esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Commission Error Removal from the RF50 Land Cover Change Map  
To create the AO50 map we applied our commission removal technique to the RF50 map by reviewing all of the RF50 change polygons using the AAviewer. Of the 7832 change polygons, 472 covering an area of 87 ha were found to be erroneously classified as change, giving a commission error rate of 2.55% by area (Table 2). The resulting AO50 change map contained 3779 ha of change for a slightly reduced average annual change rate of 0.46%. Because of the relatively low commission error  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Change** & **Reviewed** & **Modeled** & **Removed** & **Verified** & **Dissolved** \\  & **Probability** & **Change** & **Change** & **Commission** & **Change** & **Change** \\  & **Threshold** & **Polygons** & **Area (ha)** & **Area (ha)** & **Area (ha)** & **Locations** \\ \hline RF50/AO50 Map & \(\geq\)50\% & 7832 & 3866 & 87 & 3779 & 2348 \\ RF25/AO25 Map & \(\geq\)25\% & 16,524 & 5354 & 833 & 4521 & 5607 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall summary of the two change models before and after commission removal. The table shows the number of polygons reviewed for each model, the area covered by those polygons, the area of commission errors removed, the final mapped change area and the count of change locations. Note that the AO25 map almost doubled the number of change locations. Change locations were derived from aggregating adjacent change polygons.  
rate, the overall change rate statistics changed little between the RF50 map and AO50 map. The total area reviewed for the AO50 map represented about 1.5% of the study area, which showed that eliminating commission error from the RF50 map, the base model output, required reviewing only 1/67 th of the total study area. We used this fraction as an estimate of efficiency as compared to all manual photo interpretation.  
While reviewing change polygons using the AAViewer, we attributed each to one of three change agents: development, forestry or natural. After removing commission errors, The AO50 map included 330 ha of development, 3002 ha of forestry, and 433 ha of natural related change. The average size of the forestry change polygons (5.43 ha) was considerably larger than the average size for development (0.37 ha) or natural changes (0.49 ha). Thus, the review process provided two benefits (1) the elimination of commission error and (2) an opportunity to designate a change agent.
## 8 Experiments and Results
### Fault-Oriented Prescription  
To enhance interpretability and support maintenance decision-making, the predicted amplitude values of the fault frequency component were mapped onto a three-tier risk scale that included green, yellow, and red zones. This health classification scheme enables the intuitive visualization of the asset's condition and helps prioritize interventions.  
* GreenZone: This zone corresponds to low-amplitude variations within the expected operating range. When the predicted values remain within this region, the belt system is considered to be in stable condition, with no indication of significant wear or misalignment. In this scenario, no immediate action is required beyond regular monitoring.
* YellowZone: As the predicted amplitude begins to enter this intermediate zone, it indicates an accelerated degradation trend. This suggests the emergence of wear or tension irregularities in the belt system. Although the asset remains operational, it is recommended that the maintenance team prepares for further inspection, as the likelihood of progression toward failure increases.
* RedZone: This level reflects a critical condition in which the amplitude of the fault frequency is significantly elevated. It signals that a failure is likely under development and that immediate action is required. The system automatically recommends an inspection of the transmission mechanism, with particular focus on verifying belt tension, pulley alignment, and possible belt replacement if necessary.  
Importantly, the thresholds used are not generic but are personalized for each asset, which is key to the system's real value. Generic thresholds could lead to many false positives or false negatives, reducing reliability. However, this tool is not a _plug-and-play solution_, it requires a learning period, approximately 15 days in this study, during which the model learns the asset's normal behavior to adjust and fine-tune the thresholds.  
Figure 9: Belt failure predicted in the future with three-tier risk.  
Although this process is not fully automatic, the learning phase is essential to capture the typical operational profile of the asset. This allows the system to set accurate limits, minimizing false alarms and providing meaningful insights to maintenance teams and asset managers, tailored to the specific operating conditions.  
This zone-based prescription mechanism not only aids in the interpretation of predictive results, but it also shifts maintenance from predictive to prescriptive. By linking specific actions to each risk level, the system transforms passive condition monitoring into an intelligent decision-oriented process and proactively recommends targeted interventions based on the expected progression of the fault, enabling teams to act before failures occur, improving operational continuity, reducing unplanned downtime, and optimizing asset life-cycle management.
## 2 The Proposed Method
### Comparison Experiments and Analysis  
#### 3.4.1 Comparisons of Space Conversion Methods  
LE adopted in this study is a nonlinear local feature space conversion method, in order to make comparative analysis and highlight its effectiveness for the work, several contrast experiments with linear space conversion method PCA and global nonlinear space conversion method; Isomap that are proverbially applied for feature transformation were carried out. Considering fairness and rationality, the most suitable DNN structures for PCA and Isomap are, respectively, constructed as follows  
\[\text{DNN}_{3}[6;\,100,\,50,\,25,\,5;\,1]\]  
and  
\[\text{DNN}_{4}[6;\,100,\,50,\,25,\,10;\,1]\]  
The evaluation results of DNNs with PCA and Isomap-based space conversion techniques applying the same procedures in Section 3.3 are shown in Figure 9, it can be discovered that the waveforms of PCA-based and LE-based results have the same abnormal performance: Both of them began to appear abnormal at 5300 th min and mutated around 7000 th min, but the former has greater volatility before the start of the anomaly and the end is chaotic. While the result of Isomap-based technique performs worse in the beginning normal period, and its mutation at 7000 th min is not so obvious, but of which, the unidirectional drastic descent (at about 9400 th min) demonstrated the validity of this method in the detection of serious failure of bearings. It is easy to find that LE performs best in general in the comparisons.
## 3 Results
### Removal of Ammonium and Phosphate in PAP Systems
At the initial rainfall event, all the available sites \begin{table}
\begin{tabular}{c c c c} \hline
**PAP System Materials** & **Units** & **NH4\({}^{*}\)-N** & **TP** \\ \hline Influent concentration & mg/L & 5.2 & 1.2 \\ Total added & mg & 39 & 9 \\ Limestone bed, q\({}_{\text{total}}\) & mg & 12.91 & 4.22 \\ Zeolite bed, q\({}_{\text{total}}\) & mg & 20.28 & 3.47 \\ \hline Zeolite bed followed by MZP-PM, q\({}_{\text{total}}\) & mg & 32.01 & 7.37 \\ Limestone bed a & \% & 33.1 & 46.9 \\ Zeolite bed a & \% & 50.2 & 38.5 \\ Zeolite bed followed by MZP-PM a & \% & 82.2 & 81.9 \\ \hline \end{tabular} \({}^{*}\) Percentage of cumulative nutrients adsorption = (cumulative nutrients added—cumulative nutrients in effluent)/cumulative nutrients added.  
\end{table} 
Table 2: Cumulative adsorption of nutrients (q\({}_{\text{total}}\)). Figure 4: Removal efficiency of NH4\({}^{*}\)-N (**a**) and TP (**b**) in limestone, zeolite bedding and zeolite followed by MZP-PM during a typical rainfall. of adsorption on MZP-PM were accessible and involved in removing nutrients so that the overall adsorption was more efficient. While for the fixed MZP-PM dose, as the cumulative contents of N and P enhanced, the utilization of accessible adsorption sites enhanced and as a result, the removal efficiency was reduced [26; 31]. Due to the differences in the features of water quality and quantity of rainfall in different regions, and the adsorption of N and P by MZP-PM would gradually become saturated. Therefore, it is necessary to discuss the replacement cycle of the filter column. The tests were performed with continuous rain at an intensity of 82.31 mm h\({}^{-1}\) for 2 h, which is equivalent to 59.76 mm of total rainfall that on mean value occurred in Nanjing. According to [PERSON]'s reports [32] and the adsorption characteristics of MZP-PM in this study (Equation (6)), the approximate replacement period of the medium is about 15 months. \[S\ =\frac{Q_{\mathrm{e}}\times 1000}{\overline{R}\times D/q\ \times\ 1100} \tag{6}\]  
where S is the replacement cycle of the filter column, Q\({}_{\mathrm{e}}\) is the adsorption capacities of MZP-PM, \(\overline{R}\) is the average removal concentrations, D is the water volume, q is the average intensity of this rainfall and the average annual rainfall in Nanjing is about 1100 mm year\({}^{-1}\).
## 2 Materials and Methods
### Chemical Composition and Fatty Acid Analysis of Total Mixed Ration  
The dry matter (DM) of TMR samples was measured after oven drying at 65 \({}^{\circ}\)C to constant weight and expressed as a % of the feed. The dry samples were ground through a mill (Retsch Muller, Haan, Germany) to pass a 1 mm screen and used for chemical analysis and spectrometric determination. Samples were analysed for ash, crude protein (CP), ether extract (EE) [32], and total starch using a K-TSTA assay kit (Megazyme International, Bray, Ireland). Neutral Detergent Fibre (aNDF), Acid Detergent Fibre (ADF), and Acid Detergent Lignin (ADL) were analysed using an Ankom200 Fiber Analyzer (Ankom Technology, Macedon, NY, USA) according to [PERSON] et al. [33]. The crude fibre (CF) content was determined with the same instrument, according to the Weende method [32]. Chemical data are reported as percentages of DM.  
The extraction of FAs followed the method of [PERSON] et al. [34] using a chloroform/methanol mixture (2:1, \(v/v\)). Fat dissolved in hexane was methylated with methanolic KOH, and a 1 \(\mu\)L sample was injected into a gas chromatograph with a flame ionisation detector (GC-FID) (Agilent Technologies, Santa Clara, CA, USA), equipped with a CP-Sil88 capillary column (100 m, 0.25 mm, 0.20 \(\mu\)m, 100% cyanopropyl, Agilent Technologies). The fatty acid 19:0 (Sigma-Aldrich Merck, Darmstadt, Germany) was used as the internal standard. GC-FID conditions were as follows: spitless inlet at 250 \({}^{\circ}\)C; FID at 250 \({}^{\circ}\)C; helium carrier gas at 1.2 mL/min. The oven temperature programme began at 60 \({}^{\circ}\)C (held for 4 min), then ramped up by 13 \({}^{\circ}\)C/min to 175 \({}^{\circ}\)C (held for 27 min), followed by an increase of 4 \({}^{\circ}\)C/min to 215 \({}^{\circ}\)C (held for 25 min), and a final increase by 2 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C (held for 5 min) as previously reported by [PERSON] et al. [35]. Fatty acid peaks were identified by comparing them to peaks in a Supelco mix 37 standard ([PERSON], Darmstadt, Germany); which includes the main fatty acids typically found in the TMRs such as myristic acid (14:0), palmitic acid (16:0), palmitic acid (16:1), stearic acid (18:0), oleic acid (18:1 cis-9), vaccenic acid (18:1 cis-11), linoleic acid (18:2 n-6), \(\gamma\)-linolenic acid (18:3 n-6), and \(\alpha\)-linolenic acid (18:3 n-3). Fatty acid methyl esters (FAMEs) were expressed as a percentage of the total FAME content. The total amounts of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) were also calculated.
# DNA Barcoding as a Plant Identification Method
## 4 Chloroplast DNA Barcodes  
Chloroplast DNA is a circular molecule with a size between 120 and 220 kb and consists of a large and a small single-copy region (LSC and SSC) intervened by two copies of a large, inverted repeat (Ira and Irb). There are about 100 functional genes that can be used for species identification and, according to some researchers, besides single-locus markers, the whole plastid genome could be used for DNA barcoding besides single-locus markers. DNA barcodes from chloroplast genes are extensively used in plant phylogenetic studies; the design of primers is easy, gene order in the genome of the organelle is conserved, and amplification is much easier due to the high copy number per cell. Nevertheless, compared with the nuclear genome genes of the chloroplast genome, they are characterized by a low evolutionary rate [3; 23; 31]. Among the chloroplast markers, the following have been successfully used:
## 2 Forecasting Model for Nutrient Deficiency Identification and Data Pre-Processing
### Ensemble Averaging  
Another method used in our study is Ensemble Learning. The idea of ensemble methods is that the results of multiple machine learning models can be combined to increase the accuracy of identification and forecasting. [PERSON] et al. designed several experiments to indicate the effective performance of ensembles using deep CNN for image classification. In this study, we used ensemble averaging to improve the robust level of recognition accuracy from the results of two models above, as shown in [37]. Ensemble learning is a technique that allows one model to combine the group of predictors in order to achieve greater accuracy in prediction. There are two categories of ensemble learning, including  
* Basic ensemble techniques, include max voting, averaging, and weighted average.
* Advanced ensemble techniques, include stacking, blending, bagging, and boosting.  
In our study, we select and use ensemble averaging technology to perform the combination of both of the predictive models outlined in Sections 3.1 and 3.2 based on the average when calculating the bias and variance of each model. Thereby, from the results of the two forecasting and classification models described above, we calculate the average of each case of nutrient deficiency for each model. Then, we summarize these average values, and select the highest final value as the final predictive result for the purposes of validation. Therefore, this can increase the efficiency of classification and identification of nutrient shortage status, while also increasing the accuracy of identification and forecasting. We calculate the weighted average value for each model based on the following equation:  
\[\hat{\gamma}(X)=\ \sum_{j=1}^{N}\eta log\frac{1-r_{j}}{r_{j}}\gamma_{j}(X) \tag{2}\]  
where \(N\) is the number of predictors, \(\eta\) denotes the learning rate, \(r_{j}\) is weighted error rate of the \(j\)-th instance.
## 3 Numerical Results  
In this section, we present numerical results to verify the effect of energy efficiency scheme and illustrate the impact of various MEC network parameters. In simulations, we use the MEC network parameters as shown in Table 1.
# Special Issue \"Multi-Agent Systems\": Editorial
## 6 MAS as Execution Infrastructure  
The last usage destination--that is, exploiting an MAS as the execution infrastructure for a given system--is quite common in MAS literature, as the agent abstraction is a general-purpose programming concept with applications in many business domains and for heterogeneous systems.  
In [12], an MAS is used in the context of multi-robot formation: first, a distributed consensus algorithm is simulated on a multi-agent based simulation software to assess desired properties despite uncertainty of data and delay in communications, then such algorithm is implemented as an MAS and deployed on a real robotic platform comprising four mobile robots, further assessing effectiveness. In this work, the value added of the MAS lies in its natural predisposition to distribution and tight coupling with environment sensing and actuation, which are necessary features of multi-robot systems.  
In [13], instead, an MAS is used as the platform for training unmanned surface vehicles: agents in the MAS correspond to vehicles' controllers and implement a distributed learning algorithm meant to achieve optimal coordinated behavior. Here, the agent abstraction is chosen for its capability to express adaptive behavior by learning new behavioral rules (likewise plans in BDI architectures) while operating.  
Both contributions showcase the ability of MAS architectures to provide a suitable infrastructure for effective and efficient execution of heterogeneous tasks (consensus in the former, learning in the latter).
## 4 Discussion: Analysis of Factors Affecting Drilling Forces
### Influence of Rock Mass Structural Characteristics on Drill Bit Forces
During the stable drilling phase, the thrust force and torque in the soft rock layer are noticeably lower than those in the hard rock layer. When drilling through the soft rock layer, the thrust force and torque for the Level IV rock layer are notably lower than those for the Level III rock layer. Furthermore, it is worth noting that variations in the upper soft rock layer's rock quality have a relatively minor impact on the thrust force and torque when drilling the lower hard rock layer. As illustrated in Figure 19c,d, when drilling in a rock formation with an upper layer of hard rock and a lower layer of soft rock, the drill bit initially comes into contact with the hard rock layer. After penetrating the upper hard rock layer, it reaches the lower soft rock layer, and drilling continues within the soft rock until completion. Upon the initial contact between the drill bit and the hard rock surface, significant impact pressure and torque are generated. Subsequently, when the drill bit encounters the interface between the hard and soft rock layers, there is a slight reduction in pressure and torque. This phenomenon is a result of the continuous action of drilling speed and rotational velocity during the transition from the hard rock surface to the soft rock layer. Once drilling stabilizes, both thrust force and torque remain within a certain range. During the stable drilling phase, the thrust force and torque in the hard rock layer are noticeably higher than those in the soft rock layer. The variations in rock quality within the lower soft rock layer have a relatively minor impact on the thrust force and torque when drilling through the upper hard rock layer. When drilling through the soft rock layer, the thrust force and torque for the Level IV rock layer are slightly lower than those for the Level III rock layer. The drilling results in the soft-hard interlayered rock formation clearly demonstrate significant differences in thrust force and torque during the drilling process in different rock layers. Whether transitioning from a soft rock layer to a hard rock layer or from a hard rock layer to a soft rock layer, whenever there is a change in rock quality, the thrust force and torque relationship curves are distinctly reflected. Therefore, it is possible to deduce the distribution pattern of rock layers by observing changes in thrust force and torque during the drilling process, which serve as indicators of specific variations in rock quality within the formation.
# An Introduction of a Modular Framework for Securing 5G Networks and Beyond
## 3 Proposed Network Service Security (NSS) Framework
### Security Modelling
#### 3.2.2 Network Access Security  
The NAC was well-defined by 3 GPP in 5G security standards and has been extensively studied in various related works [10, 14, 39, 40, 41, 42, 43, 44]. The 3 GPP specifies that, for UE to access the network, it requires a primary authentication process, which addresses the NAC. This includes the AKA protocol, which enables the UE and HN to authenticate each other.  
Mobile subscribers will be able to access network services through ngRAN using their UEs, taking advantage of a variety of wireless communication technologies. As a result, secure access is critical to the 5G principle design, and 3 GPP has defined the security requirements in [6] as well as the system architecture in [45] to support its objectives. The UE's connections should be secured by 3 GPP's standardised security mechanisms. Both subscribers and MNOs require these mechanisms to provide security guarantees, such as  
Figure 3: Security framework.  
the authentication and trust of involved parties, as well as the confidentiality and integrity of the user's data. For the UE to access the network, the UE and the network must mutually authenticate, and then the UE must further authenticate using the security context to access services provided by the SP, which might be the same network provider or a third party via the DN function. Through layered access and security, the UE can gain access to network services. The AKA protocol mutually authenticates the UE and the HN and establishes a session key for UE and SN in which they can have secure communication over a wireless channel to provide network access security. As mentioned earlier, the 5G standard recommends 5G-AKA and EAP-AKA' protocols as preferred methods for primary authentication to address the most significant security requirements in 5G [6].  
The network-level authentication is responsible for verifying that the UE has access to the right network when it connects to it [6]. In 5G, the messages between the UE and the access network via radio interface are to be encrypted. The security at the network level was extensively investigated in [39, 42]. Furthermore, under the 3 GPP definition, the AKA messages were implemented according to a security standard which outlines certain security properties that must be met. The security context obtained from the authentication on this level can be utilised for further authentication when the UE wants access to other services. The security on this level is concerned with authenticating devices in the network and mobility of the UE. In addition, the handover authentication requires the UE and new AN to re-authenticate both parties. To access the network services provided by the MNO, the UE and the network must mutually authenticate each other to establish a secure channel of communication, trust and the authenticity of the device in the network. After this level of security assurance, the UE can request to access other services from the servers in the CN or from third party service provider.