esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mathematical Formulation Based on Linear Programming
#### Feasible Region for Energy Minimization in Proposed Protocols  
The objective function to minimize the EC is given in Equation (16):  
\[Min\ \Sigma_{r=1}^{max}E_{consumption}(r)\quad\forall\ r\in max, \tag{16}\]  
whereas constraints for Equation (16) are as follows:  
1. The \(E_{trans}\) and \(E_{rec}\leq E_{trans}^{rem}\),
2. The \(E_{trans}\) and \(E_{rec}\leq E_{i}\),
3. The \(DP\) should be transmitted within the \(R_{t}\) of the node.  
Total energy consumed is the sum of transmission and receiving energy of the nodes, i.e.,  
\[\Sigma_{r=1}^{max}E_{consumption}(r)=E_{trans}+E_{rec}\quad\forall\ r\in max, \tag{17}\]  
where  
\[E_{trans}=T_{p}(feq)\left(\frac{PS}{DR}\right). \tag{18}\]The \(E_{trans}\) ranges from (0.693-2.775) is used in the current work, where \(T_{p}(feq)\) is representing the transmission power with frequency feq.  
\[E_{rec}=R_{p}(feq)\left(\frac{PS}{DR}\right). \tag{19}\]  
The \(E_{rec}\) ranges from (0.002-0.0087) is used in the current scenario, where \(R_{p}(feq)\) is representing the receiving power with frequency feq. Feasible region for minimum EC are shown in Figures 13 and 14.  
The points on the feasible regions boundary in EP-VIR-Three are as follows:  
\(P_{1}\left(0.496,0.7\right)\)\(mI\),  
\(P_{2}\left(0.162,0.7\right)\)\(mI\),  
\(P_{3}\left(0.496,2.7\right)\)\(mI\),  
\(P_{4}\left(0.162,2.7\right)\)\(mI\).  
Figure 14: Feasible region for EC minimization in BF-SPR-Three.  
Figure 13: Feasible region for EC minimization in EP-VIR-Three.  
The points on the feasible regions boundary in BF-SPR-Three are as follows:  
\(P_{1}\)\((0.315,0.7)\)\(mJ\),  
\(P_{2}\)\((0.18,0.7)\)\(mJ\),  
\(P_{3}\)\((0.315,2.7)\)\(mJ\),  
\(P_{4}\)\((0.315,2.7)\)\(mJ\).
# Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN
## 3 Problem Formulation
### Different Ways of ACL Policy Implementation  
**Near the source policy implementation:** Near the source policy implementation is more efficient by blocking the unwanted traffic as generated by the source node; if the source nodes are distributed then this way of policy implementation requires a large number of ACL policies and, in turn, causes a longer packet process at switches.  
Suppose that a company has an enterprise network shown in Figure 5. This network contains eight legacy switches LE1-LE-8, and three OpenFlow switches OF1-OF3. Users are connected to switches, i.e., LE1, LE3, LE5 and LE7, and data centers are connected to switches, i.e., LE2, LE4, LE6, and LE8 as shown in Figure 5. The company has the network policy, say P1, that the traffic generated by only subnet A1-A2 is allowed to reach the data center AD1-AD2. Near the source, policy implementation requires to implement Eight number of policies at the switches LE1, LE3, LE5 and LE7 that the traffic destined for AD1-AD2 should be blocked as shown in Figure 5. Now every packet generated by the nodes attached to the switches (LE1, LE3, LE5, LE7) will have to pass through these policies. Consequently, it causes a longer delay for the packets.  
traverse up to LE2 and then will be dropped here. This consumes more network resources like bandwidth, energy and processing power on the path from D1 to LE2 as shown is Figure 6 using a red dotted line.
3. **Egress port policy implementation:** Similarly, we can implement policies at the egress ports of the switch LE2 where data centers AD1 and AD2 are attached. In the case, if destination nodes are less, e.g., 5-10 nodes, then it may be feasible to configure all the ports but if nodes are greater in number, e.g., more than 100 then it is challenging and sometimes impossible to configure all the ports for the desired policy. Similarly, any update in that policy is also quite cumbersome to configure.
4. **Ingress port policy implementation:** we can implement policies at the ingress port of the switch LE2 where it is connected with LE1. In this case, we have to implement only two policies to block the traffic from all other subnets, i.e., B, C, and D, but in case of topology updates, this scheme may not be more feasible. So, optimum policy implementation is required that has less number  
Figure 5: An example Scenario containing SDN and Legacy switches connected with end Nodes.  
Figure 6: An example Scanrio containing SDN and Legacy switches connected with end Nodes.  
of rules, on less number of interfaces, stop unwanted traffic as soon as possible, load balancing strategy for all routers, etc.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 3 Results  
The efficiency scores of each DMU are presented in Table 7, while Table 8 shows the Potential Maximum Reduction in the inefficient producers performed by the inverse DEA. DMUs 1, 2, 4, 9, and 11, with an efficiency score of zero, operate efficiently, while DMUs 3, 5, 6, 7, 8, 10, and 12 do not. The efficient DMUs exhibit minimal flaring, indicating they have implemented effective measures for managing flare gas during production. These practices could serve as benchmarks for the less efficient DMUs. In contrast, DMUs 3, 5, 6, 7, 8, 10, and 12 are considered inefficient due to their high flaring rates. These companies could enhance their operations by investing in targeted interventions, such as hydrogen production via the steam methane reforming process and the adoption of carbon capture and storage (CCS) technology.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Value (USD)** & **References** \\ \hline \multirow{4}{*}{Direct labor cost} & 4,911,802.68 & \multirow{4}{*}{[62; 65]} \\  & (Annual average salary of = 60,000 \(\mathrm{\SIUnitSymbolCelsius}\)/y (This will be \\  & converted to the US dollar), Assumed number of staff = 43) * (1+ ECI-Employment cost index = 76.10\%) \\ \multirow{4}{*}{Administrative and general overhead cost} & 3,236,490.145 & \multirow{4}{*}{[62; 65]} \\ Insurance & 0.5\% TPC & & [62; 65] \\ Local Taxes and Fees & 0.5\% TPC & & [62; 65] \\ Annual Operating and Maintenance & 1.5\% TPC & & [62; 65] \\ Land rent & Land included under owner’s cost & & [65] \\
**Fixed cost (FC)** & **20,307,150.334** & \multirow{4}{*}{[62; 65]} \\ Chemicals & 91,735.05 Adjusted to 2025 & & [62; 65] \\ Water makeup & 111,660.623 Adjusted to 2025 & & [62] \\ Feedback + Fuel & 72,531,550.392. adjusted to 2025 & & [62] \\ Catalyst & 293,552.588 & & [62; 65] \\
**Variable cost (VC)** & **73,034,498.65** & \multirow{4}{*}{[65]} \\
**Operating cost** & **(FC +VC) = 93,341,648.98** & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cost Estimation of the annual operating cost (OPEX).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Value (USD)** & **References** \\ \hline Direct materials/equipment & 291,812,580.213 & [62] \\ Constructions & 15\% TPC & [62; 64] \\ EPC services & 15\% Direct material/Equipment & [62; 64] \\ Contingency & 10\% TPC & [62; 64] \\ Other cost & 10\% Direct material/Equipment & [62; 64] \\
**Total Plant Cost** & USD 486,354,300.35 & [62] \\
**Indirect cost** & & \\ Owner’s cost & 7\% TPC & [62] \\ Spare part cost & 0.5\% TPC & [62] \\ Start-up & 2\% * TPC + 25\% * (1 month fuel) + 3 months *(maintenance + labor) + 1 month * (chemicals + catalysts). & [62] \\ Working capital & 99,031,148.098 & [62] \\
**Total Capital Investment** & TPC + (Owner’s cost + Spare part cost + Start-up cost + Working capital) = USD **634,308,226.202** & [62; 65] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cost estimation of the total capital investment parameters.
## 3 Model Architecture  
Due to the extremely large scale of operation and maintenance objects involved in the same business, the method of directly analyzing the fault location conclusion from the data source has become feasible. It is necessary to rely on the accurate abnormal perception of each operation and maintenance object to carry out analysis and location for the abnormal alarm of each operation and maintenance object. Therefore, this method relies on rich and complete alarm sources, including the system, network, application, equipment, and other professional monitoring alarms. The key technical point is to screen multiple source alarms and construct key features. Based on the complete and accurate dynamic operation and maintenance map data and various professional alarm data, this method studies the troubleshooting process triggered by an abnormal business flow, mainly including database access flow, read-write I/O flow, and microservice access flow. The business flow is shown in Figure 1. Through the analysis of the commonalities of various data flows (deployment relationship commonalities, connection commonalities (physical, logical), access flow commonalities), triggered by abnormal business flow, the possible root cause of the failure is located forwardly.  
Figure 1: Business flow.  
The model is divided into three main modules: dynamic operation and maintenance map construction, multi-source alarm screening and feature extraction, and map addressing and common point analysis. This method is based on dynamic operation and maintenance maps and various professional alarm source data, with complete fault coverage types. Compared with methods that lack topological information and indicator correlation analysis, it has obvious advantages in accuracy. At the same time, due to the aggregation and simplification of alarm redundancy information in the alarm feature extraction process, the common point analysis logic itself is relatively concise, leading to this method having obvious advantages over knowledge graphs and other methods.
## 3 Antibiotic Resistance in Hospital and Municipal Wastewater
### Municipal Wastewater
Further, the antibiotic resistance-based grouping through screening for the presence of AMR genes such as quinolone resistance genes (\(qnrA\), \(qnrB\), \(qnrC\), \(qnrD\), \(qnrS\), \(qnrVC\), \(qepA\), \(aqxB\), \(anc(6^{\prime})\)-\(lb\)-\(cr\), \(bla_{OXA}\), \(incU\)) and beta-lactam resistance genes (\(blaOXA\)-1-4, \(bla_{OXA-7}\), \(bla_{OXA-10-11}\), \(bla_{OXA-13-17}\), \(bla_{OXA-19}\), \(bla_{OXA-21}\), \(bla_{OXA-28}\), \(bla_{OXA-30-32}\) and \(bla_{OXA-34-35}\)) in those isolates was performed using PCR amplification. It was found that the quinolone and the beta-lactamase resistance genes _aac(6^{\prime})-\(lb\)-\(cr\)_ and \(bla_{OXA}\), including gene \(bla_{OXA-101}\), were prevalently detected in urban WWTP wastewater. [PERSON] et al. [22] investigated the presence of ARGs in HWW and urban MWW of Girona, Spain, using an RT-PCR assay. ARGs such as \(qnrS\), \(tetW\), \(sul1\), \(blat_{TEM}\) and \(ermB\) conferring resistance against antibiotics such as quinolones, tetracycline, sulfonamides, beta-_lactam_ and macrolides were detected in significant amounts in both HWW and urban MWW. Similarly, qPCR was employed to elucidate and compare the composition of ARGs from a WWTP present in six hospitals and three rivers in Kathmandu Valley, Nepal. The results revealed that the resistances of all sample sources were composed of ARGs \(qnrS\) (quinolone resistance), \(bla_{NDM-1}\) (beta-lactam resistant), \(sul1\) (sulfonamide resistant), \(bla_{CTX-M}\) (beta-lactam resistant) and \(tetB\) (tetracycline resistant) [19]. It was reported that the ARG patterns observed in river water and WWTPs were almost similar. The river water samples reported to contain the \(sull\) gene and \(bla_{NDM-1}\) gene as the most and least abundant ARGs, whereas in the HWW, the carbapenem resistant \(bla_{CTX-M}\) was found to be the most predominant in followed by \(sull\), \(bla_{NDM-1}\), \(qnrS\) and \(tetB\). [PERSON] et al. [67] further outstretched qPCR for the detection of ARGs in HWWs and MWWs in Gothenburg, Sweden for a period of five years. The authors stated that the following ARGs, namely \(mcr\)-1, \(mcr\)-3, \(mcr\)-4, \(mcr\)-5, \(sul4\) and \(gar\), were prevalent across all samples, whereas the genes \(optA\) and \(cft\)(\(A\)) were detected especially in HWW. In addition, the most abundant genes in both HWW and MWW were found to be \(mcr\)-3 and \(mcr\)-5, which were relatively more abundant in MWW.
## 3 Results
### Comparison of Mean of Incidence Rates (IRs) and Mortality Rates (MRs) of Each Accident Type  
Figure 2 below shows the mean ratio computed when comparing the IRs and MRs each accident type from 2011-2017. From the analysis, it was indicated that the highest mean MRs accident type \"fall of person from height\" was (11.96). Followed by the Mean MRs of \"contact with electricity or electric discharge\" and \"struck by falling object\" respectively. On the other hand, the mean IRs of most of the accident types were lower than the mean MRs value. However, the results indicated the greater value of means of IRs of \"strip, trip or fall from same level\", \"striking against fixed or stationary object\" and \"injured while lifting or carrying\" accident types when compared with the mean MRs from 2011-2017. This indicated that there is a significant difference between IRs and MRs of each accident type from 2011-2017 among Hong Kong construction workers.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Accidents Types**} & \multicolumn{8}{c}{**IRs**} & \multicolumn{8}{c}{**MRs**} \\ \cline{2-13}  & **2011** & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** & **2011** & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** \\ \hline
**Fall of person from height** & 0.62 & 0.60 & 0.55 & 0.45 & 0.40 & 0.33 & 0.32 & 15.97 & 16.83 & 18.91 & 8.45 & 9.46 & 6.49 & 7.58 \\ \hline
**Slip, trip or fall on same level** & 1.04 & 1.04 & 1.01 & 1.07 & 1.00 & 0.98 & 0.92 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
**Striking against fixed or stationary object** & 0.45 & 0.41 & 0.39 & 0.47 & 0.40 & 0.36 & 0.38 & 0 & 1.40 & 0 & 0 & 0 & 0 & 0 \\ \hline
**Striking against or struck by moving object** & 0.83 & 0.77 & 0.77 & 0.78 & 0.61 & 0.54 & 0.46 & 4.79 & 2.81 & 2.52 & 2.42 & 1.05 & 0.93 & 2.53 \\ \hline
**Contact with electricity or electric discharge** & 0.01 & 0.02 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 3.19 & 9.82 & 0 & 2.42 & 3.15 & 1.86 & 0 \\ \hline
**Injured while lifting** & 0.97 & 0.74 & 0.62 & 0.55 & 0.71 & 0.58 & 0.61 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
**Trapped in or between objects** & 0.17 & 0.16 & 0.17 & 0.25 & 0.17 & 0.15 & 0.12 & 1.60 & 1.40 & 0 & 2.42 & 1.05 & 0 & 2.53 \\ \hline
**Struck by falling object** & 0.12 & 0.08 & 0.06 & 0.08 & 0.12 & 0.10 & 0.10 & 4.79 & 2.81 & 2.52 & 6.04 & 3.15 & 0 & 0.84 \\ \hline
**\textasci{} Others** & 0.89 & 0.74 & 0.61 & 0.66 & 0.64 & 0.53 & 0.55 & 6.39 & 1.40 & 3.78 & 2.42 & 2.10 & 0 & 5.06 \\ \hline \hline \end{tabular}  
* Others: includes all accidents types other than above mentioned in occupational safety and health statics, Labour department of Hong Kong.  
\end{table}
Table 3: The overview of incidence rates (IRs) and mortality rates (MRs) correspond to accident type from 2011 to 2017.
## 5 Evaluation of OWTG Loadings under OC4 Phase I Load Cases
### Load Case 3.2  
The OWTG (including RNA and tower) is assumed to be fully flexible and the steady wind flow is applied. Load Case 3.2 is focused on the influence of tower shadow effect. The dynamics of low-speed shaft torque is revealed in Figure 13. These data are obtained during the time interval of 30-40 s of the simulation period (the simulation data of the first 30 s is dropped according to the OC4 recommendation). The torque fluctuation is induced due to the tower shadow effect. Since there are three blades equipped with the OWTG, the blade passing frequency is three times the OWTG rotating frequency. The trend of torque fluctuation of the present results is similar to those by most of OC4 research teams.  
Figure 12: Analysis Results for Load Case 2.4b of OC4 Phase I. **(a)** PDF_Tower-Top Fore-Aft Shear Force; **(b)** PDF_Fore-Aft Shear Force at Mudline; **(c)** DEL_Fore-Aft Shear Force at Mudline. PDF: Power Density Function; BSX: Fore-Aft Shear Force at Mudline; DEL: Damage Equivalent Load.  
Figure 13: Analysis Result for Load Case 3.2 of OC4 Phase I (Low-Speed Shaft Torque).
# Timing Performance Testing and Regularity Analysis of eLoran System
## 4 Testing and Analysis
### Receiver Signal Source: Long-Wave Signal from Analogue Source
#### 4.1.2 Receiving Sensitivity of Each Device  
According to the mapping in Section 4.1.1, if the equipment presented in Section 4.1.1 cannot lock the station signal at 40 dB \(\upmu\)V/m but can lock it at 50 dB\(\upmu\)V/m, the analogue source will generate signal strengths ranging from 50 to 40 dB\(\upmu\)V/m (with 1 dB\(\upmu\)V/m intervals), and the signal searching phenomenon of the equipment will be recorded. If the equipment can still lock the signal at 30 dB\(\upmu\)V/m, the measurement will start at 30 dB\(\upmu\)V/m, testing at 1 dB\(\upmu\)V/m intervals until the device cannot lock the signal. For Receiver 1, the analogue source broadcast field strength is 18 dB\(\upmu\)V/m, while that of Receiver 2 is 10 dB\(\upmu\)V/m. The test broadcast time differences are TD1 = 14,990.1 \(\upmu\)s and TD2 = 33,428.1 \(\upmu\)s.  
The locations of the test points and each station are shown in Figure 6. The propagation distances from different stations to the receiving points are relatively long, and the propagation paths are quite complex. The field strengths and SNR of each receiver are listed in Table 1. The test reduced the signal source strength to its lowest level and both receivers could still track the signal, indicating their high sensitivity. For the simulation, the output of a weak signal could be considered.  
Figure 4: Receiver 2’s field strength and signal\(-\)to\(-\)noise ratio parameters.  
Figure 5: Comparison of the field strength and signal\(-\)to\(-\)noise ratio parameters of Receivers 1 and 2.  
Figure 6: Schematic of test points from existing stations.
## 4 Experimental Results and Discussion
### Compared Methods  
To validate the effectiveness of the proposed ESA-GCN model, we compared it with four representative methods, which can be divided into two categories: balanced network embedding methods (i.e., GCN and GraphSAGE) and imbalanced network embedding methods (i.e., DR-GCN and GraphSMOTE).  
* GCN: A graph convolutional network (GCN) [10] is the most representative balanced network embedding method, which obtains node embeddings by aggregating the features of neighboring nodes.
* GraphSAGE: GraphSAGE [11] is another representative GNN method. Unlike GCNs, which use full-sized neighboring nodes to obtain node embeddings, GraphSAGE saves memory by using a fixed number of neighboring nodes for each target node. Additionally, it learns three different aggregators: mean-aggregator, LSTM-aggregator, and pooling-aggregator. We report the best-performing aggregator among these three as the final result of GraphSAGE.
* DR-GCN: The DR-GCN [3] is an imbalanced network embedding method based on GCNs. It proposes to use conditional adversarial training to enhance the separation of different classes. Additionally, distribution alignment training is applied to balance the majority and minority nodes.
* GraphSMOTE: GraphSMOTE [2], based on SMOTE [13], has two types of edge generators that can connect synthetic nodes to the original graph through pretraining. We report the results of the best variant of the generator for each dataset.  
From Table 1, it can be seen that our ESA-GCN method has the greatest improvement in the performance of the GNN (GCN) when the dataset is imbalanced. This indicates that the ESA-GCN can effectively alleviate data imbalances and enhance the classification performance of the GNN.
## 1 Introduction
### Purpose  
As discussed before, a high-speed sensor network system with clock and data acquisition synchronization using various kinds of sensors has not been developed. We herein propose a data-acquisition synchronization method for high-speed sensor network systems based on an accurate clock synchronization. In clock synchronization, we adopt the precision time protocol (PTP) [23], which works on Ethernet networks. Because Ethernet is highly flexible in communication hardware, it can be utilized over a considerably wide area. In addition, the expandability of the sensor network system in the case where new sensor nodes are added is extremely high. Because a general analog to digital (AD) converter board can be used to acquire data from tactile sensors, the proposed method can be applied to any analog sensors such as acceleration sensors, gyro sensors, and force sensors. Based on the background, we developed a high-speed tactile, visual, acceleration, and gyro sensor network system to observe and control robots, machines, etc. After developing the high-speed sensor network system with the proposed method, we verified the effectiveness of the proposed methods in three kinds of sensor network systems experimentally.  
The characteristics of the system include:  
1. good data synchronization (approximately ten microsecond order);
2. high sampling rate (over 500 Hz); and
3. general-purpose property (not requiring special hardware).  
In particular, the data synchronization improves data analysis, sensor fusion technique, and hardware (e.g., precise machine, robot, etc.) control. The first and second terms correspond to Specifications 1 and 2, respectively, described in Section 1.1. The third term is considered to be a common required specification.
# Removal of Phosphate from Aqueous Solution by
## 2 Materials and Methods  
### Materials  
Zeolite was bought from Xinyang City, Henan Province, China. Its particle size is 150 \(\upmu\)m, and its cation exchange capacity is 53.15 cmol/kg. The mineralogical composition of zeolite was clinoptiolite (66.3%), montmorillonite (29.3%), and quartz (4.4%). Figure 1 illustrates the XRD pattern of the material.  
Corn straw was collected from farmland in Niudachang, Longli County, Qiannan Buyi, and Miao Autonomous Prefecture, Guizhou Province, China (26\({}^{\circ}\)23\({}^{\prime}\)2\({}^{\prime\prime}\) N, 106\({}^{\circ}\)45\({}^{\prime}\)41\({}^{\prime\prime}\) E). After collection, corn straw was dried naturally and crushed for subsequent use.  
The reagents used in these experiments, such as potassium dihydrogen phosphate (KH\({}_{2}\)PO\({}_{4}\)), ammonium molybdate ((NH\({}_{4}\))\({}_{2}\)MoO\({}_{4}\)), and ascorbic acid, were purchased from Tianjin Kemeiou Chemical Reagent Co. Ltd. (Tianjing, China). Sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) was bought from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). All reagents were purely graded. Deionized water was used to reach the corresponding concentration when needed.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Energy-Efficient Task Scheduling
#### 5.3.1 Event-Driven Approach
The authors of [68] also introduced an event-driven neuromorphic processor which employs a level crossing (LC) sampling method. The LC-ADC captures the level-crossing event and encodes it as a single bit. This approach not only simplifies the ADC quantization process but also minimizes the number of sampling points, thereby reducing the overall data volume. The event-driven approach must be designed carefully to minimize resource overheads and reduce the latency associated with activating components. Additionally, it is crucial to design the event controller to minimize false positive events, which can lead to unnecessary module activation. Conversely, failing to detect true events can compromise the reliability of the physiological monitoring system.