esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing Multi-Agent System Organisations for Flexible Runtime Behaviour
## 2 Organisation-Oriented MAS Design and Development
### MAS Design  
Agent oriented software engineering (AOSE) is based around the design of systems using agents and key characteristics and abstractions relating to agents. The software engineering process, particularly the analysis and design process, is coupled with the abstractions and models adopted. A methodology considers the life cycle of development, including analysis, modelling, design, and implementation. A meta-model describes the components that should be included in a successful model design.  
The development of multi-agent-based systems can involve multiple dimensions, including modelling the environment and using organisational models as a layer of abstraction above and separate from agents. Recently, attention has been given to merging multiple dimensions of development into a single paradigm aiming toward the development of complex distributed systems [34].  
A number of methodologies for agent-oriented MAS design are reviewed in [35; 36; 37; 38]. We note a selection of design methodologies, including methodologies related to organisation centred multi-agent systems: O-MaSE [30; 31], Trops [39], Gaia [40], Prometheus [12], INGENIAS [41], GORMAS [42; 43; 44], and ASPECS [45]. [PERSON] and [PERSON] [46] highlight similarities between of a number of agent-oriented software engineering methodologies. In particular, common elements are the adoption of use case scenarios and adopting models for goals and entities such as roles and capabilities. An overview of AOSE more broadly can be found in [33; 46; 47; 48]. The reader is also directed to the series of workshops on engineering multi-agent systems (EMASs)--for example, [35; 36; 49].  
If a MAS is to be deployed as part of an open environment in which heterogeneous agents interact, then agents may not share a predefined architecture or organisation structure. A service oriented architecture (SOA) is an integrated system wherein interoperability between distributed resources is achieved. In these systems, agents' functionalities are modelled as services. Previous work has included an organisation level to define social context and functional objectives [50], but the organisational level in such cases does not specify how agents achieve their outcomes, so the coordination of agent interactions has to be modelled separately, outside the organisation.  
One approach to coordination within an organisation is to employ middleware or special coordination agents [51]. Another approach is to embed coordination implicitly in the plans that agents use; however, this limits agent awareness and flexibility. More recently, it has been proposed to coordinate agent interactions using artefacts and commitments external to the agents [25]. It is also possible to consider an organisation of agents as if it were an agent at a higher level of abstraction [52]. In that case, the agent organisation can be closed and comprising of homogeneous agents capable of achieving a particular goal. Other coordination approaches are described in [53].
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 1 Introduction
### Inverse Halftoning Technology  
Inverse halftoning is an ill-posed problem because of the inevitable information loss during halftoning. From Figures 2-4, we can see that there are halftone noise patterns existing in both digital halftone images and scanned halftone images, and more complex quality degradation in scanned halftone images. Thus, the critical issue of inverse halftoning is not only removing halftone noise patterns but also restoring fine details from halftone images. Over the last decades, many inverse halftoning methods have been proposed, such as low-filtering methods [6; 7], projection onto convex sets method (POCS) [8], maximum a posteriori (MAP) estimation method [9], wavelet-based method [10], look-up-table method (LUT) [11; 12], dictionary learning method [13; 14], and neural networks method [15]. The references [1] and [16] reviewed these methods in detail. Although these traditional methods achieved state-of-the-art performance at that time, the restored continuous-tone images still suffer from visual artifacts and subtle details loss. Hence, inverse halftoning is still a promising but challenging problem.  
Recently, deep convolution neural networks (DCNN) have demonstrated their powerful ability in many image processing tasks. As a result, more and more researchers are focusing on inverse halftoning based on DCNN [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. In particular, the first work using deep learning for inverse halftoning was presented by [PERSON] and [PERSON] [17]. To promote the research of inverse halftoning, we reviewed the recently development of inverse halftoning method based on deep learning. Because complex and compounded degradation processes exist for scanned halftone images, such as ink smear, paper fiber structures, and paper damage with time, and there are no paired real data for learning [24], the inverse halftoning method for scanned halftone images is different from that for digital halftone images. Thus,  
Figure 4: Sample images showing continuous-tone image, digital halftone image, and scanned halftone image from left to right.  
Figure 3: Color halftoning process. R, G, and B channels of RGB image; color halftone image; and continuous-tone image are shown from left to right in that order.  
the review on inverse halftoning based on deep learning is arranged in terms of following the two directions of digital halftone images and scanned halftone images.  
The remainder of the paper is organized as follows. Section 2 introduces the inverse halftoning method for digital halftone images. Accordingly, the inverse halftone method for scanned halftone images is investigated in Section 3. Section 4 presents and discusses the existing image quality evaluations through experiments, and the prospects are drawn in Section 5. Section 6 provides a summary.
## 2 Materials and Methods
### Determination of PAHs  
For the measurement of the 16 PAHs, namely, Acenaphthene (Acy), Anthracene (Ace), Benz[a]anthracene (BaA), Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Benzo[ghi]perylene (BghiP), Benzo[k]fluoranthene (BkF), Chrysene (Chry), Dibenz[a,h]anthracene (DBaHA), Fluoranthene (Flu), Fluorene (Fin), Indeno[1,2,3-cd]pyrene (Ind), Naphthalene (Nap), Phenanthrene (Phe), and Pyrene (Pyr), 50 mg of dried sieved soil (<20 \(\upmu\)m) was placed in a flask for the addition of the internal PAH standard. The fine soil particle size was chosen due to its potential for easier absorption by the human body. The soil sample was then extracted by adding dichloromethane (DCM) 300 with purity 99.5% and using ultrasonic extraction for 10 min. This extraction process was repeated three times. The filtrate was evaporated at 600 torr using a rotary evaporator (Buchi Rotavapor R100, Flawil, Switzerland). The residue was then dissolved in 30 mL of n-hexane 300 with purity 96.0%, filtered through a burette, and further concentrated with the rotary evaporator at 180 torr. To remove the residue, a silica gel solid-phase cartridge (Sep-Pak Silica Plus, 690 mg sorbent, 55-105 \(\upmu\)m particle size, Waters, Milford, MA, USA) was used. Solid-phase extraction was then performed using nitrogen gas until the extract was concentrated to 200 \(\upmu\)L [27]. The final extract, concentrated to 0.1 mL, was analyzed for PAHs using a gas chromatography-mass spectrometer (GC/MS, Model Agilent 5973N, Agilent Technologies, Inc., Santa Clara, CA, USA, 1999) [28] mentioned in Figure S1.  
The concentration of PAHs was determined using GC-MS. The analysis was conducted with a 0.25 mm i.d. \(\times\) 30 m InertCap 17 column (GL Sciences Inc., Shinagawa-ku, Tokyo, Japan) featuring a film thickness with 0.25 \(\upmu\)m, coupled with an HP6890 gas chromatograph interfaced with an HP5973 mass spectrometer (Model Agilent 5973N, Agilent Technologies, Inc., Santa Clara, CA, USA, 1999). The temperature (initial column) was set at 50 \({}^{\circ}\)C and continued for two minutes. The temperature was then ramped to 185 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min rate, followed by an increase to 320 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min, where it was held for 22 min. Mass spectrometry detection was accomplished within selected ion monitoring (SIM) mode. A 1 mL sample was injected in splitless mode, using pure He as carrier gas with 1.3 mL/min flow rate. PAH concentration was identified considering the retention time of the targeted ion peaks, with a tolerance of 0.05 min from the calibration standard's retention time. Data collection and processing were managed using HP Chemstation software (G1701 EA E.02.02.1431) installed on Agilent 5973N GC-MS. Each PAH was identified within the specified retention time window, and samples were analyzed in triplicate, with statistical processing conducted using Microsoft Excel 2022.
## 4 Experimental Verification
### Comparing Location Results with and without Outliers  
For each event, the mentioned TSWLS and LLS methods in Sections 1 and 2 are applied for comparison with the PCFWE with additive outliers and without additive outliers. Figure 7 shows the location results in a three-dimensional space. It can be seen that the location results of the PCFWE method are approximate to the true sources, regardless of whether the additive outliers are contained in the TDOA measurements or not. Location results, which are determined by the TSWLS and LLS methods, also achieve desirable location results with minor deviations from true sources, but it deviates dramatically if outliers exist. Figure 8 shows the absolute distance errors of location results for three methods with and without outliers. When there are no outliers in the measurements, the absolute distance errors of the three methods are all small, while the PCFWE is the smallest, because of the applied preconditioning method. When the outliers are contained in measurements, the location errors of the traditional methods are dramatically large, while the location performance of the proposed method always remains stable with a higher location accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sources**} & \multicolumn{4}{c}{**True Source Coordinates**} & \multicolumn{4}{c}{**Location Results**} & \multirow{2}{*}{**Absolute Distance Error**} \\ \cline{3-3} \cline{5-8}  & & \(x\) **(mm)** & \(y\) **(mm)** & \(z\) **(mm)** & \(x\) **(mm)** & \(y\) **(mm)** & \(z\) **(mm)** & **(mm)** \\ \hline \multirow{4}{*}{Without filtering} & P & 80.00 & 120.00 & 84.00 & 93.94 & 106.20 & 76.60 & 20.96 \\  & Q & 160.00 & 60.00 & 84.00 & 121.71  
Figure 5: The process of locating the AE source \(T\) using the PCFWE method. The weights in this figure have been standardized by \(w_{i}/\text{sum}(w_{i})\). From Table 2, it is obvious that the biggest absolute distance errors of traditional methods can occasionally exceed 50 mm. There are two main reasons for the poor location results of traditional methods. Firstly, due to the influence of an outlier in the measurement, residuals are significantly intensified by the least squares principle, which results in a large deviation to the final location result for traditional methods. Secondly, traditional methods take no account of the problem of ill-conditioned linear equations, which also affect the location accuracy to some extent. It is worth mentioning that the proposed method in this paper can achieve an accurate location, where the best absolute distance error can reach about 1 mm. The good performance of the new method is attributed to the fact that, it not only reduces the ill condition of linear equations, it also eliminates outliers. Therefore, location accuracy is improved effectively. Figure 6: Comparison of location errors before and after filtering using the LLS method. Figure 8: The absolute distance error of the location results for three methods.
## 5 Collision Avoidance Trajectory Tracking Control  
### Structure of Collision Avoidance Controller  
Figure 6 illustrates the principle of the collision avoidance controller, which combines the collision avoidance algorithm with the tracking controller. The sensor collects the environment and USV status information and then transmits it to the collision avoidance algorithm. The evasion trajectory is generated by the collision avoidance algorithm and used as the tracking trajectory of the tracking controller. Finally, the collision avoidance control of the USV is realized.  
It is difficult for the tracking controller to accurately track the desired trajectory in the control cycle, so the collision avoidance algorithm needs to re-plan the trajectory according to the USV state and environmental information, as shown in Figure 7. As can be seen from Figure 7, the USV starts to sail from the point of \(A\) and plans the evasion trajectory of \(AB_{0}\). However, due to the tracking error of the tracking controller, it only reaches the \(B\) position; that is, the actual trajectory is \(AB\). The evasion trajectory \(BC_{0}\) is planned again to compensate for the tracking error. The red solid line is the actual trajectory.  
Figure 6: The collision avoidance controller.
## 5 Results and Discussion  
The effect of the individual processing steps on both the backscattering time series as well as the resulting soil moisture estimations are presented and discussed in this chapter. Focusing on the main processing steps relevant for preparing the multi-orbit SAR time series for the short-term change detection approach under changing vegetational influence, the incidence angle normalization, Fourier Series transformation, and vegetation correction are discussed in detail. Finally, the soil moisture product is assessed in terms of its spatial and temporal accuracy over the Hur catchment and the Apulian Tavoliere test site. For the evaluation metrics, we applied both the squared Pearson correlation coefficient (R\({}^{2}\)) as well as unbiased Root Mean Square Error (uRMSE) as described by [PERSON] et al., 2020 [85].
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 4 Experimental Setup
### Datasets
#### 4.1.1 CMAPSS  
This dataset contains the simulation of various NASA turbofan engine degradation. The trajectories from 21 different sensors are generated under four different simulation settings.  
As outlined in Table 1, the dataset consists of four sub-datasets: FD001, FD002, FD003 and FD004, according to the operating states and fault mode. Each sub-dataset is split into a training set and a test set. The training set of each sub-dataset is made up of run-to-failure histories of different engines. In contrast, the simulation of each test engine is terminated before its failure, so that the RUL of each engine in the test set is required to be predicted for reporting the final results. Among the four sub-datasets, we run our experiments on the FD001. Instead of using the remaining three sub-datasets, we evaluate our method on the N-CMAPSS dataset described below.  
One additional note is that the data of each engine consist of 21 multivariate time series, but seven time series that do not show changes over time are discarded. Thus, we use only 14 time series as inputs. All the sensor readings and the RUL prediction are updated at the same frequency; the time unit for both the RUL prediction and the sensor measurements is referred to as a _cycle_.
## 1 Introduction
The inter-class closeness evaluates whether the distances from each author group cluster to the target malware are considerably far, meaning that none of the author group clusters are sufficiently close to the target malware to be classified as one of them. For intra-class closeness, if nearby author groups have approximately the same distance from the target malware, the system is unable to decide to which author group a target malware should be assigned to with certainty. If both cases are applicable to the target malware, the system considers the malware to be ambiguous and postpones the classification to human experts. We also developed a graphical user interface that provides the points of ambiguity for helping human experts to effectively determine the author group of the target malware (i.e., ambiguous case). We evaluated our approach through extensive experiments using a real-world dataset labeled by a group of domain experts. The results demonstrated that our proposed metrics, inter-class closeness, and intra-class closeness were effective in avoiding the risk of misclassification. The accuracy of the inter-class inliers and outliers showed significant differences, with accuracies of 96.6% and 76.0%, respectively. For intra-class closeness, we were able to find the optimal hyperparameter value that balances the accuracy and human engagement rate. After the engagement of human experts, we were able to increase the accuracy of the machine-only classification of 93.7% to 95.7% with a much smaller number of samples turned over for manual inspection. The remainder of this paper is organized as follows: Section 2 introduces the dataset and details of its extracted features for this research and explains how to construct the malware-feature graph. With the graph constructed, Section 3 describes the graph embedding for the KNN-based classification and the mechanism of human expert intervention. Section 4 presents the experiments and evaluates the performance of the proposed framework. Section 5 presents the conclusions of this study.
## 3 Collaborative Secure Localization Algorithm
### Overview of Secure Localization Algorithm CSLT
Based on our previous work MANCL in [29], the proposed secure localization algorithm in this paper consists of the following five parts: (1) trust evaluation of anchor nodes; (2) initial localization of unknown nodes; (3) trust evaluation of reference nodes; (4) selection of reference node; and (5) secondary localization of unknown node, as shown in Figure 2.  
In the first sub-process, the trust values of anchor nodes are calculated based on the main idea of the reference [30] to detect malicious anchor beacons. The anchor node which sends legal localization beacons is assigned with a higher trust value. Only trusty anchor nodes can be used to localize unknown nodes in the rest of processes. In the second sub-process, the unknown nodes are localized based on the multilateral localization method by using positioning reference information from trusty anchor nodes. However, not all the unknown nodes can be successfully localized in the second sub-process. Therefore, in the third sub-process, we first evaluate trust value for each successfullylocalized unknown node. Then, the trusty and successfully localized unknown node can be selected as a reference node in the forth sub-process. Finally, in the fifth sub-process, we use two-hop trusty anchor nodes and reference nodes to help localize unknown nodes. How to calculate unknown nodes has been carefully discussed in [29]. Thus, in the next section, we mainly introduce how to evaluate trust values for sensor nodes, which is the most important part in the process of secure localization.
## 4 Method
### The Proposed Scheme Uses Extended Chaotic Maps
#### 4.2.2 Registration Phase  
In this phase, the entity (e) registers its identity (ID) with the registration center (RC) and generates the corresponding private key (\(SK\)). This \(SK\) is then sent back through a secure channel to the entity (e), whose identity (ID) is published. Figure 2 schematically depicts the registration phase.  
Step 1: Network entity(e) send IDe to the registration center (RC).  
Step 2: Registration center (RC) has known \(x\), \(p\), \(T(\cdot)\), \(h(\cdot)\). After the registration center (RC) receiving the IDe from the network entity(e), the registration center (RC) calculates its corresponding privacy key \(SK_{i}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(E\) & Entities on the network. Can be wearable devices, mobile devices, and medical center servers \\ \hline \(S\) & Wearable devices \\ \hline \(M\) & Mobile devices \\ \hline \(MCS\) & Medical center server \\ \hline \(ID_{c}ID_{S},ID_{M},ID_{MCS}\) & Identity of network entity, wearable devices, mobile devices, and medical center servers \\ \hline \(SK_{c}\), \(SK_{c}\), \(SK_{M}\) & Private key of network entity, wearable devices, and mobile devices \\ \hline \(Mk\) & Private key of registration center (RC) \\ \hline \(x,r_{1},r_{2}\) & Random number \([-\infty,\infty]\) \\ \hline \(T\) (\(\cdot\)) & Chaotic map function \\ \hline \(H\) (\(\cdot\)) & One way hash function \\ \hline \(p\) & Big large prime number \\ \hline \(K\) & key \\ \hline \(Kxy\) & Communication key \\ \hline \(p\) & Origin data \\ \hline \(C_{1},C_{2}\) & Encrypted data \\ \hline \(V\) & Verification data \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbol of the scheme.  
\[SK_{i}=T_{h(ID_{e})\ast mk}(x)\bmod p.\]  
After calculating the private key \(SKe\), send it back to the network entity (e) through the secure channel.  
Step 3: After the registration center (RC) sends back the private key \(SKe\) and publishes the identity IDe of the network entity.
# Equivalence of the Frame and Halting Problems
## 2 Results
### Equivalence of the FP and HP  
We can now state our main result--not only does the known undecidability of the HP show that the FP is undecidable, but the reverse holds as well.  
**Theorem 1**.: _The Frame Problem (FP) and the Halting problem (HP) are equally hard._  
**Proof:**  
**FP \(\rightarrow\) HP:**: Let \(a\) = the algorithm that generates the next state of \(A\times E\) and let \(i\) be some instantaneous state of \(A\times E\). Let \(A\)'s state be \(i_{A}\), and the states of \(E_{A}\) and \(E_{U}\) be \(i_{EA}\) and \(i_{EU}\) respectively. Consistent with the above, we assume \(A\) knows \(i_{EA}\) but not \(i_{EU}\), and knows one component of \(a\), namely the algorithm \(a_{A}\) implemented by \(A\). Now assume the FP is undecidable: \(A\) cannot deduce the state of \(E_{A}\sqcup E_{U}\) at \(t+\Delta t\) from knowledge of \(a_{A}\) and \(i_{EA}\) at \(t\). In this case, \(A\) cannot deduce either \(i\) or \(a\). In this case \(A\) cannot build an oracle \(f(a,i)\) that decides whether \(a\) halts on \(i\) and cannot recognize such an oracle if it exists _a priori_. Hence the HP is also undecidable by \(A\). Since \(A\) is a generic finite agent, the HP is undecidable by any such agent.
**HP \(\rightarrow\) FP:**: Assume that the HP is undecidable (as shown in Reference [20]) and hence that no oracle \(f(a,i)\) exists. In this case, \(A\) cannot deduce, even if given all of \(i\) at the current step \(t\), that \(A\)'s next state (at \(t+\Delta t\)) is not a halting state. Hence \(A\) cannot deduce even its own next state \(i_{A}\) at \(t+\Delta t\), let alone the full state \(i(t+\Delta t)\). Hence the FP is undecidable.  
As the FP centrally concerns what an agent can deduce, it is also of interest to formulate it as an instance of Rice's Theorem. The latter states that the problem of determining what programs compute a given, non-trivial function is equivalent to the HP, and is therefore undecidable. Suppose then that there is a set \(S\) of programs that, given an action on an open environment \(E\), compute all (object, property) pairs that must be updated in consequence of the action. The FP is then the problem of deciding whether \(P\in S\) for any given program \(P\). This problem is, by [PERSON]'s Theorem, undecidable.