esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Synthesis of Porous Polymer
#### 2.2.2 Preparation of Dendrimer  
**Dendrimer-(COOMe)4:** In a solution of 0.7 g tetraallyl silane (3.64 mmol, 1 equiv.) in dry DMF (6 mL), a catalytic amount of 2,2-Dimethoxy-2-phenylacetophenone (DMPA) was added. The reaction mixture was degassed by bubbling argon for 5 min. After addition of 2.32 g of methyl thioglycolate (0.0218 mol, 4.4 eq.), the solution was degassed again (1 min). The reaction mixture was then exposed to UV light for 30 min. The solvent and excess methyl thioglycolate were then evaporated under reduced pressure. The crude product was dissolved in diethyl ether and extracted with 1% of NaOH solution (3\(\times\)), water (2\(\times\)), and finally brine (1\(\times\)). The organic layers were collected, dried over MgSO\({}_{4}\), filtered, and evaporated. Crude product was purified using column chromatography (PE:EtOAc 1:4 \(\rightarrow\) 1:2) and isolated as viscous yellow liquid (1.57 g, 87%).  
Data for **1**: NMR (CDCl\({}_{3}\)): \({}^{1}\)H NMR (400 MHz) \(\delta\) 0.56-0.61 (m, 8H, SiC\(H_{2}\)), 1.46-1.56 (m, 8H, SiC\(H_{2}\)CH\({}_{2}\)), 2.58 (t, \(\rightarrow\) 7.2 Hz, 8H, SiC\(H_{2}\)CH\({}_{2}\)), 3.31 (s, 8H,SCH\({}_{2}\)COMOe), 3.63 (s, 12H, _Me_). \({}^{13}\)C NMR (101 MHz) \(\delta\) 11.2 (SiCH\({}_{2}\)), 23.3 (SiC\(H_{2}\)CH\({}_{2}\)), 32.6 (CH\({}_{2}\)COOMe), 35.6 (SiCH\({}_{2}\)CH\({}_{2}\)), 52.0 (_Me_), 170.7 (CO), and \({}^{29}\)Si NMR (79 MHz) \(\delta\) 3.66.  
HRMS (ESI): \(m/z\) [M + H] \({}^{+}\) theoretical for [C\({}_{24}\)H\({}_{45}\)O\({}_{8}\)S\({}_{4}\)Si]\({}^{+}\):617.1761; found:617.1759.  
**Dendrimer:** In a 0.5 g of dendrimer-(COOMe)\({}_{4}\) (0.81 mmol), ethylenediamine was added under argon in large excess (10 mL). Reaction mixture was then stirred for 12 h (70 \({}^{\circ}\)C). Excess ethylenediamine was subsequently evaporated to obtain the product as viscous liquid (0.681 g, 98%).  
Data pro **4**: NMR (DMSO-\(d_{6}\)): \({}^{1}\)H NMR (400 MHz, H-H COSY): 0.55-0.59 (m, 8H, SiC\(H_{2}\)), 1.45-1.53 (m, 8H, SiC\(H_{2}\)CH\({}_{2}\)), 2.55 (t, \(\rightarrow\) 7.5 Hz, 8H, SiCH\({}_{2}\)CH\({}_{2}\)), 2.57 (t, \(\rightarrow\) 6.2 Hz, 8H, CH\({}_{2}\)NH\({}_{2}\)), 3.04 (td, \(\rightarrow\) 6.2, 5.7 Hz, 8H, NHCH\({}_{2}\)), 3.08 (s, 8H, CH\({}_{2}\)CO), 8.0 (t, \(\rightarrow\) 5.7 Hz, 4H, NH\({}_{2}\)). \({}^{13}\)C [\({}^{1}\)H] NMR (101 MHz, HSQC, HMBDC):11.3 (SiCH\({}_{2}\)), 23.5 (SiC\(H_{2}\)CH\({}_{2}\)), 34.3 (COCH\({}_{2}\)), 35.6 (SiCH\({}_{2}\)CH\({}_{2}\)), 41.2 (CH\({}_{2}\)NH), and 42.3 (CH\({}_{2}\)NH\({}_{2}\)). \({}^{29}\)Si [\({}^{1}\)H] NMR (79 MHz): \(\delta\) 3.56.  
HRMS (ESI): \(m/z\) [M + H] \({}^{+}\) theoretical for C\({}_{28}\)H\({}_{60}\)N\({}_{8}\)O\({}_{4}\)S\({}_{4}\)Si:729.3462, found: 729.3481.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### A Performance Comparison of the Various Flooding Mechanisms
#### 4.3.1 The Number of Valid Frames and Dissemination Time  
Figure 10a compares \(\overline{F}_{val}\) when \(N_{node}\) changed from 60 to 140. In the case of BASE, \(\overline{F}_{val}\) decreased from approximately 860 to 790 as \(N_{node}\) increased because of the broadcasting storm problem. On the other hand, the \(\overline{F}_{val}\) of DBF gradually increased as \(N_{node}\) increased. As a result, the \(\overline{F}_{val}\) of DBF was higher than that of BASE when \(N_{node}>80\). However, compared to the other mechanisms, the performance of DBF was not acceptable in terms of \(\overline{F}_{val}\). Except for BASE and DBF, the \(\overline{F}_{val}\)s of the other mechanisms were not much affected by the increase in \(N_{node}\) and these values much larger compared to BASE; the \(\overline{F}_{val}\)s were maintained at approximately 998 in the cases of PBF, DRBFR0, and the \(\overline{F}_{val}\) of CBF slightly increased from 932 to 981 when \(N_{node}\) was increased from 60 to 140. The notable results observed in Figure 10a were as follows: (i) due to the duplication suppression, the reliability of flooding was nearly immune to the change of node density; (ii) the \(\overline{F}_{val}\) of DRBFR0 kept strictly close to the ideal; and (iii) DRBFR0 outperformed CBF that used the optimal value of the counter threshold.  
Figure 9: The effect of \(\alpha\) on the performance of DRBF in terms of (**a**) the number of valid frames \(\overline{F}_{val}\) and (**b**) dissemination time \(\overline{T}_{dis}\).  
We can see in Figure 10b that the performance of PBF was significantly degraded in terms of \(\overline{T}_{dis}\), although it seemed to be ideal in terms of \(\overline{F}_{val}\). Compared to BASE, the \(\overline{T}_{dis}\) of PBF was greater by 2.8-4.8 times because the latter is designed to achieve high reliability by deferring the transmission of queued duplicate frames instead of deleting them. \(\overline{T}_{dis}\) increased almost linearly with \(N_{node}\). In all of the mechanisms, CBF mostly maintained the smallest value of \(\overline{T}_{dis}\), which was comparable to the \(\overline{T}_{dis}\) of DBF. Although the \(\overline{T}_{dis}\) of DRBF\({}_{\text{RQ}}\) was not the shortest due to the re-queuing scheme, it was smaller than BASE and PBF by up to 24% and 6.3 times, respectively.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN  
NB-Fi, Sigfox, and LoRaWAN networks operate in unlicensed ISM radio bands that impose limitations on the emitted power. For example, in Russia, NB-Fi, Sigfox, and LoRaWAN networks use the band of 868.7-869.2 MHz with the limit of 100 mW for emitted power.  
A typical NB-Fi, Sigfox, and LoRaWAN network has a \"star of stars\" topology and consists of end-devices (further related to as _sensors_), base stations (BSs), and a server. In the considered LPWANs, BSs are connected with the server via a broadband link or the server may be implemented on the BS. Sensors communicate with the server via BSs using wireless NB-Fi, Sigfox, or LoRaWAN links, while BSs act as transparent retransmitters between the sensors and the server. Sensors are not associated with a single BS, so any BS that receives a frame from a sensor redirects it to the server, while, in the reverse direction, the server chooses which BS transmits a frame to the sensor. NB-Fi specification is centered around local regulations in European countries, while Sigfox [21] and LoRaWAN specifications [22; 23] indicate radio parameter values for a set of countries. Therefore, in this paper, we consider European operational mode, since all of them--Sigfox, NB-Fi, and LoRaWAN--can operate in the same European band.  
Let us initially compare the nominal parameters of these technologies, i.e., their modulation schemes, frame formats, channel access rules, and operation modes.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm
### Reinforcement Learning Used in Routing Protocol
#### 3.1.3 Estimation Values Based on Rewards  
An initial value must be set for each node when the protocol starts, which is often called cold start. The RLRP initially defines all neighbors with a value of 0 when a source node has no route information towards a destination node. The available range of estimated values is defined as: [0, 100]. When the protocol starts the route discovery process the estimate values are set as follows:  
\[Q_{\mathrm{n}}=100/N_{\mathrm{hops}}, \tag{10}\]  
where \(Q_{\mathrm{n}}\) is the estimated value for destination IP towards neighbor n; \(N_{\mathrm{hops}}\) is the number of hops in which RREQ or RREP messages has traversed from the source to the destination node  
After the path discovery procedure ends all nodes in the network have the initial estimated values for all routes. According to the calculation presented in (10), the estimation value is initially defined based on the number of hops between the source and the destination. It can be defined that the RLRP uses an initial approach of the hop count metric, in which the routes with the least hop are chosen.  
However, afterwards the values be adjusted since the route with the least number of hops is not always the best one. For, a route may have the least number of hops but present an overloaded link or have nodes that present malfunctions. The adjustment is made according to the received reward value. The estimation value \(Q\) like described in Section 2.1 is calculated as follows:  
\[Q_{k+1}=Q_{k}+\alpha*[r_{k+1}-Q_{k}], \tag{11}\]  
where \(Q_{k+1}\) represents the new estimation value for the action; \(Q_{k}\) is the actual estimate value; \(r_{k+1}\) define the reward value obtained; \(\alpha\) represents step size parameter; and \(k\) is the current step number. Therefore, the estimated value as stated above is impacted by the reward value.  
In e-RLRP, the reward is associated with the successful delivery of packets. Then, in general, the local reward is given to the route that has the best rate of success in delivering packets, and the long-term reward is related to the global network performance by always looking for routes with the highest success rates. As explained in Section 2.1, the RL algorithm has to consider two approaches in order to obtain a long-term reward, the selection of actions that obtain the highest reward values or explore new actions that can generate even better rewards. For this decision task, the e-RLRP uses the Softmax method [60].
## 2 Theoretical Framework and Network  
The schematic diagram of the OAM transmission system shown in Figure 1 mainly includes three parts: the generation of LG beams, the propagation of beams under atmospheric turbulence, and the detection of modes at the receiving end. At the transmitting end, the laser generates a Gaussian beam first. This Gaussian beam is then converted into Laguerre-Gaussian (LG) beams carrying different single orbital angular momentum (OAM) modes through a spatial light modulator (Spatial Light Modulator, SLM) loaded with a phase mask. In this process, each OAM state is regarded as a data symbol, and the corresponding information is modulated into different modes. At the receiving end, charge-coupled devices (Charge Coupled Devices, CCD) are utilized to capture the intensity images of the distorted LG beams after being transmitted through the turbulent channel. Finally, they are sent to the modal classifier to output the category of OAM state, thereby achieving encoding and decoding of the optical signal.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 4 Simulation's Results
### Single Event Effect Simulations on the CP  
The current caused on a sensible node by an SEE can be modeled as a double exponential function [23] in Equation (5). In this work, the parameter model of Equation (6), which was taken from a previous high-frequency design we have done in the same technology [24], has been used:  
\[I(t)=Q\frac{e^{-\frac{t}{\tau_{1}}}-e^{-\frac{t}{\tau_{2}}}}{\tau_{1}-\tau_{2} }. \tag{5}\]  
\[\tau_{1}=200\div 400\ ps\,\ \ \tau_{2}=50\div 100\ ps\,\ \ Q=67\div 800\ fC, \tag{6}\]  
Figure 16: Phase/Frequency Detector’s layout.  
Figure 15: Charge Pump’s layout.  
where \(\tau_{1}\) and \(\tau_{2}\) are the constant times of the double exponential shape and \(Q\) is the charge released in the silicon substrate during a particle strike, which corresponds to a particle LET (Linear Energy Transfer) between 5 and 60 Mev-cm\({}^{2}\)/mg.  
The SEE analysis is focused on the most critical block, the CP, since the PFD adopts a TMR mitigation approach, the passive Loop Filter avoids the use of feedback loops and of active circuits, while the VCO was inherited from a previous SEE-tested designed block in [16]. Hence, double exponential current generators from the Cadence's analogLib library have been placed on every sensitive node of the CP (all the drains and sources of the MOSFETs that were not connected to the supply voltage), taking care of the direction of the current. Then these generators have been delayed to analyze their effect separately, and the output current has been measured in order to see how an SEE on each node of the CP affects the output current. Then, these results have been reported to the ADS behavioral model of the PLL to see how the PLL reacts to Single Event Transients (SETs) on the CP (see Figure 18).  
As shown in Figure 18, the PLL, which loses lock after an SET, is able to recover the locked state in less than 600 ns. The largest peaks, which are indicated in Figure 18 with the numbers 1 and 2, are the ones derived by an SET on the output nodes, as highlighted in Figure 19 with the corresponding numbers, because the charge injected into these nodes goes directly through the output node, charging or discharging the Loop Filter and then modifying the control voltage of the VCO.  
Figure 17: PFD/CP characteristic for different technology corners at 27 \({}^{\circ}\)C (**a**) and different temperatures in typical case (**b**).  
Figure 18: Output frequency of the ADS PLL model as function of time, for Single Event Transients (SETs) spaced 1 \(\upmu\)s apart, hitting every sensitive node of the CP and with a Linear Energy Transfer (LET) of 60 MeV-cm\({}^{2}\)/mg.
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology
### State Evaluation of Protection System Based on Matter-Element Extension
#### 3.2.1 State Evaluation Characteristic Quantities  
In the smart substation information processing platform, the information can be divided into incident-level information, general-level information, and forecasting-level information [25]. Among them, incident-level information such as protection activation, circuit breaker operation, etc. will directly enable fault diagnosis algorithms or start smart grid self-healing procedures once they appear, and are generally used as post-fault diagnosis and tracking algorithms. The general and forecasting level information appears more frequently and with lower priority, and is generally used only in the substation as a basis for post-fault maintenance, but the frequent occurrence of such information often indicates the tendency of deterioration of a part of the equipment. In this paper, the frequency of general and forecasting level information is used as an indicator for the state evaluation of the equipment.  
Figure 4: State Evaluation Process of Matter Element Extension Method.  
In this paper, the historical operation data of a 220 kV substation protection system in the recent five years (2016-2021) are collected, including SV/GOOSE state information, bus/line sampling value information, communication channel information, online operation information, etc., as well as the protection system SOE frequent action signal records, and equipment defect processing records. Combined with the equipment instructions provided by equipment manufacturers, the abnormal operating conditions of the protection system are studied. During the operation of the protection system device, a total of five types of warning signals were recorded, with a total of 77 signals. Some warning signals will cause protection function exit or device locking, such as software setting errors, board configuration error alarms, etc. Some warning signals have a clear direction to the fault, and the fault causes can be obtained by simple reasoning in the existing knowledge base, such as equipment or plug-in power failure alarms. In order to improve the efficiency of the algorithm, the above types of characteristic quantities were not involved in the subsequent study, and only nine alarm signals that occurred relatively frequently, could characterize the equipment defective faults, and whose fault-type was difficult to determine by simple inference, were selected as characteristic quantities, as shown in Table 2.
## 3 Metabolic Responses to Different Antibiotic Classes
### Quinolones and Oxidative Stress  
Quinolones, such as ciprofloxacin and levofloxacin, inhibit topoisomerases II and IV, resulting in double-strand DNA breaks [47; 48].  
However, their activity is amplified by extensive production of secondary ROS [49].  
Metabolic modulation can thus interfere with the drug's efficacy [50].  
In particular, suppression of the TCA cycle and the respiratory chain reduces mitochondrial ROS accumulation, limiting the secondary damage induced by quinolones [6; 7].  
In _Mycobacterium tuberculosis_ and _Salmonella enterica_, activation of the glyoxylate cycle represents a metabolic adaptation strategy under environmental and nutritional stress conditions [4; 51; 52].  
Although direct experimental evidence linking this pathway to a reduced NADH pool or to the mitigation of antibiotic lethality mediated by ROS is not yet available, it is plausible that rerouting of metabolic flux through non-decarboxylating anaperiodic pathways may contribute to lower reactive oxygen species generation, thereby influencing sensitivity to quinolones and other bactericidal antibiotics [51; 52].
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 3 Background
### OT WAN Device Risk Identification and Mitigation
Staying up to date with current threat intelligence is essential, as implied by both NIST CSF DE.DP-4 and ISA/IEC 62443 4-1 SR 1.8, which is necessary for effective threat management. Security awareness training, guided by NIST CSF PR.AT-1 and ISA/IEC 62443 2-4 SR 3.1, aim to reduce human errors [6, 19]. This is further supported by various NIST CSF and ISA/IEC 62443 guidelines in implementing resource budget management, compliance with regulations, business continuity, and backup solutions. In summary, assessing risks and mitigating risks for WAN devices in OT infrastructure involves a comprehensive approach guided by frameworks like NIST CSF 2.0 and ISA/IEC 62443. These frameworks provide detailed guidelines to identify vulnerabilities, minimize risks, and ensure robust security measures, contributing to the overall resilience and reliability of OT systems. Those frameworks provide clear guidelines about the identification of weaknesses, risk reduction, and ways to assure strong security levels among other resiliency features and dependability of OT systems. Table 3 summarizes OT WAN device risks identification and mitigations per framework. The identified risks and mitigations serve as the factors used in the proposed risk scoring calculation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Risks/Mitigations** & **NIST CSF** & **ISA/IEC 62443** \\ \hline \multirow{5}{*}{OT WAN Device Risk} & Vulnerability Risks & ID.RA-1 & 3-3 SR 7.7 \\ \cline{2-3}  & Attack Surface Risks & ID.AM-2 & 3-3 SR 7.3 \\ \cline{2-3}  & Zero Day Existing Risks & DE.CM-4 & 4-1 SR 1.4 \\ \cline{2-3}  & Incident Risk Impact when Down & ID.BE-5 & 2-1 SR 2.6 \\ \cline{2-3}  & Third-Party Dependencies & ID.SC-1 & 2-4 SR 1.1 \\ \cline{2-3}  & Hardware Failure Risks & PR.DS-4 & 3-3 SR 7.4 \\ \cline{2-3}  & Environmental Risks & PR.PT-5 & 3-3 SR 5.1 \\ \cline{2-3}  & Human Error Risks & PR.IP-9 & 2-4 SR 3.1 \\ \cline{2-3}  & Software Update Risks & PR.IP-12 & 3-3 SR 7.5 \\ \cline{2-3}  & Access Control Risks & PR.AC-1 & 3-3 SR 1.1 \\ \cline{2-3}  & Communication Network Risks & PR.PT-3 & 3-3 SR 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OT WAN device risks identification and mitigations per framework.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 2 Materials and Methods  
### Study Area and Field Measurements  
The 6 km \(\times\) 9 km study area is located in Estonia, in Rannu parish (26\({}^{\circ}\)13\({}^{\prime}\)10\({}^{\prime\prime}\)E, 58\({}^{\circ}\)14\({}^{\prime}\)17\({}^{\prime\prime}\)N), see Figure 1. The area is relatively flat and is predominantly used for agriculture. In total, 37 agricultural grasslands used to grow red clover (_Trifolium pratense_ subsp. _satitum_), alfalfa (_Medicago sativa_), timothy-grass (_Pheum pratense_), meadow fescue (_Festuca pratensis_), red fescue (_Festuca rubra_), and other species were used in the experiment. Six grasslands (further as G1 through G6) were monitored from May to September 2015 on a weekly basis, providing information about the vegetation height, wet and dry above-ground biomass, and soil moisture. The measurements were done using the transect method. Ten measurements were taken on a straight line every 25 m. Vegetation height was measured with a measuring tape, and the recorded value represents the height of the majority of vegetation interpreted visually by the field worker. Soil moisture in the upper 5 cm layer was measured using two hand-held conductivity probes: Delta-T ML2x and Extech MO750. All vegetation inside a 0.5 m \(\times\) 0.5 m square was cut and collected. The vegetation was weighed to measure the wet above-ground biomass. It was then dried and weighed again to provide the dry above-ground biomass.
## 2 Materials and Methods
### Feature Selection Method  
Feature selection (FS) is also known as the variable selection in statistics and machine learning (ML). FS is a process for selecting the most informative features to improve the performance of ML-based algorithms. FS is needed for the following reasons: (i) to simplify models to make them easy to interpret by readers [48]; (ii) to reduce overfitting and the complexity of problems the model [49]; (iii) to reduce the training time and cost [50]; (iv) to avoid the curse of dimensionality [51]; and (v) to improve the accuracy of ML-based models [52]. In this study, we used LR as an FS method [53; 54] to extract the most significant risk factors of the children with ADHD. LR is used as supervised learning in the community of ML. In statistics, LR is also used to extract the most informative features [36; 38; 41; 54; 55]. The LR-based feature extraction procedure is described as follows:  
LR is used when the output variable is binary (1/0) and the input variables may be discrete or continuous. LR evaluates the connection between the output and one or more input variables by estimating the probability of the logit function. The logit function is the linear combination of input variables (X) and output variable (Y) (here, ADHD), which can be represented as follows:  
\[\text{logit}(P_{j})=\log_{e}\left(\frac{P_{j}}{1-P_{j}}\right)=\sum_{i=0}^{r }B_{i}X_{i} \tag{1}\]  
where, \(P_{j}\) is the probability of children who have ADHD and takes a value, \(\text{Y}=1\), and \(1-P_{j}\) is the probability of healthy children and takes a value, \(\text{Y}=0\). \(B_{i}\) (\(i=0,1,\ldots,\text{r}\)), are the unknown parameters, known as regression coefficients that need to be estimated, where, r represents the total number of the input variables. The steps of LR-based FS method are as follows: (i) Write down the likelihood function; (ii) Estimate the regression coefficients by maximum likelihood estimator (MLE) and one can get easily odds ratio (ORs) by taking the exponent of the regression coefficients (ORs = exp(B)); and (iii) Test the regressioncoefficients using a normal/z-test and calculate the \(p\)-values. We select the features that correspond to regression coefficients with \(p\)-values less than 0.05 [53; 54; 55].