esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Modelling  
This section describes the research problems, and our primary goals are explained. Consider that a set of sensor nodes monitors a field \(F(A)\) for a time horizon \(T\). This monitored field is divided into \(N\) equal-sized zones. The set of zones is denoted by \(A=\{A_{1},A_{2},\cdots,A_{N}\}\). A time-varying weight metric is assigned for each zone \(i\in A\)where \(t\in T\). This weight metric specifies the significance of each zone's observations across horizon \(T\). (surveillance requirements).  
Prior studies were based on static and location-aware sensor nodes. Additionally, the data from the monitored field sensors is collected by using multiple sink nodes. Networks are represented using undirected weighted graph \(G\) (\(V\), \(E\)), where \(V\) is the nodes set, and \(E\) is the edges, where \(x\), \(y\in V\). Vertices represent sensor nodes, whereas edges indicate communication connections between them. An edge exists between nodes \(x\) and \(y\) only if they can communicate. Additionally, the MAC layer \(PRR\)[31] measures connection quality.  
Let us start with one of the main goals in this paper, which is the selection of the optimal sink for data collection that plays a vital role in conserving the sensor nodes' energy to extend the network lifetime. So, the selected sink node should meet the following restrictions:  
1. Provides the shortest transmission distance to minimize energy consumption.
2. Provides the minimum value for the proposed load metric to achieve balanced energy consumption.
3. Away from the danger zones to keep the path of the data transmission safe as much as possible.
4. Provides the lowest end-to-end latency for real-time data transmission and eliminates sensor node buffer overflow due to limited buffer capacity.  
The second goal of this paper is to shorten the communication distance along the routing path to save energy and make the network last longer. Additionally, energy management is considered while designing the proposed routing protocol. It also examines how to reduce environmental factors' influence on urgent data packet routing by avoiding hazardous zones and finding the safest paths to the sink. Real-time transmission of urgent data packets is discussed. Additionally, the issue of reliable routing paths is discussed. To attain this aim, the proposed route must fulfill the following criteria:  
1. Minimum communication distance.
2. Less likely to be cut off as a result of environmental reasons.
3. Maximum reliability.
4. Minimum end-to-end delay.
5. In order to establish a better energy balance, the nodes participating in the path have the highest value resulting from the new proposed energy load function.
## 3 Accuracy and Robustness Simulation of the Improved Modular Vector Method
### Analysis of Anti-Gross-Error Interference Ability  
The initial exploration values, exploration step size, and termination step size for the improved modular vector method remain consistent with those detailed in Section 3.1. In this analysis, the measured data are intentionally subjected to gross error, with a total of two gross errors introduced [37]. Specifically, two 200 mm gross errors are added at both ends, inflection points, and the maximum sinking points, respectively, for the purpose of parameter inversion using the improved modular vector method.  
Upon reviewing the inversion results (Figure 5), it becomes evident that the relative error of the improved modular vector method for inversion results with gross error at the inflection point is notably higher than that observed at both ends and the maximum sinking point. In general, when two 200 mm gross errors are introduced, the relative errors for the subsidence coefficient \((q)\), the main influence angle tangent value \((tg\beta)\), the propagation angle of mining influence \((\theta)\), and the sum of inflection point offsets \((S)\) all remain below 5.1%. This outcome verifies that the improved modular vector method possesses a certain  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & \(q\) & \(tg\beta\) & \(\theta\)(\({}^{\circ}\)) & \(S_{1}\)/m & \(S_{2}\)/m & \(S_{3}\)/m & \(S_{4}\)/m \\ \hline Empirical value & 0.90 & 2.14 & 78.0 & 14.00 & 14.00 & 14.00 & 14.00 \\ Inversion value & 0.91 & 2.18 & 76.7 & 13.75 & 13.75 & 13.25 & 12.90 \\ Relative error/\% & 1.11 & 1.87 & 1.67 & 1.79 & 1.79 & 5.36 & 7.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter inversion results.  
Figure 4: Inversion results of anti-random-error interference ability.  
degree of resilience against gross error interference. In practical observation scenarios, it can effectively mitigate the influence of human factors on inversion results.
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
## 4 Case Studies and Discussion  
Figure 3 shows the test distribution system, which is the modified version of feeder 4 at Bus 6 of Roy Billinton Test System (RBTS) [16]. The residential, commercial, and small industrial customer load points are indexed with \"RL\", \"CL\", and \"IL\", respectively. Each residential customer load point \"RL\" is assumed to have 79 customers with a peak load of 0.27 MW. Corresponding values for \"CL\" and \"IL\" are 7 and 0.5 MW, and 1 and 1 MW, respectively. Recloser, fuse, and automatic sectionalizer are indexed with \"R\", \"F\", and \"S\", respectively, in Figure 3. Times to failure and repair are characterized by exponential distributions. The permanent failure rate and average repair time for lines are taken to be 0.046 failures/yr-km and 5 h, respectively. Switching time for manual switches/sectionalizers is 0.5 h. The fuse replacement time is taken as 1.5 h.  
The line impedance and other short circuit parameters are taken from Reference [22]. Mean values of fault clearing time for fuse and reclosers are 50 ms and 300 ms with 10% standard deviation.  
Figure 4: Tree diagram of scenarios following a contingency.  
Ride-through capability for different customer categories is assumed as follows: sag magnitude of 0.85 and duration of 40 ms for commercial/industrial customers. Corresponding values for residential customers are 0.75 and 300 ms, respectively. The damage cost is a function of a customer category, outage duration, and average demand of a customer [16]. This work uses a sector customer damage function obtained from the Canadian Survey [16] as shown in Table 1 to estimate financial losses due to reliability events. The damage cost associated with sag event is assumed to be equal to that of momentary interruption which corresponds to the interruption duration of 1 min in Table 1. Damage cost data for sustained interruptions are determined using the data corresponding to interval of more than 1 min presented in Table 1. Results associated with customer financial losses in the presented studies complement information obtained from the reliability indices.
## 3 Results and Discussion
### Comparative Thermal Management System Properties  
Table 5 compares the main properties of the thermal management systems, considering panels 2 cm thick. A qualitative and quantitative comparison of the material performances can be represented by the thermal management ability (_TMA_) for a panel of thickness 2 cm, expressed as the normalized energy for surface unit [35].  
\[TMA=\Delta H_{m2}\cdot\rho\cdot\frac{V}{A} \tag{4}\]  
where \(V\) is the volume of a panel 100 \(\times\) 100 \(\times\) 2 cm\({}^{3}\), and \(A\) is the unitary area of 1 m\({}^{2}\). Preliminary panels of the three PCM compounds were prepared with a constant weight of 70 g and a constant surface dimension of 62 \(\times\) 62 mm\({}^{2}\). The resulting thickness (\(t\)) and thermal energy capacity (_TEC_) values, calculated according to Equation (5), can be considered peculiar parameters of the realized thermal management panels.  
\[TEC=\Delta H_{m}\cdot m \tag{5}\]  
where \(m\) is the mass of the panels, equal to 70 g for all the systems.  
RT44/EG14 clearly appears to be the best candidate in terms of the ability to subtract heat and have a high thermal conductivity. However, the melting temperature range plays a fundamental role in material selection; hence, RT35/EG14, even though it presents a lower thermal management ability, could be preferable for setting a lower temperature control. Outdoor tests to verify the PV cell performance will be necessary. MPA/EG14 had the worst ability to store heat, even when, considering the higher melting temperature range, the daily temperature peak was completely smooth.
# Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
## 2 Methods
### Fuzzy Tracking and Control Algorithm  
The block diagram of the fuzzy tracking and control algorithm is shown in Figure 3. For the \(k\)th frame, \(X(k)\), the prediction error \(e_{k}\) of a fuzzy algorithm was estimated as  
\[e_{k}\,=\,X(k)-\tau_{k-1}, \tag{1}\]  
where \(\tau_{k-1}\) is the threshold at the \(k\)-1 frame. The initial threshold value, \(\tau_{0}\), is a predefined value; it can be estimated by using the average power energy of first \(M\)-frames.  
Because the Mamdani inference model [20] combines the sets and rules for fast and simple calculation, it was selected with linguistic rules for defining the relation between the input and the output. The fuzzy set \(A\) (Figure 4) and \(B\) (Figure 5) were used in the fuzzification and defuzzification processes. The input range of fuzzifier and the output range of defuzzifier were from \(-R_{f}\) to \(R_{f}\) and \(-R_{d}\) to \(R_{d}\), respectively. Considering the performance and stability of a recognition algorithm in a microprocessor, the number of fuzzy sets was five [21]. The linguistic parameters of these five fuzzy sets were negative large (LN), negative small (SN), zero (ZE), positive small (SP), and positive large (LP). According to the fuzzy set calculations, the fuzzy inference rules were as follows.  
If \(e_{k}\in LN\) then \(\widetilde{e}_{k}\in LN\), if \(e_{k}\in\) SN then \(\widetilde{e}_{k}\in\) SN, if \(e_{k}\in ZE\) then \(\widetilde{e}_{k}\in ZE\),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Number of Frames** & **Period (ms)** & **Frequency (Hz)** & **Simulated Signal** \\ \hline
6 & 100.00 & 10.00 & 111000 \\
7 & 116.67 & 8.57 & 1111000 \\
8 & 133.33 & 7.50 & 11110000 \\
9 & 150.00 & 6.67 & 111110000 \\
10 & 166.67 & 6.00 & 1111100000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Frequencies for a screen with a refresh rate of 60 Hz.  
Figure 3: Structure of fuzzy tracking and control algorithm. The variable \(z^{-1}\) is a unit delay for the next step.  
if \(e_{k}\in SP\) then \(\widetilde{e}_{k}\in SP\), and  
if \(e_{k}\in LP\) then \(\widetilde{e}_{k}\in LP\),  
where \(e_{k}\) and \(\widetilde{e}_{k}\) are the input variable of fuzzy sets \(A\) and the output variable of fuzzy sets \(B\), respectively.  
A defuzzification process was then used to obtain a finite number as an output. In this study, using the center of gravity method [20], the output variable, \(e_{k}^{\prime}\), of fuzzy threshold could be calculated following  
\[e_{k}^{\prime}=\frac{\sum\limits_{i=1}^{n}{{S_{i}}\left({{e_{k}}}\right){B_{i} }\left({{\widetilde{e}_{k}}}\right)}}{\sum\limits_{i=1}^{n}{{S_{i}}\left({{e_ {k}}}\right)}}, \tag{2}\]  
where \(S_{i}\) is the membership grade of the \(i\)th premise in the inference rule, and \(B_{i}\) is the central value of the \(i\)th conclusion in the inference rule. The threshold value was then updated by  
\[\tau_{k}=e_{k}^{\prime}+\tau_{k-1}. \tag{3}\]  
Finally, the output of the control signal was 1 if \(X\left(k\right)\geq\tau_{k-1}\); otherwise, the output of the control signal was 0.
## 4 Experimental Section
### Genomic Library Construction  
The genome library was constructed by using _Bam_HI (to cut the vector) and _Sau_3 AI (to cut the genomic DNA) restriction enzymes (New England Biolabs, Beverly, MA, USA) that produce compatible overhangs. Since there are two _Bam_HI sites in the ASKA plasmid pCA24N-_ortT_ (_i.e._, one in the _N_-terminus linker of _ortT_ and the second downstream of _ortT_), the vector was modified by deleting the first _Bam_HI site in the pCA24N-_ortT_ plasmid via site-directed mutagenesis with the _Bam_HI-F/R primer pair (Table 3). The resulting plasmid, pCA24N-_ortT_-B (with a single _Bam_HI site) was digested with _Bam_HI and dephosphorylated with rSAP phosphatase (New England Biolabs, Beverly, MA, USA) to prevent self-ligation. BW25113 genomic DNA was partially digested with _Sau_3 AI and \(\sim\)2-4 Kb genome fragments were gel-purified and ligated into the linearized vector using T4 DNA ligase. The ligation mixture was transformed into BW25113 competent cells. The library construction was verified by sequencing three random clones (that were not challenged with IPTG),  
\begin{table}
\begin{tabular}{c c} \hline
**Purpose/Name** & **Sequence (5’ to 3’)** \\ \hline
**Verification of _Kan_ insertion/removal** & \\ _ortT_-up-F & ATGGATAAGGCAAAGTTGCTGTTTGATG \\ _ortT_-down-R & CCAATTGTGGCGAATTTTACTGTG \\
**qRT-PCR** & \\ _gyrA_-F & GTCATGCCAACCAAATTCCTAAC \\ _gyrA_-R & TCATCATCAATAATACGCCAAGACAAC \\ _ortT_-F & AATGCGCATTCTTATCCGCG \\ _ortT_-R & GAAAACTCATGGCCATGTTG \\ \hline \end{tabular}
\end{table}
Table 3: Cont.  
which had inserts mapping to different places in the genome (_i.e._, fragments including _bepA_, _viaU_, and _elfG_). The average insert size of the library was 622 bp found by digesting the plasmids obtained from 25 random clones with SapI (cuts near the origin of the plasmids). The library was then screened by growing the clones with 1 mM IPTG to induce _ortT_ expression. Surviving clones (\(\sim\)0.25% of the library) that grew in presence of IPTG were analyzed by digesting their plasmids with _A/f_III which cuts within the _ortT_ coding region; none of the plasmids were cut by _A/f_III whereas the plasmids could be digested with _SapI. Moreover, the linearized plasmids were shorter compared to the plasmid that contains _ortT_ (pCA24N-_ortT_-B) which indicates the toxin gene was missing in the survivor plasmids. Approximately, 21,314 clones were screened in this experiment; hence, the genome coverage was 2.9 times (_i.e._, 21,314 clones \(\times\) 622 bp/4.6 Mbp).
## 4 Results and Discussion
### Photochemical Oxidant Formation  
Concerning photochemical oxidant formation (POF) seen over the vehicle's lifetime, Figure 5 shows the significant impact of diesel technology originating from the tank-to-wheel phase, as its combustion inherently causes high emissions of NO\({}_{\mathrm{x}}\). By including real-world driving emissions (RDE), the'struggle' for European car manufacturers to control NOx emissions is emphasized. Disregarding the dominance of diesel powertrains for POP, we see that the higher the battery capacity becomes, the higher the impact on POP gets. Thus, the petrol-fueled plug-in hybrid performs worse than the conventional petrol car, although the latter's impact would be far higher if it were diesel-fueled. Linked to the battery size are the vehicle weight and the electric consumption, which is reflected in the well-to-tank emissions for the electrified powertrains. Thus, the 90 kWh EV once more presents a POP contribution that is nearly twice what is reported for the 30 kWh model. Principal sources for POP among the feedstocks for electricity production are coal, gas, and oil, indicating the potential reductions if the more renewable energy sources would be applied. Considering the trailer combinations, we report a very similar POP contribution when compared to the 60 kWh EV, while representing about 35 percent lower POP emissions when compared to the 90 kWh EV. Compared to the PHEV, the occasional application of a generator or a battery trailer generates 9 to 14 percent higher POP contributions, respectively.  
If we were to assess only the POP emissions caused locally, the scope narrows down to a well-to-wheel analysis for Belgium, for which we consider the earlier mentioned refinery-to-tank (RTT) emissions for the contributions occurring upstream the use phase. Figure 6 represents the results of this exercise and shows the potential for EVs in strategies to bring down local emissions without ignoring the impact of manufacturing their powertrains. Locally, the TTW phase of the generator trailer makes it perform like the PHEV while contributing roughly 60 percent more to photochemical oxidant formation when compared to the 90 kWh EV. Substituting the generator with an additional battery pack would level its impact with that of the 60 kWh EV, or about 40 percent less than what is reported for the 90 kWh EV. If we compare the battery trailer combination to the PHEV, local POP  
Figure 4: Impact per kilometer on climate change considering the entire LCA.  
contributions for the former end up to be two-thirds less. No POF originates from tyre, brake or road abrasion.
## Materials and Methods
### 5G-BS Interference Model  
A detailed examination of 5G-BS effect on satellite-received signals are provided in the following analysis, which considers theoretical and experimental evidence. Figure 1 shows the design scheme of the interference model via 5G-BS to FSS-ES. The desired received signal power DL from the satellite at FSS-ES is denoted by dRSS. The DL-received signal power from the 5G-BS at the user equipment (UE) is denoted by iRSS. The elevation angle of the received signal of the BS is denoted by 05G-BS, and the received signal of FSS-ES is 0 FSS-ES. The height of the BS is h5G-BS, and the height of FSS-ES is hFSS-ES, as shown in Figure 1. The worst case is when the 5G-BS is using sectorial antennas because it generates interference in the whole area. The main parameters for 5G-BS are summarised in Table 1. The 5G-BS DL interferes with satellite receivers due to C-band spectrum sharing. We suppose that 5G-BS is covering a particular area. Sector antennas are considered for the 5G-BS to guarantee the worst-case interference model scenario.  
Figure 1: Diagram of 5G-BS interference measurement.  
The bandwidth required to support 5G requirements in terms of capacity and latency is 100 MHz [10] at frequencies below 6 GHz. Table 2 depicts the three 5G-BS carrier frequencies assigned to a sampling frequency (frequency 2 in the first spectrum block, frequency 1 in the second spectrum block, and frequency 3 in the third spectrum block). One 5G-BS zone may have different equipment from different operators, and each carrier frequency is considered to operate in at least one of these zones. The extracted C-band low-noise block (LNB) with a bandpass filter (BPF), the C-band LNB with BPF, and the new C-band LNB with BPF are used. A guard band (GB) of 100 and 50 MHz is used for 5G-BS. After being idle near an FSS-ES for a few minutes, the 5G-BS mobile device started downloading the full buffer to check the effect of interference. The direct-facing FSS-ES and the indirect-facing FSS-ES are 85 m between the 5G-BS and FSS-ES [10].
## 2 Optimization Design Process and Modeling Method
### Numerical Method
#### 2.3.4 Experiment Validation  
According to the Bernoulli equation, the pump head \(H\) is formulated as:  
\[H=\frac{p_{2}-p_{1}}{\rho g}+\frac{{{v_{2}}^{2}}^{2}-{{v_{1}}^{2}}}{2g}+(z_{2}- z_{1}), \tag{12}\]  
where \(p_{1}\) and \(p_{2}\) imply the inlet static pressure and outlet static pressure respectively; \(v_{1}\) and \(v_{2}\) denote the average velocities of the inlet and outlet section respectively; \(z_{1}\) and \(z_{2}\) are the heights in vertical direction at the inlet and outlet of the model pump; \(\rho\) denotes the fluid density, 997 kg/m\({}^{3}\); \(g\) denotes the local acceleration of gravity, 9.8 m/s\({}^{2}\).  
The efficiency of centrifugal pump is the ratio of useful power to input power, defined as follows:  
\[\eta=\frac{\rho g\mathcal{Q}H}{P_{e}}, \tag{13}\]  
where \(P_{e}\) denotes the shaft power calculated by the input power and motor efficiency. The way to obtain the value of \(P_{e}\) in an experiment is different from the simulation because the torque \(T\) acting on the rotor is easy to obtain in CFD. \(P_{e}\) is equal to \(n_{\text{d}}\)\(\cdot\)T/9550.  
In order to validate the accuracy of the CFD results, a test rig as shown in Figure 6 was set up to measure the pump performance. The model pump is connected to the rigid pipes by two rubber hoses. The flow rate is obtained by a LWGY-MIK-DN20 liquid turbine flowmeter with a range of 0.8-8 m\({}^{3}\)/h and accuracy of 0.5%. The head of the pump is measured by a CYG1204F type differential pressure transmitter with an uncertainty of 0.1% and range of 0-200 kPa. The PSW 30-36 programmable DC power is used to drive the pump. The output voltage range of the power is 0-30 V, and the maximum output current is 36 A. The data acquisition system consists of LabVIEW acquisition program and NI6343 data acquisition card. The estimated uncertainty of head and efficiency measurement is below 0.32%, and the random uncertainty is no more than \(\pm\)0.1%.  
The obtained pump hydraulic performance curves from CFD and experiment are compared, as shown in Figure 7. The pump efficiency obtained from the test has been converted into the hydraulic one by removing motor efficiency, bearing efficiency, and leakage efficiency. As seen from the results, the numerical ones are always consistent with the changing trends of the experimental curves. Both head and hydraulic efficiency of the simulation results are higher than the experimental values, which might be because the material roughness is not considered in the numerical simulation. The deviations of head at 0.8\(Q_{\text{d}}\), 1.0\(Q_{\text{d}}\), 1.25\(Q_{\text{d}}\) are 4.9%, 4.3%, 1.7%, respectively. The deviations of efficiency at 0.8\(Q_{\text{d}}\), 1.0\(Q_{\text{d}}\), 1.25\(Q_{\text{d}}\) are 3.6%, 4.9%, 4.8%, respectively. Therefore, it can be stated that the employed numerical method is reliable for the optimization at three selected flowrates.
## 3 Results and Discussion
### Link Budget Simulation  
The link budget calculation involves several parameters, such as transmit power, receiver sensitivity, path loss and antenna gains, which are taken into account to determine the power budget of the wireless link. The simulation is performed by evaluating the power received at the receiver end and comparing it with the minimum required power for successful communication.  
Figure 16: Twenty\(-\)four hour revisit time on 3D view.  
Figure 17: Seven\(-\)day revisit time comparison.