esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Object Detector Based on Images Captured Using Unmanned Aerial Vehicle
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Object Detection  
The main challenge of small object detection is that the small objects not only have low saliency relative to the background but also contain less information due to their small size, which makes their recognition and localization more difficult [15]. Small objects are often surrounded by complex backgrounds and are susceptible to interference from background noise. In datasets, the relatively low number of samples representing small objects often leads to susceptibility to overfitting or underfitting during model training. To address the challenges associated with small object detection, researchers have proposed a variety of methods, including increasing data samples, refining network structures, using multiscale feature fusion, etc. The traditional object detection algorithms include Viola-Jones [16], Histogram of Oriented Gradient [17], Deformable PartModel [18], etc., which are considered to be representative algorithms. With the continuous development of deep learning technology--especially the broad application of Faster R-CNN [19] and models such as Transformer [20] and the Yolo series [21; 22]--the performance of small object detection has been significantly improved. Yolov8 is a member of the YOLO (You Only Look Once) family. Its core design concept revolves around performing rapid and accurate object detection directly on the input image through a singular neural network architecture. The backbone network of Yolov8 is responsible for extracting multiscale feature representations from the input image. Yolov8 incorporates the C2f module, which borrows ideas from both ResNet and ELAN [23]. The backbone utilizes the Bottleneck [24] module to progressively enhance the performance of small object detection. To progressively deepen the network, the branching part retains the input-layer channels and integrates them with the features of the trunk part rather than utilizing the C3 module. In the neck part of the module, Yolov8 employs a hybrid structure combining a PAN (Path Aggregation Network) and FPN (Feature Pyramid Network) to effectively aggregate the multiscale features extracted via the backbone network. Finally, three detection head sets of varying sizes--corresponding to different scales of feature maps generated by the backbone network--are utilized to detect objects of different sizes within an image. During the prediction process, Yolov8 adopts Distribution Focal Loss (DFL) [25] as the classification loss function and CIoU (Complete Intersection over Union) [26] as the localization loss function.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 3 Enabling Technologies
### Management and Orchestration  
NVF MANO system provides a dynamic control of network infrastructure resources, it helps to manage cloud environment and network infrastructure resources in SDN and VNF manner. The NFV MANO framework presented by ETSI envisioned as reference model is illustrated in Figure 4[21]. The framework is grouped into the following functional blocks:  
* Management and Orchestration (MANO): Manages and coordinates the virtualization in the architecture. It consists of the following three entities: (1). Virtualized infrastructure manager (VIM): Helps to manage the network functions virtualization infrastructure (NFVI) resources. (2). VNF manager (VNFM): Helps to provide connectivity and to configure the VNFs. (3). Orchestrator: Helps to manage the life cycle of network infrastructure resources and ensures the integrity of process-to-process service.
* Network Functions Virtualization Infrastructure (NFVI): It is connected to the VNFs.
* Network Management System (NMS): It consists of element management (EM) and operation/business support system (OSS/ BSS). Collectively, it helps to provide general management by communicating with MANO functions.  
Figure 4: NFV reference architecture integrated with SDN controllers.  
Two SDN controllers are mentioned in the architecture namely, the infrastructure SDN controller (ISDNC) and the tenant SDN controller (TSDNC).
* Infrastructure SDN Controller (ISDNC): Dynamically manages the infrastructure resources, which enables them to communicate with the VNFs [22].
* Tenant SDN Controller (TSDNC): Operate and dynamically manages the underlying data plane resources via interface.  
These controllers need to communicate with each other to manage the network slices effectively.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 1 Introduction
* Due to the environmental dynamic, we elaborate the decision-making task as an MDP problem. Then, we improve the DQN algorithm by applying a parameterized indexed function. With the parameterized indexed function, the user agents can fully explore and exploit their action space and choose the optimal action for task offloading. * Lastly, we compare it with traditional DQN algorithm and research the impact of system parameters. The experimental results validate that the task-offloading scheme that we propose greatly improves the MEC system utility. The rest of this paper is organized as follows. In Section 2, we present the related works. In Section 3, we introduce the system model and the MDP model. In Section 4, the improved DQN algorithm is proposed to solve the MDP problem. In Section 5, the performances of the proposed algorithm are evaluated by several simulations. The conclusions are drawn in Section 6.
# Combination of Chemo- and Biocatalysis: Conversion of Biomethane to Methanol and Formic Acid
## 2 Materials and Methods
### Biocatalysis  
The heterologous expression of FDM from the _Pseudomonas putida J3_ strain by _E. coli_ and its purification was described earlier [38]. _Pseudomonas putida_ was isolated from sewage sludge at Fraunhofer-Institute for Interfacial Engineering and Biotechnology IGB Stuttgart and cultivated in a broth consisting of peptone (10 g-L1), meat extract (10 g-L1) and NaCl (5 g-L1) at a pH value between 7.0 and 7.2. After chemo-mechanical extraction of the genomic DNA the heterologous expression of the FDM J3 gene was carried out by the _E. coli_ strain BW3110 with the expression plasmid pJOE5940.1 in the rhamne promotor system. _E. coli_ was further cultivated on LB medium consisting of bacto peptone (10 g-L1), yeast extract (5 g-L1) and NaCl (10 g-L1) at a pH value of 7.0. For cell disruption cells were centrifuged, washed with aqueous NaCl solution (0.9%), suspended in aqueous Na2 HPO4 (100 mM) at a pH value of 7, treated by ultrasound and again centrifuged. The resulting supernatant protein extract was used for further experiments.  
Particles of the company Align Chemicals (Amersham, UK) were used for the immobilization of enzymes. Each immobilization was done in a column with a volume of 1 mL. To prepare for immobilization, the empty column weight was first recorded, the particles were then packed into columns, the dry weight of the packed column was determined, the package was rinsed with two column volumes of 2-propanol and weighed again after 10 min. The pore volume can be calculated from the wet weight of the column and the density of the 2-propanol. The column was then rinsed with five times the column volume of phosphate buffer (0.5 M, pH 6.8). For immobilization, 3 mL of centrifuged raw extract was used and flowed through the column using a syringe. The column was than incubated for 12 h at 4 \({}^{\circ}\)C with the remaining raw extract. After immobilization, an air-filled syringe was used to press the residual raw extract solution out of the column and the protein concentration was measured. The amount of immobilized protein can be calculated from the initial and residual protein concentrations. As reference, unloaded particles treated in the same way as described above were used but without protein loading.  
The activity of the immobilized proteins in the respective columns was determined by rinsing the columns with 3 mL of a 1 g\(\cdot\)L\({}^{-1}\) formaldehyde solution, squeezing out the liquid after 30 min with an air-filled syringe and measuring the formaldehyde concentration. The enzyme activity was derived from the difference of the formaldehyde concentrations between protein loaded and reference column.  
Biocatalytic reactions were performed in a pressure reactor (Buchi AG, Uster, Switzerland) at 37 \({}^{\circ}\)C on 500 mL scale. The formaldehyde and methanol concentrations were determined by quadrupole mass spectroscopy with liquid inlet (Variolytics, Stuttgart, Germany). Calibration of the mass spectrometer was done with methanol/formaldehyde solutions of known concentration.
###### Abstract  
Due to the lack of full agreement as to the best indicators for obesity diagnosis and type, the aim of this study was to assess the comparative classification capabilities with the use of BIA results and selected anthropometric indices in individuals aged 20-60 years. This was a cross-sectional observational study among 368 Caucasian subjects aged 20-60 years. Body size and four skinkolds measurement were taken. To assess individual body composition, the bioelectrical impedance (BIA) method was applied. The results of fat mass (FM, kg) and fat-free mass (FFM, kg) were taken to calculate FM/FFM, fat mass index (FMI), and fat free mass index (FFMI). Receiver operating characteristic (ROC) curve analysis was employed to compare the predictive power of different anthropometric indices in differentiating the classification of obesity in adults. The results of this study demonstrated and confirmed the need to change the approach to commonly used indicators such as BMI (body mass index) or WHHR (waist-to-height ratio), which should lead to the establishment of new criteria for the diagnosis of obesity that will also be sex-specific, in the adult population. The measurement of body fat content should become a generally accepted indicator for effective diagnosis, as well as for screening, of obesity.  
body composition; body size; fat mass; fat free mass; somatic indexes; obesity; adults Article
## 2 Materials and Methods
### UHPLC-MS/MS Analytical Features  
The UHPLC-MS/MS features were adjusted to provide the highest sensitivity and resolution of the DVB and EFB analytical peaks, as listed in Table 1. The chromatographic qualities of the HPLC technology were enhanced through a comprehensive analysis of several factors, involving mobile phase, the pH level, and the stationary phase features. The objective of this optimization experiment was to enhance the resolution and sensitivity of the peaks related to the objectives DVB and EFB, as listed in Table 1. The isocratic mobile phase consisted of two components--line A, which consisted of an aqueous solution (0.1% HCOOH in H\({}_{2}\)O) at pH of 3.2, making up 55% of the mobile phase; and line B, which consisted of AGN, making up 45% of the mobile phase. The measured flow rate was found to be 0.5 mL/min. If the pH value reached around 3.2, the use of a 10 mM NH\({}_{4}\)COOH in H\({}_{2}\)O caused the appearance of a chromatographic peak tailing in the DVB analysis, as well as a longer running time. If the concentration of acetonitrile (ACN) surpasses 45%, the analytical chromatograms of DVB and EFB exhibit noticeable overlapping peaks. Conversely, a lower % of AGN generates a prolonged running time. The ESI was functioned in the positive ionization mode to aid ionization, as the DVB and EFB targets contained nitrogen atoms that could catch protons, generating positively charged ions.  
The optimization of MS features for DVB (C\({}_{21}\)H\({}_{21}\)FN\({}_{6}\)O) and EFB (C\({}_{22}\)H\({}_{27}\)CIFN\({}_{7}\)O\({}_{4}\)S) was successfully performed using the IntelliStart(r) software, a smart software component of the MassLynx 4.1 software package (version 4.1, SCN 805), by directly introducing DVB and EFB (10 ug/mL) inside the mobile phase stream. The mass-resolving power of MRM was used for the quantitative estimation of DVB and EFB. The implementation of this technique resulted in a higher degree of sensitivity and specificity for the UHPLC-MS/MS methodology that was devised. The ions of interest, specifically DVB and EFB, underwent fragmentation in the fragmentation quadrupole (collision cell), generating their corresponding product ions. The dissociation process was improved by employing argon gas (collision gas) with a high level of purity (99.999%). The measured dwell period for the conversion of mass from parent ions to daughter ions in DVB and EFB was found to be 0.025 s. The acquired outcomes were scrutinized and evaluated utilizing the QuanLynx program. Table 2 lists a comprehensive summary of the various parameters associated with MRM and mass transition in DVB and EFB.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
###### Abstract  
The sideslip angle is crucial for the lateral stability state and stability control of intelligent commercial vehicles. However, sensors that can be used for direct measurements are often complex, expensive, and difficult to install in commercial vehicles. To estimate the vehicle sideslip angle, a state observer derived from the extended Kalman filter (EKF) method is proposed, and the state observer is estimated based on steering torque rather than steering angle. The transfer functions between the sideslip angle-steering torque and sideslip angle-steering angle are established, respectively, and the analysis shows that the steering torque signal has a more rapid and more direct reaction due to the hydraulic pressure in the steering system. Finally, the proposed method is validated using Simulink/TruckSim simulation hardware-in-the-loop bench test, and the results show that the proposed method can accurately reflect the actual state of the sideslip angle with good reliability and effectiveness.  
sideslip angle estimation; extended Kalman filter; vehicle dynamic; intelligent commercial vehicles 2023  
13794
## 5 Discussion
### Accuracy-Influencing Factors Analysis
#### 5.1.3 Other Factors  
Other factors, such as the hyperparameter of YOLO and the size of image resolution, also have effects on the detection result of structural components. Hyperparameter refers to the parameter that is preset based on the experience before training rather than obtained through training. The selection of the hyperparameter has a significant influence on the detection results. For instance, the batch size, which is one of the most common hyperparameters, represents the number of samples sent into the model of the neural network in each training. Generally, the smaller batch size is prone to longer training time and non-convergence of the model, while the larger batch size makes the model converge faster, but easily results in the out-of-memory error or program crash. Therefore, choosing the appropriate batch size within a reasonable range can achieve the best balance between time and accuracy when training the YOLO model. Moreover, raising the size of the input image may also affect  
Figure 7: The relation between the number of structural components and the detection accuracy: (**a**) In the column layout plan image; (**b**) In the framing plan image.  
the accuracy of the structural component detection based on YOLO. This is because when the size of image resolution increases, the number of pixels occupied by a single component will also increase. In consequence, the image features of each component will be richer than before. A disadvantage of raising the image size is that it greatly increases the computing workload as well as the time for training.
_Article_  
Dynamic Multi-Objective Auction-Based  
(DYMO-Auction) Task Allocation  
[PERSON]\({}^{\text{\textregistered}}\), [PERSON]\({}^{\text{\textregistered}}\), [PERSON],*\({}^{\text{\textregistered}}\) and [PERSON]  
Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; [EMAIL_ADDRESS] (U.B.); [EMAIL_ADDRESS] (M.A.) Computer Science, Prince Sultan University, Riyadh 12345, Saudi Arabia CISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal National School of Computer Science, University Campus of Manouba, Manouba 2010, Tunisia; [EMAIL_ADDRESS] Correspondence: [EMAIL_ADDRESS]  
Received: 15 April 2020; Accepted: 28 April 2020; Published: 8 May 2020  
In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors' knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks' quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.  
multi-robot system (MRS); multi-robot task allocation (MRTA); auction-based task allocation; task quality; online decision making
## 4 TSN Network-Wide Scheduling--Approaches Overview, Challenges, and Future Directions
### Methods and Approaches for Scheduling in TSN
#### 4.1.3 Efficient Resource Management  
Resource management is a traditional and crucial optimization objective in computer networking, including Time-Sensitive Networking (TSN). Among the key objectives in this domain, load balancing and minimizing bandwidth loss take precedence.  
Several studies ([87, 88, 89, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,  
genetic algorithm that combines routing, and task binding. Ref. [94] normalized the cycle between CQF and CSQF to solve the cycle, queue, and bandwidth mismatch. The combination of TAS and CQF was deployed in [11] to support different traffic types on the network. The authors started by selecting an appropriate cycle and scheduling unit, then injected the streams that had similar cycles together to avoid computational complexity. Other combinations are TAS with preemption for enhancing schedulability [95], combining TSN and 5G using constraint programming [63], and TSN for fog computing with VM migration. Ref. [96] suggested a rescheduling algorithm based on Breadth-First Search (BFS), and Ref. [57] proposed a configuration agent managing runtime reconfiguration using a heuristic based on list scheduling. Although some works have started to appear more recently, this area of TSN is still very much unexplored.
## 5 Broader Implications
This ability is especially important for border security, where dynamic flexibility is crucial inmanaging unauthorized flows and acting on possible threats on a real-time basis as and when they are sensed. The hybrid optimization approach suggested here may have the potential to bring revolutionary change in a number of areas, reshaping the operation dynamics of autonomous drone networks in dynamic environments. Its promise to reconcile efficiency, flexibility, and strength positions it as a priority tool for use in situations from emergency response to infrastructure management and beyond. With the technology of autonomous drones evolving further, the integration of reinforcement learning with genetic algorithms will only continue to be more instrumental in enabling them to be more efficient in their operations.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
###### Abstract  
A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicele Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.  
Keywords:feature extraction; feature selection; audio cough classification; machine learning