esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Oil Viscosity on Oil Production with Injection of Colloidal Dispersions  
To evaluate how oil viscosity affected oil recovery from saturated reservoir rocks, we performed core flooding experiments using oils varying in viscosity. As viscosity increased recovery decreased, from 79.1% for a 1.3-cp oil to 57.8% for a 500-cp oil (Figure 8a). Thus, the viscosity difference between the colloidal dispersion (the water phase) and the displaced oil phase affected displacement efficiency. At the pore scale, when the shear stress at the pore throat increases, the residual oil flow velocity falls with increasing oil viscosity; colloidal dispersions readily bypass trapped residual oil. Oil viscosity also affected the oil-water relative permeability curves; the higher the viscosity of the residual oil, the lower the relative water permeability at the endpoint (Figure 8b), reflecting excessive viscous 'fingering' induced by formation of an unstable interface between the colloidal dispersion (the water phase) and oil. Furthermore, the fractional flow curves for oils of higher viscosity were much steeper than those for lower-viscosity oils, as explained by \(f_{w}=\frac{1}{1+\left(\frac{k_{H}}{k_{H}}\right)\left(\frac{k_{H}}{k_{H}} \right)}\), where \(f_{w}\) is the fractional flow of water, \(k_{ro}\) and \(k_{rw}\) are the relative permeabilities of oil and water, respectively, and \(\mu_{o}\) and \(\mu_{w}\) are the viscosities of oil and water, respectively. Thus, water breakthrough occurs at an early stage when oil viscosity is high (Figure 8c). The saturation points are shown as in Figure 8c; lines were drawn from the initial water saturation points tangential to the fractional flow curves. The arrows show the mean water saturation at the breakthrough points of the injection fluid (S\({}_{w\!f}\)): the values ranged from \(\sim\)0.44 (100-cp oil) to 0.76 (1.3-cp oil) and S\({}_{w\!f}\) increased with decreasing oil viscosity. The fractional flow curves thus show that residual oil saturation decreased at higher oil viscosities because the flow bypassed the oil more easily.  
Figure 8: (**a**) Ultimate oil recoveries. (**b**) Relative permeabilities. (**c**) Fractional flow by oil viscosity. (S\({}_{w\!f}\): water saturation at injection fluid breakthrough. \(k_{ro}\) and \(k_{rw}\) are the relative permeabilities of oil and water, respectively).
## 2 Point-to-Point QKD Mechanism
Finally, [PERSON] and [PERSON] complete the post-processing process via PCh, including information reconciliation and privacy amplification, to obtain the final secure bits (called secret keys). As shown in Figure 1, two channels are required for QKD, i.e., QCh for transmitting quantum signals and PCh for classical information exchange. To save fiber resources, we multiplex quantum signals and classical signals into the same optical fiber for transmission based on WDM technology. Given the crosstalk between classical and quantum signals, there is always a certain wavelength separation between QCh and PCh [28]. In recent years, researchers have paid more attention to the measurement-device-independent (MDI) protocol [38]. In the MDI protocol, the detection device is controlled by a third party, [PERSON]. [PERSON] and [PERSON] jointly send quantum signals to [PERSON] to complete the key sharing. This protocol eliminates the detector vulnerability of the actual QKD system. Since the proposal of the MDI protocol, research on long-distance and high-rate MDI-QKD in practical networks has increased [39; 40], and its use has been extended to free space [41]. In addition, the multi-user scheme of the MDI protocol is useful for resisting detection attacks [42]. Similar to the MDI protocol, the twin-field (TF) protocol is also a three-party protocol. Based on single-photon interference, it can break through the rate-distance limit of QKD under the condition of guaranteeing the security of keys [43]. [PERSON] et al. implemented a 454 km TF-QKD [44]. [PERSON] et al. extended the security key distribution distance to 833.8 km [45]. Figure 1: Point-to-point QKD mechanism based on BB84 protocol.
## 2 Deep Neural Network Based on Alignment
### Alignment Mechanism  
In this work, we select a Hidden Markov Model (HMM) as the alignment technique in all the experiments. In text-dependent tasks, we know the phrase transcription that allows us to construct a specific left-to-right HMM with no skip states for each phrase of the data and obtain a Viterbi alignment per utterance.  
One reason to employ a phrase HMM alignment was due to its simplicity for training independent HMMs for different phrases used to develop our experiments without the need of phonetic information for training. Another reason was that, using the decoded sequence provided by the Viterbi algorithm in a left-to-right architecture, it is ensured that each state of the HMM corresponds to at least one frame of the utterance, so no state is empty.  
The process followed to add this alignment to our system is detailed below. Once the models for the alignment are trained, a sequence of decoded states \(\overline{q}=(q_{1},...,q_{t},...,q_{T})\) where \(q_{t}\) indicates the decoded state at time t with \(q_{t}\in\{1,...,Q\}\) is obtained. As we can see in Figure 3, we encode this information using an alignment matrix \(A\in\mathbb{R}^{T\times Q}\) where \(a_{tq_{t}}=1\) if frame \(t\) belongs to state \(q_{t}\) and 0 otherwise. Since only one state is active at the same time, \(\sum_{q}a_{tq}=1\). Thanks to this matrix encoding, we can include the alignment information in the gradient estimation increasingly.  
For example, if we train an HMM with four states, we obtain a vector \(\overline{q}\) and apply the previous transformation, the resultant matrix \(A\) would be:  
\[\overline{q}=[1,1,1,2,2,3,3,4]\to A=\left(\begin{array}{cccc}1&0&0&0\\ 1&0&0&0\\ 1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right). \tag{1}\]  
The input employed to this alignment layer is the output of the \(L\) convolutional layer. The alignment as a matrix multiplication can be expressed as a function of the input signal, \(x_{dt}^{L}\), with dimensions \((D^{(L)}\times T)\) and the alignment matrix of each utterance \(A\) with dimensions (\(T\times Q\)):  
\[x_{dq}^{(L+1)}=\frac{\sum_{t}x_{dt}^{(L)}\cdot a_{tq}}{\sum_{t}a_{tq}}, \tag{2}\]  
where \(x_{dq}^{(L+1)}\) is the supervector of the layer \((L+1)\) with dimensions \((D^{(L)}\times Q)\), where there are \(Q\) state vectors of dimension \(D^{(L)}\) and we normalize by the number of times state \(q\) is active.  
Figure 3: Process of alignment, the input signal \(x\) is multiplied by an alignment matrix A to produce a matrix with vectors \(s_{Q}\) which are then concatenated to obtain the supervector.
## 6 Experiments
### Exact Algorithm  
In order to determine the performance of the exact algorithm (Algorithm 2), we can count the number of shortest path computations that are needed to obtain the full distribution, and compare this value to \(n\), the number of iterations performed by the naive algorithm (Algorithm 1). The number of iterations performed by our exact algorithm is displayed in the fourth column of Table 2, followed by the speedup factor compared to the naive algorithm. We note that significant speedups can be observed in terms of the number of iterations, especially for datasets for which the eccentricity distribution is wide, _i.e._, the difference between the radius and diameter is very large. We believe that this is due to the fact that the nodes that form the periphery of the network are sufficiently eccentric to have a large influence on the more central nodes, so that their lower and upper eccentricity bounds can very quickly be fixed by the bounding technique. We mention that the performance of our exact algorithm does not appear to be directly related to the number of nodes. The reduction in terms of the number of nodes as a result of our pruning strategy (see Section 4.2) is displayed in the column labeled \"Pruned\", and is diverse, yet sometimes very significant (anywhere between \(1\%\) and \(34\%\)).  
To get a better idea of the performance of the algorithm, we propose to look at the quality of both the lower and upper bounds as the exact algorithm iterates over the candidate nodes. For this we define the measure of _bound accuracy_ as the percentage of bound values that are correct at that iteration:  
\[\textit{bound accuracy}=\frac{1}{n}\;\mid\{v\in V\mid\varepsilon_{\textit{ real}}(v)=\varepsilon_{\textit{bound}}(v)\}\mid\]  
Here, \(\varepsilon_{\textit{real}}(v)\) is the actual eccentricity value, and \(\varepsilon_{\textit{bound}}(v)\) is the (lower or upper) bound that we investigate. Figure 7 shows the lower and upper bound accuracy for a number of datasets. We can observe that for each of the datasets, after just a few iterations, the lower bound gives a very accurate indication of the actual eccentricity values. Apparently, the majority of the iterations are spent lowering the upper bound, whereas the lower bound quickly reflects the actual eccentricity distribution. Thus, in order to get an online estimate of the distribution, we could choose to consider only the lower bound, and obtain an accuracy of around 90% after only a handful of iterations.  
Figure 7: Bound accuracy _vs._ number of iterations for Algorithm 2.
## 2 Materials and Methods
### PV Model  
The single diode model was considered for this study since it provides accuracy and simplicity [35]. This theory suggests that PV should be considered as a DC source of an electric circuit, as shown in Figure 2, which produces a current \(I_{ph}\), generated by the irradiation of the sun. In addition, two resistances are involved, where \(R_{sh}\) is related to the leakage current of the diode of the p-n interface [36] and \(R_{s}\) represents the resistance athwart the PV [37]; the latter causes a detrimental phenomenon in terms of the maximum power of the system [38].  
The Kirchoff current law provides Equation (1), where the \(I_{sh}\) and \(I_{c}\) are defined according to the research of [39].  
\[I_{c}=I_{ph}-I_{d}-I_{sh} \tag{1}\]  
\[I_{c}=I_{ph}-I_{o}\Big{(}e^{\frac{g(V+R_{s}I_{c})}{aR_{c}I_{c}}}-1\Big{)}-\frac {V+R_{s}I_{c}}{R_{sh}}, \tag{2}\]  
where \(I_{o},K,q\) and \(T_{c}\) are the reverse saturation current, Boltzmann constant, elementary charge and the operating temperature, respectively [40]. The generated current from the PV panel is also expressed as Equation (3), where \(G/G_{SRC}\) represents the relation of the solar radiance measured and the radiance at standard rating conditions (SRC); the \(I_{sc\_ref}\) is the short circuit current of the PV and \(T_{ref}\) is the PV temperature, both variables at SRC [41]. The term \(k_{I\_ref}\) comprises a thermal factor of the short circuit current.  
\[I_{ph}=\frac{G}{G_{ref}}\Big{(}I_{sc\_ref}+k_{I\_ref}(T-T_{ref})\Big{)}. \tag{3}\]  
A PV panel was built with several modules in parallel (\(N_{p}\)) and in series (\(N_{s}\)), which led to an output current (\(I_{m}\)) and voltage (\(V_{m}\)) of the whole module based on Equation (4). Therefore, with the previous equations, the output current of the PVG can be expressed as Equation (5).  
\[I_{m}=N_{p}I_{c} \tag{4}\]  
\[V_{m}=N_{s}V_{c} \tag{5}\]
# How Human Mobility Models Can Help to Deal with COVID-19
## 1 Introduction
These models, used often in OppNets evaluation, are shown to be quite unrealistic for evaluating the COVID-19 spread. Therefore, we propose the use of a pedestrian mobility simulator for building realistic scenarios in order to generate people mobility traces. The utilisation of these mobility simulators was originally proposed in [11] for OppNets evaluation. A pedestrian simulator can model the microscopic (individual) and macroscopic (crowd) dynamics of pedestrian mobility, an option that is not supported by the synthetic mobility models [12]. More specifically, we consider a square (particularly, a plaza) and a subway station, which are typically crowded scenarios with a high degree of people renewal. Summing up, the idea is to generate a mobility trace to detect possible contacts and risky spots. We introduce two methods to evaluate the exposure risk to contagion. The first one is based on the method used by smartphone-based contact tracing applications, considering only the time and distance (a contact is considered risky if two persons are within 2 m for more than 15 min). Since this model does not consider the environment (that is, indoor/outdoor, ventilation etc. ), we introduce a new expression where the quality of the medium is determined by the type of scenario and its characteristics. The evaluation of the scenarios using these models shows that pure synthetic models cannot offer realistic human mobility patterns and therefore the risk maps obtained using these models are not useful. On the contrary, the results obtained using the scenarios generated with the pedestrian simulator are very realistic, and we can obtain spatial heat maps and temporal plots of the exposure risk. Furthermore, using the second method to evaluate the exposure risk, we can verify that outdoor scenarios, such as the plaza, exhibits a very low-risk exposure when compared to indoor scenarios, such as the station. These temporal and spatial maps would allow authorities to take measures to avoid risky spots in order to reduce the transmission of the COVID-19. The outline of this paper is as follows. Section 2 provides an overview of related works. Section 3 is a short survey of mobility models used in Opportunistic Networks. The generation of the scenarios used in the paper is described in Section 4 which are temporally and spatially evaluated in Section 5. Section 6 analyses how the SARS-CoV-2 is transmitted in order to characterise the exposure risk, which is used in Section 7 to evaluate the risk on the different scenarios. Finally, Section 8 details the main conclusions.
## 3 Proposed Methodology
### Trust Value Computation
#### 3.4.1 Optimized Link State Routing Protocol  
The proposed OLSR represents a proactive routing system that commonly exchanges network structure information from nodes [32, 33]. Each node in the network chooses a set of neighboring nodes to serve as multipoint relays (MPR). The OLSR proposes to function independently of the network's additional protocols. In addition, the connection between layers that exist from the OLSR is not used in any computation. It is utilized for groups of ad-hoc networks such as MANETs.  
In the OLSR, the MPRs assume the responsibility of transmitting control traffic intended for distribution over the complete network. MPRs reduce the number of transmissions necessary for transmitting control messages, leading to a dependable and efficient system. Additionally, a particular task is to announce connection state details across the networks. It is utilized in route processing to build a path from two network nodes, beginning at a single source network and terminating at the additional destination node. Figure 9 demonstrates the OLSR protocol.  
Figure 9: Structure of the OLSR.  
The two forms of messages used in the OLSR are topology control and HELLO messages. Each node in a system may acquire information about the link state and adjacent nodes in two hops through HELLO messages. The multi-point relay (MPR) of every node utilized to interact is determined based on this data. Each node in a network transmits messages, which are used to update a database essential to packet routing. To build an MPR selection set, various nodes frequently transmit TC messages. By reducing the optimum periodic interval of time and response to issuing TC messages, the OLSR is routinely enhanced.  
An integrated administration scheme is not necessary for the OLSR to manage the routing procedure. It may be useful for certain network operations to have routes accessible through a routing list because there will be no delay associated with finding a route in that area. In addition, the MPR approach enables the OLSR most effectively in large networks. On the opposite side, the OLSR norms do not provide any capabilities for the quality of link testing. Then, the utilization of bandwidth rises as an outcome of the routing table's regular updates. Moreover, sometimes it becomes harder to detect MPR, at last, the routing is accomplished utilizing the OLSR.
# Cooperative Guidance Law for the Mother-Cabin of the Anti-UAV Cluster Mother-Son Missile
## 2 Description and Analysis of Cooperative Guidance of Multiple Mother-Cabins
### Analysis of Cooperative Guidance  
In the guidance law design of time coordination and angle constraint, a common method is to design the controllers that independently control the flight time and the LOS according to the LOS direction and the normal direction of the LOS. Although this design is simple and there is no coupling in the controller design, the following three problems must be considered:  
1. The independently designed controllers only focus on the convergence speed and accuracy of their respective states, but there is a coupling between the missile flight states. The result of the LOS control will affect the time-to-go state. When the control  
Figure 2: Two-dimensional geometry of the missile-target relative motion model.  
effects of the two controllers do not match, the missile will miss the target or cause significant bending or oscillation of the trajectory.
2. To achieve good control, the output of the controller is often a large value in the initial stages of cooperation. However, in practice, the output capacity of the missile actuator is limited, and the theoretical guidance output cannot be achieved. This limitation will lead to a poor actual guidance effect or cause instability and non-convergence of the controller.
3. In the case of the zero LOS constraint, the LOS and LOS rate will only converge to zero when the missile reaches to the same altitude as the target. In our experimental test, we found that the convergence time is the longest under the zero LOS constraint in angle-constrained guidance laws, and the ballistic trajectory is most likely to be significantly bent. Problems (1) and (2) are also the most prominent in this case.  
In order to enable the multi-missile to simultaneously intercept the target with zero LOS and the same speed, this paper considers the acceleration constraint and the three aspects described above. This paper designs the cooperative guidance law in two parts: the first part designs the controller of the LOS direction to realize the impact time and speed coordination of multiple missiles. The second part designs the controller in the normal direction of the LOS to satisfy the constraint of zero LOS.
###### Abstract
[38] utilized the CFOSAT scatterometer (CSCAT) to analyze the temporal variations in CSCAT winds. They proposed a marine calibration based on the operating window to assess the consistency of CSCAT radar backscatter. In previous studies, the HY-2 series satellite Ku-band scatterometer was utilized by some researchers [39; 40] for cross-calibration of backscatter measurements. Furthermore, scatterometer wind data from the HY-2B satellite were employed to characterize the intensity of tropical cyclones. The applicability of the HY-2C and CFOSAT satellite-borne microwave scatterometers for retrieving sea surface wind speeds over the Yellow Sea was verified in this study using WRF-simulated wind speeds. This verification aimed to enhance the utilization of the retrieved sea surface wind speeds from these satellite-borne microwave scatterometers for estimating the spatial distribution of wind power density over the Yellow Sea. The findings can provide a scientific basis for the development of offshore wind power, facilitate the establishment of reasonable wind power planning, optimize resource utilization, and promote the coordinated development of the economy and the environment.
## 5 Experiment and Results Analysis
### Experimental Method and Results
According to the experimental results, the performance was found to be superior when the intervals overlapped rather than that in the case of non-overlapping divisions, as well as when the division was more flexible than the equal division. Next, we will look at the overfitting problem in the construction of the interval-based IGM. In the case of the CSS data set, the number of intervals and clusters per interval was increased from 2 to 50, respectively. As a result, it was confirmed that overfitting occurs when the number of intervals is large. Figures 16 and 17 show the variation of performance index as the number of intervals and clusters per interval increase for training and testing data set, respectively. As shown in Figure 17, we found that performance index decreases when the number of intervals is more than 7. As to the number of cluster per interval, the performance index increases slightly. We will study the optimal allocation of clusters for future research. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**DB** & **IGMs** & **Num. of Intervals** & **Num. of Clusters** & **Num. of Rules** & **Training PI** & **Testing PI** \\ \hline \multirow{3}{*}{CCS DB} & Case 1 & 7 & 10 & 70 & 0.391 & 0.392 \\  & Case 2 & 7 & 10 & 70 & 0.480 & 0.491 \\  & Case 3 & 5 & 10 & 50 & 0.436 & 0.443 \\ \hline \multirow{3}{*}{ASN DB} & Case 1 & 7 & 10 & 70 & 0.448 & 0.427 \\  & Case 2 & 7 & 10 & 70 & 0.534 & 0.522 \\  & Case 3 & 6 & 7 & 42 & 0.457 & 0.441 \\ \hline \multirow{3}{*}{MPG DB} & Case 1 & 7 & 9 & 63 & 0.399 & 0.362 \\  & Case 2 & 8 & 8 & 64 & 0.577 & 0.464 \\ \cline{1-1}  & Case 3 & 5 & 9 & 45 & 0.448 & 0.388 \\ \hline \end{tabular}
\end{table} 
Table 7: CCS prediction result for interval-based IGM (Cases 1, 2, and 3). Figure 15: Results of PI method for interval-based IGM (Case 3).