esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Heavy Metal Resistance
#### 2.5.1. Cobalt/Zinc/Cadmium Resistance  
In the genome of the ZCR6 strain, the presence of a _czc_ operon comprising three structural genes, _czcC_, _czcB_, and _czcA_ (Table 5), was found. Proteins encoded by the _czc_ operon constitute a branch of metal-transporting resistance-modulation-division-type (RND-type) efflux systems, corresponding to the first layer of metal resistance in bacteria [69]. The RND family plays an important role in the heavy metal resistance of gram-negative bacteria. These pumps function as a tripartite complex composed of the inner membrane and the periplasmic and outer membrane components [70]. In the CzcCBA efflux system, CzcC is a cell wall \"outer\" membrane protein, CzcA is an \"inner\" plasma membrane transport protein, and CzcB is a membrane fusion protein functioning as a dimer. The CzcCBA efflux pump functions to transport divalent cation/proton antiporters. This efflux pump is responsible for the detoxification of Zn\({}^{2+}\), Co\({}^{2+}\), and Cd\({}^{2+}\)[71].
## 3 Proposed PBFT Architecture and Model
### Consensus Process of Proposed PBFT
#### 3.3.3 Communication Complexity Analysis  
Two assumptions were made to compute the communication complexity of the proposed PBFT method. The first is that all clusters comprise an identical number of nodes. The second is that the hierarchical structure of each cluster forms a perfect tree. During the Request phase, each Cluster Primary Node (CPN) broadcasts consensus messages to their respective Group Primary Nodes (GPNs). Upon receiving these messages, the GPNs then relay the consensus messages down to the lower-level GPNs in their hierarchical network structure. This process is repeated according to the number of groups present in each cluster. The total number of groups within a single cluster is defined by Equation (9). Consequently, as the Request phase is conducted for each cluster, its communication complexity can be described by Equation (11). In the Pre-Prepare phase, each group's Group Primary Node (GPN) broadcasts consensus messages to the Normal Nodes (NNs) within its group. In this phase, \(N_{\textit{g,node}}-1\) communications occur for each group. Therefore, the total number of communications for the Pre-Prepare phase is represented by Equation (12). The Pre-pare and Commit phases serve as consensus verification processes. Within each group, \((N_{\textit{g,node}}-1)^{2}\) communications take place. Consequently, the communication complexity for these phases is outlined by Equation (13). In the Pre-Reply phase, each group's GPN returns the consensus results to its corresponding CPN. Thus, communication occurs as frequently as the number of groups, as denoted by Equation (14). The final Reply phase involves delivering the consensus results of each cluster to the client, with communication occurring for each cluster as shown in Equation (15). Hence, the overall communication complexity of the proposed PBFT method can be represented by Equation (16).  
\[\text{The number of groups in each cluster}=\left\lceil\frac{N_{node}}{N_{g\_ node}}\right\rceil \tag{9}\]  
\[\text{(Pre-Request Phase)}\qquad N_{cluster} \tag{10}\]  
\[\text{(Request Phase)}\qquad(\frac{N_{node}}{N_{g\_ node}})\times N_{cluster} \tag{11}\]  
\[\text{(Pre-Prepare Phase)}\qquad((N_{g\_ node}-1)\times\frac{N_{node}}{N_{g\_ node}})\times N_{cluster} \tag{12}\]  
\[\text{(Prepare and Commit Phase)}\qquad(\frac{N_{node}}{N_{g\_ node}})\times(N_{g\_ node}-1)^{2}\times N_{cluster} \tag{13}\]  
\[\text{(Pre-Reply Phase)}\qquad(\frac{N_{node}}{N_{g\_ node}})\times N_{cluster} \tag{14}\]  
\[\text{(Reply Phase)}\qquad N_{cluster} \tag{15}\]  
\[\text{(Communication Complex)}\qquad O(N_{cluster}\times N_{node}\times N_{g\_ node}^{2}) \tag{16}\]
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Discussion  
In this study, we have presented a comprehensive evaluation of the proposed Dual-View Knowledge Transfer (DVKT) framework for RSID. Our results demonstrate the efficacy of the DVKT approach in improving both the performance and efficiency of RSID models.  
Firstly, the quantitative and qualitative evaluations on benchmark datasets (Sate-Haze1k, RS-Haze, and RSID) confirm that the DVKT framework almost achieves a state-of-the-art dehazing performance, comparable to heavy backbone models like SCSNet, while requiring significantly fewer parameters and computational resources. This highlights the effectiveness of our knowledge distillation approach in transferring structured knowledge from a well-trained teacher network to a lightweight student network.  
Secondly, the ablation study investigating the impact of different channel preservation rates further validates the robustness of our framework. By progressively reducing the number of channels, we show that the DVKT framework can maintain high dehazing quality even with extremely compressed models. This finding underscores the practicality of our method for deployment on edge devices and mobile platforms with limited computational resources.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CPR** & **Params** & **FLOPS** & **PSNR** & **SSIM** & **LPIPS** \\ \hline
1 & 1.53 & 10.79 & 25.1759 & 0.9223 & 0.0386 \\
1/2 & 0.39 & 2.71 & 24.6717 & 0.9184 & 0.0407 \\
1/3 & 0.18 & 1.20 & 24.3804 & 0.9162 & 0.0424 \\
1/4 & 0.10 & 0.68 & 24.1163 & 0.9140 & 0.0441 \\
1/5 & 0.06 & 0.44 & 23.9271 & 0.9105 & 0.0509 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study about channel preservation rates (CPR) on SateHaze1k dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transfer Settings** & **PSNR** & **SSIM** & **LPIPS** \\ \hline W/T Transfer & 19.8206 & 0.8625 & 0.1357 \\ Only Intra-KT & 22.3718 & 0.9051 & 0.0521 \\ Only Inter-KT & 21.5281 & 0.8937 & 0.0768 \\ Intra-KT + Inter-KT & 23.9271 & 0.9105 & 0.0509 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study about different transfer settings on SateHaze1k dataset.  
Finally, the proposed DVKT framework addresses two critical issues in remote-sensing image dehazing: lack of labeled data and high computational costs. By leveraging the abundance of natural-image-dehazing datasets and transferring knowledge from a pre-trained teacher model, we overcome the data scarcity challenge. Additionally, the lightweight student model generated through DVKT achieves significant speedups compared to heavy backbone networks, demonstrating its potential to enable real-time dehazing for various remote-sensing applications.
# An Analytical Survey of WSNs Integration with Cloud and Fog Computing
## 7 Conclusions  
CC will be an innovative standard that gives convenience; the on-demand system gets an imparted pool of configurable computing resources. That might have a chance to be a quick provision of computing resources and settle for insignificant low-cost utilization of service providers. The perfect coordination of WSN includes a vast number of low cost, low control multi-working nodes with CC, which is another rising area that gives a strong and versatile foundation for a few requirements. In this paper, we surveyed the requirements, tests, and results identified for coordination between WSN and cloud. Furthermore, issues like security, protection, and coordination are still needed to be attended to. The scalability, process, delay constraint, routing, and heterogeneity are other issues that need to be addressed.  
Executing FC on an ad hoc system helps to decide immediately before any problem arises. FC moves the edge of the system with the least idleness, less processing, and system service benefits. This kind of framework can be utilized as a part of health, augmented reality, and in numerous ongoing Internet of Things (IoT) applications like visual security, etc.  
Conceptualization, Data Creation, Investigation, Resources, Validation, Writing--review & editing, Formal analysis Q.S.; Project administration, Resources, Supervision M.S.; funding acquisition A.G.; Visualization M.A.K.; methodology, Formal analysis, Validation, Writing--review & editing S.A.B. All authors have read and agreed to the published version of the manuscript.  
This is partially collaboration work with University Malaysia Sabah, Malaysia.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Acronyms** & **Meaning** \\ \hline CC & Cloud Computing \\ \hline FC & Fog Computing \\ \hline WSN & Wireless Sensor Network \\ \hline LEACH & Low Energy Adaptive Clustering Hierarchy \\ \hline FOGG & A Fog Computing Based Gateway to Integrate Sensor Networks to Internet \\ \hline VM & Virtual Machines \\ \hline IBM & International Business Machines \\ \hline AAL & Ambient Assisted Living \\ \hline LQRP & Link Quality Routing Protocol \\ \hline HEF & High Energy First \\ \hline TPSS & Time and Priority-based Selective Data Transmission \\ \hline IAMU & Identity and Access Management Unit \\ \hline CCTV & Closed-Circuit Television \\ \hline VSN & Visual Sensor Network \\ \hline \hline \end{tabular}
\end{table}
Table 6: Abbreviated acronyms.
# Towards a Real-Time Description of the Ionosphere:
## 6 The _f_oF2 statistics
Mean_ trend related to the solar activity for both IRI and IRTAM is found; IRI and IRTAM models show an increasing _RMSE_ trend as the solar activity increases; a clear decreasing _NRMSE_ trend is detected as the solar activity increases at Jicamarca, Ascension Island, and Rome for IRTAM, and at Thule and Sondrestrom for IRI. For the other cases it is difficult to establish a clear solar activity dependence. However, patterns characterized by higher _NMRSE_ are almost always observed for lower solar activity.
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 2 Materials and Methods
### Training Request  
To enable accurate monitoring of specific motion patterns, a learning routine is implemented, which relies on repeated sampling of the target movement sequence. This routine is initiated by the master node and continues until 10 complete iterations of data acquisition are performed.  
Once activated, the routine enters a continuous loop running at the microcontroller's operating frequency, monitoring for variations in motion across all axes. Upon detectingmovement, the system validates the condition for data recording and begins acquiring readings from the motion sensor. Sensor data--acceleration and rotation along the X, Y, and Z axes--are sampled every 50 ms. Each iteration consists of 40 readings, resulting in a 2 s data capture window per iteration. Given that each reading includes six parameters (three for acceleration and three for rotation), a total of 240 values are collected per iteration (40 \(\times\) 6 = 240).  
To account for potential inconsistencies or execution errors in individual motion sequences, the learning process is repeated across ten iterations, resulting in the storage of 2400 values per slave node. Since the routine is executed in parallel across three nodes, the complete system captures a total of 7200 data points during the learning phase.  
At the end of the routine, the values from all iterations are aggregated element-wise across vectors and averaged to produce a representative template of the target motion. This averaged motion profile is then used in the voting routine for real-time comparison with incoming sensor data. Averaging reduces the impact of anomalies such as spikes or sudden deviations in sensor readings, thus improving pattern recognition reliability.  
Finally, the resulting average values are stored in non-volatile memory, allowing the learned motion pattern to persist through system resets or when repeated learning is not desired.  
This routine can be repeated multiple times until the mean set of values shows no significant deviations across the 10 executed repetitions. Visualization of the received data can be performed by plotting it using data from the master device. The training phase should be conducted under the supervision of a professional coach to validate all recorded movements.
# Satellite Quantum Communications When Man-in-the-Middle Attacks Are Excluded
###### Abstract  
An application of quantum communications is the transmission of qubits to create shared symmetric encryption keys in a process called quantum key distribution (QKD). Contrary to public-private key encryption, symmetric encryption is considered safe from (quantum) computing attacks, i.e. it provides forward security and is thus attractive for secure communications. In this paper we argue that for free-space quantum communications, especially with satellites, if one assumes that man-in-the-middle attacks can be detected by classical channel monitoring techniques, simplified quantum communications protocols and hardware systems can be implemented that offer improved key rates. We term these protocols photon key distribution (PKD) to differentiate them from the standard QKD protocols. We identify three types of photon sources and calculate asymptotic secret key rates for PKD protocols and compare them to their QKD counterparts. PKD protocols use only one measurement basis which we show roughly doubles the key rates. Furthermore, with the relaxed security assumptions one can establish keys at very high losses, in contrast to QKD where at the same losses privacy amplification would make key generation impossible.  
quantum communication; quantum cryptography; quantum key distribution; satellite QKD; access control; threat model; photon key distribution; PKD +
Footnote †: journal:
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 5 Experiment and Evaluation
### Simulation Setup  
In this paper, the performance of the Ad-BBR algorithm is evaluated using the NS3 network simulation environment, which includes 5G and Wi-Fi simulations. NS3 simulation experiments are conducted by integrating new algorithms and Python APIs into the TCP module of NS3 via Typeld. This integration enables the construction of simulation scenarios--such as multi-stream competition scenarios--using NS3's Python binding interface, as illustrated in Figure 4. A dumbbell network topology is constructed using NS3\({}^{\prime}\)s Python binding interface to simulate multi-stream contention scenarios, with parameters such as RTTs, bottleneck buffer sizes, and bottleneck bandwidths configured according to experimental requirements. The 5G network simulation experiments utilize LteHelper and NrHelper to set up 5G gNB and UE terminals for testing the TCP performance of Ad-BBR under 5G/NR networks. Wi-Fi network simulation experiments employ WifiHelper to configure Wi-Fi AP and STA terminals, evaluating the TCP performance of Ad-BBR under Wi-Fi 802.11n/ac/ax standards. Finally, a Python script iterates over various experimental parameters--including 5G power levels, Wi-Fi rates, and the number of streams--to record experimental results for the throughput, RTT, packet loss rate, fairness index, and other metrics.
## 4 Results and Discussion
### Leave-One-Subject-Out Evaluation  
Table 8 shows the results obtained by performing the leave-one-subject-out evaluation. In this case the training set is made of activity samples of subjects not included in the test set. This evaluation is also known as subject independent evaluation and shows the feasibility of a real smartphone application for human activity recognition [35; 59; 60] where data of a given subject are usually not included in the training set of the classifier.  
From the results it is almost evident the drop of performances with respect to the case of 5-fold evaluation. Human subject performs activities in a different way and this influences the recognition accuracy especially when it is necessary to distinguish between fine grained types of activities, that is in the case of AF-17, A-9 and F8 recognition tasks. In particular, in the case of AF-17 the best MAA is 56.58% using RF and magnitude. In the case of A-9 the best MAA is 73.17% using RF and raw data. In the case of F-8 the best MAA is 49.35% using SVM and magnitude. In contrast, distinguishing between coarse grained activities, such as falls vs no falls, is quite easy with a MAA of 97.57% with SVM for both raw data and magnitude. Overall the magnitude feature vector performs slightly better than the case of raw data. This suggests that using the magnitude as a feature vector in the case of subject-independent evaluations could be more reliable than raw data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**5-fold**} \\ \hline  & \multicolumn{3}{c}{**Raw Data**} & \multicolumn{6}{c}{**Magnitude**} \\ \hline Data & KNN & SVM & ANN & RF & KNN & SVM & ANN & RF \\ \hline AF-17 & **82.86** & 78.75 & 56.06 & 81.48 & 65.30 & 65.71 & 41.95 & **65.96** \\ AF-2 & 97.78 & **98.71** & 98.57 & 98.09 & 95.56 & **97.42** & 96.71 & 95.74 \\ A-9 & 87.77 & 81.62 & 72.13 & **88.41** & _77.37_ & **78.94** & 62.81 & 75.14 \\ F-8 & **78.55** & 75.63 & 55.07 & 78.27 & 53.31 & 56.34 & 37.66 & **57.26** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Five-fold evaluation. Mean Average Accuracy for each classification task using raw data and magnitude as feature vectors. In bold are the best results for each classification task and feature vector employed.  
The low performance achieved in the case of subject-independent evaluation permits the researcher to investigate the following issues:  
* the study of a more robust feature vector that is able to reduce as much as possible the performance gap between the subject-dependent and subject-independent evaluation;
* the study of on-line learning classification schemes that permit us, with the use of a few subject-dependent data, to improve the performance as much as possible.
# Selection of a Photovoltaic Panel Cooling Technique Using Multi-Criteria Decision Analysis
## 3 MCDM Approach to Evaluate PV Panel Cooling Methods Evaluation  
The objective is to determine suitable weights of evaluation for the PV panel cooling methods criterion, and the entropy weight method by [PERSON] is used. The lower the entropy value, the lower the amount of disorder in the cooling climatic zone criterion. In this article, the entropy weighting method is adopted to determine the weight of the criterion and sub-criterion related to the solar panel cooling methods. The multi-criteria decision approach can be applied where a set of alternative methods for cooling photovoltaic panels must be selected on the basis of a set of criteria. The multi-criteria decision-making approach adopted is simple, and the concept allows one to find the best alternatives for each criterion described by selecting the alternative PV panel cooling method that is the closest to the ideal PV panel cooling method and farthest from the negative ideal PV panel cooling method. For a specified set of m alternative PV panel cooling and n attributes/criteria and the rating of each PV cooling method against each criterion, refer to Table 5 above. The following sub-sections present the PV panel cooling method evaluation approach.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**\%Efficiency Enhancement**} & \multicolumn{2}{c}{**Reliability**} & \multicolumn{2}{c}{**Cost**} & \multicolumn{2}{c}{**Emission**} & \multicolumn{2}{c}{**Ergonomics**} \\  & \multicolumn{2}{c}{**(MAX)**} & & \multicolumn{2}{c}{**Negative**} & \multicolumn{2}{c}{**USD/m\({}^{2}\)**} & \multicolumn{2}{c}{**CO\({}_{2}\) in Kg**} & \multicolumn{2}{c}{**Ranks**} \\ \cline{2-9}  & \multicolumn{2}{c}{**BWh**} & \multicolumn{2}{c}{**Dwa**} & \multicolumn{2}{c}{**Rank (MAX)**} & \multicolumn{2}{c}{**(MIN)**} & \multicolumn{2}{c}{**(MIN)**} & \multicolumn{2}{c}{**(MIN)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Summer Winter**} & \multicolumn{2}{c}{**Summer Winter**} & \multicolumn{2}{c}{**C5**} & \multicolumn{2}{c}{**C6**} & \multicolumn{2}{c}{**C7**} & \multicolumn{2}{c}{**C8**} \\ \hline Aluminum fins cooling (AFC) & 4\% & 2\% & 3\% & 0\% & \(-1\) & 58 & Good & 1 \\ \hline Heat pipe cooling (HPC) & 8\% & 2\% & 6\% & 0\% & \(-4\) & 168 & Acceptable & 5 \\ \hline Phase change material cooling (FCC) & 10\% & 2\% & 8\% & 0\% & \(-2\) & 1125 & Poor & 3 \\ \hline Thermosiphon cooling (TCC) & 8\% & 2\% & 6\% & 0\% & \(-4\) & 25 & Poor & 4 \\ \hline Thermosiphon with clay pot cooling (TPC) & 10\% & 2\% & 8\% & 0\% & \(-5\) & 25 & Good & 6 \\ \hline Forced convection fan cooling (FCC) & 7\% & 2\% & 1\% & 0\% & \(-3\) & 68 & Good & 2 \\ \hline Water spray cooling (WSC) & 15\% & 2\% & 12\% & 0\% & \(-5\) & 75 & Good & 7 \\ \hline Evaporative cooling (EVC) & 15\% & 2\% & 12\% & 0\% & \(-5\) & 75 & Good & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Consolidated attribute list for different factors in the cooling method selection.
# Comparison between Laser and Stamping without Die (SWD) for Micro Tapered Hole Forming
## 2 Finite Element Software DEFORM-3D
### Simulation Results and Discussion  
The simulation results of SWD are shown in Figure 4. Figure 4a shows the result of flat-plate workpiece, while Figure 4b shows the result of grooved workpiece. According to the simulation results, when the stamping depth is 0.38 mm at the same punch head angle with flat plates, the workpiece is largely bent, and the workpiece is unperforated. When the workpiece is grooved and the punch angle is 24\({}^{\circ}\), the workpiece is perforated. No buckling is observed, and only deformation around the outlet is shown in the simulation results. However, the workpiece cannot be perforated as the punch angle increases, and it is largely bent.  
Figure 4: Schematic diagram of workpiece shape/punch angle in punch forming (**a**) workpiece-flat plate (**b**) workpiece-grooved (**c**) deflection of flat plate with a 24\({}^{\circ}\) punch head.  
Figure 3: Stamping model specification.  
This phenomenon is analyzed in the view of mechanics, where the lower die supporting the workpiece is an 8 mm \(\times\) 8 mm \(\sqcap\)-shaped groove, and the distance between the application point to the bearing point is very large, which results in a very large bending moment. In stress analysis, the area other than the grooving increases the moment of inertia due to a thicker sheet. This groove contributes to enhancing the punching effect. The relationship between stamping depth (stroke) and punch load is as shown in Figure 5. The punch load is relatively high with a flat plate, and because the tapered punch is V shaped, the lateral force on the punch increases as the workpiece bends (as shown in Figure 4c), and the punch load is high.  
When the workpiece is grooved, the punch at 24\({}^{\circ}\) has good punching result. The microhole forming process is as shown in Figure 6. When the stamping depth is 0.34 mm, the workpiece begins to be perforated, and the outlet diameter increases with the stamping depth. While the outlet is slightly bent, it is restricted to the nearby area of the outlet. This result can predict the relationship between stamping depth and perforation. The simulation results will be compared with experiments in the next section.  
Figure 5: Relationship between stamping depth (stroke) and punch load.  
Figure 6: Stamping depth/punch forming relationship simulation analysis.