esa-sceva/satcom-chunk-collection huggingface dataset:
# LBTM: Listen-before-Talk Protocol for Multiclass UHF RFID Networks
## 6 Hospital Example
### Scenario Layout  
The layout considered for the hospital models a single floor with several rooms for patient care as well as several rooms for storage of equipment and drugs, as shown in Figure 7. We assume that patients can be tagged for knowing their location or for other uses like breath monitoring, such as in [40]. Besides, items or drugs are also tagged for continuous inventory. Finally, this layout has several entrances to control people and items passing by. The key performance indices selected in this example are the number of inventories (cycles) per second that each reader type can do. The readers for patient monitoring will have the highest priority, followed by the readers at doors due to the limited identification time for this traffic, while those readers doing a continuous inventory of items inside rooms have the lowest priority. In addition, the next setup has been selected:  
* There are 20 readers installed at 2.5-m height distributed as follows: 8 perform patient monitoring (class 1, highest priority), 8 do continuous inventory of storage rooms (class 3, lowest priority), and the other 4 control items and patients crossing doors (class 2, medium priority).
* Each reader has an initial load of 20 tags.
* Arrival rate at each door is a Poisson of rate 1 group of people every 120 s, with the people in each group also given by a Poisson distribution of rate 3.
* Cycle time is set to the minimum of 100 ms for all reader classes.
* Tagged equipment has a reorganization rate (they change their rooms) every 4 min, involving a random number between 20 to 60 items.
* Simulation stop condition is 1 h of operation.
* As in the grocery shop example, all readers cause mutual interferences; in this case, the minimum level of interference is \(-\)26.3 dBm for readers at 42 m.
## 2 Improvements and Comparative Analysis of IEEE 802.11 bd
### Key Improvements in Data Transmission Reliability
An example of repeated transmission is shown in Figure 2.  
The NON_NGV_10 repetition transmission mode supports broadcast services for both NGV STAs and non-NGV STAs, enhancing the frame reception reliability and facilitating the transmission of crucial security warnings. Incorporating a radio environment measurement mechanism can further enhance this mode through an adaptive repetition transmission scheme. The decision and quantity of repeated transmissions are contingent on the measured radio environment congestion levels. Ref. [23] have shown that this adaptive packet repetition scheme can provide approximately a 3 dB gain in SNR. Furthermore, the non-NGV duplication operation enables NGV STAs to transmit the Request To Send (RTS) frame in a non-NGV duplicate PPDU to safeguard an NGV PPDU with a 20 MHz bandwidth. This approach enhances the stability and reliability of the RTS/CTS mechanism, providing better assurance for data transmission.
## 3 Results and Discussion
### Structure and Hardness
After deformation at n = 10 (e = 4.13), the maximum hardness of the samples reaches 160-170 HV, which exceeds the hardness of the as-cast counterparts by a factor of 4. Based on the above-presented results of the structural studies and hardness measurements, one can conclude that the grain boundary, solid solution, and dispersion mechanisms make the greatest contributions to the hardening and strengthening of the deformed AlFe alloy and AlFeGn composite. The addition of 0.12% graphene did not affect the hardness values. This result needs an explanation. Previous research and comparison of the hardness values for single-phase as-cast and UFG AlGn and AlMgGn composites showed that structural refinement resulted in the increase in these material hardness [36]. The hardness of the AlGn composite increased by 25%, and the hardness of the matrix of the AlMgGn composite increased by no more than 15-20%. Concerning the hardness of AlFeGn composites, it is necessary to take into account both the grain size and the number of intermetallic particles. In particular, after HPT, a heterogeneous multiphase UFG structure is formed in the AlFe alloy, in which dispersed Fe aluminides increase the material hardness due to their small size. The structure of the UFG composite after HPT is more homogeneous, with fewer aluminides and a more iron-supersaturated Al solid solution. Given the equal contribution of grain boundary strengthening (the equal grain-subgrain size of the HPT AlFe and AlFeGn samples) to the total hardness of the AlFe alloy and AlFeGn composite, a decrease in the dispersion strenthening and an increase in the solid solution strengthening in the AlFeGn composite can equalize the absolute values of hardness. Figure 9: Hardness of the AlFe alloy and the AlFeGn composite before and after HPT. Thus, HPT produced an aluminum-graphene composite with a very high hardness, the structure of which consists of a large number of disperse iron aluminides alongside an SMC iron-supersaturated Al solid solution.
###### Abstract  
This paper compares three different methods capable of estimating the deflection of the vertical (DoV): one is based on the joint use of high precision spirit leveling and Global Navigation Satellite Systems (GNSS), a second uses astro-geodetic measurements and the third gravimetric geoid models. The working data sets refer to the geodetic International Terrestrial Reference Frame (ITRF) co-location sites of Medicina (Northern, Italy) and Noto (Sicily), these latter being excellent test beds for our investigations. The measurements were planned and realized to estimate the DoV with a level of precision comparable to the angular accuracy achievable in high precision network measured by modern high-end total stations. The three methods are in excellent agreement, with an operational supremacy of the astro-geodetic method, being faster and more precise than the others. The method that combines leveling and GNSS has slightly larger standard deviations; although well within the 1 arcsec level, which was assumed as threshold. Finally, the geoid model based method, whose 2.5 arcsec standard deviations exceed this threshold, is also statistically consistent with the others and should be used to determine the DoV components where local _ad hoc_ measurements are lacking.  
deflection of the vertical; tie vectors; ITRF; QDaedalus; ITALGEO2005
## 3 Optimization
### Algorithms
#### 3.3.1 Single-Objective Optimization Algorithms  
The Genetic Algorithm (GA) was introduced by [PERSON] in the 1960s and formalized in his 1975 book [32]. Inspired by natural selection in biological evolution, it employs selection, crossover, and mutation to iteratively refine solutions.  
The Pattern Search (PS) algorithm [33], introduced by [PERSON], et al. in 1961, is a direct search method. It iteratively evaluates candidate solutions within a structured pattern, adjusting the search area based on the observed results. Pattern search is particularly well-suited for objective functions that are non-smooth, non-linear, or noisy.  
The Evolution Strategy (ES) algorithm [34], developed by [PERSON] in 1978, is another evolutionary algorithm that aims to evolve a population of candidate solutions to identify the optimal one. This algorithm is based on Gaussian random mutations with optional recombination and often employs self-adaptive step sizes for local exploration.  
Differential Evolution (DE) [35], developed by [PERSON] and published in 1994, is a biologically inspired optimization algorithm that maintains a population of candidate solutions. It generates new candidate solutions using mathematical operations based on differences between randomly selected individuals within the population and ensures the preservation of superior solutions.  
In 2000, [PERSON], [PERSON], and [PERSON], developed the Stochastic Ranking for Constrained Evolutionary Optimization (SRES) algorithm [36]. This derivative-free global optimization method is designed to solve constrained optimization problems by combining fitness values and constraint violation information into a single ranking system.  
The Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm [37], proposed by [PERSON], [PERSON], et al. in 2005, is specifically tailored for constrained optimization and is particularly effective in addressing non-linear problems. This algorithm combines stochastic search and evolutionary strategies.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 2 State-of-the-Art in Non-Uniform and Low Latency Resampling  
This section starts with a short review of resampling methods that are relevant for this work. After evoking classical Shannon and Lagrange methods, examples for current resampling applications are given. Solutions, which provide a low or even single sample group delay, are lacking. The literature review is followed by a summary of the mathematical implementation of the selected methods.  
Resampling can be generalized to signal reconstruction or interpolation, where the desired time points can be picked from the reconstructed signal. In addition to being able to convert between fixed ratios of input and output sampling rates, the resampling unit must be able to handle non-uniform input sampling, which is also referred to as non-equidistant, irregular, or arbitrary resampling.  
Since [PERSON]'s famous paper \"Communication in the presence of noise\" in 1949 [5], 100s of articles on resampling have been published. A review paper [6] lists 148 references, although it only focuses on the uniform case.  
According to the Nyquist-Shannon sampling theorem, a signal can be perfectly reconstructed if it has a band limit \(f_{\mathrm{L}}\) of 50% of the sampling frequency \(f_{\mathrm{S}}\), which is also called the Nyquist frequency.  
The Lagrange polynomial interpolation [7] provides a theoretical solution for the non-uniform case. Perfect reconstruction is possible for time-infinite signals if the average sampling interval fulfils the Nyquist condition. The theory of Lagrange interpolation is not limited to simple polynomials, a more detailed overview can be found in [2].  
The fact that a \"slight oversampling is required in most signal-processing systems in order to make them practically implementable\" is generally recognized, as [2] states. To reconstruct a signal, assumptions need to be made about the signal. The most common assumption is that the signal consists of sine functions that are bandlimited to the Nyquist frequency. A more generalized assumption extends from sine functions to shift-invariant spaces [6; 8], such as splines or wavelets.
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
###### Abstract  
The French tokamak WEST supports the ITER design and operation. IRFM is designing a new Thomson scattering diagnostic to measure plasma density and temperature profiles. The diagnostic system consists of an endoscope inside a vacuum vessel, composed of actively cooled optical components. In order to validate and guarantee the diagnostic performances during normal operations, mechanical, thermal, hydraulic and vibratory behavior must be checked. Moreover, perpendicular displacement of the optical surface shall not be higher than 40 \(\upmu\)m. Since this diagnostic operates in near infrared light, the temperature of all components must stay lower than 200 \({}^{\circ}\)C as not to bias the measurements. The differences in water temperature and pressure between the inlet and outlet of the diagnostic must be lower than 50 \({}^{\circ}\)C and 5.6 bar, respectively. The natural frequencies of the structure must be higher than 20 Hz and far enough from the frequency of external components. In this study, the worst radiative plasma scenario was chosen. The results of this study validate the accuracy of the measurements. Before manufacturing, electromagnetic disruption events must also be considered.  
tokamak; WEST; Thomson scattering; plasma radiation; optical system; thermomechanical; thermal-hydraulics; modal analysis; computational fluid dynamics +
Footnote †: journal: Applied  
Accepted: 24 January 2022  
Published: 26 January 2022
## Abbreviations  
The following abbreviations are used in this manuscript:  
API Application Programming Interface ARCHES Autonomous Robotic Networks to Help Modern Societies DLR Deutsches Zentrum fur Luft- und Raumfahrt (German Aerospace Center) DPSS Diode Pumped Solid State FDM Fused Deposition Modeling GPIO General Purpose Input/Output GUI Graphical User Interface IMU Inertial Measurement Unit LED Light Emitting Diode LIBS Laser-Induced Breakdown Spectroscopy LRU Lightweight Rover Unit NIR Near Infrared PBIMS Payload Box Infrastructure Management System PCB Printed Circuit Board PDU Power Distribution Unit PEL Planetary Exploration Laboratory PETG Polyethylenenterephthalat-Glycol PNG Portable Network Graphics PWM Pulse Width Modulated ROBEX Robotic Exploration of Extreme Environments SBC Single Board Computer SNR Signal-to-Noise Ratio SSD Solid-State Drive UV Ultraviolet
## 4 DDPG Optimization of Control Parameters for Active Disturbance Rejection Controller
### Deep Deterministic Policy Gradient-Active Disturbance Rejection Controller Algorithm Framework  
Reinforcement learning is an algorithm that allows an agent to adjust its behavioral strategies based on observations made during interactions with the environment, to maximize cumulative rewards. The schematic diagram is shown in Figure 3.  
Figure 3: Reinforcement learning diagram.  
In response to the online adjustment problem of fixed parameter active disturbance rejection controllers, this paper employs the Deep Deterministic Policy Gradient algorithm (DDPG), which can handle continuous action control. The proposed control strategy, DDPG-LADRC, treats the entire underwater robot control system as the environment, using the system's control performance as the reward evaluation criterion. The DDPG-LADRC agent determines actions based on the current environment, and then the environment provides a new state based on the output value and calculates the reward value. The DDPG-LADRC agent makes judgments, optimizes and updates the next action, and interacts with the environment until the reward converges.
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 5 Evaluation Result
### Analysis of the Retrieval Robustness
#### 5.2.2 Comparison Analysis on Retrieval Robustness of Different Robustness Training Approaches  
The results shown in Tables 6 and 7 reflected the robustness of the models under different robustness training methods when dealing with adversarial attacks. We calculated the evaluation metrics for the IR models and demonstrated the extent of the decline in their performance when subjected to attacks. The Dual Contrastive method on the DR model achieved the best retrieval performance, but its defensive ability against query attacks did not show a significant improvement compared to the original model. We believe that the improvement was mainly attributed to the optimization of query and document representations in the latent space during model training, which further enhanced the PLM's ability to represent documents. Although slightly inferior to Dual Contrastive in retrieval performance, our proposed approach could significantly improve the robustness of the model. The typo-aware method, on the other hand, exhibited excellent robustness when facing adversarial attacks despite a slight decline in retrieval performance compared to the original method. Additionally, the performance of different training methods varied  
Figure 3: The avg d. and worst d. of MAP, MRR, P@10, and NDCG@10 of different IR models. We selected a subset of traditional retrieval models from the baselines, as well as PLM-based IR models, represented by BERT. Both BERT and our proposed method employed Cross Encoder (Reranker). The data in the top two rows represent the results for WikiQA, while the data in the bottom two rows represent the results for ANTIQUE. The values shown in the subfigures represent the absolute values of the actual results. A higher value indicates poorer robustness. Each color signifies the robustness of the corresponding model under different datasets and evaluation metrics.  
across different datasets. On WikiQA, the typo-aware method demonstrated the best robustness for the DR model, whereas our method exhibited the best robustness in all other experiments. Considering both retrieval performance and robustness as training objectives, the method proposed in this study achieved better robustness while ensuring nearly optimal retrieval performance.
# Performance Comparisons of Broadband Power Line Communication Technologies
## 5 Conclusions  
This study compared the physical layer performance of broadband PLC technologies being deployed or tested in Korea. The PLC technologies included IoT PLC, HPGP, and HS PLC. The bit rate, power spectrum, and BER were evaluated. For the transmission rate, an expression for the bit rate for IoT PLC and HPGP was derived while taking the padding bits and the number of tones in use into consideration. The expression was compared with an approximate formula. IoT PLC provides 31 different bit rates ranging from 0.930 to 25.568 Mbps. HPGP and HS PLC each provides three different bit rates. The power spectrum was obtained through computer simulations. IoT PLC was found to have good power spectrum properties in terms of the notch depth and out-of-band power. For the BER performance comparisons, the upper bound of the BER for each PLC standard was evaluated through computer simulations. From the results, the STD mode of HPGP provides similar performance to the QPSK mode of IoT PLC with a diversity number of 4. Finally, we observed that IoT PLC in QPSK mode has better BER performance than any other PLC technologies at each diversity number.  
This research was supported by a grant from the Korea Energy Efficiency Cooperative.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 1 Introduction
However, guaranteeing a constant battery temperature over time can represent a difficult task for the thermal regulation system, and slight temperature oscillations can occur even in best case scenarios. A number of studies have currently begun to assess this issue. The authors of this paper conducted a one-year long experimental campaign to validate the battery lifetime predicted by a _SOH_ sensitive HEV EMS and found that an increase in the average cell surface temperature of only 1 \({}^{\circ}\)C can culminate in as much as a 15,000 km or 7% decrease in the cell lifetime [23]. [PERSON] et al. recently considered energy management for a parallel plug-in hybrid electric bus sensitive to both battery temperature and _SOH_. They suggested that noticeable equivalent cost savings might be attained by limiting the overall battery temperature increase while driving [24]. Although these works represent steps in the right direction, extensive research is still needed to develop a battery thermal management system whose calibration can guarantee a satisfactory high-voltage battery kilometrical lifetime. The present work introduces an optimization-driven calibration method for the on-board energy and thermal management of a plug-in HEV. The aim is to minimize operative costs of fuel and electrical energy consumption and battery degradation over the entire vehicle lifetime. The optimization-driven calibration workflow considers different ambient temperatures, driving conditions, payload conditions, and cabin conditioning system states. The developed HEV energy and thermal management strategy proved to flexibly adapt to different ambient temperatures. Moreover, the strategy limits the increase in the overall HEV energy demand that may be expected when ambient temperature rises above 30 \({}^{\circ}\)C.  
The paper is organized as follows: the retained HEV powertrain model and baseline on-board EMS are presented first. Section 3 then describes the numerical models implemented to reproduce the electrical, thermal, and ageing behavior of the high-voltage HEV battery. The battery cooling system and its numerical model are also described. The optimization-driven calibration of the HEV EMS is discussed in Section 4. Results are presented in Section 5, with conclusions drawn in Section 6.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 5 Experimental Results and Analysis
### Experimental Settings  
Mininet 2.2.1 and WOSS 1.5.0 software were used to design the simulation platform. We set up VirtualBox 5.2.12 as the virtual machine. Open vSwitch v2.5.1 was installed in DNs to build an SDN switch, and OpenDayLight was deployed on the surface sink node as the SDN controller. FPO-MST and FCO-MST algorithms were running on the controller. The experiment was run on a clustered system consisting of eight blade servers. Each server was configured with two E5-2698 v4 processors and 512 GB of memory. The clustered system was configured with an IBM v3700 storage of 10 TB disk capacity. Eight controllers and 200 DNs were given in the experiment. Each controller was allocated 20 core CPU resources, 64 GB of memory, and 2 TB of disk capacity. A DN provided one core CPU resource, 8 GB of memory, and 10 GB of disk capacity. The network is assumed to be randomly deployed in a 1000 \(\times\) 1000 \(\times\) 100 m\({}^{3}\) submerged area. We supposed that the depth range of the nodes is 10-110 m, and the underwater nodes are relatively stationary and the motion is insignificant. Let the data communication distance be 100 m, the cluster radius 50 m, and the control communication distance be 1000 m. Other parameters are shown in Table 4.  
We compared the PDR, end-to-end latency, and throughput of FPO-MST and FCO-MST with the three related algorithms in different scenarios. The low-energy adaptive clustering hierarchy algorithm (LEACH) [42] divides UANs into multiple clusters. Each cluster consists of one CH and multiple DNs. The CH is predefined in this algorithm. When the cluster is formed, the CH broadcasts the communication schedule based on the time division multiple access (TDMA) technique. Each DN in the cluster transmits data to the CH according to the allocated time slots. The CH aggregates the data and sends them to a BS. [PERSON]'s algorithm is a clustering-based routing [43] method for hydroacoustic networks. It consists of two phases: CH selection and data transmission. In this algorithm, the CH implements a multi-hop routing scheme by aggregating data. Energy-efficient hybrid clustering algorithm (EEHC) [44] is a routing protocol with a predetermined percentage of clusters.