esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 2 Operation and Maintenance: Influential Factors  
When scheduling maintenance for offshore wind farms, many different factors need to be considered. Many different researchers analyzed the influences on the maintenance and presented similar results. According to most publications, the factors influencing the planning and cost of maintenance are the occurrence of failures, availability of maintenance crew, spare parts and vessels, weather and external factors, the chosen maintenance strategy, and economical parameters such as the electricity price and subsidies. While laws and legal restrictions also influence the operation of offshore wind farms, they are only included in the models in form of working-hour restrictions and often not included at all. This is possibly due to the fact, that a wind farm maintenance provider or operator cannot wield influence on these factors. Table 1 summarizes the factors mentioned by the different authors.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \multicolumn{1}{c}{**Publication**} & \multicolumn{1}{c}{**Factors Mentioned**} \\ \hline [PERSON] et al. [9] & accessibility, reliability \\ \hline [PERSON] and [PERSON] [10] & weather, power production, damage, inspections, repairs, transport strategies, rate of interest on capital \\ \hline [PERSON] et al. [11] & failures of turbines, repair time, wave height, wind speed, weather windows, the number of turbines, vessel availability, spares provisions \\ \hline [PERSON] et al. [12] & weather, component failures, vessel fleet size, vessel type, size of the maintenance crew, travel time, maintenance strategy \\ \hline [PERSON] et al. [13] & location of maintenance accommodation, crew transfer vessels (type and number), use of helicopters, work shift organization, spare part stock management, technical support, crane ship availability (purchase or contracting), environmental conditions (dependent on time and season), economical parameters (electricity prices, vessel charter costs) \\ \hline [PERSON] et al. [14] & as [PERSON] et al. [11] and costs \\ \hline [PERSON] et al. [15] & investment costs, vessel costs (time charter, variable costs), failure probabilities, downtime costs, weather data \\ \hline \end{tabular}
\end{table}
Table 1: This is a table summarizing the factors influential to the offshore wind farm operation and maintenance scheduling.  
In the following subsections, we comment on the individual influencing factors presented in the literature. First, we investigate the modeling of degradation and failures. Next, we discuss the availability of maintenance supply, such as crew, vessels, and spare parts. Then, the routing of vessels and transportation are discussed. Subsequently, weather modeling, the uncertainty in weather and forecasts is presented. Finally, this is followed by economical parameters and cost estimation.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 5 Conclusions and Future Work
The goal of the evaluation process is to find out how robust the used camera-based traffic measurement system under certain conditions is. There are three main aspects which are crucial for the proposed system: Generalization, classification and counting. The main goal of the system is to work under different traffic conditions without the need for manual user input or configuration changes. In other words, the system should be capable of abstracting well enough to achieve also proper performance in difficult conditions, e.g., high traffic scenes or on rainy days. Another requirement of the system is regarding the classification. The program should be capable of classifying different vehicle types correctly. The most important vehicle types are trucks, cars and motorbikes. Last but not least, the traffic measurement consists of counting vehicles passing by. If the counting is done right this information helps to provide estimations about the current traffic flow. These predictions can then be used for intelligent driving assistants, e.g., in navigation systems to bypass congestions on the road. The evaluation is made on an ordinary computer system with a GPU supporting CUDA to enable real-time processing of video data. The data sources were available on the Youtube Live platform where several organizations were streaming traffic in real-time. To run the video sequence with different settings some passages of the videos were recorded. All video streams were analysed multiple times using different request intervals, hence simulating the restricted environment regarding the frequency of frames. As a conclusion, the robustness of the proposed system under real-world scenarios could not be proven. For some restricted cases such as simple, mono-directional roads the error rate of lost vehicles can be held under 10%. There, the prediction model can estimate the driving paths correctly. For more difficult scenarios such as long distance roads or multi-lane highways with several exits the prediction model has its problems finding the right driving paths. For such cases the error rate is above 40%. In addition, the detection and classification framework has its flaws regarding different weather conditions such as rain, fog or darkness. Due to the fact that the prediction model only relies on the detection from the preceding module the overall performance decreases. Furthermore, if the traffic is not equally distributed between multiple driving directions the prediction model often estimates positions wrong because of no or less available driving paths. Depending on the request interval this leads to more or less additional counts of vehicles because the tracking is not possible anymore. Summarizing, the proposed system is capable of processing simple roads only, a generic approach is not found yet.
## 2 Supervisory and Control System Based on Real-Time Power Management
### Operation Layer  
The energy optimization layer finds the optimal power flow of the sources and the EVs based on \(p_{PV\text{ }MPPT\text{ }pred}\) and \(p_{IIREV\text{s }D}\). The coefficient \(k_{D}\) is calculated based on the optimized power flow obtained by CPLEX [49]. This coefficient controls the operational layer for the IHEVs in real-time operation. The advantage of \(k_{D}\) is balancing the power flows, coupling the energy management easily while respecting all constraints [45].  
The operational layer must consider optimized power flow in real operating conditions, \(p_{PV\ MPPT}\) and \(p_{IIREVs\ D}\). In addition, the operation management must ensure robustness and withstand uncertainties in the forecast data. Then, this layer calculates the power references and performs PV shedding or EV shedding when necessary. The actual operating conditions lead to a reference power \(p_{ref}\) to stabilize the DC bus voltage, defined by (40) and (41):  
\[p_{ref}(t_{i})=p_{PV\ MPPT}(t_{i})-p_{IIREVs\ D}(t_{i})-C_{P}(V_{ref}-v_{DCbus}), \tag{40}\]  
\[p_{ref}(t_{i})=p_{G\_ref}(t_{i})+p_{S\_ref}(t_{i}), \tag{41}\]  
where \(C_{P}\), \(V_{ref}\) and \(v_{DC\ bus}\) are the proportional controller gain, reference voltage, and the actual voltage of the DC bus, respectively. The stationary storage power reference can be calculated as in (42):  
\[p_{S\_ref}(t_{i})=k_{D}(t_{i})\cdot p_{ref}(t_{i}), \tag{42}\]  
where \(k_{D}\) is defined in the interval [0, 1].  
The grid power reference \(p_{G\_ref}\) is calculated taking into account the stationary storage physical limit, which means \(p_{S\_ref}=0\) if the storage reaches its maximum \(SOC_{S\_max}\) or minimum \(SOC_{S\_min}\) limits or its maximum power \(P_{S\_max}\), and the grid power reference becomes \(p_{G\_ref}=p_{ref}\). Figure 4 shows the control algorithm of the power balancing strategy for the IIREVs.  
Figure 4: Control algorithm for IIREVs.  
To prove the feasibility of the optimization problem, it is compared with a storage priority algorithm simulation without optimization \"Sim w/o opt\", where \(k_{D}\) is one in this operation mode. Moreover, these operation modes are compared with an ideal case, \"Opt for real conditions\", which is based on real PV MPPT and IIREV powers.
# Fire Risk Analysis in Large Multi-Compartment Structures Using a Hybrid Multiscale Approach
## 2 The Hybrid Multiscale Approach
### The Compartment Fire Zone Model
Although established for open fires, the correlation of Babrauskas [21] is used to account for the functional dependence of the mass loss rate on the pool diameter \(D\):  
\[\dot{m^{\prime\prime}}_{D}=\dot{m^{\prime\prime}}_{\infty}\left(1-e^{-\kappa \beta D}\right) \tag{6}\]  
The values of \(\dot{m^{\prime\prime}}_{\infty}\), the mass loss rate of an infinite diameter pool, and \(k\beta\), an extinction coefficient, were determined by [PERSON] for a large number of fuels [21]. For a given fuel, Equation (6) allows for calculating the maximum \(HRR\), \(HRR_{max}^{D}\), of a pool fire of diameter \(D\) from that of a pool fire of smaller diameter \(d\) for which \(HRR_{max}^{d}\) is known:  
\[\frac{HRR_{max}^{D}}{HRR_{max}^{d}}=\frac{\dot{m^{\prime\prime}}_{D}\Delta h_{ ch}}{\dot{m^{\prime\prime}}_{d}\Delta h_{ch}}\left(\frac{D}{d}\right)^{2}=\frac{1- exp^{-k\beta D}}{1-exp^{-k\beta d}}\left(\frac{D}{d}\right)^{2} \tag{7}\]  
In the present study, the scaling procedure is used to determine the design fires for large fires from those validated on smaller fires [14]. When applied to 70 cm diameter heptane and JP5 pool fires, using the standard set of parameters given previously, it satisfactorily reproduces the experimental HRR curves, as shown in Figure 3b. Note that the effect due to the thermal feedback from the pan walls to the remaining fuel at the end of burning (lip effect), which is clearly visible in the evolution of the HRR, especially for heptane pool diameters of 50 and 70 cm, cannot be reproduced by Equation (1), which is not bad insofar as this experimental artifact does not exist under real fire conditions. Figure 3: Predicted and measured HRR time curves of (**a**) heptane and (**b**) JP5 pool fires with diameters of 50 cm and 70 cm.
## 3 Results
### Mixed Dual-/Triple-Frequency Performance Analysis
As was explained in Figure 6, the reason behind this difference is the fewer number of satellites broadcasting on three frequencies. Figure 9 shows the number of satellites broadcasting on two and three frequencies and the evolution of the numbers over one day. In the case of triple-frequency processing, all dual-frequency-only satellites are rejected, leading to the number of satellites corresponding to the orange line and dropping as low as 4 satellites at around 4 am. Including both dual- and triple-frequency satellites leads to the number of satellites being the sum of both blue and orange graphs. Figure 8: Float, dual-frequency fixed, and mixed dual-/triple-frequency fixed histogram of horizontal convergence time of all processed datasets. When looking at the fixed solutions, the triple-frequency solution performs the worst out of the three, as it uses a lower number of satellites. However, when comparing dual-frequency and dual-/triple-frequency results, the benefits of the third frequency become obvious. The 3D convergence time of the dual-/triple-frequency solution reaches 1.5 min, compared to 3 min for that of the dual-frequency solution. This reduction in the convergence time is a direct consequence of the third frequency, since it helps quicken the correct fixing of the ambiguities due to the correlation between ambiguities from the same satellite. After convergence, all accuracies are relatively similar, showing that most of the benefits from the additional frequency are in the form of faster convergence and smaller position errors before convergence. The fluctuations in the solutions after convergence are due to the receiver coordinates being estimated in kinematic mode, leading to noise affecting the position estimates.
## 4 Discussion
However, it should be borne in mind that the type of geometry used in the construction of the reflective surface of a heliostat has a significant influence on the shape and size of the image generated in the plane of the receiver, and therefore on the energy density and the amount of energy intercepted by this element [42]. Concentrating solar power is clean and reliable, can be produced during high demand, and has the potential to meet a country's growing needs in the future. In addition, thermal storage systems prevent fluctuations in supply, allow production to continue in the absence of solar radiation, when direct generation is not possible, and allow production peaks to be transferred in accordance with demand requirements [43]. The research carried out in this work will be useful to optimize the performance of solar tower power plants of molten salt external receivers and to plan its design properly [44]. The results can hopefully help the Algerian government to decide on policies related to performance of solar tower power plants of molten salt external technologies. It has been proven that although energy production is lower in the northern regions of Algeria, it is profitable and allows for the efficient supply of electricity to regions that do not currently have electricity grids. As prospects for future research, it will use the performance of the solar advisor model for integrating financial modeling into project models. It is also advisable to evaluate the incorporation of a wind barrier in the perimeter of the CSP in order to protect the components from high wind levels and the dust it carries. The barrier protects the heliostats from bursts and prevents the continuous movement of sand that may enter the solar field and deposit on the components.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 5 Discussion
### Ephemeral Identifiers Reduce Traffic Overhead  
As it is hard to determine if addresses managed by TA-SLAAC are still in use, the operating system must maintain deprecated IIDs for several days after they stop being used in new flows. This results in the host maintaining a large pool of addresses, despite almost all of them being unused. When performed by all hosts on a network, the resulting gratuitous IPv6 Neighbour Discovery Protocol (NDP) traffic may adversely impact network performance, resulting in pathological behaviour in caching, and in handling of multicast traffic [46]. eNIDs avoid this problem, as ILNP behaviour can already immediately determine a NID that is no longer in use, and remove it from the end-system state, preventing gratuitous NDP overheads. So, in addition to providing stronger privacy protection than TA-SLAAC, eNIDs alleviate a known performance limitation for which TA-SLAAC does not yet have a satisfactory solution.  
With eNIDs, all NDP traffic is 'useful'--it relates to a NID that is either in use, or potentially about to be used, and never to a value that will not be used or is no longer being used (as can happen with TA-SLAAC). However, excess traffic is still undesirable, especially if each host maintains a large pool of available eNIDs. The overheads could be reduced further by using Optimistic DAD [25; 26]. Optimistic DAD is an optimisation of IPv6 DAD that allows a host to generate an IID value and begin using it immediately, rather than waiting to complete DAD.  
With Optimistic DAD, a host could generate eNID values just-in-time, rather than maintaining a pool of pre-generated values. This would reduce NDP traffic to only that related to in-use addresses, allowing an inactive node to maintain a minimal (even zero) eNID pool. While this could cause bursts of NDP traffic when a node initiates many flows at once (such as browsing to a webpage with lots of third party content), these bursts would be short, and inter-spaced with periods of low or no activity. The aggregate behaviour of all nodes on the network would therefore have little adverse impact overall.  
Even in a default deployment scenario using unmodified DAD, eNIDs have the potential to reduce the high volume of control traffic incurred by alternative solutions like TA-SLAAC. In large networks where scalability is a particular concern, Optimistic DAD--which would already be desirable in such cases for improving the scalability of any IPv6 deployment--complements well the use of eNIDs to reduce control messaging even further. So, eNIDs can be deployed to large or corporate networks without a scalability concern. There may still be an additional administrative cost in correctly configuring eNID pool size or Optimistic DAD parameters if default values were not appropriate for some specific deployment scenario, however this is typical when deploying any configurable system.
## 4 Formulation for Optimal Solution
It is known in WSNs that communication energy is related to the transmission distance. The quickest way considerably decreases energy use. However, the shortest path approach did not prolong the network's lifetime. The network's energy usage must be balanced for energy-efficient routing. Relying entirely on sensor nodes' residual energy is not the ideal way to establish network energy balance [7]. The routing protocol must refrain from using low-energy, high-traffic nodes as next-hops in order to achieve a better energy balance. Using the recommended new function, the energy load function, it is feasible to include the remaining energy and traffic load of sensor nodes and let them significantly impact choosing the next hop [7]. The total number of messages sent by each node should match that of the node. This should contain the number of messages at such node and neighboring nodes to relay. Equation (16) presents the proposed traffic load function of node \(y\) at time \(t\). \[NL_{y}(t)=\left\{ \begin{matrix}TM_{y}(t)&if\ ED_{(y,MS)}(t)\leq ED_{(x,MS)}(t) \\ 0&otherwise\end{matrix} \right. \tag{16}\]  
\[ECL_{y}(t)=\left.1\right/\exp\left(\frac{IE_{y}-\left(RE_{y}(t)-\left(NL_{x}( t)*HE_{xy}\right)\right)}{IE_{y}}\right) \tag{17}\]  
Equation (17) suggested new energy consumption load function expresses node \(y\)'s energy use after sending all messages. Any tiny change in the exponential function input causes a huge output change. By exponential function, a slight change in nodes' energy leads to selecting the most energy-efficient relay node [33]. Urgent, real-time data must be sent to the chosen sink within predefined deadlines; performance is assessed by how many of packets received before the deadline. Therefore, real-time messages must be transmitted with the proper speed, reducing packet loss. Equation (18) defines the required message delivery speed for candidate node \(y\) as follows:  
\[Rd_{y}(t)=\frac{ED_{(x,s_{i})}-ED_{(y,s_{i})}}{delay_{xy}} \tag{18}\]  
For every neighbour node \(x\), its candidate neighbours \(CNEN_{x}\) with relaying delays smaller than the required delay are added to the final candidate neighbour set \(FNEN_{x}\).
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 7 Future Works  
For future directions, we will improve on server encryption because the server encryption and decryption processes are many times greater than on the buyer's device. In addition, we plan to use an attack detection method that will help to improve network performance significantly. Furthermore, one of the future contributions that we would like to improve in our protocol is to focus on the merchant's requests from suppliers and companies, because when the merchant buys a large commodity, it costs him/her a lot of money, as any breach of the merchant's information in e-commerce will cause a great loss compared to the regular customer, which can even affect the country's economy. Moreover, we plan to study post-quantum cryptography (PQC) to support the security of our protocol. Finally, to broaden the scope of analysis, we aspire to analyze more attacks such as fault and side-channel attacks on our protocol in addition to the attacks analyzed in this research.  
All authors provided contributions to the work. Conceptualization, G.S.S. and M.A.-Z.; methodology, G.S.S. and M.A.-Z.; software, G.S.S. and M.A.-Z.; validation, G.S.S. and M.A.-Z.; formal analysis, M.A.-Z.; investigation, G.S.S. and M.A.-Z.; writing--original draft preparation, G.S.S. and M.A.-Z.; writing--review and editing, M.A.-Z.; supervision, M.A.-Z.; project administration, M.A.-Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Protocol** & **Keys Length** & **Encryption Algorithm Key Generation Algorithm Key Support Technique** & **Encryption Type** \\ \hline [PERSON] et al. [10] & 512/1024/2048 & RSA & RSA & - & Asymmetric \\ [PERSON] et al. [11] & 256 & EAS & AES & SRFG & Symmetric \\ [PERSON] et al. [30] & 1024 & ElGamal & ElGamal & - & Asymmetric \\ [PERSON] et al. [13] & 1024 & ElGamal & ElGamal & Scanning & Asymmetric \\ [PERSON] et al. [15] & 256 & EAS & RNG & PBKDF2 & Symmetric \\ [PERSON] and [PERSON] [16] & 256 & EAS & AES & - & Symmetric \\ Our protocol & 256 & EAS & ElGamal & CRT & Symmetric and Asymmetric \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of performance parameters between our protocol and existing protocols.
## 3 Fully Decoupled Extended Kalman Filter
### Fully Decoupled Extended Kalman Filter
In Chapter 5 of [12], the general recommendation is to use a scaled identity matrix, either the scaling factor is kept constant during training or it is updated keeping directly proportional to the cost function, thereby that its value decreases as training improves. In Algorithm 1, it is shown the pseudocode for FDEKF [11]:  
```
Require:\(P_{0}\): Initialize diagonal matrix of estimation error covariance Require:\(Q_{0}\): Initialize diagonal matrix of process noise covariance Require:\(R_{0}\): Initialize measurement noise covariance Require:\(\theta_{0}\): Initial parameter vector \(t\gets 0\). Initialize timestep while\(\theta_{t}\) not converged do \(t\gets t+1\) \(H_{t}\leftarrow\
abla_{\theta}y_{t}(\theta_{t-1})\). Get gradients of network output w.r.t. the parameters at timestep \(t\) \(P_{t|t-1}\gets P_{t-1|t-1}+Q_{0}\). Compute a priori state estimation error covariance \(S_{t}\gets H_{t}P_{t|t-1}H_{t}^{T}+R_{t-1}\). Compute output estimation error covariance \(K_{t}\gets P_{t|t-1}H_{t}^{T}S_{t-1}^{-1}\). Compute Kalman gain \(\theta_{t}\leftarrow\theta_{t-1}+K_{t}(y_{t}^{*}-y_{t})\). Update parameters \(P_{t|t}\leftarrow(I-K_{t}H_{t})P_{t|t-1}\). Compute a posteriori state estimation error covariance \(R_{t}\). Applying a measurement noise covariance update rule endwhile return \(\theta_{t}\). Resulting parameters
```  
**Algorithm 1** FDEKF