esa-sceva/satcom-chunk-collection huggingface dataset:
# A Petri Net Approach for Business Process Modeling and Simulation
## 2 Fundamental Concepts
### Supply Chain  
The Supply Chain (SC) refers to the interconnectedness of the various actors involved in fulfilling a consumer's needs through the acquisition of a product or service. This cycle commences with the production of raw materials, which are then transformed into supplies used in the manufacturing or assembly of goods or delivery of services [12].  
The fundamental element of a supply chain is the collaboration and joint effort of two or more companies with the aim of creating a competitive advantage and generating profits that would be difficult to achieve if each company acted independently. This collaboration involves inter-company relationships in which information, resources, rewards, risks, and responsibilities are shared among the participating entities. By working together, companies can leverage their combined strengths and resources to achieve mutual benefits and improve overall performance [12].  
The supply chain involves various business processes that are essential for the smooth operation of the chain. These processes include transportation, storage, transformation, rental of services, sales, and more. Each organization participating in the supply chain performs these processes to ensure the efficient flow of goods and services throughout the chain. Transportation processes ensure the movement of products from one location to another, while storage processes involve the proper handling and management of inventory. Transformation processes may involve manufacturing or value-added activities, while rental of services can include leasing or outsourcing certain functions. Sales processes are responsible for promoting and selling products to customers. Each organization within the supply chain contributes to these processes in order to fulfill customer needs and drive the overall success of the chain [12].  
Effective supply chain management is essential to ensure the availability and timely delivery of products, keep costs in check, reduce waste, and improve customer satisfaction. To achieve this, different strategies and tools such as demand planning, inventory management, forecasting, optimization of transport routes, and use of information technologies are used to improve visibility and coordination throughout the chain [13].  
A well designed and managed supply chain can provide competitive advantages to companies by allowing them to quickly respond to market demands and adapt to changes in economic and business conditions. In addition, an efficient supply chain can have a positive impact on sustainability and corporate social responsibility by reducing waste and improving energy efficiency in production and distribution processes [13].
# Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network
## 3 Modeling And Problem Formulation  
### System Model  
As shown in Figure 1, the system's architecture has three layers: the user layer, edge server layer, and the cloud layer [40]. The user layer includes IoT devices. IoT devices collect the data needed for various services and transmit them to the nearest edge server through wireless communication for further processing. These IoT devices can send service requests and broadcast them to all edge servers. These service requests will be received and processed by the nearest edge server. They can also be offloaded to other idle edge servers via access networks [41]. In the edge server layer, a service can respond to requests from multiple IoT devices. Each task generated by devices is served by a corresponding service, which shares its physical resources with other services on the same edge server [42]. Edge servers not only provide computational resources for task processing, but also determine the computation offloading strategy [43]. The cloud layer is far from IoT devices and edge servers. The cloud is mainly responsible for data replication and load-balancing, not computing.  
Based on the system model described above, we present some of the notations used in this paper. \(F=\{F_{1},F_{2},\ldots,F_{n}\}\), representing the set of edge servers described by the following attributes: the unique identifier of each server \(F_{i}.id\), the geographic location \(F_{i}.loc=(L_{x},L_{y})\), the processing capacity of edge server \(F_{i}.cap\), the list of services currently hosted by the edge server \(F_{i}.lostLst\), and the list of tasks currently being processed by the edge server \(F_{i}.pt\). The main purpose of this paper is to find a better way to reduce the delays in task processing and the energy consumption of the edge networks, instead of studying the edge server heterogeneity. Therefore, we assume that all edge servers have the  
Figure 1: System architecture.  
same physical resources, which include CPU processing capacity, bandwidth, memory, and storage. In the realistic setting, services are highly diverse. Without loss of generality, we assume that the edge server layer includes \(m\) types of services for simplicity, labeled as a set \(H=\{H_{1},H_{2},\ldots,H_{m}\}\), which is described as having the following attributes: the unique identifier of the service \(H_{i}.id\); the service capacity \(H_{i}.bytes\). Dividing the time period \(T\) evenly into time slots, the time slot set can be represented by \(T=\{T_{1},T_{2},\ldots,T_{t}\}\). In each time slot, the IoT devices at the user layer are assumed to issue \(k\) task requests, labelled as \(C=\{C_{1},C_{2},\ldots,C_{k}\}\), which can be described by the following attributes: the unique identifier of the task \(C_{i}.id\), the geographic location \(C_{i}.loc=(L_{x},L_{y})\), the service type of the task \(C_{i}.H_{j}\), the capacity of the task data \(C_{i}.bytes\) and the number of CPU cycles required for task processing \(C_{i}.cir\).
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 3 Background of V2X
### Platoon Communication  
A platoon, in the context of autonomous driving technologies, consists of two primary components: the Lead vehicle and Member vehicles, as shown in Figure 2[10]. The Lead vehicle, typically manually driven, operates at the front, perceiving the environment and responding to changes. Member vehicles follow the Lead vehicle and communicate with other platoon vehicles to maintain coordination. These vehicles operate either autonomously or semi-autonomously, depending on their role.  
Key technical requirements are crucial for ensuring stable platoon communication, as standardized by the 3 GPP[9]. Minimizing communication delay is essential, as platoon vehicles travel at high speeds in close proximity. To prevent collisions and enable safe platooning, the 3 GPP has set a communication delay threshold of 10 to 25 ms. Reliability of at least 90% is also mandated, as transmission failures could lead to accidents. Additionally, platoon communication requires data packets of 300 to 400 bytes to be transmitted over 30 times per second. Compared with the Basic Safety Message (BSM) or Cooperative Awareness Message (CAM) sent by general vehicles (50 to 300 bytes), platoon communication involves larger data volumes[33].  
Platoon communication methods are categorized into centralized and decentralized models[10]. In centralized communication (Figure 3a), the Lead vehicle manages all communications, transmitting packets to all Member vehicles. This enables rapid coordination but imposes a heavy communication load on the Lead vehicle as platoon size grows. Conversely, decentralized communication (Figure 3b) involves each vehicle transmitting packets to the vehicle directly behind it. This reduces the Lead vehicle's communication burden and allows each vehicle to adjust its position and speed independently. However, decentralized communication introduces challenges, such as instability when vehicles join or leave the platoon and difficulties in maintaining overall platoon integrity if communication is interrupted.  
The proposed resource allocation algorithm in this paper is designed for platoon vehicles using decentralized communication. Efficient resource allocation is critical, as decentralized models require higher resource consumption due to communication among all vehicles. Maintaining smooth and stable communication is essential for ensuring the safe and reliable operation of the platoon.  
Figure 3: (**a**)—Centralized communication of platoon, (**b**)—Decentralized communication of platoon.  
Figure 2: Components of a platoon: Lead vehicle and multiple Member vehicles.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 8 Conclusions  
The present study proposed four FS methods based on outliers detection using the Project Pursuit. Two PP indices along with two AI optimization methods were used to find the optimal projections that reveal the outliers. The proposed PPFS methods were validated using three classifiers and 19 datasets. They yielded promising results.Moreover, these methods were successfully compared with three Wrapper FS methods on eight datasets. They also yielded the best or equivalent accuracy against five Filter FS techniques on 15 datasets (among 19 datasets). Statistical analysis was performed and showed that PPFS are equivalent, and their results are statistically significant. It is also demonstrated that PPFS are comparative with PCA, CFS, and WOA. Therefore, our assumption to consider the outlier detection methods for revealing the irrelevant features from a dataset to increase the classification accuracy was validated. For the computation time, PPFS was faster than the five Filter FS methods when handling large datasets (Colon, Spam, and Fifa2019).  
However, no computation time was reported for the three wrapper FS methods. Furthermore, PPFS yielded promising results in handling a large dataset reaching 13,000 instances (Fifa2019 dataset) and 2000 features (Colon dataset). The only limitation of this study is not getting the best result when dealing with the Waveform and Segment dataset. Hence, some future research directions are suggested:  
* Make the dataset spherical before seeking the outliers [27] to reduce the noise.
* Use another optimization method such as PSO, which is already available in EPP-Lab.
* Use large datasets reaching 30,000 features to test the extent of PPFS in enhancing accuracy.
* Explore other PP indices such as Friedman Turkey and Discriminant indices.
* Investigate other Machine Learning classifiers.
# Novel Drug Combinations in Lung Cancer: New Potential Synergies Between 5-FU and Repurposed Drugs
## 3 Results
### Safety Assessment of 5-FU and Levosimendan and Their Combination in Non-Cancerous MRC-5 Cells  
In a previous study of our group [41], we thoroughly evaluated the safety of 5-FU and Levosimendan over 24, 48, and 72 h, assessing their cytotoxicity both as single agents and in combination, using non-cancerous MRC-5 cells. The results demonstrated that neither 5-FU alone nor its combination with Levosimendan at the IC\({}_{50}\) concentration for the A549 cell line exhibited any significant cytotoxic effects on normal cells. Specifically, no changes in cell viability or morphology were observed, even at higher concentrations (up to 50 \(\upmu\)M) and across all tested time points. However, it is noteworthy that Levosimendan alone, at a concentration of 100 \(\upmu\)M, caused approximately 25% toxicity in MRC-5 cells after 48 h of exposure. Despite this level of cytotoxicity, the morphology of the MRC-5 cells remained unaffected at these time points.  
For the current study, we extended this investigation by testing the combination of the IC\({}_{50}\) value of 5-FU determined for A549 cells with increasing concentrations of Levosimendan in MRC-5 cells. This assessment aims to further confirm the non-toxic nature of this drug combination in non-cancerous cells, reinforcing the potential of Levosimendan as a safe candidate for combination therapy with 5-FU. As indicated in Figure 8, the combination of the IC\({}_{50}\) 5-FU for the A549 cell line with increasing concentrations of Levosimendan did not indicate cytotoxicity towards normal MRC-5 cells and did not result in any morphological changes in these cells, as corroborated by the observations in Figure 9.  
Figure 6: Cell viability of A549 cells following treatment with Levosimendan alone and in combination with 5-FU. Data are presented as percentages relative to the control group and represent the mean \(\pm\) SD. Each experiment was performed independently three times (\(n=3\)); \({}^{\star}\) statistically significant vs. drug alone at \(p<0.05\); \({}^{\star\star}\) statistically significant vs. drug alone at \(p<0.01\); \({}^{\star\star\star}\) statistically significant vs. control at \(p<0.0001\). The green, red, black and gray bars are used solely to differentiate between data groups and do not represent specific experimental variables.  
Figure 7: Morphological assessment of A549 cells treated with 5-FU, Levosimendan alone, and their combination. Cells were exposed to the vehicle (0.1% DMSO) as a control. The results are representative of three separate experiments. Scale bar: 50 \(\upmu\)m.
## 5 Experimental Verification  
To verify that the model established in this study provided a realistic description of a lathe processing a slender shaft, an experiment was conducted on a CZ6132A universal machine, shown in Figure 11a. To obtain a more sensitive vibration signal for the cutting parameters and entire rotor system, a novel three-axis wireless on-rotor sensing (ORS) system and a prototype for the turning system were developed, with a sampling rate of 3200 Hz [33]. The installation size of the three-axial acceleration sensor was designed according to the shaft diameter and lathe system. The sensor was installed directly on the shaft rather than on the tool holder or lathe. The vibration signal was collected based on the ORS and directly transmitted to a smartphone through a wireless network [34]. We collected the vibration data once for each layer of workpiece material turned, each sampling time was about 2.5 min with a sampling rate of 3200 Hz. Three different DOCs were set, 0.25 mm, 0.5 mm, and 0.75 mm, and the workpiece was turned at these docs from the original diameter of 22 mm all the way down to a diameter of nearly 12 mm.  
The cutting parameters are listed in Table 2. The material of the cutting tool is coated cemented carbide. The dimensions of the tool, such as the corner radius of 0.2 mm and thickness of 4.76 mm, are shown in Figure 10(b). Figure 10(c) illustrates workpieces of three different diameters.  
Two methods of clamping the lathe processing shaft are in common use. In both, one end of the shaft is fixed with a three-jaw chuck. In the first ('fixed-free') method, the other end is left free; in the second ('fixed-pinned') method, it is supported by a tailstock (Figure 11). When the first method is used, a cantilever beam model can be applied to study the dynamic characteristics directly. There has been little research on the second clamping method; therefore, we studied the dynamic characteristics of transverse vibration with a fixed-pinned shaft model.
## 2 Related Work
### Wireless Sensor Node Clustering Strategies
In a previous study, the authors [17] have proposed an adaptable cluster formation technique that employs Voronoi tessellations to define the cluster zones to be used to track Mobile Nodes in congested noisy environments. The technique partitions the tracking space into a number of regions, each of which are to be monitored by one cluster comprised of a sink node and multiple anchor nodes. _Ad hoc_ deployment of large number of wireless sensor nodes is proposed as an alternative approach to the explicit cluster formation techniques described previously. Due to the unique characteristics and behaviours of _ad hoc_ networks, there is a growing interest in developing protocols for _ad hoc_ wireless sensor networks including clustering, location discovery protocols, and position based routing protocols among others. [PERSON] _et al._[18] presented a clustering based solution to the problem of location discovery in wireless _ad hoc_ networks with no Mobile Nodes or wireless nodes with limited mobility. The proposed localization algorithm is an anchor based method that relies on the _a priori_ coordinates of sparsely located anchor nodes. Clusters of nodes surrounding anchor nodes cooperatively establish confident position estimates through assumptions, checks, and iterative refinements. Once established, these positions are propagated to more distant nodes, allowing the entire network to create an accurate map of itself. [PERSON] [19] proposes a distributed approach to the clustering of quasi-static and mobile _ad hoc_ networks. The proposed approach groups the nodes by following a new weight-based criterion that allows the choice of the nodes that coordinate the clustering process based on node mobility-related parameters. The proposed algorithm is executed at each node with the sole knowledge of the identity of the one hop neighbors. Table 1 provides a summary of the recent work on wireless sensor node clustering strategies. This work has primarily focused on the properties of specific clustering approaches (e.g., energy efficiency, security, _etc._). Although some work has been done on node clustering strategies that dynamically adapt to changing industrial conditions, very little work has been done on the trade-offs between competing approaches to this problem. In the remainder of this paper, we tackle these open issues by combining a multi-agent approach with the recent work on efficient sensor node partitioning using Voronoi clusters. In the next section, we describe this general approach. Although considerable work has been accomplished on WSN node management, the trade-offs between alternative clustering approaches are not well understood. In this paper, we compare two general approaches that span the different clustering approaches proposed in the literature. In order to assess the adaptability of these approaches, the comparisons are performed in a harsh, industrial environment with a network. In the next section, we provide a description of the general architecture of the WSN used in this research along with an overview of the clustering approaches.
## 3 Proposed Work
### Choosing Factor for Cluster Heads  
Each node's \"cluster head choosing factor (CCF)\" determines which routing table node will be the next-hop node. CCF is a function that takes into account three variables: the congestion level \(C_{l}\) at the next hop, the SINR value of the link \(\ell(\mathrm{e}_{i})\) between the current and next-hop nodes, and the \"survivability factor\". \(P_{s}\) of the path to \"the destination through that next hop\". The CCF is given as  
\[CCF=(\alpha*\ell(\mathbf{e}_{i}))+(\beta*P_{s})+(\gamma*(1-C_{l})) \tag{5}\]  
The three components \(\ell(\mathbf{e}_{i})\), \(P_{s}\), and \(C_{l}\) The CCF are given different weights, in this case, using the values \(\alpha\), \(\beta\), and \(\gamma\). In the cluster head selection, the intensity of these three components can be selected based on the requirement. The simulation is carried out in terms of three \"weighting coefficients\" as \(\alpha=\beta=\gamma=\frac{1}{3}\), to demonstrate an equivalent influence from each PCF component. The following goals are reached by standardizing the values as follows:  
\[\alpha+\beta+\gamma=1 \tag{6}\]  
According to condition (6), the input for the \"adaptive fuzzy c-means clustering\" will be the \"CCF factor\". Finally, this task is completed using the three factors, including \"SINR\", \"congestion level\", and \"survivability factor\" in the network of clustered \"sensor nodes\" [14; 18].
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 2 Background
### Adaptive Optimizers
While adaptive optimizers, like Adam, require careful adjustment of multiple parameters, including learning rate, beta values (for momentum), and epsilon (for numerical stability), CLR only requires specifying the minimum and maximum learning rate boundaries. These boundaries can be efficiently determined using a Learning Rate Range Test (LRRT), which empirically identifies the optimal range for the learning rate based on model performance during an initial short training phase [4]. Thissimplicity is particularly beneficial in federated learning, where client devices may have limited computational resources, and minimizing communication overhead is a priority. The use of pre-training on augmented data helps to initialize the model with strong feature representations before federated training begins, by providing a strong knowledge base that facilitates faster convergence and improved final model performance, mitigating the effects of data heterogeneity across clients [5]. When combined with CLR, pre-training accelerates convergence by providing a stable starting point and reduces the number of communication rounds needed to reach optimal performance. In summary, while adaptive optimizers face challenges in federated learning with non-IID data due to biases introduced by pseudo-gradients and heterogeneity in local updates, implementing pre-training with CLR offers a more robust approach to improving model accuracy and convergence.
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 3 Preliminary
### Feature Extractors  
Feature Extractors extract specialized features pertaining to a particular classification problem. These features are distinct for the given problem and can be used to distinguish between multiple classes. Specialized CNN architectures such as YOLO [29], MobileNet v2 [30], AlexNet [31], Inception v2 [32], and ResNet [33,34] are used for feature extraction [35]. MobileNet architecture is a prominent architecture for real-time scenarios. It uses depthwise separable convolution layers, which have a reduced computation cost compared to standard convolution layers. MobileNet v2 is an improved version of the MobileNet architecture. This architecture is highly efficient and can be deployed in low-power real-time scenarios. Inception is a popular architecture for feature extraction. It has a good balance between accuracy and cost but is far costlier than MobileNet. The architecture was developed after VGG and is more accurate and computationally efficient. It employs convolution layer factorization, which splits larger convolutions into a combination of smaller ones to achieve this. Inception v2 is used for a comparison of performance for the given task. ResNet is a state-of-the-art feature extractor, which obtains a high accuracy using residual learning. This improvement in accuracy is with'shortcuts', which are the connections that skip levels. However, ResNets have a very high computation cost and cannot be employed in real-time systems.