esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems  
In the context of computing, specifically in the adaptive time-triggered multi-core architecture (ATMA), a metascheduler is an advanced scheduling component that operates above standard task and resource schedulers. It is responsible for creating a comprehensive framework of schedules, known as a multi-schedule graph (MG), which outlines how tasks are to be executed, resources allocated over time in a multi-core system, and how communication should flow across the network on a chip (NoC), and how the system should adapt to changes or events. The metascheduler takes into account the system's various operational contexts, including different workload scenarios, environmental conditions, and potential system faults. It uses input from the application model (task graph) [29], the platform model, and the context model to generate multiple time-triggered schedules, each tailored to specific sets of conditions or \"contexts\" the system may encounter. These schedules dictate when and where computational tasks should run, how communication should flow across the network-on-chip (NoC), and how the system should adapt to changes or events. The metascheduler operates offline, meaning it computes schedules before runtime based on predictions and models of possible states the system may experience. It ensures that the system can adapt to changes in context by switching schedules in a time-triggered and predictable manner. The metascheduler thus plays a critical role in maintaining the system's efficiency, reliability, and adaptability to various runtime conditions.  
We use Figure 2 to describe the architecture of our proposed metascheduling algorithm, which mainly consists of an application, platform, and context model; conditionally timed models; and a scheduling component. The context module is used to create an event calendar, which the metascheduling algorithm utilizes in conjunction with the baseline application and platform model to generate conditional, timed models (application, platform, and context). The conditional timed models are used to construct an optimization problem, which a scheduler solves to produce a multi-schedule graph. In the following sections, we provide a detailed description of the building blocks.
## 3 Results
### Time Lag Effect  
The Pearson correlation coefficients between the extreme climatic indices factors (TXn, TNn, TXx, TNx, TMAXmean, TMINmean, RX1 day, RX5 day, and DTR) and the corresponding NDVI in the current month and after 1-3 months are provided in Figure 16 for the growing season (April-October) in the Loess Plateau. While the effect of the time delay of some factors, such as TXn, TNn, TXx, TNx, TMAXmean, and TMINmean, on vegetation growth was 0 month, that of DTR showed various time lengths in different areas of Loess Plateau (Table 6). The time lengths of time lag effects of DTR on vegetation growth differed for various vegetation types in the Loess Plateau.  
Figure 14: Distribution of Pearson correlation coefficients of NDVI with extreme climatic indices in the Loess Plateau region, the extreme climatic indices are (**a**) FD0, (**b**) ID0, (**c**) SU25, (**d**) TR20. (**e**) WSDI, (**f**) CSDI. (**g**) GSL.  
Figure 15: Percentages of Pearson correlation coefficients of NDVI with extreme climate indices over the Loess Plateau region.  
The distribution of time delay impacts on vegetation growth caused by meteorological factors in the Loess Plateau (PWV, RHU, and SSD) is presented in Figure 17. Among other factors, the correlation between PWV and NDVI was the highest when the time delay was 0, accounting for 98.55% of Loess Plateau. The time delay impacts on vegetation growth associated with RHU and SSD differed. Statistical results reveal that only 66.67% of Loess Plateau shows maximum Pearson correlation coefficients between NDVI and RHU when the time delay is 0. The time lag effect of SSD on vegetation growth was distributed from 0 to 3 months, and the response of vegetation to SSD in the northern part of the Loess Plateau was mainly two months, accounting for 30.43% of Loess Plateau. In the middle and south of Loess Plateau, the lag response was mainly 3 months, accounting for 34.78% of the total Loess Plateau area. SSD has no lag effect on 15.22% of the regional vegetation growth in the western region of the Loess Plateau and 19.57% of the vegetation growth in the northeast region.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Subregion** & **I** & **II** & **III** & **IV** & **V** & **VI** & **VII** & **VIII** \\ \hline Time delay(month) & 3 & 2 & 0 & 1 & 0 & 3 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time delay effects of DTR with different time lengths on vegetation growth in various subregions of the Loess Plateau region.  
Figure 16: Time delay effects of extreme climate factors on NDVI over the Loess Plateau region.
# Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic Bearing System
## 2 System Identification on Controllers for Radial Magnetic Bearings
### System Identification on Controllers for LRAMB  
Similarly, the controller for lower radial AMB (LRAMB) along the X-axis, \(G_{\text{LX\_C}}\), comprises a notch filter and a regulator, i.e., \(G_{\text{LX\_Notch}}\) and \(G_{\text{LX\_Reg}}\), respectively, shown in Figure 4. That is, \(G_{\text{LX\_C}}=G_{\text{LX\_Reg}}G_{\text{LX\_Notch}}\). The chirp signal is externally imported to _Point_**S7** shown in Figure 4, i.e., the output of lower gap sensor along the X-axis, and the corresponding response is recorded at _Point CP8_, i.e., the output of the regulator for LRAMB along the X-axis. The Bode plot of the controller for LRAMB along the X-axis is depicted in Figure 8. Unlike the notch frequency of the controllers at URAMB, the notch frequency is designed as 446 Hz for the controllers at LRAMB.  
Since the order of the dynamic model of controller for LRAMB along the X-axis is 4, the state space representation and its discrete-time transfer function are as:  
\[\text{A}_{\text{LX\_C}}=\begin{bmatrix}-4559.940&0&0&0\\ 0&-701.529&-2635.884&0\\ 0&2635.884&-701.529&0\\ 0&0&0&-25,679.137\\ \end{bmatrix} \tag{11}\]  
\[\text{B}_{\text{LX\_C}}=\begin{bmatrix}-67.522\\ -12.302\\ 2.900\\ -96.335\\ \end{bmatrix} \tag{12}\]  
\[\text{C}_{\text{LX\_CC}}=\begin{bmatrix}-39.352&-14.682&29.212&221.189\\ \end{bmatrix} \tag{13}\]  
\[\text{D}_{\text{LX\_CC}}=0.1937 \tag{14}\]  
\[\text{G}_{\text{LX\_CC}}=\begin{pmatrix}0.1937&\text{z}^{4}-1.048&\text{z}^{3 }+1.961&\text{z}^{2}-1.558&\text{z}+0.4515\\ &\text{(z}^{4}-2.987&\text{z}^{3}+3.207&\text{z}^{2}-1.422&\text{z}+0.2055), \end{pmatrix} \tag{15}\]  
where \(T_{\text{S}}=0.05\) ms  
On the other hand, the controllers for LRAMB along the X-axis and along the Y-axis are to levitate the lower part of the rotor together; the characteristics of the controller for LRAMB along the Y-axis are similar to those of the controller for LRAMB along the X-axis. The Bode plot of the controller for LRAMB along the Y-axis is close to that of the controller for LRAMB along the X-axis. The identified models of the controller for LRAMB along the Y-axis are:  
\[\text{A}_{\text{LY\_C}}=\begin{bmatrix}-4600.290&0&0&0\\ 0&-674.501&-2639.121&0\\ 0&2639.121&-674.501&0\\ 0&0&0&-24,903.283\\ \end{bmatrix} \tag{16}\]  
Figure 8: Bode plot of controller for LRAMB along the X-axis.  
\[\text{B}_{\text{LY\_C}}=\begin{bmatrix}-67.886\\ -11.936\\ 2.836\\ -95.533\end{bmatrix} \tag{17}\]  
\[\text{C}_{\text{LY\_C}}=\begin{bmatrix}-39.548&-14.883&28.915&213.690\end{bmatrix} \tag{18}\]  
\[\text{D}_{\text{LY\_C}}=0.1811 \tag{19}\]  
\[\text{G}_{\text{LY\_C}}=\begin{pmatrix}0.1811\;\text{z}^{4}-0.9952\;\text{z}^ {3}+1.879\;\text{z}^{2}-1.502\;\text{z}+0.437\end{pmatrix}/\\ \begin{pmatrix}\text{z}^{4}-2.999\;\text{z}^{3}+3.238\;\text{z}^{2}-1.45\;\text{ z}+0.2138\end{pmatrix},\end{pmatrix} \tag{20}\]  
where \(T_{\text{S}}=0.05\) ms
## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method
### Data Augmentation of Multi-Dimensional Feature Engineering
#### 3.1.3 Time Characteristics  
Given that the price volatility in the future market follows distinct patterns across various time frames, incorporating temporal features can enhance the model's ability to adjust and grasp these cyclical variations.  
(1) Hour and minute characteristics  
Retrieve the hour and minute details from the transaction timestamp, which is directly obtained from the transaction time data. It does not involve complex calculations, but only refines the time dimension information to the hour and minute levels. Different trading periods may have different market activity and price fluctuation characteristics. For example, during the period after the opening of the morning session, the market may experience large price fluctuations due to the digestion of overnight information and the influx of new trading orders; at the close of the market, the market trading activity may gradually decrease, and the price fluctuation is relatively small.  
(2) Characteristics of the day of the week The day (day_of_week) is included as a feature, which is also a simple extraction based on transaction time data. It is encoded in the order of the seven days of the week (0 on Monday and 6 on Sunday), because in different trading days of the week, the market may be affected by macroeconomic data releases, industry news, and other factors to show different price movements. For example, some important economic data are usually released on a specific working day, which may lead to large fluctuations in futures prices on that trading day.  
(3) Trading period characteristics  
Create a binary feature of whether it is morning (_is_morning_) and whether it is afternoon (_is_afternoon_), and the calculation formulas are as follows:  
\[\textit{is\_morning}=\left\{\begin{aligned} & 1,\ \ \textit{if}\ \ 9\leq hour<12\\ & 0,\ \ \ \ \ \ \textit{otherwise}\end{aligned}\right. \tag{9}\]  
\[\textit{is\_afternoon}=\left\{\begin{aligned} & 1,\ \ \textit{if}\ \ 13\leq hour<15\\ & 0,\ \ \ \ \ \ \textit{otherwise}\end{aligned}\right.. \tag{10}\]  
By differentiating various trading intervals, we can enhance the model's understanding of market price volatility patterns during distinct time frames. For instance, the composition of market participants, their trading tactics, and the pace of information dissemination might vary between the morning and midday, and these variations will be mirrored in the price movements.  
(4) Characteristics of intra-day minute count  
To calculate the number of minutes from the opening to the current time (_Minutes from Open_), the calculation formula is as follows:  
\[MinutesfromOpen=hour\times 60+minute. \tag{11}\]  
This functionality enables the model to grasp the temporal patterns of price variations within a single trading day. For instance, after the market opens, prices might exhibit a distinct rising or falling trajectory, which could shift as the trading session progresses. The intraday minute count serves as a temporal reference point for the model.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 5 Enhancement Measures for Power System Resilience
### Measures after an Event
* Microgrids  
A microgrid can be seen simply as a subset of the power grid, consisting of distributed loads, generation, storage, and protection devices that are synchronized with the main grid through a control system. The microgrid can operate in both grid-connected and island mode, allowing it to provide power during emergencies. For example, microgrids can be connected to the main grid to assist in restoring power to critical loads on distribution feeders. Alternatively, microgrids can be disconnected from the main grid and utilize DERs to provide power specifically for critical loads [57; 81]. Furthermore, the coordinated operation of multiple microgrids can increase the flexibility of grid operations to help the system adapt to the impacts of extreme events [85]. Due to their ability to effectively supply power to loads, microgrids are an important measure to enhance the resilience of future power systems. They are also a significant focus of many research studies [86; 87]. * Advanced smart grid technologies  
One of the concepts of resilience is to restore more load as quickly as possible after a power outage, and this can be achieved through advanced smart grid technologies. For example, distribution automation (DA) enhances the reliability of the system through remote monitoring and automated switching operations, reducing the number of affected customers and the recovery duration for system performance [54; 81]. Furthermore, network reconfiguration refers to the process of modifying the topology structure of the system. By utilizing remote-controlled tie switches and creating alternative paths with existing distribution lines, the power grid becomes less vulnerable to the impact of events [13]. This flexibility also facilitates the integration of microgrids with the distribution system.
## 1 Introduction
The suitability of a site for radio astronomy observations is primarily characterized by the opacity statistics in atmospheric windows or by the PWV statistics, which are related to each other under clear sky conditions, although the LWP statistics are also important. Both \(\tau_{\text{O2}}(\
u)\) and \(\beta(\
u)\) are determined by the vertical distributions of the atmosphere physical parameters (pressure and temperature), molecular oxygen and water vapor. They can be derived empirically for a certain site or calculated using the existing models of the atmosphere in conjunction with spectroscopic databases (e.g., [12; 13]). Then, nowadays, global dynamic models of the atmosphere with a high spatial and temporal resolution are available (see below). The dependencies of microwave absorption by molecular oxygen and water vapor on physical parameters and altitude in the atmosphere were analyzed many years ago [14]. In the paper in [15], the dependencies of \(\tau_{\text{O2}}(\
u)\) and \(\beta(\
u)\) on altitude for the 1.3 mm window were calculated. The dependence of the molecular oxygen optical depth on altitude (\(h\)) is well described by the exponential function:  
\[\tau_{\text{O2}}(\
u)=\alpha(\
u)e^{-\frac{h}{h_{0}}}\,, \tag{2}\]  
where \(\alpha(\
u)\) is the O\({}_{2}\) optical depth at the sea level and \(h_{0}\) is the characteristic height, usually adopted to be 5.3 km [16], although it can be somewhat different in different seasons [14]. Atmospheric transparency research is important not only for radio astronomy but also for telecommunications and radars. Millimeter-wave communication channels can provide the highest throughput (e.g., [17; 18; 19; 20; 21; 22; 23]) but are strongly affected by the atmosphere. Millimeter-wave radars are promising facilities for monitoring space debris and dangerous asteroids (e.g., [24; 25; 26]). There are several ways to measure the atmospheric opacity and PVW, which are described in Section 2. In Section 3, we present some results of such investigations. They are discussed in Section 4.
## 3 Proposed Methodology
### Cost Functions Selection  
The next step, is to find proper criteria to evaluate the performance of the converter. Nevertheless, there are many possible cost functions that could be considered. Thus, the well known integral of square error (ISE), integral of absolute error (IAE), integral of time multiplied by square error (ITSE), and integral of time multiplied by absolute error (ITAE) performance indexes described in [25], are options that can be easily suited into the design application.  
Henceforth, as described in [26], the main features that can be derived by the selection of the performance indexes are:  
1. Integral of the square of the error (ISE): \[\text{ISE}=\int_{0}^{\text{T}}e^{2}(t)dt\] (1) where \(e\) is the error signal along to an evaluation period T. The ISE index is associated to the error energy, giving more importance to larger errors.
2. Integral of the absolute magnitude of the error (IAE): \[\text{IAE}=\int_{0}^{\text{T}}|e(t)|dt\] (2) The IAE index, is related to the cumulative error, giving more importance to the distance between the reference and the obtained response.
3. Integral of time multiplied by absolute error (ITAE): \[\text{ITAE}=\int_{0}^{\text{T}}t\cdot|e(t)|dt\] (3) The ITAE index, is related to the absolute error value in time, giving more importance to the last errors than the initial ones.
4. Integral of time multiplied by squared error, (ITSE): \[\text{ITSE}=\int_{0}^{\text{T}}t\cdot e^{2}(t)dt\] (4) The ITSE index, also associated to the error energy, is weighted by time, giving more importance to the most recent errors.  
However, considering their main features and drawbacks, designing using the ITAE index may lead to small overshoots and damped oscillations (as explained in [15; 18]). Therefore, it is highly recommended as a cost function for this methodology.  
Moreover, since a fast voltage response is a priority for DC-DC converters design (as discussed in [4]), the ITAE allows an optimization that prioritizes the convergence and velocity of the converter, ensuring that the last errors will be weighted with greater importance.
## 3 Results and Discussion
### Overview and Final Remarks
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Physical**} & \multicolumn{3}{c}{**Thermal**} & \multicolumn{3}{c}{**Mechanical**} & \multicolumn{3}{c}{**Durability**} \\ \cline{2-13}
**ID** & \(\gamma\) & \(\varphi\) & \(\omega\) & \(\mathbf{R}\) & \(\lambda\) & \(\alpha\) & \(\sigma_{\mathbf{c}}\) & E & \(\mathbf{UPV_{30 cm}}\) & \(\sigma_{\mathbf{f}}\) & \(\mathbf{C_{b}}\) & \(\rho\) & \(\mathbf{W_{1g^{\prime}}}\) & \(\mathbf{D}\) \\ \hline R1-28D & 1.72 & 36 & 1.40 & 0.30 & 0.50 & 3.37 \(\times\) 10\({}^{-7}\) & 2.54 & 55.65 & 1018.65 & 0.25 & 0.19 & 1.06 & 20 & 310.67 \\ R2-28D & 1.90 & 31 & 1.40 & 0.23 & 0.65 & 4.04 \(\times\) 10\({}^{-7}\) & 2.27 & 53.61 & 1004.57 & 0.21 & 0.09 & 1.44 & 15 & 342.67 \\ R1-180D & 1.73 & 37 & 0.79 & 0.30 & 0.50 & 3.29 \(\times\) 10\({}^{-7}\) & 1.78 & 40.46 & 974.71 & 0.25 & 0.20 & 1.27 & 20 & - \\ R2-180D & 1.85 & 32 & 0.60 & 0.23 & 0.65 & 3.99 \(\times\) 10\({}^{-7}\) & 2.03 & 46.21 & 1079.09 & 0.21 & 0.15 & 1.57 & 16 & - \\ \hline  & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 12: Overview of the results obtained at 28 and 180 days. to the nature of the material and may not reproduce actual conditions. Properly laid, sheltered, and plastered blocks--at least for exterior walls--are hardly exposed to extreme conditions such as water sprays or total immersion. However, for scientific purposes, it is of great interest to perform them. [PERSON] et al. [57] classified the capillary absorption test as a long-term durability that can be used to assess durability under non-extreme conditions. On the other hand, the total immersion and accelerated erosion tests are classified as short-term tests to assess the effectiveness of stabilisers and, more likely, extreme events rather than to predict the rate of erosion. Testing more than one disintegration mechanism allows not to draw approximate and/or erroneous conclusions about the resistance of the blocks to weathering [58]. In both water absorption tests, the finer-textured R1 blocks absorbed more water than the coarser-textured R2 ones. The water absorption coefficient by capillarity was 51% higher in R1 CEBs than R2 at 28 days and 26% higher in R1 CEBs than R2 at 180 days. After 15 min of soaking, R1 CEBs absorbed 24% more water than R2 at 28 days and 23% more water than R2 CEBs at 180 days. Due to the low stabilisation rate, the accelerated erosion test did not produce interesting results. In fact, the blocks eroded at a very high rate in both mixtures and were completely disintegrated after only a few minutes. No mechanism could be identified, so the test was not repeated at 180 days.
## 2 Development of a Process Model for Two-Stage Bio-Oil Hydrogenation (HDO)  
### Specification of Compounds from Bio-Oil  
A process model was developed by using Aspen Plus to simulate the process of two-stage bio-oil hydrogenation. According to the properties of raw bio-oil and products shown in Table 1, and for simulation purposes, the hundreds of substances presenting bio-oil and products were therefore divided into 9 model components according to the reported study [10]: (1) BIO-OIL (organic compounds of raw bio-oil, CH\({}_{1.47}\)O\({}_{0.56}\)); (2) H\({}_{2}\); (3) ODF (oil-phase organic compounds of mild stage, CH\({}_{1.47}\)O\({}_{0.11}\)); (4) AQO (aqueous phase organic compounds of mild stage, CH\({}_{3.02}\)O\({}_{1.09}\)); (5) CO\({}_{2}\); (6) H\({}_{2}\)O; (7) COKES (soluble coke deposit, Cs); (8) COKEIS (insoluble coke deposit, C\({}_{\text{is}}\)); and (9) PRODUCT (model compound that represents the final product of bio-fuel, with same properties of gasoline, CH\({}_{1.71}\)). Model compounds were selected from the Aspen Plus database to represent the above-mentioned substances in the bio-oil and the upgraded products [11]. The developed HDO process model is adaptable for different types of bio-oil feedstock with given properties and reaction kinetic parameters.
# Textural Properties of Bakery Products: A Review of Instrumental and Sensory Evaluation Studies
###### Abstract  
Bakery products are an important sector of the food industry globally and are part of the regular diets of many people. Texture encompasses many product characteristics and plays a pivotal role in consumer acceptance. This review focuses on the studies that evaluate textural properties in a set of bakery products, either using instrumental texture measurements or sensorial evaluations. A search was conducted on scientific databases, and selection was based on some eligibility criteria, resulting in a total of 133 articles about the textural properties of bakery products. Of these studies, the majority reported only instrumental analysis of texture (62 out of 133), and a minor number of studies reported only sensorial analyses (n = 14). Still, there was an expressive number of studies in which both methodologies were used to assess the texture of the bakery products (n = 57), i.e., instrumental measurement complemented with sensory evaluation. The results showed that most studies focused on bread (37%) and cakes (33%). With respect to instrumental texture analysis, most tests were TPA (texture profile analysis), and the most commonly used probe was a cylinder disc with a 75 mm diameter. Instrumental tests usually determine textural properties like hardness, cohesiveness, chewiness, and springiness. Regarding the sensorial analyses of texture, mostly descriptive tests were used (72%), particularly sensory profiling, with a lower number of studies performing discriminating (18%) of preference/acceptance tests (10%). In most cases, untrained panels were used, with a most common number of panelists equal to ten, and the most representative evaluated textural attributes were hardness, cohesiveness, chewiness, and springiness. In conclusion, this review provides insight into the methods used to assess the texture of bakery products and which characteristics of these products should be on focus. Furthermore, it was verified that both types of methodologies are complementary in evaluating texture for these types of food products.  
bakery product; bread; cookie; cake; texture measurement; texture profile analysis; sensorial analysis; sensor panel +
Footnote †: journal: Applied Sciences