esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Current Limitations and Future Trends  
Although, as proven in this review, substantial progress has been made toward developing production processes, specifically in MaaS implementation, there are still some limitations at this state and therefore plenty of developments yet to be met. It is often observed that executives lack decision-making capacity [64], and as a result, they must meet decisions based on immediate benefits rather than sustainable and holistic outcomes [64]. To provide efficacy and sustainability in decision-making by the managers, modeling is useful to simulate scenarios, predict participation levels and design mechanisms [47]. The majority of the MaaS systems proposed could be evolved further by taking more parameters into consideration, such as demand forecast, seasonal variation, or raw material pricing [40]. Of course, to arise a better understanding of the MaaS implication, its involved parts, the associated risks and the obtainable benefits, more MaaS platforms need to be examined to achieve a profitable comparison [77], and more information needs to be selected from industrial practitioners, for example, through questionnaires or interviews [62]. The managerial implications of MaaS affect strategic decision-making, cost optimization, and market adaptability [13, 23]. Managers must use predictive analytics to improve supply chain resilience [48], while SME adoption barriers, such as digital infrastructure limitations, require policy interventions and workforce training [10]. Academically, further research on scalability, dynamic pricing, and real-world applications is essential to bridge theory with practice [2, 14], ensuring holistic MaaS integration. The current limitations in MaaS adoption, such as inadequate decision-making capacity and trust issues, significantly hinder its scalability and industry-wide implementation. For instance, [PERSON] [55] and [PERSON] [60] highlight that real-time data collection and transparency are crucial for smart manufacturing, yet many enterprises struggle with integrating secure decision-support systems, leading to inefficiencies and reduced adoption rates. Similarly, [PERSON] and [PERSON] [2] and [PERSON] et al. [38] emphasize blockchain-based trust mechanisms as potential solutions, but concerns about implementation costs and interoperability with existing infrastructures remain key obstacles to widespread MaaS adoption. In addition, despite the great improvements MaaS offers, its integration into small- and medium-sized enterprises (SMEs) presents important challenges. Limited or zero access to advanced digital infrastructure, lack of technical expertise, and concerns related to data protection, or trust, may hinder adoption. Moreover, MaaS mainly addresses collaborative and complex industrial frameworks, which are difficult for SMEs to align with. In order to
## 3 Results
### Metabolic Parameters of the Strains in Cultivated in Raw Glycerol  
Using raw glycerol, the NCYC4007 strain at 15 \({}^{\circ}\)C presents a maximum production of DHA (23% DHA in ODW) at 144 h of cultivation and could maintain this production for four days (Figure 4a). A similar situation was observed for the NCYC1146 strain but with respect to carotenoid production (Figure 4b). For each strain, the live microbial biomass (ATP) was kept at basal levels until the culture reached the maximum DHA or TC  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Carbon Source** & **SM** & **AGM** & **RGM** \\ \hline
**Fatty Acids** & & **(\%) TFA** & \\ \hline C14:0 & 0.2 & 0.2 & 0.8 \\ C15:0 & 0.2 & 0.2 & 0.6 \\ C16:0 & 3.8 & 4.5 & 8.9 \\ C17:0 & 0.6 & 0.5 & 1.1 \\ C18:0 & 0.0 & 4.7 & 10.5 \\ C20:0 & 0.1 & 0.1 & 0.3 \\ C22:0 & 0.2 & 0.2 & 0.5 \\ C23:0 & 0.0 & 0.0 & 0.2 \\ C24:0 & 0.6 & 0.5 & 1.0 \\ C25:0 & 0.0 & 0.0 & 0.2 \\ C26:0 & 0.0 & 0.0 & 0.1 \\ C16:1 (\(\upmu\)9) & 1.5 & 0.7 & 1.2 \\ C17:1 (\(\upmu\)7) & 1.0 & 0.3 & 0.7 \\ C18:1 (\(\upmu\)9) & 53.0 & 55.4 & 0.0 \\ C18:2 (\(\upmu\)6) & 4.6 & 3.0 & 42.2 \\ C20:1 (\(\upmu\)9) & 0.1 & 0.1 & 0.2 \\ C20:5 (\(\upmu\)3) & 7.2 & 10.2 & 9.2 \\ C22:6 (\(\upmu\)3) & 27.0 & 19.2 & 22.3 \\ \hline Total \% SFA & 5.6 & 11.2 & 24.1 \\ Total \% PUFA & 94.4 & 88.9 & 75.8 \\ \hline Total FA (mg g\({}^{-1}\)) & 46.2 & 45.3 & 53.6 \\ Total DHA (mg g\({}^{-1}\)) & 12.5 & 6.8 & 11.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fatty acids profile from the NCYC 4007 strain cultivated in three different culture media at 15 \({}^{\circ}\)C: Sabouraud medium (SM), media formulated with analytic grade glycerol (AGM) and raw glycerol (RGM). The results are shown as the percentage of total fatty acid (TFA). In addition, the amount of fatty acids (FA) and DHA are showed in mg g\({}^{-1}\) freeze-dried weight (FDW) biomass.  
Figure 3: Carotenoid pigment profile of the NCYC1146 strain acquired from the total biomass produced from each of the bioprocesses carried out in the culture media SM, AGM and RGM at 15 \({}^{\circ}\)C. The results are shown in \(\upmu\)g g\({}^{-1}\).  
production; from here on, an increase of ATP was observed and, subsequently, an increase in the cell count, which reached the maximum at 216 and 240 h for the NCYC4007 and NCYC1146 strains, respectively. (Figure 4a,b). In addition, observations were made at 24 h when the culture began the synthesis of carotenoids in the NCYC4007 strain, reaching values of 15% of CT in ODW at the end of the culture (Figure 4a). The intracellular glucose concentration determined in both strains presented a decrease in the time, contrary to what was observed in the culture medium, where an accumulation was observed during the cultivation (Figure 4c,d).
## 1 Introduction
As a result, attackers cannot judge whether the session content is from the same user, cannot distinguish the specific identity of the user, and therefore cannot trace the user's behavior. * _Message Integrity_: Integrity means that the data has not been destroyed or altered during transmission or storage. The recipient of the message must be able to verify the integrity of the received message. If the message was found to have been tampered with or lost, the session should be stopped immediately and a resend requested. At the same time, the sender of the message cannot deny that it has ever sent the message. The most common methods of achieving integrity are encapsulation and signing. * _Mutual authentication_: Each communication node in the network can authenticate with each other to confirm the legitimacy of the identity of the communicating nodes. * _Confidentiality_: Data transmitted in the public network must be transmitted in encrypted cipher text, and the transmission of sensitive information in clear text is prohibited. It is mainly divided into forward and backward confidentiality. If the long-term private key of the protocol participant is intercepted by attackers, and attackers cannot thereby roll out the session key that the participant negotiated to obtain before the private key was compromised. Forward confidentiality means that a node leaving the network cannot gain access to confidential data transmitted afterwards. Backward confidentiality means that a newly joined node cannot read confidential data that has been transmitted before. * _Freslness_: The recipient of the message should first check the timestamp of the received message to determine whether it meets the freshness requirement. As a result, the main purpose of this paper is to design a safe and reliable UAV communication protocol to achieve mutual authentication between UAV and GCS and establish a secure session. The main contributions of this paper are as follows: 1. We propose a certificate signing based on an Elliptic Curve multiple authentication scheme (CSECMAS) to achieve secure sessions between UAVs and the GCS. 2. The key node is selected using the fitness function, through which a secure session can be established between any two UAVs in the network. Increasing the resistance of the entire network to destruction. 3. The proposed scheme is analyzed using formal and informal analysis techniques to ensure the security of the protocol. 4. Compared with other related protocols in terms of security properties and node computational overhead, the results shows that the protocol is resistant to a wide range of attacks and performs better. The rest of the paper is organized as follows. Section 2 describes the current research on UAV communication protocols. Section 3 describes the proposed protocol in detail and Section 4 presents a formal and non-formal security analysis of the protocol. The final section gives the performance analysis, simulation results and conclusions respectively.
###### Abstract
Furthermore, the integration of multiple piezoelectric transducers substantially enhances the system's power output capacity. To validate the proposed PEH, a self-powered wireless sensor node has been developed for assessing its performance in CO\({}_{2}\) monitoring within the context of transformer substations. The remainder of this paper is organized as follows: the system configuration is presented in Section 2. Experimental studies and discussions of the system for self-powered CO\({}_{2}\) monitoring are presented in Section 3. Finally, the conclusions are provided in Section 4.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 3 Self-Organizing Coverage Based on Gradient  
### Gradient Generation Mechanism  
The gradient is generated by setting different gradient values according to the distance between the robots. As shown in Figure 2, the gradient value of the leader robot (red cross mark) is set to 0, and the gradient value of the robot nearest to the leader robot is 1, and so on. According to the distance from the leader robot, the gradient value of the robot increases by 1 each time. For ordinary robots, by periodically querying the gradient of neighbors in the range of \(d_{safe}\), and setting its gradient value to the minimum gradient value of the neighbor plus 1. When the robot moves to other positions, its gradient value can be continuously updated to ensure the gradient value of the swarm robots is always increased layer by layer and increases from the inside out from the leader robot. The number marked on the robot in Figure 2 is the gradient value of the robot.  
The gradient generation algorithm is shown in Algorithm 1. Initially, the gradient of the leader robot is set to 0 (lines 1-2). Robot \(\alpha\) searches for the minimum gradient (\(grad_{min}\)) of adjacent robots within \(d_{min}\) distance (lines 4-5). If the gradient of the robot is greater than 1 than the minimum gradient, then the gradient of the robot will be updated to \(grad_{min}+1\) (lines 6-7).
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
###### Abstract  
In the study of biomass assessment, geospatial modeling-based analysis becomes crucial for the sustainable management of agriculture. Currently, there is no integrated sustainability assessment of the geographic information system (GIS) cluster or suitability analysis for the feedstock of crop residues. In order to fill this research gap and support the strategy of bioenergy formulation with the circular economy concept in agriculture residues in Myanmar, this study aims to assess the energy generation potential and site locations of treatment facilities for crop residue, utilizing the integrated assessment of GIS cluster and suitability modeling. The cluster analysis identifies the rice straw as the highest feedstock of crop residues and township-based high/low clusters. In addition, the electricity generation potential is estimated at 279.14 MW for different clusters of rice straw. Moreover, the suitability analysis in the study uses the conceptual model of variables for constraints and factors with the analytical hierarchy process (AHP) technique to evaluate the weights. The suitability analysis found high suitability areas of 14,603 hectaforement facilities within the high/low cluster of feedstock for rice straw. The multiciteria and GIS integrated assessment model adopted in this research can support the decision-makers in developing spatial-based strategic planning for bioenergy promotion which will support sustainable farming practices in Myanmar. Additionally, the proposed model is adaptable in study areas with similar feedstock.  
crop residue; bioenergy; geographic information system (GIS); cluster analysis; suitability analysis; analytical hierarchy process (AHP); energy generation  
1  
[PERSON], [PERSON], [PERSON], T. GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar. _Appl. Sci._**2022**, _12_, 11822. [[https://doi.org/10.3390/app122211822](https://doi.org/10.3390/app122211822)]([https://doi.org/10.3390/app122211822](https://doi.org/10.3390/app122211822))
## 5 A Case Study
### Experiment 1
#### 5.1.2 A Discussion  
In order to present the rationality and some superiorities of the proposed method, the experiment results of the case study were compared with some other methods.  
* Compare the experiment results with the hypothesis of a single fault. For the system under test where \(T_{1}\), \(T_{3}\), and \(T_{11}\) are successfully passed and the rest of the tests are unqualified, if there is a single fault assumption, according to the diagnosis logic, the system will have no solution--while with the proposed method, the fault status can be located on \(F_{(4,6)}\), \(F_{(5,6)}\) or \(F_{(4,5,6)}\), with a possibility suggestion of each diagnosis result for reference.
* Compare the experiment results with the method in the literature [29]. The method in Reference [29] shows that if the fault prior probability and the test results are known, the multiple fault diagnosis can be achieved through the criterion of maximum posterior probability. Although the method is effective, sometimes the result can be insufficient, because it only gives one diagnosis result (\(F_{(4,6)}\) in this case study), while the failure may exist in other fault types according to the proposed method.
## 2 Proposed Topology and Principle of Operation
### Buck-Boost Converter  
The buck-boost converter is one of the basic converters that was used during the initial stages of the power electronics field. It works either to step-up or step-down the input voltage based on the duty cycle; if \(D<0.5\) the converter works in buck mode, while if \(D>0.5\) it presents the boost mode. It is an inverting converter in nature, where the converter output is negative with reference to the ground input. Figure 4 shows the buck-boost converter building blocks and its operation.  
During the ON state, \(Q\) is closed and \(D\) is open, as illustrated in Figure 4b. The current flows from the input supply into ground through the inductor \(L\), while the output capacitor supplies the load current during this state. Figure 4c shows that during the OFF state \(Q\) becomes open and \(D\) closes, and again the inductor nature works since the current cannot be interrupted, such that the inductor becomes a current source and the charged energy in the inductor flows to charge the output capacitor and supply the load current. This charging and discharging takes place in every switching cycle. According to the inductor volt-second law, in steady state the average voltage applied on the inductor must be zero [24; 25]. Applying the volt-second balance equation, the inductor average voltage equals  
\[\langle V_{L}\rangle=\frac{1}{T_{\mathrm{S}}}\int_{0}^{T_{\mathrm{S}}}V_{L}(t )=0 \tag{2}\]  
Figure 4: Buck–boost circuit and its operation: (**a**) buck–boost basic structure; (**b**) ON state circuit; (**c**) OFF state circuit.  
Figure 3: Proposed transformerless single-phase grid-tied inverter.  
\[\frac{Vout}{Vin}=\frac{D}{1-D} \tag{3}\]  
Therefore, according to Equation (3), by controlling the duty cycle \(D\) we can generate the required inverted output voltage, which will be used in the proposed topology to charge \(C_{\text{FC}}\).
# Anomaly Detection Method Considering PLC Control Logic Structure for ICS Cyber Threat Detection
## 3 Proposal for PLC Control Logic Embedding and Anomaly Detection
### Overview  
The proposed PLC control logic anomaly detection approach targets PLC control logic composed of IL code. It detects anomalies by embedding IL code and training an AI model. Since control logic programming methods are standardized under IEC 61131-3, other control logic languages can be converted into IL, ensuring the generalizability of the proposed method. Figure 1 presents an overview of the proposed PLC control logic anomaly detection approach.  
The proposed PLC control logic anomaly detection approach first performs embedding on IL code. Since the opcode and operand of IL code have distinct characteristics, they are separated and embedded using different embedding models. The opcode and operand are then combined to restore the original IL code structure. This embedding method preserves the structure of the control logic, ensuring that the embedded control logic retains the original IL code's structure and context. Finally, the anomaly detection model learns only the embedded normal control logic and detects anomalous control logic that deviates from the normal range.
## 3 Proposed Optimum Mother Wavelet Selection Method  
### Selection of the Optimum Wavelet  
For signal contaminated by white noise, the denoising is based on the different behaviors of pulses and noise in frequency sub-band. According to the theory of wavelet denoising, when hard threshold function is adopted, the detail coefficients with absolute values smaller than threshold are considered to correspond to the noise, while the detail coefficients with absolute values larger than threshold are assumed to associate with PD pulses. The threshold is estimated based on the detail coefficients at each scale, and the coefficients are related with the mother wavelet. Thus, the SNR is reflected by both the coefficients and  
\[E_{AN}(j)=\sum_{k\in I_{AN}(j)}\left|{{cA(j,k)}}\right|^{2} \tag{9}\]  
where \(I_{AS}(j)\) and \(I_{AN}(j)\) are described as Equations (10) and (11), respectively.  
\[I_{AS}(j)=\left\{{k\left\|{{cA(j,k)}}\right\|\geq\lambda_{j}}\right\} \tag{10}\]  
\[I_{AN}(j)=\left\{{k\left\|{{cA(j,k)}}\right|<\lambda_{j}}\right\} \tag{11}\]  
at \(j\)th scale, the energy of signal and noise are expressed as follows:  
\[E_{S}(j)=E_{AS}(j)+E_{DS}(j) \tag{12}\]  
\[E_{N}(j)=E_{AN}(j)+E_{DN}(j) \tag{13}\]  
The SNR of the \(j\)th scale is calculated by (14) at this frequency sub-band:  
\[SNR(j)=10\cdot\log_{10}(E_{S}(j)/E_{N}(j)) \tag{14}\]  
Based on the perspective to improve the SNR, the proposed method identifies, at \(j\)th decomposition scale, the optimum mother wavelet is selected as the one maximizing the \(SNR(j)\) value.  
It is worth mentioning that the proposed method for optimum wavelet selection is more time-consuming than the other energy based wavelet selection methods; the extra time is mainly spent on the threshold estimation for each candidate mother wavelet.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 5 Adversarial Defense Methods in IoT Networks  
In addition to the inherent nature of IoT devices, the ML-based security systems in IoT networks are vulnerable to adversarial attacks. As demonstrated in the preceding section, there are various ML-based techniques capable of creating adversarial examples that can easily fool or degrade the performance of the ML models.  
To detect and mitigate the various attack strategies discussed in Section 4, there has been a surge in promising defense techniques introduced in recent years, all geared towards enhancing the robustness and resilience of ML models against such attacks. However, the challenge of countering adversarial attacks remains open and continues to elude researchers to find an effective global solution. Most existing defense strategies lack adaptability against various forms of adversarial attacks. While a particular method may successfully counter one type of attack, it often exposes vulnerabilities that can be exploited by attackers who are aware of the fundamental defense mechanism. Additionally, implementing those defense strategies might result in performance burdens and potentially reduce the prediction accuracy of the model in practical usage.  
In this section, we discuss the recent advancements in adversarial defense methods, and basing on various defense methods classifications in the literature [17; 24; 52; 72; 73; 74], we propose our two-dimensional classification. The first dimension is a defense mechanism that can be a proactive defense mechanism or a reactive defense mechanism [52; 72]. The second dimension is a defense strategy of three types: network optimization strategy, data optimization strategy, and network addition strategy [17]. In Figure 7 we summarize the most famous defense methods in use today classified according to our two-dimensional (2D) classification.
## 3 Sink-Based Centralized Transmission Scheduling
### Using Multiple Channels for Shortening Data Collection Time  
In the SC-Sched method, the sink node activates a pair of sensor nodes per hop. When a sensor node wakes up, the sensor node performs WuC detection and address matching and activates the data module for transmitting/receiving. These operations take a relatively long time and occur every hop as shown in Figure 3. Therefore, the SC-Sched method takes a relatively long time to collect all sensor data.  
We suggest using multiple channels to shorten the data collection time of SC-Sched, where the WuC and data frames are transmitted over different channels in parallel. This adopts the concept of pipelined wake-up [13], but all of the transmissions are controlled by the sink node.  
Figure 6 shows the mechanism of using multiple channels. The next WuC can be transmitted immediately after the WuC detection time. When the number of data frames relayed by a node is large, it is possible that the transmission of the next pair of nodes will start before the transmission of the current pair is finished, and the two transmissions will collide with each other. To prevent this problem, the sink node schedules the transmission timing of the next WuC based on the calculated data transmission completion time. In this way, the next pair of nodes wake up properly, neither too early (with transmission collision) nor too late (with extra waiting, which increases data collection time), and the transmission of the next data frame exactly follows the current one.  
Figure 5: Conceptual diagram of the retransmission slots for data frames.  
Figure 6: Reducing data collection time by parallel transmission of WuCs and data frames over multiple channels.
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.9 PAC HD50 Sensor  
This sensor is designed to screw into a threaded hole with a small hexagonal rod housing (6.3 mm across) and with a limited sensing element size. Tested with a short, threaded-brass hexagonal rod, it showed a peak at 500 kHz to normal incident waves as the sensor designation implied (Figure 17). Again, peak response frequencies to bar waves shifted down to 100-230 kHz. The aperture effects are again obscure as many peaks and dips are present.