esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions
## 2 Digital Forensics
### Network Forensics  
Network forensics is the practice of analyzing information from a host or an entire network [5]. The forensic information can be obtained through logs or traffic captures.  
Three of the layers of the TCP/IP Model can provide investigators with useful information. These layers are the application, transport, and network layers. The only layer not included in this is the network interface layer, which includes ethernet frames and the physical connections of a network. Forensic information can be gathered from the application layer through logs that hosts gather. This can be information regarding failed logons or timestamps, which could be critical information in an investigation. The transport and network layer are where firewalls are classified. Firewalls, if properly configured, can contain log data of traffic that has been dropped from the network [6]. This can give investigators information about potentially malicious traffic that has been seen by the firewall. Figure 2 shows the relationship between the layers and the information that investigators can gather by showing the flow of traffic though the network model and the devices it affects.  
Figure 2: Evidence gathered from network layers.  
The largest challenge facing investigators in network forensics is the amount of traffic and log data that can be present in an investigation. Although it is possible in theory, it is impossible in practice for investigators to collect and analyze every single packet in a capture of an entire network. The amount of data will not only take too much time to analyze, but also can incur on significant, and often, unmanageable costs [6].  
Another challenge that investigators face with network forensics is the growing trend of the Internet of things (IoT). These devices rely on networks to function, meaning there are more end hosts on networks that create logs and traffic. This not only increases the challenge of log and capture size, but it also complicates investigations when determining the scope of the investigation [7].
# New Retailing Problem for an Integrated Food Supply Chain in the Baking Industry
## 4 Model Formulation  
The article considers a two-echelon supply chain with one manufacturer (central kitchen, CK) and one retailer (branch warehouse). The structure is developed under a coordinated case. The purpose of this study was to evaluate the effect of revenue-sharing, buy-back, and quantity- flexibility under new retailing framework.  
* Temperature control One of the major preoccupations of food processing in the past decade has been investigating the nature of temperature control. Temperature is one of the important factors to consider during the bulk production of food items. Storage temperature and time is discussed by [PERSON] et al. [60], as follows.  
\[q(T,t)=q_{0}e^{-b(T)t^{n(T)}}, \tag{1}\]  
where \(b(T)\) and \(n(T)\) are temperature-dependent coefficients. In particular, \(b(T)\) can be described by the empirical model as follows.  
\[b(T)=\ln[1+e^{m(T-T_{C})}], \tag{2}\]  
where \(T\) is the temperature (\({}^{\circ}K\)), where \(m\) and \(T_{C}\) are constants. It is possible to empirically obtain the values of parameters \(b(T)\), \(n(T)\), m, and \(T_{C}\) so as to use the equations above to calculate the expected quality of food products after storage, at given time periods and temperature levels.  
* Temperature coefficient  
The cost for storage and transportation depends on the temperature in central kitchen. The coefficient of performance (COP) for refrigeration is calculated as follows.  
\[COP=\frac{T_{L}}{T_{H}-T_{L}}, \tag{3}\]  
where \(T_{H}\) and \(T_{L}\) are higher (environment) and lower (cooling) temperature measured in Kelvin. For example, if \(T_{H}=293\) K (20 \({}^{\circ}\)C), and \(T_{L}=273.15\) K(1 \({}^{\circ}\)C), then \(COP=13.76\). It means that for each unit of energy drawn from an electrical source, the coolant can absorb as much as 13.76 units of heat from the inside of the refrigerator.  
We assume the cost of electrical energy at 1 \({}^{\circ}\)C is 1, then relative cost at higher temperatures can be calculated by multiplying the cost with the ratio of \(COP\) values (where we use an environment temperature \(T_{H}\) of 20 \({}^{\circ}\)C = 293 K).  
* The quality degradation cost (\(C_{QD}\)) at central kitchen  
The loss value of a batch of size \(Q\) is  
\[p_{2}\bigg{[}Q-\int_{0}^{Q}e^{-b(T)(Q-q)/D}dq\bigg{]}=p_{2}\bigg{[}Q-\frac{D} {b(T)}(1-e^{-b(T)Q/D})\bigg{]}, \tag{4}\]  
where \(n(T)=1\).
## 5 The Proposed Protocol
### Communication Overhead of the Proposed Protocol
#### 5.6.4 Data Redundancy and Data Fusion Optimization
Medical applications have prioritized redundant data by identifying critical SNs, such as PDFHs, FSDFHs, and SSDFHs, and making sure they keep duplicate copies of crucial activities and data since they deal with urgent and high-importance data. By doing this, the total resource consumption of the network remains relatively low while enabling swift recovery from node failures or communication interruptions. In order to establish secondary channels for data transmission in the event that the primary route fails, selective redundancy is also incorporated into multipath routing. The trade-off between fault tolerance and resource utilization is lessened with the aid of this adapted redundancy. The proposed protocol guarantees that vital services continue to function by concentrating redundancy efforts on the most important parts, improving the network's overall performance and stability. We conclude by introducing a new variable, the Redundancy Energy Threshold (RET), in light of the previously discussed methods. It is interesting to note that SNs with energy levels below RET will undoubtedly limit redundancy and optimize data fusion to preserve power, but SNs with high-importance data (urgent and critical) and energy levels above RET can afford to store and transmit redundant data.
## 4 Empirical Case Study
### Case Company's Background as a Supplier of Industry 4.0 Based Solution Provider  
DT Company is a leading industrial automation manufacturer, offering efficient and reliable products and solutions to serve global customers. One of their specialties is an automated batch process for low-volume, high-complexity customization. It has had a loyal client, Company A, for years.  
Company A is a business-to-business (B2B) information technology (IT) company as a supplier of Industry 4.0 based solution provider, specializing in the flexible manufacturing of desktops, laptops, and other IT devices. Established for years, Company A has been running smoothly with its standard operation procedure, including sale interviews with their clients,'make-to-order' services, and batch production. However, recently, they have received increasingly frequent customized orders in greater diversity from their client, Company B.  
Company B is a laptop retailer, which is supplied mainly by Company A. In their standard collaborative process, individual consumers visit the outlet of Company B, expressing their needs to the sales personnel of B and hoping to obtain a desirable product. If their needs are beyond the existing products available in shops, the sales personnel would inform the procurement team to place the customized order. The procurement team would then engage Company A's sales personnel for an interview to discuss the cost and feasibility of the customization. At this point, A's sales personnel would also seek technical advice from their technical personnel to validate the feasibility. Along with this communication chain, information might be misaligned sometimes. A diagram is developed to enhance the understanding of this communication chain, as shown in Figure 6.  
In recent years, B has noticed that the consumer expectations have become more diverse. The communication activities between A and B had become more frequent, and thus, a more severe misalignment of information was observed. Both A and B agreed that the current process had consumed a considerable amount of manpower and showed inefficiency when the communication became more intense. They were keen to find a solution to address this issue.  
In addition, Company A also noticed that there could be a communication barrier between A's sales personnel and B's procurement team. However, neither of them knew how to translate customer needs to EC's. For instance, they may want the laptop to be good at graphics processing, but they have no idea which graphics card is sufficiently capable of satisfying that need. Apparently, the best way to recommend an optimal laptop configuration is direct communication with A's technical personnel. However, this requires more manpower and incurs more workload on the technical personnel.  
Therefore, Company A engaged DT Company to streamline the communication process. DT wishes to propose a recommendation system that considers the customer's evaluation priority or weight as the input to generate the optimal product configuration under the given budgetary conditions.
## 4 Discussion  
### Explainable Analysis with SHAP  
The interpretability of machine learning is relatively weak compared to traditional generalized linear models. Even though some models can train the importance of features, the scale standards of feature roles are inconsistent and lack intuitiveness; hence, they are often referred to as \"black boxes\". This study uses the SHapley Additive exPlanation (SHAP) method to interpret the model [45; 46]. SHAP analysis is a method of post hoc model interpretation and can interpret the output of any machine learning model. Based on the training status of tree models or neural networks, this method unifies the scale of feature importance for each sample and reflects the importance of features through SHAP values while also presenting the specific roles of each feature in each sample. We apply the SHAP method to the interpretive analysis of three types of rock thin-section images. By calculating the contribution of each pixel to the model's predictive results, we visualize the decision-making process of the model in judging the rock. As shown in Figure 5, red pixels indicate a positive correlation, meaning the larger and darker the red area, the more favorable the model's judgment of the rock. Conversely, the larger and darker the blue pixel area, the more unfavorable it is for the model's judgment. The results show that our method can more accurately locate and interpret the key features in the rock compared to the other three advanced models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Categories Metrics (\%)**} & \multicolumn{5}{c}{**Models**} \\ \cline{3-6}  & & **ResNet-152** & **ViT** & **Swin Transformer** & **Ours** \\ \hline \multirow{4}{*}{C7} & Precision & 0.7949 & 0.8286 & 0.9333 & 0.9355 \\  & Recall & 1.0000 & 0.9355 & 0.9032 & 0.9355 \\  & F1-score & 0.8857 & 0.8788 & 0.9180 & 0.9
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 3 Triple Modular Redundancy in FSMs
### Model 1--Triplicated State Register DFF  
The first model analyzed represents a situation in which the state register DFF is triplicated. The architecture of the FSM is shown in Figure 9.  
1. Injecting an error at the output block \(\lambda\) of the FSM will cause the output error.
2. If the error is injected into the transition block \(\delta\), all three registers will latch the incorrect state.
3. If the error is injected into the state register DFF, then voter will operate and the FSM will not only produce the correct output state, but also the next state for all state registers--the 'broken' FSM will return to the correct cycle.  
TMR can be implemented independently on the output block.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 5 Results
### Parameter Reconstruction Error  
In [26], the sAE was able to achieve the lowest reconstruction error, 0.086, while the technique that came the closest to its accuracy was kPCA with support vector regression, achieving an error of 0.09. The sAE technique still outperforms all the other combinations of techniques, as can be seen in Table 4, achieving an overall error 0.074. It should also be noted that the sAE is able to reconstruct the most parameters of the equaliser (6) more accurately than any other combination of techniques.
# Shear Strength Analysis and Slope Stability Study of Straight Root Herbaceous Root Soil Composite
## 3 Results
### Significance and Correlation Analysis of Shear Resistance Indicators and Influencing Factors  
#### 3.3.1 Significance Analysis of Shear Resistance Indicators and Influencing Factors  
Figure 6 depicts the influence of different root contents on the shear strength indexes of undisturbed soil for different moisture contents.  
Figure 6a shows that the cohesion of undisturbed soil is maximized for the root content of 0.2%, followed by 0.31% and 0.12%. The correlation between the cohesion of RUS and undisturbed soil is highly significant for the root contents of 0.2% and 0.31% and not significant for the root content of 0.12%. The correlation between the cohesion of undisturbed soil with the root contents of 0.12% and 0.2% is highly significant and very significant for the root contents of 0.12% and 0.31%, respectively. In addition, the correlation between the cohesion of undisturbed soil with the root contents of 0.2% and 0.31% is insignificant. At the same moisture content, the median value increases with the root content and decreases slightly when the root content reaches 0.31%. The same trend in cohesion is observed at other levels. The cohesive force exhibits a slow downward trend with an increase in the moisture content and root burial depth.  
The internal friction angle of undisturbed soil is maximized at the root content of 0.09%, followed by 0.07% and 0.06% (Figure 6f). The RUS exhibits the lowest internal friction angle. The correlation between the internal friction angle of RUS and undisturbed soil is highly significant for the root contents of 0.07% and 0.09% and significant for the root content of 0.06%. The correlation between the internal friction angle of USRS at the root contents of 0.06% and 0.09% is significant, while that for root contents of 0.06%, 0.07%, and 0.09% is insignificant. At the same moisture content, the median value increases with the root content, indicating a continuous increase in the internal friction angle. Moreover, as the moisture content and root burial depth increase, the internal friction angle exhibits a significant downward trend. This trend is significantly different from the cohesion trend.  
The results indicate that further analysis beyond significance testing is required to determine the factors influencing the shear strength index.
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 3 Results and Discussion
### Inference of Test Images and Validation of GIS Application Implementation  
Figure 11 illustrates the process of verifying the accuracy of building information. In Figure 11a, the building is located within the image. After identifying its position, Figure 11b showcases the roof detected by Resnet18, represented as a rectangle. Using this detected area, its location on the map in Figure 11c is determined and compared with the building information system. The verification results of this comparison are displayed in Figure 11d. The comparison method checks for a match between the roof value of the building information system and the recognized value; if it matches the IOU threshold, the database containing the building information is updated. In this study, a match of 0.6 was set as the threshold. Additionally, the area enclosed by the rectangle was checked for inclusion in the database using the Contain function of PostGIS, and the database was updated accordingly.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Global Accuracy** & **Mean Accuracy** & **MeanIoU** & **WeightedIoU** \\ \hline Before & 0.7841 & 0.77669 & 0.6392 & 0.64405 \\ refinement & 0.78896 & 0.78383 & 0.64729 & 0.6514 \\ \hline \end{tabular}
\end{table}
Table 4: Performance results of the refinement on the training dataset.  
Figure 10: We identified images from the GIS-based labeled training data with low IoU values, where (**a**) visually appears like a rectangular building in (**b**), and (**c**) has a labeling error that makes it appear like a walkway in (**d**).  
Methods for recognizing buildings from satellite or aerial imagery have been extensively researched [23; 24]. In such building recognition tasks, various techniques are employed, including the incorporation of GIS information as input and the use of edge detection [25; 26]. Generalizing these methods for application across different regions is challenging due to the diverse roof structures in different areas. For instance, some regions might require waterproofing for building roofs, while others might maintain a consistent style due to sunlight exposure. Furthermore, images captured by drones have high resolution, allowing for more accurate roof detection using precise models. The significance of this study lies in selecting an appropriate model for drone-captured images, refining the data, and verifying the buildings in the GIS.
## 6 Results and Discussions
### Discussions  
The close agreement between the corresponding results of Figures 8-16 verifies the validity of the FPGA-based real-time simulator to operate in the HIL configuration for testing external controllers. The stability and the accuracy of the simulations for long running durations are also verified as the FPGA-based simulator along with the controller are left continuously running in HIL configuration for hours.  
The slight discrepancies between the simulation results obtained from the FPGA-based real-time simulator and those from the Matlab/simpowersystems are due to:  
* the delays in the controller outputs imposed by the computation time required by the controller platform to perform the arithmetic calculation of the control algorithm and of the PWM scheme. This delay time is not accounted for in the Matlab/simpowersystems simulation,
* the discretization errors since the Matlab/simpowersystems adopts floating point representation whereas the FPGA-based real-time simulator is based on the fixed point number representation, and
* the errors introduced due to the D/A and A/D converters.
## 3 Experimental Details
### Bending Test  
The three-point bending is adopted to conduct the bending test for RPC and BFRPC beams. WAW-300 KN microcomputer controlled electro-hydraulic servo universal testing machine was selected as the loading device. The calculated span of the test beam was 300 mm. Loading layout and AE sensors layout are shown in Figure 7.  
AE equipment SAEU2S was used to monitor crack propagation of RPC and BFRPC beams under bending loads. The AE data acquisition system was used to collect AE parameters and AE wave data during the bending test. First, AE sensor with a frequency range of 60-400 kHz and model SR150M were fixed on one side of the beam to monitor the crack propagation. Then, signal connection judgment should be made before test to ensure that AE sensor was well fixed. The next step is to set the AE acquisition parameters. The threshold of AE acquisition system was set as 40 dB to filter environmental noise. Low-pass and high-pass filters were set to 20 kHz and 400 kHz, respectively, to filter out electrical noise. The sampling frequency was 5 MSPS.The peak definition time, hit definition time and hit lockout time were set to 50, 150 and 300 ms, respectively, to ensure correct identification of the signal. Figure 8 shows the experimental setups for the bending test and AE test.  
Figure 6: Experimental procedure.  
Figure 7: Loading and AE sensors layout (mm).
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of Upper Level Distributed Coordination Strategy
#### 5.3.1 Ring Network Topology  
Based on the double-layer cooperative guidance architecture, when the network topology of the formation is a loop, the variation curves of the flight trajectory, the leading angle, time-to-go and acceleration command of each member of the formation obtained by simulation over time are shown in Figure 9.  
As can be seen from Figure 9, the initial time-to-go of the three UAVs is different, respectively, 30.29 s, 37.97 s and 22.96 s. However, after mutual coordination, they gradually become consistent in about 12 s. In the later stage, the cooperative guidance law degenerates into the UAV's own control item, and finally the saturation attack on the target is carried out simultaneously in 33.86 s. The leading angle and acceleration command converge to 0 at the terminal moment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**UAVs** & \begin{tabular}{c} **Initial Position** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **Velocity (m/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Initial Flight Path** \\ **Angle (deg.)** \\ \end{tabular} &
\begin{tabular}{c} **Target Position** \\ **(km)** \\ \end{tabular} \\ \hline M1 & (0,0) & 330 & 0 & \\ M2 & (5,8) & 320 & 30 & \\ M3 & (15,5) & 310 & \(-120\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of upper distributed coordination strategy.  
Figure 8: Different network topologies.
## 3 Results and Discussion  
### Effect of the Process Parameters on Overall Extraction Yield  
Experiments showed in Table 2 illustrate the influence of the extraction conditions on the overall extraction yield (mass of extract / mass of plant) obtained from the SFE of heather, marigold and heather + marigold (50:50). Regarding the extraction of each plant separately, as expected, higher extraction yields were achieved by using a co-solvent (Run E2 and E3-2) due to the extraction of more polar phytochemicals. In general, values around 4.5%-7.0% for both, heather and marigold, were obtained when 10% (w/w) ethanol and 30 MPa were applied. When neat SCCO\({}_{2}\) was used, the yield obtained at 50 MPa and 4 h for marigold was 3.4% (sum of yield obtained from both separators, that is, S1 + S2), while the yield obtained at 25 MPa and 1.5 h for the same material was 2.8% (E3-1). Moreover, the value reached from heather at 25 MPa was 2.6%, which is even higher than the obtained at the highest pressure studied (2.2% extraction yield for S1 + S2). Accordingly, the amount of extract recovered did not considerably vary as pressure increased from 25 MPa to 50 MPa. A possible explanation could be formulated on the basis of compound solubility. Despite the increase of pressure leading to a higher CO\({}_{2}\) density and, therefore, a greater solvation power, the majority of extractable compounds from these plants could be appreciably soluble in SCCO\({}_{2}\) at 25 MPa. It could be the reason for the low extraction yields obtained from the first separator (Run E1-S1) by the fractionation of the extracts. In this case, the yield of the fractionated extracts from the first separator (30 MPa) was lower than 0.4% for both plants. Just as for pressure, long extraction times did not seem to exert a strong effect on the extraction yield. Taking into account the extraction yields mentioned for 50 and 25 MPa (Run E1-S1 + S2 and run E3-1), the extraction time prolonged from 1.5 h to 4 h did not result in a considerable increase in the amount extracted. In the case of the combined extraction (heather + marigold 50:50), a comparison between the obtained experimental and theoretical extraction yields (calculated as thearithmetic mean of the experimental extraction yield obtained from single plants) was done. As can be observed from Table 2, the overall experimental extraction yields obtained from the combined extraction were 1.1-1.6 times higher than that of theoretical values, except for sequential extraction (E3-1 and E3-2).