esa-sceva/satcom-chunk-collection huggingface dataset:
# A Micro-Processor-Based Feedback Stabilization Scheme for High-Q, Non-Linear Silicon Resonators
## 2 Materials and Methods
### Optical Transmission Measurement Setup  
All of the measurements presented in this work were carried out using an end-fire setup as shown in Figure 3. Light from a tunable laser, Agilent 81640A (Keysight Technologies Deutschland GmbH, Boblingen, Germany), was coupled onto the silicon chip via polarization control optics and a lensed optical fibre. The top scattering from the chip was measured using a compact InGaAs camera (Xenics nv, Leuven, Belgium), and the output facet was imaged onto a photo-detector via a microscope objective (Comer optics, Linton, UK). Transmission spectra were measured by using the tunable laser in swept mode and measuring the time dependent detected power on the oscilloscope (Keysight Technologies Deutschland GmbH, Boblingen, Germany).  
Figure 2: (a) Schematic of a silicon \(\pi\)-phase shifted grating with resistive heating element for wavelength tuning and thermal stabilization; (b) Scanning Electron Microscope (SEM) image of the phase shift section at the centre of a grating device (a device with \(d\) = 70 nm is shown to best illustrate the grating modulation profile).
# Applications of Probabilistic Forecasting in Demand Response
## 3 Results
### Application 1: Quantifying the Coverage Probability for Daily DR Actions  
In this section, we illustrate how to compute the daily DR capacity of the aggregation in terms of coverage probability. That is, for each day of the test period, we want to know which interval of uncertainty with respect to the predicted baseline can be covered by DR actions.  
The process can be summarized as follows:  
* Step 1: Select a day of the test period and obtain the predicted baseline of consumption for that day. In this step, the predicted baseline is given by the predicted alpha-quantile for each of the 24 h of the selected day, with alpha = 0.5 (the median) and a prediction horizon of 48 h.
* Step 2: Use the PBLM to simulate the maximum increase and maximum decrease in consumption by means of DR actions on the selected day. It is important to recall that comfort conditions must hold. The 24 h curves of this step will be subtracted and added, respectively, to the baseline, providing the lower and upper bands of the energy covered by DR actions.
* Step 3: Determine the predicted quantile curves (horizon 24 to 48 h) that are closer to the bands obtained in the previous step.  
Figure 5: Sharpness properties of the _QRF_ models: _PINAW_ for the QRF48 model (black dots) and the QRF1 model (blue dots).  
Figure 6: Comparation of sharpness properties for the LQR (black dots) and _QRF_ (blue dots) models: (**a**) _PINAW_ for a prediction horizon of 48 h; (**b**) _PINAW_ for a prediction horizon of 1 h.  
For example, Figures 7 and 8 depict the three steps of the process for one day in the test period (7 February 2011) and the two predictive methods. In the case of the LQR method, we can ensure that at least 75% of the uncertainty can be potentially covered by DR actions during the main hours (from 10 a.m. to 8 p.m.), whereas in the case of the _QRF_, the percentage of coverage could reach 79%.  
Let us focus on what happened from 10 a.m. to 8 p.m. on 7 February, using the _QRF_ method (Figure 8). It can be seen that the quasi-homogeneous control group considered for the two end-uses was able to cover nearly 100% of the uncertainty at 10 a.m., a little less at 11 a.m., and so on. In particular, the lowest value of the upper coverage interval (95%, filled dot in blue color) was reached at 5 p.m., whereas the greatest value of the lower coverage interval (16%, filled dot in green color) was reached at 8 p.m. This means that during all the intervals of interest (from 10 a.m. to 8 p.m.), the flexibility provided by the QHCG would cover the interval determined by quantiles 16% and 95%, which implies that 79% (i.e., 95-16%) of the uncertainty due to predictions would have been covered.
## 4 Mitigating the Data Protection Risks
### Protection against Reidentification Attacks
#### 4.1.2 Centralized Approaches  
Apart from these decentralization approaches, another way to alleviate data protection risks with respect to training data is to anonymize them; however, this comes with a well-known problem that is related to every anonymization, namely, how to answer affirmatively with certainty the question \"are the resulting data indeed anonymous?\" Even if we can ensure that proper anonymization has taken place, there is still another important question that needs to be considered: \"Are the anonymous data still useful for the training purposes?\" To this end, several techniques have been proposed. The researchers in [15] propose a differential privacy (DP) technique for the training dataset that is based on the stochastic gradient descent algorithm, i.e., noise is added into each iteration of the stochastic gradient descent algorithm with the aim of preserving the privacy of the said algorithm at a minimal cost in terms of software complexity, training efficiency, and ML model accuracy. DP focuses on achieving the following: the inclusion or exclusion of a single individual from a dataset should not significantly change the results of any analysis carried out on the dataset to make it difficult to conclude anything about that individual; to achieve this, noise is added appropriately to the data. For the ML algorithm, as the noise introduced into the stochastic gradient descent algorithm increases, higher probabilities for achieving anonymization areachieved. But, as stated before, there is a need to configure the hyperparameters of the model appropriately in order to find the proper balance between anonymization and the validity of results [15].  
In [56], a new technique is proposed, which applies a \"confusion\" function to the training dataset before the model training process takes place. This function either introduces random noise into the samples of the training dataset or adds new samples to it, with the aim of hiding personal information regarding the characteristics of individual samples or the statistical characteristics of a group of samples. Clearly, this technique lies within anonymization techniques; however, it should be stressed that by this method, the ML model trained with the \"confused\" dataset can achieve accuracy to a high extent.  
An alternative approach to making private computations over the training dataset relies on the use of homomorphic encryption (see, e.g., [13; 16; 57]). As stated above, in the context of decentralized settings, by these means, operations take place over encrypted data so that the entity performing these operations, i.e., the training of the model, does not have access to the actual training data.
###### Abstract  
The development of global navigation satellite systems (GNSSs) and multi-system compatible radio occultation (RO) techniques provides favorable conditions and opportunities for increasing the number of occultation events and improving their spatiotemporal coverage. The performance of the multiple GNSS RO event number, spatiotemporal coverage, and uniformity need assessments by robust and functional approaches. Firstly, a simulation system of RO events, which took the orbit perturbations into account, was established, and the concepts of global coverage fraction and uniformity of RO events were defined. Secondly, numerical experiments were designed to analyze the GNSS RO performances of a single-receiving satellite and satellite constellations under the condition of using current multiple GNSSs as transmitting satellite systems, in which the Earth was divided into 400 \(\times\) 400 km\({}^{2}\) grids. Finally, the number, timeliness, global coverage fraction, and uniformity of GNSS RO events for a single-receiving satellite and receiving satellite constellations were numerically calculated and analyzed. The results showed that \(\heartsuit\) multiple GNSS integration improved the number of GNSS RO events and their global coverage for a single polar-orbit satellite significantly, e.g., the 24 h multiple GNSS RO event number was about 7.8 times that of the single GNSS system, BeiDou navigation satellite system-3, while the corresponding 24 h global coverage fraction increased nearly 3 times. \(\heartsuit\) In the multiple GNSS integration scenario, the constellation composed of 12 polar-orbit low-Earth-orbit satellites achieved 100% RO event global coverage fraction within 24 h, of which the RO detection capability was comparable to the 100 Spire weather satellites and global positioning system (GPS) RO system. \(\heartsuit\) More GNSS RO events of the polar-orbit constellations were distributed in the middle- and high-latitude zones. Therefore, multiple GNSS integration could increase the RO event number and global coverage significantly to benefit the global climate monitoring and global numerical weather prediction, and the polar-orbit constellations were more favorable to atmospheric detection in middle- and high-latitude regions.  
multiple GNSS; radio occultation; orbit and constellation parameters; event number; global coverage +
Footnote †: journal: _atmosphere_2022, 13, 654. [[https://doi.org/10.3390/atmos13050654](https://doi.org/10.3390/atmos13050654)]([https://doi.org/10.3390/atmos13050654](https://doi.org/10.3390/atmos13050654))
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 7 System Performance
### DC to DC Efficiency  
The DC to DC efficiency of a WPT system given in Table 4 indicates the overall performance of the WPT system as a whole. The system with wound copper coils had an optimal 52% DC to DC efficiency under full load at 5 mm separation distance [24; 25]. The DC to DC efficiency of the system equipped with copper tape and printed coils were measured at 5, 10, 15, and 20 mm separation distances with varying load. The highest efficiencies given in Table 4 occur at the maximum output power point, which was as expected since the Qi WPT system is designed to achieve optimal efficiency under full  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Separation** & **Symbol** & **Wound Copper Coils** & **Copper Tape Coils** & **Printed Coils** \\ \hline \multirow{2}{*}{5 mm} & \(I_{Load}\) & 0.39 A & 0.37 A & 0.37 A \\  & \(\eta_{DC-DC}\) & 52\% & 51\% & 37\% \\ \hline \multirow{2}{*}{10 mm} & \(I_{Load}\) & 0.37 A & 0.36 A & 0.36 A \\  & \(\eta_{DC-DC}\) & 46\% & 44\% & 30\% \\ \hline \multirow{2}{*}{15 mm} & \(I_{Load}\) & 0.28 A & 0.28 A & 0.27 A \\  & \(\eta_{DC-DC}\) & 39\% & 38\% & 22\% \\ \hline \multirow{2}{*}{20 mm} & \(I_{Load}\) & 0.16 A & 0.16 A & 0.14 A \\  & \(\eta_{DC-DC}\) & 24\% & 24\% & 10\% \\ \hline \end{tabular}
\end{table}
Table 4: Maximum output current and DC to DC efficiency of wireless power transfer system deployed with different coils.  
Figure 10: Output powers of wireless power transfer system deployed with different type of coupled coils against output DC current at 5 mm and 10 mm separation between transmitter and receiver coils. An approximate 1.51 W output power can be achieved for all types of coils at 5 mm separation, and 9% less output power, 1.37 W, can be achieved at 10 mm separation.  
load. The WPT system equipped with the printed coils has up to a 60% reduction in the DC to DC efficiency compared to that of the wound copper coils, this reduction in efficiency is attributed to the losses associated with the ESRs of the printed coils.  
The DC to DC efficiency at optimal output current against varying separation distance for the different types of paired TX and RX coils is shown in Figure 11. It can be seen that the efficiency reduces as the separation distance increases for all the types of coils tested.
# Towards the Implementation Process of Accessible Virtual
## 5 Process for the Implementation of an Accessible Virtual Campus
### Methodologies for the Implementation of an Accessible Virtual Campus  
In the multivocal literature review conducted, there were methodologies identified that consider the different organizational levels in HEIs with respect to the implementation of virtual learning environments. [PERSON] et al. (2018) proposes the systematic creation of a virtual learning environment as a set of defined phases and dimensions that enable the implementation of virtual spaces for learning. [PERSON] and [PERSON] (2019) employ a holistic approach to e-learning founded on usability, accessibility, local factors, infrastructure, and available learning results. Lastly, [PERSON] (2019) proposes criteria that should be considered for the creation of accessible e-learning platforms. These following criteria are:  
1. **Establish Consciousness**: learn the requirements of users, the uses of resources, and approaches towards inclusivity.
2. **Research**: identify usability best practices for accessible platforms and content.
3. **Comprehension**: evaluate the adaptation of current practices and their potential application as learning objects.
4. **Implementation**: execute changes based on current and researched practices. Work on adjustments as they are needed, in addition to search for possible alternatives and enhancements.
5. **Evaluation**: verify the quality as well as the effectiveness of learning objects used by students.  
In the methodologies described, some common elements stand out, such as discerning the requirements via an analysis or diagnosis; pinpointing practices that increase usability and accessibility in learning environments; making necessary adjustments; and the evaluation of steps taken towards the objectives and requirements established.  
When assessing the processes involved in HEIs, a wide array of factors and stakeholders must be considered in order to balance the multiple requirements identified and generate a high-quality virtual campus ([PERSON] _et al._, 2019).
# Effects of Pretreatment on the Volatile Composition, Amino Acid, and Fatty Acid Content of Oat Bran
## 3 Results and Discussion
### Effects of Heating Methods on Amino Acids of Oat Bran
#### 3.4.2 Analysis of the Correlation of Amino Acids and Flavor Components in the Pretreated Oat Bran  
The Spearman correlation coefficient is a nonparametric index to measure the dependence of two variables. It uses a monotone equation to evaluate the correlation between two statistical variables. If there are no duplicate values in the data, and when the two variables are completely monotonically correlated, the correlation coefficient rho (R value) is the value between \(-1\) and \(1\). Red represents positive correlation, blue represents negative correlation. As shown in Figure 3 and Table 2, most of the amino acids, except Pro, Met, and Tyr, displayed an extremely significant positive correlation with components: VFC42, VFC34, VFC43, and VFC26(\(p\) < 0.01). These components were identified as acetic acid, 4-ethylcyclohexan-1-ol, octanoic acid, and decanoic acid ethyl ester. The umami amino acid glutamic acid (Glu) displayed an extremely significant positive correlation with components VFC17, VFC38, VFC55, VFC53, VFC33, VFC35, VFC36, and VFC48, and a very significant negative correlation with components VFC5, VFC4, VFC22, and VFC2 (\(p\) < 0.01). These components were identified as aldehydes, esters, alcohols, and alkanes. The bitter amino acid leucine acid (Leu) showed an extremely significant positive correlation with components VFC17, VFC38, VFC55, VFC53, VFC33, VFC35, VFC36, VFC48, VFC62, and VFC12, and a very significant negative correlation with the components VFC5, VFC4, VFC7, and VFC51 (\(p\) < 0.01). Most of those components were identified as aldehydes, esters, alcohols, and alkanes. The above comprehensive analysis demonstrated that most amino acids were mainly related to alcohols, esters, and aldehydes. Amino acids are the precursors in the metabolic pathway of volatile composition. Since heat treatment aggravates the Maillard and Strecker degradation reactions, the relationship between aromatic ester substances and amino acids is mainly due to amino acid metabolism. Transamination and decarboxylation occur. Hence, the changes in the free amino acids are closely related to the metabolism of volatile flavor substances [20].  
Figure 2: Radar graph of the sensory results of pretreated oat bran. OB, Oat bran; S-OB, Steamed oat bran; M-OB, Microwaved oat bran; HA-OB, Hot air dried oat bran. Sweet = Ala + Gly + Ser + Thr + Pro; Umami = GLU + ASP; Bitter =Val + Met + Leu + Ile + Phe + His + Arg; Tasteless = Lys + Tyr; Other = Cys.  
Figure 3: Correlation heat map between amino acids and flavor substances in pretreated oat bran. Red represents positive correlation, blue represents negative correlation, * represents \(p<0.05\), ** represents \(p<0.01\).
# Source Microphone Identification Using Swin Transformer
## 4 Experiments and Discussion
### Investigating the Preprocessing of Inputs to Improve System Accuracy  
#### 4.4.1 Frequency Band Effect on Accuracy  
This experiment aims to investigate the impact of bandwidth on classification accuracy for microphone intra-model classification. Various experiments were conducted with different frequency bands on all microphones in the database. The frequency range from 0 to 16 kHz was segmented, and the study was conducted by using a portion of the frequency spectrum while neglecting the remaining frequencies to determine the optimal frequencies that could potentially yield a microphone identification.  
As shown in Table 2, the results indicate that the frequency range from 0 to 4 kHz produces the best outcomes for microphones M1, M2, M3, and M5, while the frequency range from 4 to 8 kHz yields the best results for microphones M4, M6, M7, and M8. High frequencies ranging from 8 to 12 kHz, as well as frequencies from 12 to 16 kHz, do not yield satisfactory results when compared with other frequencies. Additionally, another experiment was conducted using the entire frequency spectrum, and the system achieves better accuracy for microphones M7 and M8 compared with only using parts of the frequency spectrum.  
Figure 3: Classification of the intra-model performance of microphones. Comparison among Swin_T, AST, and KNN.  
Based on the previous experiments, it can be concluded that each microphone model may have a unique pattern that is more pronounced in a specific frequency range compared with others. In other words, it is impractical to specify a single frequency range that can be used for all microphones.
## 4 Evaluation over Use Cases
### ICT Gateway
#### 4.1.6 Treatment  
Based on the results obtained from the testing tools, we improve the initial version of the extended MUD, which was created by the ResilBlockly tool during the risk identification phase. To automate this process, we created the MUD Updater tool (Figure 5, treatment phase). It collects empirical results from the security testing phase to refine the initial values of the extended MUD, adding new configurations to reduce the attack surface of possible attacks and mitigations for the vulnerabilities encountered. Figure 9 shows an overview of the relationship between the MUD and the methodology in which the original MUD file created by the manufacturer is extended by the assessment report of ResilBlockly and refined later by the MUD updater.  
Figure 8: Security label obtained for ICT GW.  
The MUD updater uses the metrics derived from the test's execution, which are stored in the **TestSuite-output.json** file. In particular, the MUD updater analyses the field **matchWith** shown in Listing 2 (line 12) to locate the name of the corresponding ACL within the extended MUD that needs to be updated or extended. Then, based on the metric's name and value, the tool defines the specific policy to be integrated into the MUD.  
The outputs of the ICT GW testing, in addition to their MUD file, were then used as inputs for the MUD updater to derive new security configurations. In particular, the maximum number of simultaneous connections (**num-connections**) was established at 4822 (C22), and the cryptographic algorithm was set up to RSA-OAEP-256 (C46). The MUD was extended 160'69% with respect to the original MUD file, and 40% of the non-binary test results were integrated into it. The resulting extended MUD file can be found in [44].
## 3 Results and Analysis
### Processes and Data Inputs in the Planning of Road Schedules
#### 3.1.3 Sequencing Construction Activities  
Sequencing construction activities involves identifying and documenting the order in which the project's construction activities should be based on the activities identified during the planning phase. This information helps establish the chronological order of construction activities, ensuring an efficient construction process that aligns with the project's scope. Identifying these relationships also helps pinpoint the constraints activities will face during construction, allowing the contractor to manage the release of constraints on predecessor activities. Furthermore, planners can use this information to estimate the duration and critical path of the project, identifying activities that could delay the project if they are delayed. This approach enables planners to highlight essential construction activities and those with float, where schedule deviations can occur without affecting the project's delivery date. Key types of precedence relationships include finish-to-start (FS), finish-to-finish (FF), start-to-start (SS), and start-to-finish (SF). Additionally, it is crucial to account for leads or lags that may impact the start of successor activities. When defining precedence relationships for road infrastructure construction processes, it is essential to consider the work zones where the construction process will occur. This consideration allows for alternating between different work zones, coordinating the construction process with the project's traffic management plan, minimizing disruptions to road users, and enhancing construction efficiency.