esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Design of Bi-log Hybrid Antenna with Taguchi's Method for EMI Measurements
## 2 Antenna Configuration and Benchmark Performance  
The Bi-log antenna has the favorable ability of receiving interference and wideband in radiation emission tests, due to its very good directivity characteristics and flat gain curve. Figure 1 depicts the main geometrical characteristics of the Bi-log antenna. This antenna consists of a set of LPDA antenna elements and a pair of bowtie antennas, as depicted in Figure 1a, and these two parts are connected together. The overview of the proposed Bi-log hybrid antenna is illustrated in Figure 1b. A planar bowtie and a compact LPDA element have been utilized to provide an optimal design, and they are based on the concept and method of broadband antenna theory for optimal designs. A part of the LPDA element in the Bi-log hybrid antenna describes the proportionality factors such as the scaling factor (\(\tau\)), flare angle (\(\alpha\)), and spacing factor (\(\sigma\)) for design parameters. After specifying two of the design parameters, the third parameter can be determined. The relationships between lengths, diameters, and spacings among elements are derived from directivity contour curves, which depend on \(\tau\) for different values of \(\sigma\). Another part of the antenna is the applied bowtie structure design. It depends on dimensions of its triangular shape and its flare angle values to obtain miniaturization, bandwidth, and impedance match performance. As mentioned above, the scope of the proposed antenna is attractive and provides many practical applications for EMI measurement.  
Figure 2 illustrates the performance benchmark for the proposed antenna. As shown in Figure 2a, the impedance matching band is wide enough to cover the 30 MHz to 6 GHz range, without the attenuator. In the purposed acceptance conditions in the whole bands, the voltage standing wave ratio (VSWR) is below two while the AF is stable and linear, as demonstrated in Figure 2b. The ratio of antenna symmetry has an up and side-down measurement setup, respectively, in vertical polarization. It provides a slight deviation of 0.5 dB with a small gain variation, as shown in Figure 2c. The remarkable performance of the antenna is also summarized in Table 1. This table determines several items such as the antenna type, operation frequency band, number of antenna elements, scaling factor (\(\tau\)), fractional bandwidth (FBW), and gain by observing and comparing variation, respectively. Furthermore, the overall dimensions of the proposed antenna can be as small as 95 cm (L) by 148.5 cm (W) by 60 cm (H), and it can be as lightweight as 2.5 kg. The proposed antenna demonstrates outstanding performance, surpassing previously published works in optimized design of antennas. Furthermore, it boasts a high antenna gain and covers a broader frequency range, as shown in Table 1.  
Figure 1: Construction details of (**a**) LPDA antenna element and bowtie antenna; (**b**) overview of the proposed Bi-log hybrid antenna.  
Figure 2: _Cont_.
## 2 Characterization and Types of Phenolic Compounds
C6-CN phenolic compounds possess a basic C6-CN structure, where N is found between 1 and 3. Within this phenolic group, three subdivisions can be made in C6-C1, C6-C2, and C6-C3 and correspond to phenolic acids and aldehydes, which are characterized by having phenol with carboxylic group substitution [87]. Other examples are phenolic acid, hydroxybenzoic acids (C6-C1), acetophenones, phenylacetic acids, coumarins (C6-C2), coumarins, hydroxycinnamic acids, phenylpropanoids (C6-C3), naphthoquinnes (C6-C4), xanthones (C6-C1-C6), stilbenoids, anthraquinones (C6-C2-C6), flavonoids, isoflavonoids (C6-C3-C6), lignans, neoligans ([C6-C3]2), lignins ([C6-C3]n), and condensed tannins ([C6-C3-C6]n). Gallic acid is one of the simplest phenolic acids used as a standard for total phenol content estimation. It can be found in high concentrations in the brown alga _Halopteris scoparia_. Other simple acids such as 4-hydroxybenzoic acid have been also reported in the brown alga _Undaria pinnatifida_[90]. C6-C2 are not very common in nature but the red alga _Tichocarpus crititus_ was studied for the C6-C3 category of phenolic compounds such as coumarins, isocumarins, chromones, monolignols, hydroxycinnamic acids, and cinnamic aldehydes [87; 91]. Moreover, [PERSON] et al. [92] found the presence of coumarins in the green algae _Dasycladus vermicularis_. Other phenolic compounds include xanthonoids (C6-C1-C6), stilbenoids, anthraquinones, anthrones (C6-C2-C6), flavonoids (C6-C3-C6), and diarylheptanoids (C6-C7-C6). (C6-C3-C6) can be classified based on the arrangement of the C3 group that connects two benzene rings such as chalcones, aurones, and flavonoids. The last compound is further classified into different classes such as flavonoids, flavones, isoflavones, anthocyanins, and flavanones. [PERSON] et al. [81] found a high content of flavonoids in red algae, which was higher than in green and brown algae. In addition, [PERSON] et al. [90] reported a good number of different flavonoids, catechchin, epicatechin, gallate, and epigallocatechin, in brown algae species such as _Eisenia bicyclis_, _Sargassum fusiforme_, and _Saccharina japonica_.
# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 2 Study Area Description and Dataset
### Datasets
#### 2.2.2 Global Precipitation Products  
Global precipitation data with fine spatial and temporal resolution provide optional homogeneous timeseries information for data-scarce areas, going back in time (30+ years) as far as possible for hydrological applications and climate studies (Kal  
The Climate Hazards Group Infrared Precipitation with station data version two (hereinafter CHIRPSv2) was developed by the United States Geological Survey (USGS) and University of California, Santa Barbara (USCB); it merges estimates using blending satellite, global climatology, and gauge observation data from the Global Telecommunication System (GTS). The CHIRPSv2 dataset incorporates 0.05\({}^{\circ}\) spatial resolution with ground reference measurements to generate a daily sequence of data points for an area coverage of 50\({}^{\circ}\) S-50\({}^{\circ}\) N since 1981 [12].  
The Tropical Applications of Meteorology using SATellite version three (hereinafter TAMSATv3) estimate, developed by Reading University in the UK, features Meteosat thermal infrared (TIR) fine-resolution observations on a daily timescale employing attuned cold cloud duration (CCD) data measurements for Africa by downscaling pentadal total measurements. The TAMSATv3 estimate incorporates 0.0375\({}^{\circ}\) spatial resolution through ground rainfall measurements to generate timeseries for all of Africa from January 1983 to date [11].  
The Precipitation Estimation from Remotely Sensed Information using Artificial Neutral Network Climate Data Record (PERSIANN-CDR) system was developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI); it uses a neutral function classification procedure to determine the product of precipitation amount for each 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) grid in an IR temperature spectrum offered by a geostationary satellite. The rainfall product features an area coverage of 60\({}^{\circ}\) S-60\({}^{\circ}\) N globally from 1983 to 2015 [13].
## 4 Evaluation
### Comparison of Various Features
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**w/o Event-Wise**} & \multicolumn{2}{c}{**w/ Event-Wise**} \\  & \multicolumn{2}{c}{**Post-Processing**} & \multicolumn{2}{c}{**Post-Processing**} \\ \cline{2-5}  & **F1-Score [\%]** & **F1-Score [\%]** & **F1-Score [\%]** & **F1-Score [\%]** \\  & **(Micro)** & **(Macro)** & **(Micro)** & **(Macro)** \\ \hline NMF(iterative) & **35.06** & 31.58 & 40.12 & 39.23 \\ \hline NMF (non-iterative) & 34.87 & 30.16 & 40.02 & 38.45 \\ \hline MelSpec & 34.41 & 32.31 & **40.41** & 39.72 \\ \hline Log-Mel & 30.27 & 29.88 & 35.11 & 36.60 \\ \hline GAM & 32.15 & **33.09** & 37.23 & **39.81** \\ \hline CQT & 32.25 & 28.76 & 37.28 & 35.36 \\ \hline [PERSON] et al. [46] & - & - & (with own post-processing) \\ \cline{3-5}  & & & 42.48 & 39.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Averaged results of various features. The performance of the Cornell system was provided as a reference of comparison. The boldface means the best performance of each measure. water, and alarm bell) contained several impulsive sounds. Thus, the _non-iterative_ method appeared to be useful for the impulsive sounds. However, we cannot easily come to this conclusion, as the frying sounds also contain impulsive ones. There are two main differences between the iterative and non-iterative NMFs: divergence function and optimization method. The _iterative NMF_ uses KLD and multiplicative update, and the _non-iterative NMF_ employs Euclidean and closed-form solution. To analyze the effect on the class-wise performance, we compare the performances of the NMFs with an _iterative NMF with Euclidean_ (for convenience, we call it _NMF-EUC_ here). The _iterative NMF_ and _NMF-EUC_ have the same parameters and settings except for the divergence function. Figure 7 shows the comparison results. The _iterative NMF_ is superior to the _NMF-EUC_ in the classes of electric shaver, speech, frying, and blender, where the _iterative NMF_ is superior to the _non-iterative NMF_. In other words, the tendencies in performances are similar for the _NMF-EUC_ and _non-iterative NMF_. However, the difference in the performance of _non-iterative NMF_ compared to _iterative NMF_ is greater than that of _NMF-EUC_ and _iterative NMF_. Therefore, it seems that the difference in the optimization method had a greater effect on the performance than that in the divergence function in this experiment.
## 4 Application to Staffing Optimization of a Multi-Skill Call Center
### Problem Statement  
Consider a one-period multi-skill call center, where various types of calls reach randomly and distinct groups of agents handle these calls. The calls arrive randomly following a stochastic process which can be doubly stochastic or non-stationary. If an agent with the required skill is available, this arriving call will be handled promptly; otherwise, it will wait in a queue. When the waiting time of a call surpasses the given time limit, an abandonment occurs and this call is lost.  
Agents in a same group can only serve the assigning call type. Every agent group has a specified subset of skills. Any type of call requires a particular skill. For a given type of call, preferences may happen to some agent groups due to either managers prefer to reserve some agent groups for other call types, or certain agent groups are better than others to handle calls. Agents with more skills are also more expensive, hence a compromise is made when choosing the skill groups.  
The goal of considered problem is to minimize the staffing cost subject to the service level constraints. The staffing cost is the total cost for all agents, where the cost of an agent relies on its skills.  
The service level is expressed as the fraction of calls served within a predefined time limit over a longer period. The decision variable is the number agents for every skill group.  
Figure 3 shows an example of a one-period multi-skill call center consisting of \(J\) call types and \(K\) agent groups. The \(\lambda_{j}\) indicates the arrival rate of the \(j\)th call types. The \(x_{k}\) represents the number of agents in the \(k\)th magnet groups. The mean service rate of the \(j\)th call types in the \(k\)th agent groups is \(\mu_{k,j}\), which depends jointly on the skill group and call type. At the beginning of the period, the call center is assumed to operate in a steady state relative to the staffing levels. The scheme to assign a newly-free agent for any queued call is that agent groups select call types according to increasing sequence of call-type number. The policy for assigning calls to agent groups is that calls select agent groups in order of increasing sequence of group number. Every agent group has its own prioritized list of call types. For the static routing, it is assumed that each call type has an ordered list of agent groups, used to select an available agent upon arrival. This ordered list is generally a strict subset of the agent groups, because not all agent groups can deal with a specific call type. If every agent group in this list is busy, then the call enters a queue with the same type calls. When an agent is available, this agent serves the longest-waiting call from the first non-empty queue in the ordered list.
# Joint Spreading Factor and Channel Assignment in Multi-Operator LoRaWAN Deployments
## 5 Multi-Operator Game for SF Assignment
### Distributed Approach to SF Assignment  
In order to make the SF assignment algorithm completely distributed, it is sufficient for any individual operator to have an estimation of the total packet delivery ratio (success rate) at the time they play the game. For simplicity, we assume that the number of devices present in its network is known by each operator. For instance, if 1000 devices are present and transmitting at one packet per hour, the operator expects to receive 1000 packets every hour. If it receives 600, and then it estimates that it has a packet delivery ratio of 0.6. With this estimation, any operator can play the game in a distributed manner, from simple knowledge of its own traffic.
## 5 System Engineering Analyses
### Mass Budget  
The mass budget shown in Table 14 lists estimates for the contribution of various components to the mass of the nanosatellite.  
A mass margin of approximately 120.5 g is obtained, according to Table 14. The predicted mass budget respects the total mass constraint, which was set at less than 1.33 kg.
# Spatiotemporal Modeling of the Smart City Residents' Activity with Multi-Agent Systems
## 3 Research Methodology
### Citizen Modeling--Agents  
An agent models a city resident and has a set of features that reflect its social character:  
* Age, gender, marital status, and number of children.
* Trust in other residents, a number in the range <0,1> that determines to what extent a resident trusts other people. It is not a pejorative trait; it does not mean naively, but faith in the capabilities of others.
* Trust in institutions, a number in the range <0,1> that determines to what extent a citizen trusts institutions; that is, governmental agencies, healthcare, or educational institutions.
* Altruism, a number in the range <0,1> that determines to what extent a person wants to work for the society and engage in the social life for the sake of common interests.
* Education, a number in the range <0,1> that determines the degree of education.
* Life satisfaction, a number in the range <0,1> that determines to what extent a citizen is satisfied with her/his life.
* Wealth, a number in the range <0,1> that determines the material status of a citizen.
* Identity, a number in the range <0,1> that determines the emotional connection of the citizen with the city or district in which s/he lives.  
Besides the features above, each agent is assigned to a place in which s/he lives (a residential building) and a workplace (an office building or a factory). During the simulation, the agents are moving around the city according to the daily rhythm. Residents navigate the city along a network of roads; eventually, they can go to a demonstration.  
Demonstrations take place in the so-called attractors--places that attract social interest and provoke extreme emotions. One such attractor is the Palace of Culture and Science in Warsaw, as its demolition is a continuing matter of dispute. A demonstration causes a clash of extreme emotions of the participants and often results in a change of stance regarding the fate of a given attractor.  
The devised model assumes that from Monday to Friday, each agent (human agent) leaves for work in the morning (06:00 to 08:00). An agent stays at work for eight hours and then returns home. Some people go to a governmental agency during work hours (10:00 to 12:00) or to a doctor (09:00 to 10:00). After work, and on the weekends, some of the agents leave the city (18:00 to 23:00). Agents meet and interact when traveling, walking around the city, arriving at work, or places of entertainment.  
In addition to ordinary residents, there are also the so-called leaders: Social activists who want to impact on the society so that, following the idea of [PERSON]'s \"The Power of the Powerless\" essay [46], many of the weak gain influence over the molding of the urban fabric through mutual interactions. Leaders are close to controversial events. Positive leaders are those with extreme and affirmative trust in people; they have a beneficial influence over the social trust in others. Negative leaders place extreme and negative trust in people, thereby lowering the trust of people with whom they interact towards others.
## 2 Mathematical Formulation
### Inverse Problem Formulation  
Equation (12) allows the formulation of the inverse problem as the detection and localization of bars within a predetermined number \(N_{b}>N\) of \"candidate\" positions. Following the same approach as in [9], we define a new discrete function \(\gamma(n)\), \(n=1,\ldots,N_{b}\), whose value is \(1\) if the \(nth\) position is occupied by a bar; otherwise, it is equal to \(0\). Thus, the problem is recast as a discrete equation as follows:  
\[\hat{E}(k,\mathbf{r}_{o})=\sum_{n=1}^{N_{b}}C_{n}(k)\Big{[}H_{0}^{(2)}(k| \mathbf{r}_{o}-\mathbf{r}_{n}|)\Big{]}^{2}\gamma(n) \tag{15}\]  
where \(\gamma(n)\) plays the role of an unknown.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 3 Key Management Taxonomy in DTNs
### Key Revocation  
Unitil now, only a couple of works about key revocation in DTN have been proposed. In particularly, the authors in [56] propose a new validation and revocation mechanism as well as a new design for a lightweight CRL in compliance with PKI (X.509) for DTNs. The new designed CRL is of reduced size and arranges the revocation list in the form of a Hash Table (Map) data structure to increase the searching efficiency. Moreover, in [57] the authors present a secure and fully distributed key revocation and update scheme for DTNs called Distributed Signing (DS) revocations. This is based on a DTN without a centralised PKI that ensures entity authentication and utilises the LCF trust system presented in [33]. More specifically, neighbouring friendly nodes attest and vouch for a node's identity during the key revocation process. Table 5 summarizes and compares these two key revocation schemes using the same criteria as in the previous tables.
## 4 Numerical Study of SC Walls Perforation
### Free Surface Boundary Effect  
Although cavity expansion analysis was successfully applied to projectile deep penetration in concrete, the typical penetration resistance equation proposed by [PERSON] et al. [23] was not suitable for a projectile perforation scenario with a concrete panel of limited thickness. The front and back free surfaces might degrade the material strength thus reducing the penetration resistance during cratering and shear plugging, as shown in Figure 12. Therefore, the penetration resistance prior to shear plugging should be revised in the case of a concrete panel with limited thickness.  
To develop a penetration resistance equation with respect to penetration velocity, numerical simulations with constant projectile velocity were performed to derive the penetration resistance acting on the projectile nose. Since the front and rear free surface might negatively affect the penetration resistance, concrete panels with thicknesses of 150, 200, 250, 300, 400, 500, 600 and 700 mm were selected for simulation. According to the results plotted in Figure 13, it is interesting that penetration resistant force increased to a plateau. This can be explained by the fact that the front surface degraded the penetration resistance until reaching about 6\(d_{\text{p}}\). The rear free surface effect was estimated by penetration simulation of panels with 250 mm and 300 mm thickness. Both had a stable plateau during tunneling and started to drop at a position about 68 mm to 70 mm away from the rear surface. The shear plugging height was about 2.5\(d_{\text{p}}\) which matches well with experimental data in Ref. [34]. For 150 mm and 200 mm thickness concrete panels, both the front and rear free surface affected the penetration resistance, implying no stable tunneling.  
Figure 11: Numerical simulation results. (**a**) Effects of steel plate; (**b**) effects of concrete panel; (**c**) projectile velocity history; (**d**) residual velocity corresponding to striking velocity.  
Figure 12: Front and rear free boundary effect on projectile penetration responses.
## 3 System Models
### Semi-Synchronized Model Aggregation  
In each global iteration, clients from different tiers upload their local models, and the FL server can estimate when a client may upload its local model according to its associated tier. Figure 2 provides one example to illustrate the scheduling of the clients from four tiers in LESSON. For example, the clients in Tier 1 are expected to upload their local models in each global iteration, and the clients in Tier 2 are expected to upload their local models in every two global iterations. Denote \(k\) as the index of the global iterations, and let \(y_{jk}\) be the binary variable to indicate whether the clients in Tier \(j\) are expected to upload their local models by the end of \(k^{th}\) global iteration, where  
\[y_{jk}=\left\{\begin{array}{ll}1,&\text{if }k\%j=0,\\ 0,&\text{otherwise},\end{array}\right. \tag{8}\]  
where % is the modulo operation, and so \(k\%j=0\) indicates \(k\) is divisible by \(j\). Meanwhile, let \(z_{ik}\) be the binary variable to indicate whether client \(i\) is expected to upload its local model by the end of \(kth\) global iteration (\(z_{ik}=1\)) or not (\(z_{ik}=0\)), where  
\[z_{ik}=x_{ij}y_{jk}. \tag{9}\]  
Based on the value of \(z_{ik}\), the FL server would expect which clients will upload their local models in global iteration \(k\), and then aggregate all the received local models based on  
\[\mathbf{\omega}^{(k)}=\sum\limits_{i\in\mathbf{\mathcal{I}}}\frac{|\mathbf{\mathcal{D}}_{ i}|}{\sum\limits_{i\in\mathbf{\mathcal{I}}}|\mathbf{\mathcal{D}}_{i}|z_{ik}}\mathbf{ \omega}_{i}^{(k)}z_{ik}, \tag{10}\]  
where \(\sum\limits_{i\in\mathbf{\mathcal{I}}}|\mathbf{\mathcal{D}}_{i}|z_{ik}\) indicates the total number of the data samples among all the clients, who would upload their local models in global iteration \(k\).  
Please note that, in synchronous FL (e.g., FedAvg), each selected client would update its local model \(\mathbf{\omega}_{i}^{(k)}\) based on Equation (11) in each global iteration.  
\[\mathbf{\omega}_{i}^{(k)}=\mathbf{\omega}_{i}^{(k-1)}-\delta\
abla f_{i}\Big{(}\mathbf{ \omega}_{i}^{(k-1)}\Big{)}, \tag{11}\]where \(\delta\) is the step size, which is the same for all the selected clients. In LESSON, different clients update their local models in different frequencies, depending on their associated tiers, so it is reasonable to adopt different step sizes for the clients in different tiers [24]. Here, we adjust the step size of a client proportional to its tier (i.e., \(\delta\times j\)). Thus, when \(k\%j=0\), client \(i\) in Tier \(j\) would update its local model based on  
\[\mathbf{\omega}_{i}^{(k)}=\mathbf{\omega}_{i}^{(k-j)}-j\delta\
abla f_{i}\Big{(}\mathbf{ \omega}_{i}^{(k-j)}\Big{)} \tag{12}\]
## 4 Experiments and Results
### Results and Discussions  
In this section, the proposed PIANet is firstly compared with the comparison methods to show the superiority of the proposed PIANet in short-term OD prediction performance. Then, ablation studies are conducted to investigate the impacts of the proposed OAMs and feature interaction on the prediction performance of the proposed PIANet.