esa-sceva/satcom-chunk-collection huggingface dataset:
## 2. Sulphate Reduction Metabolism  
In anoxic habitats, SRB generally use sulphate as an electron acceptor for anaerobic respiration and gaining energy for growth. The source of the electron donor can come from the oxidation of a carbon source, hydrogen or metal. The process of sulphate reduction occurs through a dissimilatory sulphate reduction (DSR) metabolic pathway catalysed by enzymes. Generally, the DSR process includes four steps (Figure 1). The first step is the activation of free sulphate around bacterial cells by the enzyme sulphate adenylyltransferase (ATP-sulfurylase) to produce adenosine-5-phosphosulfate (APS) and pyrophosphate (PPi). The potential of sulphate/sulphite is too negative (\(-516\) mV) for reduction by NADH or ferredoxin which are the primary intracellular electron mediators [20]. The next step is the reduction of cytoplasmic APS to sulphite/bisulphite and adenosine monophosphate (AMP) by the enzyme APS reductase. Then, sulphite is catalytically reduced to the DsrC trisulphide intermediate by the dissimilatory sulphite reductase (DsrAB) enzyme. The final step is the reduction of trisulphide to sulphide and reduced DsrCr by a membrane bound enzyme DsrMKJOP complex. The steps described above are shown in Figure 1.  
Figure 1. Dissimilatory sulphate reduction in sulphate-reducing bacteria (SRB) [21].  
SRB metabolic activities have been shown to be related to environmental conditions [22]. The metabolic flexibility of SRB helps to increase their chance of survival when environmental conditions change, for instance, the respiration of a group of _Desulfobvibrio_ sp. under aerobic conditions where the same substrate as sulphate are oxidised with oxygen [23].  
Another example is the survival and growth of SRB in sulphate depleted environments. [PERSON] et al. [24] reported _Desulfovibrio_ sp. was able to grow with methanogenic bacteria in the absence of sulphate. A product of SRB metabolic activity is H\({}_{2}\) which can be consumed by methanogenic bacteria. This coexistence helps SRB to obtain enough energy for their survival and growth. The coexistence of SRB and other microorganisms was also found in extreme pH environments where the metabolism of the other microorganism changes the pH to a suitable level for growth [24]. Sulphate reducers also have their own strategy to survive and grow in extreme pH environments. The following sections explain their strategy to adapt to acidic environments.
## 4 Results
### Optimal Parameter Settings for the Individual Performance Measurements
#### 4.1.2 Dimensional Accuracy
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ASTM D638** & **DF** & **Sum of Square** & **Mean Square** & **\(F\)-Value** & **\(p\)-Value** \\ \hline Layer Thickness (L) & 1 & 0.95570 & 0.95567 & 23.35 & 0.000 * \\ Build Orientation (O) & 1 & 3.50630 & 3.50631 & 85.68 & 0.000 * \\ Printing Speed (P) & 2 & 0.28090 & 0.14043 & 3.43 & 0.049 * \\ L \(\times\) O & 1 & 0.49120 & 0.49120 & 12.00 & 0.002 * \\ L \(\times\) P & 2 & 0.10290 & 0.05144 & 1.26 & 0.303 \\ O \(\times\) P & 2 & 0.31940 & 0.15968 & 3.90 & 0.034 * \\ L \(\times\) O \(\times\) P & 2 & 0.13450 & 0.06724 & 1.64 & 0.214 \\ Error & 24 & 0.98210 & 0.04092 & - & - \\ Total & 35 & 6.77290 & - & - & - \\ \hline
**ASTM D695** & **DF** & **Sum of Square** & **Mean Square** & **\(F\) -Value** & **\(p\)-Value** \\ \hline Layer Thickness (L) & 1 & 0.41158 & 0.41158 & 42.90 & 0.000 * \\ Build Orientation (O) & 1 & 0.03670 & 0.03670 & 3.83 & 0.062 \\ Printing Speed (P) & 2 & 0.00587 & 0.00293 & 0.31 & 0.739 \\ L \(\times\) O & 1 & 0.00402 & 0.00402 & 0.42 & 0.523 \\ L \(\times\) P & 2 & 0.00077 & 0.00039 & 0.04 & 0.961 \\ O \(\times\) P & 2 & 0.00184 & 0.00092 & 0.10 & 0.909 \\ L \(\times\) O \(\times\) P & 2 & 0.00503 & 0.00251 & 0.26 & 0.772 \\ Error & 24 & 0.23027 & 0.00959 & - & - \\ Total & 35 & 0.69607 & - & - & - \\ \hline
**ASTM D3039** & **DF** & **Sum of Square** & **Mean Square** & **\(F\) -Value** & **\(p\)-Value** \\ \hline Layer Thickness (L) & 1 & 0.32211 & 0.32211 & 34.70 & 0.000 * \\ Build Orientation (O) & 1 & 0.00922 & 0.00922 & 0.99 & 0.329 \\ Printing Speed (P) & 2 & 0.01597 & 0.00799 & 0.86 & 0.436 \\ L \(\times\) O & 1 & 0.00401 & 0.00401 & 0.43 & 0.517 \\ L \(\times\) P & 2 & 0.01403 & 0.00701 & 0.76 & 0.481 \\ O \(\times\) P & 2 & 0.00438 & 0.00219 & 0.24 & 0.791 \\ L \(\times\) O \(\times\) P & 2 & 0.00491 & 0.00246 & 0.26 & 0.770 \\ Error & 24 & 0.22278 & 0.00928 & - & - \\ Total & 35 & 0.59741 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ANOVA results for dimensional accuracy (* \(\alpha<0.05\)). Figure 7: Main effect plots of the significant factors for dimensional accuracy. **(a)** ASTM D638; **(b)** ASTM D695; **(c)** ASTM D3039.
## 5 Conclusions  
In this study, coarse-resolution SIF data for the Saihanba region were downscaled to a higher resolution (0.0005\({}^{\circ}\)), and the effects of downscaling TROPOSIF data and eSIF data were compared. The results demonstrate that both the downscaled TROPOSIF and eSIF data exhibited a strong linear relationship with SIF simulated by the SCOPE model. Notably, the downscaled eSIF data showed a more consistent seasonal variation pattern with the GPP, indicating its greater suitability for studying the relationship between SIF and GPP. Furthermore, the comparison of the linear relationships of coarse-resolution SIF, SIF\({}_{\rm FECW}\), and SIF\({}_{\rm FECW}\) with GPP revealed that the relationship between SIF\({}_{\rm FECW}\) and GPP was the strongest. This indicates that matching the SIF with the flux footprint can improve the relationship between the SIF and the GPP. Additionally, an examination of the relationship between the SIF and GPP at varying distances from the flux tower revealed that this relationship deteriorated as the distance increased, reaching its weakest point beyond 1 km from the flux tower. Moreover, at the SHB2 site, which is characterized by significant landscape heterogeneity, the relationship between the VIs and GPP was found to be poor, with no clear pattern in the relationship between the observation scale and GPP. Overall, these findings emphasize the importance of using high-resolution SIF data and ensuring consistency between the flux footprint and SIF observations for accurately assessing the relationship between the SIF and the GPP.  
Conceptualization, L.Z. and R.S.; methodology, L.Z., R.S., Z.L. and J.Z.; validation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z., R.S., J.Z., Z.L. and S.L.; visualization, L.Z.; supervision, R.S.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (42271330).  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
### Contribution of This Work  
In this paper, we develop a distributed cooperation scheme to coordinate AVs and MVs to pass through signal-free intersections. The core idea is to cooperatively regulate each AV to cross the intersection while keeping a safe distance with the vehicles in conflict relationships. Firstly, a conflict graph is constructed to describe the conflict relationships of the different approaching lanes. Based on the conflict graph, the communication topologies of vehicles are identified in real time. Then, the future trajectories of AVs and MVs are predicted by the respective car-following models and are shared with their neighboring vehicles in conflict relationships. By utilizing the predicted trajectory information of the conflicting MVs, AVs proactively optimize their trajectories to avoid conflicts while assisting the MVs to cross the intersection smoothly. In addition, the elimination of conflicts, traffic mobility, and fuel economy are synchronously considered in the proposed scheme. The intersection coordination issue is formulated into a multi-objective optimization problem aiming to minimize the weighted performance indexes of AVs in a prediction period. Finally, a receding horizon optimization algorithm is presented and tested with a typical four-phase intersection in simulations. The contributions of this study are listed below:  
1. Different from the existing literature only considering coordinating vehicles under a fully AV environment, this paper proposes a coordination scheme under which AVs and MVs can cooperatively pass through a signal-free intersection;
2. The proposed scheme develops a practical application which concurrently considers the restrictions of control inputs, vehicle states, safety conditions, global conflict relationships, and the mixed-automated driving environment;
3. A distributed multi-objective optimization algorithm is presented to synchronously eliminate the traffic conflicts and improve mobility and fuel economy in a receding horizon framework.  
The remainder of this paper is structured as follows. Section 2 describes the problem statement. Section 3 details the methodology to formulate and solve the cooperative control problem and gives the receding horizon optimization algorithm. Simulations are conducted in Section 4; and Section 5 concludes this paper.
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 1 Introduction
[29] used three criteria to find the optimal wavelet: the degree of similarity, regularity, and accuracy of correct recognition during classification processes. [PERSON] [30; 31] applied four evaluation criteria, signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), MSE, and the cross-correlation method for wavelet selection. [PERSON] et al. [32] proposed a wavelet selection scheme by evaluating MSE, SNR, and correlation. [PERSON] et al. [33] proposed a selection method for the wavelet and scale using digital signal energy spectral density. Considering the decomposition ability of a wavelet and the similarity of decomposition coefficients and analysis signals in information, the Ecom criterion proposed by [PERSON] et al. [34] integrates parameters such as entropy, energy, mutual information, joint entropy, relative entropy, and conditional entropy. [PERSON] et al. [35] proposed the Information Quality Ratio (IQR) as a new metric for wavelet selection in beef quality. IQR has a better capability to quantify the modifications of signal structure than MSE and correlation coefficients. [PERSON] et al. [36] proposed the selected wavelet evaluation utilizing five metrics: SNR, SNR improvement, MSE, root mean square error (RMSE), and percentage root mean square difference (PRD). [PERSON] et al. [37] found that time-error minimization and F1-score maximization can offer the relevant wavelet selection for gait event detection. [PERSON] et al. [38] proposed the selection metrics based on the percentage root mean square difference (PRD), the SNR, and the simulated frequencies to find the best and suitable wavelet for assessing normal, seizure-free, and EEG signals showing seizures. The abovementioned researches provide many methods and strategies for wavelet selection. These wavelet selection methods can extract representative features for specific signal types, laying a foundation for further analysis. However, these methods are proposed for particular types and ranges of signals and are often unsuitable for large amounts of seismic waveforms. On the other hand, with the development of data acquisition, especially in earthquakes, the amount of data is becoming larger and larger. Therefore, the intelligent processing method of seismic data based on deep learning technology has also been commonly studied. In these intelligent data processing methods, the requirements for the prominent role of the signal after wavelet transformation are reduced because the deep learning methods can mine the deep features of the data. Based on the above changes, the demand for wavelet selection is more to stably characterize a particular type of signal after wavelet transformation. Therefore, we propose a novel wavelet selection method, mainly used in seismic data intelligent processing, to perform stable wavelet transform on big seismic data to improve intelligent processing.
# Evaluation of Resource Exhaustion Attacks against Wireless Mobile Devices
## 6 Discussion
* Growth of wireless ongoing or outgoing traffic on a device. This type of attacks is specific to autonomously working devices and modules in the field of wireless communication, involving the receipt and transmission of digital multimedia content. The volume of traffic transmitted is determined by semantic rules of a particular application area, which are rather difficult to validate by automated means on particular traffic patterns. * Creation of electromagnetic interference on wireless channels, causing an increase in transmitter power. This type of attack is specific to components of receiving/transmitting wireless devices, including those capable of dynamic self-organization and rebuilding, as well as physically moving in space with varying relief, presence of natural or artificial obstacles and other factors that can affect the availability, speed and quality of communications between individual devices. An example of such systems is a system for ensuring operational management and response in emergency and crisis situations. The system is deployed and rebuilt on the ground to ensure reliable and secure communication between devices of rescue service by using ZigBee, LoRa (Long Range), and other mesh protocols. At that, the situational nature of the structure and composition of the self-organized communication network provides the possibility of alternately increasing and decreasing the power of the receiving/transmitting components of the devices, balancing between raising the signal to meet communication requirements and reduce its power to minimize the energy expenses. As a result, there is a need to monitor the validity of a possible increase of the transmitter power of the autonomously operating network nodes. * Untypical usage (misuse) of a devices' software, including forcing incorrect settings and others. This type of attack is typical in multi-task devices--smartphones, laptops, etc.--assuming the possibility of background running of software applications, running hidden computing processes, deploying local data storages and other capabilities that are potentially suitable for their exploitation by an intruder to deplete energy. In addition, the choice of \"non-optimal\" trajectories and automatic ways to move unmanned aerial vehicles with unjustified hangs, speed acceleration and slowdown and other features can also adversely affect the battery consumption of the drone and reduce the maximum technically achievable time of its flight.
## 3 The Study
### The Scenarios  
The first scenario is a one-tier network architecture including communication between two parties, the sensors, and the server as is shown in Figure 2(left). The sensors are deployed along a river; each is assigned to a base station and communicates with the server through the LTE network. The challenges in this scenario are to measure the propagation of a pollutant in a river and the reaction (alarm) on the server side, taking into consideration the QoS of the network in two conditions: periodic monitoring and alarm reaction. In systematic monitoring, each sensor sends the river's water measurements at a specific time interval. The server continuously checks the measurements, i.e., it compares them against some given thresholds, and when this comparison indicates the presence of a pollutant, it enters into the alarm condition by sending back a command to all the sensors to start sending their data more often (i.e., lowering their sampling and sending time interval) to track the pollutant appropriately while it flows along the water.  
The second scenario is a two-tier network architecture including communication between three parties, (i) the sensors, (ii) the aggregators, and (iii) the server, as Figure 2(right) shows. The aggregators collect all the data from the sensors transmitted through ZigBee, aggregate them, and then send them to the server, again through LTE. In this case, we  
Figure 1: Design mode of the NED file in the OMNeT\(++\) environment represents a system with four sensors and two aggregators (6 UEs) attached to an eNb each.  
Figure 2: **(left)** Scenario A, one-tier architecture: the sensors are connected directly to the server through the base stations. (right) Scenario B, two-tier architecture: the sensors are connected to an aggregator, and the aggregators are connected to the server through base stations.  
simulate pollution through a river to investigate the server's reaction considering the QoS of the network, again in the two conditions as before. The difference with the previous scenario is that when the server detects anomalies in the measurements, it cannot reach the sensors directly; instead, it sends a message to the aggregators, which coordinate their group of sensors to lower their sampling and transmission interval.
## 5 Evaluation
### Metrics and Scoring
#### 5.2.1 Confusion Matrix  
This matrix is the basis for further calculations. For simplicity, the positional explanation is relative to the position within the matrix, subdivided into:  
* **True positive (TP)**, top-left: correctly classified as malicious
* **True negative (TN)**, bottom-right: correctly classified as benign
* **False positive (FP)**, bottom-left: falsely classified as malicious
* **False negative (FN)**, top-right: falsely classified as benign  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Malicious** & **Benign** \\ \hline.docm & 3 & 1 \\.docx & 4 & 3 \\.dtom & 0 & 1 \\.dotx & 0 & 1 \\.pptx & 1 & 1 \\.xlsm & 3 & 1 \\.xlsx & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assessment of each file type; those incorrectly classified by Claude 3.5 Sonnet are circled.  
Figure 17: Evaluation of the maliciousness scores for the 25 test files.  
It can be observed that only one malicious and one benign document were incorrectly identified. A decision boundary of 4 would result in no false positives and two false negatives. However, this would be a less favourable outcome, as incorrectly classifying a malicious document as benign could have more severe consequences, potentially leading to the mishandling of the file.
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 6 Conclusions  
Due to the many privacy restrictions and regulations regarding sharing PI/patient data, obtaining enough medical data for sufficient ML model training is often challenging. This research addressed these difficulties, especially in the eye healthcare/ophthalmology space, by proposing a novel FL architecture explicitly designed to allow for private, collaborative ML model training within this field. In doing so, this research leveraged TL with the VGG-19 model and horizontal FL training. Additionally, this study developed and applied k-client selection training and a custom, dynamically weighted _FedAvg_ model aggregation method (_wFedAvg_).  
Overall, this study's proposed FL architecture keeps data private, yet is scalable and ready to be deployed in an industrial/commercial eye healthcare environment. Firstly, although only four clients were used in this study, this architecture was designed to take advantage of FL's ability to create collaborative models securely using an arbitrary number of clients (m). Secondly, the novel _wFedAvg_ aggregation algorithm was designed to be scalable, as although it takes a weighted average of the client's local models, it will dynamically adjust the weights as more clients are added on the fly to the FL architecture. Lastly, differential privacy was utilized throughout the architecture to ensure patient PI stays secure. By adding noise, this architecture prevents outside attackers from tracing published model weight updates back to the patients and their PI.  
Compared to CL models, this study's proposed architecture maintained most of the model accuracy and model training time efficiency while ensuring all data remain private. As shown in Table 2, the proposed _DataWeightedFed_ approach with _wFedAvg_ aggregation had only an average 1.85% accuracy decrease, compared to the conventional FL average of up to about a 55% accuracy decrease. This meant that this study's proposed architecture improved how FL can be efficiently applied to the eye healthcare space and allowed for collaborative ML model training.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 5 Evaluation  
In this section, we conduct a series of experiments to evaluate the impact of our proposed mobility support method on the performance of mobile users. We use three performance metrics to quantify mobile user performance: average packet delay, packet loss rate, and handover delay. Therefore, in order to verify the performance of our proposal, we developed a late-binding node selection algorithm based on the proposed mobility support system on Mininet [29]. We also implemented the principle concerning MAP-ME [20] and the OPT [21] using special packets to update a router's forwarding table to support mobility.
## 2 Methods
### Sensor Modeling
#### 2.2.3 FEM 3D Model  
A more sophisticated model of the system was developed in COMSOL Multiphysics for 3D finite-element analysis. The fundamental eigenfrequency analysis was done using structural mechanics, dynamics and vibration modules. Keeping the analytical results in mind, a parametric analysis was performed to determine the mesh size. A fine mesh was generated with 18,141 degrees of freedom, 1048 mesh points and 2097 tetrahedral elements. The minimum element quality was set to 0.31, as shown in Figure 5. The model was created with a Lagrange-Quadratic element type. The computations employed second-order polynomials, which was a good tradeoff between memory and accuracy of results. Finer meshes did not have a significant effect on the results. The simulation was done to calculate the eigenfrequencies for the first six modes.  
Figure 4: The typical considerations while doing frequency response analysis of a cantilever beam-based sensor. The natural frequency of a cantilever beam changes depending on type of loading and boundary conditions.