esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Feasibility Test of the API Protocol via Simulations
### Performance Analysis of the API Protocol
#### 4.2.1 Simulation Operation and Conditions for Analyzing the Performance of the API Protocol  
The simulation is built using MATLAB software and its operation is explained as follows:  
* The simulation considers the initialization stage between a master node and a number of slave nodes.
* The position of a master node and slave nodes are randomly determined in the beginning of a simulation. It is assumed that the master node and any slave node can be connected directly in the initialization stage.
* A master node and slave nodes operate based on the initialization process as specified in Section 3.
* In the simulation, an initialization process between a master node and slave nodes can be executed up to \(K\) times. \(K\) is given as the same as the number of slave nodes. This is determined based on the assumption that at least one slave node can be initialized in one round such that as many rounds as the number of all the slave nodes can be iterated in the worst case.
* At the end of one process, the master node calculates the initialization success rate, and compares it to the given application requirement.
* Simulations are executed case by case with respect to the initialization rate and bit error rate (BER).
* For one case with a fixed BER and initialization rate, 10,000 simulation tests are performed, and the average values of the performance metrics are derived. After performing the simulation tests in the same way by changing the BER and the initialization rate, the performance of the API, and the FI are analyzed and compared with each other.  
The parameters used in the simulations are as follows:  
* The number of simulation experiments per case is 10,000.
* The number of slave nodes is 100.
* The maximum number of initialization repetitions is 100.
* The initialization rate is 0.2:0.2:1.0.
* The length of initialization messages is 100 bits.
* The BERs are \(7\times 10^{-3},\;5\times 10^{-3},\;3\times 10^{-3},\;10^{-3}\), and 0.
## 5 Simulation and Performance Analysis
### Performance and Analysis  
Firstly, we verified the convergence of the proposed method on the training set. With the same number of rounds but a different number of trainings in each round as the reference factor, the convergence of the method was compared, as shown in Figure 11. In the training and testing process, we set the rewards for correct and incorrect identity authentication to 10 and \(-\)100, respectively. Each point in the figure represents the average reward value corresponding to the number of trainings. It can be seen from the figure that, based on the training set data, the method showed a gradual convergence trend under different numbers of trainings, and the method basically reached the initial convergence state at 500 rounds in six subgraphs, thus indicating the correctness of the method parameters setting. The set parameters played a positive guiding role in the learning of the model method. In addition, we can also observe from the figure that, as the number of trainings in each round increased, the training convergence curve of the method became gradually smoother, and the convergence results of each subgraph were 9.13, 9.22, 9.35, 9.46, 9.65, and 9.82, respectively (the closer the convergence value is to 10, the more in place the model training is), which also corresponded to the objective reality. The more the model explores and utilizes, the better it can understand the rules of getting along with the environment. However, in the setting of model parameters, more trainings corresponded to a longer training delay.  
For the test set of CSI data corresponding to six sensing terminals, we verified the identification performance of various methods. Figure 12 (on the last page of this paper) shows the authentication confusion matrix of the utilized methods. The horizontal axis in each subgraph represents the terminal discrimination of the method, while the vertical axis represents the true identity of the terminal. It can be observed from the eight subgraphs that the DQN-PIA method proposed in this paper achieved an accuracy rate of over 98% in the authentication of six terminals, with an overall authentication accuracy of 98.9%, which was the scheme with the highest authentication accuracy of all methods. In comparison, the long short-term memory (LSTM) method, which is good at processing time series data, showed a performance second only to the method proposed in this paper, with an overall authentication accuracy of 97.7%. The CNN method was also satisfactory for terminal authentication performance, with an authentication accuracy only slightly lower than DQN-PIA and LSTM; this is because this method can obtain the features of the CSI matrix through convolution, which can be used for terminal identity authentication. Among the eight methods, the logistic regression method assumed that there was a certain direct nonlinear mapping relationship between the CSI data and the terminal identity. Because the assumption made was relatively simple and intuitive compared with other methods, the performance of the method was the worst, with an overall authentication accuracy of 88.5%.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Proposed Method  
The structure of the proposed GRU model is depicted in Figure 1a. The GRU consists of a reset gate that determines the amount of past information to be discarded, an update gate that determines the amount of past information to be passed to the future, candidate output, and output. The reset gate (\(\mathbf{r}^{(t)}\)), candidate output (\(\mathbf{h}^{(t)}\)), update gate (\(\mathbf{z}^{(t)}\)), and output (\(\mathbf{y}^{(t)}\)) are defined in Equations (1)-(4), respectively.  
\[\mathbf{r}^{(t)}=f_{r}^{\sigma}\Big{(}\mathbf{x}^{(t)}\mathbf{W}_{r}+\mathbf{y}^{(t-1)}\mathbf{V}_ {r}+\mathbf{b}_{r}\Big{)}, \tag{1}\]  
\[\mathbf{h}^{(t)}=f_{h}^{tanh}\Big{(}\mathbf{x}^{(t)}\mathbf{W}_{h}+\big{(}\mathbf{r}^{(t)} \odot\mathbf{y}^{(t-1)}\big{)}\mathbf{V}_{h}+\mathbf{b}_{h}\Big{)}, \tag{2}\]  
\[\mathbf{z}^{(t)}=f_{z}^{\sigma}\Big{(}\mathbf{x}^{(t)}\mathbf{W}_{z}+\mathbf{y}^{(t-1)}\mathbf{V}_ {z}+\mathbf{b}_{z}\Big{)}, \tag{3}\]  
\[\mathbf{y}^{(t)}=\Big{(}1-\mathbf{z}^{(t)}\Big{)}\odot\mathbf{y}^{(t-1)}+\mathbf{z}^{(t)} \odot h^{(t)}, \tag{4}\]  
where \(\mathbf{x}^{(t)}\) is the input vector at time t, \(\mathbf{W}\) is the weight matrix of \(\mathbf{x}^{(t)}\) for each gate, \(\mathbf{V}\) is the weight matrix of \(\mathbf{y}^{(t)}\) for each gate, and \(\mathbf{b}\) is the bias vector input to each gate. Furthermore, \(f^{\sigma}\) is the sigmoid function and \(f^{tanh}\) is the hyperbolic tangent function. The operation symbol \(\odot\) indicates the Hadamard product.  
The architecture of the proposed GRU model is illustrated in Figure 1b, where \(T\) represents the number of time points and \(C\) denotes the number of input data dimensions. As the input data were single-trial EOG data, \(T\) was 101. Furthermore, C was 2 because the EOG data consisted of two channels. The \(H\) in the hidden layer and \(1\) in the output layer are the number of neurons in each GRU. The hyperbolic tangent function was used as the activation function for the output of GRU 1, and the softmax function was used as the activation function for the output of GRU 2. The model was trained using Adam, which is a stochastic gradient descent algorithm, to minimize the cross-entropy error between the output of Layer 2 and the label data of the input data (epoch number: 25, batch size: 256). The optimal number of \(H\), which is a hyperparameter, was estimated using a grid searchfrom a range of 25 to 150 (in increments of 25) based on the mean absolute error between the label data and the output of Layer 2 in three-fold cross-validation using the training data. The model was evaluated using leave-one-subject-out cross-validation.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 2 Networked Microgrids' Configuration
### Power Distribution  
The configuration of NMGs is significantly reliant on power flow (PF) calculations. Analyzing the power flow or voltage profile is crucial for understanding the distribution of power within the network. This information plays a key role in dispatching microgrids optimally, ensuring their stable and reliable operation. Additionally, it aids in identifying areas with high load concentration and interconnected DERs, which are deemed as promising candidates for microgrid formation. According to Table 2, researchers used different PF techniques in configuring NMGs.  
PF calculations frequently employ various numerical techniques to linearize nonlinear equations and solve them within electrical power systems. The PF calculation typically consumes a significant amount of execution time and involves complexities, mainly because it necessitates updating the voltage magnitude and angle in each iterative process [65]. These challenges become particularly pronounced in NMGs due to their dependency on various factors, including the operational mode, types of microgrids, and network topologies. The detailed discussions on these factors will be presented in the subsequent subsections
## Conceptual Scheme
### Residential LoRa Node  
As with the personal node, the residential LoRa node is composed of an ESP32-based microcontroller (MCU) with various sensors coupled and placed in areas that do not interfere with the use of the home. The data are sent periodically, in previously defined periods, and can also be sent immediately, whenever certain values read on the sensors exceed the previously established limits, considering that there will be a situation of alert or threat to the safety of residents and housing.  
All the necessary data modeling is supported on a platform developed for this purpose and hosted on a dedicated server, which serves as a form of service infrastructure.  
To take advantage of IoT technologies, namely LoRa communications, The Things Network (TTN), which is a collaborative communication infrastructure for Internet of Things, is used as a reference, and is accessible in [53].
## Materials, Methods, and Concept
### Methods  
#### 2.2.1 Method of Evaluation  
To evaluate the effect of personalized orthodontic treatment with Dental Monitoring(r) (Dental Monitoring Co., Paris, France) in this paper, the following criteria were used:  
1. A comparison of average treatment refinements per patient from a 3 year average before implementation of DM and after. Implementation of DM into a treatment workflow has been compulsory since 2019;
2. A reduction in the number of in-person appointments required for patients who received dental monitoring and in the period without DM;
3. An online patient satisfaction feedback provided after every appointment; an improvement in patient satisfaction and comfort level with the use of Dental Monitoring(r) (DM) (Dental Monitoring Co., Paris, France);
4. Evaluation of the nurse's/assistant's daily activities in relation to the organization of time.  
Only comprehensive aligner treatments longer than a year were selected for evaluation to prevent possible bias resulting from extremely short treatments. This preliminary report refers to preliminary findings on the sets of 372 DM patients and 192 non-DM patients (control), although the complete retrospective cohort study is the subject of separate research and is not pivotal to this preliminary report. The average number of refinements per patient in orthodontic aligner treatment can vary depending on the specific case and treatment plan. In general, it is common for patients to have approximately two refinements during their treatment with aligners. However, this depends on practitioners' and patients' attention to detail and many other factors. Some cases may require more adjustments, while others may not.  
Only aligner treatments by Invisalign (Align Technology, Inc., San Jose, CA, USA) were included as they represented the majority of aligner cases treated in the observed period in selected offices. Spark cases (Ormco Corporation, Brea, CA, USA) were excluded.  
The time periods evaluated were non-DM workflow 1/2016-1/2019 and DM 1/2019-1/2022 with an approximately stable patient count per year and staff numbers.  
An online patient satisfaction survey was provided after every appointment to ascertain the improvement in patient satisfaction and comfort level with the use of Dental Monitoring(r) (DM) (Dental Monitoring Co., Paris, France). The online form was provided in an electronic appointment reminder and calendar event as a link to an online form created in Typeform.com (Typepform S.L., Barcelona, Spain).  
A more complex cost-benefit analysis of Dental Monitoring(r) (DM) (Dental Monitoring Co., Paris, France) compared to traditional orthodontic treatment methods would require separate research and is a possible topic for a separate clinical trial that would demonstrate the efficacy and safety of Dental Monitoring(r) (DM) (Dental Monitoring Co., Paris, France) as a means of delivering personalized orthodontic care.
## 4 Experimental Results
### Timing Fluctuation  
Figure 4 shows the timing fluctuation results for our fiber-based frequency transfer experiment. We had measurements for three scenarios, which are the frequency transfers without phase compensation, with phase compensation, and with shorted fiber optic links. Figure 4a shows the timing fluctuation result of the recovered 10 GHz RF signal on the receiver site without phase compensation. Its RMS value is about 21 ps in a 10,000 s measurement time. Figure 4b shows the timing fluctuation result of the recovered RF signal on the receiver site with phase compensation. Its RMS value is about 103 fs in a measurement time of 10,000 s. Comparing these two figures, it can be seen that the timing fluctuation is reduced by about 100 times using our joint phase compensation technique. Figure 4c shows the timing fluctuation result when the optical fiber link is shorted. The shorted link is shown in Figure 3, and it is a 1-m long fiber which directly connects the transmitter and receiver sites, by replacing the 50-km fiber link. Only the noise of the electric and optical components contributes to the result, which can also be regarded as the measurement floor of this system. The RMS value of this floor is about 29 fs in a 10,000 s measurement time.  
Figure 3: Schematic of fiber-based RF transfer with optical–electronic joint phase compensation. CW: continuous wave, EOM: electro-optical modulator, PS: phase shifter, BPF: band-pass filter, HV: high voltage, PD: photodiode, API: analog proportional-integral controller. DPI: digital proportional-integral controller.
# Sulfonic Resins as Catalysts for the Oxidation of Alcohols with H\({}_{2}\)O\({}_{2}\)/KBr
## 1 Introduction  
Fatty acids are among the most useful renewable materials as they are able to produce many types of useful compounds, such as surfactants, lubricants or polymers [1, 2, 3]. Hence, the development of new sustainable methods for their transformation, or the adaptation of existing ones to the particularities of fatty acid derivatives, is an important objective in sustainable chemistry [4, 5]. Our group has been working on methods that include heterogeneous catalysts for the transformation of fatty esters, starting from unsaturated ones, through a first epoxidation step and the further conversion of the epoxide via rearrangement [6], hydrogenolysis [7], hydrolysis [8] or oxidative cleavage [9].  
The oxidation of alcohols to carbonyls is one of the most useful reactions for the transformation of functional groups [10]. Some oxidation methods include the combination of organic molecules, such as in the Swern oxidation method (oxalyl chloride and triethylamine in dimethyl sulfoxide) or the Corey-Kim oxidation method (N-chlorosuccinimide/Me\({}_{2}\)S/NE\({}_{3}\)), or the use of hypervalent iodine compounds, as in the Dess-Martin oxidation method. In other cases, the oxidant systems are based on metals, such as chromium trioxide (Jones oxidation), aluminum alkoxide (Oppenauer oxidation), pyridinium chlorochromatic, barium permanganate, manganese dioxide, ruthenium tetroxide or silver carbonate. However, the oxidation of hydroxyd fatty esters has been very seldom described in the literature. The use of CrO\({}_{3}\) as an oxidant [11, 12], or the Swern oxidation [13], are examples of non-sustainable oxidation methods applied to the synthesis of ketofatty esters.  
Different catalytic systems have been developed for use with benign oxidants, mostly using transition metal complexes [14]. Molecular oxygen has been used as a primary oxidant with Pd [15, 16], V [17, 18] and Ru [19] complexes. Hydrogen peroxide and alkyl hydroperoxides have been used with Fe and Cr complexes [20, 21]. However, metal catalysts have a certain environmental impact and some disadvantages. Therefore, transition-metal-free methods are being developed; this includes, for example, using nitrosyl radicals as catalysts [22]. The Anelli-Montanari method includes the use of sodium hypochlorite as an oxidant and sodium bromide as a catalyst [23]. [PERSON] et al. have described twoclosely related oxidation methods [24], in which potassium bromide is the catalyst and the oxidants are the stabilized monopersulfate Oxone(r) or hydrogen peroxide, in this case using a sulfonic acid as an additional catalyst. The first method was successfully applied to the oxidative cleavage of fatty diols [9]. In this paper, we describe the application of the H\({}_{2}\)O\({}_{2}\)/KBr/sulfonic acid method for the oxidation of secondary hydroxyfatty esters, the heterogenization of the procedure via the substitution of the sulfonic acid with a solid using sulfonic groups and its application to the oxidation of other alcohols.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 3 Experimental Setup
### In-Air Testing  
After performing the on-ground experiments, real-time in-air testing was conducted for 20 charge-discharge cycles. This testing was conducted to gather data under high discharge rates. Real-time data were collected using an electric propulsion vehicle or quadcopter (a FLYWOO Explorer), with 2750 kV motors that can spin 2750 RPM per volt. The quadcopter includes a GOKU GN405 Nano flight controller with an Atomic 5.8 GHz antenna for point-to-point communication. Without the battery, the quadcopter weighs 162.8 \(\pm\) 2 g. The quadcopter was chosen for its real-world usage and ability to draw high currents from the battery pack [41]. The battery pack was installed in a 3D-printed mounting frame to support the BMS on top, as seen in Figure 3c. The BMS weighed \(113\pm 2\) g. The battery charging protocol remained consistent with the on-ground procedure, utilizing the datasheet of the battery pack. After each charging cycle, approximately 0.167 h were required to prepare for in-air discharge. Unlike the controlled discharge patterns employed during ground testing, the in-air discharge cycles were randomized to simulate real-world variations. The quadcopter flew approximately 1 foot above ground level during discharge. When the BMS indicated that the battery voltage had reached 10 V, the in-air discharge was stopped. After a 0.167 h rest period, a charging cycle was performed.
## 3 Materials and Methods
### Two Different Combinations of MOEAS  
As we mentioned, two different approaches are compared. They differ in the way the solutions are obtained:  
* _Approach A_: Apply a single MOEA for obtaining non-dominated solutions.
* _Approach B_: Combine a set of MOEAs to obtain a global group of non-dominated solutions.  
Figure 1 shows the functional diagram of both approaches. Under _Approach A_, a single MOEA is used, whereas under _Approach B_, a set of them is used, but in both cases, the result of the optimization process is a set of non-dominated solutions. These are applied in the market to obtain daily results. In both approaches, the solutions are selected according to a group of objectives under a non-dominating selection process. The fitness values are calculated by simulating the market with the selected dataset. _Approach A_ allows us investigation of various implementations of different MOEAs, while _Approach B_ constitutes the main contribution of this work. We selected a set of the most popular MOEAs found in the literature:  
* Non-dominated Sorting Genetic Algorithm-II (NSGA-II) [14].
* Strength Pareto Evolutionary Algorithm (SPEA2) [15].
* Pareto Archived Evolution Strategy (PAES) [16].
* Niched Pareto Genetic Algorithm (PESA-II) [17].
* Cellular Genetic Algorithm (MOCell) [18].
## 3 Materials and Methods
### Design, Implementation, and Installation of a Low-Cost GNSS Monitoring System  
The previous research [15] showed that, even with mass-market receivers, it was possible to detect displacements imposed up to five millimeters in planimetry and one centimeter in altimetry. In the previous study, the best results were achieved with an uBlox EVK7T (Thalwil, Switzerland) receiver, a Garmin GA38 (Olathe, KS, USA) mass-market antenna and when the distance from the master station was about few hundred meters. The phase ambiguity fixing was performed with the use of the L1 GPS frequency only [15].  
The instrumentation installed on the Madonna del Sasso sites involved the use of mass-market uBlox EVK8T receivers that provide data on the first frequency of the three GNSS constellations: GPS, Galileo and Beidou. It also based on the Garmin GA38 antenna able to trace these frequencies and a distance from the nearest permanent station of less than 10 km. A short distance between master and rover makes it possible to neglect the tropospheric and ionospheric residues in the double phase differences, and it has almost always been possible to integer fix whole-phase ambiguities with multiple constellations and one frequency.  
In the next paragraphs, we better describe the instrument installed on the site and the GNSS data processing.