esa-sceva/satcom-chunk-collection huggingface dataset:
# A ROTI-Aided Equatorial Plasma Bubbles Detection Method
## 4 Discussion  
In order to adequately extract the TEC depletion by EPBs, the key is to adequately fit the background TEC data during the period of the plasma bubble event. Compared to the IBS method, the ROTI-aided method in this study can more adequately fit the background TEC data during the period of the plasma bubble event. For the IBS method, a partial linear fitting is employed to fit the background TEC during the period of EPB event [17]. In this method, the TEC data during the period of EPB event are not used in the fitting process. Therefore, the effect of EPB on the fitting is removed. However, it is well-known that the variation of the background TEC is not linear. For a short duration, the linear fitting error is small (see Figure 6); the linear fitting error increases with duration (see Figure 7). Therefore, the low-order polynomial fitting is a more suitable fit for the background TEC data. Then, the TEC depletion by the EPB can be adequately extracted (see Figure 7).  
It is worth noting that the magnitude of the polynomial order for the ROTI-aided method should not be too high, as in the case of the sixth order. For the EPB with a long duration, the partially fitted TEC may be unreliable with a high-order polynomial. Figure 8 presents the above two cases with a third-order and a sixth-order partial polynomial fitting strategy, respectively. As shown in the figure, for short-duration EPB, both third-order and sixth-order partial polynomial fitting results are suitable. However, for a long-duration EPB, the sixth-order partial polynomial fitting result is not reliable.  
As shown in Equation (1), the effect of satellite elevation is calibrated when computing the ROTI value as suggested by [PERSON] et al. [22]. So, the magnitude of the ROTI values is less than that without the satellite elevation correction [19]. However, it will not affect the extraction of EPB signals because we only used the ROTI values to seek the EPB occurrence time. Certainly, the parameters of the ROTI-aided EPBs detection method should be adjusted when using ROTI values without satellite elevation correction. In addition, the ROTI values were different for various sampling rates [24]. The effect on the detection method was similar to that of the satellite elevation correction.
## 4 Conclusion  
In summary, we have proposed and experimentally demonstrated an all-optical modulation-format and high bit-rate transparent wavelength conversion at the 40 Gbit/s using a QD-MLL as a dual pump source and FWM in an XN-SOA. A performance comparison of the OOK and DPSK signals using the proposed wavelength converter is reported. We reported error-free signal conversion over 16 channels with 100 GHz spacing for both OOK and DPSK with moderate power penalty (less than 2 dB for OOK and than 3 dB for DPSK). The high conversion efficiency values were obtained at relatively low input pump powers over a wide wavelength range. We demonstrated that optical SOA wavelength converters, transparent to bit rates and modulation formats, are very good and viable solutions for next generation optical communication networks. The significant sidelobes of the DPSK spectrum (relative to OOK), leading to a modest power penalty (1 dB) for DPSK relative to OOK.
## 4 Management for the WSN
### Fault Management  
Once a WSN is deployed, it works for a long time, during which a node or a link may break down, affecting the normal operation of the WSN. Although the WSNs based on the ZigBee protocol have a self-recovery function, it doesn't work in the case of hardware faults [22]. Hence, a fault management scheme is necessary to diagnose the network faults and repair them in time, so as to ensure the normal operation of the WSN.  
As mentioned in Section 2.2, the mesh WSN for an underground coal mine is composed of two layers. In the backbone network, the routing nodes communicate with the GMDC via the sink node; in the perception network, the sensor nodes communicate with the sink node via the routing nodes. Based on this hierarchical structure, a hierarchy and cluster machinery is adopted to manage the WSN. The WSN is divided into two layers to manage it. In the backbone network, the sink node is the Cluster Head (CH), the routing node is the Cluster Member (CM); in the perception network, the routing node is the CH, while the sensor node is the CM. The CH is mainly responsible for managing the CMs, reporting the environmental information and the network status within its cluster to the upper network, and forwarding the commands from the upper network. By using this hierarchy and cluster machinery, management tasks are decentralized, realizing effective management for the WSN.  
In the process of the WSN gathering the environmental information periodically, the information gathered by CMs must be reported via the CH to the upper network. Therefore, in each cycle, the CH knows which CMs have reported and which not. If the CH does not receive information from a CM twice, it considers that the CM may be broken. In order to determine whether the CM is really broken, the CH sends a state request command to the CM, and starts a Tcm timer. If the CH receives a reply from the CM within Tcm, it means the CM is normal; otherwise, the CH considers that the CM is really broken, and reports to the GMDC to inform the user of the failure node.  
Because a sensor node can only communicate with its parent routing node, if the parent node fails, the sensor node is isolated from the sink node even if it is normal. Based on this, it is designed that if a sensor node does not receive an acknowledgement frame (ACK) from its parent node N (e.g., N = 3) times, it sets the transmitting power to maximum, and then tries to communicate with its parent. If it still fails, it considers the parent has failed, and scans the channel to rejoin the network.  
According to the network status information that the GMDC receives, the failed nodes can be known and the causes can be diagnosed. Based on the location scheme introduced in Section 4.2,it is easy to locate the corresponding failure node and then remove the fault. After failure recovery, the node should be put back in its place, and its physical address should stay the same, so that the node's network address and neighbor table can be the same as before after it joins the network again, guaranteeing the stability of the network topology.
# Differential Signalling in Free-Space Optical Communication Systems
## 2 Differential Signalling Scheme
To recover the transmit bit stream, the optimal DTL should be set to the value given by Label (6a), with the variance given in Label (6). In the electrical domain, \(S\) is represented by two distinct signal levels of \(v_{\mathrm{low}}\) and \(v_{\mathrm{high}}\) corresponding to bits \"0\" and \"1\", respectively. For \(\overline{S}\) bits, \"0\" and \"1\" are recognised as \(v_{\mathrm{high}}\) and \(v_{\mathrm{low}}\), respectively. This means that, for the link 1 in Figure 1, the received electrical signals corresponding to bits \"0\" and \"1\" are \(v_{\mathrm{low}}\eta h+n_{1}\) and \(v_{\mathrm{high}}\eta h+n_{2}\), respectively. Therefore, the corresponding received differential signals for bits \"0\" and \"1\" are \(\Big{(}v_{\mathrm{low}}-v_{\mathrm{high}}\Big{)}\eta h+\Delta n\) and \(\Big{(}v_{\mathrm{high}}-v_{\mathrm{low}}\Big{)}\eta h+\Delta n\), respectively. Note that the difference between the two bits is twice that of a single link, so without the loss of generality we replace the levels for bits \"0\" and \"1\" with \(2v_{\mathrm{low}}\eta h+\Delta n\) and \(2v_{\mathrm{high}}\eta h+\Delta n\), respectively, and substitute the subtraction of channel responses by \(h_{\mathrm{DS}}\), and rewrite Label (4) as  
\[v_{t}=\eta h_{\mathrm{DSS}}S+\Delta n. \tag{7}\]  
The BER expression for a NRZ-OOK FSO link with an equiprobable data transmission condition is \(P_{\epsilon}=0.5(P(e|1)+P(e|0))\), provided that \(P(e|0)\) and \(P(e|1)\) are the conditional probabilities given as  
\[P(e|0)=P(e|1)=\int\limits_{0}^{+\infty}f_{h}(h)Q\bigg{(}\frac{\eta h}{\sqrt{2N _{0}}}\bigg{)}dh, \tag{8}\]  
where \(Q(\cdot)\) denotes the Gaussian \(Q\)-function defined as \(Q(x)=\int_{x}^{+\infty}\exp\bigl{(}-t^{2}/2\bigr{)}dt\), \(f_{h}(\cdot)\) is related to the channel condition, and the electrical signal-to-noise ratio (SNR) \(=(\eta h)^{2}/2N_{0}\). In the rest of the paper, we will show that, under different channel conditions, in an FSO system with DSS, the average of DTL is always zero and the variance of DTL has the minimum value if the channels are highly correlated. In addition, for highly correlated channels condition, we derive the BER expressions for different channel fading conditions. Figure 1: A schematic block diagram of differential signalling scheme (DSS). The labels OS, BC, BS, OR, OF, and TIA are optical source, beam combiner, beam splitter, optical Rx, optical filter, and transimpedance amplifier, respectively. \(T\) is the bit duration.
## 4 Performance Metrics  
### Performance Metrics for Emergency Packets  
#### 4.1.1 Emergency Packets' Blocking Rate  
When an emergency packet arrives, if other emergency packets occupy the CH at this time, the incoming emergency packet will be blocked. Emergency packets' blocking rate \(\beta_{1}\) is expressed as follows.  
\[\beta_{1}=\sum_{x=1}^{K+1}\pi_{x,1,1}\bar{s}_{1}p_{1} \tag{19}\]
## 7 Performance Evaluations
### Results and Discussion
#### 7.2.1 QoS Improvement with Heterogeneous Data  
In an edge intelligence system utilizing DML, QoS is primarily determined by two factors: training latency and model test accuracy. An improvement in QoS is achieved either by reducing the time required to produce a deployable global model or by attaining a global model with higher test accuracy within the specified latency constraints.  
Figure 5 shows a training latency reduction by the proposed Algorithms 1 and 2. The test accuracy is calculated by averaging global model test accuracy values across 20 smartphones. An observation related to the CIFAR-10 dataset is that the global model converges smoothly and quickly to 90% accuracy using our approach. Latency is reduced by more than 25% compared with benchmarks. However, an additional observation is that the test accuracy of our proposed protocol and FedProx is relatively close when \(\alpha=10\). Therefore, our solution may not boost test accuracy significantly when local data distributions are more homogeneous.  
Figure 6 demonstrates a more challenging scenario using the CIFAR-100 dataset, where the global model is trained within a time constraint \(T_{n}\) to classify testing instances into 100 distinct categories. A notable improvement in test accuracy is achieved by our approach, while the benchmarks are below 50% accuracy and are thus not usable. Therefore, the model utility is shown to be enhanced. We also remind the reader that it is very challenging to reach a test accuracy close to 100% on CIFAR-100 in heterogeneous networks where \(\alpha=0.5\). Although model design is not the focus of this paper, an advanced model structure can be considered atop our solution for improved model utility.  
Compared with the benchmarks, our proposed Algorithm 2 utilizes decentralized data valuation to assess actual contributions. Therefore, local weights trained from a dataset that closely resembles an IID dataset have larger summation weights in aggregation. As mini-batch SGD is designed for training on IID data, our proposed scheme can make mini-batch SGD function well under non-IID data settings.  
Figure 5: Training latency reduction: proposed approach shows more than 25% faster convergence speed to reach 90% test accuracy on CIFAR-10 dataset.
## 4 Single Level Equivalent Linearization
### Simulations and Discussion
Also, the negative price that should be paid due to the load reduction is low. Therefore, the aggregator would not obtain substantial profit from taking part in the negative balancing market. So, it offers high prices to avoid profit losses. Figure 11: Expected positive balancing procurement for different levels of \(\beta\). Figure 12: Expected negative balancing procurement for different levels of \(\beta\). Figure 10: Expected DA procurement for different levels of \(\beta\). From 3:00 to 6:00 and 21:00 to 24:00, the understudy aggregator offers lower prices because it buys the energy from the DA market at lower price rates. During these hours, the aggregator manages to provide a high amount of energy from DA to benefit from low prices (see Figure 10). Moreover, during the working period (i.e., 7:00-21:00), as mentioned before, the aggregator mostly participates in the positive balancing market. Also, the aggregator participates in the negative balancing market and obtains adequate revenues due to the load reduction. So, its low offered price would be compensated with the earnings from the negative balancing market. In the working period, although it purchases energy with higher prices, it suggests cheap selling prices to keep its clients and compete against its rivals, especially aggregators 1 and 2, which offer low prices in this period. Also, during this period, the aggregator participates in the negative balancing market and could achieve some money due to load decrement. In this period, although the negative balancing prices are high, the aggregator does not participate in the negative balancing market, since the requested load is low. The EVs' demand supplied by the aggregators during the whole day is illustrated in Figure 14. It is observed that when the rivals offer lower prices and the price offered by the understudy aggregator is high (i.e., 1:00-2:00), the total demand is supplied by other rivals. On the other hand, at hours that the aggregator's price is the lowest value (3:00 to 6:00 and 21:00 to 24:00), the total demand is supplied by the understudy aggregator. However, in the working period, the aggregator contributes to supply a high portion of demand. As it is observed, rivals 1 and 3 supply the demand only in some of the hours. Generally, it should be noted that EV owners select the most competitive aggregator to purchase energy which leads the aggregator to compete against other aggregators that offer similar services. Figure 14: EVs’ demand share during the whole day. Figure 13: Price offered to the EV owners by the aggregators.
## 2 Materials and Methods
### Functional Connectivity Analysis  
Using functional connectivity analysis, we would like to examine the possible SMA-related functional connectivity changes induced by long-term driving. Considering that SMA can be divided into sub-regions with distinct cognitive functions, we first conducted a functional segmentation algorithm that we have previously proposed to divide SMA into sub-regions [36]. To be more specific, this segmentation algorithm established a two-level clustering procedure to obtain a consistent result across all subjects. The first level was the individual level, where functional connectivity (i.e., Pearson's correlation coefficients) between voxels in SMA (\(n\) voxels) and the whole brain was calculated as the data feature for each subject (denoted as \(x_{i}\)\((i=1,2,\ldots,n)\)), and the maximum margin criterion used in SVM was then applied to assign \(x_{i}\) a cluster label and a SVM classifier \(f(x)=\omega^{\prime}\phi(x)\) (\(\omega\) was the optimal separating hyperplane, \(\phi\) was the feature map; linear kernel was used in this study). After finishing the above clustering procedure for all subjects (\(j\) subjects), for each voxel \(i\)\((i=1,2,\ldots,n)\), a set of SVM scores was obtained (marked as \(s_{i}=\left[f_{1}\Big{(}x_{i}^{(1)}\Big{)},\ldots,f_{j}\Big{(}x_{i}^{(j)}\Big{)} \right]^{\prime}\)). The second level was the group level, where the data feature was the SVM scores \(s_{i}\)\((i=1,2,\ldots,n)\), and maximum margin criterion was then performed to assign \(s_{i}\)\((i=1,2,\ldots,n)\) to two clusters. Therefore, the final clustering result reflected the consistency of brain segmentation across different subjects (for more detail please see [36]).  
After performing functional segmentation algorithm, the SMA was divided into two sub-regions, i.e., the pre-SMA and the SMA proper (see Figure 1). On this basis, we, respectively took the pre-SMA and the SMA proper as seed regions for functional connectivity analysis. [PERSON]'s correlation coefficients between the seed time series (the averaged time series of all voxels in the seed region) and time series of all voxels in the brain were calculated, and [PERSON]'s z-transforms were performed for normality. The obtained correlation maps across subjects were then used to identify significant driving-related functional connectivity changes, where two-sample \(t\)-tests (\(p<0.001\), uncorrected, and \(p<0.05\), Topological FDR corrected, respectively) were conducted to identify group differences [37]. Age, sex and educational level were included as covariates in the statistical models.  
Using the same procedure, we additionally evaluated the effect of precise segmentation of brain regions on functional integration analysis; namely, the group differences in functional connectivity between the entire SMA and the whole brain were also calculated.
## 4 Performance when Using CCIS in FSO Applications  
Figure 4 shows the block diagram of an FSO system based on CCIS-OCDMA architecture. This scheme of the recommended SAC-OCDMA configuration employs FRS coding with three simultaneous clients. In FSO, a variety of link lengths are examined at various visibilities. The encoder-containing transmitter is made up of a CCIS as a source that provides an OCDMA encoding process. To create the information signals, a pseudo-random bit generator (PRBG) and non-return-to-zero (NRZ) coding are employed in conjunction with an external Mach Zehnder modulator. Finally, an ideal multiplexer is employed to combine the signals. The FSO channel mixes the signals that are propagating. The receiver houses the decoding process, which includes FBGs and photodetectors. FBGs with the same bandwidth but different Bragg wavelengths are employed for signal decoding depending on the detecting strategies used. The photodiode is used to convert optical to electrical signals. A Bessel LPF with an order of four is used to cancel the interference, noise, and components. The proposed models are simulated using Optisystem software version 13 with parameters values given in Table 2[25; 26]. The proposed FSO model that uses CCIS at the transmitter is simulated using the parameters given in Table 2 using Optisystem software [23; 24; 25]. Figure 5 shows the BER versus the variation of the FSO range under clear weather conditions. As the range increases, the BER increases for all optical sources. The figure reveals an acceptable BER of the FRS code (CW laser) at an error free transmission code which is lower than that for LED sources and CCIS when similar parameters were applied. For comparison, at an FSO transmission distance of 9 km, the values of BERs are 1.86 \(\times\) 10\({}^{-7}\), 1.96 \(\times\) 10\({}^{-6}\) and 1.83 \(\times\) 10\({}^{-5}\) for the laser, CCIS, and LED, respectively. The eye diagram at an FSO range of 9 km is displayed in Figure 6. It shows a larger eye opening which assures the successful transmission of the information data at the receiver. Additionally, the effect of different haze levels on the performance of the proposed FSO model that uses CCIS is studied. As low haze weather causes a lower attenuation coefficient (1.537 dB/km) compared to that of medium (4.285 dB/km) and heavy (10.115 dB/km) hazes weather conditions, the information signal can propagate a longer range under it. Figure 7 shows the BER versus different ranges for our proposed model under different haze levels, and one can observe that at a BER of approximately 10\({}^{-6}\), the FSO ranges are 3.7 km under low haze, 2.5 km under moderate haze, and 1.5 km under heavy haze. So, as the FSO and the haze level range increase, the BER increases.
## 2 Problem Definition
### Cost Analysis  
#### 2.2.1 Travel Time and Travel Speed Considering the Congestion Level  
The travel speed and travel time of a MCV change with the change in the traffic congestion degree, which further leads to changes in vehicle management and use cost, transportation cost and mixed carbon emission cost. The following two formulas are obtained in reference [20; 40]. The travel time considering traffic congestion can be calculated according to Equation (1). The travel speed considering traffic congestion can be calculated according to Equation (2). Here, \(s\) indicates that traffic congestion is not considered. \(t_{ij}^{bs}\) represents the free-flow travel time--that is, the travel time of MCV \(k\) on road \(d_{ij}\) without consideration for traffic congestion. \(t_{ij}^{k}\) represents the travel time of MCV \(k\) on road \(d_{ij}\) while considering traffic congestion. \(v_{ij}^{bs}\) represents the free-flow travel speed--that is, the travel speed of MCV \(k\) on road \(d_{ij}\) without consideration for traffic congestion. \(v_{ij}^{k}\) represents the travel speed of MCV \(k\) on road \(d_{ij}\) while considering traffic congestion. \(TR_{ij}^{k}\) representsthe actual traffic flow of \(d_{ij}\). \(TR_{ij}^{d}\) represents the initial design traffic flow of \(d_{ij}\). \(TR_{ij}^{a}/TR_{ij}^{d}\) represents the congestion degree coefficient of \(d_{ij}\). \(\alpha\) and \(\beta\) represent the parameters of the BPR function. This function reflects the relationship between the travel time and the traffic flow of the road section, and the parameter values are recommended by the Bureau of Public Road (renamed to Federal Highway Administration) [40].  
\[t_{ij}^{k}=t_{ij}^{ks}.\left[1+\alpha\left(\frac{TR_{ij}^{a}}{TR_{ij}^{d}} \right)^{\beta}\right] \tag{1}\]  
\[v_{ij}^{k}=\frac{d_{ij}}{t_{ij}^{k}}=\frac{d_{ij}}{t_{ij}^{ks}\left[1+\alpha \left(\frac{TR_{ij}^{a}}{TR_{ij}^{d}}\right)^{\beta}\right]}=\frac{\frac{d_{ ij}}{t_{ij}^{ks}}}{\left[1+\alpha\left(\frac{TR_{ij}^{a}}{TR_{ij}^{d}} \right)^{\beta}\right]}=\frac{v_{ij}^{ks}}{\left[1+\alpha\left(\frac{TR_{ij} ^{a}}{TR_{ij}^{d}}\right)^{\beta}\right]} \tag{2}\]
# Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks
## 2 Experimental Work and Arc Waveform Analysis
### Experiment Setup  
A majority of faults that occur in power distribution lines are single-phase faults. Given that an arc is created due to contact with a conductor immediately or shortly after the fault, we implemented an experiment model for a single phase fault with a contacted conductor. The power distribution network in the case of the fault with arc is depicted in Figure 1. Two time-controlled switches were added to the fault model. Switch 1 was used to simulate the fault. Switch 2 was closed to calculate arc conductance after switch 1 is opened. The distribution network operates and consists of a 3-phase 4-wire distribution feeder. Feeder remote terminal units (FRTUs) were used to measure the phase voltage and phase current at fault location to observe the wave distortion due to the arc phenomenon and was installed in the protection device and switched to verify the proposed method. FRTUs measure 1-cycle data (e.g., voltage, current magnitude, phase angle difference) through the CT and PT installed at the corresponding points. For signal processing, CPU (TMS320F2812, Texas Instrument) and A/D converters (AD7606C, Analog Device) were embedded. RS422 communication protocol was implemented for parallel data transmission. The fundamental frequency and sampling frequency correspond to 60 Hz and 20 kHz (about 333 samples per cycle), respectively. The experimental setup for arc generation is shown in Figure 2. Artificial open conductor faults were experimented on in the Gochang PTC (power testing center) of the KEPCO. The Gochang PTC had a high-voltage power distribution network, and received 22.9 kV from a substation, but no load was connected to it. For this experiment, a single branch line in the middle of the line was grounded after the open conductor, and its behaviors were analyzed.  
Figure 1: Description for power distribution network in case of fault with arc [6].  
Figure 2: _Cont._