esa-sceva/satcom-chunk-collection huggingface dataset:
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 2 State of the Art
### Synthesis  
The authors in [2] propose to distinguish systems that integrate PV and EV according to three criteria:  
* Their spatial configuration. The authors distinguish systems that span over households, buildings, charging stations or territories. We also propose to add the size of the PV plant and the number of EV users as criteria;
* Their technological environment. It consists of the technological components that are included in or are added on. Such components could be BES, WT, Heat Ventilation Air Conditioning (HVAC), network technologies (such as DC micro-grids), etc.;
* Their smart control strategy. The authors in [2] also distinguish the strategies by their objectives that can be expressed in monetary terms (such as maximizing the revenues or minimizing electricity costs, etc.), in energy efficiency terms (such as improving self-consumption or reducing the impact on the grid) or in ecological footprint terms (such as reducing the CO\({}_{2}\) emissions). The authors also distinguish the strategies by their \"coordination method\", that is, their mathematical formulation. These methods could be based on \"optimization methods\" (such as MILP, etc.), \"heuristics methods\" (such as those based on expert rules) or \"hybrid methods\" (i.e., a combination of the two previous methods).  
As explained in Section 2.1, prefeasibility studies aim at exploring several potential solutions, whereas feasibility studies focus on the most promising ones. In practice, the two phases are distinguished by their levels of complexity, specifically in terms of required data and level of expertise. The level of expertise is mainly about the complexity of the charging schedule algorithms and the software that is used for their implementations. Heuristics methods are mostly used during prefeasibility studies, whereas optimization methods are rather used in feasibility studies.  
Table 1 summarizes these reviewed papers in Section 2.2 using the aforementioned criteria along with additional ones: targeted project development stage, EV fleet and PV size, EV mobility data, sizing methods, and the variables/parameters to be sized.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 2 Materials and Methods  
### Dataset  
In our study, we employed the UniMiB-SHAR dataset [3], a publicly available and widely used benchmark for human activity and fall detection research. This dataset comprises accelerometer data collected from smartphones carried by participants while performing a variety of activities, including both falls and activities of daily living (ADLs). Each observation contains tri-axial acceleration readings measured along the X, Y, and Z axes with corresponding metadata such as the activity label, participant identifier, and trial number.  
The dataset includes data from 30 people (of 24 women and 6 men), aged between 18 and 60 years. In total, it contains 7013 labeled samples. The original annotation defines 17 distinct activity classes, encompassing 8 types of falls (e.g., falling leftward, generic backward falls, falls with protective strategy, and syncope) and 9 types of routine activities (e.g., walking, running, jumping, sitting down, and going up or down the stairs).  
The relatively diverse participant pool and rich annotation, makes UniMiB-SHAR a valuable resource for developing and evaluating machine learning models aimed specifically at fall detection and activity recognition. Its inclusion of multiple fall types provides an especially relevant testbed for algorithms intended to distinguish dangerous events from benign motions in real-world conditions.  
In this paper, we focused on a binary problem to distinguish falls from ADLs. Figure 1 shows sample smartphones' accelerometer data for eight different types of falls and, additionally, a sitting-down activity. It can be noticed that there is only a slight difference between those activities, which is not obvious at first glance. The subtle variations in accelerometer readings caused by different user activities present a formidable obstacle to achieving robust and precise classification.
## 4 Hydrocarbon Potential of formations in Malaysia
### Organic Geochemical Characteristics of Formations in Sarawak Region
#### 4.3.3 Thermal Maturity  
The \(\rm R_{o}\) values of the formations in the Sarawak region ranged from 0.37% to 2.39%, with an average value of 0.65% (Table 15). The Belaga formation had the highest \(\rm R_{o}\), with the Mukah formation having the lowest \(\rm R_{o}\) value of 0.37% (Table 15). Except for the Belaga formation, the average \(\rm R_{o}\) for all the other formations was below 0.60. According to the standard interpretations of the stages of organic matter maturity by [PERSON] [88], all the formations studied in the Sarawak region were immature, apart from the Belaga formation, which was matured to the extent of generating dry gas (Table 15). The Tmax vs. PI plots provided results like the Dow [88] classification. All the nine (9) other formations studied in the Sarawak region were in the immature zone, with only the Belaga formation in-between the wet gas and dry gas window (Figure 16). According to the HI vs. Tmax plots, all the formations studied were in the pre-oil stage, apart from the Belaga formation which is in the dry gas zone (Figure 17). Therefore, the results from all the three classifications for thermal maturity used were in line with one another.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Formation** & **Av R\({}_{o}\) (\%)** & **Dow [88]**, Thermal Maturity Interpretation** & **Reference(s)** \\ \hline
**1** & Bintulu & 0.58 & Immature & [63] \\ \hline
**2** & Belaga & 2.39 & Dry Gas & [12] \\ \hline
**3** & Nyalau & 0.54 & Immature & [55,66] \\ \hline
**4** & Begrih & 0.45 & Immature & [55] \\ \hline
**5** & Liang & 0.44 & Immature & [55] \\ \hline
**6** & Balingian & 0.40 & Immature & [55,63] \\ \hline
**7** & Mukah & 0.37 & Immature & [63] \\ \hline
**8** & Lambir & 0.42 & Immature & [67] \\ \hline
**9** & Miri & 0.44 & Immature & [67] \\ \hline
**10** & Tukau & 0.43 & Immature & [67] \\ \hline \hline \end{tabular}
\end{table}
Table 15: Thermal maturity interpretation for formations in Sarawak according to [PERSON] [88] using \(\rm R_{o}\).  
Figure 15: Ol versus HI plot showing the type of kerogen of formations in Sarawak.  
Figure 16: Tmax vs. PI plot showing kerogen maturity level of formations in Sarawak.  
Figure 17: Tmax vs. HI plot kerogen maturity level of formations in Sarawak.
## 1 Introduction
Increasingly, mHealth applications are being used to support health care teams in early diagnosis and monitoring patient progress [15]. Many of the existing mHealth solutions for TB case management are custom software developed for individual organizations and use cases, they are not freely available for other organizations to try. There are also general purpose mHealth solutions available, such as Medic Mobile or ODK Collect and its cousins. These are direct-to-digital systems that do not involve any paper record keeping or paper archives. Among the various reported uses of mHealth tools the most reported are: data collection, electronic decision support, provider's education and training, and communication [10]. However, the particular use of mHealth for data collection has a greater likelihood of improving public health in low- and middle-income countries [16]. Noteworthy are those unpredictable implementation challenges and complicated situational factors that affect an introduction of mHealth applications in primary healthcare settings of the developing world [17; 18]. Since there are a number of freely-available open source mHealth applications aimed at improving data collection, only one tool leveraged the strengths of the existing paper system with the flexibility and efficiency of the digital records. We chose the ODK Scan paper-to-digital application to test improved data collection in the public-private mix model of the TB control program of Pakistan. The ultimate goal of this intervention was to reduce the time and resources spent on TB data collection by improving the data collection and digitization process and reducing reliance on manual data entry. Additionally, ODK Scan was chosen because it allows supervisors and managers to assess the data quality by reviewing the original image snippets. The specific objectives of this pilot study were to: (1) assess data quality aspects at both the app and user levels, (2) assess the quantitative impact of ODK Scan on the data management processes, and (3) collect feedback on application user experiences to ascertain app usability, user interface design and workflow changes.
# Optimization of the Housing Shape Design for
## 3 Finite Element Modeling of the Gearbox
### Modeling of the Internal Machine Elements  
To numerically analyze the noise and vibration of the gearbox using the FE model, the bearing reaction force should be inputted as an excitation force. The gear mesh force is transmitted to the housing through the shaft and bearing, and the force transmitted to the housing is called the bearing reaction force. In a previous study, the bearing reaction force calculated by the dynamic analysis was only applied as a loading condition. However, this method is not suitable because the bearing reaction force does not change and is constant despite the housing shape changes during the optimization process. In this study, internal machine elements such as gears, bearings, and shafts were modeled as substructures using a reduced-order model technique [15] and connected to the FE model of the gearbox housing using RomaxNEXUS (R19) and Opti-struct (v2019) commercial software. This method is suitable for use in the optimization procedure because the excitation force changes according to the shape change of the housing [16]. The first and second harmonic components of the first stage of the gear train, which is shown as the main source of noise in Section 2, were applied as the excitation forces, and the bearing node and housing nodes were connected by a rigid body element.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 3 Methods
### Vehicle Counting Based on Multiple Loops
#### 3.2.4 Counting Strategy  
To count vehicles, we set up a counter variable, which is used to count the cumulative number of vehicles in each lane. Slightly different from the setting of the loop, for each lane, only one counter that matches the lane is set and no secondary counters are set. The counter could be expressed as \(Counted^{i}_{k}\), where \(i=1,2,\cdots,N^{lane}\), which represents the cumulative number of vehicles driving on \(Lane^{i}\) that have been counted until frame \(k\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(SLoop^{1}\) & \(PLoop^{1}\) & \(SLoop^{2}  
The strategy of increasing the value of \(Counted^{i}_{k}\) is as follows: if and only if the lane is not occupied by a vehicle in the previous frame and is occupied in the current frame, the cumulative number of vehicles on \(Lane^{i}\) should be increase by 1; otherwise, the cumulative number remains the same. In this case, the cumulative number of vehicles driving on \(Lane^{i}\) until frame \(k\) could be expressed as follows:  
\[Counted^{i}_{k}=\left\{\begin{array}{ll}Counted^{i}_{k-1}+1,&\text{if $Lane^{i}$ is not occupied at $k-1$ but occupied at $k$}\\ Counted^{i}_{k-1},&\text{otherwise}\end{array}\right. \tag{13}\]  
As can be seen from Equation (13), in order to accurately count the vehicles on the lane, two important nodes need to be accurately discriminated. The first one is from the frame in which the lane is not occupied to the frame in which the lane is occupied. At this point, the counter for the lane increases by 1 in the frame in which the lane is occupied. The second important node is from the frame in which the lane is occupied to the frame in which the lane is not occupied. This is in preparation for the next increment in the counter. Therefore, these four frames are key for the counter to count properly.  
Take the left black vehicle in Figure 2a as an example. The loop states of its four key frames is expressed in Table 6 and displayed in Figure 14. Using the counting strategy described above, the counter for \(Lane^{1}\) should be increased by 1 in frame 534.
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
## 3 Methods of Test and Verification
### The Strain Distribution and Changes over Time  
Figure 10 shows the intensity of the strain distributed over the tested rail in the 24 h test period. The calculation was based on the BFS of the reference track in Equation (5):  
\[\Delta\epsilon=\Big{(}\Delta v_{B_{nat}}-\Delta v_{B_{ref}}\Big{)}/C_{t} \tag{5}\]  
Figure 8: The 3D measured temperature profile: the temperature distribution of the tested rail in the 3 dimensions of time, distance, and temperature, which is quite different from the temperature profiles of other single-point measurement results.  
Figure 9: A temperature comparison of rail and atmospheric temperature.  
where \(\Delta v_{B_{test}}\) is the BFS of the tested rail and \(\Delta v_{B_{n_{ref}}}\) is the BFS of the reference track.  
The tested rail was found to have less free stretch than the reference track due to the restrictions of sleepers and ballasts.  
As shown in the figure, the strain near the unmovable section was negative, as shown on the right of the figure, which means the expansion was less than that of the reference track. On the left side of the figure, the strain in the movable section is positive. It can be understood that because greater restraining force was from sleepers and ballasts in the unmovable section, an axial strain that was even smaller than that of the freely extending reference track was measured. In contrast, farther away from the unmovable section, a larger amount of strain was measured, and the maximum strain was reached at the extension joint (EJ) at the end of rail, as shown at distance zero (0) on the left of the figure.  
In Figure 10, regular vertical dark stripes appear along the direction of increasing length. A total of 167 stripes were calculated and are shown. Taking the sensing fiber segment D as 101.45 m, the stripe interval was calculated to be 0.61 m, which roughly coincides with the average distance between the sleepers in the verification area.
# BELMKN: Bayesian Extreme Learning Machines Kohonen Network
## 2 Methodology
### Cluster Prediction Using Bayesian Information Criterion (BIC)  
The second phase in BELMKN is to predict the number of clusters using Bayesian Information Criterion (BIC) [29]. BIC is a model selection technique that is used to predict the number of clusters in the dataset. In real-world problems, most of the datasets are usually unlabeled, and, traditionally, most of the clustering algorithms require apriori information of the number of clusters. BIC is useful for solving aforementioned problem by statistically predicting the number of the clusters from the data distribution.  
BIC associates with Gaussian distribution in which each cluster has mean and covariance matrix as parameters. In our study, we estimate these parameters using the Expectation Maximization (EM) algorithm. The Bayesian Information Criterion (BIC) is given by [6; 29],  
\[\text{BIC}=\ln(N)k-2\ln(\overset{\sim}{L}) \tag{7}\]  
in which \(N\) is the total number of samples, \(k\) is the number of free parameters to be estimated, and \(\overset{\sim}{L}\) is the maximized value of the likelihood function. The BIC values are computed for \(n_{c}\), in which \(c=1\), \(2\),..., \(N\), the model with the lowest BIC value, is chosen, which gives the optimal number of clusters for the dataset.
# Resilient Green Cellular IoT for Landslide Monitoring Using Voice Channels
## 3 VRITHI: System Design and Characteristics
### Data Flow in VRTTHI  
In this section, we elaborate on the design of VRTTHI in terms of the control channel and the data channel. The reference diagram of the data flow using these two channels of the Telecom network is shown in Figure 5. It shows that the control channel and data channel take different paths to reach the peer IoT node. The control channel uses the voice traffic and Public Switched Telephone Network (PSTN) to reach the peer IoT node. Here, both the IoT nodes are connected using the International Mobile Subscriber Identity (IMSI) in PSTN, where the traditional DoS attack from botnets is not possible. However, the control channel has minimal bandwidth and could be used only for control parameters of data channel, such as changed IP addresses, the port for listening, duration of port open, etc. In contrast, the data channel is used for sensor data traffic that consumes bandwidth. During communications, both the control and data channel function simultaneously in an IoT node. Based on the parameters received in the control channel, the data channel is dynamically configured at run-time.  
The parameters are encoded in voice traffic using Dual Tone Multi-frequency (DTMF) tones [32]. DTMF is a method used for converting the bits to tones. The tones are sent in the form of voice signals between the peer IoT nodes using voice frequency bands. The DTMF table, along with different frequencies and corresponding hexadecimal bits, is shown in Table 3, and a detailed protocol handshake process between the sender and receiver is explained in Section 3.4.  
The data channel uses the usual IP (Internet protocol) channel to communicate between the nodes. By default, none of the nodes listen for any incoming messages in the data channel. The data communication takes place after the configuration parameters are received through the control channel. This prevents unnecessary listening in the data channel and saves a significant amount of power in the node.  
Figure 5: Communication architecture.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 4 Conclusions  
This paper presented the design of a single element monopole antenna and its array operating at 60 GHz. The proposed antennas were designed from a monopole patch and DGS ground structures, employing a corporate feed network that evenly distributed the power to each element to achieve wide bandwidth and high gain. The impedance and radiation characteristics results showed that the proposed antenna array achieved a bandwidth greater than 6.6 GHz with an optimal gain around 11.6 dB, which would allow a high data transmission rate for 5G applications. The proposed antenna array achieved the requirements of mmWave to operate at the 60 GHz band, in addition to its simple design and ease of fabrication at a low cost. Through comparing the return loss, the measured response was quite close to the simulated values, verifying the HFSS simulation. The design bandwidth and radiation gain of the proposed antennas made them highly desirable for mmWave applications and 5G technology systems.  
Conceptualization, T.S.M., A.I.Z., R.K.H. and W.A.E.A.; methodology, T.S.M.; software, T.S.M.; validation, T.S.M.; formal analysis, T.S.M.; investigation, T.S.M.; resources, T.S.M., A.I.Z., R.K.H. and W.A.E.A.; data curation, T.S.M.; writing--original draft preparation, T.S.M.; writing--review and editing, A.I.Z., R.K.H. and W.A.E.A.; visualization, T.S.M.; supervision, A.I.Z., R.K.H. and W.A.E.A.; project administration, T.S.M.; funding acquisition, T.S.M. All authors have read and agreed to the published version of the manuscript.  
This research has no external funding.  
The authors declare no conflict of interest.
# FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks
## 1 Introduction
It is intended for high data rates and can achieve a considerably low energy per bit. The price to be paid is the receiver complexity that comes from the stringent requirements for precise timing synchronization. The FM-UWB is a low complexity alternative that targets low and moderate data rates. Although it cannot achieve as low energy per bit as IR-UWB, it compensates with a smaller silicon area, easier implementation and lower peak power consumption. Thus, in applications that do not require very high data rates FM-UWB may usually be a better choice; however, in the IEEE 802.15.6 standard, it is only used as an optional mode. The standard FM-UWB signal is generated in two steps (Figure 2. First, the input bits are mapped to FSK symbols that use a triangular waveform (sub-carrier). Then, in the second step, these triangular  
Figure 1: Illustration of the _WiseSkin_ concept, wireless sensor nodes embedded in an artificial skin provide a sense of touch to the patient. waveforms are used to modulate the RF carrier and create the wideband signal. In this way, a flat spectrum with a steep spectral roll-off is obtained. The Physical (PHY) layer specifications in the IEEE 802.15.6 standard only define a 2-FSK sub-carrier modulation at a single mandatory RF channel and a single sub-carrier channel. As we intend to show in this paper, standard FM-UWB can easily be extended to higher data-rates and higher order FSK modulations without a significant increase in complexity and power consumption, which would result in a higher efficiency of the entire system. Since the IEEE 802.15.6 was not intended for a large number of devices, the potential of the FM-UWB was not fully exploited. The sub-carrier FDMA (SC-FDMA), which was demonstrated in [5; 6; 7], allows multiple nodes to communicate at the same time, which can bring a lot of benefits as the number of nodes in a network increases. Here, capabilities and limitations of multi-user communication are discussed, and the case with up to four users at the same RF channel is demonstrated. The concepts from [5] are further extended to provide a simple approximation for Bit Error Rate (BER) estimation. Furthermore, multi-channel transmission is proposed via a simple modification of the standard FM-UWB, which could allow simultaneous transmission to multiple nodes, as yet another technique that might find use in HD-WSNs. The paper is organized as follows. The review and analysis of existing FM-UWB transmitters and receivers, pointing out the power reduction trends, are given in Section 2. In the following sections, the potential of FM-UWB beyond a simple 2-FSK sub-carrier modulation is demonstrated using one of the existing receivers. Single carrier communication is described in Section 3 and the SC-FDMA approach is explored in Section 4. The new multi-channel transmission is explained in Section 5, while Section 6 provides concluding remarks.