esa-sceva/satcom-chunk-collection huggingface dataset:
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 5 Experiment Description  
### Virtual Testbed Description  
To carry out the experiments we used a GNU/Linux virtual testbed created by Virtualbricks [22]. The testbed consists of eight Virtual Machines (VMs), one for each DTN node, connected by virtual switches and channel emulators, which are essential for adding the desired delay on Mars to Earth links (see Figure 7). VM clocks are perfectly synchronized, as VMs set their clocks from the host. All VMs run GNU/Linux and ION, the NASA implementation of the DTN architecture, including BP, LTP and CGR. The last is a routing algorithm specifically designed by NASA JPL to cope with scheduled intermittent connectivity, typical of space networks. We used ION 3.6.0 b [11], with a patch to fix problems encountered during previous tests. DTNsuite including DTNbox and DTNperf is also installed on all nodes.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 5 Conclusions  
A low-cost, low-power, star-type wireless network capable of measuring and recording the angle of rotation of a set of bell wheels was designed and validated using data from both the laboratory and the field. The sensors had a fixed sample rate of 100 Hz, and could produce x-, y-, and z-axis acceleration data with a resolution of 61 \(\upmu\)g. Typical values for the drift and noise magnitude in the angle were 0.927\({}^{\circ}\) per hour and 0.0785\({}^{\circ}\), respectively. The network is capable of supporting 254 sensor nodes but only 5 nodes were built and tested as a proof of concept. The periods of small oscillations of the five lightest bells in Durham Cathedral were measured and the values agreed within \(\pm\)0.7% with the theoretical values in [6].  
For the first time, it was proven experimentally that the initial velocities of all the bells in Durham Cathedral during ringing were negligible, allowing simplifications in the equations used to calculate the forces produced by a ringing bell. The sensors can, with confidence, be used to determine the forces produced by a ring of bells and to identify the ones that may lead to structural damage. The forces produced by bell ten in Durham Cathedral were computed and found to be much larger than the weight of the bells in agreement with the theoretical values in the literature [6]. The sensors have been proven to be reliable and accurate; they will soon be used to undertake further research into the vibration of the tower of Durham Cathedral and also many others around the UK. The network also has potential applications in low-frequency structural-health monitoring because of its capability to measure acceleration in three axes.  
All sections and experiments, J.M.; supervision, Z.R.; application concept, C.C. This research was supported by the School of Engineering, Durham University and received no external funding.  
The authors would like to thank the Durham Bell Ringers society for invitations to Durham Cathedral bell tower and also for sharing information about the art of change ringing.  
The authors declare no conflict of interest.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 6 Discussion  
One of the most relevant challenges in cybersecurity is the detection of zero-day attacks, which can easily evade all the products based on signature or pattern detection. The proposed approach leverages various characteristics that are known to perform well when facing zero-day threats [40] like, for example, the absence of malicious samples in the training set, the training set sanitization process, and the absence of signature-based features and filters.  
On the other hand, in Section 4.4, we suggested the usage of whitelists as a way of reducing false positives. While in the experimental evaluation of Section 5, we intentionally used them as little as possible (i.e., only 11 of the 758 machines in the benign test set were actually whitelisted), a security analyst could customize such whitelists in order to filter out machines that do not require monitoring. Adding domain knowledge to machine learning algorithms in the form of data (in our context, machines) that should not be modeled or monitored can not only reduce the amount of alerts that an analyst has to evaluate, but also increase model performance. In conjunction with the integration of whitelists, the number of false positives generated by our approach can be tuned in two other ways. The first one is represented by the threshold \(c\) which, as already described in Section 5, controls the sensitivity of the ANN; in turn, it impacts the false positive rate because the lower the minimum value of the ANN output confidence considered as \"high\", the harder to match the conditions we have defined for the connection to be an anomaly (see Section 4.4). The second way of reducing false positives is a periodic retraining of proposed models. As briefly described in Section 7, once the prototype will be included in a streaming architecture, the training phase will be performed periodically. Real networks changes over time, so keeping the models updated is the key to maintain an accurate modeling of what is the current state of the network.  
Finally, as already pointed out in Section 3, differently from other approaches, we extract features directly from the raw traffic without relying on network analyzers that reconstruct network traffic metadata. This allows to save computing resources and to speed up the analyses. Furthermore, the use of bit-stream representation is independent from protocol specific fields (e.g., DNS query field), making the prototype also able to detect tunneling attacks on different clear-text protocols.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
###### Abstract  
Malicious software presents significant challenges in cybersecurity, leveraging rapidly evolving technologies to bypass traditional defense mechanisms. This research introduces a novel image-based malware classification framework that uses hybrid-model Convolutional Neural Networks to process RGB images generated from assembly code. We present MalevisAsm, an enriched dataset that merges MaleVis malware samples with benign files, and propose a hybrid deep learning model that combines EfficientNetB0 and DenseNet121 for robust feature extraction. The approach transforms Portable Executable files into assembly code, maps opcode transitions into three-channel images, and uses a fine-tuned CNN to classify malware families. Additionally, we implemented Uniform Manifold Approximation and Projection a contemporary nonlinear dimensionality reduction technique, to enhance the identification of previously unseen malware samples via binary classification. Our experiments achieve a top-tier accuracy of 98.45%, surpassing existing benchmarks on the MaleVis dataset. This research contributes to the field by integrating static binary analysis with advanced computer vision techniques, offering a scalable and effective solution for malware detection.  
image-based malware classification; assembly code visualization; hybrid deep learning model +
Footnote †: journal: Applied Sciences
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis
### Aodv
#### 3.1.5 Local Connectivity Management  
Once a node receives a broadcast form a neighbor node, it calls upon the insert function or the update function to update the local routing table information, so that all of the latest information of the neighbor nodes will be included in the local routing table. If one node doesn't broadcast any information to its neighbor nodes for a long time, it needs to broadcast a hello message to them. A hello message is a special \(RREP\) message, sent by the node initiatively, and contains the broadcast ID and sequence number of the node owners. The hello message only has a \(TTL\) (Time to Live) of 1, which ensures that it is not forwarded by its neighbor node. In other words, the hello message won't be broadcast outside its neighbor range. Nodes that receive the hello message update the connection information with the node that sends it. Receiving the hello message from a new node or no longer receiving any message from the original node implies a change in the local network.  
The main advantage of AODV is the ability to reduce the routing overhead. The others are the possibility to use an extension ring search to control the flooding of \(RREQ\) packets. In addition, the deployment of the destination sequence number allows nodes to pursue more updated routing. However, there are several issues worthy of attention in AODV protocol. Firstly, the two-way link and periodic link confirmation are required to detect link damage. Then, it needs to maintain the routing table. In particular, this flooding scheme might cause some security issues. Furthermore, the flooding attacks generated excessive traffic to the lead to DoS in MANET.
## 4 Works Execution and Control
### Execution Procedure. Test Stretches  
With the construction of earth moving work it is fundamental to verify the behavior of the materials on a real scale as well as the adequacy of the machinery used and the work control methodology [12; 13]. A test stretch was completed for this purpose; the system for controlling the embankments consisted of the following (Figure 7A-D depicts the described stages).  
The materials were mixed using a 25 ton static weight pad foot drum (Caterpillar 825b model), with a minimum of four passes. At the same time, the materials were wetted. Once this first compacting process was complete, the layer was profiled with a bulldozer in order to create an adequately finished layer, which will ensure water from any precipitation is evacuated as quickly as possible.  
Figure 6: A panoramic view of the earth moving work to build the main roads.  
Figure 7: Embankment execution procedure. (**A**) Extender line on the dry soil; (**B**) mixed with lime and clays; (**C**) profiling motor grading layer; (**D**) compacting process.  
The thickness of each layer was 0.25 m, and comprised of soil obtained from the clearing, which, as mentioned above, was clay of varied mineralogical composition (montmorillonite, saponite, stevensite, sepiolite, etc.). No logical grade could be deduced as far as the composition because of the spatial distribution. For this reason, and due to the difficulties of accurately determining the mineralogical composition of the materials, as well as the conditions for implementation, the choice was made to perform an exhaustive control of the embankment execution procedure, from the preparation of the foundation ground to the exploitation of the clearings and the compacting.  
The quality control was performed following the standard procedure and was supplemented with density and humidity tests using nuclear methods that were duly calibrated and contrasted with sand and stove drying methods. The obtained results showed average values of 99% regarding the respective used pattern densities and average humidity, corresponding with the optimal humidity of the standard Proctor tests. Similar experiments have been recorded with fine non-cohesive soil. The backfills were completed with lime stabilization using the same clay as was used for the embankments to stabilize the ground; backfills were mixed with 6% lime. The treated thickness was 0.30 m, and was spread over the esplanade layer with:  
* A 0.20 m layer of clean gravel, protected with a geotextile to prevent the contamination of the gravel. This layer features a slope that drains toward the manifold situated at the foot of the road
* A 0.80 m layer of adequate soil, where the services were arranged
* A 0.25 m layer of artificial ballast
* The concrete pavement and tread layer of bituminous mix.
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 4 Validation of the Proposed Solution
### Simulation Validation of Solution Functionality
#### 4.1.6 Summary of Experiment 1b and Experiment 2b  
The simulation results of the experiment with a change of master manipulator speed are depicted in Figures 10 and 11. Results of the experiment with a change of slave manipulator speed are shown in Figures 12-14. Both experiments results, where an advanced synchronization algorithm was used, are aggregated in Figures 12 and 15. These results prove that the proposed algorithm can identify a global change of speed of the master element (Experiment 2a) and also of the slave element (Experiment 2b). Most importantly, the algorithm is able to modify the speed of the slave element (elements) feedforward unlike in the case of the standard algorithm. Based on monitoring the operation duration of master and slave manipulators (Figures 10 and 13), and according to evaluation of percentage proportion deviations (Figures 11 and 14), it is clear that the algorithm ensured the time-synchronized movement in the 3 rd step of the production cycle from such a change. This resynchronized activity is indicated by a minimized absolute value of percentage proportion deviation \(D_{ij}\) in the two operations after this change.  
Figure 11: Percentage proportion deviation with feedforward synchronization—Experiment 1b.  
Figure 12: Brief summary of Experiment 1b results.  
Figure 13: Operation duration times of master and slave manipulators with feedforward synchronization—Experiment 2b.
# Using Interval Analysis to Compute the Invariant Set of a Nonlinear Closed-Loop Control System
## 6 Application Scenarios
### Stabilization of the Pendulum Motion towards Zero
#### 6.4.3 Computation of the Invariant Set  
The invariant set for this case is depicted in Figure 7. As can be seen, the whole line is part of the invariant set, implying that once on the surface, the system will stay there for ever. To see that the pendulum will actually converge to its equilibrium point, the vector field in Figure 8 is helpful. It can be seen that once on the surface, the trajectories tend to the origin.  
Figure 7: Invariant set of the pendulum, where the sliding surface is a straight line through the origin.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 3 Energy-Based RPL Routing for IoT-Fog-Enabled Forest Region
### Energy-Based Routing Protocol for Low Power Lossy Networks (E-RPL)  
The proposed idea for forest fire monitoring is that all the sensor nodes are randomly deployed. For RPL communication, DODAG has to be formed first. All the nodes select their parent from the list based on the RANK, which is calculated based on the objective function. For E-RPL, the objective function is the residual power (RP) of the node. Though residual energy plays an important role in the energy-constrained environment, it also needs an expected transmission range (ETX) to improve the performance. In E-RPL, both ETX and residual energy are used as an objective function in validating the network performance in terms of delay, packet delivery ratio (PDR), and CPU utilization for varying network sizes of 10, 20, 30, 40, and 50 nodes.  
When all the sensor nodes send the data directly to the border router via RPL routing, it takes more time to reach the destination. Specifically, if the number of nodes is increased, it results in an increased end-to-end delay and less PDR. Additionally, the total energy consumption of the network increases with a varying number of nodes for reaching the border router or Fog node where real-time analysis is done. The level of nodes in the RPL DODAG ROOT increases when scalability increases. Though an optimized rank calculation is done with expected transmission range (ETX) and energy to reduce the size of DODAG in the entire network, the role of the aggregator is highly appreciated.  
In the proposed work, instead of routing the data packets from all nodes to Fog nodes using E-RPL, aggregation and data compression are carried out at the aggregator for every region in the network area using E-RPL, where energy and ETX are applied to all aggregators, which will improve the performance of the network in terms of PDR and less overall energy consumption.
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 3 Architecture
### Traffic Engineering Architecture  
We design traffic engineering for our system to achieve approximate optimal performance. All of the functions are deployed on the server and controllers. The server accounts for seeking optimized routing paths among clusters while controllers split flows among multiple paths to balance traffic. Figure 4 shows an overview of the traffic engineering architecture.  
The controllers operate over the following aspects:  
1. The information collection module provides information for traffic prediction. It is responsible for gathering statistics information, including packet size and the number of every flow, the queue size for every port of the switch, _etc._
2. The link traffic allocation module allocates traffic evenly among multiple links between any two connected clusters. Besides this, it also answers for installing and updating flow tables.
3. The topology abstract module detects switch connection information when the system starts and abstracts away topology information. When there is a link state change at run-time, it reports this information to the topology aggregation module and traffic prediction module. We call it link-level abstraction, which lays the basis for the link traffic allocation module in the controllers and the traffic aggregation module in the server.  
The server achieves the following functions:  
1. The topology aggregation module chalks up the network topology graph in which vertices represent clusters and edges represent the links between clusters by consolidating topology information from multiple controllers and then aggregating trunks between clusters. We call it cluster-level abstraction, which is the basis of inter-cluster routing computation. This abstraction significantly reduces the size of the graph, thus simplifying the routing optimization algorithm.
2. The traffic prediction module is responsible for predicting future traffic for a period of time in terms of traffic statistics information of the past time. Treating prediction values as inputs to the optimization algorithm can gain more reasonable routing congestion avoidance. The reason why traffic can be predicted is mainly due to the characteristics of data in the Internet of things (IoT) environment.
3. The traffic optimization algorithm computes optimal or suboptimal routing paths according to the global network topology and traffic value,s which are obtained from the traffic prediction  
Figure 4: Traffic engineering overview.  
module and the topology aggregation module mentioned above. This module will be described in detail in the remainder of this paper.
# Performance Validation of Control Algorithm Considering Independent Generator Torque Control in PCS
## 4 Experimental Validation
### Simulation Result for Scaled WT  
The control algorithm design described earlier has been completed for the scaled wind turbine model using Matlab/Simulink and finalized in DLL format. The external controller in DLL format was integrated with the Bladed software for elastic analysis to conduct dynamic simulations for the performance verification. The simulation conditions included  
Figure 10: Experimental environment of the wind tunnel center.  
Figure 9: Scaled wind turbine for the wind tunnel test.  
an average wind speed of 6 m/s and a turbulence intensity of approximately 10%, and each algorithm was executed for a duration of 60 s.  
Figure 11 shows the simulation results for the reference power control, PCS torque control, and control switching logic control. Similarly, it can be observed that the generator torque value is predominantly influenced by the rotor speed due to the consideration of the PCS torque control. The proposed control algorithm alleviated the sharp drop in the generator torque observed in the reference power control. However, while the reference power control maintains the generator torque value consistently at the rated value in Region III, the PCS torque control exhibits variable values depending on the rotor speed. This has resulted in an increase in the standard deviation of both the generator torque and the power.  
Tables 7 and 8 present the quantitative values of the rotor speed and generator power based on the simulation results. In the case of the rotor speed, a tendency for the standard deviation to decrease relative to the reference power control was observed, with a reduction of approximately 17.73% under the PCS torque control conditions and about 29.58% under the modified control switching logic conditions. For the generator power, an increase of 3.21% was noted under the PCS torque control conditions due to the oscillation of the generator torque, which was analyzed to result from the generator torque command being output as a variable value rather than a constant value, unlike in the reference power control. In contrast, under the modified control switching logic conditions, the generator power decreased by approximately 13.33%, attributed to the alleviation of the rotor speed spikes due to the application of the control switching logic.
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 4 Upstream DFT-Spread Hybrid OFDM-DFMA PON Performance
### Upstream Transmission Performance  
Figure 5 presents the overall channel BER as a function of the received optical power, where the total optical launch power into the SSMF transmission system is fixed at 0 dBm. In computing Figure 5, a resolution of 7 bits was used for the ADC/DAC, as shown in Figure 4. All other parameters are specified in Table 1.  
It can be seen in Figure 5 that, for the case of including/excluding DFT-spread, the calculated channel-1 (CH1) and channel-2 (CH2) BER performances are identical across the entire received optical power range, and these two channels also have very similar signal bit rates. Most importantly, for the same signal bit rates, the case with DFT-spread can achieve an upstream power budget improvement of more than 1.4 dB compared to the case without DFT-spread. Such performance improvement is mainly because of the DFT-spread-induced PAPR reduction, resulting in a considerable decrease in quantization noise, thus giving rise to an increase in the effective OSNRs.