, an index that could represent the substrate resource economy, implied by the mapped node distribution methodology, was not considered.  
One common factor of overview incompleteness among the contemplated solutions is the lack of a comparison between all the different coordination methodologies. Our research aims to experimentally explore the potential of those solutions, on the basis of their coordination taxonomy, and observe the factual limits of their capabilities.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 2 Background
### Oxidative Degradation Mechanisms
#### 2.1.2 Oxidation of Unsaturated Compounds  
In food and beverages, there are several components that are prone to oxidation as the organic substrate in the previous section. They can be differentiated according to the photosensitization type.  
Unsaturated compounds in packaged food products are usually the first to become oxidized. Unsaturated fatty acids are the most typical examples, but also terpen and vitamin A possess the same sensitivity issues [41].  
Due to the presence of pigments and dyes in foodstuffs, activation of molecular oxygen can occur as described earlier and the resulting singlet oxygen (type II photosensitization) will oxidize unsaturated bonds at room temperature. Type I photosensitization may also occur with unsaturated molecules, but only significantly for those that are susceptible to H-abstraction. Polyunsaturated fatty acids such as linoleic acid (C18:2) and linoleic acid (C18:3) provide a weak C-H bond in between the double bonds, as indicated in Figure 6. Even though the singlet oxygen initiation still maintains a higher reaction rate in the order of magnitude 4, the radical initiation is sensitively increased by this [39; 42; 43].  
Figure 5: Bimolecular decay of organic peroxyl radicals. After a tetroxide transition state, different products may be formed, either (**a**) terminating the radical chain or (**b**) propagating it [32].  
Figure 6: Molecular structures of (**a**) linoleic acid and (**b**) linoleic acid, highlighting the hydrogen atoms that are the most sensitive to H-abstraction due to conjugative stabilization after radical formation.  
Oleic, linoleicic and linolenic acid are present in beer after the malting of barley, where they are released by malt lipases from oils containing the esterified fatty acids. The breakdown of these components has been widely related to a cardboard taste after beer staling. The fatty acids are oxidized to form new carbonyl structures of which trans-2-nonenal is the most prominent [44].  
In organic electronics, the semi-conductive polymer chains and chromophores are highly conjugated unsaturated structures. They are also predominantly degraded by singlet oxygen in a type II photosensitization. In Section 2.1.1, it was noted that a type I-like photosensitization is also possible after charge separation. Both mechanisms are therefore relevant, but initiation involving a singlet oxygen will consistently occur at a higher rate [45; 46].
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 5 Research Trends and Opportunities
Consequently, the benefit of low electrical energy consumption for absorption chillers is reduced, which leads to low electrical efficiency (i.e., low electrical COP). Moreover, theoretically, reducing the pump/fan volume flow rate by decreasing the pump/fan speed about 50% reduces electricity consumption by 87% [100]. Therefore, new control and operation strategies can be implemented by actively controlling external volume flow rates via variable speed pumps and fans. By so doing, the whole system consumes less electricity and electrical COPs are higherThis new type of control strategy has already been applied in field tests with newly developed absorption chillers, new-AHP, in solar and tri-generation (CHP plants) applications [24; 100]. Because of the intermittent nature of the solar resource and the variation in the cooling load during the day (and also the year), these newly developed absorption chillers need to be controlled if they are to perform optimally and operate safely. Hence, in recent years, control strategies have been developed on the basis of chiller performances in field tests and simplified models (such as the characteristic equation method) to describe the full and partial load operation of the chillers.
## 6 Discussion
Furthermore, the _TWVC_ mission subsystem consumes approximately 8.50 W in the transmission mode without a power amplifier. Having a power amplifier [60] integrated into the system could significantly improve the system link margin. On the other hand, this would negatively impact the power budget, mass, and volume of the system depending on the limited resources of a small satellite. As a solution, the use of high-power amplifiers demands either the implementation of supplementary power systems such as deployable solar arrays [67], or an increase in the size of the satellite or a reduction of the mission execution time must to meet the requirements of the limitation factors. If these suggested solutions could be implemented, a positive system link margin will be achieved for the proper ISL communications in orbit. Some limitations due to the processing power of the RPi were encountered as RPi 4 efficiency declined at sampling rates above 3 MSPS; however, the SPATIUM-II satellite achieved the in-orbit demonstration of SS signal demodulation and time delay detection at 1 MSPS based on the RPi CM3+ module, which has a lower processing power compared to the RPi 4 model B [19; 35; 42]. Also, during ground tests, it was observed that when multiple processes run in the RPi desktop Ubuntu Linux operating system, communication fluctuations and frequency reconfiguration delays occurred. These can be alleviated using high processing power microprocessors, or implementing software interrupt functions which allow the prioritization of important tasks such as frequency reconfiguration and data processing. Using a lighter OS or running the programs in Ubuntu-based servers could also deliver better performance since all system resources would be dedicated only to server tasks rather than having additional desktop resources [68]. The use of precise atomic clocks such as the chip-scale atomic clock (CSAC) or GPS clock [19] to alleviate the clock jittering effect on the SDR or other radio transceivers has been demonstrated in SPATIUM-I and SPATIUM-II. In this study, an external 10 MHZ GPS clock has been used to eliminate the jittering error of 35 us that was consistently recurring. As a result, all errors associated with the SDR and instruments' clock jittering were removed, and the bits of information could be perfectly received without noise, shifting or flipping of the information bits. Furthermore, considering a constellation of more than 1000 satellites, large volumes of data will be captured. In such cases, it is recommended to utilize high-data-rate communication networks such as the S-band, C-band, and X-band on the satellite bus for data downlink to the ground [63; 69].
## 4 Description of Subsystems
### The NFTSMC and TDE Subsystems
#### 4.2.2 The NFTSMC and TDE Systems Design
Furthermore, in light of the application of the TDE Subsystem as presented above and reflected in Figure 1, the TDE-based component can be derived with the estimate \(\hat{\mathbf{H}}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) of \(\mathbf{H}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) using (16), instead of having to compute \(\mathbf{H}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) directly, thereby making the control scheme highly efficient.