esa-sceva/satcom-chunk-collection huggingface dataset:
# Securing Infrastructure-as-a-Service Public Clouds Using Security Onion
## 4 Methodologies
### Experimental Setup  
The experimental environment is a highly flexible, centralized virtual lab infrastructure, which consists of an ESXi server hosting three VMs: Security Onion (SO) [34], Kali Linux [35] and Metaspolitable [36] (see Figure 3). In this setup, SO runs the IDS instance, Kali hosts the attacker and Metaspolitable is the victim/target machine. ESXi 6.0 is used which is a robust, high-performance hypervisor that virtualizes hardware resources and makes them shareable by several VMs inside VMware Workstation 12 on Intel (R) Core (TM) i7, 16 GB RAM and 734 GB hard disk space.  
To implement the IDS, SO is configured for network monitoring, intrusion detection and log management in one of our three VMs. The major factor that influenced the decision to use SO is that it comes with pre-installed security tools, such as Snort, Bro, Suricata, OSSEC, Sguil, etc., which makes network monitoring a less difficult task for security administrators. SO can be used in two ways, either proactive: When it is used to identify vulnerabilities or expiring SSL certificates, or reactive: When used for network forensics or incident response. Although SO provides visibility into a network and context around the anomalous event, it still requires commitment from a security administrator to monitor network activity, review alerts and incline to learn new things. There are other commercial solutions which offer similar tools as SO, but only a selected few contain the vast capabilities of SO in one package.  
SO offers the choice of Snort or Suricata for rule-driven intrusion detection. In this work, Snort [37] is considered as it is a de-facto standard IDS with millions of downloads and is the most extensively deployed IDS technique worldwide. Snort IDS scans network traffic for identifiers or fingerprints that matched known malicious or suspicious traffic.  
Figure 3: Experimental Setup.  
With full-packet capture and Snort logs, there is an overwhelming amount of data to be analyzed. Fortunately, SO integrates analysis tools, such as Sguil, Sguert and ELSA. In this experiment, we use the Sguil console for data analysis. Sguil [38] provides visibility into the data that is being collected and the justification to validate the alert.  
To test the effectiveness of our IDS, Metasploitable is installed in one VM to serve as a victim machine. Then attacks are simulated from Kali Linux to mimic real world scenario.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 3 Proposed Method  
This section presents the proposed method. It consists of three main steps: feature point detection, MN-SIFT descriptor construction and regression model training & testing. Figure 1 shows a block diagram for the proposed method. Each block is briefly described below:
## 6 Discussion  
### Solutions Quality  
On average, the TLBA was able to obtain solutions with gaps between 11.48% and 33.22% (Table 10). Even when the minimum gaps were obtained in a mid-sized instance (TI4:10.58%) and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**B\&B GMOP**} & \multicolumn{2}{c}{**Initial Solution**} & \multicolumn{3}{c}{**TLBA**} \\ \cline{2-9}  & **Optimal Cost** & **Time** & & & & & **Min. Comp.** & **Avg. Comp.** \\  & **(COP)** & **(secs)** & **GAP** & **Min. Gap** & **Avg. Gap** & **Time (secs)** & **Time (secs)** \\ \hline TI1 & 10,326,117 & 969.4 & 14.54 & 0.2666 & 0.2952 & 1424.9 & 1482.2 \\ TI2 & 2,683,534 & 972.5 & 23.7 & 0.2757 & 0.2895 & 1258.3 & 1279 \\ TI3 & 4,890,778 & 972.8 & 72.96 & 0.1659 & 0.1722 & 1321.5 & 1350.2 \\ TI4 & 106,247,444 & 994.56 & 30.23 & 0.1058 & 0.1148 & 7227.1 & 7260.2 \\ TI5 & 74,585,367 & 999.25 & 10.84 & 0.1705 & 0.1773 & 7741.5 & 7792.7 \\ TI6 & 43,278,154 & 993.24 & 21.84 & 0.2871 & 0.3041 & 8030.7 & 8042.5 \\ TI7 & 1,585,713,234 & 1001 & 2.62 & 0.2186 & 0.2328 & 24,959.6 & 25,002.3 \\ TI8 & 765,235,068 & 1020.1 & 4.66 & 0.2309 & 0.3322 & 27,000.4 & 27,145.6 \\ TI9 & 252,626,622 & 1017.2 & 16.08 & 0.1301 & 0.1486 & 23,741.6 & 23,741.6 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results for total planning cost and total computational times.  
Figure 5: Solution generation process.  
a large instance (TI9:13.01%), there is no statistical evidence of better performance in a specific problem size.  
As shown in Figure 6, the average gaps also showed no significant difference when comparing test instance groups.  
Even though the lot-for-lot method allowed for obtaining feasible initial solutions in relatively short times (15-180 s), the gaps for the initial solutions were considerably high. The effect of the initial solution quality on TLBA was not measured, and further experimentation is necessary to improve the algorithm's efficiency [43; 44; 45].  
Taking into account these results, the exact method remains a convenient option in terms of quality. A cost-benefit analysis may be carried out, due to the sometimes expensive acquisition of solver licenses [10], especially in small and medium enterprises.
## 4 Discussion
Another work [71] correlates NDVI and SPEI over semiarid regions with a similar methodology used here and also shows very high correlations between the two variables over South African grasslands. South African regions are also those where optimal correlations between VHI and SPEI present the largest improvement when compared with baseline values estimated using the traditional approach with \(\alpha=0.5\) for all pixels (Figure 3). The coherence between SPEI time-scales for correlations estimated with the three datasets is also worth noticing (Figure 1, bottom). The maps show that vegetation health responds predominantly to short drought time-scales of 1- and 3-months, which is expectable according to Ref. [70] and followingthe rationale that vegetation typically has a shorter memory to temperature variations. However, to the best of our knowledge, there are no works focusing on the relationship between SPEI and temperature alone. Future work on this specific topic may include a study that compares temperature (e.g., TCI) and SPEI in order to understand the drought time-scales associated to temperature conditions of vegetation. Another topic that may be tackled is the sensitivity of the contribution of \(\alpha\) to the drought time-scale by estimating \(\alpha\) with one time-scale for all pixels (e.g., 1- or 3-month time-scale). Although such choice will simplify the analysis, it may also introduce biases on the results, in particular since there is still no knowledge on the typical time-scales associated with temperature condition. When comparing the three datasets, differences of about 0.2 (RMSD) were obtained between \(\alpha\) as estimated with PU LST and CM-SAF LSTs. These differences include some pixels below (above) 0.5 when using PU LST and above (below) 0.5 when using CM-SAF LSTs. These cases represent about 19% of the total pixels. However, if cases where \(\alpha\) jumps from 0.425 to 0.575 are dismissed, then only about 15% of the values of \(\alpha\) estimated with PU LST and CM-SAF LSTs present relevant differences in terms of the dominant contribution. Furthermore, when comparing \(\alpha\) estimated with the different LST CDRs there are no associated systematic errors (bias = 0). A comparative study of severe drought episodes was finally made between the optimal VHI with SPEI-based \(\alpha\) (VHI\({}_{\text{SPEI}}\)) as derived from one of the LST datasets (CM-SAF SMW) and the baseline VHI estimated with \(\alpha=0.5\) (VHI\({}_{\text{K}}\)). Results obtained with the metric \(\mu\), which allows a comparison between the traditional VHI and the SPEI-based VHI by assessing the capability of capturing a drought event as identified by SPEI, point out that the SPEI-based VHI systematically presents larger absolute values and therefore it better represents the drought episodes.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 3 Results
### RF-Acoustic Computed Tomography (RACT) of the Homogeneous Phantom  
The RACT simulation used the same finite element mesh that was used for the photoacoustic simulation, utilizing a typically used radio frequency source of 434 MHz. For the parameters given in the methods section, the electric field magnitude is provided in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Tissue** & \(\mu_{a}\) (mm\({}^{-1}\)) & \(\mu_{s}^{\prime}\) (mm\({}^{-1}\)) \\ \hline Background & 0.0005 & 1.5742 \\ Glandular & 0.0059 & 1.12 \\ Tumor & 0.0021 & 0.625 \\ \hline \end{tabular}
\end{table}
Table 1: Optical properties (\(\mu_{a}\) the absorption coefficient, and \(\mu_{s}^{\prime}\) the reduced scattering coefficient) of different breast tissue at 800 nm.  
\begin{table}
\begin{tabular}{c c c} \hline
**Tissue** & \(\epsilon_{r}\) & \(\sigma\) **(S/m)** \\ \hline Background & 5.51 & 0.0353 \\ Glandular & 61.3 & 8.86 \\ Tumor & 25.25 & 13.03 \\ \hline \end{tabular}
\end{table}
Table 2: Electrical properties (\(\epsilon_{r}\) the relative permittivity, and \(\sigma\) the conductivity) of different breast tissue at 434 MHz.  
Unlike the PACT case, the electric field alone does not provide any information on the location of the absorbers. Since the wavelength is much larger than the simulation domain, and there is small difference in conductivity between the absorbers and background, the electric field intensity is approximately constant. The results of the RF acoustic simulation of this homogeneous phantom are shown in Figure 5.  
Figure 3: Photoacoustic computed tomography (PACT) simulations using an 800 nm source on the homogeneous phantom with two light absorbing inclusions; (**a**) the true optical absorption distribution; (**b**) the initial pressure rise induced by the photoacoustic effect; (**d**) the reconstructed pressure, calculated using the time-reversal algorithm; and (**c**) the reconstructed absorption, obtained by dividing the reconstructed pressure with the fluence.  
Figure 2: Total optical fluence distribution inside the homogeneous phantom with two absorbers using an 800 nm source. The circular and elliptical absorbers, simulating hemoglobin with absorption coefficient \(\mu_{a}=0.425\) mm\({}^{-1}\) at 800 nm, are located 4 mm to the left and right of the center. The background has a \(\mu_{a}=0.001\) mm\({}^{-1}\), and a reduced scattering coefficient \(\mu_{s}^{\prime}=1\) mm\({}^{-1}\). \(\mu_{s}^{\prime}=0\) for the absorbers.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 2 Stakeholder and System Interoperability
### System Interoperability Promoted by Industrial IoT Frameworks
The key principles of Service-Oriented Architectures (Lookup, Late Binding, Loose Coupling) allows Arrowhead-compatible systems to dynamically connect, produce and consume services to/from each other (i.e., exchange information). When becoming available (i.e., after wake-up or getting their resources freed), service producing systems get in contact with the Arrowhead Service Registry core system, which records the service availability. On the other hand, when a system requires to consume a given service (e.g., reading vibration or stream processing a data-set), it contacts the Arrowhead Orchestrator and the Authorizer core systems, so it receives access to a service producer. After this match-making procedure, systems communicate with the protocol of their preference, hence real-time operation is not disturbed by the capabilities of the Arrowhead core [20]. System compatibility issues have to be clarified during the orchestration phase, or handled through translators that Arrowhead can also provide [21]. Arrowhead also promotes ecosystem-wide interoperability [7; 22]. Local automation clouds defined on the production floor can have inter-cloud connections to data processing systems and Manufacturing Execution Systems (MES) on the Manufacturing Operations Management level, as well as to Enterprise Resource Planning (ERP) systems on the Business Planning and Logistics level. This approach for secure information exchange between loosely coupled systems can be extended inter-enterprise, throughout whole ecosystems and supply chains [23], as Figure 4 suggests. Following this thread, the telco-grade solutions described in the current paper can also be part of the System of Systems formed within industrial production ecosystems. A high-level representation of this approach is shown by Figure 4.
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 3 Methodology
### Use of Deep Learning for Tabular Sets--Use of Fastai Deep Learning Library
The model structure is shown in Table 2.  
In the model, three dense layers were utilized with different dropout values for each of the dense layers. The activation function used for the layers is also depicted in the table. Within the model summary, it can be seen there are embedding layers for each categorical \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output Shape** & **Number of Parameters** \\ \hline Embedding & 3 & 12 \\ Embedding & 3 & 9 \\ Embedding & 5 & 40 \\ Embedding & 10 & 260 \\ Embedding & 5 & 40 \\ Dropout & 26 & 0 \\ BatchNorm 1d & 9 & 18 \\ Linear & 50 & 1800 \\ ReLU & 50 & 0 \\ BatchNorm 1d & 50 & 100 \\ Dropout & 50 & 0 \\ Linear & 10 & 510 \\ ReLU & 10 & 0 \\ BatchNorm 1d & 10 & 20 \\ Dropout & 10 & 0 \\ Linear & 1 & 11 \\ ReLU & 1 & 0 \\ BatchNorm 1d & 1 & 2 \\ Dropout & 1 & 0 \\ Linear & 1 & 2 \\ \hline \hline \end{tabular}  
* Total Parameters: 2824, Total trainable parameters: 2824
* Optimized with ‘torch_optim.adam_Adam’, betas = (0.9, 0.99)
* Using true weight decay, Loss function: Flattened Loss  
\end{table} 
Table 2: The layer structure of the model utilized. Figure 3: FastAI tabular module structure. variable and there are dropout layers provided to help reduce biases and variances as previously indicated. Additionally, all the parameters used for the model were trainable and there utilized. The data were split between training, testing and validation sets with 80% allocated for training and 20% for training and validation. It is critical to note that only the most important features as set out by the feature importance tool in Section 3.2 were utilized in the model. The model setup code parameters developed and used is further given in Appendix A. Once these parameters were set up, the learning rate finder was employed to suggest an appropriate learning rate and using this learning rate (lr) the model was trained with 80 epochs (training steps) and using a weight decay factor (wd) of 0.2 to help regularization and to prevent overfitting. All of these factors implemented using the 'learn.fit_one_cycle' function within the library. The final output of the model is a prediction of the year, which should be designated as the next point of maintenance intervention for a given road within the database. Any user using the model at the road agency would have to provide the same information for that road as used in this model configuration. However, as the data used are from easily available sources, input data pose an easier task than other methods or techniques.
## 4 Experimental Methodology and Measurement Results
### Link Performance  
The link performance was measured by transmitting 40.96 MHz-OFDM signals (256-QAM) centered at 3.6864 GHz. The root-mean-square EVM values provided in this paper are normalized to the average constellation power.  
1. DL: from the DU to an RRU via an optical link with different fiber lengths, then from an RRU antenna to a user (electrical back-to-back);
2. UL: from a user to an RRU antenna (electrical back-to-back), then from the RRU to the DU via an optical link with different fiber lengths.  
The combined length of the break-out fibers, and therefore the fiber length of the optical back-to-back cases, was about 8 m. The RF in/out ports of the RRU and the user were connected directly with a cable. Appropriate attenuation was applied to prevent the receiver chains from saturation. No reference clock was provided; the carrier frequency offset was estimated and canceled using the algorithm proposed in [22].
## 3 Results and Discussion  
The Physical Mechanism of dual material gate-engineered heterostructure junctionless tunnel field-effect transistor (DMGE-HJLTFET)  
The physical mechanism of the DMGE-HJLTFET is shown in Figure 2a-d. Figure 2a shows the nonlocal BTBT tunneling rate of electrons and Figure 2b shows the nonlocal BTBT tunneling rate of holes of the DMGE-HJLTFET when Vds = 1 V and Vgs = 1.2 V. It was obvious to observe that BTBT mainly occurred at the source and channel interface, and the rate of BTBT increased significantly due to the heterojunction between source and channel. In Figure 2c, the electric field distribution of DMGE-HJLTFET at Vds = 1 V and Vgs = 1.2 V is illustrated, it is not difficult to find from this figure that value of electric field was markedly improved near the hetero junction and hetero dielectric, so the ON-state current significantly increased. In addition, the total current density of DMGE-HJLTFET was researched in order to well understand the physical mechanism, as shown in Figure 2d.  
Figure 2: (**a**) Nonlocal band to band tunneling (BTBT) rate of electrons; (**b**) nonlocal band to band tunneling (BTBT) rate of holes; (**c**) electric field distribution in DMGE-HJLTFET; (**d**) total current density of DMGE-HJLTFET.  
Figure 3a shows the energy band diagram of the HJLTFET and DMGE-HJLTFET in ON-state (Vds = 1 V, Vgs = 1 V), where it was noticed that the conduction band and valance band of DMGE-HJLTFET at the source-channel interface were very close to each other and the tunneling distance of DMGE-HJLTFET was much smaller than the HJLTFET, i.e., tunneling width and effective tunneling area of DMGE-HJLTFET were obviously improved by appropriate workfunctions for the divided gate electrode as compared with HJLTFET, resulting in enhancement on electron probability to tunnel from the source to channel junction in the DMGE-HJLTFET. Figure 3b indicates the energy band diagram of HJLTFET and DMGE-HJLTFET in OFF-state (Vds = 1 V, Vgs = 0 V), and from this figure it is not difficult to find that the distance of conduction band and valance band in source/channel interface of the DMGE-HJLTFET was much larger than HJLTFET. In addition, an extra barrier height occurred in the channel region for DMGE-HJLTFET. This is because the gate electrode was divided into three parts in the DMGE-HJLTFET namely the auxiliary gate (M1), control gate (M2) and tunnel gate (M3), and the auxiliary gate (M1) produced the extra barrier height in OFF-state.
## 3 Results and Discussion
### Simulations for Chain-Based Network
#### 3.1.3 Evaluation of Complexity of Information Fusion  
In chain-based WSNs, each node has only one child for AVG and proposed method. Thus, each node needs to combine only two data frames/packets during the fusion process for AVG and proposed methods. For EBDKF fusion technique, the average number of neighbors is two such that there will be 3 data frames/packets to combine in a single time for a node. Further, there are 3 batches of such data frames/packets per data gathering cycle. Therefore, the complexity is high in EBDKF. The number of CPU cycles of the microprocessor and the corresponding complexity \(T_{A}\) for each fusion function is given in Table 2. We use Atmel Studio 7 to evaluate the complexity of the fusion function.  
Single precision 32-bit floating-point data types are used in AVG, EBDKF and the proposed information fusion functions. The complexities of these functions are therefore high compared to simple information fusion functions such as OR and AND. The average method fusion function will require only two additions and one division. In the proposed statistical information fusion two additions, five divisions, and one multiplication are used. It was discussed in a previous section that the average number of neighbors for diffusion technique in a chain-based platform as two. For a single diffusion estimate, the Estimation based Diffusion Kalman Filtering will require 10 additions, 4 divisions and 8 multiplications. Thus, the EBDKF fusion function has a much higher computation complexity than the proposed method and AVG method as proved by results in Table 2. It can be identified that the complexity of diffusion technique has been 7 times complexity of average method whereas the proposed method's complexity is only 1.97 times that of Average method for chain-based sensor networks.
## 6 Discussion
### Benefits
#### 6.1.4 Decentralized Intelligence  
In centralized intelligent systems, a centralized agent is in charge of gathering information from every node in the network and creating a centralized entity that can create global knowledge. However, the centralized intelligence architecture is less scalable and consumes more networking resources than the distributed knowledge generation approach. Furthermore, centralized intelligence is prone to sole spot of breakdown and security attacks. As an alternative, in distributed intelligence systems, many advantages such as low latency, low power consumption, etc. are realized due to the distributed approach. However, if decentralized machine learning is applied without a secure mechanism for privacy protection, users privacy can be leaked, as proven by existing studies [391]. This is where blockchains can come in handy by enabling decentralized machine learning and ensuring privacy is preserved. By utilizing blockchain, ML models can be trained, knowledge can be generated locally, and knowledge can also be shared securely using blockchain, such that a centralized authority in a KDN can aggregate the distributed knowledge in the blockchain for making global-level decisions. First, machine learning models can be trained in a secure and trustworthy manner by using legitimate and verified data stored on the blockchain. Then, trained machine learning models and model parameters can also be exchanged securely in the Knowledge-Defined Network thanks to the immutable and verifiable ledger technology. This promotes collaborative knowledge generation and network optimization, which can enhance the performance of Knowledge-Defined Networks. In order to generate a secure, optimized global model of intelligence, decentralized knowledge agents (controllers) can audit the blockchain of knowledge or machine learning models for poisoning attacks.
## 5 Simulation Results and Discussion
### Performance Comparison of Attention-Mechanism-Based PFL with Traditional FL Methods
Hence, PFL and PFL with attention mechanism lead to an approximately 30% reduction in latency, that comprises queuing delay and processing delay, compared to conventional FL approaches such as FedAvg and FedSGD. However, a significant discernible difference in the latency of PFL and PFL with attention mechanism is not observed in Figure 11. This is attributed to the delay accumulated in calculating the attention weights and to identifying the dominant local model hyper-parameters to be transmitted from vehicles to the UAV. Although this difference in the two approaches is negligible over a smaller road length, this difference may be larger and significant in real-world traffic scenarios. Figure 10: Variation in average packet delay (ms) in PFL for a varying number of vehicles (\(V\)) for different road lengths (\(R_{L}\)). Figure 9: Variation in average latency (ms) in FedSGD and PFL with attention mechanism scenarios with a varying number of vehicles (\(V\)) for road length (\(R_{L}\)) = 2 km.