esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement and Data Collection
### Influential Factor Analysis Based on AHP  
This paper utilizes the AHP method to determine the weights of each influencing factor of the compound interchange auxiliary lanes to determine the paper's main research object. Its calculation steps are as follows:  
(1) Establishing the Hierarchy Structure Model  
Considering that each influencing factor directly impacts the design of auxiliary lanes, this paper divides the hierarchy into two levels: the upper-level objective layer represents the design form of auxiliary lanes, and the lower level consists of the element layer, which includes traffic volume, merging/diverging ratio, vehicle composition, number of mainline lanes, number of ramp lanes, driver characteristics, and external conditions.  
(2) Constructing the Judgement Matrix  
To compare each factor pairwise and obtain the importance comparison results between element \(i\) and element \(j\), the judgment matrix is constructed based on Table 1.  
(3) Consistency test  
Define the consistency indicator as:  
\[CI=\frac{\lambda-z}{z-1} \tag{1}\]  
where \(CI\) denotes coherence indicators; when \(CI\) = 0, it means that there is perfect consistency. The larger the \(CI\) is, the more serious the inconsistency is. \(\lambda\) denotes the largest eigenvalue of the corresponding judgment matrix, and \(z\) denotes the number of factors.  
Calculation of the consistency ratio \(CR\).  
\[CR=\frac{CI}{RI} \tag{2}\]  
where \(RI\) denotes the random consistency indicator, \(CR\) denotes the consistency ratio, and if \(CR\) < 0.1, the consistency test is passed.  
\begin{table}
\begin{tabular}{c c} \hline
**Importance of Element \(i\) over Element \(j\)** & \(\mathbf{b_{ij}}\) \\ \hline Same & 1 \\ Slightly higher & 3 \\ Stronger & 5 \\ Very Strong & 7 \\ Extremely strong & 9 \\ Falls between adjacent levels & 2, 4, 6, 8 \\ \hline \end{tabular}
\end{table}
Table 1: Comparative results of the importance of elements.  
Figure 2: Baqiao–Tianwang compound interchange.  
Calculate the weight matrix.  
Apply the arithmetic mean method to obtain the weight vector:  
\[w_{i}=\frac{1}{z}\sum\limits_{j=1}^{z}\frac{b_{ij}}{\sum\limits_{k=1}^{z}b_{kj} },(i=1,2,\cdots,z) \tag{3}\]  
In this paper, seven experts are selected to calculate the weight vector using AHP. Take expert 1 as an example. Its judgment matrix is shown in Table 2.  
The largest characteristic root is:  
\[\lambda_{1 MAX}=5.266 \tag{4}\]  
The consistency test passes according to Equation (5).  
\[CR=\frac{CI}{RI}=0.06<0.1 \tag{5}\]  
Other experts use the same analysis method; each expert weights the same to get the final weight of each influence factor, as shown in Table 3.  
From the analysis results in Table 3, the weights of the influencing factors can be seen, so the traffic volume and the ratio of emerging and diverging are selected as the main research object of the article.
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 4 A Test Problem
### Verification and Validation  
To verify the efficacy of ALPPL, we evaluate its adequacy and the necessity in returning robust solutions. Adequacy--We tested 20 RMC values randomly spread in [0, 1] and identified the relatively insensitive range(s); see Figure 14. If the best RMC returned by the ALPPL falls into the relatively insensitive range(s), then we show that the ALPPL can identify the best RMC. It turns out that the best RMC (0.55) ensures the solutions to fall in a relatively insensitive range. For each pair of EIs (the mean and standard deviation), we identify the range(s) of RMC values, within which, both the mean and the standard deviation have acceptable values and are flat (stable). For example, for the deviation Z, we desire both its mean \(\mu_{z}\) and standard deviation \(\sigma_{z}\) to be low values and flat. The two ranges bounded by the dotted rectangles are such ranges. For all EIs, the overlapped desired range is [0.5, 0.55], so this is the relatively insensitive range. The value of 0.55 is in the insensitive RMC range for all EIs, so it is verified that when RMC is 0.55, it gives a relatively robust performance. Necessity--The insensitive range is sufficiently explored during the RMC tuning. First, we identify the insensitive range of RMC values--testing 20 RMC values uniformly distributed in [0, 1] with their EIs, we identify two ranges where the solutions are relatively insensitive to scenario changing, [0.35, 0.4] and [0.5, 0.55]. In Figure 15a, we illustrate all fourteen RMC values in RMC tuning. The horizontal axis represents the iteration number and the vertical axis represents the RMC value. Four of the fourteen RMC values fall in the two insensitive ranges, so 28.5% of the tested RMC values fall in the insensi  
Figure 14: Identifying the insensitive range of RMC value using twenty RMC values. Subfigure (**a**–**c**) show the insensitive ranges of the mean and standard deviation of Z, Naoc, and Nab, respectively. tive ranges, whereas the insensitive ranges only occupy 10% of the whole RMC range. Hence, we conclude that our RMC tuning enables a relatively sufficient exploration of the insensitive ranges. Validation of the improvement of ALPPL versus the ALP. We compare the tested RMC values using parameter learning (ALPPL) in Figure 15a with the tested RMC values using the golden section search (ALP) in Figure 15b. The best RMC identified using the golden section search is 0.65, which is not in the insensitive range; in addition, the RMC values tested in the golden section search are concentrated in [0.57, 0.77], which misses the insensitive ranges [0.35, 0.4] and [0.5, 0.55]. For a solution algorithm that sequentially linearizes the problems in multiple synthesis cycles, the computational complexity is O(n\({}^{2}\)) [37]--this applies to both the ALP and ALPPL, so the parameter-learning method in this paper does not require much computational power. Other improvements of ALPPL over ALP are summarized in Table 11.
## 1 Introduction
For some medical applications such as implantable sensors/actuator devices, long lifetime requirements (10-15 years) for the sensor node may lead to energy harvesters to be employed, which puts further constraints on the available energy budget. This work focuses on the reduction of communications power consumption in the WBAN sensor node by using novel communication methods that take advantage of the wireless wake-up functionality enabled by the our Wake Up Receiver (WUR) [2]. Implementation of the WUR has benefits in networks that do not have high data rates and traffic but need to have on-demand functionality with fast response times. As recent low power DSP processors have enabled low power signal analysis and pre-processing, WBAN sensors have increasing processing capabilities and can be split in two groups depending on whether they transmit only raw measured data or do DSP preprocessing. Some signals (ECG for example) contain more than one type of information about the body (ECG values, heart rate, respiratory rate, _etc._\(\ldots\)) and the DSP enabled sensors can extract this information. This leads to lower communication data rates \"over the air\" where nodes transmit extracted physiological values instead of raw physiological signals. On the other hand, this increases the need for a network that is responsive and can handle the events to transmit these physiological values when they are available, computed, or demanded from an external monitor. We will present a communication method that takes this into account and, using the WUR, formalises the communication within a WBAN for overall power efficiency and prolonged sensor battery lifetime and independence.
## 4 Modeling  
The process of modeling TEC based on SVR is shown in Figure 4:  
(1) Divide the dataset: The parameters \(C\) and \(g\) of the SVR model need to be determined through cross-validation. Considering the temporal nature of time prediction sequences and the seasonal variations of the ionosphere, 12 training sets, and their corresponding validation sets were manually established. As shown in Figure 4, the validation datasets span one year and cover all four seasons: spring, summer, autumn, and winter.  
(2) Determine the parameter search method and search range: Grid search is widely used to determine the hyperparameters in SVR models [32]. Therefore, the grid search method is selected here, with the search range for \(C\) set from 1 to 30 with an interval of 1 and the search range for \(g\) set from 0.01 to 0.1 with an interval of 0.01.  
(3) Train the SVR model using the training data: According to the SVR model's input settings: solar activity index (only \(R_{12}\), only \(F_{12}\), or both \(R_{12}\) and \(F_{12}\)), month, hour, season, and the TEC observation value simultaneously last month, the output is the current TEC.  
(4) Validate the trained model using the corresponding validation data to obtain the model validation values.  
Figure 3: \(R_{12}\) and \(F_{12}\) data.  
Set the model selection strategy to minimize RMSE. When the RMSE is minimized, the solar activity index input and the values of \(C\) and \(g\) are determined, and this combination is used in the final model.  
Figure 5 shows the RMSE between the model and observed values within the parameter search range. It can be seen that the RMSE values within the search range are between 0.7 and 0.84. Table 1 presents the minimum RMSE and the corresponding \(C\) and \(g\) obtained using different solar activity indices as SVR model inputs. The table indicates that the smallest RMSE is obtained using \(R_{12}\) as the solar activity index for SVR modeling. In this case, the corresponding \(C\) is 12, and \(g\) is 0.01, establishing this as the final model for predicting TEC.  
Figure 4: Flowchart of modeling TEC based on SVR.  
Figure 5: _Cont._  
Figure 6 compares the model validation data with the observations (OBS), marking the corresponding RMSE in the upper left corner of each subplot. This figure illustrates that the SVR model validation data can replicate the trend of the changes in OBSs; however, there is a variation in the fit quality across different months. In January 2006, the RMSE between the validation data and the OBS was the smallest, at 0.36 TECU, whereas in May 2005, it was the largest, at 1.16 TECU.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Solar Activity** & **RMSE\({}_{\text{MIN}}\)** & **C\({}_{\text{RMSemini}}\)** & **g\({}_{\text{RMSemini}}\)** \\ \hline \(R_{12}\) and \(F_{12}\) & 0.727 & 12 & 0.01 \\ \(R_{12}\) & 0.708 & 12 & 0.01 \\ \(F_{12}\) & 0.730 & 12 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 1: The minimum RMSE and corresponding \(C\) and \(g\) were obtained using different solar activity indices as SVR model inputs.  
Figure 5: RMSE between model and observed values within the parameter search range.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
This step corresponds to Box 8 in Figure 2.  
Step 9: When \(j>N\), the \(i\)-th UAV in the \(t\)-th time slot has completed all possible attempts at a communication coverage radius. Based on the estimated CRLB corresponding to each communication coverage range for the \(i\)-th UAV, the optimal policy (including the selection of communication coverage range and motion) for the \(i\)-th UAV can be determined. Step 10: When \(i>M\), all UAVs in the \(t\)-th time slot complete the selection of an optimal policy. At this point, the states of all UAVs are updated simultaneously, and the system enters the \(t+1\) time slot. Step 11: When \(t>T\), the JRCO algorithm terminates. At this point, the UAV swarm returns to the departure area and transmits the AOA measurement information of the \(T\)-th time slot to the base station in this area.
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 5 Main Strategies for Nanoencapsulation of Essential Oils
### Biopolymer-Based Nanoencapsulation  
Biopolymers are widely used as a wall material for the encapsulation of antimicrobial agents. Various sources serve as the origin of the biopolymer, which can be used in its native form or modified to acquire certain required properties. In general, EOs can be encapsulated in food-grade biopolymers such as proteins (whey proteins, caseins, zein, gelatin, etc.) and polysaccharides (starch, chitosan, cyclodextrin, pectin, alginate, etc.), as well as derived composite materials [2]. Biopolymer-based nanoencapsulation can produce nanocapsules (Figure 2D) or nanospheres (Figure 2E). In nanocapsules, the oily core is surrounded by polymer walls, while nanospheres are matrix systems in which the core is dispersed in the polymer matrix [80]. Several different technologies are normally used for the production of nanocapsules or nanospheres, such as complex coaervation [71], freeze-drying [81], spray-drying [61], and ionic gelation [82].
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Multisource Data Integration
radar extrapolation nowcast, and then seamlessly blends with the NWP forecasts for longer lead times. The blending also works in a probabilistic manner. Figure 3 shows the skill of a probabilistic Lagrangian radar extrapolation nowcast, an NWP ensemble forecast and a Bayesian blending of the two, averaged over four precipitation events in the central European Alps. The blending can also take place in multiple stages of the processing, as is the case of [59], where NWP forecasts of growth and decay over the orography were used to locally modify precipitation rates inside a Lagrangian radar extrapolation. Hail is another example for which multisource data integration can be used. Single-polarisation techniques, used to estimate the probability and maximum expected size of hail, combine three-dimensional radar reflectivity measurements with information on the height of the zero-degree isotherm, which is commonly taken from NWP models. These single-polarisation radar hail products have routinely been used for hail monitoring in mountainous regions for many years, see, for instance, [52; 54]. Dual-polarisation offers the possibility of identifying hail and other types of hydrometeors [99], as well as the level of mixing of different hydrometeor types [100], in the pulse volume of a radar measurement. Since 2015, hail has also been monitored in Switzerland by means of a crowdsourcing approach, based on a reporting function in the popular MeteoSwiss weather app. In the hail season of 2021 alone, the app users submitted 100,000 reports of hail observed on the ground in real time, an impressive number compared to the small size of the country, that is, 41,000 km\({}^{2}\). In addition, a network of 80 automatic hail sensors has been deployed in the three hail hot spots, that is, in central Switzerland, southern Ticino and over the Jura mountains. A drone, equipped with a high-resolution camera, has been used to take pictures of hail stones on green areas, such as football fields, shortly after a hailstorm, as a fourth option to obtain information on hail. Research on combining the different sources of hail information is ongoing; for the first results, see [75; 98].
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies
### Planner
#### 6.4.2 Rendezvous Channels
**Find:**  
* Center frequencies, \(F=\{f_{1},\ldots,f_{n}\}\)  
**Constraints:**  
* \(\forall_{f,f_{i}\
ot\in F}\bullet[f_{i}-b,f_{i}+b]\cap[f_{j}-b,f_{j}+b]=\emptyset\)
* \(\forall_{f_{i}\in F}\exists_{g_{i}\in F}\bullet[f_{i}-b,f_{i}+b]\subset R_{j}\)
* \(\forall_{f_{i}\in F}\exists_{m\in\mathcal{N}}\bullet f_{i}=m\cdot\Delta\)
* \(|F_{P}|/n\leq r\)  
The algorithm depends on the following three functions:  
* \(\boldsymbol{func_{1}}\) --builds the list of opportunities with relaxed constraints, considering only the time-frequency map of the geographically all-encompassing rules of the active spectrum access policy and from there, carving out the time-frequency map of the union of disallowed rules, which is also expanded to the full geographical area covered by the active policy. The resulting set of opportunities is then passed through a \"channel provider\" filter that converts it to a list of \"channels\" determined by their center frequencies that obey parameterized constraints and limit the number of such channels returned to \(n\cdot r\). * \(\boldsymbol{func_{2}}\)--builds the list of opportunities with relaxed constraints, considering only the time-frequency map of \"allow\" rules of the active spectrum access policy and ignoring the disallowed rules of the said policy. The resulting set of opportunities is also passed through the \"channel provider\" filter while limiting the returned set of channels to \(n\cdot(1-r)\)* _func\({}_{3}\)_--consolidates the results by only retaining results from \(func_{1}\) and \(func_{2}\) that are within the fully constrained list of opportunities as computed from the active policy. The three functions are leveraged in the main control flow of the rendezvous algorithm as follows:  
* Retrieve the time-frequency map of the geographically all-encompassing rules of the active spectrum access policy (assuming there are such rules). * Retrieve the set of \"disallowed\" rules from the active policy. * Append the ordered list of frequencies returned by \(func_{2}\) to the list of frequencies returned by \(func_{1}\). * Then, apply the filter \(func_{3}\) to the list of channels obtained from the previous steps. * Return the filtered list, ordered and freed of any duplicates.
## 3 The Proposed EMCR Protocol  
This paper proposes the Energy-Mobility-Connectivity aware routing protocol (EMCR) to overcome the disconnection problem in a hybrid network of VANET-WSN. The EMCR system model is presented in Figure 3. EMCR uses geographical positions for routing. The nodes periodically broadcast beacons (i.e., Hello messages) with their node's ID, current position, speed, heading, and energy level. The location service maps the node's ID to its current position. EMCR is based on opportunistic forwarding. Therefore, there is no route discovery before the data dissemination. The HELLO module is part of the routing protocol that is responsible for generating HELLO messages. These messages aim to discover direct neighbors and learn their energy level and mobility (velocity and heading).  
The two primary repositories in EMCR are the neighbor set and the position association set. The neighbor set is used to construct and maintain the one-hop neighbor's table. Each time a HELLO message is received, we check this repository and either create a new neighbor or update the neighbor information. Each entry will be automatically deleted after a period of \((2.5\times helloInterval)\) time. This time allows a node to remove neighbors that are not in the vicinity and hold the information if a HELLO is lost. The Position Association Set (PAS) keeps track of the position information of any destination that the specific node has data to deliver. It is a local copy of the location service. If an entry in this set is also a neighbor, it is updated with HELLO messages. Otherwise, a location service will provide this information to maintain the repository.  
In the EMCR protocol, the mobility module is based on the fact that some nodes are stationary roadside wireless sensors. The sensor nodes are not equipped with Digital Maps; instead, they use Euclidean distance in their calculation. Other nodes are vehicles. Vehicles can find the road they are traveling on since they are equipped with Digital Maps of the road topology. The position and velocity information are used to predict the position of neighbor (and destination) nodes. Employing position prediction will indeed result in less overhead. The position prediction calculation is  
\[D_{x} =d_{x}+v_{x}\times\Delta t \tag{1}\] \[D_{y} =d_{y}+v_{y}\times\Delta t,\]  
where \(D_{x}\) and \(D_{y}\) are the future positions based on the current node position (i.e., \(d_{x}\) and \(d_{y}\)), velocity (i.e., \(v_{x}\) and \(v_{y}\)), and \(\Delta t\) (i.e., the elapsed time between the present time and the time of the last received beacon).  
The energy module is an essential component of the protocol for routing decisions. The EMCR protocol routes a packet by computing each neighboring sensor node's residual energy ratio (RER). Sensors that consume less energy will be selected as a forwarder node. All this information is jointly combined in a weighting function that calculates a weight for each neighboring node, based on which forwarding selection is performed.
###### Abstract  
In the near future, it is highly expected that smart grid (SG) utilities will replace existing fixed pricing with dynamic pricing, such as time-of-use real-time tariff (ToU). In ToU, the price of electricity varies throughout the whole day based on the respective utilities' decisions. We classify the whole day into two periods with very high and low probabilities of theft activities, termed as the \"theft window\" and \"non-theft window\", respectively. A \"smart\" malicious consumer can adjust his/her theft to mostly targeting the theft window, manipulate actual usage reporting to outsmart existing theft detectors, and achieve the goal of \"paying reduced tariff\". Simulation results show that existing schemes do not detect well such window-based theft activities conversely exploiting ToU strategies. In this paper, we begin by introducing the core concept of window-based theft cases, which is defined at the basis of ToU pricing as well as consumption usage. A modified extreme gradient boosting (XGBoost) based machine learning (ML) technique called dynamic electricity theft detector (DETD) has been presented to detect a new type of theft cases.  
AMI smart meter; theft detection; machine learning; XGBoost; time-of-use (ToU) pricing Article  
ToU Pricing-Based Dynamic Electricity Theft Detection in Smart Grid Using Gradient Boosting Classifier. April 202111401. [[https://doi.org/10.3390/app11010401](https://doi.org/10.3390/app11010401)]([https://doi.org/10.3390/app11010401](https://doi.org/10.3390/app11010401))  
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