esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Relations between Parameters
#### 4.1.1 Influences of Temperature, Pressure and Soil Water Content  
Plots representing soil temperature as a function of CO\({}_{2}\) and O\({}_{2}\) have the same shape. Two groups exist, one corresponding to \"low season\" data acquisitions, with soil temperature around 10 \({}^{\circ}\)C and less, and the other corresponding to \"high season\" data with a higher temperature (over 15 \({}^{\circ}\)C). When the data are considered as a whole, the relation between soil-gas concentrations and the soil temperature is masked: e.g., CO\({}_{2}\) concentrations around 10% were monitored during both winter and summer. This is not contradictory with previous statements (see Section 3.2) that have established the mean CO\({}_{2}\) concentrations were lower during the \"low season\". This is only the result of considering the entire dataset in which local effects (topography, soil temperature, soil water content) are hidden. When the data are considered more in detail, the influence of temperature can be highlighted, e.g., for CO\({}_{2}\)-flux measurements that show a tendency to be higher when the temperature is higher (inset in Figure 4). Flux equal to zero is often measured during winter, when the soil temperature is only a few degrees Celsius and the air temperature hovers around 0 \({}^{\circ}\)C. Depending on the soil biological activity, gas flux is generally higher during \"high season\" but high flux, over 10 cm\({}^{3}\cdot\)min\({}^{-1}\cdot\)m\({}^{-2}\), may be measured locally during the \"low season\". This illustrates the effect of thermal draw on gas exchanges between soil and surface.  
At the opposite, the relationships with pressure or with soil water content (Figure 4) are non-informative in the present case study.  
Figure 4: Correlation matrix; inset shows the relation between air temperature and CO\({}_{2}\) flux taken as mean values during each campaign.
## 2 Materials and Methods  
### Chemicals and Reagents  
Glacial acetic acid and ethanol (99.8%) were obtained from Panreac (Castellar del Valles, Barcelona, Spain). High-performance liquid chromatography (HPLC) grade acetonitrile (ACN), methanol (MeOH), and chloroform (CHCl\({}_{3}\)) (99%) were purchased from Merck (Darmstadt, Germany). Ethyl acetate (EtOAc) (HPLC-grade, >99.5%) was obtained from Alfa Aesar (Karlsruhe, Germany). Sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)), sodium hydroxide (NaOH), and dimethyl sulfoxide (DMSO) were acquired from VWR (Saint-Prix, France). Sulfuric acid (96%) and hydrochloric acid (HCl) were obtained from Merck (Whitehouse Station, NJ, USA). Deionized water (resistivity >18 M\(\Omega\) cm\({}^{-1}\)) was prepared in the laboratory using a Milli-Q SP Reagent Water System (Millipore Corporation, Bedford, MA, USA). ABTS (2,2\({}^{\prime}\)-Azino-Bis-3-Ethylbenzothiazazoline-6-Sulfonic acid), Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), AAPH (2,2\({}^{\prime}\)-azobis-(2-amidiopropane) dihydrochloride), and potassium persulfate (K\({}_{2}\)S\({}_{2}\)O\({}_{8}\)) were purchased from Sigma-Aldrich (Steinheim, Baden-Wurttemberg, Germany). Tris(hydroxymethyl) aminomethane, potassium phosphate monobasic (K\({}_{2}\)HPO\({}_{4}\)), potassium phosphate dibasic (K\({}_{2}\)HPO\({}_{4}\)), and sodium phosphate dibasic (Na\({}_{2}\)HPO\({}_{4}\)) were purchased from Merck (Darmstadt, Germany). Sodium fluorescein was obtained from Fluka Chemie AG (Bunds, Switzerland); 8-16% Mini-PROTEAN(tm) TGX(tm) Precast gels, molecular weight marker Precision Plus Protein(tm) 5-250 kDa, and Coomassieie brilliant blue R-250 were purchased to BioRad (Hercules, CA, USA). Dithiothreitol (DTT) was obtained from VWR (Leuven, Belgium).  
Mycotoxins standards of AFB1 (\(\geq\)98% purity), AFB2 (\(\geq\)98%), AFG1 (\(\geq\)98%), AFG2 (\(\geq\)98%), ZEA (\(\geq\)99%), OTA (\(\geq\)98%), BEA (\(\geq\)97%), ENA (\(\geq\)95%), ENB1 (\(\geq\)95%), and ENB1 (\(\geq\)95%) were supplied by Sigma (St. Louis, MO, USA). Individual stock solutions were prepared at 100 mg/L in methanol. All solutions were stored in the dark at \(-\)20 \({}^{\circ}\)C until LC-MS/MS-IT analysis.
## Improving Placement Accuracy  
### Causal Analysis of Projection Stability Degradation  
'Image Target,' the model positioning method used by AROS_\(\alpha\), employs a QR code that contains the size, projection location, and directional coordinates of the projected AR  
Figure 8: Result of projection stability improvement in AROS_\(\beta\).  
model. When AROS_\(\alpha\) recognizes the QR code as the anchor point, a real-sized model is projected at the specified location and direction.  
Proper calibration is achieved by first matching the model projection with the real structure, then placing a QR code in a fixed location that locks in these coordinates. Subsequent projections should align correctly when the QR code is recognized by AROS_\(\alpha\). Any change in the location or direction of the user will result in the projected model undergoing appropriate adjustments to maintain the previously specified location. Hence, the model should be correctly projected as long as the QR code is correctly and consistently recognized.  
However, this also implies that model projection is highly dependent on the QR code. Although accurate projection is possible within relatively close distances (e.g., 1-3 m), such may not be the case where recognition of the QR code becomes difficult. Using a single QR code may not be practical at construction sites where inspection needs to be performed in different locations and flexibly performed. Existing AR studies recommend that QR codes be installed in multiple locations and scanned periodically to refresh the coordinates of the model [39]. Unfortunately, it is often not feasible to place QR codes in permanent locations, as physical changes continuously occur throughout the site. Therefore, a complementary approach was needed in which these limitations could be addressed.
## 4 Modified Functional Observer with Unknown Input  
Considering an unknown non-square continuous-time impulsive-mode-free singular system with more control input channels than output channels and all stable control zeros, it is desirable to propose a high-performance optimal analog state estimate tracker with an EID estimator for the system, with unknown matched and mismatched input and output disturbances, as shown in Figure 1. The newly developed modified functional observer uses the system output to estimate the integrated control input and estimated EID, while the EID estimator estimates the EID such that the desired control input can be determined consequently. It is worthwhile noting that for effective determination of the EID estimator in the conventional method, it is required to have the known control input vector with unknown disturbances. To overcome this constraint problem, in this paper the conventional EID estimator is extended for a system with both an unknown control input vector and unknown disturbances. Then, the aforementioned mechanism can be implemented as depicted in Figure 1.  
In Chapter 7 of [11], the system of interest has dependent input and output disturbances, where the same disturbance \(d(t)\) appears in both input and output terminals, and has no direct feedthrough term, as below  
\[\dot{x}_{c}(t)=Ax_{c}(t)+Bu(t)+G_{i}d(t), \tag{9a}\] \[y_{c}(t)=Cx_{c}(t)+G_{o}d(t), \tag{9b}\]  
where \(x_{c}(t)\in\Re^{n}\), \(y_{c}(t)\in\Re^{p}\), and \(u(t)\in\Re^{m}\) are the state, measured output, and input vectors, respectively; and \(A\in\Re^{n\times n}\), \(B\in\Re^{n\times m}\), \(C\in\Re^{p\times n}\), \(G_{i}\in\Re^{n\times l}\), \(G_{o}\in\Re^{p\times l}\) are known real constant matrices. The unknown disturbance \(d(t)\in\Re^{l}\) directly affects both the state and output of the system, and it is also assumed that \(\text{rank}(C)=p\) and \(p\geq l\).  
Based on the advanced functional observer originally presented for strictly proper systems, in this section, a modified functional observer for a system with an input-output direct feedthrough term is derived to estimate the EID, which can be used to deal with proper systems with mismatch and independent input and output disturbances, i.e., \(d_{i}(t)=G_{i}\overline{d}_{i}(t)\) and \(d_{o}(t)=G_{o}\overline{d}_{o}(t)\), where \(G_{i}\in\Re^{n\times l_{i}}\) and \(G_{o}\in\Re^{p\times l_{o}}\) are assumed to be unknown and there is no more constraint for \(p\geq l=\text{max}(l_{i},\ l_{o})\).
## 1 Introduction
Piezoelectric materials do not need an additional power supply and can generate potential differences when subjected to impact pressure. Piezoelectric sensors have gradually become the mainstream of particle impact sensors due to their advantages such as wide response band, high sensitivity, high signal-to-noise ratio, simple structure, reliable operation, and light weight. [PERSON] (2016) (2016) of Deere Company designed a collision sensor for distinguishing grains from other stray particles. The structure of the sensor is divided into the upper conductive layer, the impact response layer, the lower conductive layer, and the base layer from top to bottom, in which the material of the impact response layer can be polar piezoelectric polymer. The upper layer can be divided into an array of isolated regions, which represent the transverse and longitudinal positions of the grain collision, while the lower layer is a continuous conductive layer. The upper regions and the lower layer respectively transmit signals to the signal processing circuit, through which the position of the grain collision sensor can be judged. [PERSON] (2016) (2016) (2016) designed an eight-channel PVDF piezoelectric film sensor measurement system based on a single-chip microcomputer. The piezoelectric effect of the piezoelectric film was used to convert the sensor's knock signal into a measurable voltage signal, so as to realize grain loss measurement. By comparing the studies of various scholars, it is found that the loss sensor based on the acoustic principle is easy to be disturbed by acoustic noise in the working environment. The loss sensor based on machine vision is easy to be affected by light, occlusion, etc. Piezoresistive material loss sensor sensitivity is not high, and reaction lag; the PVDF piezoelectric film is easy to wear and has a low piezoelectric coefficient. In the practical application of piezoelectric materials, [PERSON] and [PERSON] discovered that compared to piezoelectric polymer materials, piezoelectric ceramic components offer several advantages, including simple structure, easy wear, and high sensitivity to force (2016). As a result, researchers in the field of micro-vibration and acceleration detection prefer these components (2016). Most scholars have done more studies on loss detection sensors of different materials, but there are insufficient studies on the vibration and noise interference of sensors. Therefore, in order to improve the detection accuracy and sensitivity of loss detection sensors and reduce vibration and noise interference in the application process of sensors [17]. For this research, a double-layer piezoelectric ceramic disc has been selected as the sensing element for the sensor. Through dynamic analysis of vibration disturbance, a double-layer vibration-damping structure has been designed. Additionally, a verification test bench has been established to verify the vibration-damping effect, and the optimal approach for verifying the vibration absorption effect of the piezoelectric sensor has been proposed.
## 3 Methodology
### Modeling and Validation
#### 3.3.2 Validation Methods  
A fivefold cross-validation was employed for an objective and reliable assessment of model accuracy, as it expands the testing dataset. The final accuracy was determined by calculating the average of the results obtained from the fivefold cross-validation. Moreover, we also evaluate the accuracy in different flux tower sites. Specifically, observations from a designated site were used as the testing set while data from the other five sites served as the training set.  
Additionally, to quantify the model's performance, we utilized two metrics: the coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE). These metrics were calculated using the following formulae:  
\[R^{2}=\frac{\sum_{i=1}^{n}{(Var_{Mi}-\overline{Var_{M}})}^{2}}{\sum_{i=1}^{n}{ (Var_{Obi}-\overline{Var_{Ob}})}^{2}} \tag{11}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(Var_{Mi}-Var_{Obi})}^{2}}} \tag{12}\]  
where \(Var_{Mi}\) is the estimated ET or NEE given by the proposed model and \(Var_{Obi}\) is the observed ET or NEE by flux tower. \(n\) is the number of samples, which is 428 for ET estimation and 509 for NEE estimation.  
Figure 4: The scatter plot of DOY with (**a**) ET and (**b**) NEE.
# Improving the Energy Efficiency of the Modular Buildings
## 3 Methodology  
This research was conducted at two sites: The first one was the whole drying room of Everton stadium construction site, and second one encompassed only one drying unit in Wincham site. The point of using two sites is that in the original case study, the stadium was in the phase of construction, and the drying room onsite had open access to the workforce; then, no experiments took place in there, and only temperature and humidity sensors were installed to identify the usage profile of the fan heaters (3 kW) in the drying room to be able to model it using IESVE software to predict the energy use onsite which would represent the baseline against which energy reduction interventions will be compared. IESVE tool was used to generate the building model and to run the thermal simulation since it is easy to manipulate and gives accurate calculation results at no cost; also in the context of the current study, it enables the user to investigate and compare between the different suggested energy conservation measures in a virtual environment and to evaluate their viability before opting for real implementation. In addition, there were no historical data on the energy consumption platform onsite that state the energy used by the drying room separately from the whole site cabin due to the combined electrical distribution. In addition, it includes an extensive database and a wide selection of different structural building components with various energy consumption profiles [17]. CFD simulations were conducted to analyse air velocity, temperature, and moisture distribution in the room. This process helps identify optimal locations for different drying racks and system components. However, to be able to visualise real drying test results, CFD simulation on its own was not enough, as it is unable to determine the drying time of garments; hence, there was a need for a private drying room to test different drying scenarios other than only fan heaters that would reduce energy consumption; therefore, it has been resorted to the drying unit in Wincham, which was dedicated only for experimentation.  
The dimensionless fraction of moisture in the fabric \(W_{W}\) is expressed by Equation (1):  
\[W_{w}=\frac{m_{w}}{m_{f}} \tag{1}\]  
where \(m_{w}\) is the mass of water (kg), and \(m_{f}\) is the mass of dry fabric (kg) [18].
## 4 Discussion, Limitations, Conclusions, and Future Works
Regarding the conclusion validity, although the sample was modest, the authors believe it was significant enough to assess the framework. Internal validity could be threatened by the fact that subjects may not present similar levels of knowledge or expertise. To ensure internal validity, participants were chosen with the requisite of presenting a comparable level of competence (knowledge and experience). External validity is about the generalization of findings. Here, the authors assume two different threats: Sample was not taken randomly and, in the second term, the limited size of the sample. The proposed framework, built upon the current literature, sheds some light on the differentiation of input areas that need to be considered to guarantee compliance in cloud computing settings. Future work can be focused in different areas of research. Firstly, a customization of the framework for various functional domains can be developed and assessed. In a second term, a mapping study with relevant standards and initiatives on IT governance, including ISO 38500 or COBIT, should be aimed for in the future, naming just some of the most relevant mappings. Third, the implementation of the framework in various settings to compare national and organizational cultures is also a future aim. In the fourth term, the authors want to expand the framework to tackle aspects of quality of service (QoS) and quality of experience (QoE). Both aspects are very relevant aspects nowadays in the literature of the topic, e.g., [48; 49; 50; 51; 52; 53; 54]. While it is true that these nascent initiatives are more relevant to mobile networks, it is also true that these aspects are quite applicable also in organizational cloud scenarios. Finally, the authors are aware of the importance of artificial intelligence technologies in the cybersecurity arena [55; 56; 57; 58; 59; 60]. Taking this into account, the authors plan the inclusion of assessment factors to these aspects in future developments of the framework. Conceptualization, K.B., S.D., R.C.-P. and V.S. ; Data curation, K.B. and S.D. ; Formal analysis, K.B., S.D. and R.C.-P.; Investigation, K.B. and S.D. ; Methodology, K.B. and S.D. ; Supervision, R.C.-P. and V.S. ; Validation, R.C.-P. and V.S. ; Writing--original draft, K.B. and S.D. ; Writing--review & editing, R.C.-P. and V.S. This research received no external funding. Authors would like to thank subjects for the contributions provided. The authors declare no conflict of interest.
## 5 The Concept of IoV
### Security Issues in IoV Communication
#### 5.5.2 Encrypted Data/Communication  
Electronic data produced when transmitting between fog and cloud should be protected by encryption. The two most widely used data encryption methods are the Advanced Encryption Standard (AES) algorithm and the Data Encryption Standard (DES) algorithm. The AES algorithm is more appropriate in a fog environment compared to the DES algorithm due to its suitability for sensitive data. Moreover, DES codes are easier to crack compared to AES codes [96].
## 4 CILS Framework: Enhancing Cross-Cultural Language Learning
### Cultural Sensitivity and Adaptability
#### 4.1.3 Adaptive Learning Technologies in Cultural Education  
Adaptive learning technologies are critical in tailoring language learning experiences to meet the diverse cultural backgrounds of learners within the CILS. These technologies enable the system to adjust educational content dynamically, ensuring that cultural nuances are respected and integrated into the learning process [29]. By leveraging data-driven insights from learners' interactions, these technologies facilitate a responsive learning environment that evolves according to individual learning trajectories and cultural comprehension levels.  
In the CILS, adaptive learning technologies utilize sophisticated algorithms to analyze learners' responses to culturally embedded language tasks. This analysis helps identify areas where learners may struggle due to cultural misunderstandings or lack of knowledge about specific cultural contexts. The system then adapts by providing additional resources, such as explanations of cultural norms, practices, or idiomatic expressions prevalent in the target language's culture.  
This technology facilitates scenario-based learning, allowing learners to engage in various culturally nuanced situations. For example, in a virtual marketplace scenario, learners might be required to practice bargaining, a skill that is emphasized differently across cultures. The AI system dynamically adjusts the complexity of the scenario and the cultural norms it presents based on the learner's previous interactions and demonstrated cultural understanding. For instance, if a learner is less familiar with a culture where bargaining is prevalent, the AI might initially simplify the negotiation process and provide contextual hints and tips. As the learner becomes more adept, the scenarios become more complex, incorporating subtler cultural cues and advanced negotiation strategies, thereby enhancing their ability to interact effectively and appropriately in diverse cultural settings.  
Adaptive learning technologies in the CILS are designed to offer culturally sensitive feedback. Figure 5 illustrates the effectiveness of the CILS in various educational settings, highlighting different regions' performance metrics. This feedback mechanism is crucial for correcting language errors and guiding learners on how to express themselves in culturally appropriate ways. The immediate feedback AI provides helps learners understand and correct their cultural and linguistic missteps in real time, which is vital for building cultural competence [30].  
Figure 5 illustrates the comparative performance outcomes of learners using different language learning methodologies, including the CILS. The data for this figure were collected through a randomized controlled trial conducted over a period of six months, involving 300 participants from diverse linguistic backgrounds. The methodology included pre- and post-assessments of language proficiency and cultural competence, along with regular feedback sessions to gauge learner engagement and adaptability to the learning tools.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 3 Simulation and Signal Test
### Simulation Results and Discussion
#### 3.2.2 Verification of Different Frequency Sinusoidal Magnetic Field Compensation Effects  
In order to further verify the compensation effect of the dynamic compensator on sinusoidal input magnetic fields with different frequencies, sinusoidal magnetic field signals with different frequencies are used as input signals of the magnetometer and the output signal of the magnetometer after the dynamic compensator is collected. A Fourier transform is performed on the output data of the magnetic sensor after the dynamic compensation, amplitude, and phase are extracted, and the amplitude is normalized. The amplitude-frequency and phase-frequency response curves of the magnetometer after dynamic compensation are obtained, as shown in Figure 15.  
Figure 15 shows that, after dynamic compensation, the amplitude-frequency characteristic curve remains flat in the low-frequency range (0.01-4 Hz), the attenuation of amplitude is reduced based on the original, and the normalized amplitude is close to 1, consistent with the input amplitude. In the higher frequency band (4-240 Hz), the dynamic compensator plays a more obvious role in reducing the amplitude attenuation, which is a great improvement compared with before dynamic compensation, so its dynamic error will also decrease accordingly. The amplitude-frequency response improvement ratio is from 0.02% to 0.69%, which represents measurement accuracy improvements of 10-345 nT in the geomagnetic background field (average of 50,000 nT). Furthermore, the dynamic compensator improves the phase frequency characteristics of the magnetic sensor more obviously; the phase difference is reduced to less than half of the original, the time delay between the output and input signals is reduced, and the input magnetic field signal can be quickly responded to the change.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Overshoot/\%** & **Response Time/ms** \\ \hline Before dynamic compensation & — & 10.91 \\ \hline After dynamic compensation & 4.3\% & 0.63 \\ \hline \end{tabular}
\end{table}
Table 4: Dynamic performance index of the magnetometer before/after dynamic compensation.  
Figure 14: Output of the fluxgate magnetometer after dynamic compensation.  
On the whole, the dynamic compensator improves the amplitude-frequency and phase-frequency response characteristics of the magnetometer, reduces the amplitude attenuation and phase delay of the magnetometer to the input signal to some extent, and achieves the effect of reducing the dynamic error.