esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation  
The developed model assumes calculating statistics, such as the mean and standard deviation, for the duration of the cabin door drive operation cycle. In order to reduce the amount of memory needed to collect measurement data from the entire duty cycle (this is necessary because the drive controller chip does not have enough memory), online statistics should be calculated and updated as each new sample arrives. In order to avoid the problem of so-called catastrophic cancellation [41] the use of the Welford algorithm has been proposed [42; 43] updating the mean and variance after the appearance of a new measurement sample. The following formulas are used in the Welford algorithm for updating statistics:  
\[\overline{x}_{n}=\overline{x}_{n-1}+\frac{x_{n}-\overline{x}_{n-1}}{n}, \tag{1}\]  
\[M_{2,n}=M_{2,n-1}+(x_{n}-\overline{x}_{n-1})(x_{n}-\overline{x}_{n}), \tag{2}\]  
\[\sigma_{n}^{2}=\frac{M_{2,n}}{n}, \tag{3}\]  
where \(\overline{x}_{n}\) denotes mean of n samples, \(M_{2,n}\) is the sum of squares of differences from the current mean, and \(\sigma_{n}^{2}\)--variance.  
This preprocessing method has been successfully implemented on a cabin door drive controller equipped with an STM32F103 from ST Microelectronics, Geneva, Switzerland series microcontroller running at 72 MHz without adversely affecting the operation of the entire system. It is worth noting that the computational complexity of [PERSON]'s algorithm is O(N), making it lightweight and suitable for the microprocessor of the drive controller. Additionally, reducing the amount of transmitted data decreases the load on the CAN bus.  
Figure 13 presents a schematic of the system being implemented, which consists of a cabin door drive, a communication-measurement module, and a central server where the  
Figure 12: Confusion matrix for the random forest algorithm.  
machine learning model will be deployed. The drive controller measures quantities, such as current and rotational speed, which are essential for vector control. These data are then processed using the proposed aggregation method and transmitted via the CAN bus to the communication-measurement module. The module also measures temperature and vibrations on the drive beam and transmits the complete dataset to the central server, where diagnostics will be performed. In addition, work is underway to implement a complete system that will also enable remote viewing of the lift's operation, collection of operational data and identification of faults in real time. Thanks to the structure developed in this way, it will be possible in the future, once enough data has been collected, to upgrade the system to perform predictive maintenance functions.
## 2 Materials and Methods  
Barium zirconate and barium cerate thin films were deposited using e-beam physical vapor deposition system \"Kurt J. Lesker EB-PVD 75\", (Kurt J. Lesker Company GmbH, Frankfurt, Germany. The initial powders, (Goodfellow Cambridge Ltd, Huntingdon, UK) (BCO, 99.9% purity; barium cerate doped with 10% (BCY10) and 20% yttrium (BCY20), 99.9% purity, BZO, 99.9% purity; barium zirconate doped with 20% yttrium (BZY20), and barium zirconate doped with yttrium and cerium (BZCY), 99.9% purity) were used as target materials to form the thin films. The powders were pressed into pellets and evaporated on substrates made of Inconel alloy 600 (Ni72/Cr16/Fe8), (Goodfellow Cambridge Ltd, Huntingdon, UK). Prior to the deposition, substrates were cleaned ultrasonically with isopropanol and acetone. During the deposition, the temperature of substrate was kept constant at 500 \({}^{\circ}\)C. The acceleration voltage of the electron gun was 7.9 kV, the current was adjusted in the range of 30-100 mA with a deposition rate of 2 A/s. The initial pressure was 1 \(\times\) 10\({}^{-5}\) Pa and the working pressure range was from 1 \(\times\) 10\({}^{-3}\) Pa to 1 \(\times\) 10\({}^{-4}\) Pa.  
The resulting thin films had a thickness of \(\sim\)2.0 \(\upmu\)m for all deposited samples. The thickness was measured with a profilometer Ambios Technology XP-200, (Ambios Technology Inc., Santa Cruz, CA, US). Density of the thin film was calculated using the mass of the thin film and its thickness. The crystalline structure of the thin films was studied by an X-ray diffractometer (XRD) \"Bruker D8 Discover\", (Bruker AXS GmbH, Billerica, MA, USA). The phase identification was performed using PDF-2 database. Crystal size and lattice parameters were obtained by refinement of the acquired diffraction pattern via the Le Bail method, using TOPAS 4.2 software (Bruker, Karlsruhe, Germany). The measurements were performed using Cu K\(\alpha\) (\(\lambda\) = 0.154059 nm) radiation at a geometry of 2\(\Theta\) angle in a 20\({}^{\circ}\)-70\({}^{\circ}\) range and a 0.01\({}^{\circ}\) step. The texture coefficients that describe the preferential crystallite orientation in the thin films were calculated using the following equation:  
\[TC_{(h_{i},\ k_{i}l_{i})}\ =\ \frac{I\left(h_{i},\ k_{i},l_{i}\right)}{I_{o}(h_{i},\ k_{i},l_{i})}\left\{\frac{1}{N}\sum_{i=1}^{N}\frac{I\left(h_{i},\ k_{i},l_{i }\right)}{I_{o}(h_{i},\ k_{i},l_{i})}\right\}^{-1} \tag{1}\]  
where \(I\left(h_{i},\ k_{i},l_{i}\right)\) is the intensity of \((h_{i},\ k_{i},l_{i})\) peak.  
The surface topography and cross-section images were obtained using the scanning electron microscope (SEM) \"Hitachi S-3400N\", (Hitachi High-Technologies, Tokyo, Japan). The elemental composition was estimated using an energy-dispersive X-ray spectroscopic (EDS) \"BrukerXFlash QUAD 5040\", (Bruker AXS GmbH, Billerica, MA, USA). Grain size distribution was evaluated by ImageJ software 1.53K ([PERSON] and contributors, National Institute of Health, MA, USA).
# Emerging Topics in Joint Radio-Based Positioning, Sensing, and Communications
## 3 Emerging Topics in JPSAC Context
### ELAA, XLMIMO, mMIMO, umMIMO, and LIS
Examples of the research questions to be addressed in order to reach this potential include: how to overcome the beam-squirt or beam-squirt effects (i.e., the unintentional change in the main beam direction during the beam-steering process, due to the frequency-dependent phases), and how to overcome the complexity and hardware challenges in the implementation of ISAC algorithms with ELAA/XLMIMO systems.
## 2 Background Concept and Problem Modeling
### Reinforcement Learning  
As a standard solution to the MDP problem, the fundamental method in the reinforcement learning algorithm uses an optimization-based method to maximize the sum of the reward signal by letting the agent interact with a stochastic environment and choose the action over sequences of discrete-time steps [27]. The MDP is defined by the tuple \(\{S,A,P,r,\gamma\}\), where \(S\) denotes the set of the state, \(A\) the set of action, \(P\) the transition probability, \(r(s,a)\) the reward function, and \(\gamma\) the discount factor [28]. The transition probability \(P(s^{\prime}|s,a)\) represents the probability of moving the current state \(s\) to the next state \(s^{\prime}\) when action \(a\in A\) is applied. Regarding the agent, the distribution of action \(a\) for the given state \(s\) from the environment is implied by the policy that is denoted by \(\pi(a|s)\). At each time step \(t\), the agent selects action \(a_{t}\in A\) based on the policy \(\pi:S\to A\) and state \(s_{t}\in S\), and applies it to the environment. After that, the environment returns both the next state \(s_{t+1}\in S\) and reward \(r_{t+1}\in\mathbb{R}\) according to the transition probability \(P:S\times A\rightarrow\mathcal{P}(S)\), where \(\mathcal{P}(S)\) means that the probability of the next state and the reward function \(r:S\times A\rightarrow\mathbb{R}\). By repeating this procedure, the learning process updates the policy until it reaches the optimal policy, defined by \(\pi^{*}\), which maximizes the expected return \(E_{\pi}[\sum_{k=0}^{\infty}\gamma^{k}r_{t+k+1}]\). In order to acquire the optimal policy, the optimal value function (i.e., estimate of the maximum return) is approximated by value-based methods like deep Q-network (DQN) as function approximation [29]. The other way to get the optimal policy is using the policy gradient methods to compute the optimal policy directly from the agent experience. Representative methods of the policy gradient are REINFORCE, actor-critic method, deterministic policy gradient (DPG), deep DPG (DDPG), asynchronous advantage actor-critic (A3C), trust region policy optimization (TRPO), maximum a posteriori policy optimization (MPO), and distributed distributional DDPG (D4 PG) [30; 31; 32; 33; 34]. In the reinforcement learning, the training performance mostly depends on the set of samples of state \(s_{t}\), action \(a_{t}\), reward \(r_{t+1}\), and next state \(s_{t+1}\). In addition to the various reinforcement learning algorithms, efforts are directed at devising techniques to utilize the samples (called episode) effectively for better learning, for example replay memory [29] and HER [35]. In this paper, for the purpose of tackling the path planning problem for the multi-arm manipulators with periodically moving obstacles, a soft actor-critic (SAC)[36] algorithm is employed.
## 3 Results and Discussion
### Chemical Environment via FTIR  
FTIR profiles of commercial dolomite (CO-Dolo), handpicked dolomite (HP-Dolo) and handpicked, ball-milled dolomite (HP-Dolo-BM) samples have been recorded for IR transmittance with wave numbers in the mid-IR region, ranging from 500 to 4000 cm\({}^{-1}\) (see Figure 4). In general, the lower energy peaks, for example around 727, 876, 1023, 1418 and 1823 cm\({}^{-1}\) matches with dolomite bands reported elsewhere [47; 48]. The handpicked dolomites, both in its pristine and ball-milled stages, follow the same spectral trends as the commercial dolomites in the lower energy region. The dolomite stretch around 1023 cm\({}^{-1}\) is, however, not prominently shown in the ball-milled HP-Dolo BM sample, whereas the sharpness of the 1418 cm\({}^{-1}\) is increased. An additional peak around 2358 or 2362 cm\({}^{-1}\) appears in the handpicked dolomite and its ball-milled counterpart, which indicates the stretch due to the quartz (SiO\({}_{2}\)) phase [49] previously mentioned in the XRF, Automated SEM-EDX Mineralogy and XRD sections.  
Figure 4: Fourier Transform Infrared (FTIR) spectra of CO-Dolo, HP-Dolo and HP-Dolo-BM samples in transmittance mode.
## 5 Machine Learning Model Computational Cost. Machine Learning Model versus FEA
### Optimisation Process  
The optimisation procedure is performed using the HEEDS MDO software package. HEEDS MDO searches the best and the most robust solutions in a given design space, while drastically reducing the design time, compared with other optimization software [46].  
The optimisation algorithm is specific to the used software. HEEDS presents a search strategy named SHERPA (simultaneous hybrid exploration that is robust, progressive and adaptive) [46]. The main advantages of the chosen algorithm are: it allows an intuitive implementation, even for users with no expertise in the field of optimisation; uses multiple strategies simultaneously, and not sequentially, for a single search; adapts itself to each particular problem, without needing tuning parameters coming from the user; identifies the global and the local optimum simultaneously; conducts optimisation by evaluating the actual model, rather than using approximate response surface models; has a high efficiency, saving days or even weeks of CPU time [46].  
The optimisation problem can be described as follows: to maximise the function f([x]), representing the natural frequency of Mode 2, subject to a set of constraints, \(C_{min}\leq C([x])\leq C_{max}\), where \([x]\) represents the stator geometry DOFs vector with its upper and lower boundaries vectors [UB], [LB], described in Table 2 and \(C_{min}\) and \(C_{max}\) are the minimum and maximum allowed values for each constraint.  
\[\text{maximise f}([x]),\,\text{subject to}\begin{cases}[LB]\leq[x]\leq[UB]\\ C_{min}\leq\text{C}([x])\leq C_{max}\end{cases} \tag{4}\]  
The natural frequency of Mode 2, average torque, torque ripple and total losses belong to the constraint vector \(C([x])\).
## 7 Conclusions and Future Works  
This paper has presented a support system for travelers with special needs in public highway transit. The system improves the accessibility and safety of this group of people when they use this type of public transportation service in urban area and rural areas. The most important aspects of the design of the proposed system include its architecture, which is based on Ubiquitous Computing and Ambient Intelligence, its deployment in different components of the public transport infrastructure, particularly in fleet vehicles, and its use of hardware resources (devices, sensors and communication infrastructure) and services (positioning system, control system planning and traveler information systems) that are usually available in these infrastructures. As a consequence of the re-use of common components of transport infrastructure, the cost of operation and maintenance is significantly reduced.  
As a result, the system is able to offer various services that can improve accessibility and safety during the trip of a person with special needs during different phases: boarding a vehicle, en route and disembarking from a vehicle along routes in both urban and rural areas. This ability to provide services in the context of public transit by highway in rural areas distinguishes it from other existing systems because these cases refer to systems that only operate in urban environments.  
We conclude that the use of Ubiquitous Computing, Ambient Intelligence and the sensory instrumentation available in public transport infrastructure enables the development of innovative solutions to classic public transportation problems and the development of new services that can improve the use of transportation by and the quality of life of citizens.  
For future work we are considering two main lines of action. The first relates to improving the availability of data and services provided by the system. To this end we will incorporate 802.15.4(ZigBee) technology for communications between the OBC and mobile user devices in order to minimize battery consumption in the user device. We will also look at the possibility of using long-distance mobile communications (3G or 4G) to enable communication between user devices and the operations control center in the event of OBC failure. The second line of action is to develop assistants for people with cognitive impairments, such as the elderly, or mobility impairments. This second area is particularly relevant as it should enable us to respond to certain challenges, such as monitoring passenger safety during boarding and disembarking by controlling the elements involved (access ramps and doors) and, during the journey, by controlling aspects such as wheelchair fasteners and seat belts.  
The authors of this paper express their gratitude to the public transportation firm Global Salcai-Utinsa S.A. for their collaboration in this study and for the use of their infrastructure for field tests of the prototype system.
## 4 Optimization of Channel MIMO Capacity by Combining OSTBC and Polarization-Diversity with XPI  
This section describes the effects and benefits of adding transmission-time-polarization-diversity MIMO techniques to the communication model under rain distortion. This combination is possible with the coding of the OSTBC on the transmitter side, the MRRC technique on the receiver side, and an XPI power allocation mechanism in the transmitter-receiver antenna array.  
Our approach is to create a comparative basis of the system's performance between the relative communication model discussed in Section 3 and the model discussed in this section. From this basis, it will be possible to draw significant insights to deal with these challenging scenarios.  
Figure 3: Average Capacity Of Channel MIMO With Dual-Polarization And XPI Power Allocation.  
We assume that the same optimization processes described in Section 3 are also implemented in this analysis. The optimized channel values are based on Table 1, and the architecture of the transmitter-receiver is described in Figure 4. Note that the OSTBC block is implemented between the QPSK modulator and the OFDM scheme transmitter (The OSTBC operation will be explained in the following section).  
This section is divided into Sections 4.1-4.3: the Section 4.1 describes the OSTBC operation and the MRRC-combiner. The Section 4.2 describes the performance of OFTD-OFDM combined with the DP model under rain distribution. The Section 4.3 describes numerical results of MIMO-channel-capacity vs. \(\frac{F_{b}}{N_{b}}\), under combining OSTBC operation to the transmitter, optimizing XPI value, and MRRC decoding in the receiver, under the same rain conditions as described in Section 3.
# A Molecularly Imprinted Polymer Based SPR Sensor for 2-Furaldehyde Determination in Oil Matrices
## 2 Materials and Methods
### Deposition of the Specific MIP Layer  
The prepolymeric mixture was composed of the reagents at molar ratio 1 (2-FAL):4 (MAA):40 (DVB), according to the method previously described [14]. No porogen solvent was used. The cross-linker divinylbenzene (DVB) also acted as the solvent in which the functional monomer (methacrylic acid (MAA)) and the template, 2-FAL, were dissolved. The mixture was uniformly dispersed by sonication and de-aerated with nitrogen for 10 min. Then, the radical initiator AIBN (23 mg/mL of prepolymeric mixture) was added  
Figure 1: Optical chemical sensor system. (**a**) Outline of the experimental setup with a schematic representation of the D-shaped multilayer SPR-POF platform. The brown block represents the resin support (1 cm \(\times\) 1 cm \(\times\) 1 cm) in which the POF (in red) is embedded. The grey layer is the photoresist buffer, and the blue layer is the receptor. SMA is the subminiature version of the connector. The different parts are not in scale. (**b**) Picture of the SPR-POF platform.  
to the mixture. An MIP with the same molar composition, but containing ethylene glycol dimethacrylate (EGDMA) instead of DVB, was prepared for comparison purposes.  
The MIP layer was prepared by dropping directly over the flat part of the platform a small volume (about 50 \(\upmu\)L) of the prepolymeric mixture on the platform maintained in a flat position with the help of the resin support. The prepolymeric mixture expanded spontaneously to cover the erased surface of the POF and of the resin support. The whole structure was spun at a given spin rate, typically at 1000 or 500 rpm for 2 min, and then placed in an oven for 16 h at 75 \({}^{\circ}\)C for thermal polymerization in air. Finally, the template and oligomeric polymer fragments were removed by repeated washings with 96% ethanol.  
Layers with different thicknesses could be obtained by spinning the prepolymeric mixture at different rates or by depositing multiple layers of MIP. The thickness of the MIP layer is particularly relevant when liquids with high RI are examined.
## 2 Related Work
### 3 DGS-Based SLAM  
Compared to NeRF-based SLAM, 3 DGS-based SLAM not only benefits from training with various loss functions but also leverages 3 DGS's explicit representation, allowing for more effective scene editing and optimization. SplaTAM [27] utilizes a silhouette mask to capture scene density, quickly determines whether a region has been mapped previously, and expands the structured map by adding more Gaussian functions. GS-SLAM [26] employs an adaptive expansion strategy that adjusts existing regions from new perspectives by adding or removing noisy Gaussian volumes. This strategy dynamically extends 3D Gaussians to reconstruct the entire scene. MonoGS [28] derives the analytical Jacobian matrix of the camera extrinsics with respect to 3 DGS, enabling optimization of the camera pose and scene geometry. CG-SLAM [39] introduces a novel uncertainty-aware 3D Gaussian with a high consistency and geometric stability for efficient computation. It also features a new depth uncertainty model to optimize the tracking process, improving both tracking efficiency and accuracy. RD-SLAM [25] employs a generalized iterative closest point for pose estimation, enhancing tracking accuracy and speed. It also introduces a dual keyframe selection strategy for dense scene processing, achieving high-quality reconstruction results.
# Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal
## 1 Introduction
The rest of this article is organized as follows. The signal model under the condition of impure reference signal interfered by target echoes and its influence on traditional target detection are described in Section 2. Then, Section 3 presents the proposed reference signal clean-up method and target detection method, and the effectiveness of the proposed methods via the numerical simulations is demonstrated in Section 4. The conclusions are summarized in Section 5.
## 3 Description of Research Methods
### Kullback-Leibler Divergence  
KL divergence is a measure used to quantify the relative entropy between two probability distributions. It provides us with a means to quantify the information lost when approximating one probability distribution (usually the true distribution) with another (usually the model distribution). Mathematically, for the discrete probability distributions \(P\) and \(Q\), the KL divergence is defined as:  
\[D_{KL}(P\parallel Q)=\sum_{i}P(i)\log\biggl{(}\frac{P(i)}{Q(i)}\biggr{)} \tag{4}\]  
For continuous distributions, it can be expressed as:  
\[D_{KL}(P\parallel Q)=\int_{-\infty}^{\infty}p(x)\log\biggl{(}\frac{p(x)}{q(x) }\biggr{)}dx\]
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## Gas Sensor
### Graphene Gas Sensors with Different Working Principles
#### 2.2.2 FET Gas Sensor  
FET gas sensors detect the gas using the dependency of the current on the field strength. FET devices have three electrodes: the source (S), the gate (G), and the drain (D). The source-drain current (\(\mathrm{I_{DS}}\)) of the FET has a significant relationship with the gate voltage (\(\mathrm{V_{g}}\)), and there is a significant change in the gas detection. As for p-type semiconductor, an induced charge is generated in the semiconductor near the gate insulating layer to form a conductive channel under the influence of a negative gate bias (\(\mathrm{V_{G}}\)) [16]. Graphene exhibits a strong field effect [17]. Thus, graphene based FET sensors have become promising sensors. When a gas molecule is adsorbed on the surface of graphene, the local concentration of carriers changes, resulting in a change in \(\mathrm{I_{DS}}\)[18].
## 3 Results and Discussion  
### Material Selection  
Both Ti64 and MS1 exhibit excellent specific properties, however, while the exceedingly high strength of Maraging Steel MS1 may enable a lower mass to be used for a similar performance, its reduced ductility may degrade the fatigue capability of the component. Therefore, Ti64 was selected as the material of choice.