esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Production System Case Study  
Firstly, the goal of the case study, the scope, and the methodology were defined. The goal of the simulation case study was to design a generic simulation model for the quantification of cost-benefit parameters for each stage of the optimization sequence. For each specific improvement strategy, a new simulation model was generated in which the simulation logic was adapted according to the differences in the improvement sequence step to which the model was related. The scope of the study was to create:  
* A generic simulation model that would serve as a basis for developing specific simulation models. It would provide the required complexity level as well as implement the criteria for later comparisons.
* Specific simulation models for each main step in the optimization sequence determined by the data generated in Section 3, which included LM, SS, TOC, QRM, and AM (i.e., five simulation models plus a model into which no lean method had been introduced). The scope of these models did not include all methods, techniques, and systems within the concepts, but only a selection of them.  
The hypothesis of the case study was:  
* The improvement of all relevant indicators along the improvement transformation line; this means that a better response of the target indicators is expected after each optimization step starting from the LM implementation and finishing with the AM implementation.  
For the methodological framework, the following steps were followed:  
1. Definition of the objective, scope, hypothesis, and methodology including a general description of target simulation models and scenarios;
2. Definition of the production system and its flow and characteristics;
3. Definition of quantitative parameters and key performance indicators (KPIs) to obtain results and compare models;
4. Development of the interrelationship among variables within the model;
5. Description of the main assumptions to simplify the complexity of the model;
6. Conditions that made a comparison between models possible;
7. Creation of the simulation models that were dependent on the improvement status;
8. Validation of the behavior of the simulation models;
9. Determination of scenarios;
10. Simulation and extraction of results;
11. Evaluation of the results and conclusions.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 2 Background Material
### Self-Supervised Speech Representation Learning  
Self-supervised speech representation learning (S3 RL) enables deep neural network models to capture meaningful and disentangled factors from raw speech waveforms [17]. S3 RL can be seen as a special case of unsupervised learning as both schemes learn without annotations. Although conventional unsupervised methods rely on reconstruction or density estimation objectives, S3 RL approaches rely on pretext tasks that exploit knowledge about the data modality used for training. Although supervised learning methods tend to learn stronger features than unsupervised learning approaches, they require costly and time-consuming work from human annotators to generate the required labels. S3 RL techniques aim for the best of both worlds: training a powerful feature extractor using discriminative learning without the need for manual annotation of training examples [19]. The learned universal speech representations can then be used across numerous tasks [20].  
In the context of S3 RL, \"upstream models\" are designed for pre-training and learning general representations from unlabeled data; in essence, they serve as \"feature generators\" for downstream tasks. The goal is to capture high-level features and patterns in the input speech data. These models are trained on a large dataset without task-specific labels. In turn, the \"downstream models\" are task-specific models fine tuned on labeled data for a particular speech-related task, such as speech recognition or emotion classification. Downstream models are trained on a smaller dataset with task-specific labels. The pre-trained upstream model is thus fine tuned on this data to adapt its learned representations to the specific requirements of the target task.  
While several universal representations exist (e.g., wav2 vec, wav2 vec 2.0, wavLM, and HuBERT), here we will focus on the HuBERT representation, as several variants have been proposed in the literature recently to either make it more robust to environment noise or to build more robust compressed versions for edge applications. This allows for several benchmark methods to be used for comparisons with the proposed method. It is important to emphasize that while the results reported herein will be based on a HuBERT representation, the proposed environment-aware method is applicable to any speech representation.
###### Abstract  
(1) Background: The study involves an assessment of the frequency of selected gene variants related to folate uptake and distribution (_FOLRI_ rs2071010, rs630074, _FOLHI_ rs61886492, _GGH_ rs11545078, rs3758149 and _SLC19A1_ rs1051266) in a group of women with fetal demise in the Polish population. (2) Methods: A total of 310 subjects were enrolled in the study. There were 110 females with idiopathic recurrent miscarriages (RM), 80 with stillbirth (IUFD) and 120 healthy controls. Designated SNVs were determined by using PCR-RFLP methods. The difference in fetal demise prevalence was assessed using a chi-square test and logistic regression analysis. (3) Results: The rs630074 variant of the _FOLRI_ gene is associated with a statistically significant increase in the risk of IUFD in a recessive model (OR = 2.03, 95%CI: 1.06-3.90, \(p\) = 0.033). The rs61886492 variant f FOLHI is linked to an increased risk of IUFD in co-dominant (\(p\) = 0.030), dominant (OR = 2.62, 95%CI: 1.07-6.38, \(p\) = 0.032) and log-additive models (OR = 2.64, 95%CI: 1.15-6.06 \(p\) = 0.030). In female carriers of the \(A\) allele, the risk of IUFD was 2.8 times higher compared to the control group. No relationship between the mother's genotype and the newborn's birth weight or placental weight was observed for the studied SNVs. (4) Conclusions: Our study finds that the rs61886492 variant of the FOLHI gene is associated with IUFD in Polish women. However, pregnancy failures have a multifactorial pathology and other genetic or environmental factors may also contribute to their complex etiology. Further research, preferably with larger groups of women from different ethnic backgrounds, is needed to confirm the results of the current study.  
recurrent miscarriage; folate uptake and distribution; gene variants +
Footnote †: journal: applied sciences
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
## 4 Simulation Results  
To validate the potentiality of the proposed FOCV MPPT algorithm, the proposed system is simulated with an electrically modeled PV cell that has the value of \(V_{OC}\) and \(I_{SC}\), 3.79 V and 100 \(\mu\)A, respectively. Furthermore, \(\alpha\) is set in 8-bit resolution in the proposed FOCV circuit for the 99.6% tracking efficiency of \(\alpha\) The Cadence Spectre with TSMC 0.18 \(\mu\)m process parameters was used for SPICE (Integrated Circuit Emphasis) simulation. Figure 14 shows the simulation results of the operating voltage, \(V_{PV}\), \(I_{PV\_V}\), \(I_{PV\_PWM}\), \(P_{PV\_PWM}\), and \(P_{\max}\), and the maximum power point tracking in the time domain of the proposed FOCV MPPT algorithm.  
Figure 15 shows the simulation result of the comparison of the energy loss between the conventional and the proposed FOCV MPPT under varying \(\alpha\). The total simulated period was 4 h and the refreshing period of the proposed FOCV MPPT was 30 min; the \(\alpha\) varies every 1 h. The energy loss of the conventional and the proposed FOCV were 356.37 mWs and 0.09 mWs, respectively. The energy loss of the proposed FOCV decreased by 99.97% compared to that of the conventional FOCV. The simulated result shows that the proposed FOCV can reduce the energy loss tremendously compared to the conventional FOCV.  
Figure 14: Simulation results of the maximum power point tracking for the proposed FOCV MPPT.  
Figure 15: Simulated results of the energy loss for the conventional FOCV and the proposed FOCV MPPT under varying \(\alpha\).
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### Conventional MIMO Detection
#### 3.1.2 Zero-Forcing Detector  
The ZF detector attempts to eliminate inter-stream interference by computing the pseudo-inverse of the channel matrix:  
\[\mathbf{\hat{s}}=(\mathbf{G}^{T}\mathbf{G})^{-1}\mathbf{G}^{T}\mathbf{r}. \tag{9}\]  
Each element of the resulting symbol vector is then quantized to the nearest constellation point:  
\[\mathbf{\hat{s}_{i}}=\arg\min_{b\in\mathcal{A}}|z_{i}-b|. \tag{10}\]  
However, ZF suffers from noise enhancement, particularly when \(\mathbf{G}\) is ill-conditioned [3].
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 1 Introduction
The fuzzy inference system is able to define a complex relationship as the membership degree or fuzzy subset, and then evaluates whether the two-factor is closely related to data transmission through the method of defuzzification. To this end, this paper proposes an adaptive two-factor routing algorithm using curve-trapezoid CMFI [26], namely DTCM. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor), and then makes reasonable decisions on data transmission through CMFI. For the sake of comprehensively evaluating the impact of two-factor on data transmission, this paper proposes curve-trapezoid CMFI [27; 28; 29; 30], which determines the fuzzy uncertain relationship between two-factor and data transmission and evaluates the fuzzy control result by the de-deblurring component. DTCM is an extensible algorithm because of its high adaptability. Data packages will be transmitted from message carriers to their future two hops based on the proposed two-hop feedback mechanism. Overall, the main contributions of this paper are summarized as follows:  
1. To analyze the fuzzy uncertain impact of the remaining energy and cache on data transmission, CMFI has been proposed. As an adaptive and extensible method for energy and cache, CMFI optimizes the selections of the next hop from neighbors via three fuzzy components. \begin{table}
\begin{tabular}{c c} \hline \hline
**Routing Protocol** & **Implement of Details** \\ \hline Prophet & Frequency of encountering, cache space and contact duration \\ BubbleRap & Node community and centrality, limiting data duplicates \\ SCORP & Node sharing interest, number of encounters and contact duration, storage \\ DABBER & Energy consumption, storage and other routing metrics \\ ICMD & Cache management and node cooperation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison of wireless opportunistic network routing protocols. Different from traditional one-hop relay node selection [12; 15; 18], a novel two-hop feedback mechanism has been developed in this paper to consider not only neighbor nodes but also the second-hop nodes. 3. Our experimental results show that within an error interval of 0.05-0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon. The remaining sections of this paper are constructed as follows: In Section 2, the related works are introduced, and Section 3 develops the system model and gives the analysis of the algorithm complexity; the simulation evaluation and comparison analysis are introduced in Section 4. Sections 5 and 6 summarize this paper and outlooks the future work related to this field.
## 5 Results and Discussion  
### ResQhot 2.0 Design Results  
Figure 9 shows the final CAD design for ResQhot 2.0--a novel mobile robot stretcher bed with an inflatable neck securing device. ResQhot 2.0 consists of six main modules:  
* A differential-drive mobile base that provides mobility and flexible manoeuvrability in typical urban and indoor terrains.
* A stretcher bed tilting module that enables ResQhot 2.0 to adjust the optimal loading angle for safe casualty extraction procedures. This module is driven by a linear actuator, and a bar linkage mechanism adjusts the bed's tilting angle.
* A stretcher bed sliding module that enables the robot's stretcher bed to slide up and down in order to switch between the loading configuration (for loading a casualty) and the compact configuration (for general robot navigation and casualty transportation). This module consists of rail mechanisms at both sides of the robot's stretcher bed that allow the bed to slide smoothly along its frame and a lead-screw mechanism that drives the bed's linear movement.
* A motorised stretcher bed conveyor module is essential in order to enable ResQhot 2.0 to gently load a casualty by synchronising the conveyor belt's loading movement with the movement of the mobile base (see the loco-manipulation approach presented in [19; 23]).
* A pair of motorised stretcher strap modules that enable ResQhot 2.0 to secure, stabilise, and immobilise the casualty on the robot's stretcher bed in order to prevent any undesired movement that could cause additional harm to the casualty.
* A neck securing device module that consists of a rigid frame and several components that inflate and surround the casualty's head and neck up to the shoulders in order to prevent any undesired impact during the casualty loading procedure. It also stabilises and immobilises the casualty's head and neck during the extraction process.
# Heart Attack Detection in Colour Images Using Convolutional Neural Networks
## 3 Methodology  
The objective of this work is the development of an algorithm to automatically detect when a person is having a possible heart attack by analysing images using CNNs. This work followed the steps described next and depicted in Figure 1: (1) generation of a set of images to be used for training, validation, and testing; (2) design of CNNs in charge of the identification of heart attacks; (3) training and validation of the CNNs; and, (4) performing of tests to determine the accuracy of the system. It is important to clarify that steps (3) and (4) were repeated until satisfactory results were achieved, as shown in the figure.
# Impact of Multiple Beams and Mobility-Based Beam Alignment Error on Millimeter Wave Communication +
## 4 Numerical Results and Discussion  
In this section, we solve the derived expressions numerically using MATLAB according to the system parameter listed in Table 1.  
In Figure 1, we analyze the impact of varying cell radii (\(r_{c}\)) and \(v\) for different \(n\). It is observed that for both \(r_{c}=50\) and \(100\) m, as \(n\) increases, the beamforming gain is increased and the interference at BS is decreased (due to narrow beamwidth). Hence, CP improves initially and reaches a maximum value, giving an optimum number of beams. However, with a further increase in \(n\), the probability of beam misalignment at UE increases, and hence CP decreases. It can be noted that the optimal \(n\) required at higher \(r_{c}\) is lower compared to that required for lower \(r_{c}\), which represents a dense network with more interference, hence requiring more beams. Additionally, with the increasing \(v\), the beam misalignment probability of UE as well as the beam (re)selection and BS handovers are increased. Hence, an increasing \(v\) decreases the optimum value of \(n\) and for a given \(n\), the CP decreases.  
In Figure 2, we compare the UE with multiple beams \(k=2\) and \(4\) with a single beam UE, i.e., \(k=1\). It can be seen that an increased UE beam at \(k=4\) provides the best CP due to increased beamforming gains, even though at increased \(k\) the probability of beam misalignment at the UE is increased. However, when \(k=2\), as \(n\) increases, a small gain in CP performance is obtained as compared to a single beam due to the overhead associated with the SSB measurement of multiple beams.
## 5 Experimental Validation and Benchmarking
### Experimental Validation of Design 1  
The prototype of Design 1 including the FSS wall and the remaining components as described earlier is shown in Figures 11b and 18a. The S-parameters of the MIMO system are tested using the 10 MHz to 40 GHz vector network analyzer PNA-X N5242A (Agilent Technologies, Santa Clara, CA, USA). Figure 18c shows the comparison between the simulated and measured S-parameters. It can be seen that the measured mutual coupling is below \(-22\) dB at 24 GHz, and it is further improved beyond 26 GHz, achieving less than \(-40\) dB between 26.5 GHz and 27 GHz. The discrepancies between the two data sets, especially noticeable in the 24 GHz to 25.5 GHz frequency range, result from the inaccuracies of the fabrication and manual assembly process. The FSS wall with metallic strip is inserted between the two monopole antennas using glue, which also contributes to the mentioned misalignments. The deviations of the electrical properties of the substrate material and the connector from their nominal values at millimeter wave frequencies constitute another reason for the discrepancies between the simulation and measurement data. A comparison of simulated and measured radiation patterns can be found in Figure 19. Also in this case, the agreement between the two data sets is satisfactory.  
Figure 18: Fabricated prototype of Design 1, (**a**) perspective view, (**b**) back view, (**c**) simulated and measured S-parameters.
## 4 Nested Computational Grid  
PALM provides the capability of computational grid self-nesting. In the self-nesting mode, there is a root/parent computational domain with the ability of nesting up to 63 levels of nested/child computational subdomains. Each subdomain may be root/parent to another nested subdomain. As a result, the second subdomain is simultaneously nested referring to the first parent, and also root, to the third nested. The nested computational subdomain receives all the appropriate and mandatory information for the three components of the velocity and all the prognostic variables of the vectors from its boundaries with the root computational domain. The flow field data are interpolated from the coarse to the finer computational grid. By the end of each time step, the corrected solution from the solver is reversely interpolated to the root/parent domain.  
Figure 5 shows the grid arrangement used in the simulations. The root computational grid (coarse grid) is shown at full height of the computational domain in contrast with the nested (finer grid-darker parts), which is shown at two building heights.  
For the simulations, three different computational grids are used with an incremental resolution from the coarse to the finer: the coarse computational grid with 924,400 cells, the medium grid with 1,248,400 cells, and the fine grid with 1,842,400 cells. The grid errors using two computational grids are estimated from the following equation [4]:  
\[GCI=\frac{f_{2}-f_{1}}{1-r^{p}} \tag{26}\]  
where \(f_{2}\) is the numerical solution that results from the medium computational grid and the corresponding from the finer is \(f_{1}\). \(r\) is the refinement factor between the two computational grids and \(p\) is the accuracy of the algorithm, which is 3 for the present study. The refinement factor between the medium and finer computational grid is approximately 1.47.  
Figure 5(a) presents the profile of the error bars of the normalized velocity, at the axial position 0.5 H from the rear surface of the building for scenario A. Additionally, Figure 5(b) shows the profile of the normalized concentration of the pollutant at the same axial position and scenario.  
The mean value of the errors of the normalized velocity between the coarse and the medium computational grids is approximately 3.77% and the corresponding value between the medium and the fine grids is about 1.54%. The mean value of the errors of the pollutant between the coarse and the medium computational grids is approximately 2.77% and the corresponding value between the medium and the fine grids is about 1.24%. For both of the aforementioned variables, the decrement of the errors going from the coarse to the finer computational grids is clearly observed.  
Figure 5: Root and nested computational domains of the flow field.
## 2 VSI-Derived CSI SVM Modulator
### Switch-State Mapping and Implementation  
A critical aspect of this modulator is the clear mapping between VSI switching states and CSI switching states as originally introduced and explained by [7] as shown in Table 1 below, while Figure 5 illustrates the switch operation pattern [33]. Researchers and engineers can evaluate and improve the performance of these inverters in a variety of applications due to this mapping, which enables simulation models to accurately represent the switching behavior of CSIs. Consistency between theory and practice is ensured by the table's notation, which includes switch states (e.g., S1, S2), corresponding to the simulation models and theoretical framework offered in this study.  
The relationships for the CSI vectors using VSI can now be determined from Table 1 and simplified using the Boolean Algebra AND-Distributive Law, which states that \(A+BC=(A+B)(A+C)\) as shown in Equation (A1) in Appendix A.  
The equations from (A1) are used in PLECS Blockset 4.9, shown below in Figure 3. This method uses reliable SVM techniques from VSIs, which have been adapted for CSI operations, simplifying the modulation procedure. The analog input signals obtained from the VSI modulator are utilized to produce the necessary switching patterns for the CSI [7]. This method reduces the complexity of the CSI modulation process. The figure highlights the fundamental components of the modulator, including the analog input signals and the associated switching patterns, providing a clear visual description of the application of VSI-derived modulation to the CSI.