esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionic Liquid Electrolytes for Safer and More Reliable Sodium Battery Systems
## 4 Conclusions  
To summarize, the 0.1 NaTFSI-0.9 IL mixtures, investigated in the present work, show potentially appealing characteristics as innovative ionic liquid electrolytes for realizing safer and more reliable sodium battery systems. Room temperature conductivities of interest for practical devices, i.e., largely overcoming 10\({}^{-4}\) or 10\({}^{-3}\) S cm\({}^{-1}\), are displayed by all investigated IL mixtures. The EMI-based electrolytes are seen to exhibit the best ion transport properties, especially at low temperatures: at \(-\)20 \({}^{\circ}\)C from 10\({}^{-4}\) to 10\({}^{-3}\) S cm\({}^{-1}\) are observed, this extending the applicability of these electrolytes even at low temperatures. The FSI mixtures display faster transport properties than the TFSI- and IM\({}_{14}\)-based ones. A conductivity well above 10\({}^{-2}\) S cm\({}^{-1}\) is approached at 80 \({}^{\circ}\)C.  
The 0.1 NaTFSI-0.9 IL electrolytes were found to be thermally robust up to 275 \({}^{\circ}\)C (EMITFSI-based electrolyte) with the exception of the FSI-based ones (up to 150 \({}^{\circ}\)C). This important peculiarity enables these sodium electrolytes to be used in battery systems operating at temperatures (i.e., \(>\)100 \({}^{\circ}\)C) which are not allowed in standard organic solutions. At the same time, in conjunction with the flame-retardant properties and the very low volatility of the ionic liquid components, it strongly increases the reliability and the safety of the device, particularly in the case of prolonged overheating.  
Investigation, validation, M.B.; investigation, validation, E.S.; investigation, M.D.F.; conceptualization, methodology, data curation, supervision, writing, review, editing, G.B.A. All authors have read and agreed to the published version of the manuscript. This research was funded by the Italian Ministry of the Economic Development (MISE) within the ENEA-MISE Program Agreement on Electric System Research. The authors declare no conflict of interest.
## 6 Flight Test Experiment  
### Testbed Design  
The environmental disturbances and nonlinear aerodynamic forces are difficult to generate, replicate and analyze in the disturbance rejection experiment. Despite this, a block dropping device was designed to generate a quantifiable and replicable disturbance, as shown in Figure 6.  
Figure 5: Attitude tracking with model uncertainty. (**a**) Tracking of pitch angle; (**b**) tracking of roll angle; (**c**) tracking of yaw angle.  
A model-scale helicopter called Align T-REX500 was chosen to be the flight testbed in this work. It was produced by ALIGN, which is a company founded in Taiwan, China. In addition to this helicopter, the entire system consists of an avionic box, an attitude and heading reference system (AHRS), a radio and a block dropping device. There is a Digital Signal Processor TM320F2812 developed by Texas Instruments (TI) in the avionic box, which is used to realize the control algorithm, analyze sensor information from the AHRS and generate pulse width modulation (PWM) signals to control the helicopter. And TI is a semiconductor company founded in Texas, USA. The AHRS consists of a three-axis gyroscope, a three-axis accelerometer, a three-axis magnetometer, a barometric altimeter and a microcontroller unit (MCU), which is able to provide the information of attitude and angular velocity. The command signal could be transmitted through the radio. Both the AHRS and the radio were connected to the DSP through RS232. The program was developed in embedded C for DSP by an Integrated Development Environment (IDE) called Code Composer Studio (CCS), which is also provided by TL.  
The dropping device is made of a heavy block and a servo, while the block is fixed by the servo, which is controlled by the signal from radio. In this work, the mass of the block is \(m=0.26\) kg and the distance from the block to the center of gravity is \(l=0.4\) m, therefore, the disturbance generated by the device, \(Q_{D}=-mgl=-1.02\) N - m, is sufficiently intense for this testbed. The whole experimental system is shown in Figure 7.
## 2 Methodology and Strategy
### Back-Propagation Neural Network  
Back-propagation neural network (BPNN) is one of the most popular neural networks which was proposed by [PERSON] et al. [26]. Like a neural network, it also consists of an input layer, one or multiple hidden layers, and an output layer, each of which has one or more neurons. The neurons in different layers are connected to each other. After we feed the data into the model, the result of the hidden layer can be calculated by Equation (5):  
\[H_{j}=f\left(\sum_{i=1}^{n}\omega_{ij}x_{i}+a_{j}\right) \tag{5}\]  
where \(x_{i}\) is the input value, \(H_{j}\) is the result of the hidden neuron \(j\), \(n\) is the number of the hidden neuron, \(\omega_{ij}\) is the weight on the connection between the input and the hidden neuron \(j\), \(a_{j}\) is the bias from the input layer to the hidden layer, and \(f\) is the activation function.  
From the hidden layer to the output layer, for the regression model, in most cases, a linear model is used, which can be expressed as Equation (6):  
\[y_{k}=\sum_{j=1}^{m}\omega_{jk}H_{j}+b_{k} \tag{6}\]  
where \(H_{j}\) is the value of the hidden neuron \(j\), \(y_{k}\) is the output value of the output neuron \(k\), \(m\) is the number of the hidden neuron, \(\omega_{jk}\) is the weight on the connection between the hidden neuron \(j\) and the output neuron \(k\), and \(b_{k}\) is the bias from the hidden layer to the output layer.  
To account for the nonlinearity, the activation is used in most cases, and in this paper, some famous activation functions such as'relu', 'tanh', and'sigmoid' will be used. Those activation functions can be expressed as  
\[relu(Input)=max(0,Input) \tag{7}\]  
\[tanh(Input)=\frac{e^{Input}-e^{-Input}}{e^{Input}+e^{-Input}} \tag{8}\]  
\[sigmoid(Input)=\frac{1}{1+e^{-Input}} \tag{9}\]  
where the _Input_ is similar to the process in hidden layer, and \(e\) is the natural constant, approximately equal to 2.71828.  
When the signal finally reaches the output layer, the model will compare the output result with the real value, and calculate the error between them, and then backpropagate the error to adjust the weights and biases in each layer of neurons until the error of the model is within the acceptable range. This is the core function of BPNN, and over a number of learning cycles (epochs), it can achieve a high-precision prediction [27]. The entire structure of the model is shown in Figure 1.  
Figure 1: Neural network structure.
## 2 Materials and Methods
### Analysis of FMT Bacterial Composition Using Next-Generation Sequencing (NGS)  
Genomic DNA was isolated from faecal samples using the ZR Fecal DNA MiniPrep(tm) kit (Zymo Research, Irvine, CA, USA) according to the manufacturer's protocol. The concentration and purity of the extracted DNA were evaluated using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Library preparation, sequencing, data processing, and analysis were performed by Novogene Europe (Cambridge, UK). Briefly, following an initial quality control assessment, amplicon libraries were constructed using universal primers 341F (5'-CCTAYGGGRBCASCAG-3') and 806R (5'-GGACTACNNGGGTATCTAAT-3') targeting the V3-V4 regions of the 16S rRNA gene. These libraries were subsequently subjected to paired-end sequencing on the NovaSeq 6000 platform (Illumina, San Diego, CA, USA) in accordance with the manufacturer's guidelines. The sequencing run generated 2 \(\times\) 250 bp reads, yielding approximately 30,000 raw tags per sample.
## 3 System Model
### Mobile Devices  
When tasks arrive at mobile devices, the devices have the responsibility to execute the tasks as quickly as possible. In out model, each mobile device is connected to at most one edge node. Connecting to more edge nodes is not allowed here, as maintaining multiple connections incurs more power consumption for mobile nodes, which need to conserve their batteries. Additionally, it complicates decision-making for mobile nodes, which requires them to dedicate more power for task offloading decisions and in turn, consume more energy.  
We define \(e(m_{i})=e_{j}\) to denote the edge device \(e_{j}\) that the mobile device \(m_{i}\) is connected to and \(r(m_{i})=r\) to denote the data rate \(r\) of the connection between mobile device \(m_{i}\) and its connected edge.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 3 Preliminaries
### Federated Learning  
Federated Learning (FL) is an emerging privacy-protecting and decentralised learning scheme that enables edge-devices (local users) to learn a shared global model without disclosing their personal and private data to the cloud server. In FL, user download a shared global model from the cloud server, train this global model over individuals' local data, and then send the updated gradients back to the cloud server. Afterwards, the cloud server aggregates these updated gradients in order to compute a new global model. The following are some unique features of FL compared to traditional centralised learning.  
1. The learned model is shared between the users and the cloud server. However, the training data which is distributed on each user is not available to the cloud server.
2. Instead of the cloud server, the training of learning model occurs on each user. The cloud server receives the local gradients and aggregates these gradients to obtain a global gradient and then send this global gradient back to all the users.  
In this paper, we consider the standard settings of FL, where large-scale of local users train the global learning model in a distributed and collaborative manner.
# A Power Planning Algorithm Based on RPL for AMI Wireless Sensor Networks
## 1 Introduction
Differently, in this paper, we present an RPL-based heuristic approach for transmission power level assignment. Most of the RPL evaluations that motivated the previous critics paid little attention to the configuration of the transmission power of the RPL nodes. However, adjusting transmission power is important in AMI networks because they present strong performance requirements in terms of two-way communication, and the adjustment could assure the quality of every bidirectional link. In addition, power planning allows the search for optimal connectivity in terms of the number of alternative parents in the DODAG. Power planning is feasible in AMI networks because their nodes do not have strict energy restriction, and it can be executed offline because the positions of nodes are fixed and well known. This paper presents a study about the planning of transmission power of the nodes in AMI networks, in particular those based on the IEEE 802.15.4 technology. We defend the viewpoint that a careful planning of the transmission power of the communication nodes can significantly reduce the problems raised for RPL. More precisely, this paper presents a method for planning the transmission power that assures that the size of the parent set of the RPL nodes is as close as possible to a predefined size. Another important feature of the proposed method is that the resulting power setting allows all nodes in the parent set to offer connectivity through links of similar quality. The consequence is that a node is capable of making more realistic path cost offers for their children. We evaluate our algorithm considering the ability of RPL to find reliable parent sets under different situations, such as highly concentrated urban and sparse rural areas. As far as we know, this is the first power-planning algorithm proposed for RPL-based networks. The remaining of this paper is organized as follows: Section 2 details the problem and gives some examples that motivate the use of a power planning algorithm. Section 3 presents a theoretical model to estimate the ETX metric. We use the theoretically estimated ETX to compute the transmission power of the nodes and to give an initialization value for the DODAG construction. In Section 4, we describe the algorithm, showing how the transmission power of nodes and of their parents are adjusted to assure that all links have the ETX below a given threshold. Section 5 evaluates the algorithm, comparing the results with the default power setting and initial values of ETX used in most RPL evaluations based on Cooja/Contiki. Finally, Section 6 presents the final remarks and concludes the paper.
## 4 Conclusions  
We designed a novel composite chalcogenide-core tellurite-cladding photonic crystal fiber (PCF) with high non-linearity, high birefringence, and a tunable zero dispersion wavelength using a numerical approach. By utilizing an elliptical-core, three rings of air-holes, and realistic hole dimensions, we obtained an ultra small effective mode area of nearly 0.6 \(\upmu\)m\({}^{2}\), a huge non-linear coefficient of 28 \(W^{-1}\) m\({}^{-1}\), a large birefringence of 7 \(\times 10^{-3}\), nearly-zero confinement loss of 2.47 \(\times\) 10\({}^{-12}\) dB/cm, and a zero dispersion at 1550 nm. We compared all of the optical characteristics of the proposed elliptical-core PCF with a regular circular-core PCF to highlight the differences between the two designs. We further showed that by modifying the core and air-hole dimensions one can easily control the dispersion curve and tune the zero dispersion wavelength of the proposed fiber to any excitation wavelength ranging from near-infrared to short-wave-infrared, including optical telecommunication windows close to 1550 nm. The proposed PCF with large non-linearity, polarization-maintaining capabilities, and the possibility of tuning zero dispersion wavelengths, could have important  
Figure 8: Non-linear coefficient (main plots) and effective mode area (inset plots) of PCFs with (**a**) circular-core, and (**b**) elliptical-core, with different core dimensions as a function of excitation wavelengths. The C-PCF and E-PCF parameters are: \(d=0.692\), \(\Lambda=0.867\), \(a=1\)\(\upmu\)m.  
applications in non-linear optics, optical telecommunications, optical signal processing and even sensing devices.  
A.A. (investigation, data curation, software, methodology, formal analysis); Y.E.M. (visualization, formal analysis, validation, writing-original draft, writing-review and editing, project administration  
This research received no external funding. Y.E.M. acknowledge the support of the Dalhousie University and Killam trusts.  
The authors declare no conflict of interest.
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 2 Materials and Methods
### What Is 'Roughness'
An example of this is given in Figure 1e, where the unfiltered surface profile of this sample is shown using the black line. A cut-off length of 1 mm is used here to extract the surface form (blue line). The next cut-off length of 0.025 mm was used to separate the waviness (green line) from the surface roughness (red line). The correlation length used is in line with ISO 4288 for these types of surfaces and is discussed to a greater detail in the literature [23]. Beyond being used as a cut-off length, the correlation length has a further importance in acoustics for describing the attenuation of the SAW due to the surface roughness. The attenuation and frequency shift of SAWs have been explored at length by [PERSON] and various collaborators [31], primarily through perturbation theory. This body of work concludes with two primary findings, namely thatSAWs propagating across a rough surface are subject to (1) attenuation and (2) dispersion, and that the degree to which these effects occur has a complex dependency on the acoustic wavelength, surface height, and spatial frequency. For example, in the Rayleigh regime, attenuation varies as the fourth power of the frequency, suggesting that for high-frequency surface wave propagation, the surface roughness may play a critical role [31]. [PERSON] and [PERSON] have previously measured these effects in machined aluminium specimens by laser ultrasound [32], but the extent to which this effect is observed in additive manufacturing samples is yet to be studied. Conversely, there has been shown to be an improvement in generation on rough surfaces. [PERSON] et al. demonstrated [33] that the amplitude of a SAW generated by laser-excitation increased with increasing roughness. [PERSON] et al. later demonstrated by ray-tracing that this effect was due to multiple scattering and increased with increasing roughness [34], with it being particularly pronounced in high-reflective metals. Whilst the \(R_{a}\) value is widely used to describe the roughness of the surface, when comparing a variety of surfaces of similar \(R_{a}\), it quickly becomes clear that many different types of surface can present with a similar \(R_{a}\). This is discussed in more detail in Section 2.3.1.
###### Abstract  
Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.  
IoT; IIoT; LPWAN; LoRaWAN; GNSS-free; outdoor localization; smart cities; smart transportation; smart healthcare; smart manufacturing +
Footnote †: journal: _Sci. 2021_, 11, 10793. [[https://doi.org/10.3390/app112210793](https://doi.org/10.3390/app112210793)]([https://doi.org/10.3390/app112210793](https://doi.org/10.3390/app112210793))
## 4 Case Study
### Anomaly Identification of Uplift Pressure
The reduction factor of the P2-2 point exceeds the expected value for a long time, indicating that the grouting curtain is more significant. Figure 7: Anomaly identification result of uplift pressure at the P2-3 monitoring point. curtain nearby the P2-2 point may be destroyed. 3 The P2-3, P2-4, and P2-5 points are located on the downstream side of the grouting curtain, and their reduction coefficients have increased and shown abnormal operation since 2017. 4 The reduction coefficient of P6, P7, and P8 points meet the expectation in 2015-2016 and increase significantly in 2017 -2020. 5 The reduction coefficient of other uplift pressure points in the vertical section is stable at 0.2-0.3, which accords with the experience. Through the annual maximum reduction coefficient analysis, the anomaly analysis results are consistent with those of the anomaly identification model. The accuracy of anomaly identification of the DBSCAN is verified. It is concluded that the anomaly uplift pressure occurred locally in 2016 and developed in 2017, mainly in the dam section of the riverbed. To further verify the accuracy of the anomaly identification result of the DBSCAN, SIR-4000 geological radar and 100 MHz shielding detection antenna are used for non-destructive testing. Non-destructive testing of the dam foundation in the middle of the riverbed is carried out in the corridor at the bottom of the dam body, and the testing result is shown in Figure 9. At a depth of 7-15 m near the P2-4 monitoring point, the overall continuity of the foundation is relatively poor, and the in-phase axis dislocation is intense. There are apparent small-scale wave groups, and the local reflection wave appears as an arc. The high frequency has a specific loss, and the low frequency is locally prominent. It is speculated that the geological body here is not dense, and there may be cracks. Therefore, the non-destructive testing results further verify the possibility of seepage failure in the riverbed and the accuracy of anomaly identification model based on the DBSCAN. Figure 8: Variation of annual maximum reduction coefficient of uplift pressure monitoring points.
## 2 Related Studies
### Data Augmentation  
Deep-learning models require significant amounts of data to achieve effective learning. However, the data required for learning are difficult to obtain and the obtained data may cause an imbalance in the amount of data for each class. When a deep-learning model is trained with such data, it can be biased toward a specific class, thereby significantly affecting the model performance. Data augmentation is performed to overcome this problem. Because data augmentation can increase the number of training data in various methods, it can address any insufficient data or imbalance for each class. Data augmentation techniques include geometric transformation, color space transformation, kernel filter-based augmentation, random erasing, image mixing, and mosaic [36]. Geometric transformation is a method of changing the shape of an image by rotating, translating, scaling, cropping, and flipping the image. Color space transformation is a method of changing the color or brightness of an RGB (red, green, and blue) image by adjusting the pixel values. It is used to compensate for biased data due to limited lighting environments. Kernel-filter-based augmentation can generate blur or sharpen images by adjusting the internal value of an n \(\times\) n filter. Random erasing refers to a method of inserting a patch with a random size, position, and value into an image [37]. In this method, dropout regularization is implemented in the input data space instead of in the network to improve the performance degradation of the model caused by occlusion between objects in the image. Meanwhile, mixing images is a method of synthesizing images of different classes with the average pixel value and it is performed in the classification model [38]. Mosaic refers to combining four images used for training into one image at a specific ratio [25]. Mosaics can learn to identify objects on a scale smaller than the normal scale, thereby improving the recognition ability of models in complex backgrounds. In addition, it can significantly reduce the necessity for a large mini-batch size during training.