esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A wearable low-profile antenna with circular polarization is designed. Slit-loaded parasitic elements are used to improve the CP bandwidth in the 2.4 GHz band. CM theory is used to reveal the modal current distributions of the antenna. According to these current distributions, we find that the antenna can achieve a wide CP bandwidth by loading slits on the parasitic elements whilst the antenna profile remains low. Full wave simulations and measurements validate our design. The size of the prototype antenna is 1.04 \(\times\) 1.04 \(\lambda_{0}\)\({}^{2}\) while the profile is only 0.027 \(\lambda_{0}\). The antenna has an impedance bandwidth of 2.18-2.61 GHz (18%) and a CP bandwidth of 2.2-2.54 GHz (14.3%) and realized a peak gain of 8.9 dBic. Compared to other low-profile wearable designs in the 2.4 GHz band, this design achieves a bandwidth that is 3-5 times larger.  
Figure 12: Simulated and measured realized gain pattern in (**a,b**) free-space and (**c,d**) on-phantom scenarios at 2.3 GHz and 2.4 GHz.  
**Author Contributions:** Z.C., Conceptualization, methodology, software, validation, writing; X.Z., validation, writing--review and editing, supervision; C.S., validation, writing--review and editing; J.Z., conceptualization, methodology; V.V., software, measurement; Y.L., measurement; G.A.E.V., validation, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** [PERSON] is grateful for the scholarship from China Scholarship Council (CSC). [PERSON] and [PERSON] are grateful for KU Leuven Internal Funds: the C1 project (C14/19/083).  
**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.
## 3 Results and Discussion
### Performance of Spray-Dried Powder
The eugenol loading of the F1 sample powder-chi 0.33% (16.8%) was higher than the result of [PERSON] and [PERSON] [33]. They stated that eugenol loading in their eugenol-loaded chitosan nanoparticle study ranged from 0.85% to 12.80%. Research on eugenol loaded in zein/casein nanocomplexes resulted in a loading of 5.5% [23]. The encapsulation with the WPMD conjugate produced a loading ranging from 5.0% to 7.9% [11]. Generally, the encapsulation efficiency of the F2 powders was higher than that of the F1 powders, except for F1-chi 0.33%. We found that a higher eugenol concentration lowered the encapsulation efficiency. This result is in line with that of [PERSON] et al. [22], who reported that EE decreased with an increase in the initial essential oil content. This could be due to the saturation of essential oil loading into the wall material during the encapsulation process and also the effect of chitosan concentration. The level of saturation of eugenol was known from experiments encapsulating eugenol with four levels of eugenol concentration. The result showed that the percentage of EE increased at eugenol concentrations up to 2%, and then decreased when loaded with 4.0% eugenol. The LC increased until 4.0% eugenol concentration, and then decreased at 8% eugenol concentration (Figure S1). Volatile compound retention can be influenced by an optimum percentage of infeed solid that varies for each carrier or encapsulating agent. However, in this research, all formulas used the same mass and type of infeed solid (WPMD conjugate and chitosan). Each formulation is distinguished by the percentage of eugenol. In general, the encapsulation process with spray drying usually used 20% flavor load based on carrier solids. The higher percentage of that flavor load usually causes unacceptably high losses of flavors during spray drying [34]. The formula with a concentration of eugenol 2% (F-2%) has a maximum percentage of encapsulation efficiency (EE). The EE is influenced by the mass of eugenol in feed, which also involves a solid carrier in its calculation. The percentage of eugenol in feed for formula F-2% is 35.71% (data not shown). This value was greater than the formula with 1% eugenol (21.74%) and smaller than the eugenol formula 4% (52.63%), and the eugenol formula 8% (68.97%). The percentage of eugenol in feed for F-2% was probably the optimum value in the formulation of this study because it produced the maximum percentage of EE, although the maximum loading was at F-4%. At F-4% the percentage of EE decreased. The ANOVA test results showed that the loading percentage between F-2% and F-4% was not significantly different, while the percentage of EE was significantly different. Therefore, it can be concluded that the 2.0% eugenol concentration was the best at obtaining the optimal percentages of loading and encapsulation efficiency in the encapsulation of double-layer eugenol using WPMD conjugates and chitosan.
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 6 Conclusions  
This paper presented a new optimization algorithm using the compact bat algorithm (cBA) for the uneven clustering problem in wireless sensor networks (WSN). A probabilistic model was used to generate a new candidate solution in space search using the cBA which has been promising so far. The candidate solution was found by studying explicit probabilistic models of promising existing solutions and sampling the built models. The probability density functions (PDF) and cumulative distribution functions (CDF) thoroughly are used to construct the operations of selecting and optimizing behaviors. The probabilistic model is used as a valid alternative optimization strategy for available devices with even limited hardware sources. Experimental results demonstrate that the proposed algorithm achieves the effective way of using limited memory devices, and provides competitive results.  
Figure 6: Comparison of converging curves of the cBA-WSN (with adjusted cluster size for BAs with altered loudness) and the cBA-WSN (with no adjusted).  
Figure 7: Comparison of advanced cBA-WSN for the number of nodes alive for a WSNs with PSO-TVAC, PSO-TVIW, LEACH, and LEACH-C approaches.  
**Author Contributions:** Conceptualization, T.-T.N. and T.-K.D.; methodology, T.-T.N.; software T.-K.D.; validation T.-K.D., T.-T.N. and J.-S.P.; formal analysis T.-T.N.; investigation, J.-S.P.; resources J.-S.P.; data curation T.-K.D.; writing--original draft preparation T.-T.N.; writing--review and editing J.-S.P.; visualization T.-T.N.; supervision J.-S.P. project administration T.-T.N.; funding acquisition J.-S.P.  
**Funding:** This work was supported by the Natural Science Foundation of Fujian Province under Grant No. 2018J01638.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Case Analysis
### Simulation Attack and Performance Analysis
#### 4.4.2 Analysis of the Impact of Deliberate Attacks on Network Performance  
Using the method of identifying key nodes mentioned earlier, we observe the extent to which the global performance of the transportation network changes under deliberate attacks on different nodes as follows:  
Step 1: Select the top 16 nodes in terms of importance as key nodes;  
Step 2: Carry out deliberate attacks on the key nodes in descending order of importance, that is, continuously remove key nodes;  
Step 3: Calculate the network performance indicators after removing the nodes;  
Step 4: Plot and analyze to obtain the results.  
Based on the level of importance, the key nodes calculated in the article are deliberately damaged in ascending order, that is, deliberate attacks, causing them to fail and compromising the network's connectivity performance. Assuming the initial performance is 100%, 16 key nodes are subjected to deliberate attacks, causing node damage and failure. Analyze the relationship between network performance degradation and the number of attacked critical nodes, as shown in Figure 6.  
The change in network performance after damage to each node in Figure 6 is not consistent. Critical nodes with higher importance have a greater negative impact on the overall network performance. Network efficiency and network connectivity follow a similar trend in the early stages, dropping quickly to 70%. Afterward, the former continues to rapidly decline to around 40%, while the latter gradually drops to 60%. In comparison,natural connectivity changes faster, and the final network performance remains around 50%. Overall, network efficiency and network connectivity have similar characteristics. Natural connectivity doesn't change much under deliberate destruction, with the decline being relatively steady.
## 3 Results and Discussion
The specific capacitance of the composite electrodes was larger when the scan rate was larger than 250 mV\(\cdot\)s\({}^{-1}\). Because metal oxide was deposited only on the surface of the MCMB, a section of the pores was blocked, and the specific capacitance of the composite electrodes was smaller than the specific capacitance of the carbon electrodes. However, a chemical adsorption/desorption or redox reaction occurred on the surfaces of the electrodes at a high scan rate; therefore, the capacitance was excellent. The charging and discharging curves of the NiO/MCMB electrodes at R.T. in a voltage range of \(-3\) to 3 V at a constant current density of 6 A\(\cdot\)g\({}^{-1}\) is shown in Figure 7a. The equivalent series resistance (ESR) of the devices was calculated according to the following equation [20]:  
\[ESR=\frac{iR_{drop}}{2\times I} \tag{2}\]  
where \(I\) (ampere of 0.03 A) is the discharge current, and \(iR_{\text{drop}}\) (volts of 2 V) is defined as the electrical potential difference between the two ends of a conducting phase during charging/discharging. The ESR is calculated as 33.3 \(\Omega\) (\(I\) = 0.03 A, \(iR_{\text{drop}}\) = 2 V). The electrochemical stability of the NiO/MCMB electrodes was also studied. Figure 7b shows the composite electrode test charge/discharge efficiently (\(\eta\)) at 5000 cycles. The following equation was used to calculate the charge/discharge efficiently:  
\[\eta=\frac{Q_{dch}}{Q_{ch}}\times 100\%=\frac{t_{dch}}{t_{ch}}\times 100\% \tag{3}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scan rates** & **MCMB** & **NiO/MCMB** & **WO3/MCMB** \\ \hline
**25 mV/s** & 221.5 & 187.2 & 194.8 \\
**50 mV/s** & 186.4 & 146.5 & 160.4 \\
**100 mV/s** & 150.1 & 119.6 & 122.7 \\
**200 mV/s** & 101.7 & 94.1 & 84.3 \\
**250 mV/s** & 71.4 & 84.2 & 72.5 \\
**500 mV/s** & 26.7 & 41.2 & 39.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The specific capacitance (F\(\cdot\)g\({}^{-1}\)) of the composite electrode at various scan rates. Figure 6: Specific capacitances of the carbon electrodes and the composite electrodes at various scan rates. where \(Q_{dch}\) is the discharge capacity (Coulomb), \(Q_{ch}\) is the charge capacity, \(t_{dch}\) is the discharge time, \(t_{ch}\) is the charge time. The results show that the first charge/discharge efficiency was 93%. As the charge/discharge times increased, the charge/discharge efficiency gradually increased. The charge/discharge efficiency was near 100% after 10 cycles. After 5000 cycles, good adhesion was retained between the electrode material and the substrates.
# Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach
## 4 Proposed Approach
### Automated Labeling Stage  
#### 4.1.1 Labeling Training Data  
In many environments, including the CERN storage deployment, a precise definition or label for \"failed\" disks is not a priori available but needs be derived from other existing information. At CERN, we have put in place a probe that several times per day determines the presence of all disk drives (identified by their serial numbers) in all server or compute nodes. Changes between these configuration snapshots are used to track interventions by the data center operations team, which usually take place for one of the following reasons: A disk is  
* removed/disabled after repeated I/O or self-test errors;
* retired/replaced at the end of its planned lifetime;  
Figure 1: Data Collection and pre-processing phases.  
Figure 2: DOE, modeling and prediction phases.  
* relocated within the data center(s) (e.g., due to floorspace reorganization).  
In Figure 3, the hard disk \(d_{2}\) is an example of temporary suspension during the interval \(I_{\text{g}}=[g_{1};g_{2}]\). A typical scenario of a host decommissioning is also shown in the Figure 3. All drives belonging to a host that has to be shut down are drained and removed. Due to the high disk reliability, it is quite unlikely that more than one disk would fail at the same day on the same host. Thus, whenever multiple shutdowns are observed (e.g., \(d_{1},d_{2},d_{3},d_{4}\)) in a small day interval, it is necessary not to classify these disks as replaced disks due to a failure.  
As there is no available label classifying a disk as broken, we propose a heuristic approach based on the following definition for \"disks at risk\", which is aimed to identify disk replacements due to individual device failures from the larger amount of disk replacements due to other data center operations (e.g., host or disk model replacements or retirement campaigns).  
**Definition 1**.: _A disk is classified as broken if it has been removed and all other hard disk belonging to the same host machine continue to operate nominally._  
In other words, replaced hard disks are classified as broken if and only if neither if other HDDs are removed in the same day nor if the host machine is decommissioned within 30 days (e.g., \(d_{k}\) at \(t_{k}\) instant in Figure 3).
## 3 Proposed Algorithm
### CIM-Based ART with Edge and Age
#### 3.3.4 Node Learning and Edge Construction
Create the new node as \(\mathbf{y}_{K+1}=\mathbf{x}_{l}\). Calculate the kernel bandwidth \(\sigma_{K+1}\) by (8) and (9). if\(K=\lambda/2\)then
5 Calculate the vigilance parameter \(V_{\text{threshold}}\) by (5). 6
7 else
8 Search the indexes of winner nodes \(k_{1}\) and \(k_{2}\) by (10) and (11), respectively. Update the edge age \(a_{(k_{i},j)}\) by (17). if\(a_{(k_{i},j)}>a_{\text{max}}\)then
9 Delete the edge. 10 if\(V_{k_{1}}>V_{\text{threshold}}\)then
11 Initialize a counter \(M_{K+1}=1\). Update a set of counter \(\mathcal{M}\leftarrow\mathcal{M}\cup M_{K+1}\). Create the new node as \(\mathbf{y}_{K+1}=\mathbf{x}_{l}\). Calculate the kernel bandwidth \(\sigma_{k+1}\) by (8) and (9). 12
13 else
14 Update the state of \(M_{k_{1}}\) by (18). Update the state of \(\mathbf{y}_{k_{1}}\) by (19). if\(V_{k_{2}}\leq V_{\text{threshold}}\)then
15 Update the state of neighbor nodes \(\mathbf{y}_{j}\) by (20). Create a new edge \(e_{(k_{1},k_{2})}\) between \(\mathbf{y}_{k_{1}}\) and \(\mathbf{y}_{k_{2}}\). 16
17 if the number of data point inputs \(l\) is multiple of a topology adjustment cycle \(\lambda\)then
18 forall\(k\in 1,2,\ldots,K\)do
19 if\(\mathbf{y}_{k}\) does not have any edge then
20 Remove \(\mathbf{y}_{k}\) from \(\mathcal{Y}\). 21
22
23
24 return the CAEA model. ```  
**Algorithm 2**Learning Algorithm of CAEA [16]  
To visually understand the functionality of CAEA, Figure 4 shows a self-organization result for a synthetic dataset. The dataset consists of six distributions, and each distribution has 15,000 data points. Each data point is given to CAEA in random order and only once. The parameters of CAEA are set as \(\lambda=50\), and \(a_{\text{max}}=50\). In Figure 3(b), CAEA successfully generates six distributions by topological networks (i.e., nodes and edges).
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 3 Entanglement Generation and Distribution
### Memory and Channel Depolarization  
Depolarization describes an application of isotropic noise to a quantum state and can be interpreted as a uniform contraction of the Bloch sphere. Depolarization is a form of time decoherence that can be modeled mathematically as the quantum channel  
\[\mathcal{D}_{T}(\rho)=e^{-Tt}\rho+(1-e^{-Tt}\frac{\mathds{1}}{2}), \tag{6}\]  
where \(T\) is the decoherence decay rate and \(\mathds{1}\) is the identity matrix. The depolarizing channel can be seen as a particular case of a single-qubit Pauli channel, and its action of a maximally entangled state is described by  
\[\mathds{1}\otimes\mathcal{D}_{T}(\Phi^{+})=e^{-Tt}\Phi^{+}+(1-e^{-Tt}\frac{ \mathds{1}}{4}). \tag{7}\]  
Depolarization can also affect the qubits transmitted through a physical channel, namely photons in an optic fiber, and in this case, this noisy transmission can be represented by the formula  
\[p_{\text{depol}}=1-(1-p_{\text{in}})10^{-L^{2}\cdot\eta/10} \tag{8}\]  
where \(p_{\text{in}}\) denotes the probability of photon loss upon entering the fiber, \(L\) (Km) is the channel length, and \(\eta\) (dB/Km\({}^{2}\)) is the unit channel attenuation. \(p_{\text{depol}}\) is the probability that the qubit (the carrier photon) becomes completely depolarized (\(\mathds{1}2\)) and is useless for communication or computing.  
In this paper, memory decoherence is modeled through a depolarization channel that exponentially disturbs the quantum state \(\rho\), as above in (6), whereas channel depolarization exhibits a probabilistic behavior, which is not that of (8). We introduce our probabilistic model for depolarization in Section 5.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 3 The Proposed Method
### Rule Mining and Reasoning
#### 3.2.2 Rule Reasoning  
The logical rule set is denoted as \(\mathcal{F}=\{(f,c)\}\), where \(f\) takes the form \(\forall x,y,z:(x,r_{p1},y)\wedge(y,r_{p2},z)\overset{\triangle}{=}(x,r_{c},z)\). In this rule, \(x\), \(y\), and \(z\) represent variables for different entities, while \(r_{p1}\), \(r_{p2}\), and \(r_{r_{c}}\) represent different relations. The left side of the \(\Rightarrow\) symbol represents the premise of the rule, which consists of multiple connected atoms. On the right side, we have a single atom representing the conclusion of the rule. These Hore rules are closed [25], meaning that the intermediate entity is shared by the continuous relations, and the first and last entities of the premise appear as the head and tail entities of the conclusion. Such rules can provide interpretive insights. A rule's length is equal to the number of atoms in the premise. For example, \(\forall x,y,z:(x,born\_in,y)\wedge(y,city,z)\overset{0.8}{\Rightarrow}(x,national,z)\) is a length-2 rule. This rule reflects the reality that, most likely, a person's nationality is the country in which he or she was born. The rule \(f\) has a confidence level of 0.8. The higher the confidence of the rule, the more likely it is to hold.  
The reasoning procedure consists of instantiating the rule's premise and obtaining a large number of fresh conclusion triples. One of the most common approaches is forward chaining, also known as the match-select-act cycle, which works in three-phase cycles. Forward chaining matches the current facts in the KG with all known rule premises to determine which rules can be satisfied in one cycle. Finally, the selected rule's conclusions are derived, and if the conclusions are not already in the KG, they are added as new facts. This cycle should be repeated until no new conclusions are derived. However, if soft rules are used, forward reasoning will lead to incorrect conclusions. Therefore, we run one reasoning cycle in every iteration.
# A Real Case of Implementation of the Future 5G City
## 2 5G Network Architecture
### 5G Physical Infrastructure
#### 2.1.2 Roll-Out Phase--NSA Architecture  
During the first period, the roll-out phase focuses on NSA architecture: several BBUs with one or more AAUs are deployed on specific areas of the two cities and connected to the LTE-CN in order to evaluate the transport network, including fronthaul and backhaul links. Transport network is managed via software, thus allowing a flexible reconfiguration of all the network elements using the FlexE [22] standard that allows for flexible use of the links capacity, making the physical layer transparent to the service layer. Connections of BBUs is provided using 100G Ethernet rings, while connections with the core network is supported by using optical links.  
The NSA 5G NR utilizes the existing LTE radio and CN as an anchor for mobility management and coverage while adding a new 5G carrier. More specifically, in this case, the 5G NR is connected to the LTE-CN by means of the LTE-RAN. This configuration reflects the 3 GPP NSA deployment scenario Option 3x [23]. In this configuration, even if the 5G coverage is initially jeopardized, services can be provided thanks to the 4G coverage. Hence, there is a strong interest in evaluating the coexistence and the integration with the already existing 4G network and the portability of the services that currently can be provided by means the 4G network. Moreover, dual-connectivity tests are ongoing.  
Although Network slicing and SDNs are not officially part of the tests planned by the Ministry of Economic Development, the NSA phase represents a precious chance to test network slicing and a software-defined network (SDN) [24] technology that allows one to control and coordinate different network slices on a common physical infrastructure.  
During the NSA phase basic network functions are validated and related KPIs are as follows:  
* _control plane latency_: time to switch from an IDLE or INACTIVE state (low battery consumption) to an ACTIVE state (data transfer). Expected value: \(\leq\)20 ms for URLL and eMBB scenario. Reference International Telecommunication Union (ITU) 5G requirement: \(\leq\)20 ms.
* _user plane latency_: time to successfully transmit an application-layer packet/message, from the input level of the protocol 2/3 to the output level of the protocol 2/3, for both the uplink (UL) and downlink (DL) radio interface. Expected value: \(\leq\)4 ms for eMBB scenarios. Reference ITU-5G requirement: \(\leq\)1 ms user plane latency for URLLC and \(\leq\)4 ms for eMBB.
* _cell peak data rate_: maximum theoretical cell data rate. Expected value: \(\geq\)4 Gbps (DL), 550 Mbps (UL). Reference ITU-5G requirement: \(\geq\)10 Gb/s cell peak data rates for eMBB.