esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Similar to external load, it is essential that the selected methods for monitoring internal load are appropriate for the specific sport or type of exercise practiced and that internal load responses can differ between individuals even when experiencing the same external workload [5]. Although different methods of monitoring training load can provide useful information for adjusting planning according to individual needs and goals, it is crucial to highlight the inter- and intra-individual variability when assessing external and internal load during training. Indeed, training load is influenced by factors such as training level, body composition, team position, environmental condition, and health status [7]. For example, resistance-trained individuals perform a higher number of repetitions at a given relative strength (e.g., 60% and 80% 1 RM--one maximal repetition) than low or moderate-trained individuals, evidencing the influence of muscle endurance, strength, and power on externalload (i.e., volume load) monitoring [8]. Similarly, displacement during soccer matches varies by player position (e.g., midfielders cover \(\sim\)11.5 km, while defenders and attackers cover 10 to 10.5 km) [9]. Furthermore, RPE scores have shown differences when comparing experienced individuals regarding the level of fatigue and pain during exercise [5; 7], as well as the athletes who train at a target similar HR zone might show differences indifferently perceived and metabolic responses (e.g., RPE and blood lactate concentration) if the mode of exercise differs (e.g., cycling vs. running: 12.8 \(\pm\) 0.4 vs. 11.6 \(\pm\) 0.3, and 6.2 \(\pm\) 0.3 vs. 2.9 \(\pm\) 0.3 mmol/L), due to the differences in the level of muscle activation and fiber type II recruitment (both higher in cycling than running) [10]. Understanding, selecting, and applying methods and tools for monitoring external and internal load in physical training programs is crucial for controlling and improving training planning. Therefore, this study aimed to present and describe the use of the main tools and methods for assessing internal and external load to assist in the monitoring of the level of responses to the planning (according to the expected level of training) or the analysis of the planning (rethink the strategies according to the proposal of training, i.e., whether sports performance or healthcare). The search for this revision was not constrained to a given period and was conducted in the Embase, ESPORTDiscuss, LILACS, PEDro, PubMed, and SciELO electronic databases, covering studies published until 30 May 2024. The search used the terms \"training load\" OR \"internal load\" OR \"external load\" OR \"training monitoring\" OR \"planning\" OR \"sport performance\" OR \"athletic enhancement\" OR \"health improvement\" OR \"humans\". Manual searches were conducted in the references of eligible articles in the PubMed, Scopus, and Google Scholar databases to add other relevant titles. Seventy-eight references were selected among the titles screened.
## 3 Methodology
### GitHub Modeling Using DTMC
#### 3.2.2 Transition Matrix Construction  
The transition probability matrix \(P\) defines the probabilities of moving from one state to another based on the observed activities in the data. The construction of the transition matrix involves the following steps:  
1. Data Collection and Preprocessing: Extract activity logs from the selected GitHub repositories. Identify and categorize each activity into one of the defined states.
2. State Transition Identification: Sequence the activities for each participant to trace the transitions between states. Record the frequency of each transition.
3. Probability Calculation: Calculate the transition probabilities for each state based on the observed frequencies. If \(N(i,j)\) represents the number of transitions from state \(i\) to state \(j\), then the transition probability \(P_{ij}\) is given byTransition Matrix Construction: Construct the transition matrix \(P\) where each element \(P_{ij}\) represents the probability of transitioning from state \(i\) to state \(j\).
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 3 Experiments
### Experimental Setup  
In the experiments, a total of 400 images of around 100 different objects from the Cornell Grasping Dataset were used as the training data. Only the images from the dataset were used; the other grasping information was discarded. Any oriented object detector can be used, but for this study, a modified version of You-Only-Look-Once version 5 (YOLOv5) algorithm called YOLOv5 OBB (Oriented Bounding Boxes) [23] was chosen as the choice of oriented object detector. Training was conducted on a Linux server equipped with a 32 GB Tesla V100-DGXS Graphics Processing Unit (GPU) over 1000 epochs, taking around 10 h to complete. The robot arm used is a Universal Robot UR5e equipped with an OnRobot RG2 Robot Gripper and the camera attached is an Intel RealSense. Both the robot and camera are connected to a PC running a 64-bit Ubuntu 18.04.5 LTS Operating System with an Intel Core i5-7500 CPU (Intel Corporation, Santa Clara, CA, USA) and a NVIDIA GeForce RTX 2080 SUPER GPU (NVIDIA, Santa Clara, CA, USA). A white background is used for the object to be grasped. For each object tested, 10 attempts were made. In total, 40 different objects were used for a total of 400 grasp attempts. In the 10 attempts of each object, the position and orientation of the object are also varied within the field of view of the camera. The tactile sensors in the robot's gripper are set to a fixed threshold. Once the gripper closes in on the object and detects the threshold force, it assumes that the object is already grasped and stops closing the gripper further. The arm then proceeds to lift the object up. The robot commands are executed based on the inner controller of UR5e with closed architecture and no external feedback controller is required. A grasp is considered successful if the robot is able to grasp it and return it to its ending position without dropping it in the moving process. The hardware used is shown in Figure 15.
# A Conceptual Design of Deployable Antenna Mechanisms
## 2 Materials and Methods
### Performance Metrics of the Deployable Antenna Mechanism  
Given satellites to launch, it is worth noting that space structures are required to minimize their volume and weight (density) to reduce their launching cost while maximizing the efficiency of the limited payload of the launching vehicle. In addition to these, considering that the primary objective of most of deployable antenna mechanisms is to enlarge the aperture diameter upon deployment, this addresses two evaluators: (1) height to radius ratio, which is denoted as HR (\(h/\pi\cdot R^{2}\) [\(m/m\), dimensionless]), and (2) area to mass ratio, which is denoted as AM (\(\pi\cdot r^{2}/m\) [m\({}^{2}\)/kg]). These key performance metrics are related to variation in height and area with respect to variation in radius and mass, respectively. The height and radius upon the stowed state are represented as \(h\) and \(r\), while the height and radius upon the deployed state are denoted as \(H\) and \(R\). The total weight of the deployment mechanism is represented by'm', including the frame, joints, and all other components.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
### The Need for Dense Sampling  
Conventionally, for clinical voice assessment one tries to _eliminate_ as much as possible of the variability in the voice, by eliciting only a few sustained vowels at \"comfortable\" (i.e., unspecified) loudness and pitch. While this may be clinically expedient, we submit that it may amount to a gross under-sampling, with a high risk of being poorly representative of the person's vocal status. It is rather like trying to assess a person's face from only a few scattered pixels, at ill-defined locations in a photograph. In effect, we are taking sparse samples from an unknown function of SPL and \(f_{\rm o}\), when it is actually the function itself that carries the sought information. Measurements that are based on a few sustained vowel productions are susceptible to differences in _elicited_ SPL and \(f_{\rm o}\). These differences can easily be so large that the associated trends in the observed metrics will mask the effect that one is trying to measure. Some real-life examples will be given later in this article. We believe that this is one important reason why the evidential value of many individual physical voice metrics appears to be weak, as found by numerous studies [1; 2; 3]. It is often not because the metrics are unsuitable as indicators of vocal status, but rather because we have not been collecting and collating them effectively enough.
## 3 Materials and Methods
### Antimicrobial Activity  
The synthesized compounds were tested for their in vitro growth inhibitory and bactericidal (fungicidal) activity against _Staphylococcus aureus_ (ATCC 25923), _Staphylococcus aureus_ (ATCC 43300, MRSA), _Escherichia coli_ (ATCC 8739), _Escherichia coli_ (ATCC 25922), _Mycobacterium smegmatis_ (mc(2)155/ATCC 700084), and _Candida albicans_ (ATCC 10231). Cefazolin, cefotaxime, amikacin, rifampicin, isoniazid, and fluconazole were used as control antimicrobial (antifungal) agents. The cells were grown overnight at 37 \({}^{\circ}\)C in a glass tube with 5 mL of LB broth containing 1% of glucose for _C. albicans_ and 0.05% Tween 80 for _M. smegmatis_. The grown cultures were then diluted 1:100 with fresh medium and cultivated for 5 h. The _M. smegmatis_ culture was grown for 20 h with agitation on a shaker GFL1092 (GFL, Germany) (200 rpm, 37 \({}^{\circ}\)C). Then, the cultures were adjusted to OD 0.1 (A625) and diluted to 1:10 for _M. smegmatis_ and 1:100 for the other microorganisms. The resulting suspension was used to determine the MIC using the serial dilutions method in 96-well plates with modifications. The synthesized compounds were dissolved in DMSO at a concentration of 20 mg/mL and a series of two-fold dilutions was prepared in the same solvent. Then, 10 \(\upmu\)L of the solutions was added to the wells of the plate containing 190 \(\upmu\)L of the cell suspension. To the control wells, 10 \(\upmu\)L of DMSO was added. The plates were incubated at 37 \({}^{\circ}\)C under static conditions for 24 h (72 h for _M. smegmatis_) for the determination of minimum inhibitory concentration. The minimum bactericidal concentration and minimum fungicidal concentration were determined as the lowest concentration of an antimicrobial agent required to achieve a 99.9% reduction in the colony-forming units (CFU) number in the initial inoculum. In total, 10 \(\upmu\)L from each well of a plate for the MIC determination was inoculated on Petri dish with LB agar (containing 1% of glucose for _C. albicans_), and colonies were checked after incubation (24 h, 37 \({}^{\circ}\)C) to determine the MBC or MFC.
# Machine-Vision-Based Algorithm for Blockage Recognition of Jittering Sieve in Corn Harvester
## 4 Discussions
### Future Work  
For the purpose of portable application and online monitoring, the embedded-system-based hardware and software should be developed in the future. As presented in Figure 17. The implementation is composed of four parts, the RGB camera, the light source, the embedded system, and the host computer. The natural light source is suggested. It is because the proposed blockage recognition algorithm requires the thresholds of three bands to judge whether a pixel belongs to kernel blockage, and the natural light source could provide a standard light environment for the judgment. As indicated by step 5 of Algorithm 2, the judgment based on the thresholds may be influenced if the light is too weak or too strong. To address issue, the following improvements can be considered.  
* Use the adjustable light source such that the thresholds can be tested and adjusted in advance.
* Normalize the RGB data of each pixel, that is, \(\underline{\mathbf{X}}(i,j,:)\), and then compute the correlation between the normalized data and the dictionary to judge whether the pixel belongs to kernel blockage. The dictionary is trained by using training samples, and various of dictionary learning methods can be employed for this purpose [24].  
Besides a light environment, the paint color is another factor that may influence blockage recognition. Therefore, for better performance of recognition, painting jittering sieves a color that is obviously different from kernels is suggested.  
Figure 17: Outline of hardware implementation.  
The image processing system is composed of lower computer and upper computer, and they communicate wirelessly. The lower computer is realized by an embedded system with an RGB camera. The embedded computer module, Jetson TX2, is selected as the hardware of the lower computer. This module contains the graphics processing unit with 256 Nvidia cube cores, the Denver dual-core central processing unit, and the advanced reduced instruction set computer machine of A57 complex. For the purpose of portability, a storage battery is utilized to provide power for the motherboard. The software of lower computer is developed based on the Python programming language. Based on the software, the camera is controlled to acquire the image of the objection. The data of image is transferred to upper computer by using the Socket library.  
The hardware of an upper computer can be realized by the normal personal computer. Therefore, the main development task of upper computer is the software that can be developed by the Python. The interactive interface contains the functions of start-stop, online monitoring, parameter input, and so forth. The data are transformed into images by using the V4L2 library, which is the genetic kernel driver for video devices of the Linux.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## 4 Dataset, AE Design and Implementation
### AE Design
#### 4.2.2 All-in-One Autoencoding  
For the second set of experiments, we tested different network designs before choosing a good configuration of the parameters. We found that adding **width** and **depth** to the AE is beneficial. In particular, Table 5 shows the design used for the all-in-one autoencoding experiment. The chosen AE is made up of **five hidden layers**. These layers are densely connected and include \(N\)-64-24-6-24-64-N neurons, where \(N\) is the number of features of the data points. Again, the rectified linear unit (ReLU) has been selected for the encode layer, the decode layer and the bottleneck layer, while for the output layer the hyperbolic tangent (Tanh) activation function has been used. We train the autoencoder on NORMAL data points for 100 epochs with batch size 2048 using the RMSProp optimizer with learning rate value \(lr=0.0001\).
## 3 Simulations and Discussion  
### The RO Data Used in the Assimilation  
Figure 2 shows the locations of GPS RO soundings assimilated in the DA period of 0000 UTC 2-4 July 2016. There are a total of 5230 RO soundings in the globe with 211 soundings located in the region of 0-40\({}^{\circ}\) N and 110-170\({}^{\circ}\) E spanning the typhoon track. This local region was chosen for the verification against the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis shown later. The assimilated RO data that came from FS3/C, Meteorological Operational satellites (MetOp), Gravity Recovery and Climate Experiment (GRACE), X-Band TerraSAR satellite (TerraSAR-X), and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) are irregularly distributed but with quite uniform data density in the global and local regions. A reduced data density can be found in the tropical and polar regions as FS3/C is a constellation of six LEOs at 72\({}^{\circ}\) inclination that can provide denser occultation in mid-latitudes.
## 3 Results and Analysis
### Microstructure of Lime-Stabilized Slurry
#### 3.3.2 SEM Test Results  
Figure 9 below shows the SEM images of the Zhoushan slurry before and after lime stabilization. The remolded slurry soil samples were mainly flaky and granular, and the contact mode between the particles was face-to-face contact, forming a relatively dense soil skeleton. Whereas the pore distribution was dominated by intergranular pores which were mostly round or elliptical in shape and small in diameter, the microstructure of the soil samples had significantly changed after lime stabilization, when the contact mode between  
Figure 7: Pore volume distribution curves of stabilized slurry for each group with different lime dosages. (**a**) Wenzhou soil sample, (**b**) Hangzhou soil sample, (**c**) Taizhou soil sample, (**d**) Zhoushan soil sample.  
particles had become edge-to-face and edge-to-edge contact. The loose soil skeleton was found to have led to an increase in the number of overhead pores which were irregular in shape and relatively large in diameter. With the increase in lime dosage, the particles developed from local edge-to-face contact to overall edge-to-face contact, with a gradual rise in the number of overhead pores.  
The SEM images of the four types of stabilized slurry with a 1% lime dosage are shown in Figure 10 below. The number of clay mineral aggregates and pore diameters after lime stabilization of the slurry with high clay content was larger than those of the slurry with  
Figure 8: Pore classification results of stabilized soil samples for each group with different lime dosages.  
Figure 9: Microstructure of Zhoushan soil samples with different lime dosages. (**a**) Lime dosage: 0%, (**b**) lime dosage: 1%, (**c**) lime dosage: 2%, (**d**) lime dosage: 3%.  
high silt content. This was hypothesized to indicate, firstly, that the stabilization had a more significant effect on the slurry with high clay content than that with high silt content for the same lime dosage. Secondly, the pore diameters had obviously increased, which manifested macroscopically as noticeably enhanced permeability.
## 1 Introduction
proposed a new formulation of stochastic mixed-integer programming and described an effective TSA [13]. [PERSON] studied the operational characteristics of DRT in-depth, established the DRT path optimization models for single and multiple access points, and used an improved TSA to find the optimal solution [29]. [PERSON] et al. developed a customized SAA to introduce flexible quality of service into the DRT feeder transportation problem, which can reduce infeasible solutions and improve search efficiency [30]. [PERSON] et al. selected a collaborative ACO to compute the DRT model with fuzzy demand, redefined pheromones, heuristic information, etc., and solved the limitations of the traditional ACO [31]. [PERSON] et al. combined the advantages of the GA and the ACO and added the destination dimensionality reduction operator, which gave the algorithm a stronger solving ability and better stability [32]. [PERSON] et al. designed an algorithm based on GA by constructing an optimization model that considered the operating costs and reliability of the DRT line design problem. The results showed that the new algorithm had obvious advantages regarding computational efficiency and performance [33]. [PERSON] et al. improved the GA by increasing the number of iterations of the genetic operator and schedulable passengers to generate highly accurate schedules and reduce passengers' travel time [34]. Existing studies have achieved rich research results and formed a relatively perfect theoretical system. However, the scheduling models in the literature have only considered the case of a single vehicle size. But due to the single limitation of vehicle size capacity, coupled with the complexity and variability of the roads, it is easy to lead to a mismatch between the number of passengers and vehicles in practice, resulting in a waste of resources, so it is necessary to consider the case of multiple vehicle sizes. Moreover, established research also did not consider the case of multiple vehicle yards or just set both departures and returns to enter the same yard to reduce the difficulty of solving. Still, the reality is often to take a multiple vehicle yard scheduling mode. Therefore, this paper establishes a DRT scheduling model suitable for multiple vehicle sizes and yards. The contributions of this study are summarized as follows:  
1. A directed acyclic graph is used to describe the scenario of DRT based on the reservation scheduling problem;
2. The constraints integrate the practical limitations such as multiple vehicle sizes, maximum pick-up interval time, etc., and an ant colony algorithm is designed according to the characteristics of the model to solve the problem. 3. The model gets rid of the limitation of single vehicle yards, so the applicability of the model is wider, and it can be applied to multi-model vehicle scheduling in single and multiple vehicle yards.
###### Abstract  
Experience shows that dilution of lubricating oil with diesel oil is unfavorable to the engine, causing issues including deterioration of engine performance, shortening of oil life, and reduction in engine reliability and safety. This paper presents the verification of the hypothesis that the changes in lubricity, friction coefficient, and decreasing oil film thickness (using a relative approach, given as a percentage) are similar for lubricating oil and diesel mixtures prepared from fresh lubricating oil and used lubricating oil. To validate this hypothesis, an experiment is conducted using a high-frequency reciprocating rig (HFFR), in which the lubricity is determined by the corrected average wear scar \(WS_{1,4}\), the coefficient of friction \(\mu\), and the percentage relative decrease in oil film thickness \(r\). A qualitative visual assessment of the wear scars on the test specimens is also performed after the HFFR tests. The testing covers mixtures of SAE 30 grade Marinol CB-30 RG1230 lubricating oil with Orlen Efecta Diesel Biodiesel. The used lubricating oil is extracted from the circulating lubrication system of a supercharged, trunk-piston, four-stroke ZUT Zgoda Sulzer 5 BAH 22 engine installed in the laboratory of ship power plants of the Maritime University of Szczecin. Mixtures for the experiment are prepared for fresh lubricating oil with diesel oil and used lubricating oil with diesel oil. Mixtures of these lubricating oils with diesel oil are examined for diesel oil concentrations in the mixture equal to 1, 2, 5, 10, 15, and 20% m/m. The results of the experiment confirm the hypothesis, proving that, for up to 20% m/m diesel oil concentration in lubricating oil, the changes in the lubricity of used lubricating oil diluted with diesel oil can be evaluated based on reference data prepared for mixtures of diesel oil with fresh lubricating oil. The linear approximation of \(\mu\) and \(r\) trends is made with a certain margin of error we estimated. The experiment also confirms the results of previous studies which state that oil aging products in small quantities contribute to improved lubricity.  
fresh lubricating oil; used lubricating oil; diesel oil; fuel dilution; lubricity; high-frequency reciprocating rig; HFFR +
Footnote †: journal: Applied Sciences
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors  
To demonstrate the robustness of our proposed protocols, we consider their resilience against several standard cybersecurity attack vectors.
## 4 Topology-Based Routing Protocols in FANETs
### Hybrid Routing Protocol
ZRP utilizes a route discovery technique for destinations outside the zone that takes advantage of the zones' local routing information, referred to as inter-zone routing [107].