esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 2 Theory Background
### Clustering Algorithm  
Due to the large scale of space solar-power stations, there are many nodes to be repaired, which greatly increases the difficulty of space-robot cluster to perform maintenance tasks. The current task planning algorithm is not very effective for planning large-scale problems, and as the number of tasks increases, the planning time increases significantly. Therefore, how to reduce the scale of the problem is one of the elements to solve the task planning problem. This study uses clustering algorithm to reduce the scale of the problem and reduce the difficulty of solving the planning problem.  
Clustering has been extensively studied for many years, and researchers have proposed many clustering algorithms [34; 35; 36; 37]. For large-scale space problems, space task segmentation can be effectively divided into disjoint subsets, and only allow each space robot to select from subsets of the whole task set. According to the target of space maintenance task, a group of data points is divided into several clusters by cluster analysis, and then the task planning of space-robot cluster is carried out for the maintenance points after clustering, so as to reasonably arrange and organize space robots.  
The movement of the space robot is divided into the movement of the space robot base and the movement of the mechanical arm. For space maintenance operations, the movement of the satellite base moves between the clustering centers points, and the space robot only needs to operate the mechanical arm to complete the maintenance task within the clustering scope, without moving the base of the space robot. According to the results after clustering, the task planning of space robots is carried out. This hierarchical planning idea can effectively reduce the planning complexity and shorten the planning time.  
If point \(P_{x}\) is taken as the center of the clustering, when the distance between point \(P_{x}\) and point \(P_{i}\) is less than _eps_, then point \(P_{i}\) is put into set \(Cluster_{x}^{1}\), as shown in Equation (9), and the rest points are followed in turn.  
\[Cluster_{x}^{k}=\left\{\begin{array}{cc}P_{i}\big{|}Dist(i,x)<eps&k=1\\ P_{j}\big{|}P_{i}\in Cluster_{x}^{k-1},Dist(i,j)<eps&k>1\\ Cluster_{x}^{area}=Cluster_{x}^{1}\cup\ Cluster_{x}^{2}\cup\cdots\cup \ Cluster_{x}^{k}\end{array}\right. \tag{9}\]  
Among them: _eps_ represents the clustering radius, and \(k\) represents the number of layers of clustering, which can be set according to the requirements.  
Repeat the above process to obtain other clustering sets. The set of regional maintenance targets clustered by point targets is \(Task=\left\{Cluster_{x}^{area},Cluster_{y}^{area},\cdots,Cluster_{M}^{area}\right\}\), and \(M\) is the total number of clusters.
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 8 Conclusions and Perspectives  
In our study, new results on degree-constrained minimum spanning hierarchies in graphs with non-uniform capacity constraints are presented. DCMSHs can be built in several cases, where DCMSTs do not exist. An ILP-based exact solution and a heuristic are proposed for the NP-hard DCMSH problem. Certain approximation ratios are also discussed. As the solution does not always exist, the necessary and sufficient conditions for the existence are formulated.  
Our study shows that the set of specified leaves with degree bound 1 is crucial for the properties of the solutions. If this set is large, the solution may not exist and the approximation within a constant is not trivial (we consider, it to be an open question). The DCMSH exists in several cases where the DCMST does not exist. The average cost of DCMSHs is lower than the average cost of DCMSTs. The weaker the constraints, the more the solution converges to the MST (or to the MSTSL). Note that the heuristic provides good results relatively close to the optimum.  
In future work, the approximation of spanning hierarchy problems with different degree constraints in various graphs should be analyzed. We conjecture that in some cases, depending on the positions of leaves in \(V_{1}\), approximations can be found even if \(|V_{1}|>3\). Another important perspective is the analysis of partial spanning problems like degree-constrained Steiner problems. We believe that an important part of the results presented in this paper can be applied to partial spanning problems. Applications of the degree-constrained spanning hierarchies can be found, for instance in optical broadcast/multicast routing. In these applications, additional constraints (e.g., the uniqueness of the used wavelength in the fibers, limited length of paths, etc) exist, and various constraints must be fully satisfied. Analyzing these issues promises further interesting challenges.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 3 System Proposed  
### System Architecture  
Below is a general description of the architecture of the proposed system. Its parts and functions are explained later. Figure 2 shows a general diagram of the proposed system. On the left side (a), there is a representation of the WSN composed by the developed nodes, and their connection with the platform is shown on the right side (b). This side also presents an image of the different subsystems, communication protocols and applications provided by the system.  
The platform includes four subsystems of information:  
* Geographic Information Subsystem: this subsystem of the architecture stores geographic data regarding the region where the nodes are deployed. These geographic data are mainly related to the information about the routes between node locations in the system and their geocoding information. The services of this system are provided through a REST API (Application Programming Interface) to other subsystems.  
Figure 1: This figure shows a graphic representation of the input of a Capacitated Vehicle Routing Problem (CVRP) problem and the output route solutions.  
* Data persistence subsystem: This system stores the information related to the management of the collection platform (data from the sensors and the deployed network, vehicles and the routes they perform, users, etc.).
* Alert subsystem: This system manages the incidences related to the information obtained through the container sensors and the vehicle fleet. They notify possible events that may happen in both the container and the vehicle fleet.
* Route optimization system: This system is responsible for searching the best collection routes for the vehicle fleet using the information obtained from the sensors.  
Subsystems inside the platform use Message Queuing Telemetry Transport (MQTT) and REST web services as communication protocols. The platform also includes two user applications which employ the communication protocols and the information subsystems previously mentioned. Described below are all the components of the system emphasizing the developed node as well as the searching process to calculate the best routes for the vehicle fleet using the sensors' data.
## 3 Interactions of Antitoxins and Toxins with Cellular Components
### Interactions Involving Toxins
#### 3.1.2 Interfering with DNA Replication  
Direct interactions with DNA-gyrase: Plasmid-encoded CcdBr and ParERR2 type II toxins target DNA-gyrase, a type II topoisomerase [97; 98; 99; 100]. CcdBr-resistant mutants were isolated and mapped in the g\(yrA\) gene encoding the GyrA sub-unit (mutation GyrA\({}_{462}\)) [101]. CcdBr interacts with gyrase in an open conformation and stabilizes the DNA-gyrase complexes. This results in inhibition of the DNA replication step and leads to DNA double-strand breaks formation, inhibition of replication, SOS induction, cell filamentation and, eventually, cell death [4; 101; 102]. ParERR2 toxin was shown to inhibit DNA-gyrase and induce double-strand break formation _in vitro_[99]. ParE\({}_{2}\) from _Vibrio cholerae_ was also shown to interact with the GyrA sub-unit, although to distinct sites from CcdBr, and unlike CcdBr, requires ATP to stabilize DNA-gyrase cleavable complexes. This indicates that ParE inhibits DNA-gyrase in a different manner than CcdBr[100]. Another member of the ParE family (ParE2 from _E. coli_ 0157:H7) was shown to induce the SOS response and to colocalize with the nucleoid [34].
## 3 Model Formulation
### Problem Statement  
The presented model allows for answering the following questions:  
* What change in the level of competence structure of \(SG^{n}\) will result in the acceptance of additional \(EX\) orders to the already adopted \(EP\) collection project plan? This occurs in the so-called analysis problem.
* Is it possible to accept additional \(EX\) orders for a successful \(EP\) portfolio while maintaining the required level of competence structure \(SG^{*}\)? This occurs in the so-called synthesis problem.  
Related problems can be formulated in terms of the following Constraint Satisfaction Problem (CSP):  
\[CS=((\mathcal{V},\mathcal{D}),\mathcal{C}) \tag{12}\]  
where: \(\mathcal{V}=\left\{y_{j,i},x_{k,i^{\prime}}^{n},g_{k,i^{\prime}}^{n}\left|k=1,\ldots,K;\ i=1,\ldots,Q;\ j=1,\ldots,J\right\}\right\}\), a set of decision variables representing schedule \(Y_{j}\), assignment \(X^{n}\) and competence structure \(G^{n}\); \(\mathcal{D}\) is a finite set of domains of decision variables; \(\mathcal{C}\) is a set of constraints specified in inequalities (1)-(11).  
To solve problem \(CS\) (12), one should determine such values of decision variables \(y_{j,i},x_{k,i^{\prime}}^{n},g_{k,i^{\prime}}^{n}\) for which all the constraints given in the set \(\mathcal{C}\) are satisfied. Solving \(CS\) means determining the schedule \(Y_{j}\), assignment \(X^{n}\), and competence structure \(G^{n}\) which guarantees the project portfolio \(EP\) execution with a given level of \(SG^{*}\) for occurrence in new projects \(EX\).  
The proposed \(CS\) model (12) is part of an iterative (alternating) method of solving analysis and/or synthesis problems [20]. The essence of this method is a scheme of alternating approaches. To solve analysis problems, use the question:  
* Is there an admissible solution for the input data (competence structure with a given competence level)? For synthesis problems, use the question:
* Are there other inputs (e.g., assignment of workers to tasks) resulting in an admissible solution (i.e., required level of competence structure)? This method was used in the experiments described in the next section.
## 3 Results
### Temporal Trends  
Quite a few models predict an overall increase of LAI with time in most of the Northern Hemisphere, which is consistent with the satellite observations (Figure 7) which show a greening over the whole Eurasia and almost no negative trend in the whole Northern Hemisphere, with a small exception over western North America and few locations in the Eurasian boreal forest.  
Figure 7: Observed and simulated Leaf Area Index (LAI) trends (%) computed over the period 1986-2005 for 11 Earth System Models (ESMs) and satellite observations over the Northern Hemisphere (30\({}^{\circ}\)–90\({}^{\circ}\)N). For each model we masked out all the grid points where the seasonal amplitude is less than 0.5 (see Figure 3). The value in the box represents the spatial correlation between modelled and satellite data.  
From the whole compendium, BNU-ESM, GFDL-ESM2G, HadGEM2-CC and NorESM1-ME display the highest increase in LAI (see also Figure 1), mostly over the eastern coast of North America, Europe and the boreal forest of Asia. IPSL-CM5A-MR, MIROC-ESM, BCC-CSM1 and INMCM4 have an intermediate signal with the increase shown over the same regions, and some patchy areas where LAI decreased. We found that none of the models were able to reproduce the correct spatial pattern, the spatial correlation being close to 0 for almost all the models, except MIROC-ESM, which shows a positive correlation of 0.15.  
The models also show a general increase in the growing season length, with patchy areas where it decreases (Figure 8). It is clear that from the 11 ESMs, those that perform better at calculating the growing season (both on the onset and dormancy) and LAI also do better for the trends, despite there being no spatial correlation between CMIP5 models and satellite data.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 4 Results
### Path Simulation Comparison
#### 4.2.2 Simple Scenario  
As shown in Figure 5, the rectangular warehouse environment is established. The robots' initial and target positions are mainly distributed on the area's periphery. The green circles represent the initial positions, and the red squares represent the target positions.
## 2 System Overview and Design
### Remote Identification  
When an external reader attempts to locate a particular IC among a population of ICs, variations in the output of the ID generators may cause errors. For example, consider an external reader that tries to locate IC \(A\) by transmitting the chip code for chip \(A\), \(C_{A}\). All chips in the population receive this code, and compare it to their generated codes, \(G\). If \(C_{A}=G_{A}\) then chip A concludes it is being addressed and can reply to the external reader. However, this may lead to false negative errors if unreliable bits in chip \(A\)'s ID generator result in \(C_{A}\
eq G_{A}\).  
The common way to deal with this problem is to use a slightly different approach. To find chip \(A\) in a population of \(M\) chips, an external reader transmits an inquiry to all the chips, which all reply with their generated codes, \((G_{0},G_{1},...,G_{M-1})\). The external reader then computes the Hamming distance between all the received codes and chip code \(A\), \(H(C_{A},G_{i})\) for all \(i\). The value of \(G_{i}\) that has the smallest Hamming distance to \(C_{A}\) is then concluded to have come from chip \(A\).  
We call this approach remote identification, since the target chip \(A\) is unable to positively conclude it is being addressed. Rather, identification happens remotely (from the perspective of the chip), at the external reader. The primary drawback of this approach is that the chip must transmit its generated code, increasing the energy consumed by the radio. This may also cause problems for systems involving large numbers of chips: some system for staggering their replies must be implemented to avoid collisions.
## 3 Materials and Methods
### ML Models  
After preprocessing the data, multiple ML models were employed to evaluate their effectiveness in classification. KNN classified data points based on their closest neighbors, with k set to 3 for optimal performance. SVM constructed a decision boundary for classification using a linear kernel. LR models the relationship between dependent and independent variables to predict outcomes. RF, an ensemble of decision trees, was implemented with tuned hyperparameters to improve accuracy. NB, a probabilistic classifier based on Bayes' theorem, assumes feature independence and is useful for sparse datasets. Additionally, Ensemble Learning techniques were explored, including Boosting, which iteratively improves weak models; Bagging, which enhances accuracy through resampling; and Majority Voting, where multiple classifiers' predictions are aggregated to determine the final output. Furthermore, an ANN model was applied, outperforming all other ML algorithms in accuracy and robustness. For this study, a single-hidden-layer ANN was chosen to balance model complexity and interpretability and align with our focus on explainable models. In the following section, the proposed ANN model is elaborated.
# Model Predictive Control of Non-Linear Systems Using Tensor Flow-Based Models
## 4 Process Identification
### Model-Based Predictive Control  
MPC is a control strategy that uses the model to predict the output. Using these predictions, the aim is to find the control signal that minimizes the cost function that is dependent on those predicted outputs, desired trajectories, and control actions. As these controllers depend on the system's model, their performance heavily relies on the identified model. Figure 9 represents the basic structure of an MPC algorithm.  
The idea of this approach is to minimize the criterion presented in (4).  
\[J(t,U(t))=\sum_{k=N_{1}}^{N_{2}}[r(t+k)-\hat{y}(t+k)]^{2}+\rho\sum_{k=1}^{N_{n }}[\Delta u(t+k-1)]^{2} \tag{4}\]  
Subject to:  
\[\Delta u(t+k)=0,\ N_{u}\leq k\leq N_{2}-d \tag{5}\] \[u_{min}\leq u(t)\leq u_{max} \tag{6}\]  
with respect to the first \(N_{u}\) future control inputs:  
\[U(t)=[u(t)\ \dots\ u(t+N_{u}-1)]^{\top} \tag{7}\]  
\(d\) is the time system delay (we assumed it equal to 1), \(r(t+k)\) is the signal with the future reference samples, \(\hat{y}(t+k)\) is the signal with predicted output samples based on the model, \(\Delta u(t+k-1)\) is the signal with the changes in the control signal, \(N_{1}\) is the minimum prediction horizon, \(N_{2}\) is the prediction horizon, \(N_{u}\) is the control horizon and \(\rho\) is the weighting factor for penalizing changes is the control actions.  
The minimization of this criterion, when the predictions are determined by a non-linear relationship, constitutes a complex non-linear programming problem. This problem draws more attention when real-time implementation is required as, under this condition, it is necessary to impose an upper bound to the control law synthesis solution time. In this implementation, we use the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm to minimize the cost function (4) [23], implemented in the [PERSON]'s ([[https://github.com/bgranzow](https://github.com/bgranzow)]([https://github.com/bgranzow](https://github.com/bgranzow))) framework [31]. This is a quasi-Newton method that uses an approximation of the Hessian matrix to reduce the memory requirements of the optimization problem.  
Figure 9: Structure of MPC algorithm.  
The MPC strategy can be summarized in the following steps [1]:  
1. The future outputs, \(\hat{g}(t+k)\), are calculated over the prediction horizon at each sampling time using the model of the system, which in this case is a neural network. These values of \(\hat{y}\) depend on the past input and output samples and the future control samples, \(u(t+k)\).
2. The values of \(U(t)\) are calculated by an optimization algorithm in order to minimize the cost function (4). This criterion tries to approximate the future outputs to the future reference signal.
3. After optimization, the first sample of the signal \(U(t)\) is applied to the system and the other samples of this signal are ignored. When a new sampling time is available the described cycle starts over.
## 3 Results and Discussion
### Optimization of HPLC Chromatographic Conditions  
A range of analysis parameters, including types of reversed-phase C\({}_{18}\) column according to the manufacturer (SunFire (Waters, Milford, MA, USA), Gemini (Phenomenex, Torrance, CA, USA), Capcellpak UG120 (Shiseido, Japan), OptimaPak (RStech Corp., Korea)) and column temperature (30, 35, 40, and 45 \({}^{\circ}\)C) were varied to achieve efficient separation of the 12 marker analytes that were extracted from the SGJT sample. The optimal analysis was achieved with a SunFire C\({}_{18}\) column (4.6 \(\times\) 250 mm, 5 \(\upmu\)m), a column temperature of 40 \({}^{\circ}\)C, and two mobile-phase systems of distilled water-acetonitrile, both containing 0.1% (_v/v_) formic acid. Under the optimized analysis conditions, 12 marker analytes were baseline separated and eluted within 45 min with _Rs_ values \(\geq\) 3.45 (Figure 1 and Table S3).  
Figure 1: High-performance liquid chromatography (HPLC) chromatograms of mixtures of the 12 marker analytes (**A**), distilled water extract of the freeze-dried Sogunjung-tang (SGJT) sample (**B**), and 70% methanol extract of the freeze-dried SGJT sample (**C**). Gallic acid (GA) (1), albifiorin (ALB) (2), paeoniforin (PAE) (3), liquiritin apioside (LIAP) (4), liquiritin (PIQ) (5), benzoic acid (BA) (6), coumarin (COU) (7), liquiritigenin (LIQG) (8), cinnamic acid (CINA) (9), benzoylpaeoniforin (BPAE) (10), cinnamaldehydehyde (CINDA) (11), and glycyrrhizinic acid (GLYA) (12).
## 3 Materials and Methods
### The Proposed FMEA-DEA Method  
The conventional FMEA ranks the FMs in descending order of their risk priority number (RPN). For each FM, the RPN is equal to the product of its probability of occurrence (O), severity (S), and non-detection (D). Nevertheless, sometimes, solving a set of FMs with moderate RPNs would be more efficient than solving a single FM with a high RPN. This means that allocating the time and costs needed to solve one single FM with a high RPN to solving a group of FMs whose RPNs' sum is equal to or larger than the former may be more reasonable. Consequently, it is recommended that the notions of cost and time be incorporated into the traditional FMEA and the risk prioritization method be revised. Furthermore, the causes and effects of the same failure mode are most likely to have different severity, occurrence, and detection rates. This makes it necessary to consider the scaled-down rates related to each cause separately, as well as the interrelationships between them. Since the causes of a given parent failure mode may have different ranks with some gaps, it is necessary to assign them a common or synthesized rank. Moreover, in some cases, the RPN number alone is not efficient in risk prioritization; this leaves us with a question regarding how to introduce the notion of efficiency to deal with this shortage.  
The proposed solution in this paper will answer the question concerning which FMs should be fixed first or must be fixed prior if we want to consider the efficiency along with the risk associated with the causes and effects rates. Thus, in order to determine solving which FM is efficient, we are going to use a DEA model that allows us to minimize time, cost, O, S, and D by also considering the highest RPN. Additionally, causes and effects will be treated as sub-failure modes belonging to a given parent failure mode. Each of these sub-failure modes will have its own ratings, and they will be grouped in a later step in order to obtain their common rank. Therefore, in the first stage, a modified FMEA will be proposed; this will be followed by the application of the DEA approach in the second stage.