esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 5 Conclusions  
The article presents a method of optimizing the performance of the oxygen generator using PSA technology. It was experimentally found that the low-capacity adsorption oxygen generators currently offered for sale are not optimized in terms of energy consumption or efficiency. The measured energy consumption of a small oxygen generator at factory settings was, on average, 2490 kWh/t with an oxygen separation capacity of 0.6 m\({}^{3}\)/h.  
The proposed optimization consisted of changing the recipe for conducting the separation process. Two key conditions for the optimal separation process have been shown. Firstly, it is necessary to maintain an appropriate pressure difference between the adsorption pressure and the pressure inside the oxygen tank. For the maximum oxygen purity, this difference should not be larger than 0.5 bar. Secondly, to minimize the energy consumption of the oxygen separation process, the energy consumption of peripheral devices should be reduced in relation to the energy consumption associated with the separation itself. For this reason, adsorbers should always operate at maximum capacity for given pressure conditions. It means that an appropriate adsorption pressure should be selected for the required yield of the oxygen separation process.  
Based on the presented analysis of the experimental results, it was found that it was possible to optimize the energy consumption of the low-capacity adsorptive oxygen generator. As a result of the applied optimization, energy consumption of an average value of 1460 kWh/t was obtained in a wide range of received oxygen stream from 0.7 m\({}^{3}\)/h to 1.1 m\({}^{3}\)/h. Compared to the reference measurements, the optimization effects the reduction in the energy consumption of oxygen separation is about 40% with a possible increase in the maximum oxygen capacity by about 80%. The limitation of this method is the changing pressure in the oxygen tank according to the changing generator capacity.  
The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.  
Figure 7: Optimized oxygen separation energy consumption.  
* [PERSON] and [PERSON] (2013) [PERSON]; [PERSON] [PERSON] Comparison of two pressure swing adsorption processes for air separation using zeolite 5A and zeolite 13X. _Pet. Coal_**2013**, _55_, 216-225.
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] [PERSON] Energy Consumption of Air-Separation Adsorption Methods. _Entropy_**2018**, _20_, 232. [CrossRef]  
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## 2 Data and Methods
### Data Processing and Evaluation Metrics  
We used the Oceanview 6.0 software for hardware control and we needed to set the integration time before collection. The integration times of the micro-spectrometer for measuring radiance and measuring irradiance are described in detail in Section 3.1. The micro-spectrometer and Trios both measure different floating-point wavelength data; therefore, we needed to resample the Trios and STS-VIS spectrometer data to the same integer wavelength for field radiation calibration. The cubic spline interpolation method has a small error (we have also compared the results of the other three resampling methods plotted in Figure A2 Appendix A). Thus, we used this method and obtained a result that is very small, as shown in Figure 2.  
The following statistics were used to evaluate the spectral results, including the correlation coefficient (r), bias and mean absolute percentage difference (MAPD).  
\[\mathrm{r}= \frac{\sum\limits_{i=1}^{n}\left(\mathrm{S}_{i,1}-\mathrm{S}_{1} \right)\left(\mathrm{S}_{i,2}-\mathrm{S}_{2}\right)}{\sqrt{\sum\limits_{i=1}^{ n}\left(\mathrm{S}_{i,1}-\mathrm{S}_{1}\right)^{2}+\sum\limits_{i=1}^{n}\left( \mathrm{S}_{i,2}-\mathrm{S}_{2}\right)^{2}}} \tag{4}\] \[\mathrm{bias}=\left(\mathrm{S}_{i,1}-\mathrm{S}_{i,2}\right)/ \mathrm{S}_{i,2}\times 100\%\] (5) \[\mathrm{MAPD}=\frac{1}{\mathrm{N}}\sum\limits_{i=1}^{n}\left| \left(\mathrm{S}_{i,1}-\mathrm{S}_{i,2}\right)/\mathrm{S}_{i,2}\right|\times 1 00\% \tag{6}\]  
where \(\mathrm{S}_{i,1}\) and \(\mathrm{S}_{i,2}\) denote the DN values corresponding to the measured diffuse reflectance plates, the gain and offset observed under different conditions and the R\(rs\) measure with the Trios and micro-spectrometer.  
In this article, we compare the effects of different fibers on the gain and offset, which is followed by a quantitative analysis to measure the spectra of different diffuse reflectors  
Figure 2: Comparison of raw and resampled data.  
with clear water and turbid water, and finally, we perform a first application demonstration in an unmanned area.
## 2 Vehicle Modeling for State Estimation  
A planar two-track vehicle model is presented in this section. It is a 3-DOFs vehicle model, which contains the longitudinal velocity \(u\), the lateral velocity \(v\), and the yaw rate \(r\). Figure 1 shows the vehicle model and coordinate systems. The wheel positions are numbered with the subscript _ij = fl,rl,fr,rr_ denoting front left, rear left, front right and rear right, respectively.  
Figure 1: Planar vehicle model and coordinate systems.  
The following assumptions are made before the modeling work:  
1. Vehicle is moving on a flat horizontal plane;
2. Vertical, roll and pitch dynamics are omitted;
3. longitudinal acceleration, lateral acceleration and the yaw rate is measured with white Gauss noise;
4. INS sensors are mounted on the vehicle COG.
## 4 Case Study
** & **Rec. ** & **F1** \\ \hline RNN-AE ([PERSON] et al., 2014 [17]) & 0.6880 & 0.5695 & 0.6232 & 0.3514 & **0.6026** & 0.4439 & 0.2347 & **0.6093** & 0.3389 \\ LSTM-AE ([PERSON] et al., 2020 [18]) & 0.5316 & 0.5563 & 0.5437 & 0.2794 & 0.5828 & 0.3777 & 0.2083 & 0.5960 & 0.3087 \\ Bi-LSTM-AE ([PERSON] et al., 2022 [16]) & 0.5833 & 0.5563 & 0.5695 & 0.3560 & 0.5894 & 0.4439 & 0.2507 & 0.5960 & 0.3529 \\
**LSTM-WMCs-AE** & 0.4852 & 0.5430 & 0.5125 & 0.3347 & 0.5497 & 0.4160 & 0.2251 & 0.5695 & 0.3227 \\
**Bi-LSTM-WMCs-AE** & **0.8462** & **0.5828** & **0.6902** & **0. Although the training time of our proposed model is longer than that of the baseline models--specifically, models utilizing this Working Memory Connections for LSTM take more time to fit compared to those based on conventional LSTM units--we argue that, with advancements in hardware, such computational resource consumption can be deemed acceptable, especially in applications where accuracy is critical, such as in nuclear power plant scenarios. Moreover, the difference in computational resource requirements is predominantly observed during the model training and fitting phase, with only negligible differences in the anomaly detection phase. Additionally, in real-time application scenarios, where each input consists of a single time window, the decision-making time for all compared models converges to nearly zero.
# Modeling the Dynamics of Two Cooperating Robots
## 3 Description of the Dynamics of Two Robots Transporting a Beam  
Type II Lagrange equations were used to describe the dynamics of the robot formation, which in the vector notation take the form:  
\[\frac{d}{dt}\left(\frac{\partial\mathbf{E}}{\partial\dot{\mathbf{q}}}\right)^{T}-\left( \frac{\partial\mathbf{E}}{\partial\mathbf{q}}\right)^{T}=\mathbf{Q}+\mathbf{J}^{T}(\mathbf{q}) \mathbf{\lambda}, \tag{5}\]  
where \(\mathbf{E}\) is the kinetic energy of the system, \(\mathbf{Q}\) is the vector of generalized forces, and \(\mathbf{\lambda}\) is the vector of Lagrange multipliers. The right-hand sides of Lagrange's equations were determined based on the principle of virtual work. The virtual work \(\delta L\) performed by the forces acting on the formation of robots is equal to the sum of the virtual work forces acting on the individual members of the robot formation. \[\delta L=\sum_{i=1}^{4}\mathbf{F}\,\dot{\mathbf{r}}\,dt+\sum_{j=1}^{4}M\dot{\mathbf{ \kappa}}\,dt. \tag{6}\]  
Relationship (6) takes into account the sum of the virtual work performed in translational motion by the force vector \(\mathbf{F}\) and the sum of the virtual work performed by the vector of moments of forces \(\mathbf{M}\). The vectors \(\dot{\mathbf{r}}\) and \(\dot{\mathbf{\kappa}}\) are, respectively, the vector of the linear velocities of the individual members of the robot formation and the vector of their angular velocities in rotational motion. The external forces acting on the system are shown in Figure 2. Force \(P\) refers to the weights falling on the respective wheels, while force \(T\) refers to the forces of dry friction between the wheels and the ground in the respective directions. Figure 2: Distribution of external forces acting on the formation of robots. In the analyzed case, the virtual work is:  
\[\delta L=(M_{11}-N_{11}f_{11})\delta\alpha_{11}+(M_{21}-N_{21}f_{21})\delta\alpha _{21}+(M_{12}-N_{12}f_{12})\delta\alpha_{12}+(M_{22}-N_{22}f_{22})\delta\alpha_{ 22}, \tag{7}\]  
where \(M_{ij}\) is the moment driving the \(i\)-th wheel of the \(j\)-th robot, \(N_{ij}\) is the pressure on the \(i\)-th wheel of the \(j\)-th robot, \(f_{ij}\) is the coefficient of rolling friction of the \(i\)-th wheel of the \(j\)-th robot, and \(\delta\alpha_{ij}\) is the virtual displacement of the \(i\)-th wheel of the \(j\)-th robot. The virtual work can also be expressed in the form:  
\[\delta L=\sum_{k=1}^{n}Q_{k}\delta q_{k}, \tag{8}\]  
where \(Q_{k}\) is the \(k\)-th generalized force, and \(q_{k}\) is the \(k\)-th generalized coordinate. After comparing Equation (7) to Equation (8) and taking into account the vector of generalized coordinates, the vector of generalized forces was obtained:  
\[\mathbf{Q}= \begin{bmatrix}0\\ 0\\ 0\\ 0\\ M_{11}-N_{11}f_{11}\\ M_{21}-N_{21}f_{21}\\ 0\\ M_{12}-N_{12}f_{12}\\ M_{22}-N_{22}f_{22}\end{bmatrix} . \tag{9}\]  
The vector of the Lagrange multipliers is written as:  
\[\mathbf{\lambda}= \begin{bmatrix}\lambda_{1}&\lambda_{2}&\lambda_{3}&\lambda_{4}& \lambda_{5}\end{bmatrix} ^{T}.
# Automatic Mobile Warning System against People with Elevated Body Temperature
## 2 Materials and Methods
### Assumptions of Proposed Method for Localization the People with Elevated Body Temperature  
At the testing stage, several distance ranges were checked: 0.25 to 2.0 m every 0.25 m, and 1 to 3 m every 1 m. As a result of preliminary observations and measurements, obtaining accuracy of 0.25 or 0.5 m using available mobile thermal cameras was very difficult or even impossible. However, it was possible to determine the distance to a dangerous object with accuracy of 1 m. The basic assumptions that ensured the proper operation of the automatic system for determining distance to people with the highest temperature, and the limits are discussed below.  
Requirements:  
* mobile thermal camera with a minimal frequency of 8 fps;
* thermal camera mounted at chest height;
* for measuring distance, small sideways movements of the camera or the observed person;
* small movements due to the slow walking speed of the camera user or the observed person. Purposes:
* remote noncontact temperature measurement and localization of people with increased or high temperatures;
* automatic image analysis directly on a mobile device;
* no need to control and manipulate the device;
* estimating distance to a dangerous object and distance classification in ranges of 1, 2, and 3 m;
* detection of forward-, left-, or right-facing profiles;
* use of the application in closed spaces with limited influence of ambient temperature;
* the detection and tracking of one person with the highest temperature in the area of the head moving towards the thermal camera.  
Limits:
* if the image of the warm objects stops moving, the system displays the last measured distance value;
* values of the linear acceleration of the smartphone with a camera not exceeding 0.7 m/ s\({}^{2}\) in the X and Y axes for slow walking.  
Figure 2: Examples of thermograms used in study— static hot object—training set.  
Meeting these requirements allowed for preparing a system that determines the approximate distance to a person with elevated temperature using a single thermal camera. The attachment of the devices is illustrated in Figure 3. For convenience, user's comfort, and device dimensions, a good and practical solution seems to be mounting the devices onto the chest. Body temperature does not affect the camera measurement because the smartphone with the thermal camera is packed in one casing.  
The threshold temperature value of 35 \({}^{\circ}\)C was adopted on the basis of previously described temperature values with different levels of hazards for health and life. Higher temperature objects or areas are detected and tracked by the system. For the detection of a human figure (head), a temperature threshold of 35 \({}^{\circ}\)C was adopted, whereas for the detection of a person with elevated body temperature, the threshold was 38 \({}^{\circ}\)C.
# Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition
## 2 Background
### Robot Conversation Strategy Exploiting Entrainment Phenomena  
To improve the performance of indicated object recognition, humans should use as much useful information as possible when referring to an object to uniquely identify the object based on it attributes, such as its color, form, and name. For example, if humans refer to an object with speech that contains many of the object's attributes, the robot's speech recognition could be robust to the failure of speech section detection and noisy speech. Additionally, if humans refer to an object using pointing gestures, the robot could narrow down the candidates of the indicated object based on the direction of the pointing. Hence, the desirable reference behavior to improve the performance of indicated object recognition is speech that contains as much useful information as possible to identify the object and pointing gestures (hereinafter known as a _redundant reference_). Considering the three abovementioned entrainment phenomena, the following paragraphs summarize the approaches and their reasons to elicit a redundant reference from humans:  
Lexical Entrainment  
Humans tend to align their speech with their interlocutors. Therefore, robots should talk with useful lexical expressions to identify an object because humans will come to use the same or similar expressions.  
Gestural Entrainment* Humans tend to align their gestures with their interlocutors and their gestures increase. Therefore, robots should use pointing gestures because humans will repeat the pointing gestures. Entrainment Inhibition
* If robots talk with many lexical expressions, humans tend to speak with fewer lexical expressions. Therefore, although robots should talk with useful lexical expressions to identify an object, they should avoid using too many lexical expressions because humans will decrease their use of lexical expressions in response.
* When aligning with robots' pointing gestures, humans tend to use fewer lexical expressions in their speech. Therefore, robots should avoid using pointing gestures in situations where the pointing gestures are not useful for identifying an object because humans will decrease their verbal expressions.  
Therefore, to improve the performance of the indicated object recognition, robots should provide minimum information needed to identify an object, and use pointing gestures when the pointing gestures are useful to decrease the number of candidates for the object being referenced. In other words, robots should align their speech with that of humans, which contains useful information to identify an object, and use gestures considering entrainment inhibition. Robots could thus elicit a redundant reference, and the performance of indicated object recognition could improve.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 4 Hardware Implementation
### Hardware Architecture
#### 4.2.1 Architecture Overview  
The look-up tables (LUTs) corresponding to the trained mapper as well as the trainable parameters of the demapper are extracted from the PyTorch design and included in the hardware implementation. In general, both inference and training module are designed as pipelined architecture using Vivado high-level synthesis (HLS) 2019.2 and parts of Xilinx FINN library [37]. Due to the low-complexity, ANN topology with only 388 trainable parameters, all weights can be stored on-chip, resulting in minimal memory overhead and reduced latency. All fully connected layers are implemented as separate hardware modules and operate in a pipelined fashion; thus, a layer can already start its calculation after the first inputs are received. In this way, the overall latency of the complete module is approximately the same as the latency of the slowest layer. The hardware modules for training and inference access the input and output data in DRAM by using memory-mapped AXI interfaces that are internally converted to AXI streams. The DOP can be adjusted for each layer individually to trade-off resource usage against latency. It can be modified by setting the parallelism on a fine-grained level (single instruction multiple data (SIMD)), corresponding to the number of inputs that one neuron calculates in parallel, as well as on a coarse-grained level (processing element (PE)) specifying the number of neurons that are concurrently processed. The DOP can be separately set for the inference and training module to optimally fit the needs of the application and meet the given constraints. To reduce memory resources and computational complexity, we implement all datatypes using a fixed-point format with arbitrary width for fraction and integer part. The integer bit-width for the activations, MAC units, and gradients are adjusted to cover the full range of values, which is determined by simulating 10,000 input samples. The fraction bit-width is set to the minimal value without a loss in accuracy. In particular, the activations are quantized between 11 and 15 bits, the weights to 9 bits, and the gradients to 13 bits.  
For the inference module, the input is propagated through the fully connected layers by multiplying with the corresponding weights and adding up the bias to produce the output. As the implementation of ANN-training on FPGA has higher implementation complexity compared to the inference, it is explained in more detail in Section 4.2.2. It is to note that in theory, it is also possible to discard the inference module and use the forward-pass of the training module for inference. In contrast, we have decided to use separate modules for two main reasons:  
1. It allows us to set the DOP separately for inference and training module to partition the resources according to the requirements as described in Section 4.3;
2. We are able to operate on a reduced bit-width during inference as compared to training, to save power and energy during inference while keeping high precision during training.
## 3 Dynamic Response for an Accessory Structure Equipped on the Primary Structure  
### Theoretical Model  
The vibration problem of a complex multi-degrees-of-freedom (MDOF) system can be transformed into that of a single-degree-of-freedom (SDOF) system using the mode decomposition technique, so that the calculation will be much more convenient. Therefore, regarding the arch dam as the primary structure and the hydraulic gate as the subsidiary structure, respectively, a simplified representation for the complex MDOF dam-gate system is given in Figure 9.  
The dynamic equations for the above two-degrees-of-freedom system can be written in following form:  
\[m_{1}\ddot{x}_{1}+c_{2}\Big{(}\dot{x}_{1}-\dot{x}_{2}\Big{)}+k_{2}(x_{1}-x_{2 })+c_{1}\dot{x}_{1}+k_{1}x_{1}=P_{1}(t)+P_{2}(t) \tag{1}\]  
\[m_{2}\ddot{x}_{2}+c_{2}\Big{(}\dot{x}_{2}-\dot{x}_{1}\Big{)}+k_{2}(x_{2}-x_{1} )=P_{2}(t) \tag{2}\]  
where, \(m_{1}\), \(c_{1}\) and \(k_{1}\) denote the mass, damping and stiffness for the primary structure, respectively; \(m_{2}\), \(c_{2}\) and \(k_{2}\) denote the mass, damping and stiffness for the subsidiary structure, respectively; \(\ddot{x}_{1}\), \(\dot{x}_{1}\) and \(x_{1}\) represent the absolute acceleration, velocity and displacement for the primary structure, respectively; \(\ddot{x}_{2}\), \(\dot{x}_{2}\) and \(x_{2}\) represent the absolute acceleration, velocity and displacement for the subsidiary structure, respectively; \(P_{1}(t)\) and \(P_{2}(t)\) represent the time-variant external force on the primary and subsidiary structures, respectively; and \(t\) denotes the time. Considering that the system is excited by the harmonic force, the external excitations \(P_{1}(t)\) and \(P_{2}(t)\) can be expressed as:  
\[P_{1}(t)=F_{1}e^{\dot{x}_{1}\omega_{0}t} \tag{3}\]  
\[P_{2}(t)=F_{2}e^{\dot{x}_{2}\omega_{0}t} \tag{4}\]  
Figure 9: A two-degrees-of-freedom system consists of primary and subsidiary structures. where \(\omega_{0}\) denotes the frequency of the external excitations; \(i\) represents the imaginary unit; and coefficients \(F_{1}\) and \(F_{2}\) denote the amplitudes of the external harmonic forces. It is noted that the subsequent derivation will be extremely difficult if the frequencies of excitations \(P_{1}(t)\) and \(P_{2}(t)\) are assigned different values. In order to facilitate the analysis of the dynamic response of the aforementioned system, it is assumed that the external excitations generated by the high velocity discharge flows in the surface and mid-level orifices have similar frequency distributions and the values of the frequencies for excitations \(P_{1}(t)\) and \(P_{2}(t)\) are equal to each other.