esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Plastid Phylogeny
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Pigment type** & **Cyanobacteria** & **Glaucophytes** & **Red algae** & **Brown algae** & **Diatoms** & **Green algae** & **Land plants** \\ \hline PBS & + & + & + & - & - & - & - \\  & +, except in _Acaryochloris marina_ & & + & + & + & + & + \\ Chl \(a\) & and related taxa & & + & + & + & + & + \\ Chl b & except in Prochlorophytes & - & - & - & - & + & + \\ Chl c & - & - & - & + & + & - & - \\  & Only in _Acaryochloris marina_ and & & - & - & - & - & - \\ Chl d & related taxa & & - & - & - & - & - \\  & Only in filamentous cyanobacteria & & - & - & - & - & - \\ Chl f & from stromatolites & - & - & - & - & - & - \\ \(\beta\)-Carotene & + & + & Unicellular & + & + & + & + \\ Fucoxanthin & - & - & - & + & + & - & - \\ Diadoxanthin & - & - & - & Traces & + & - & - \\ Diatoxanthin & - & - & - & Traces & + & - & - \\ Violkavanthin & - & - & - & + & + & + & + \\ Lutein & Depends on species & Macrophytes & - & - & + & + & + \\ Zexanthin & Depends on species & + & + & + & Traces & + & + \\ Xanthophyll cycle & - & - & - & + & + & + & + \\ Echinenone & Depends on species & - & Traces & Traces & - & _Adonis_ sp. \\ Myxoxanthophyll & Depends on species & - & - & - & - & - & - \\ Canthaxanthin & _Anabaena_ & - & - & - & - & Depend on species & _Adonis_ sp. \\ Aphanizophyll & Depends on species & - & - & - & - & - & - \\ \(\beta\)-Cryptoxanthin & _Aphanzomenon_ & - & + & - & - & + & + \\ Lycopene & _Nostoc_ & - & - & - & Depend on species & + \\ Synechoxanthin & _Synechococcus_ & - & - & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 1: Main chlorophyll and carotenoid types in the various taxa of photosynthetic organisms. “+” and “-” mean that the pigment was detected or not in the taxa.
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 3 Methodology
### Appliance Identification Based on Multi-Feature Integrated Classification (MFIC)
#### 3.6.3 Performance Metrics  
In order to evaluate the effects of signals disaggregation and compare with existing implemented algorithms, some indices are needed to evaluate the performance. Since an event-based NILM algorithm is designed in this paper, it is essential to measure the accuracy of this method in predicting which appliance is running in each state. Classification accuracy indices, such as precision, recall, and F-measure, are suitable for evaluation. Precision denotes the positive predictive values, i.e., the correct proportion of samples identified as appliance \(c\). Recall represents the true positive rate, i.e., the proportion of samples belonging to appliance \(c\) that are recognized correctly. F-measure is harmonic mean of precision and recall. These typical classification metrics can be formulated as follows:  
\[P_{c}=TP_{c}/(TP_{c}+FP_{c}) \tag{17}\]  
\[R_{c}=TP_{c}/(TP_{c}+FN_{c}) \tag{18}\]  
\[F_{c}=2\times(P_{c}\times R_{c})/(P_{c}+R_{c}) \tag{19}\]  
where, the subscript \(c\) is used to mark different appliances or states. \(TP_{c}\) indicates true positive, i.e., the correct judgment that appliance \(c\) is ON; \(FP_{c}\) represents the false positive, i.e., appliance \(c\) is judged to be ON but actually OFF, \(FN_{c}\) denotes false negative, that is, appliance \(c\) is ON but is wrongly judged as OFF.  
It is important to feedback the detailed power consumption of each appliance to users, so the accuracy of estimated power also needs to be considered. To compare the estimated power with the actual power consumption, disaggregation accuracy (\(DA\)) and percentage of contribution in energy consumption (\(PCEC\)) are used to evaluate the effects of different algorithms for reconstructing power profiles. The \(DA\) provides a global comparison between the estimated power and the ground truth, while the \(PCEC\) is used to calculate the contribution of each appliance in total power consumption. The calculation formulas are shown as Equations (20) and (21).  
\[DA=1-\frac{\sum_{k=1}^{L}\sum_{n=1}^{N}\left|\hat{p}_{n}(k)-p_{n}(k)\right|}{ 2\sum_{k=1}^{L}P(k)} \tag{20}\]  
where, \(L\) is the number of disaggregated readings, \(N\) denotes the number of appliances in the house, \(\hat{p}_{n}(k)\) represents the estimated power consumption of appliance \(n\) at the \(k\)th sample, \(p_{n}(k)\) is the actual power consumed at the \(k\)th sample for appliance \(n\), and \(P(k)\) stands for the aggregated power at the \(k\)th sample.  
\[PCEC^{n}=\sum\
olimits_{k=1}^{L}\hat{p}_{n}(k)/\sum\
olimits_{k=1}^{L}P(k) \tag{21}\]  
where, \(PCEC^{n}\) represents the contribution of appliance \(n\) to total power consumption.
## 5 Conclusions  
We have observed the femtosecond pulses transmit in SPNW with different input energies at different temperatures by experiments. The large thermo-optic coefficient (TOC) of silicon has high temperature sensitivity for the device controlled by temperature. The femtosecond pulses transmit nonlinear characteristics such as SPM, FCA, FCD, TPA, Kerr nonlinearity, and TOE in SPNW. TPA intensity is influenced by the input pulse energy and can influence the free carrier concentration. Thus, TPA intensity can influence FCA and FCD. The FCA of the SPNW for femtosecond pulses causes pulse deceleration. The FCD of the SPNW for femtosecond pulses causes pulse red shift. The TOE caused pulse acceleration and blue shift, and this influence is of nonlinear variation. The output pulses are characterized by acceleration, splitting, compression, and broadening for different input energies at various temperatures. These results provide basic references for researching SPNW for femtosecond pulse applications such as photonic networks. They also show that the SPNW exports single peak femtosecond pulses with dual-peak femtosecond input pulses and achieves pulse acceleration by adjusting the SPNW temperatures and the input pulse energies. The silicon nanowire waveguide will output long wavelength sharp pulses with small central wavelength disparity by controlling the temperature and input energy. For different inputs of pulse energy, we should make the waveguide work in corresponding suitable temperatures. The research results will contribute to the SPNWs' development for photonic elements and networks compatible with mature CMOS.  
Conceptualization, X.L.; and X.W.; validation, M.F.; investigation, J.L.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.L.; supervision, H.Y.; All authors have read and agreed to the published version of the manuscript.  
National Science Foundation of China (NSFC) (61070040, 61108089, 61205087, 61107005 62005207), Hunan Provincial Natural Science Foundation of China (805297133191), Natural Science Foundation of Shaanxi Province (2019Q-648).  
The authors acknowledge assistance from [PERSON].  
The authors declare no conflict of interest.
## 8 System Design
### Onboard Processing Design, Software and Operations
#### Operating Software  
The operating software controls the minute-to-minute operations of the spacecraft. It is based on a loop which begins with, during each iteration, critical status indicators and queues being checked. Then a task (with the highest priority of those pending) is selected for performance (note that it is potentially possible that multiple tasks could be selected for performance in parallel). All non-selected items retrieved from queues and battery, temperature and other indicator values are stored in the onboard database. If no tasks are pending, the system waits a pre-configured period of time and then restarts the loop.  
Figure 4: Software Operations.  
Figure 5: Ground Station Tasking Flow Chart.  
Figure 6: Ground Station Communications Flow.  
If tasks are present, the selected task is performed, via sending a command to an actuator controller, sending a job to the payload processing system or creating and queuing a message for transmission to the ground station. The status of the task performed is updated, and the loop restarts.
## 4 Results
### Evaluation Metrics
#### 4.1.2 Correlation Matrix  
The correlation matrix is a frequently used multivariate technique for determining strongly connected variables. This aspect of the project focused on correlating all the variables in the error signals and some operational signals considered dependent variables, such as CraneOn, CraneOff, and twist-lock locked. Error variables with only one unique value, 0, were removed because homogenous variables contain only one unique value and do not contribute additional information to the model. The correlation between the variables was analysed and visualised to give more insight into the data. Correlation was employed to determine the relationship between variables. It can be positive, indicating that both variables move in the same direction, or negative, indicating that when one variable's value increases, the values of the other variables decrease. Correlation can also be neutral or zero, indicating that the variables have no relationship or are unrelated.
## 4 KGs Evaluation
### Dataset and Domain-Specific Evaluation
In addition, studies using datasets such as FB15K, NELL, WN18, and YAGO3-10 evaluated the performance of dynamic KG refinement and continuous updates [188], andresearch based on cerebral aneurysm and COVID-19 datasets evaluated clinical data integration and evidence-based medicine support performance [191]. Evaluations of semantic relevance have been carried out using DBpedia and 14 benchmark datasets [192], and research utilizing the MIMIC-III, MIMIC-IV, and eICU datasets evaluated the interpretability of medical diagnosis and disease prediction through path inference [193]. There are also evaluations in natural language processing using the AMR2.0 and AMR3.0 datasets [194], studies on complex entity resolution tasks using seven real-world industrial and domain-specific datasets [195], and evaluations of fall risk detection and human activity monitoring using everyday activity and event data [196]. Research based on a dedicated hydraulic engineering dataset to assess KG embedding and link prediction performance in the field of hydraulic engineering has been conducted [197]. Other examples include studies on edge attribute-based community exploration using standard graph datasets [198], evaluations of KG construction and quality in cybersecurity using datasets such as FB15K [199], assessments of reverse-engineering question generation, question quality, and filtering performance using a Wikidata-based KG [200], and research on collision detection and context recognition in the IoT field using trigger-action programming-related datasets [201]. In the question-answering field, there is research evaluating extrinsic QA performance using the NLPCC2017, cMedQA, TREC-QA, and WikiQA datasets [202], as well as studies that quantitatively evaluated the effects of relation-based graph attention and mutual information maximization techniques using the FB15K-237 dataset [203, 204, 205]. Finally, in Section 4.4. _Dataset and Domain-Specific Evaluation_, the **Evaluation Metrics/ Considerations** are summarized in Tables 3 and 4. \begin{table}
\begin{tabular}{c c c c} \hline \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **Evaluation Metrics/Considerations** \\ \hline GTS Madrid benchmark & Evaluate mapping accuracy, data consistency, processing speed, and scalability based on GTFS-standard transit data. \\ \hline \multirow{2}{*}{FB15K, FB15K-237} & Use ranking metrics (MRR, Hits@K) and classification metrics \\ FB15K, FB15K-237 & (Precision, Recall, F1-Score). FB15K-237 refines FB15K by removing redundant/inverse relations. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of evaluation metrics and considerations for various datasets in knowledge graph applications.
## 5 Simulation Results
### Energy Consumption Analysis  
The design goal of HCR-1 is to balance the energy consumption of nodes in \(\mathcal{R}(1)\). To verify the effectiveness of HCR-1, we firstly explore the distribution of the residual energy ratio and compare to the result of HCR given in Section 3.3. For fairness, the simulation setting is the same as that in Section 3.3: there are 400 nodes randomly deployed in a 200 \(\times\) 200 m\({}^{2}\) network area, and the clustering range \(R_{c}\) is set as \(70\) m.  
Figure 5: Network lifetime with different routing factor \(\alpha\) and clustering range \(R_{c}\).  
Figure 6: Network lifetime with different routing factor \(\alpha\) and node densities.  
The results are illustrated in Figure 7 with different gradient \(k\) and \(d_{toEdge}\) (Equation (4)). Compared to the results of HCR given in Figure 2, in HCR-1, the maximum residual energy ratio in \(\mathcal{R}(1)\) decreases from \(67\%\) to \(32\%\), and the variance of the residual energy ratio in \(\mathcal{R}(1)\) is reduced from \(39\%\) to \(14\%\). The result proves that the imbalance of the energy consumption in \(\mathcal{R}(1)\) is effectively alleviated by HCR-1.  
Moreover, as shown in Figure 7, the residual energy ratio in gradient \(k\geqslant 2\) also decreases in HCR-1. To clarify the improvement, we compare the residual energy ratio of HCR-1 to that of HCR when \(R_{c}\) varies from \(20\) m to \(70\) m and the network area is fixed at 200 \(\times\) 200 m\({}^{2}\). As shown in Figure 8, the residual energy ratio of HCR-1 is smaller than that of HCR, and the improvement gets larger as the \(R_{c}\) increases. When \(R_{c}=70\) m, the residual energy ratio reduces from \(52\%\) in HCR to \(35\%\) in HCR-1. The  
Figure 8: Residual energy ratio comparison with different \(R_{c}\).  
Figure 7: Residual energy ratio with different node locations.  
result indicates that, balancing the energy consumption of the nodes in \(\mathcal{R}(1)\), which is the bottleneck of the whole network, can greatly improve the energy efficiency of the whole network and prolong the network lifetime. Therefore, in the next section, the simulation results will be given to demonstrate the improvement of HCR-1 on the network lifetime.
## 2 Related Studies
They concluded that objective video quality is more appropriate to measure the performance of error recovery techniques. The authors of [43] proposed a novel Impulse Noise Detection and Mitigation (INDAM) technique. This method involves a complex Cyclic Redundancy Check (CRC) method, replacing the affected pixels by using surrounding pixels based on complex mathematical calculations. However, the error method proposed in this paper is a bit-inversion technique, which, by detecting the errors in encoded bitstreams and simply inverting bit errors, results in an improved, reduced computational cost. It can be concluded from related studies that although many schemes have been proposed to improve the quality of videos, little work is currently being undertaken on video security and error recovery applied jointly, specifically for Multimedia CRNs. This paper proposes a pioneer mechanism (to the best of the authors' knowledge) equipped with error correction for selectively encrypted compressed videos. The comparative analysis of the proposed scheme using different parameters is given in Table 1, which shows the clear distinction and research contribution of the proposed VQProtect over existing methods. \begin{table}
\begin{tabular}{|c|c c c c c c c c c c c c c c c c|} \hline \hline  & & & & & & & & & & & & & & & & & & & & & & & \\ \hline
[3] & H.264/AVC & X & X & No fixed loss rate & Reed-Solomon & X & Yes & X & X & X & X & RC Block Size Dependent & No mentioned \\ \hline
[3] & H.D Video & X & X & None Carlo & Systematic RS Block Erasure Code & Yes & Yes & X & X & X & X & OOM (\(n_{max}-k_{1}+RM\)) & 87,2,735, G3,51, 40,32, 35, 245 & (in different feedback & (in different feedback & (in different feedback & (in different feedback & (in different feedback & (in different)) & (in different) \\ \hline
[9] & Not given & X & X & WLAN & Reed-Solomon & & No but SNR provided & & Not mentioned & Not mentioned \\ \hline
[44] & Not given & X & X & GE Model & Reed-Solomon & No, but delay and redundancy provided & & O(\(\text{log}k+\text{log}(n)\)) & 70,125, and 150 (for FEC-16, FEC-16, FEC-64, and FEC-128) \\ \hline
[63] & H.264/SVC & X & X & None Carlo & Recursive Systematic Cumulative Code & Yes & X & X & X & X & X & Low-complexity Table-look-up & Not mentioned \\  & & & & & Code & & & & & & & & & & \\ \hline
[64] & IPTV data & X & AES & WLAN loss rate = 0.1 & Systematic RS Block erasure code & No, but exposure rate and recovery & Not mentioned & Not mentioned \\  & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\
[47] & H.264 & X & X & More Carlo on AWGN & & Lucky Transform and & Yes & X & X & X & X & O(\(M-1),R\)) & [0–25,10–45] (for different frame rates) & & & & & & & \\
[8] & H.264/AVC & X & X & More Carlo on AWGN & & & Lucky Transform and & Yes & X & X & X & X & O(\(N\),Log(N,)) & Not mentioned \\  & & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\ \hline
[9]
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Performance Metric  
The following three performance metrics were considered for results comparison.  
1. Number of Alive Nodes (NAN): The number of remaining alive mobile sensor nodes after \(t\) seconds of simulation time was measured.
2. Remaining Energy (RE): The average remaining energy (RE) of mobile sensor node at the end of each round was measured.
3. Packet Delivery Ratio (PDR): Packet delivery ratio (PDR) is defined as the ratio between successful delivery of transmitted packets by a source (mobile sensor node) to a destination. The source mobile sensor node receives acknowledgment reply after successful delivery of packets at destination. The performance of protocol is considered better when PDR is high.Thus, we calculated the PDR with Equation (18). \[PDR=\frac{PRD}{PTS}\] (18) where PDR is packet delivery ratio, PRD is amount of packets received at destination, and PTS is amount of packets transmitted by source.
# A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
## 5 Results and Discussion
### Processing Time  
Given that many ANPR applications need to operate in real-time, the processing time for detecting the plate characters is a critical factor in evaluating the different ANPR models. We found that our model, on average, requires around 10 ms only to detect the actual characters in the number plate in an image captured by our mobile app without communication overheads. The overall processing time, on average, was measured to be 29 ms, including the app processing and communication overheads. This shows that our model is adequate to be deployed on an IoT surveillance camera to recognize the number plates in real-time.  
Figure 7: Examples of successful recognition of all plate characters.  
To put these numbers in some context, consider a commercial surveillance camera that can run at 30 Frames Per Second (fps), which means it can generate up to one image every 33 ms. Our ANPR model can process more than 3 images every 33 ms, or support a super-resolution camera that runs at the rate of 90 fps.  
We compared the performance of our model to two state-of-the-art ANPR approaches, namely SVM [12] and YOLO detector [16]. We found that our model outperforms both methods in terms of the overall processing time to recognize all characters in the license plates over our testing dataset. Figure 10 shows the average CPU processing time of our model compared to SVM and YOLO. We found that our model outperforms both approaches in terms of the overall CPU processing time, even after including the mobile app overheads.  
Figure 8: An example of a character mismatch error.  
Figure 9: The Average classification accuracy of our model.
###### Abstract  
Leaky feeders provide seamless and uniform signal coverage in confined spaces like tunnels, mines, and buildings. Their easy scalability and integration with modern systems, like Multiple Input Multiple Output (MIMO), make them ideal for environments requiring reliable and consistent connectivity. However, using optical fiber as a radiating cable has never been investigated before. This may seem infeasible at first sight. However, our experimental study shows otherwise. We measured light leaking from a bent optical fiber transmitter. We also derived closed-form formulas to describe the amount of leakage energy and found that this energy exponentially varies with the square of the curvature radius. This allows us to design an Optical Leaky Feeder (OLF) transmission system for the first time. Then, we analytically show that a slotted optical fiber can be used as a MIMO receiver. The proposed system can ensure reliable, high-quality signal distribution even in challenging environments like tunnels, industrial settings, and dense urban areas.  
optical leaky feeder; MIMO; bending loss; optical fiber Article
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
## 5 Discussion: Recovery Protocols in Real-Life Implementation
### Secure Recovery When the Biometric Image Is Defective or Lost  
First of all, \"losing\" a biometric image only occurs following dramatic events. For example, this could happen when the user faces a grave medical condition that affects their face, or when the user passes away. The CRP mechanism generating CT-Bio tables incorporates liveness tests; therefore, showing a picture of the user to the camera of its terminal device will not work. The method of placing a screen in front of the camera with an AI-generated image of the subject can also be blocked during the CRP mechanism by incorporating additional controls regarding the source of the image. Protecting biometric authentication schemes from pictures and AI-generated images is a broader field of research that is outside the scope of this paper. It is up to the user to take precautions. As with a token, the reference table CT-bio could be stored somewhere, which would support a similar recovery process to that following the loss of a token:  
* The service could be provided by the Keystore; however, storing both CT-T and CT-Bio in the same location is not recommended.
* The user could ask the financial advisor managing their family trust to store CT-Bio, with written instructions to give back the reference table.
* Other examples of methods include the encryption of CT-Bio and its storage in the cloud, as well as the involvement of a family member.  
To protect privacy, the CRP mechanism generating the reference table CT-bio is a one-way function: it is extremely hard, if not impossible, to obtain a biometric image from a reference table. Such a recovery operation is still relatively secure, as malicious parties can do very little from the table CT-Bio. To intercept the ephemeral key K, the malicious parties still need RN1, RN2, the password, the token, and the virtual token. If needed, a new enrollment cycle should be performed to re-establish full protection.