esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Transportation System Technologies, Challenges and Security
## 4 Intelligent Transportation System Security Challenges and Measures
### ITS Cyber Attack Methods  
Cyber attacks are aimed at stopping, shutting down, or completely crashing the system. Cyber attacks can be made over wired and wireless connections. In this section, possible attacks are examined. Security problems that may occur on ITS are evaluated in terms of cyber security and shown in Figure 11.  
The most important situation to protect ITS systems is to minimize and protect against cyber security attacks. The systems should not be turned off after the attacks. As a result of the attacks, financial losses, loss of reputation, information theft, and service interruption occur in the systems. Looking at the world in general, cyber attacks on ITS systems are increasing daily. When cyber attacks in ITS systems are examined, the most common attacks are VANET MiTM attacks, DDoS, and Internal Vehicle Network attacks [48; 49].  
\begin{table}
\begin{tabular}{p{14.2 pt} p{284.5 pt}} \hline \multirow{3}{*}{4} & Vehicle Private Networks (VANETs) networks are highly vulnerable to maintaining traffic safety. Sybil attacks are one of the most dangerous cyber attacks on these networks and are very difficult to detect. In addition, Distributed Denial-Of-Service (DDoS) attacks are dangerous attacks. It creates the risk of consuming the resources of the systems against excessive intensity [41]. \\ \hline \multirow{3}{*}{5} & Incorrect transmission of vehicle location data is one of the major problems. Thus, incorrect location causes data to be transmitted incorrectly [42]. \\ \hline \multirow{3}{*}{6} & With the developing technologies, the security risk is reduced when ITS control units are configured differently from the corporate network. Having a separate firewall prevents erroneous data [43]. \\ \hline \multirow{3}{*}{7} & With Intrusion Protection Systems (IPS) and Intrusion Detection Systems (IDS), daily data are analyzed and necessary anomaly controls are made. The lack of these systems makes it very difficult to detect attacks that occur in ITS systems [44]. \\ \hline \multirow{3}{*}{8} & It is possible to prevent many cyber attacks when digital signature algorithms are used in ITS systems. These technologies encrypt and secure data. It provides protection, especially against Man-in-the-Middle (MiTM) attacks [45]. \\ \hline \multirow{3}{*}{9} & It is possible to analyze the real status of the vehicles with the data fusion software. Data from the entire field is collected and reported. Thus, it becomes easier to perform security analyses on the data [46]. \\ \hline \multirow{3}{*}{10} & Biometric authentication is a technique that authenticates people based on their physical and individual characteristics. After the information is collected, it is distributed, stored, and processed within the system. These techniques ensure that the ITS system is secure against spoofing attacks [47]. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 11: ITS cyber attack methods [42; 43; 44; 45; 46; 47].
## 5 Results and Discussion
### Creation of 3D Model  
To make the results more understandable, the linear vegetation marks of the best-preserved structures, Installations A-C, were expanded into a 3D model. Figure 12 shows the evolution of the model, from a simple 2D outline of the walls of the buildings, to a rectangular closed structure, and lastly, to a completed Pharaonic storeroom with barrel vault ceilings, as seen at the Ramesseum. To ensure the most accurate and realistic rendering of Installations A-C, textures and materials that imitate those used for ancient Egyptian construction were mapped onto the model (Figure 13). A sun-dried mudbrick material that included small vegetal and non-vegetal inclusions was used for the exterior of the buildings (Figure 8).  
Figure 11: The study area image in RGB, the digitized structures from previous excavations [32], as well as the two newly detected sites in the eastern section of Hermopolis, located outside the excavated zones.  
Figure 12: Process of constructing the 3D model for remotely sensed Installations A–C in SketchUp Pro.  
The mudbricks used in ancient Egypt were largely fabricated with clay and mud obtained from riverine silts and sediments deposited by the Nile River. Veget binding materials, such as rice chaff or chopped straw, were also added to the clay and mud concoction [62; 63]. The importance of these two extraneous tempers can be seen by a New Kingdom model letter in which there was an official complaint about the lack of local straw for making mudbrick [47]. Miscellaneous ingredients such as sand, ash, sherds, animal dung, and fragments from stone working have also been detected in ancient mudbricks [47]. On the other hand, for the inside of the structure, including the top and bottom floors, a lighter compressed dirt/clay material with small vegetal aggregates and inclusions was used (Figure 8). The ancient Egyptians are known to have used a natural clay substance, akin to mortar, to hold together the unfired mudbricks [64]. When rendered in Chaos V-Ray, these above-mentioned materials were transformed into 3D textures with significant depth (Figure 13). The key goal of expanding the remotely sensed structures to a georeferenced 3D model was to make such scientific results more understandable to the general public. In  
Figure 13: Constructed 3D model of the remotely sensed Installations A–C. (**A1–A4**) show varying angles of the structures generated via SketchUp Pro. (**B1–B4**) show the final product with rendered materials and textures via the Chaos V-Ray software.  
addition, it is meant to demonstrate how remote sensing can have a multifaceted outcome--from first seeing nothing from the raw satellite image, to uncovering possible structures using spectral indices, and finally rendering them into a more tangible format in the third dimension.
# A Real Option Pricing Decision of Construction Project under Group Bidding Environment
## 2 A Real Option Pricing Decision Model under Group Bidding Environments
### The Winning Probability and the Weighted Option Value Calculation
#### 2.3.2 The Weighted Option Value Calculation  
The winning probability and the maximization of option value are the dual objectives pursued by the enterprise. In this model, the double objectives are transformed into single objectives by multiplication as a decision function, as shown in Equations (11)-(13).  
\[V_{i}(P_{i},\ C_{i})=F_{i}(P_{i},\ C_{i})W(P_{i},\ C_{i}) \tag{11}\]  
\[V_{i}(P_{i},\ C_{i})=max\Big{\{}\Big{[}P_{i}e^{-r(T-t)}N\big{(}d_{1_{i}}\big{)} -C_{i}N\big{(}d_{2_{i}}\big{)}\Big{]}\Big{[}u\times e^{-b(|P_{i}-AP_{i}|/AP_{i} )^{n}}\Big{]}\Big{\}} \tag{12}\]  
\[\begin{cases}\Big{[}P_{i}e^{-r(T-t)}N\big{(}d_{1_{i}}\big{)}\cdot ue^{-b(\frac{ A_{i}-B_{i}}{AP_{i}})^{n}}\Big{]}+\Big{[}P_{i}e^{-r(T-t)}N\big{(}d_{1_{i}}\big{)}-C_{i}N \big{(}d_{2_{i}}\big{)}\Big{]}\Big{[}-ubn\cdot e^{-b(\frac{A_{i}-B_{i}}{AP_{i}} )^{n}}\cdot\Big{(}\frac{A_{i}-B_{i}}{AP_{i}}\Big{)}^{n-1}\cdot\frac{1}{AP_{i} }\Big{]}=0,&P_{i}<AP_{i}\\ P_{i}e^{-r(T-t)}N\big{(}d_{1_{i}}\big{)}\cdot ue^{-b(\frac{A_{i}-B_{i}}{AP_{i}} )^{n}}\Big{]}+\Big{[}P_{i}e^{-r(T-t)}N\big{(}d_{1_{i}}\big{)}-C_{i}N\big{(}d_{ 2_{i}}\big{)}\Big{]}\Big{[}-ubn\cdot e^{-b(\frac{A_{i}-B_{i}}{AP_{i}})^{n}} \cdot\Big{(}\frac{B_{i}-AB}{AP_{i}}\Big{)}^{n-1}\cdot\frac{1}{AP_{i}}\Big{]}= 0,&P_{i}\geq AP_{i}\end{cases} \tag{13}\]  
where the \(V_{i}\) is the option value weighted by the winning probability of the \(i\)th competitor. In an independent environment within a group, the prices of each competitor is not affected by other bidding decisions, that is, the \(P_{i}\) follows the random distribution in [\(C_{i}\),\(PM\)], where \(PM\) is the bidding control price, and \(AP_{1}=AP_{2}=\ldots=AP_{N}\).  
The bidder divides the competitive environment according to the actual situation and matches the estimated bids of competitors with the bidding environment through the above model. Finally, the bidder replaces itself into the decision-making body of the model and makes comprehensive decisions according to Formula (9)-(13), to enhance the scientific price and improve the winning probability.
### UAVs: Roots and Advancements
#### 1.2.2 UAVs and Civilian Applications
In addition, they can be used for traffic monitoring, crowd control, and surveillance in law enforcement applications. The widespread adoption of UAVs in civilian applications faces numerous challenges. Regulatory frameworks and guidelines need to be developed to ensure the safe and responsible use of drones in a civilian airspace [29]. Privacy concerns and ethical considerations must also be addressed, particularly in applications, such as surveillance and data collection [30]. Additionally, processing the extensive data collected by drones can be complex and requires specialized technical skills and computing capacities.
# Performance of 5G Trials for Industrial Automation
## 5G Performance for Industrial Control
It can be observed that the gains for the selection of the RAN parameter values is particularly visible for large message sizes (see Figure 7), where the achievable median latency is reduced from 12.5 ms to 4.5 ms. For small message sizes, the reduction (see Figure 5) in the latency is only up to 1.5 ms, and for device A, the 99.9 th percentile of the latency is even increased by 3.1 ms.  
One way to increase the reliability of 5G connectivity is to introduce redundant communication paths, which is enabled by, for instance, FRER, as shown in Figure 4. The benefit of redundant connectivity is that if a single communication path is experiencing link degradations and potentially long transmission times, the other link can still provide message transmission with lower latency. The FRER performance is shown in Figures 9 and 10 for uplink and downlink, respectively. The figures show that, not surprisingly for the median latency, FRER does not provide any benefits, as its primary target is improved reliability. As the average latency of the 5G system performs in-line with the configuration, it is not expected that the second transmission path can provide any substantial benefit. However, at the 99 th or 99.9 th percentile values, FRER provides extra robustness. As a result, the guaranteed latency that can be provided to the industrial system can be reduced by up to several milliseconds. Figure 9: Uplink latency with and without redundant transmissions using FRER. For 5G, many features have been standardized, and these features allow ultra-low latency and high reliability to be achieved in stringent industrial IoT use cases as needed. We have conducted experiments with the URLLC testbed system described in Section 3, in which several of the 3 GPP Rel. 15 and Rel. 16 standardized URLLC features were implemented in the network and the device. The results in in Figures 5-8. show that with the proper design and usage of standardized 5G URLLC functionality, much lower latencies are able to be achieved and can also be guaranteed with high reliabilities in comparison to MBB-focused 5G systems. For small and large messages, the median latencies are below 0.8 ms in uplink and between 0.8-0.9 ms in downlink. Even for guaranteed latencies at 99.9 th percentile, the latencies remain below 1.09-1.12 ms. A FRER configuration with redundant traffic flows over the URLLC testbed was not considered, as it is not expected to reduce latency below the already low latency bounds. While the presented results on the commercial 5G eMBB system in this article focuses on the 5G-SMART use cases (cf. [11]), we carried out the latency performance evaluation in both the UL and DL directions on the URLLC testbed for a wide range of packet sizes ranging from 100 bytes to 1500 bytes. Our empirical results indicate that the URLLC features guarantee a latency bound of approximately 1 ms for both the UL and DL directions for these packet sizes up to 99.9 th percentile value.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 3 Data Description: UWF-ZeekDataFall22
### Data Distribution and Size  
The dataset consists of network traffic, that is, 42.8 GB of Zeek logs collected across 81 subnets. The data was transferred daily from a Security Onion Virtual Machine to a Big Data platform, utilizing Hadoop's distributed file system (HDFS) for storage and analysis. This distributed approach allows for efficient handling of the high volume of network data generated [28].  
A significant feature of this dataset is the balanced representation of attack and normal traffic:  
* Non-malicious traffic: 350,339 records;
* Malicious traffic: 350,001 records.  
These records were labeled according to the MITRE ATT&CK techniques, as presented in Table 1, enabling detailed analysis of adversary behaviors and attack phases such as reconnaissance, discovery, privilege escalation, and exfiltration.
## 3 Results
### Horizontal Distance Positioning Results Comparative Analysis
Taking an incident angle of 45\({}^{\circ}\) as an example, the method's horizontal error accuracy was compared with different methods using corresponding data, considering variations in time, location, water depth, and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Incidence Angle (\(\alpha\))** & \multicolumn{2}{c}{**35\({}^{\circ}\)**} & **40\({}^{\circ}\)** & **45\({}^{\circ}\)** & **50\({}^{\circ}\)** & **55\({}^{\circ}\)** & **60\({}^{\circ}\)** \\ \hline R (m) & & 2732.32 & 2274.36 & 1913.97 & 1611.33 & 1348.49 & 1114.48 \\ \hline \multirow{2}{*}{\(F_{1}\)} & r & 2705.23 & 2255.44 & 1900.36 & 1601.33 & 1341.04 & 1108.91 \\  & \(e_{r}\) & 0.9\% & 0.8\% & 0.7\% & 0.6\% & 0.5\% & 0.4\% \\ \hline \multirow{2}{*}{\(F_{2}\)} & r & 2680.77 & 2248.20 & 1897.49 & 1600.04 & 1340.43 & 1108.60 \\  & \(e_{r}\) & 1.8\% & 1.1\% & 0.8\% & 0.7\% & 0.6\% & 0.4\% \\ \hline \multirow{2}{*}{\(F_{3}\)} & r & 2684.66 & 2250.04 & 1898.55 & 1600.72 & 1340.88 & 1108.92 \\  & \(e_{r}\) & 1.7\% & 1.0\% & 0.8\% & 0.6\% & 0.5\% & 0.4\% \\ \hline \multirow{2}{*}{\(F_{4}\)} & r & 2553.63 & 2163.75 & 1838.24 & 1556.31 & 1308.77 & 1083.16 \\  & \(e_{r}\) & 6.5\% & 4.8\% & 3.9\% & 3.4\% & 3.0\% & 2.8\% \\ \hline \multirow{2}{*}{\(F_{5}\)} & r & 2610.37 & 2247.72 & 1897.12 & 1599.77 & 1340.22 & 1108.45 \\  & \(e_{r}\) & 4.4\% & 1.1\% & 0.8\% & 0.7\% & 0.  
oceanic environmental conditions. The experimental results are presented in the Table 4. Here, \(r\) represents the horizontal localization results for four different locations, with the true horizontal positions being 574.69, 946.48, 567.39, and 953.75, respectively. \(e\) indicates the localization error in horizontal distance. The specific error comparison results are illustrated in Figure 9. On the horizontal axis, 1, 2, 3, and 4, respectively, represent the positions \"23.171,129.430\", \"19.593,121.142\", \"11.415,138.064\", and \"23.007,133.196\". The magenta curve represents the stratification method proposed in this paper, as shown in Figure 9. It can be observed that the method proposed in this paper performs relatively well in localization across different depths, times, and environmental conditions.
## 2 Network Model and Channel Fading Characteristics  
### Network Model  
In this paper, a coal mine face is taken as the network application scenario to carry out WSN monitoring in the context of unmanned mining in an intelligent mine. A coal mine face mainly comprises three pieces of working equipment: a mining machine, a scraper conveyer and a hydraulic support, herein called \"three machines\". As the mining machine moves, the scraper conveyer and hydraulic support should follow the progress of the mining machine [27]. As shown in Figure 1a, sensor nodes are deployed on the roadway wall, the hydraulic support (blue pattern in Figure 1a), the scraper conveyer (green pattern in Figure 1a) and the mining machine (yellow pattern in Figure 1a). The red rectangular area in Figure 1a shows the space region of the coal mine face; to better understand the real scenario of a coal mine face, an on-site photograph of one is also provided in Figure 1b; the space dimensions and network topology are given in Figure 1c.  
Suppose that the nodes are distributed in the coal mine face area and that the system features the following properties: (1) the nodes in the network are fixed after the initial deployment and can move together with the equipment if fixed on mobile equipment; (2) the BS is in the center of the region, is connected to an underground Ethernet, and has strong computation and memory ability with no energy limitations; (3) the power of all sensor nodes is adjustable, and direct communication with the BS is ensured; (4) all sensor nodes have the same structure, limited energy and the same initial energy; (5) the network possesses self-organization, and no artificial maintenance is required for the network after deployment; and (6) the position information of the network nodes is known.
## 3 Results and Discussion  
### Biochemical Characterization of Rhodomonas sp. Biomass  
The composition of _Rhodomonas_ sp. biomass, collected at the early stationary phase, was determined. The obtained results showed that _Rhodomonas_ sp. accumulated 34.5 mg/100 mg DW of proteins, 14.18 mg/100 mg DW of carbohydrate, 13 mg/100 mg DW of lipids, 15.73 mg/100 mg DW of ash, and 0.46 mg/100 mg DW of Chlorophyll (Table 1). These results revealed that organic constituents were the most abundant components with a proportion greater than 75 mg/100 mg DW [41]. As previously reported, proteins were the major organic components. In fact, several studies have shown the richness of _Rhodomonas_ species in proteins. _R. lens_ and _R. salina_ species were shown to contain a large amount of proteins, ranging from 30% to 60% [42; 43; 44; 45]. The carbohydrate content was relatively low compared with data reported by [PERSON] et al. [46] showing a carbohydrate content of 28.9% for _Rhodomonas_ sp. However, the obtained carbohydrate content was comparableto that reported for _R. lens_, i.e., 11% [44]. Furthermore, the majority of microalgae, including _Rhodomonas_ genus, accumulate lipids, in particular, triacylglycerlycerols. Under conditions of macronutrient deficiency, such as nitrogen starvation, the cell stores the carbon flux obtained from photosynthesis as lipids [47]. In addition, a high ash level was found in _Rhodomonas_ sp. microalgae biomasses [46]. It is worth noting that microalgae biomass composition is extremely dependent on the culture media used as well as environmental factors [48; 49].
## 3 Results and Discussion
### Moisture Content (MC) and Corrected Mass Loss (CML)
These CML results raise doubts about the effectiveness of the adopted schedules since the main benefits of the process are associated with mass loss, such as reduced hygroscopicity and increased resistance to biodeterioration by fungi [11]. In contrast, CS160 had the highest CML, reaching 18.47%. In addition to heat, the closed-system process has pressure and a saturated atmosphere as mass loss inducers,  
Figure 4: Initial and final moisture content of wood by treatment. which explains the higher CML, even at a lower temperature. This result was also observed by [PERSON] et al. [6] on the thermal modification of eucalyptus wood in closed and open systems. Studies argue that the behavior of wood degradation varies according to the heating medium used, which is due to the surrounding atmosphere [31]. The closed system (such as CS160) allows for the presence of steam, facilitating wood degradation compared to using temperature alone. In this process, hemicelluloses and cellulose are depolymerized by hydrolysis in the presence of water, which is the main cause of the loss of resistance to degradation of these polymers. Both [PERSON] et al. [31] and [PERSON] and [PERSON] [32] stated that wood exposed to high relative humidity degrades predominantly by acid hydrolysis, with a higher degradation rate than thermal or pyrolytic degradation. Water is responsible for the breakdown of acetyl groups through the formation of acetic acid, which is responsible for further wood degradation. According to the CML results, a greater effect on chemical composition and wood color is expected for CS160, followed by OS210. On the other hand, OS185 should show similar results to the untreated wood.
## 6 Conclusions  
Exploiting the efficiency of cooperative communication and the incremental error correction capabilities of the RCPC codes, an enhanced version of cooperative ARQ protocol is proposed in this  
Figure 10: **(a)** Throughput efficiency; **(b)** Energy efficiency _vs._ BER of all links.  
paper for underwater acoustic sensor networks. This protocol efficiently merges the two ARQ schemes: Type-II HARQ and CARQ, so as to utilise the benefits of both the techniques. Computer simulations were done to show the enhanced performance of this hybrid cooperation technique by comparing its performance against the present Cooperative ARQ and standard S&W ARQ. The results show that not only is the throughput efficiency boosted to a much higher value, but the energy efficiency is also improved. Further, the optimum values of various network parameters were also estimated to extract the best performance from the proposed protocol. Using this basic framework of the C-HARQ protocol, more powerful rate compatible codes (e.g., rate compatible punctured turbo (RCPT) codes) can be employed to further improve the performance.  
This work was supported in part by the Defense Acquisition Program Administration and Agency for Defense Development under the contract UD130007 DD and in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (NRF-2012R1A1A4A01).  
The authors declare no conflict of interest.
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 2 Depth Camera Modeling and Implementation
### Stereo IR Depth Camera  
A Stereo IR depth camera comprises an IR projector and two IR cameras. The IR projector emits a dot pattern on the front of the camera, and two IR cameras capture the pattern. As in Figure 2a, the Intel realsense D435 camera has two IR cameras and an IR projector. When detecting objects, the projected dot pattern from the IR projector is reflected off the measured materials and detected by the IR cameras. Because of the distance between the two cameras, the position of the pattern measured on the two cameras is different. As in Figure 2b, The depth between the camera and an object can be calculated using the disparity value from two camera measurements. The equation is written as follows [27]:  
\[\frac{b}{Z}=\frac{(b+x_{L})-x_{R}}{Z-f} \tag{1}\]  
\[Z=\frac{b\cdot f}{x_{R}-x_{L}} \tag{2}\]  
The \(Z\) is the depth value, \(x_{R}\) is the measurement of the right camera, \(x_{L}\) is the measurement of the left camera, \(b\) is the distance between the two cameras, and \(f\) is the focal length of the camera as shown in Figure 2b.  
When the detecting object is close to the depth camera, the disparity value is high, and an accurate depth value can be calculated [28]. Conversely, when the detecting object is far from the depth camera, the error of the calculated depth is increased due to a small disparity value [29]. Moreover, since different materials have different surface structures, they will affect the visibility of the pattern leading to variations in depth error.