esa-sceva/satcom-chunk-collection huggingface dataset:
# Photon Enhanced Interaction and Entanglement in Semiconductor Position-Based Qubits
## 3 Position-Based Semiconductor Qubits in the Frame of Semiconductor Photon Communication  
### Rabi Flopping Frequency of a Position-Based Qubit  
We start our analysis by considering the system of a CMOS double-quantum-dot (DQD). We assume initially that only the two lowest states can be populated. In this case, the system can be seen as a two-level system: the ground state, denoted as \(\ket{g}\), and the first excited state, denoted as \(\ket{e}\).  
In this basis, the wave-function can be expressed as a superposition of the two eigen-functions:  
\[\ket{\psi}=c_{g}\ket{\psi_{g}}+c_{e}\ket{\psi_{e}} \tag{1}\]  
where \(c_{g}\) and \(c_{e}\) are the probability amplitudes of each energy state, with \(c_{g}^{2}+c_{e}^{2}=1\). The energy difference between the ground state, \(E_{g}\), and the first excited state, \(E_{e}\), defines the occupancy frequency, \(\omega_{0}=(E_{e}-E_{g})/\hbar\). When in relaxation, the system is expected to be in the ground state. However, one can excite the system by applying a driving field, which can be a square or a sinusoidal pulse of a given width. Assuming initially a small pertubative pulse, the system will be in a quantum superposition of the first two states, and it will display occupancy oscillations [26].  
The Hamiltonian of the system can be written as:  
\[\hat{H}=\hat{H_{0}}+\hat{H_{I}} \tag{2}\]  
where \(\hat{H_{0}}\) is the Hamiltonian of the system in equilibrium, with \(\hat{H_{0}}=\hat{p}^{2}/2m_{e}^{*}+U_{0}\), \(\hat{H_{I}}=U_{BI}\cos\omega t\) is the interaction Hamiltonian and \(m_{e}^{*}\) is the effective electron mass. \(U_{BI}\) denotes the change to the initial potential energy profile, \(U_{0}\), of the DQD system, when an arbitrary bias voltage waveform of frequency \(\omega\) is applied at the gates (The interaction Hamiltonian due to a single-mode cavity field of frequency \(\omega\) will have the same effect and can be represented qualitatively by the same expression). In the rotating wave approximation, the Rabi flopping frequency, \(\omega_{R}\), is [28]:  
\[\omega_{R}\equiv\frac{1}{2}\sqrt{(\omega-\omega_{0})^{2}+(U_{BI}/\hbar)^{2}} \tag{3}\]  
and  
\[\begin{array}{l}c_{e}(t)=i\frac{U_{BI}}{U_{\rm R}\pi}e^{i\delta t/2}\sin{( \omega_{\rm R}t/2)}\\ c_{g}(t)=e^{i\delta t/2}\left\{\cos{(\omega_{\rm R}t/2)}-i\frac{\delta}{\omega _{\rm R}}\sin{(\omega_{\rm R}t/2)}\right\}\end{array} \tag{4}\]  
where \(\delta=\omega-\omega_{0}\) is the detuning term. In this work, we are not interested in investigating the effects of detuning, therefore we assume \(\delta=0\). The transition probability, \(P_{g\to e}\equiv P_{e}(t)\), can be written as:  
\[P_{e}(t)=\left|C_{e}(t)\right|^{2} =\frac{U_{BI}^{2}}{\omega_{\rm R}^{2}\pi^{2}}\sin^{2}{(\omega_{ \rm R}t/2)} \tag{5}\]
## 2 Research on PSS Design Evaluation
(2019) used intuitionistic fuzzy number operators and DEMATEL to evaluate the hesitancy of expert PSS judgments and derive all interdependencies between co-creative value propositions (CVPs), respectively. They then validated their method using a smart fridge service system. [PERSON] et al. (2019) proposed a concept evaluation model based on the fuzzy morphological matrix, and developed a fuzzy multi-objective optimization model to select the design concept with maximum customer satisfaction. After that, determining membership and non-membership in fuzzy sets and intuitionistic fuzzy sets remains a critical challenge (2018). For this issue, rough set technology can integrate the original evaluation data from various DMs, and does not require a predetermined membership function or auxiliary information. In addition, a rough-Z-number-based DEMATEL was proposed by [PERSON] et al. (2019) to address uncertainties in stakeholder evaluations and determine the risk prioritization of sustainable value propositions. To improve the delivery performance of PSSs, [PERSON] et al. (2019) proposed a method that adopts rough sets to perform fuzzy processing on a priori information, which then ranks evaluation criteria using the best worst method (BWM) and criteria importance through the inter-criteria correlation (CRITIC) method. Obviously, rough number in rough set is an objective mathematical model, which can integrate the original individual judgment of each DM from group experts. Based on Table 1, we compare our proposed approach with the pertinent literature for PSS design decisions in Table 1. Additionally, previous studies in this area have widely focused on the calculation of criteria weights for assessing the value of PSS concepts. This has resulted in the selection of PSS conceptual solutions that are relatively unsuitable for the needs of both manufacturers and customers. There are few studies that quantitatively assess the conflict of interest between customer demand and economics objectives with respect to enterprises for PSS design, and some do not even consider the uncertainty of assessments come from stakeholders' subjective judgments. The intent of this study is to fill this gap, and the coalitional game is adopted to reconcile the conflicting between customer demand and economics objectives for PSS design under uncertainty. In this study, a Shapley value is obtained to represent a stable coalition condition in which all objectives will not unilaterally leave this coalition to improve their own benefits--please refer to [11; 25].
## 3 Literature Review and Research Questions
### Usage of Information Systems and Supply Chain Management  
Empirical studies that were conducted already in the 1990s or a little later consider that information systems (IS)/information technology (IT) help significantly in the development of new organizational forms, such as strategic partnership or networks [19]. Since then, IS/IT or information and communication technology (ICT) has emerged and managed to grow its influence among the enterprises. Companies have realized that through IS they have the opportunity to implement important innovations in their processes and also improve the efficiency of those processes that are utilized to produce their products/services [20]. Several researchers through many years have examined and analyzed the innovations that are derived from ICT/IT investments, the enablers or inhibitors of their adoption or even the possible impacts on the enterprises [21]. Recently managers and researchers seem to show a great interest in the application of IS in SCM because of the general realization that IS can improve not only the performance and efficiency of an individual firm but also the supply chain (SC) performance as a whole [22].  
There are several ISs that can be used worldwide in industry in an effort to help the company's internal processes and also in regards with their supply chain management (SCM) with suppliers and customers. A brief presentation of the ISs that are used in the current research is given below:* SCM (supply chain management)
* ERP (enterprise resource planning)
* WMS (warehouse management system)
* APS (advanced planning and scheduling)
* OMS (ordering management system)
* MES (manufacturing execution system)
* MRP (materials requirement planning)
* DRP (distribution requirements planning)
* TMS (transportation management system)
* CRM (customer relationship management)
* SRM (supplier relationship management)  
It seems natural to start the description of the current state of use of IS from the industrial companies--domestic or foreign--of Northern Greece by exploring the level of penetration of IS in these companies and the kind of IS they use to conduct their business. Thus, the first research question is formulated as follows:  
RQ1: How much do (or how many) manufacturers in Northern Greece use IS to deal with their affairs? Which kind of IS they have adopted and used to process their activities?
## 4 Protocol Framework
### Methods
#### 4.4.1 Claim Generation and Verification  
In the deployment process, every mobile node is initially associated with a set of traced nodes. Each node is the witness node of all nodes in its tracking set. That is, the node is the tracer of all nodes in its own tracking set. To avoiding overloading the tracer and taking advantage of the meeting chances of nodes, it is reasonable to make tracking set scale equal to \(\sqrt{N}\), because the probability that the encountering nodes have at least one same tracked node according to the Birthday Paradox is 50%. When a moving sensor node \(L\) migrates to a new position and encounters a new neighboring mobile node, and if the mobile node is in the tracking set of the neighboring node, the neighboring node requires for verifiable time-location claim by sending a request packet including current time \(T_{n}\) to the requested node \(L\). Node \(L\) would discard the request, once the following condition holds  
\[|T_{L}-T_{n}|>\xi+\tau \tag{17}\]  
where \(T_{L}\) is the current time that mobile sensor node \(L\) receives the request; \(T_{n}\) is the time that neighboring node starts the request; \(\xi\) is the transmission delay of time-location claim; \(\tau\) denotes a maximum error during the process of temporal synchronization. Because the transmission distance is over transmission radius of a node, the time-location claim is not from the neighboring node. Otherwise, node \(L\) generates its time-location claim which can be expressed as \(\left\{ID_{L},L_{L},L_{L},\text{SIG}_{\text{SK}_{L}}(\text{H}(ID_{L}\parallel t _{L}\parallel l_{L}))\right\}\), where \(ID_{L}\) is the identity of mobile sensor \(L\), and \(l_{L}\) is node \(L\) position, \(t_{L}\) is the time of generating this time-location claim; \(\parallel\) indicates the concatenation operation, and \(\text{SIG}_{\text{SK}_{L}}(\text{H}(ID_{L}\parallel t_{L}\parallel l_{L}))\) denotes encrypting the hash value of the data that performs a cascade of the \(ID\), the time-position claim generation time and the position of node \(L\) by utilizing the private key of node \(L\) for the sake of implementing authentication of node \(L\). The neighboring node which acts as the tracer of node \(L\) would validate the data integrity of the received data packet by utilizing the public key of node \(L\) and validate the plausibility of the distance between the tracer and tracked node \(L\). If the verification fails, the time-location claim would be ignored, whereas the claim would be preserved with probability \(p\).
## 4 Discussion
Additionally, it is speculated that any difference in the effect of resistance exercise on peripheral artery stiffness between this study and the previous findings may be due to the difference in the employed methodologies--ArmPWV vs. baPWV. No improvement was noted in the LegPWV following the 8-week LRT. However, for both resistance training groups (URT and LRT), the change in leg LBM had a significant relationship with the change in LegPWV. Resistance training can reduce peripheral artery stiffness not only by decreasing the trained peripheral artery tone at rest [41; 42] but also by local vasodilation induced by increased circulating metabolites generated from resistance training [43]. Thus, this result suggests that improved vascular endothelial function afterlimb-specific resistance training via both local and systemic biochemical environment change may lead to enhanced peripheral artery stiffness even in relatively healthier young adults. Unfortunately, the related physiological mechanism could not be demonstrated because vascular endothelial function measures were not performed in this study. Resting norepinephrine concentration significantly decreased and resting epinephrine concentration showed a tendency to decrease in all three groups following the 8 weeks of intervention. In particular, the reduction of systemic norepinephrine level in CON is likely associated with the impact of seasonal ambient temperature variations on the autonomic nervous system. The plasma catecholamine level in the cold ambient temperature is higher than that in the warm outside temperature, which is likely related to the increased cardiovascular morbidity and mortality in winter [44; 45; 46; 47; 48; 49]. In this study, blood collection pre- and post-intervention was performed in March and July in 2017. The average ambient temperatures at Incheon, Korea during March and July in 2017 were 5.8 and 25.8 \({}^{\circ}\)C, respectively. It is speculated that the seasonal difference of ambient temperature might cause the significant difference in resting norepinephrine concentration between pre- and post-intervention. This study has limitations that should be considered when interpreting major findings. The study was conducted with a relatively small number of healthy young adults. Thus, the research results cannot be generalized and applied to other populations with different biological or clinical conditions. In this study, the number of study participants by gender was not able to secure statistical power, making it impossible to further analyze gender differences. This study mainly analyzed functional changes, and there was a limit to the analysis of physiological mechanisms. In the future, large-scale functional and mechanistic research studies in which a sufficient number of men and women of various ages and health conditions participate are needed.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 5 Conclusions  
In this research, we developed a SR denoising method for optically complex water hyperspectral \(R_{\text{rs}}\) data and tested the method in three hyperspectral datasets. The results indicated that the SR method can effectively remove the random errors and systematic errors of the hyperspectral data and keep the useful signals at the same time. The method is robust under different types of error distribution. The denoised hyperspectral data can improve correlation coefficients between the spectra indices and \(C_{\text{chla}}\) and the dynamic range of \(C_{\text{chla}}\) maps.  
Figure 11: \(C_{\text{chla}}\) maps yielded from (**a**) PRISMA and (**b**) denoised images and (**c**) their scatter plot.  
**Author Contributions:** Conceptualization, Y.G. and Q.B.; methodology, Y.G. and Y.L.; validation, Y.G., J.H. and W.C.; formal analysis, L.S. and G.J.; investigation, Y.G., Y.L. and C.D.; data curation, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, Y.G., Q.B. and Y.L.; visualization, Y.G.; supervision, Q.B.; project administration, Q.B. and W.C.; funding acquisition, J.H., W.C., C.D., Y.L. and G.Y All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key R&D Program of China, grant number 2021 YFD 1700900; National Natural Science Foundation of China, grant number 41701422, 42071333, and 42001296; Young backbone teachers program of Henan Province, grant number 2019 GGJS048.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Measurement Uncertainty Evaluation
### Comparison between the Optical and the Contact Measurement Methods  
This section aims the comparison between the modulus of elasticity and Poisson ratio measurements obtained from both the optical and the contact measurement methods, considering average values obtained from the four local measurements performed in each specimen. Figure 16 shows the corresponding estimates and measurement uncertainties for the modulus of elasticity and Poisson ratio.  
Figure 15: Estimates and standard uncertainties obtained from the contact measurement system for (**a**) the modulus of elasticity; (**b**) and the Poisson ratio.  
Concerning the modulus of elasticity, the contact measurement method provided estimates with a higher magnitude, namely, in the case of specimen ID M0M12. In general, the dispersion of values related to each measurement method overlap, with the exception of the specimen IDs M0 MF9 and M0M12.  
With respect to the Poisson ratio, the optical method provided slightly higher estimates in the majority of the tested specimens, noticing an overlap of the dispersion of values in most cases with a high magnitude.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 5 Simulation and Results
### Packet Loss Rate  
Figure 10 shows the packet loss rate for varying the number of vehicles and CBR connections. In macroscopical sight, when the number of nodes is increasing, the packet loss rates are reduced in all protocols. This occurs due to the increase in node density can improve the network connectivity and reduce network partition, which increases the probability that a packet successfully reaches the destination node. In the sparse environment, where the number of nodes from 30 to 70, data packets are more likely to be discarded in the process of finding the correct path. In general, REITT has a lower packet loss rate than MM-GPSR, MOPR, and GPSR in sparse scenario.  
For instance, as Figure 10a shows, in the scenario with 5 CBR connections, all the REITT points have a lower packet loss rate than MOPR, MM-GPSR, and GPSR. This can be explained as that neither GPSR nor MM-GPSR considers link quality during the process of making decisions. REITT and MOPR can avoid link disruption. MM-GPSR selects the next hop, which is not the nearest to the destination, resulting in an increase in the number of hops which can improve the probability of a packet dropped by vehicles. Compared with the results in Figure 10b, these three protocols have an increase in packet loss rate at 70 nodes. Such a phenomenon can be explained as: when the number of CBR connection pairs increases, the network load increases, and the chances of collision and interference between  
\begin{table}
\begin{tabular}{c c} \hline \hline
**simulation Parameter** & **Value** \\ \hline Simulation area & 1100 m\(\times\)1100 m \\ Number of streets & 12 \\ Number of vehicles & 30, 40, 50, 60, 70 \\ Transmission range & 250 m \\ Packet size & 512 byte \\ Simultaion time & 200 s \\ MAC layer & IEEE 802.11p \\ Simulation tool & Ns3 \\ Number of CBR connections & 5, 10, 15, 20 \\ Max speed & 15 m/s \\ Propagation model & Two-ray ground \\ Beacon Interval & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameter.  
packets increases, resulting in poor performance of packet loss rate. Another reason why REITT performs better than GPSR and MM-GPSR on packet loss rate is that REITT selects the correct path. We assume that the probability of packet loss per hop is equal, then more hops mean more packets are discarded. Real-time effective information traffic mechanism reduces the occurrence of redundant paths and further reduces the packet loss rate. For packet loss rate, the TREIT performance 5% better than GPSR, 8% better than MM-GPSR, and 7% better than MOPR. On the whole, the packet loss rate of REIT is smaller than GPSR, MOPR, and MM-GPSR in the sparse scenario, that is because REIT selects the next hop with a more stable connection, smaller distance to destination, and better connectivity.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 3 Problem Formalization
### System Model  
In order to show the responsibilities and information interaction of each entity in PPDB more clearly, we present a system model. As shown in Figure 2, the system model includes five types of entities, namely, root key generation centre (RKG), SP, AC, FNs and SMs. We assume that the area covered by the grid is divided into \(k\) sub-regions. There are \(n\) SMs in each sub-region, and these \(n\) SMs are all covered by the same FN. \(SM_{ij}(i=1,2,\cdots,k,j=1,2,\cdots,n)\) represents the \(j\) th SM in the \(i\) th sub-region, and \(FN_{l}(i=1,2,\cdots,k)\) denotes the FN covering all SMs in the \(i\) th sub-region.  
* _SMs_: SMs generate users' real-time electricity consumption data and encrypt data, then send the ciphertext to the corresponding FN periodically.
* _FNs_: After receiving reports from all SMs in the coverage area, FNs first verify the validity of all reports and then forward the valid reports to AC after aggregation. In addition, FNs can store the reports for billing.
* _AC_: Following receipt of the reports from FNs, AC validates their legitimacy, then pre-decrypts the reports and finally sends results to the SP. Furthermore, AC is tasked with the arbitration of disputes between users and the SP.
* _SP_: The SP is responsible for collecting total electricity consumption data, and charging users based on real-time prices.
* _RKG_: RKG takes charge of generating the public parameters of the system and generating keys for each entity.  
Figure 1: Relationship between Sections 2.1–2.3.
## 5 A Framework for SDN-SDR-Based Maritime Communications
### Maritime Communications Security with SDN  
An SDN-based framework can be used to mitigate attacks in an automated manner for improved resilience in the ship's communication network [69]. There are sensors and actuators attached to the different components of the ship that are responsible for controlling the bridge, the engine, and the propulsion. The sensors transmit the data related to these physical devices to the controller for analysis. The controller, known as the Monitoring Controller, plays a crucial role in overseeing the operation of the ship's components. It continuously monitors the data received from the sensors and analyzes them to ensure the proper functioning of the various ship components. Depending on the information  
Figure 4: Maritime communications platforms with SDN.  
Figure 5: SDN-based maritime network.  
obtained from the bridge devices, the Monitoring Controller issues commands to start or stop the propulsion control system and can even reroute the ship on a different route if necessary.  
With the help of the Detection Engine, the Monitoring Controller can quickly identify any anomalies or deviations from normal behavior. The Detection Engine examines the network traffic within the ship's communication network and detects suspicious or malicious activities by employing various techniques and underlying algorithms. It analyzes the network traffic patterns, protocols, and payload contents to identify potential threats or attacks. Once a suspicious activity is detected, the Detection Engine raises an alert and informs the Monitoring Controller about the potential security breach. Additionally, when the Detection Engine detects a fault or failure on the bridge device, it notifies the Mitigation Engine and the controller to divert the network traffic on a different route. The Mitigation Engine is responsible for initiating appropriate countermeasures to mitigate the impact of faults and/or failures. It collaborates with the controller to divert the network traffic onto a different route, ensuring that critical ship operations continue without disruption. By swiftly responding to faults or failures, the Mitigation Engine helps maintain the ship's communication network's reliability and availability.  
Together, the Monitoring Controller, Detection Engine, and Mitigation Engine form integral components of the SDN-based framework, working in tandem to safeguard the ship's communication network against potential threats and ensure uninterrupted operation.  
Figure 6 shows these components and the relations between them. Note that each of the three controllers within the controller layer, depicted in Figure 5, can have the Detection Engine and the Mitigation Engine, for more robust overall security.