esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 5 Discussion
### Fwd Header Length and Its Role in Detecting Reconnaissance Activities  
Our analysis revealed a notable correlation between 'Fwd Header Length' and reconnaissance activities, validating its importance in detecting the preliminary phases of cyberattacks. This feature, as highlighted in our results, serves as a critical marker for abnormal traffic patterns, particularly in IoT environments where resource constraints amplify the impact of such attacks.  
'Fwd Header Length' pertains to the length of the forward packet headers and is one of the critical features seen in the context of reconnaissance attacks, especially in an IoT environment. This feature can help detect the headers of abnormal packets, which often conclude the scanning activities or attempts by attackers to collect data. When attackers fetch information about the targeted system to identify any vulnerabilities that can be exploited, reconnaissance attacks are the preliminary phase of the cyberattack. Similar to DIS attacks [65], reconnaissance attacks may lead to an increase in power consumption, even though low power consumption and lossy networks are characteristic features of IoT networks using RPL protocols. Characteristics of network traffic, such as 'Fwd Header Length', can be analyzed to enhance the detection of reconnaissance activities and identify deviations from normal behavior. Headers of packets can be monitored in real time to detect any ongoing remote attacks and reconnaissance activities in lightweight IDSs by using hardware performance counters. This can be performed with minimal overhead in IoT devices [66]. IoT network traffic can be analyzed to detect vulnerabilities related to the transfer of data in an unencrypted format, which may be exploited during reconnaissance, using machine learning techniques, such as those used in MANDRAKE [67]. Adaptive feature engineering can be implemented using attention-based long short-term memory models to detect reconnaissance attacks. This detection can be achieved by focusing on the relevant features of the packet, such as the length of the header, thereby extracting the temporal dependencies and, finally, identifying the attack patterns [68]. Abnormal lengths of headers affect the utilization of the CPU and the sizes of the packets; hence, early detection methods can quickly identify reconnaissance attacks while analyzing the performance and traffic characteristics [69]. Protection against subsequent exploitation attempts can be generated by integrating these techniques into a comprehensive security network, significantly enhancing the ability of the network to detect and prevent reconnaissance attacks in IoT environments. Several studies based on the detection and prevention of vulnerability in IoT systems show that this approach contributes to the broader goal of improving security [70; 71; 72].
## 5 Vehicle Platoon/Swarm  
Travelling at high speeds brings more problems into the picture and makes things a lot more complicated when the vehicle is on a motorway/highway. However, these problems can be reduced if the vehicles form a group and then travel in a formation on highways and motorways. This will have two benefits: first, vehicles are synchronized (_i.e._, vehicles travel in a straight line and maintain a fixed distance from each other and the vehicle in front informs the cars behind if they should reduce speed or change lanes in advance, and then all of them can do it in tandem) and second, the randomness in the immediate vicinity of the vehicles is reduced, which makes things much simpler and safer. Safe Road Trains for the Environment (SARTRE) is an EU-financed 6.4-million-dollar project that has proven this concept in collaboration with Volvo and UK-based company Ricardo, among others [11]. The aim of the SARTRE project was to develop a road train or platoon concept in vehicular traffic where a lead vehicle driven by a professional driver takes control of a platoon of vehicles that go in a semi-autonomous state [11]. The SARTRE project demonstrated successfully that vehicle platoons are not only safe but can be very efficient in reducing congestion and improving safety.  
The vehicle swarm (shown in Figure 3) can be imagined by thinking of the motorway/highway as a conveyor belt, but where a conveyor belt moves with a constant steady speed, a group of vehicles on the motorway can move with any set constant speed. This can be possible by allowing like-minded vehicles, _i.e._, vehicles that intend to travel at roughly the same speed, to team up and form groups or a \"vehicle swarm\". Ricardo, a British engineering firm involved in the SARTRE project, believes that vehicle platooning is possible with fully autonomous vehicles [12].
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Reactor
#### 3.2.2 Mixing  
Mixing, to a great extent, can determine the performance and cost of an AD system. Mixing promotes contact between microorganisms, substrates, and nutrients and provides uniform temperature distribution in the digester. Adequate mixing can reduce sedimentation and foaming caused by floating fat with adhering gas bubbles or by filamentous microorganisms, such as Microthrix or Norcardiia [164]. Reactors equipped with mixing tend to produce more biogas [165] than those without, although contrasting results do exist [166; 167]. Mixing can be accomplished by means of mechanical (employing a mixer), hydraulic (liquid recirculation), and pneumatic (recirculation of gases) techniques [168] at various frequencies (continuously or intermittently with several hours or several times in an hour during a day) [11] and intensities (gentle, intermittent, and rigorous rotation speed). [PERSON] et al. [169] showed that employing intermittent mixing, mass transfer from the liquid phase to the gas phase greatly enhanced resulting in an increased gas release as much as 70% higher than the periods without mixing. In addition, among various mixing intensities, gentle mixing leads to the formation of aggregates and prevents methane-producing organisms from being washed out by the liquid and thereby proved to be more effective [170].  
Dispite the benefits of increased gas release, mixing does have an extra cost and requires an energy input. Continuous mixing in a full-scale AD can demand as much as ~50% of the total plant energy [171], with mixer motor start-up consuming 2.5 % additional energy [172]. Therefore, mixing should be compromised or chosen carefully, given the feedstock type and AD technology. Strategies  
Figure 4: Example of two novel anaerobic reactor types—reproduced with permission from the original authors [7; 161].  
like switching from continuous to intermittent mixing mode could save over 25% of the energy demand and ensure higher plant productivity [173]. Additionaly, using innovative technologies such as a gas-lifting reactor (that works on biogas's rising tendency for partial recirculation and digester's agitation) can provide the effect of mixing without requiring a mixer, and has been shown to result in improved plant economies [174]. Two innovative mixing configurations operating with the airlifting mixing concept are shown in Figure 5.
# Dimensional Errors Due to Overhanging Features in Laser Powder Bed Fusion Parts Made of Ti-6 Al-4V
## 1 Introduction
analysed the inter relationships between the laser power, scan speed, and various overhang angles and their results indicated that certain surface texture parameters (_Rpc_, _RSm_, and _Rc_) could indicate a shift between surfaces dominated by partially melted particles and surfaces dominated by material from re-solidified melt tracks. Another conclusion from their work also indicated that  
Figure 1: Depiction of dress formation on the down-facing surfaces of a part produced by laser-based powder bed fusion. distinct correlations between process parameters and the surface texture parameter Ra were not conclusive; however they mentioned that further analysis on down-facing surfaces is warranted [17]. A number of researchers have employed different analytical and statistical approaches to build predictive process models for various fields within Additive Manufacturing [25; 26]. [PERSON] et al. developed a Gaussian process regression formula for the prediction of porosity in L-PBF processes and discussed the difficulty in modeling the L-PBF processes due to the low process repeatability [27]. [PERSON] et al. used the Taguchi method for parametric optimization of the L-PBF process. They conducted an ANOVA analysis and established a regression equation that revealed a linear relationship between the part density, laser power, scan speed, powder thickness, and scan strategy [28]. Similarly, a popular method for the modeling and optimization of processes is conducted through the use of artificial neural networks (ANN). [PERSON] et al. used an artificial neural network to develop an ANN model based on the results of a series of experiments and were able to draw conclusions on the effects of different process parameters on the mechanical properties of L-PBF parts [29]. [PERSON] et al. also employed a similar methodology to create an ANN model for L-PBF parts to predict surface roughness in down-facing surfaces [30]. However, a research gap does exist in correlating the L-PBF process responses with input parameters to predict the dimensional deviation that occurs in overhangs. This is the problem that this research starts to solve for parts made of Ti-6 Al-4V metal alloys. Therefore, this current work aims to understand the effects of process parameters on the dimensional accuracy of printed parts, considering the stochastic effect of down-facing surfaces on the obtainable dimensional accuracy. In particular, a statistical analysis of the L-PBF processes for the hard-to-process 45\({}^{\circ}\) down-facing surfaces produced with a Ti-6 Al-4V titanium alloy was conducted. Then, a quadratic regression equation was formulated for the dimensional error in down-facing areas of L-PBF parts using various scanning strategies, layer thicknesses, and down-facing process parameters and the equation was tested by printing a number of samples in order to obtain its accuracy. Additionally, an ANOVA analysis determined the most significant process parameters within the down-facing area, as well as the insignificant parameters.
## 3 Scenario Description and Connectivity Network Configuration
Field technical visits revealed that two network points were in areas without traditional electricity supply: Almagra and La Pena. Photovoltaic systems were installed in Almagra and La Pena to power communications equipment and tower's security lights. The higher the electrical load that the photovoltaic system must maintain, the greater the technical requirements of its elements and, therefore, the investment cost. On the other hand, both technologies have software to monitor and manage their devices, providing information such as the identification of each device, working frequency, signal levels, and length links calculation to the network administrator. In addition, both technologies can automatically change their working frequency based on the electromagnetic spectrum analysis [29].
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 2 Materials and Methods
### Hu Moments  
Named after Ming-Kuei Hu, Hu moments consist of seven mathematical moments used to characterize the shape of an object in an image. These moments are derived from the normalized central moments of the object's binary image. Hu moments are invariant to translation, scale, and rotation, making them invaluable for shape analysis and pattern recognition tasks in computer vision. They provide a compact representation of an object's shape features, facilitating applications such as object classification, recognition, and shape matching.  
Hu moments are particularly well suited for analyzing simple shapes due to their robustness to translation, scale, and rotation. However, they may not perform as effectively when applied to complex shapes, such as plants, which often exhibit intricate and irregular contours. In these cases, other shape descriptors or feature extraction methods that capture the detailed structure and texture of the object may be more suitable for accurate analysis and classification.
## 2 Methods
### Scenarios of Water Reclamation Projects in Industrial Parks  
In this study, we evaluate the economic feasibility of two types of water reclamation projects in industrial parks: (1) water reclamation projects for industrial water supply and (2) water reclamation projects for the irrigation of plants and green areas. These are the primary water reclamation activities in Chonburi's industrial parks, accounting for 70.8% of water reuse. Although reclaimed water used for power plant cooling accounted for 28.7% of total water reuse, Chonburi has only one industrial park with a power plant. The majority of industrial parks lack their own power plants. As a result, water reclamation for power plant cooling is uncommon in industrial parks.  
For water reclamation projects for industrial water supply, we assumed that the system consisted of ultrafiltration (UF) and reverse osmosis (RO) units. This assumption is based on the actual systems that have been successfully constructed and operated in several industrial parks in Thailand. CBAs were conducted for different wastewater flow rates--5200, 10,000, 15,000, and 25,000 m\({}^{3}\)/day--, covering typical flow rates in industrial parks in Chonburi that reclaim water for industrial water supply. RO permeate was assumed to be 75% of the influent. The assessment was conducted for cases that included and excluded externalities.  
For water reclamation plants for the irrigation of plants and green areas in industrial parks, we assumed that the system consisted of sand filtration and chlorine disinfection. This system is considered appropriate and highly affordable in developing countries. CBAs were conducted for wastewater flow rates of 240, 480, 2400, 3600, and 4800 m\({}^{3}\)/day. These flow rates were selected based on the reclaimed water for the irrigation of plants and green areas in industrial parks in Chonburi, which were typically less than 20% of wastewater flow  
Figure 1: Locations of industrial parks in Chonburi Province, Thailand.  
rates, according to our 2018 survey [31], together with the highest maximum capacity of the central WWTPs in the industrial parks in Chonburi (24,000 m\({}^{3}\)/d). Thus, the maximum flow rate selected was 4800 m\({}^{3}\)/day, and the lower flow rates varied. This irrigation activity is assumed to have no negative environmental impact on the soil, and the volume is not too large to infiltrate groundwater and affect groundwater quality.  
Notably, the flow rates chosen for these two water reclamation activities (industrial water supply and irrigation of plants and green areas) were not the same. The irrigation of plants and green areas was usually limited by the green areas available in industrial parks; thus, the flow rates were typically less than 20% of the WWTP effluent. By contrast, water reclamation for industrial water supply is not limited in this way.
# Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines
## 4 Optimization Approach
### Case 1: Low Standard Deviation
#### 5.1.3 Interpretations  
Regarding the solution given in Table 1, we remark that the quantities produced by machines in each production period are nearly the same. This is due to the barely constant demand generated randomly by a relatively low standard deviation (\(\sigma_{d}=25\)). Given the correlation between failure rate and production rate for each machine, such a constant production plan leads to similar failure rates and automatically to similar number of failures; Table 2 shows clearly that the intervals of maintenance for machines are very close to each other in the case of an individual maintenance strategy (\(T_{1}=5\), \(T_{2}=6.66\), \(T_{3}=6.66\), \(T_{4}=5\), \(T_{5}=6.66\), \(T_{6}=6.66\)). When group maintenance policy is planned, we can remark through Table 3 that the production cost increases. This can be explained by the fact that an additional quantity of goods must be planned in order to hedge against shortages since all machines are maintained at the same interval. However, the maintenance cost thanks to group maintenance  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(T^{\star}\) & \(N^{\star}\) & **Total Maintenance Cost** \\ \hline
6.018 & 3 & 2636.7 mu \\ \hline \hline \end{tabular}
\end{table}
Table 4: Case 1: Maintenance plan for GPM policy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Period** & \(\mathbf{d_{k}}\) & \(\mathbf{p_{k}}\) & \(\mathbf{M_{k}}\) \\ \hline Period 1 & 962 & 1000 & 5 \\ Period 2 & 1005 & 840 & 5 \\ Period 3 & 1001 & 920 & 6 \\ Period 4 & 1044 & 1040 & 6 \\ Period 5 & 994 & 1320 & 5 \\ Period 6 & 1022 & 780 & 5 \\ Period 7 & 935 & 920 & 6 \\ Period 8 & 998 & 1240 & 5 \\ Period 9 & 992 & 800 & 5 \\ Period 10 & 1004 & 1000 & 5 \\ Period 11 & 1013 & 1360 & 6 \\ Period 12 & 1007 & 1380 & 5 \\ Period 13 & 1010 & 1200 & 6 \\ Period 14 & 1026 & 840 & 6 \\ Period 15 & 1005 & 1240 & 5 \\ Period 16 & 1002 & 1520 & 6 \\ Period 17 & 1018 & 1200 & 6 \\ Period 18 & 986 & 1260 & 5 \\ Period 19 & 973 & 1400 & 5 \\ Period 20 & 989 & 1080 & 6 \\ Total cost & \multicolumn{2}{c}{1.1614 \(\times\) 10\({}^{7}\) mu} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Case 1: Maintenance plan for individual maintenance policy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine** & **Ni** & **Ti** \\ \hline Machine 1 & 4 & 5 \\ Machine 2 & 3 & 6.66 \\ Machine 3 & 3 & 6.66 \\ Machine 4 & 4 & 5 \\ Machine 5 & 3 & 6.66 \\ Machine 6 & 3 & 6.66 \\ Total cost & \multicolumn{2}{c}{5179.42 mu} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Case 1: Production plan for group maintenance policy.  
strategy decreases considerably, as shown in Table 4 (from 5179.42 mu to 2636.7 mu) and all machines are maintained at \(T_{n}^{*}=6.018\) which proves the consistency of the proposed approach.
## 4 Analysis of Test Results
### Monitoring of Long-Term Settlement  
After averaging the settlement values obtained from the four settlement observation points under long-term excitation test conditions, the relationship between vertical settlement and cumulative vibration cycles, reflecting the overall settlement of the roadbed, is depicted in Figure 24.  
It is evident that the cumulative plastic settlement of the roadbed increases with the number of vibration cycles and eventually reaches a state of convergence. The settlement follows an exponential development pattern, with the final value of the cumulative settlement under dynamic loading ranging within 4.5 mm. This suggests that the on-site foundation-treatment method is highly effective in controlling settlement. It is important to emphasize that soil properties inherently possess uncertainty. The influence of rainfall and excitation loading can bring about variations in soil attributes, such as cohesion, friction angle, and permeability, ultimately resulting in alterations to the settlement value curve [36]. Furthermore, rainfall causes a reduction in the suction of the unsaturated soil matrix, alongside a decrease in the pile-bearing capacity, subsequ  
Figure 23: Variation of dynamic displacement attenuation coefficient with load: (**a**) 90 km/h; (**b**) 100 km/h.  
Figure 24: Variation of settlement with cumulative vibration cycles.  
increase in settlement [37]. Therefore, it is essential to incorporate the influence of rainfall in the design of the foundation treatment. From Figure 24, the cumulative deformation stage of the roadbed can be categorized into three distinct stages:  
* Stage I--Rapid Accumulation of Rainfall Infiltration Deformation (0-4.7 \(\times\) 10\({}^{5}\) vibration cycles): During this phase, rainfall infiltration causes an increase in the water content of the loose residual roadbed material. This, in turn, reduces the friction between particles, increases lubrication, and makes the material more prone to compaction under the excitation force;
* Stage II--Slow Increase in Natural-Condition Deformation (4.7 \(\times\) 10\({}^{5}\)-1.2 \(\times\) 10\({}^{6}\) vibration cycles): During this phase of long-term cyclic vibration, the residual roadbed gradually compacts, leading to a decrease in the rate of cumulative settlement;
* Stage III--Cumulative Settlement Stabilization (1.2 \(\times\) 10\({}^{6}\)-1.6 \(\times\) 10\({}^{6}\) vibration cycles): During this phase, the cumulative settlement exhibits minimal change and has converged, reaching a stable stage.
## 2 Fractional Derivative  
Let us consider a fractional derivative of the time-variable function \(f(t)\), where \(f:\mathbb{R}\rightarrow\mathbb{R}\). There are many definitions of a fractional derivative, which are not equivalent in the general case [24]. The most prominent ones include the Riemann-Liouville, Caputo, Marchaud and Grunwald-Letnikov definitions. For modeling electromagnetic systems, it is desirable to choose the definition which reflects certain properties, like the semigroup property  
\[D_{t}^{\alpha}D_{t}^{\beta}f(t)=D_{t}^{\beta}D_{t}^{\alpha}f(t)=D_{t}^{\alpha+ \beta}f(t) \tag{1}\]  
and the trigonometric function invariance (derivative of the complex exponent)  
\[D_{t}^{\alpha}e^{\Omega t}=\Omega^{\alpha}e^{\Omega t}. \tag{2}\]  
In (1) and (2), \(D_{t}^{\alpha}\) denotes the \(\alpha\)-order fractional derivative, \(\alpha,\beta\in\mathbb{R}\) and \(\Omega\in\mathbb{C}\)[25]. The Riemann-Liouville and Caputo definitions do not meet the conditions (1) and (2). The Grunwald-Letnikov and Marchaud definitions, however, satisfy both properties, making them more appropriate for applications in the electromagnetic theory [25]. In this paper, we employ the Grunwald-Letnikov definition given by [2]  
\[D_{t}^{\alpha}f(t)=\lim_{h\to 0^{+}}\left(\frac{1}{h}\right)^{\alpha} \sum_{l=0}^{\infty}(-1)^{l}\binom{\alpha}{l}f(t-lh). \tag{3}\]  
In the above formula, the symbol \(\binom{\alpha}{l}\) for non-integer \(\alpha\) is given by  
\[\binom{\alpha}{l}=\frac{\alpha(\alpha-1)(\alpha-2)\ldots(\alpha-l+1)}{l!}. \tag{4}\]  
Apart from satisfying the desired conditions, the Grunwald-Letnikov definition can also be naturally approximated using finite differences. It is also consistent with the Marchaud definition used by us in previous investigations [22].  
The approximation scheme for the derivative \(D_{t}^{\alpha}f(t)\) is natural for a sufficiently small time-step size \(T>0\), and is given by the infinite series  
\[D_{t}^{\alpha}f(t)\approx\left(\frac{1}{T}\right)^{\alpha}\sum_{l=0}^{\infty }(-1)^{l}\binom{\alpha}{l}f(t-lT). \tag{5}\]  
In the case of causal functions (i.e., vanishing for times \(t<0\)), it naturally simplifies to the finite sum  
\[D_{t}^{\alpha}f(nT)\approx\left(\frac{1}{T}\right)^{\alpha}\sum_{l=0}^{n}(-1) ^{l}\binom{\alpha}{l}f((n-l)T) \tag{6}\]  
when calculated for \(t=nT\).