esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experiment and Result
The 4.24% decrease in sensitivity value is a worthy exchange for the improvement of specificity. \begin{table}
\begin{tabular}{l c} \hline \hline
**Function Name** & **Time(s)** \\ \hline Load data & 0.37 \\ Deep activated features extraction & 9.99 \\ Geometric features extraction & 2.52 \\ Fusion features combination & 0.01 \\ Training classification model & 0.62 \\ Predict and evaluation & 0.28 \\ \hline Total & 13.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Total execution time for each function of the proposed system. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**References** & **Method** & **Samples** & **ACC\%** & **SEN\%** & **SPEC\%** & **PPV\%** & **NFV\%** \\ \hline \multirow{3}{*}{[11; 13]} & \(\bullet\) & Autocorrelation and GLCM features & \multirow{3}{*}{120} & \multirow{3}{*}{53.33} & \multirow{3}{*}{59.33} & \multirow{3}{*}{06.67} & \multirow{3}{*}{73.67} & \multirow{3}{*}{6.67} \\  & \(\bullet\) & SVM classification & & & & & \\ \hline \multirow{3}{*}{[12; 13]} & \(\bullet\) & Autocorrelation coefficients matrix & \multirow{3}{*}{120} & \multirow{3}{*}{73.33} & \multirow{3}{*}{77.67} & \multirow{3}{*}{53.33} & \multirow{3}{*}{90.33} & \multirow{3}{*}{53.33} \\  & \(\bullet\) & Neural network classification & & & & & \\ \hline \multirow{3}{*}{[13]} & \(\bullet\) & Multi-linear principal component analysis & \multirow{3}{*}{120} & \multirow{3}{*}{90.00} & \multirow{3}{*}{94.67} & \multirow{3}{*}{63.33} & \multirow{3}{*}{91.00} & \multirow{3}{*}{63.33} \\  & \(\bullet\) & Nonlinear programming with adaptive & & & & & \\ \cline{1-1}  & \(\bullet\) & \multicolumn{1}{c}{} & \multirow{2}{*}{Neural network classification} & & & & \\ \hline \multirow{3}{*}{Proposed method} & \(\bullet\) & Deep activated features & \multirow{3}{*}{533} & \multirow{3}{*}{91.70} & \multirow{3}{*}{90.43} & \multirow{3}{*}{92.67} & \multirow{3}{*}{91.51} & \multirow{3}{*}{93.36} \\ \cline{1-1}  & \(\bullet\) & Geometric features & & & & & \\ \cline{1-1}  & \(\bullet\) & SVM classification & & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of the proposed method and with the previous methods.
## 3 Dataset  
To our knowledge, there are two datasets for Arabic traffic signals: ArTS [16] and ATTICA [17]. A developed labeled dataset of images of Arabic traffic signs, known as ArTS, was created at Prince Mohammad Bin Fahd University in Al-Khobar, Kingdom of Saudi Arabia. Researchers can access it publicly. Applications pertaining to autonomous vehicles, traffic monitoring, and traffic safety can benefit from the dataset in various ways. Additionally, it can be helpful in creating applications for those who are blind or visually impaired and cannot read the signs. The photos were taken in Alkhobar and Dammam, two of the largest cities in Saudi Arabia's Eastern Province. The captured images were for the 24 most common Arabic traffic signs that are described in Figure 1, such as Slow, Stop, No Parking, and others.  
This dataset is available in two versions. The first version contains a total of 2718 real images that were randomly divided into an 80% training set (2200 images) and a 20% test set (518 images). The second version contains a total of 57,078 images that were created by applying some augmentation techniques to the 2718 real captured images, such as resizing, flipping, scaling, zooming, cropping, and rotation, as shown in Figure 2. The augmented images were saved as RGB images in various sizes. These 57,078 images were randomly partitioned into an 80% training set (46,200 images) and a 20% testing set (10,878 images).  
Figure 1: Most common traffic signs along with the total number of photos gathered.
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 2: Coupling of Subsystems with Lumped Parameters
#### 3.2.1 RCFBS Method and Governing Equations
**Equations of motion for subsystem A:**  
For subsystem A, the generalized coordinate of interest consists of \(\mathrm{uc}_{1}\) and \(\mathrm{uc}_{2}\) at the connection points and \(\mathrm{u}_{1}\) as an internal coordinate. The equations of motion in the time domain are formulated as follows:  
\[ \begin{bmatrix}\mathrm{M}_{\mathrm{A}}\end{bmatrix}\begin{cases}\ddot{ \mathrm{u}}_{1}\\ \ddot{\mathrm{u}}_{\mathrm{c}1}\\ \ddot{\mathrm{u}}_{\mathrm{c}2}\end{cases} +\left[\mathrm{C}_{\mathrm{A}} \right] \begin{cases}\dot{\mathrm{u}}_{1}\\ \dot{\mathrm{u}}_{\mathrm{c}1}\\ \dot{\mathrm{u}}_{\mathrm{c}2}\end{cases} +\left[\mathrm{K}_{\mathrm{A}} \right] \begin{cases}\mathrm{u}_{1}\\ \mathrm{u}_{\mathrm{c}1}\\ \mathrm{u}_{\mathrm{c}2}\end{cases} =\mathrm{f}(\mathrm{t}),\\ \mathrm{where}:\\ \left[\mathrm{M}_{\mathrm{A}}\right]= \begin{bmatrix}m_{11}^{A}&0&0\\ 0&m_{22}^{A}&0\\ 0&0&m_{33}^{A}\end{bmatrix} ,\left[\mathrm{C}_{\mathrm{A}}\right]= \begin{bmatrix} c_{11}^{A}&c_{12}^{A}&c_{13}^{A}\\ c_{21}^{A}&c_{22}^{A}&c_{23}^{A}\\ c_{31}^{A}&c_{32}^{A}&c_{33}^{A}\end{bmatrix} ,\;\left[\mathrm{K}_{\mathrm{A}} \right]= \begin{bmatrix}k_{11}^{A}&k_{12}^{A}&k_{13}^{A}\\ k_{21}^{A}&k_{22}^{A}&k_{23}^{A}\\ k_{31}^{A}&k_{32}^{A}&k_{33}^{A}\end{bmatrix}  \tag{35}\]  
By transferring these equations of motion from the time domain to the frequency domain, the receptance matrix can be determined as follows:\[ \begin{array}{c}\left[{\rm{H_{A}}}(\omega)\right]\begin{B}{\rm{F_{n1}^{A}}}\\ {\rm{F_{n1}^{A}}}\\ {\rm{F_{n0}^{A}}}\end{B}\end{array} \right\}=\left\{ \begin{array}{c}{\rm{u_{A }^{A}}}\\ {\rm{u_{n0}^{A}}}\\ {\rm{u_{n0}^{A}}}\end{array} \right\}\\ \mbox{where :}\\ \left[{\rm{H_{A}}}(\omega)\right]=\left(\left[{\rm{K_{A}}}\right]+{\rm{i}} \omega{\rm{[C_{A}]}}-\omega^{2}{\rm{[M_{A}]}}\right)^{-1}= \begin{bmatrix}{ \rm{H_{n1}^{A}}}_{{\rm{u_{1}}}}&{\rm{H_{u_{1}}^{A}}}_{{\rm{u_{1}}}{\rm{u_{1}} }}&{\rm{H_{u_{1}}^{A}}}_{{\rm{u_{1}}}{\rm{u_{2}}}}\\ {\rm{H_{u_{1}}^{A}}}_{{\rm{u_{1}}}{\rm{u_{1}}}}&{\rm{H_{u_{1}}^{A}}}_{{\rm{u_{1} }}{\rm{u_{1}}}}&{\rm{H_{u_{1}}^{A}}}_{{\rm{u_{1}}}{\rm{u_{1}}}{\rm{u_{2}}}}\\ {\rm{H_{u_{0}}^{A}}}_{{\rm{u_{2}}}{\rm{u_{1}}}}&{\rm{H_{u_{0}}^{A}}}_{{\rm{u_{2} }}{\rm{u_{1}}}}&{\rm{H_{u_{2}}^{A}}}_{{\rm{u_{2}}}{\rm{u_{2}}}}\end{bmatrix}  \tag{36}\]
###### Abstract
Collaboration between organizations can significantly improve the collective ability to respond to cyber threats. However, many companies are reluctant to share information due to privacy and competition concerns [5]. The study identified gaps in cyber risk management in the Brazilian context, particularly in the financial sector. Although financial institutions adopt international frameworks such as NIST CSF and ISO/IEC 27001 [9], challenges persist in adapting these standards to local regulations, such as the absence of strict deadlines for reporting incidents and the lack of requirements for offensive testing (red teaming) [10]. In addition, the integration between internal audit (third line) and IT and cybersecurity teams faces operational obstacles, such as divergences in terminology and objectives, limiting the effectiveness of collaborative approaches [11]. Another gap lies in the implementation of data-driven continuous auditing, where the lack of standardized metrics and the scarcity of technical resources hinder the real-time analysis of large volumes of information [12]. Therefore, this research is justified because, within financial institutions, internal audits play a central role in resolving several strategic issues. Due to the potential impact associated with cyber risks, it is urgent to identify tactics, techniques, and procedures so that the performance of internal audits as a third line can result in gains. Finally, this study aims to identify the approaches through which Internal Audits can test, evaluate, and monitor the effectiveness of cybersecurity controls implemented in a financial institution to support the continuous improvement of these practices. This work was organized into six sections to achieve this goal, starting with this introduction in Section 1. We will then explore related work in Section 2 and the context in Section 3. In Section 4, we will detail the research method. In Section 5, which focuses on the results of the research, we will present and categorize the findings based on the interview themes and analyze them in light of the existing literature. Finally, in Section 6, the conclusion will briefly present the main findings, the study's limitations, and suggestions for future research.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 8 Extensions
### New MAC Design for LoRa  
Other MAC designs that alleviate the bottlenecks of Aloha can be used on top of LoRa. For example, if CSMA based MAC protocols are included, the duty cycle can be avoided, increasing thus the throughput. In [52], authors showed that by using CSMA the packet delivery ratio is increased by seven times in the case of 10,000 nodes compared to normal Aloha based MAC. In such a case, the PDR of CSMA based MAC is more than 75%, while in case of Aloha-based LoRaWAN MAC the PDR is only 10%. At the same time, the energy consumption per node is decreased. In the case of Aloha-based LoRaWAN MAC, much energy is wasted in collisions. Another comparison study between Aloha-based and p-CSMA based MAC protocols for LoRaWAN is presented in [53].  
A time-power multiplexing approach for MAC protocol on top of LoRa was proposed in [110] to improve downlink traffic scalability. The first proposition is to decouple the uplink and downlink traffic. Both receive windows will use only high power (27 dBm) high duty cycle (10%) channel (869.525 MHz) for downlink traffic, while the rest of channels are used only for uplink. This will overcome the collision possibility between downlink and uplink traffic. Secondly, the transmission in downlink is multiplexed in time and power, by sending simultaneously multiple packets using different SFs as well as different transmit powers [110]. This approach shows that the number of retransmissions of confirmed uplink packets is decreased. At the same time, the uplink throughput is increased even for non-confirmed traffic, as they benefit from elimination of collision possibility between UL and DL traffic.  
In the case of LoRa mobile nodes, they can communicate with multiple gateways in the area. However, due to long range coverage, end nodes that are covered by different gateways can impact each other traffic due to hidden terminal problem. To avoid the hidden terminal problem, a MAC protocol on top of LoRa was studied in [111; 112]. All the nodes are always in the listening state. When they want to transmit, they will send a Request-to-Sent (RTS) message. Multiple gateways will reply with Clear-to-Sent (CTS) but due to capture effect one of the CTS will succeed. Both RTS and CTS messages will include the reservation time for the channel. Before actual data transmission, end node will send another Control CTS (CCTS) to tell to which gateway it will transmit, in order for other gateways to continue serving other nodes. At the end, the data communication happens [112]. This MAC protocol improves the data delivery ration by decreasing the impact of hidden terminal effect.
# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 2 Preliminaries
### Optimization for DNN  
Compared with the traditional machine learning approaches, the CTR prediction based on DNN has made significant improvements. These models are composed of a multi-layer neural network, or can be combined with multiple DNNs for prediction. However, the training of DNNs is very tricky, and some essential optimization techniques must be involved to build a better CTR model.
# Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
## 4 Validation and Experimental Results
### Comparison against Sibyl Framework  
Given that our focus is on evaluating routing protocols over Clos networks, we decided to validate our implementation running several experiments over the same network topologies used by the Sibyl framework [9]. Using these same scenarios in ns-3 DCE gave us the opportunity to compare the results, since Sibyl framework defines various fat-tree based scenarios.
## 3 Results and Discussion
### Limitations  
All PCM images used in this study were prepared by the authors. Since the authors followed the guidelines published by the Japanese Ministry of the Environment [31] in preparing the samples, the model may not be directly applicable to samples prepared using different methods. In that case, the model will require additional training before the model is applied.  
In addition, since this study was conducted on PCM images, the model performance could not be evaluated for thin fibers that cannot be resolved by PCM. Given that this  
Figure 8: Recall and F1 scores for the test dataset of the optimal models.  
is a limitation of PCM, it is difficult to overcome this limitation by improving the model. We focused on PCM images for inexpensive and rapid detection; however, if high detection accuracy is required for fine fibers, costly methods such as electron microscopy will be necessary.  
Because machine learning techniques are developing rapidly and continuously, there could be a more advanced CNN model architecture than the Xception. Moreover, it would be worth exploring more efficient model configurations and settings, such as filter sizes and convolutional depths, and alternative pooling techniques. It would be desired, in addition, to consider other classifiers and ensemble methods in future research.
## 5 Long-Term Stability and Lining Safety of Surrounding Rocks in Tunnels
### Timing of Lining  
According to the Technical Specifications for Construction of Highway Tunnels [32] and results of previous research [33; 34; 35], the timing of lining is generally determined by the deformation and deformation rate of surrounding rocks, as well as whether the deformation causes damage to the initial supporting and lining structures. The following specific principles are considered: (1) 80-90% of the predicted total displacement is achieved; (2) the displacement deformation rate has converged and the deformation of surrounding rocks tends to be stable, the horizontal convergence rate of the arch is less than 0.1-0.2 mm d\({}^{-1}\), or the arch settlement rate of the arch is less than 0.07-0.15 mm d\({}^{-1}\); (3) the initial supporting surface fracture (e.g., steel arch bending and shotcrete cracking) is no longer developing, and it continues to exhibit compressive (tensile) strength; (4) during tunnel operation, the pressure on the lining does not exceed the limit value. Based on these principles, two periods of lining support can be determined--the critical period of failure of the initial supporting structure (latest timing of lining) and the critical period at which the lining is damaged due to excessive pressure during long-term deformation (earliest timing of lining).
## 4 Results and Discussion
### Cost, Energy, and Throughput Analysis
#### 4.6.1 Environmental and Reliability Evaluation  
4.6.1. Waste Generation and Recyclability  
FDM generated 15% waste by weight, primarily from support structures and failed prints. SLA produced 10% waste, but its resin is hazardous if uncured. PLA is biodegradable, and ABS is recyclable, whereas SLA resin requires specialized disposal.  
Figure 4: Average build times (with approximate variation) for test parts printed in PLA, ABS, and SLA resin. Error bars show the observed range within each material.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Item** & **FDM (PLA)** & **FDM (ABS)** & **SLA (Resin)** \\ \hline Material & 1.50 & 1.70 & 4.50 \\ Labor & 0.40 & 0.40 & 0.80 \\ Energy & 0.20 & 0.20 & 0.30 \\ Depreciation & 0.20 & 0.20 & 0.30 \\ \hline Total (EUR) & 2.30 & 2.50 & 5.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Per-part cost breakdown for FDM vs. SLA.
# 5G-MEC Testbeds for V2X Applications
## 2 Background
### Fundamentals of ETSI-MEC  
URLLC and eMBB applications require computational capabilities, which becomes an inherent concern due to the limited storage and computational abilities of the end-users. Due to these minimum requirements, the 5G network will face unusual traffic volumes. At the same time, MEC is an emerging concept in the 5G network that has the capacity to process a lot of data inside the RAN. MEC provides support for applications where real-time processing is needed. Fog computing and cloudlet are alternative architectures that, like MEC, offer cloud capabilities at the network's edge. The core idea of MEC is to deploy the cloud computing capabilities within the RAN, close to the end-user [19]. The  
Figure 2: 5G system reference architecture.  
ETSI is a standardization organization for MEC that provides a reference architecture for deploying network functionalities. The ETSI divides the MEC system into different MEC building blocks and discusses the contribution of each building block.
# Ocean Surface Topography Altimetry by Large Baseline Cross-Interferometry from Satellite Formation
## 5 Methodology for Altimetric Accuracy Analysis  
### Parameters Setting  
The altimetric errors of LB-IRA are mainly introduced from interferometric phase noise, baseline inclination, and length error. The interferometric phase noise is determined by the signal coherence, therefore, LB-IRA works in cross-interferometry mode which introduces the CFS to compensate for baseline decorrelation. Apart from baseline decorrelation, time decorrelation, thermal decorrelation, and wave volume decorrelation should all be taken into consideration while selecting the system parameters. The signal coherence can also be improved by averaging neighboring resolution cells, usually referred to as multi-looking by the InSAR community, which will be explained later.  
The altimetric error from baseline inclination or length error can only be decreased by longer cross-track, higher radar band, or smaller radar look angle. Therefore, apart from the CFS method,  
Figure 13: Coherence determined by wave volume decorrelation. (**a**) Cross-track baseline length of 629 m, and (**b**) cross-track baseline length of 1000 m.  
proper system parameters must be selected for LB-IRA. Figure 14 shows the flowchart of the methodology for parameters selection. Since the LB-IRA is mainly focused on geostrophic current or marine gravity anomaly inversion, whether the systems parameters are properly selected is determined if the centimetric relative accuracy is achieved at 1 km resolution.  
The main system parameters for numerical analysis are listed in Table 1. Since the baseline length that determines the CFS varies with latitude, the CFS needs to be accommodated with varying latitude during the orbit cycle, besides, the bandwidth needs to be accommodated with varying CFS in case of radio interference. Therefore, the CFS and its corresponding radar carrier frequency and bandwidth vary within a recommended range rather than being a fixed value, as listed in Table 1.
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
###### Abstract
The results showed that the hydraulic and water quality characteristics of the Paldang Reservoir varied depending on the discharge conditions of upstream dams. The NIER [4] also conducted a rigorous investigation on the reservoir's hydraulic properties and water quality, in which focus was placed on the confluence of the South Han River and the North Han River in 2008. In the same study, peak discharge from the Cheongpyeong Dam (CPD) on a periodic basis was presented as the primary cause governing the hydraulic properties and water quality at the Paldang Dam (PDD). Multiple studies have been conducted on the spatiotemporal variations in hydraulic mixing, thermal stratification, organic matter content, and nutrient salt concentrations in the Paldang Reservoir [4; 7; 26; 27; 28; 29; 30], as well as on applications of hydraulic and water quality analyses performed by using a numerical model for the Paldang Reservoir [11; 31; 32; 33; 34], but these studies have suffered from the insufficient availability of actual field monitoring data. Currently, water quality monitoring in the Paldang Reservoir is being carried out by using samples or mixed samples collected at different water depths from key locations less than once per week. This dataset suffers from insufficient data points necessary to perform continuous assessments regarding the evolution of thermal stratification, temporary hypoxia in the lower layer, and mixing patterns of substances. Therefore, additional hydraulic and water quality analyses of the Paldang Reservoir can serve as an important basis for a variety of research projects on the transport and dispersion of water pollutants, the mechanisms of eutrophication, and the occurrence of algal blooms. This study conducted velocity and water temperature surveys at different water depths for key locations in an attempt to provide a comprehensive view on the spatiotemporal characteristics of water circulation in the Paldang Reservoir. Based on the results, an examination was made of the impacts of spatiotemporal circulation variations within the Paldang Reservoir on water quality.
# Laboratory Evaluation and Finite Element Modeling of SBS and Basalt Fiber Modified Mixtures
## 2 Experimental  
In this research, two variations of samples, one consisting of a 0.3% BF with 3% SBS mixture and the second one consisting of only a 7% SBS mixture are tested using the flexural strength test and wheel rutting test in the lab, since a conventional 7% SBS mixture would outperform the conventional 3% SBS mixture. Furthermore, the properties of these two variations are further employed in finite element modeling to perform predictions based on lab performance of these mixtures. Moreover, predictions based on finite element modeling are made for two distress mechanisms including rutting and fatigue damage.