esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup and Procedure  
The main automated hammering device used in this study included a voltage-controlled solenoid hammerhead that can automatically hammer at different frequencies, six-degree-of-freedom robotic arm to guide the hammerhead, and radio device consisting of a directional microphone, microphone power module, wireless transmission, and computer [32]. The automatic hammering device ensures that the hammering position and hammering acoustic data obtained are sufficiently standardized, and that the hammering acoustic data obtained by hammering at different frequencies can be used for machine learning, as shown in Figure 6.  
Concrete specimens with a length of 420 mm, width of 60 mm, and height of 60 mm were used in this experiment. The concrete specimen was bent through a four-point bending system, as shown in Figure 7, producing damage distributed over the entire specimen as in Figures 8 and 9. In this study, the formation of damage classification is divided into two types: two-category, as shown in Figure 8, and four-category, as shown in Figure 9. The entire sample was divided into 21 groups. In the two-category classification, the concrete samples were divided into healthy and damaged areas. The healthy area was not damaged within 20 mm, and the damaged area included surface and internal damages. In the four-category classification, the samples were divided into healthy areas with no damage within 20 mm, surface damage areas with only surface cracks within 20 mm, internal damage areas with only internal cracks within 20 mm, and compound damage areas with both surface and internal cracks within 20 mm. Acoustic data were obtained by hammering the concrete from above at a hammering frequency of two times per second (2 Hz), five times per second (5 Hz), and 10 times per second (10 Hz). In the two-category and four-category classification, the concrete was subjected to 1260 hammer blows at 2 Hz, 3150 hammer blows at 5 Hz, and 6300 hammer blows at 10 Hz. The sound acquisition device is directional, pointing vertically in the hammering direction and moving with the hammering to ensure that the sound propagation distance does not need to be corrected.  
Figure 6: Schematic of the experimental setup.  
In this study, ReliefF was used for feature selection, and SVM was used to classify the damage types of concrete. Linear, quadratic, and Gaussian functions were used as kernel functions to build the model. In this study, acoustic features extracted from the selected signals based on two different hammering methods (single-frequency hammering and variable-frequency hammering) were used for classification and comparison.
# A Novel Method for Field Measurement of Ankle Joint Stiffness in Hopping
###### Abstract
Additionally, the conventional method is restricted to lab environment where the force plate and motion capture devices are commonly available. To overcome that limitation, in 2004, a simple method was introduced and validated by [PERSON] [25], which could calculate the leg stiffness in hopping and bouncing with a simple set of technical equipment and under field condition. In this method, the vertical ground reaction force was modeled as a sine wave which is a function of contact and flight times and the leg stiffness was calculated using a simple contact mat. This showed a significant correlation (r = 0.94) between the leg stiffness calculated with this method against the conventional method. Despite this success, the model developed by [PERSON] [25] could not calculate the stiffness of the ankle as an important joint in the lower extremity. As it stands currently, to calculate ankle joint stiffness, only conventional methods of calculating joint stiffness exist in which access to the motion analyses and measurements of ground reaction force are necessary. Therefore, this study aimed to develop a simple method for calculating the ankle joint stiffness (AJS), without a need for motion analyses and measurements of ground reaction force. Upon development, this can have implications in assessing the AJS in the field in low resource settings where access to sophisticated equipment is not available.
## 2 Background
Four types of charging stations are considered, each with different specifications, characteristics, and costs (AC, DC, rated voltage, etc.). The research in [28] searches for locations, capacity options, and service types for EV charging stations, after estimating charging demand from GPS trajectory data. To solve the corresponding mixed-integer linear programming (MILP) model, a hybrid evolutionary algorithm that combines the non-dominated sorting genetic algorithm-II (NSGA-II) with linear programming (LP) and neighborhood search is proposed. The method adopted in [29] addresses the EVCS distribution problem through an approach in which, apart from the optimal location, the number of the required charging stations is calculated to cover charging needs. The EVCS placement is estimated through the employment of Genetic Algorithms that estimate the EVCS longitudes and latitudes. In summary, existing research generally considers optimization models that develop the optimal placement of charging stations upon a predefined set of candidate sites (facility-to-location configuration). Also, the number of stations composing the network is generally considered fixed, with the exception of the research studies in [20] and [29], in which the optimization variables include the number of charging stations, with [20] formulating the problem as a facility-to-location one. This paper aims to fill these gaps by developing an optimization model that determines the optimal number and placement of charging stations freely within the application area (facility-to-site formulation) to cover the distributed EV charging demand. The problem is developed in a bi-objective model with two objectives: minimizing the total cost for the charging station deployment (construction, operational, and maintenance costs) and maximizing service quality by minimizing the average traveling distance (and thus, cost) between demand spots and station locations, while considering any station capacity constraints. This analysis intends to enrich previous research efforts in several directions, in terms of both numerical and practical considerations. The work includes a number of case studies and numerical applications to assess the capability, applicability, and scalability aspects of the model. Overall, the present research aims at an in-depth study and better understanding of the problem characteristics and the quality and practicality of the solutions in a real-life environment. The rest of the paper is organized as follows. Section 3 presents the problem description and the proposed model formulation. In Section 4, 10 case studies are developed and run with the corresponding results presented and commented on. Section 5 provides a brief discussion of the obtained results and the main conclusions of the study.
# A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario
## 4 Adaptive Duty Cycle Selection with Reinforcement Learning
Consequently, there is an expected or mean reward associated with each action, referred to as the value of that action, denoted as \(q(a)\). Following the notation presented in [39], if the action selected in time step \(t\) is \(A_{t}\), and the corresponding reward is \(R_{t}\), the value of \(q_{*}(a)\) for an action \(a\) is given by  
\[q_{*}(a)\doteq\mathbb{E}[R_{t}|A_{t}=a], \tag{1}\]  
where \(\mathbb{E}\) denotes the expected value function. If the value of \(q_{*}(a)\) were known for each action \(a\), it would be trivial for the agent to solve the MAB problem by simply selecting either the action with the highest value over the period or time steps. Since these values are not known, we define a function \(Q_{t}(a)\) to approximate \(q(a)\). This function uses an average of the past received rewards for each action at each time step \(t\) and it is defined as  
\[Q_{t}(a)=\frac{\sum_{i=1}^{t-1}R_{i}\cdot 1_{A_{i}=a}}{\sum_{i=1}^{t-1}1_{A_{i}=a }}, \tag{2}\]  
where \(1_{A_{i}=a}\) is a predicate that is 1 only if \(A_{i}=a\) is true. The average is updated only for the selected action at each time step \(t\). As we are using an average, it is expected that by the law of large numbers, the value of \(Q_{t}(a)\) will converge to \(q_{*}(a)\) as the denominator tends to infinity. The best action to be selected in time step \(t\) is the action with the highest estimated value following a greedy approach as  
\[A_{t}\doteq\underset{a}{argmax}\ Q_{t}(a), \tag{3}\]  
exploiting the current agent's knowledge about the environment. However, if the agent only uses a greedy approach, it might get stuck in a suboptimal action. For that reason, the agent should also explore by selecting actions at random to discover new options. That exploration versus exploitation dilemma is a well-known concept in the reinforcement learning field, as it applies to nearly every problem. One way to solve it is to define a variable \(\epsilon\) that represents the probability of randomly selecting an option from the set of actions, each with equal probability. This strategy is known as \(\epsilon\)-greedy. At each time step \(t\), the agent has a probability of \(1-\epsilon\) to select the greedy action, prioritizing exploitation, and a probability of \(\epsilon\) to select an action randomly, promoting exploration. This approach tries to balance between exploring different actions and exploiting the actions that have shown good results.
## 1 Introduction
### Porosity Monitoring
#### 1.2.2 In-Situ Based Methods  
Research has also focused on image-based methods for in situ monitoring during metal AM applications [61]. [PERSON] et al. created a real-time approach to defect detection through the use of high-speed synchrotron hard X-ray imaging [62]. During the study, insight is provided into the formation of defects by tracking the motion of powder particles and observing melt pool as a function of laser heating in an attempt to observe the structure underneath the surface. Authors demonstrate that the approach indeed is capable of observing the melt pool development in real-time. The approach consists of using a pseudo pink beam that impinges upon the powder bed directly above the sample while the X-ray penetrates the sample from \"the side\". Then, imaging and diffraction detectors collect the distance of each component and is labeled. One particularly insightful finding is the author's observed the processes of the molten metal spreading during melt pool creation due to continuous laser heating. In doing so, the cavity depth and strong oscillation behavior is observed that occurs under the surface in real-time [62]. In [33], research is conducted through the use of acoustic emission (AE) for in situ quality monitoring of pores in the building of a workpiece. Unlike the XCT method for detecting and characterizing pores, AE is a technique performed as the build is progressing. AE is an advantageous method due to the acoustic signatures which possess potential robustness and richness of data pertaining to the material properties, process conditions, outliers in the data, and defects [36]. Research has been conducted in an attempt to improve the strength of AE during monitoring applications [63]. In the study, [PERSON], et al., aim to improve upon weak AE amplitude, especially when the signals are diluted due to environmental noise. In [64], a nondestructive technique is used for acoustic measurement during quality monitoring of machine and material state AM process.  
Many of these methods are centered around anomaly detection frameworks based on a preset threshold. Popular-visual based methods for measuring porosities are infrared thermography and optical tomography. In [65], researchers introduce a new contactless off-axis thermal detection monitoring arrangement. In the study, two cameras were used simultaneously for thermography and optical tomography.
_J. Low Power Electron. Appl._**2012**, \(2\), 211-241; doi:10.3390/jlpea2040211  
**Journal of Low Power Electronics and Applications ISSN 2079-9268**  
www.mdpi.com/journal/jlpea  
_Article_  
**A Multi-Channel Low-Power System-on-Chip for** _in Vivo_  
**Recording and Wireless Transmission of Neural Spikes**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{1,\dagger}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Golgi 40, Milano 20133, Italy  
\({}^{2}\) Italian Institute of Technology (IIT), Department of Robotic, Brain and Cognitive Science, Genova 16163, Italy  
\({}^{\dagger}\) Present address: STMicroelectronics, Via Tolomeo, Cornaredo 20010, Italy.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-02-2399-6146; Fax: +39-02-2367-604.  
_Received: 11 June 2012; in revised form: 6 September 2012 / Accepted: 21 September 2012 / Published: 28 September 2012_  
**Abstract:** This paper reports a multi-channel neural spike recording system-on-chip with digital data compression and wireless telemetry. The circuit consists of \(16\) amplifiers, an analog time-division multiplexer, a single \(8\) bit analog-to-digital converter, a digital signal compression unit and a wireless transmitter. Although only \(16\) amplifiers are integrated in our current die version, the whole system is designed to work with \(64\), demonstrating the feasibility of a digital processing and narrowband wireless transmission of \(64\) neural recording channels. Compression of the raw data is achieved by detecting the action potentials (APs) and storing \(20\) samples for each spike waveform. This compression method retains sufficiently high data quality to allow for single neuron identification (spike sorting). The \(400\) MHz transmitter employs a Manchester-Coded Frequency Shift Keying (MC-FSK) modulator with low modulation index. In this way, a \(1.25\) Mbit/s data rate is delivered within a limited band of about \(3\) MHz. The chip is realized in a \(0.35\)\(\mu\)m AMS CMOS process featuring a \(3\) V power supply with an area of \(3.1\times 2.7\) mm\({}^{2}\). The achieved transmission range is over \(10\) m with an overall power consumption for \(64\) channels of \(17.2\) mW. This figure translates into a power budget of \(269\)\(\mu\)W per channel, in line with published results but allowing a larger transmission distance and more efficient bandwidth occupation of the wireless link. The integrated circuit was mounted on a small and light board to be used during neuroscience experiments with freely-behaving rats. Powered by \(2\) AAA batteries, the system can continuously work for more than \(100\) hours allowing for long-lasting neural spike recordings.  
**Keywords:** neural recording; action potential; low-noise amplifier; wireless transmitter; bio-telemetry; FSK; Manchester-encoding
# Smart Sensing of PSC Girders Using a PC Strand with a Built-in Optical Fiber Sensor
## 1 Introduction
### PC Strand with Built-in Optical Sensor
optical (FBG) sensor (Figure 3). Owing to the high strength and stiffness of CFRP and its remarkable relaxation, creep, and fatigue characteristics, the OSPC strand has mechanical and durable performances similar to those of a conventional steel strand. In addition, the reliability of the prestressing force measured by the OSPC strand was verified by comparison with that measured by a traditional electrical resistance strain gauge through a series of tensile tests by [PERSON] et al. [11; 12].
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Setup Phase
#### 4.3.1 Initialization  
At the start of the network, the sensor nodes are randomly installed in the sensing area. During the nodes installation procedure, some points also take into consideration to avoid premature exhaustion of the network because once the nodes are employed in the field, there is no any possibility to change their batteries or their locations. The density of nodes in a specific area, the transmission distance, and the number of relaying nodes also affect the network stability.  
In the initialization phase, the BS broadcast a _BS-Hello-Msg_ at a certain power level. This message contains the coordinates of the BS. When this message is received by the sensor nodes, the nodes can estimate the distance of the BS according to the received signal strength indication. The illustration of the network initialization is given in Figure 1.  
The received signal strength indication is utilized to compute the distance of one node from another. The strength of a signal fades as long it propagates from sender nodes towards the receiver nodes as revealed in Figure 2. A radio propagation model can be used to estimate the distance between two nodes on the basis of receiving signal.  
**Lemma 1**: _The initialization of the network is stabilized in finite time._  
Proof.: The proof of this Lemma is given in Section 4 of [40] in Lemma 4.  
Figure 1: The network initialization and configuration.
## 1 Introduction
When it comes to humans, it is easy for them to read the citation text and identify the sentiment expressed in the citation text, but when it comes to training a model to automatically predict sentiments' polarity, it becomes a difficult and challenging task [17] because there exist many techniques to train the model using a dataset and to perform sentiment prediction. For the most part, the sentiment polarity of the cited text seems to be neutral, with negative or positive sentiments hidden [18]. For hidden sentiment analysis, several approaches can be used, such as lexical analysis, feature-based extraction, structure-based sentiment analysis, etc. This study leverages the machine learning approach for the automatic sentiment classification of in-text sentiments and makes the following key contributions:  
* A framework is presented for in-text citation sentiment analysis based on a customized convolutional neural network (CNN) coupled with the combined word embedding approach. In addition, fastText, fastText subword, and global vectors for word representation (GloVe) are also analyzed individually for their efficacy for sentiment analysis;
* The unbiased sentiment of various citations is predicted to highlight the importance of a research work. By classifying the sentiment of in-text citations into positive, negative, or objective sentiments, the research significance of a work can be evaluated;
* An evaluation of the proposed framework is carried out by comparing its result with state-of-the-art models used in text classification, such as random forest (RF), stochastic gradient descent (SGD), logistic regression (LR), a voting ensemble that combines LR and SGD (VC(LR+SGD)), and long short-term memory (LSTM);
* Performance analysis is carried out with recent state-of-the-art approaches to show the significance of the proposed framework. For validating the results, the \"clinicaltrail\" citations dataset is used. In addition, we also collect our own dataset to analyze the quality of selected science articles by using citation sentiments. The rest of the paper is organized as follows. Section 2 discusses important works related to the current study. Section 3 presents an overview of the methodology adopted for the current research as well as a detailed description of the dataset and models used for experiments. Results are discussed in Section 5, while the conclusion and future work are provided in Section 7.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 2 Materials and Methods
### Time Series Analysis  
#### 2.2.1 Selection of Stations  
We computed the GNSS position time series of 1077 stations over Europe using the MG3 products and the GINS software in PPP mode. We used the same models as the ones listed in Table 1 in order to maintain consistency between the products used and the individual station processing. For the tropospheric delays, we use the global mapping function (GMF) [54] tropospheric model and the global pressure and temperature empirical function GPT2 [55]. The numerous agencies and organisations providing the raw GNSS data in RINEX format are listed in the acknowledgements section.  
The selected stations have a minimum time span of five years and a completeness of 50%. Some exceptions of a time span between three and four years have been considered for recent stations (ending after 2019) but with a more selective completeness criterion (minimum 70%). The distribution of stations and the statistics of the time series are shown  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **MG3 Products** \\ \hline Gravity field & EIGEN-GRGS.RL04.MEAN-FIELD [47] \\ Ocean tides (gravity) & FES2014b (Finite Element Solution) [48] \\ Planet ephemerides & de42 lldlf.ad [49] \\ Relativistic acceleration & Schwarzschild and geodetic precession and Lense-Thirring \\ Antex & IGSR3.atx [50] \\ Mean pole (C21/S21) & IERS conventions (from geopotential model) [51] \\ Subdaily EOP model & [52] \\ Atmospheric tides (S1/S2) & [53] \\ Ocean tide loading & FES2014b [48] \\ Centre of mass correction & FES2014b [48] \\ Solid tides (station) & IERS conventions [51] \\ Reference frame & IGS\_R3 [IGSMAIL-8026] \\ Galileo ponderation & 3.5 cm/1 m \\ GPS ponderation & 3.5 mm/60 cm \\ GLONASS ponderation & 3.5 cm/2 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dynamical and loading models used for MG3 products.  
in Figure 3. The analysed network is homogeneous and dense in Great Britain, France, Spain, and Italy, while in the remaining European countries, the station distribution is not as dense but still spatially homogeneous.