esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Solution
### Detector Bank Based on Simplified Welch Algorithm
#### 3.2.3 Detector Bank  
Parameters of each interferences must be extracted and saved separately, a \"Detector Bank\" is used to achieve this target. The number of detector blocks is not limited and  
Figure 5: Simple scenario for grouping interferences.  
Figure 6: Utilized mechanism for activating each detector for an interference.  
one can add detectors as required to the design (Figure 7). However, after completing the design and synthesizing, the maximum number of detected interferences is limited to the number of embedded detectors. For instance, if the design is equipped with three detectors, then the system is capable to detect a maximum of three interferences. As it can be seen, this block has four inputs: enable pulse, estimated amplitude of power spectrum, index of spectrum samples, and data ready pulse.  
Now, let us look at the next stage to extract the characteristics of each interferences (Figure 7). The previous block makes the system capable of extracting the pulse that determines the start, end, and the interval of each interference. This pulse is utilized in Figure 7 to extract all desired parameters. There are two registers that work with the rising and falling edge. The rising edge register saves the start frequency of interferences \(f_{1}\) and the falling edge register saves the end frequency of interference. The accumulator works with the activator signal level. During the time interval that enables, the signal is high, the accumulator gathers the values of the estimated spectrum samples. This accumulation simply calculates the \(\sum\limits_{v}PSD(v)\), which is the main parameter for average power calculation. There is also a counter in each detector that counts from zero to one when it receives the activating pulse. The output of this counter is utilized as the \"select\" signal for multiplexer. When there is no activation pulse, the counter is reset to zero by the data ready pulse and related notch filter is removed by the multiplexer from the reception chain.  
The last layer of registers helps the system to save the extracted characteristic of interference and update them for each estimated packet of PSD.
## 2 Current Market Approach
### Advanced Product Quality Planning  
Advanced Product Quality Planning (APQP) is the development methodology used mainly by the automotive industry to align on a set of procedures and generic templates to reduce the amount of time spent on documentation and to be able to re-use cross-products the documentation when possible [9]. The methodology was created by Chrysler, Ford, and General Motors, USA companies, to provide guidelines for developing a product quality plan that supports the development of a product or service that will satisfy the customer [9; 10; 11].  
Figure 3: FDD development cycle—part of Agile methodologies.  
APQP determines the steps necessary to provide a satisfactory product for the customer, based on a structured method centered on product quality planning, divided into five overlapping phases in Figure 4[8].  
APQP phases are divided and scoped like [8,9]:  
1. Plan and define programme--planning is elaborated based on customer centricity and associated needs to define the common product understanding;
2. Product design and development verification--Design must be frozen and product feasibility assessments with the associated risks are communicated to all stakeholders;
3. Process design and development verification--Manufacturing capabilities are assessed, tooling vendors are nominated based on all product specifications and quality targets, while the cost and volumes desired are kept as a target;
4. Product and process validation--The testing phase for the product and its manufacturing is evaluated to estimate the process capability and the product performance versus the specifications;
5. Feedback assessment and corrective action--Emphasis is on evaluating and improving processes by reducing variations, issues identification, and corrective actions; everything is followed continuously until the product end of life.  
The product quality plan is unique for each customer, product, and process. Everything is based on the targeted launch date and implementation of product quality planning [8, 9, 10, 11].  
Core values generated, from the quality perspective between LEAN and APQP, are linked with the deep desire for quality improvement. We can say that in automotive engineering, both can be satisfied if the foundation of ISO 9001 and ISO/TS 16949 is mastered. The LEAN approach can be considered to survey and provide delivery patterns for on time and on quality delivery, and APQP is focused to ensure transversal documentation and enforcing the production capability and processes (partly also covered in LEAN). APQP helps attain the PPAP of the component with high-quality confidence and Lean will help the MSA improvement and process quality; therefore, Lean-6 sigma can be considered much closer to APQP rather than Lean alone.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 2 Access Sharing Models  
Sharing of access networks can be achieved through a number of different architectures and models. Typically there is a trade-off between the ability of Other Licensed Operators (OLOs) to control the level of service offered to their users and the complexity and amount of infrastructure that each OLO needs to deploy. For example, physical layer unbundling provides OLOs with the highest level of control, as they can tap directly into the end-user's physical transmission link, that is, in the case of copper-based Digital Subscriber Line (DSL) access. However, it requires the OLOs to have their own physical infrastructure deployed in (or near) each local Central Office (CO) where they intend to serve the customers.  
On the other hand, higher layer unbundling, like bitstream access, discussed in more details later, allows OLOs to collect their customer traffic at a small number of points of presence (POPs), thus reducing the amount of equipment deployed in the network. However, these models provide OLOs with very limited control over the type of services they can deliver to the users, as typically they can only offer a small number of broadband packages to their customers. The next sections delve into the details of the most popular access network sharing paradigms.
## 2 Literature Review
### Object Segmentation  
Object segmentation aims to extract different objects or targets from images or 3D data. The goal is to divide the input data into multiple semantic subregions or objects, each representing an independent entity in the image or scene. Currently, there are four main categories of objection segmentation methods: threshold based [30], edge detection based [31], region based [32], and deep learning based [33]. Similar to image semantic segmentation, more and more research is focusing on the application of deep learning in the object segmentation method. The main research can be divided into fully supervised [34], weakly supervised [35], and interactive 3D-object segmentation [36]. However, object segmentation through deep learning also requires a large amount of data support, especially for a point cloud data annotation with a dense 3D point cloud segmentation, which is more time-consuming. In addition, the accuracy (IoU) of object segmentation achieved through deep learning is currently not perfect [37].  
Octree is a typical spatial-based objection segmentation algorithm that presents advantages, such as efficient spatial utilization, rapid search ability, flexibility, visualization, and parallel processing when processing 3D spatial data [38]. It can efficiently process and manage 3D data, providing rapid search and segmentation outcomes, making objection segmentation tasks more efficient and accurate. The octree algorithm is currently applied in point cloud compression [39]), surface reconstruction [40], and other fields. This study achieved objection segmentation-based semantic segmentation results through octree, providing data support for the subsequent establishment of the monomer BIM.
###### Abstract  
The primary aim of this investigation was to assess the impact of varying the ratio of gum arabic to maltodextrin and employing diverse encapsulation techniques on the properties of the powdered substance and the capacity to retain the aromatic attributes of terephthal fruit oil. Distinct ratios of gum arabic to maltodextrin (75:25, 50:50, and 25:75) were employed to fabricate oil-in-water emulsions. The utmost stability of the emulsion was realized at a gum arabic to maltodextrin ratio of 75:25, characterized by a minimal creaming index and an even and small-scale dispersion. The encapsulation techniques employed included spray drying (SD), spray freeze-drying (SFD), and freeze-drying (FD). These methodologies were compared based on encapsulation efficiency, desiccation yield, powder attributes, and the capacity to retain aroma. The encapsulation efficiencies were notably higher (>90%) in SD, particularly with the application of an ultrasonic nozzle and a two-fluidized nozzle (2 FN), in contrast to those obtained through SFD and FD. Notably, SD employing an ultrasonic nozzle exhibited superior preservation of volatiles (73.19%) compared to FD (24.45%), SD-2 FN (62.34%), and SFD (14.23%). Among the various components, \(\alpha\)-pinene and linalool stood out with near-perfect retention rates, close to 100%.  
aroma; encapsulation; spray drying; spray freeze-drying; fruit oil +
Footnote †: journal: _foods_  
023
## 2 Proposed Interface Layer Using a CPW  
### Geometry of the Interface Layer Using the CPW  
Figure 1 presents the geometry of the interface layer using the CPW considering the connection part between the antenna and TRM in X-band radar systems. To enhance the transmission characteristics and minimize the leakage fields of the CPW, the proposed interface layer consists of three transition parts: \(A\), \(B\), and \(C\). Transition part \(A\) is located between the SMP connector and the CPW line of the interface layer. The SMP is a surface-mounted connector, and it is generally recommended with this connector to use a CPW without ground [24]. However, to enhance the transmission characteristics with minimum leakage of electric fields, vias should be densely inserted into the ground plane around the CPW. X-band radar systems typically use a small connector due to the narrow array distances of the antenna elements. Thus, the CPW line mounted on the SMP connector has a thin width of \(w_{1}\) and a gap of \(g_{1}\), as shown in Figure 1b. In contrast, the CPW line near the SMP connector has a greater width of \(w_{2}\) and a gap of \(g_{2}\) to prevent dropout of the inner metallic line when the feed-pin is connected to the CPW line using solder. Transition part \(B\) is the CPW, the thickness of which gradually increases between the thin and thick CPWs in the interface layer. The width of this transition line with a length of \(d_{1}\) is gradually varied from \(w_{1}\) to \(w_{2}\) to obtain suitable impedance-matching characteristics. Finally, transition part \(C\) is implemented between the ground of the CPW and the feed-pin, as shown in Figure 1c. The transition part \(C\) comprises the feed-pin radius (\(r_{\rm f}\)) and the radius of the perforated part of the ground plane (\(r_{\rm d}\)). The transmission characteristics of the interface layer can be further enhanced by adjusting the ratio of \(r_{\rm f}\) to \(r_{\rm d}\). The proposed interface layer using the CPW uses an FR-4 substrate (\(\epsilon_{\rm r}=4.4\), \(tan\)\(\delta=0.027\)) with a thickness of \(h_{1}\), and it is designed using a FEKO full electromagnetic simulator [25]. The detailed design parameters of the proposed interface layer using the CPW are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value (mm)** \\ \hline \(h_{1}\) & 0.8 \\ \(w_{1}\) & 0.75 \\ \(w_{2}\) & 1.5 \\ \(g_{1}\) & 0.1 \\ \(g_{2}\) & 0.37 \\ \(d_{1}\) & 0.85 \\ \(d_{2}\) & 1.35 \\ \(r_{\rm f}\) & 0.22 \\ \(r_{\rm d}\) & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal design parameters of the interface layer.
## 2 Materials and Methods  
### Fresh Biomass of T. virens and T. viride  
The fungi _T. virens_ (CP1) and _T. viride_ (CP4) were previously isolated from rhizosphere soil of mesquite (_Prosopis_ sp.) in the state of Jalisco, Mexico [54]. The two strains of _Trichoderma_ were reactivated in Petri dishes with potato dextrose (PDA, Merck\({}^{\otimes}\), State of Mexico, Mexico) at 28 \({}^{\circ}\)C for 5 days and then grown in 50 mL of mineral medium (g L\({}^{-1}\)) 0.1 CaCl\({}_{2}\); 0.2 KCl, 0.5 KH\({}_{2}\)PO\({}_{4}\); 0.5 (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\); 0.2 MgSO\({}_{4}\)-7H\({}_{2}\)O; 0.05 CuSO\({}_{4}\); 0.05 ZnSO\({}_{4}\); 0.43 MnSO\({}_{4}\); 0.05 (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\):H\({}_{2}\)O, 6 glucose, and pH 4.3. After six days of incubation, the living mycelium (fresh biomass) was vacuum filtered under sterile conditions. Then, half of the mycelium of each fungal isolate was washed with 50 mL of sterile distilled water and transferred to a 150 mL flask with 50 mL of sterile distilled water. The other half of the fresh fungal biomass was exposed to 0.35 g of HgCl\({}_{2}\) for five days to kill the mycelium and avoid the further synthesis of fungal enzymes [55]. This dead mycelium was subsequently filtrated and washed, as previously described, and placed in 50 mL of sterile distilled water.  
Later on, 100 mL of a stock solution of CR or MG (1000 mg L\({}^{-1}\)) were added into each respective treatment (living and dead mycelium) to obtain a final concentration of 50 mg L\({}^{-1}\) at pH 6. The treatments were incubated at 28 \(\pm\) 2 \({}^{\circ}\)C at 150 rpm for eight days, sampling every 24 h; then, 300 mL of each treatment were transferred to a microplate and analyzed in a multi-modal microplate reader (Synergy 2 SL, Biotech\({}^{\otimes}\), Winooski, VT, USA) to 541 nm (CR) and 619 nm (MG), respectively. The amount of dye removal per unit of fungal biomass was calculated using the following equation:  
\[\text{removal efficiency}(\%)=\frac{C_{\text{i}}-C_{\text{f}}}{C_{\text{i}}} \times 100, \tag{1}\]  
where \(C_{\text{i}}\) and \(C_{\text{f}}\) represent the initial and final (after adsorption) dye concentrations, respectively.
# Efficient Human Activity Recognition Using Machine Learning and Wearable Sensor Data
## 2 Data Collection and Preprocessing  
The accelerometer and gyroscope are used to measure accelerations in three axial directions of human motion and store the data. The collected data are then preprocessed, including filling in missing values and addressing outliers [16]. Next, data normalization is performed to ensure that each feature is uniformly processed within the same dimensional and distributional range, maintaining consistency for subsequent processing. Afterward, representative time-domain and frequency-domain features are extracted to describe different human activities. These features are used to establish a classification and recognition model [17]. We calculate the Mahalanobis distance between each pair of groups, which measures the degree of difference between groups in the feature space. To quantify the similarity between groups, we use the reciprocal of the Mahalanobis distance to represent it.
## 5 Conclusions  
This study proposes a two-stage time-domain equalization method based on DL. This method adopts a global-to-local equalization strategy to effectively solve the factors that affect the communication quality in the terahertz channel, such as nonlinear distortion and random interference. In an indoor channel, the frequency is 230 GHz, the line-of-sight is 2.1 m, and experimental verification is carried out. Following two-stage time-domain equalization after data enhancement, the BER of the received signal is reduced from 0.5072 to 0.0398. In contrast, Volterra and traditional LSTM will be affected by the stronger nonlinear noise brought by data enhancement and cannot learn channel characteristics, which shows that the two-stage time-domain equalization has stronger  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **T** & **RM** & **BOP** & **NABS** \\ \hline Traditional DL & 25 & 1.66 M & 192.08 M & 4.86B \\ Two-stage DL & 25 & 3.32 M & 382.16 M & 9.72B \\ Volterra & 25 & 0.0034 M & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of inference complexity for different equalization methods. RM: Real-valued multiplication; BOP: bit-level operation; NABS: addition and bit shift.  
anti-noise performance. In addition, the model shows a certain generalization ability. When applied to the newly collected received data generated by different transmission data under the same channel conditions (frequency is 310 GHz and line-of-sight is 1.5 m), the BER is reduced from 0.012 to 0.002, further demonstrating the versatility of the model. The above two-stage equalization process is an offline operation. The two-stage equalization method improves the stability and reliability of the communication quality of the terahertz communication system. However, due to the processing speed of the model, it is not currently applicable to real-time communication systems. Future research will focus on compressing and optimizing the model, as well as enhancing its adaptability to a wider range of channel environments, ultimately achieving real-time equalization across a variety of channel conditions.  
Conceptualization, Y.L. and H.Y.; methodology, Y.L.; software, Y.L.; validation, Y.L., H.Y. and Z.L.; formal analysis, Z.L.; investigation, Y.L., Z.L., S.L. and J.L.; resources, Y.L. and Z.L.; data curation, Y.L., M.J. and S.L.; writing--original draft preparation, Y.L.; writing--review and editing, J.H.; visualization, Y.L.; supervision, J.H.; project administration, J.H., Z.Y. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
* Restrictions apply to the datasets. The datasets provided in this article are not easily accessible because the experimental setup involves a custom LabVIEW control program and a deep learning-based signal processing model, and the data are part of other ongoing research. To access the datasets, please contact the authors at [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### TB Subunit Vaccines
#### 3.1.3 H56:IC31  
Clinical trials are also ongoing in post-exposure vaccines, including H56:IC31, which is specifically targeted at the _M. tuberculosis_-infected population. H56:IC31 contains a fusion protein of three mycobacterial antigens (early secreted Ag85B and ESAT-6, and the latent Rv2660c) formulated in the Th1-stimulating IC31 adjuvant [15]. Within healthy HIV-adults, previous trials showed no SAEs, with transient AEs when using the vaccine [15]. In addition, within these same trials, antigen-specific IgG responses and Th1 cytokine-expressing CD4+ T cells were observed [15]. A completed phase I trial evaluated the safety and immunogenicity of H4:IC31, H56:IC31, and BCG vaccination in previously BCG-vaccinated healthy adolescents (ClinicalTrials.gov: NCT02378207). Within the H56:IC31 group, 20/24 participants presented AEs. After administration of the vaccine (5 mcg H56/500 nmol IC31 administered as 0.5 mL) at days 0 and 56, 45% of the group presented a response rate to Ag85B on day 70, with this decreasing to 9.5% on day 168. Similarly, response rates to TB10.4 were higher at day 70 (5.6%) compared to day 168 (0%). A similar safety profile was found in another phase I trial testing the safety and immunogenicity of the vaccine in adults who had recently been successfully treated for drug-susceptible pulmonary TB (ClinicalTrials.gov: NCT02375698). Within the H56:IC31 group, 87.5% of the participants reported an AE, however no SAEs were found. The immunogenicity was assessed via a mean percent change from the baseline of participant responses to the TB antigens, Ag85A and ESAT-6, which were found to be 0.014% and 0.026%, respectively, at day 224. A further phase I clinical trial is currently active and is further evaluating the safety and immunogenicity of H56:IC31, in conjunction with a COX-2 inhibitor, to test the hypothesis that this may strengthen the vaccine response (ClinicalTrials.gov: NCT02503839). The optimal dose of the vaccine was also investigated in a phase I/II trial, which found that two or three vaccinations at a dose of 5 mg:500 nmol (H56:IC31) induced durable antigen-specific CD4 T cell responses with acceptable safety and tolerability profiles in TB-infected and uninfected adults (ClinicalTrials.gov: NCT01865487). A phase II clinical trial to evaluate H56:IC31 in preventing TB reinfection is currently recruiting (ClinicalTrials.gov: NCT03512249), while another phase II trial was withdrawn prior to study procedures being done (ClinicalTrials.gov: NCT03265977).
## 3 Results
### Meteorology Variables
Notably, both \(\text{GPP}_{\text{GR}}\) and \(\text{GPP}_{\text{VI}}\) showed a poor relationship with \(\text{GPP}_{\text{EC}}\) at the US-Rls site and US-Rws site. This situation was mainly due to the underestimated GPP values during the growing season. Figure 6: Scatter plots between \(\text{GPP}_{\text{VI}}\) and \(\text{GPP}_{\text{EC}}\) for all sites in 2017–2018. (**a–h**) represent the site the same as Figure 4.  
Figure 7: \(\text{GPP}_{\text{GR}}\), \(\text{GPP}_{\text{VI}}\), and \(\text{GPP}_{\text{EC}}\) temporal dynamics for all sites in 2017–2018. (**a–h**) represent the site the same as Figure 4.  
Spatial - Temporal Consistency between GPP\({}_{\text{MODIS-GPP}}\), GPP\({}_{\text{EC-LUE}}\) and GPP\({}_{\text{MOD17}}\)  
We compared the spatial distribution of the annual mean GPP (gC/m\({}^{2}\)/d) and maximum daily GPP (gC/m\({}^{2}\)/d) from GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) across California in North America during 2017 and 2018 at a 300 m spatial resolution. The annual mean GPP was the average of each pixel throughout the year. The maximum daily GPP was the maximum value of each pixel throughout the year. Notably, the spatial distributions of GPP\({}_{\text{GR}}\) and GPP\({}_{\text{VI}}\) were not shown here due to the insufficient calibration data for upscaling to the regional scale. Figure 8a\(-\)f show the spatial distribution of the annual mean GPP throughout California. The GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) reached the highest values in the western coastal area and decreased along a longitudinal gradient from the west (dominated by forest) to the east (dominated by shrubland and grassland). For the maximum daily GPP (Figure 8g\(-\)l), the highest value was \(\sim\)23 gC/m\({}^{2}\)/d for the Northwest Coastal Forest Belt. The southeastern shrubland region had low GPP values. From 2017 to 2018, the maximum daily GPP decreased significantly in the southwestern shrubland (35\({}^{\circ}\)N-37\({}^{\circ}\)N, \(-\)121\({}^{\circ}\)W-199\({}^{\circ}\)W). The biggest discrepancy between the annual mean GPP value and maximum daily GPP value could be found in the central croplands, where the maximum daily GPP was high, and the annual mean GPP was moderate. Overall, the GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) showed similar spatial -temporal patterns to GPP\({}_{\text{MOD17}}\). Notably, compared with GPP\({}_{\text{MOD17}}\), GPP\({}_{\text{MODIS-GPP}}\), and GPP\({}_{\text{EC-LUE}}\) relatively underestimated the GPP values for cropland.