esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
Nowadays, the conditions for producing good-quality material joints are determined by equilibrium wettability tests and the value of surface tension. In this paper, a methodology for surface tension measurement, including a general concept of the immersion experiment and calculation of its parameters, is presented. The methodology was used in the construction of an automatic research stand containing an integrated platform for automatic determination of high-temperature braze wettability, enabling a comprehensive study of the dynamic properties of brazes at temperatures of up to 1000 \({}^{\circ}\)C with various technological gas atmospheres. The surface tension parameter plays an important role in many industrial engineering processes due to its contribution to the fluid phase, including the creation of joints based on brazing or soldering technologies. Knowledge of the surface tension and its dynamics as well as the materials' physical and chemical properties allows for the design of new materials and joining technologies as well as the optimisation of the existing ones. Today, research in materials engineering also focuses on methods for automatic measurement of joining process properties, i.e., wetting force and surface tension, the quantitative description of which allows for easy comparison of the results. Example experiments for the solder material S-Sn97 Cu3 are shown. The results are in line with the values known from the literature [16; 17; 18]. In summary, our work bridges theory and practice, advancing joining technologies and reliable material design. Our contributions lie in developing a robust measurement technique and its application to high-temperature brazing, enhancing the understanding of joint dynamics and enabling informed choice of materials.  
[PERSON] conceived the research direction and collected relevant information; [PERSON] built the test stand; [PERSON] designed measurement automation processes; [PERSON] and [PERSON] conducted the experiment; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 14: A section of the bubble experiment in solder (S-Sn97 Cu3) for a ceramic capillary Al\({}_{2}\)O\({}_{3}\).
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Road Condition**} & \multirow{2}{*}{**Forklift Type**} & \multirow{2}{*}{\begin{tabular}{c} **Sensor** \\ **Location** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Unit Load Weight** \\ **(kg)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Speed** \\ **(3.22 km/h)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Speed** \\ **(4.82 km/h)** \\ \end{tabular} } \\ \cline{5-8}  & & & & & **G\({}_{\rm rms}\)** & **G\({}_{\rm rms}\)** & **G\({}_{\rm rms}\)** & **G\({}^{2}\)/Hz** \\ \hline \multirow{4}{*}{Asphalt} & \multirow{4}{*}{Electric Forklift} & Carriage & 680 & 0.044 & 3.40 & 0.071 & 2.28 \\  & & Tip & 680 & 0.096 & 2.03 & 0.140 & 2.36 \\  & & Carriage & 1134 & 0.034 & 9.15 & 0.070 & 3.12 \\  & & Tip & 1134 & 0.070 & 1.95 & 0.116 & 1.43 \\ \cline{2-8}  & & Carriage & 680 & 0.049 & 6.49 & 0.090 & 1.87 \\  & & Tip & 680 & 0.088 & 6.71 & 0.145 & 1.26 \\  & & Carriage & 1134 & 0.040 & 11.43 & 0.070 & 1.91 \\  & & Tip & 1134 & 0.059 & 2.77 & 0.106 & 2.58 \\ \hline \multirow{4}{*}{Concrete} & \multirow{4}{*}{Reach Truck} & Carriage & 680 & 0.028 & 4.68 & 0.046 & 4.22 \\  & & Tip & 680 & 0.037 & 3.62 & 0.054 & 1.05 \\  & & Carriage & 1134 & 0.025 & 10.28 & 0.034 & 8.32 \\  & & Tip & 1134 & 0.039 & 2.91 & 0.055 & 2.01 \\ \cline{2-8}  & & Carriage & 680 & 0.028 & 9.78 & 0.051 & 1.26 \\  & & Tip & 680 & 0.056 & 5.05 & 0.097 & 11.21 \\  & & Carriage & 1134 & 0.024 & 6.42 & 0.037 & 5.00 \\  & & Tip & 1134 & 0.049 & 10.34 & 0.074 & 3.64 \\ \cline{2-8}  & & Carriage & 680 & 0.033 & 8.63 & 0.066 & 8.56 \\  & & Tip & 680 & 0.054 & 2.04 & 0.091 & 11.17 \\  & & Carriage & 1134 & 0.032 & 2.47 & 0.053 & 6.33 \\  & & Tip & 1134 & 0.054 & 13.81 & 0.104 & 9.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall G\({}_{\rm rms}\) (G\({}^{2}\)/Hz) and kurtosis values for distributions of recorded events obtained for this study. Additionally, it was observed that the vibration levels decreased with the unit load weight carried by the forklift. The highest overall \(\mathrm{G_{rms}}\) observed at 680 kg was 0.145, while the lowest overall \(\mathrm{G_{rms}}\) was 0.028. Similarly, the highest overall \(\mathrm{G_{rms}}\) observed at 1134 kg was 0.116, while the lowest \(\mathrm{G_{rms}}\) was 0.024. Overall, the gas-powered forklift driving on the asphalt condition at 4.82 km/h, while supporting a 680 kg payload, experienced the highest vibration among the three forklifts studied, with an overall \(\mathrm{G_{rms}}\) of 0.145. This \(\mathrm{G_{rms}}\) level is much smaller than what is generally observed for trucks and small vehicles such vans or motor cars; however, it is only marginally smaller than what is observed for railcars. Figure 4: Overall vibration intensity measured at the carriage for each of the three forklifts (1–200 Hz). Figure 5: Overall vibration intensity measured at the tip of the fork tines for each of the three forklifts (1–200 Hz).
# An Assessment of Anthropogenic CO\({}_{2}\) Emissions by Satellite-Based Observations in China
## 1 Introduction
It is still a challenge in using XCO\({}_{2}\) data to quantitatively estimate the magnitude of anthropogenic CO\({}_{2}\) emissions. This data-driven approach, as an additional way of quantifying anthropogenic CO\({}_{2}\) emissions, can help policymakers to obtain more information for evaluating the effects for CO\({}_{2}\) emissions reduction at both regional and global scales. In this paper, we propose a method of using satellite-based observation to assess the anthropogenic CO\({}_{2}\) emissions aiming to assist the national routine investigation of carbon emissions. We focus on mainland China as the studying area since it is a major national emitter of CO\({}_{2}\)[18]. We extracted XCO\({}_{2}\) anomalies (dXCO\({}_{2}\)) using XCO\({}_{2}\) dataset obtained from GOSAT observations. The anomalies are found to be significantly correlated with anthropogenic CO\({}_{2}\) emissions from the CO\({}_{2}\) emitting sources such as power plant emission. We further introduce artificial neural network algorithm (ANN) to construct an estimation model for anthropogenic CO\({}_{2}\) emissions based on the changes of atmospheric CO\({}_{2}\) concentration derived from satellite observations.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 5 Results
### Parameter Space Evaluation
#### 5.1.3 Class Separation  
By applying JMD (Equation 4) to the dimensionality reduction techniques, we find that kPCA outperforms the rest of the techniques used, achieving 0.607, whereas the optimised autoencoder model performs slightly less favourably with a score 0.558, as shown in Table 3. The only technique that was excluded from this process was LDA, for two reasons: (1) it is a supervised technique that specifically maximizes the separability between the different classes in the low-dimensional space, and (2) in the context of our study, LDA has reduced the dataset to a single dimension, while all the other techniques have reduced the dimensionality to two dimensions. While class-separability is not necessarily correlated with accurate preservation of structure, high separability will allow users to effectively modulate between contrasting timbral descriptors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Separability Measure** & **PCA** & **pPCA** & **kPCA** & **FA** & **DM** & **sAE** \\ \hline JMD & 0.5142 & 0.5152 & 0.6076 & 0.4862 & 0.5125 & 0.5581 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Jeffries–Matusita Distance (JMD) scores showing separation across different dimensionality reduction techniques.  
Figure 4: Two-dimensional parameter-space representations using seven data reduction techniques, where the red data points are taken from parameter spaces described as _bright_ and the blue points are described as _warm_. (**a**), (**c**), (**f**), and (**g**). PCA: Principal Components Analysis; pPCA: Probabilistic PCA; kPCA: Kernel PCA; FA: Factor Analysis; DM: Diffusion Maps; LDA: Linear Discriminant Analysis; sAE: stacked-autoencoder.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis
### Model Selection and Reliability Evaluation
#### 2.5.2 Reliability Assessment  
After the optimal distribution model is determined, MTBF (mean time between failure) is used to evaluate product's reliability [25].  
The point estimation of MTBF is calculated using Equation (13):  
\[MTBF=\int_{0}^{\infty}tf(t)dt=\int_{0}^{\infty}tdF(t)=tF(t)|_{0}^{\infty}-\int _{0}^{\infty}F(t)dt \tag{13}\]  
where \(f(t)\) is the probability density function, and \(F(t)\) is the failure distribution function.
## 1 Introduction  
A biofilm is defined as a three-dimensional organized structure constituted by microorganism clusters adhered to either a biotic (e.g., host tissue) or abiotic (e.g., biomaterial/implant) surface or interface and encased in a protective self-produced hydrated ECM composed mainly of proteins, polysaccharides, and extracellular DNA. These structures are heterogenous, both in space and time, and reveal \"water channels\" that have colossal importance to the growth of cells within the biofilm since they allow the passage of essential nutrients and oxygen [1].  
Additionally, the development of biofilms is due to diverse environmental cues (e.g., nutrient limitation is the reason why the growth rate of biofilm-associated organisms is typically slower than planktonic organisms) and exhibits extreme resilience, insensitivity, or tolerance to severe conditions, including antibiotic treatment (biofilms are up to 10-1000 times more resistant to antimicrobials than planktonic organisms), UV radiation, pH stress, chemical exposure, dehydration, osmolarity, host immune response, and mechanical and shear forces [2, 3]. These features are the natural result of being in a biofilm form, which allows and eases lateral gene transfer, leading to high mutation rates and, thus, high resilience [4, 5, 6, 7, 8].  
Nanocarriers show great potential in combating infections associated with biofilms. Biofilms are protective layers created by bacterial communities that are highly resistant to traditional antibiotics. Unlike conventional treatments, nanocarriers can more effectively penetrate biofilms, delivering higher concentrations of antibiotics or other antimicrobial agents directly to the affected area. This targeted method increases the effectiveness of treatments while reducing potential side effects. Developing nanocarrier-based therapies is essential for overcoming the limitations of current treatments and ultimately improving patient outcomes and lowering the economic impact of biofilm-associated infections on healthcare systems [9]. This review explores nanotechnology-based systems for addressing biofilms, discussing their characteristics, limitations, and benefits in treating these infections. Additionally, biofilm evaluation models and the tests required for the preclinical validation of these nanosystems to aid their clinical application are also analyzed.  
The purpose of this review is to examine nanotechnology-based systems, detailing their characteristics, limitations, and potential advantages in tackling biofilm-related infections. It also discusses biofilm evaluation models and the testing required for preclinical validation of these nanosystems to support their clinical use. The structure of this review is summarized in Figure 1.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 6 Open Issue
### Scalability  
The number of IoT devices needed to be deployed in the near future will be massive. And so, the scalability factor of an IoT environment is very important to keep in mind. The scalability of IoT systems can be considered from two perspectives [43]. One is load scalability, which refers to how scalable the traffic amount is for each device in a system. The other one is structural scalability, which refers to how scalable the system is in terms of the number of devices. For ISM band-centric protocols like Z-wave, Sigfox, and LoRa, interference becomes severe with denser environments because the frequencies are not reserved. LoRa uses a pure ALOHA channel access method, which is not very load-scalable due to high packet collision probability. Sigfox's various restrictions, such as strict duty cycle, frequency hopping, and Listen Before Talk mechanisms, hinder its load scalability. Meanwhile, 3 GPP licensed spectrum-based protocols such as NB-IoT, LoRa, and RedCap use OFDMA which is more load-scalable. From a structural scalability point of view, SigFox and LoRa are comparatively more device-scalable than their 3 GPP counterparts, although Sigfox is the more scalable compared to LoRa [135]. LTE-M offers better device scalability compared to NB-IoT. Z-Wave's scalability is comparatively low [136]. 3 GPP-based protocols are much more load-scalable than ISM-based protocols due to their superior traffic handling capabilities, reserved dedicated spectrums, and fewer regulatory restrictions. Contrarily, unlicensed spectrum networks can accommodate a greater number of devices per area. Stakeholders may need to choose between these two sorts of scalability along with other desired IoT parameters during deployment. Use cases such as healthcare, smart buildings, vehicular communication, and meter readings can require high device density and, at times, high-traffic QoS. For tasks like these, NB-IoT and LTE-M can provide good load-scalable environments. On the other hand, LoRa and Sigfox will be able to accommodate the huge number of devices required in these scenarios. To support all these devices, existing LPWAN protocols may face certain challenges regarding interference, collision, massive data storage, and spectrum congestion, especially in the unlicensed spectrum. Cellular IoT using a centralized LTE model can support the present number of devices. However, soon, it will become a bottleneck with an increased number of devices. Also, duty cycle access mode in a large-scale network can result in packet collisions and retransmissions, resulting in spectrum congestion. Research needs to be conducted on service discovery among billions of devices connected to different nodes, decentralizing architecture using clustering approaches, building fog computing models, peer-to-peer communications, gateway densification, spreading factor assignment combined with joint scheduling, etc. [137; 138; 139; 9].
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 3 Results and Discussion
Furthermore, the challenges presented by the Doppler effect in establishing LoRa ground satellite connectivity have been comprehensively investigated. This classification framework provides valuable insights into identifying the most suitable parameters for satellite communication, thereby enhancing our understanding of packet loss dynamics. Furthermore, it is important to note that the computational demands and simulation duration are largely influenced by the parameters being studied. For example, using MATLAB R2023a requires around 20 s to generate the outcomes shown in Figures 19 and 20. Additionally, it should be emphasised that resistance to the Doppler impact greatly depends on the decisions made by the designer, and the overall efficiency has a better chance to increase with design advancements. Figure 19: Margin and operating point of Globalstar satellite and Perth ground station. Figure 20: Free space path loss in Decibels for the Globalstar satellite and the Perth ground station.
###### Abstract  
Glucose delivery to the brain requires transporters at the blood-brain barrier (BBB), whose downregulation may be associated with neuronal deficits in Alzheimer's disease (AD). Whether this downregulation is due to reduced demand or primary BBB dysfunction remains unclear. We investigated novel 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG-PET) measures, namely ventricles (FDGVentricles) and cortical uptake (FDGVentricles), and the FDGVentricles/FDGVentricles ratio in 224 patients with AD compared to those in 35 controls (CTRLs). AD patients showed lower FDGVentricles and FDGVentricles and higher cerebrospinal fluid (CSF) lactates than CTRLs. We found a positive correlation between FDGVentricles and FDGVentricles in both groups, although this was less strong in AD patients (AD: r = 0.358; \(p<\) 0.001; CTRL: r = 0.516; \(p=\) 0.003). Multivariate regression analyses showed that only older age was associated with reduced FDGVentricles and FDGVentricles in CTRLs. Conversely, lower FDGVentricles was associated with higher Qalb and higher plasma glucose levels within the AD group. Moreover, lower FDGVentricles and FDGVentricles/FDGVentricles ratios were associated with elevated CSF lactates in this group. Stratifying AD patients by Apolipoprotein E (APOE) genotype revealed distinct patterns. In APOE \(\varepsilon\)3 homozygotes, FDGVentricles showed no associations, while FDGVentricles and FDGVentricles/FDGVentricles/FDGVentricles were negatively associated with CSF lactate. In APOE \(\varepsilon\)4 carriers, lower FDGVentricles was linked to higher plasma glucose and QAlb, whereas FDGVentricles and FDGVentricles/FDGVentricles were positively associated with CSF p-tau/A942. Our findings suggest that, in patients with AD, FDGVentricles and the FDGVentricles/FDGVentricle ratio may reflect alterations in brain metabolism and glucose extraction capacity. These parameters are differently linked with age, BBB integrity, and metabolic dysfunction (CSF lactates), according to APOE genotype.  
Alzheimer's disease; glucose metabolism; FDG-PET; blood-brain barrier; APOE genotype; CSF +
Footnote †: journal: Applied Sciences  
Article
## 4 Simulation Results
We note that the system responses deteriorate when parameter variations exceed \(\pm 30\%\), and the system becomes unstable for variations around \(\pm 50\%\). Table 2 provides a comparison of the time-domain responses of the \(SP\) control structures from the simulation, focusing on disturbance rejection, considering the effect of airflow generated by a fan ventilator. In this table, \(IAE\) refers to the Integral of the absolute error, defined as:  
\[\int_{80}^{\infty}\left|(r(t)-y(t))\right|\cdot dt \tag{42}\]  
\(ISE\) denotes the Integral of the squared error, given by:  
\[\int_{80}^{\infty}(r(t)-y(t))^{2}\cdot dt \tag{43}\]  
and \(TV\) represents the total variation in the control signal, expressed as:  
\[\int_{80}^{\infty}\left|\frac{du(t)}{dt}\right|\cdot dt. \tag{44}\]  
The first two indexes reflect the system's ability to reject disturbance effects, while the third quantifies the control effort required. In this context, it measures the variation in the control signal during the application of the airflow disturbance on the \(TRMS\). This metric is associated with actuator wear (as well as that of other physical components), the magnitude of the control signal, and the potential for actuator saturation.
## 5 Physical Layer: Five Decades of Evolution (from 45 Mb/s to 100+ Tb/s)
### Five Decades of Physical Layer Evolution in Backbone  
#### 5.2.1 Methodology  
The evolution of fixed network generations is service driven, as it is analyzed in the introduction. In particular, it was shown in Figure 1 that the first generation ends at about 2000, whilst the next four generations extend over to 2025. This categorization highlights a significant acceleration after 2000 due to the wide adoption of Internet services, such as video, gaming, etc. However, from the physical layer perspective, noticeable advances can be found in all five decades. For this purpose, we segregate the physical layer evolution in five discrete time intervals (decades), beginning in the 1970s with the first fiber-optic transmission system and ending in 2020 with the 100 Tb/s capacity limitation of current systems. The key evolutionary steps of each time interval are presented using a dialectical procedure; that is, each interval solves the main limitations of its previous one, allowing us to overcome the burdens set on attainable capacity and transparent reach. Moreover, the key advances in each generation are discussed in order to further understand why and how each technological achievement prevailed. A unified overview of the evolution of transported capacity within a single mode fiber for research and commercial systems is illustrated in Figure 29. The most important operational parameters for each decade are also pointed out. We can observe from Figure 29 that research technology is about 8 years ahead of commercially available systems, as it is also shown in [82, 83, 84]. It is also worth mentioning that the transported capacity has been increased by about 6 orders of  
Figure 28: Attenuation and dispersion parameter of a G.652D fiber.  
magnitude over the past 40 years, which roughly means a doubling every two years. The main driving forces behind this evolution are first the need to increase the channel line rate, mainly due to the advances on modulation format, and second the introduction of WDM, which allowed to transmit multiple channels within a single fiber. A summary of the main technological advances in optical transmission that upscaled the B x L product is shown in Figure 30. In this figure, a degree of arbitrariness on the presented dates is considered as the time between the introduction of each technology and its wide adoption may vary greatly.  
Figure 30: Overview of the key technological advances which allowed to increase the B \(\times\) L product in optical transmission.  
Figure 29: Evolution of transported capacity within a single mode fiber over the last 50 years (based on [82, 83, 84]).  
Figure 31 describes pictorially the evolution of the commercial line rates for a single channel, starting at 45 Mb/s for the first systems and ending with up to 800 Gb/s for state-of-the-art systems. We can observe that the line rate per channel shows an increase of about four orders of magnitude over the past 40 years, which implies an increase of about 20-25% per year. In the next paragraphs, we detail the main achievements of each decade.