esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Monitoring Campaign
#### 2.3.2 Indoor Microclimate Monitoring Campaign of Vasari Corridor  
To investigate the thermal and hygrometric conditions inside the Vasari Corridor, a continuous monitoring system was installed in July 2016. The monitoring setup, described in detail in [30], consists of a set of data loggers that measure dry-bulb air temperature (T) and relative humidity (RH) [38]. These types of sensors respect the uncertainties defined in the European standards UNI EN 15758:2010 [39] for temperature and UNI EN 16242:2013 [40] for relative humidity.  
In this study, data monitored throughout 2017 for the zones Lungarno and Ponte Vecchio are analyzed and discussed.  
According to the procedure for the measurement of microclimate parameters suggested by UNI 10829:1999 [15], a preliminary survey was carried out in each zone using portable devices for measuring spot T and RH in the nodes of a horizontal grid with sides of 1.5 m at a height of 1.5 m from the floor. The spot measurements were made in the standard conditions of the environments, in a time interval not greater than 1 h for each zone. On the basis of the results obtained in this preliminary phase and taking into account the functional needs of the zones (i.e., aspects related to the use of the environments), the operating requirements of the monitoring systems (avoiding proximity to heat sources, direct light, and direct contact with local disturbance causes that may affect their proper operation), and the geometric characteristics of the zones, the positions of the continuous monitoring system were identified. Figure 6 depicts the selected monitoring positions, named A (Lungarno zone) and B (Ponte Vecchio zone). The sensors continuously monitored the indoor microclimate parameters (T and RH) in the two zones every 15 min.
# Galileo Receiver Performance Analysis with New I/NAV Improvements Live Data +
## 1 Introduction  
In order to compute its time and position, a Galileo receiver needs to decode the Clock and Ephemeris Data (CED) information of the satellites present in the message obtained via the received signals from space. The speed of the retrieval of CED from four different satellites will have a direct impact on the Time to First Fix (TTFF). The legacy I/NAV message in Open Service E1B and E5B [1] contains the CED information on words 1-4, and all need to be decoded in the same Issue of Data (IoD) to provide the necessary information of the satellite. These words are distributed in the subframes according to Table 1, meaning that, in perfect conditions (with no data loss), the minimum time needed to retrieve CED is 14 s. With four satellite channels tracked in parallel, the TTFF computation is straightforward, although, in environments with less visibility and/or degraded reception, the computation of the CED of four different satellites will take significantly longer.  
The inclusion of Reduced CED (hereafter RedCED) word 16 and Outer Forward Error Correction (FEC2) based on Reed-Solomon (hereafter RS) words 17-20 [2; 3] was proposed in order to improve the conditions of CED information retrieval. The RedCED word 16 contains the information of legacy words 1-4 compressed in a single word with reduced bits (and, therefore, reduced accuracy), while RS words 17-20 complement words 1-4 with information protected via the FEC2 RS algorithm that will complement words 1-4, as shown in Table 1. By computing the new words, the data decoding and TTFF value are greatly improved in comparison to the legacy configuration.  
The introduction of the Secondary Synchronization Pattern (hereafter SSP) [2] is intended to improve the time needed for time convergence when a coarse estimate is available (within \(+/-3\) s), as it will resolve the ambiguity when the receiver retrieves SSP bits pattern every 2 s among three possible patterns. The SSP information is present on the raw navigation data bits with no need to decode further data.  
In this paper, the improvements of RedCED, RS, and SSP are shown by comparing the performance of the configuration using the new words with the configuration using the legacy I/NAV message with live signal data. For further reading on Reduced CED and FEC2 RS CED, refer to [4, 5].
# Shear Resistance Assessment of the Y-Type Perfobond Rib Shear Connector under Repeated Loadings
## 4 Shear Behavior under Repeated Loads  
### Residual Shear Performance after Repeated Loads  
#### 4.1.1 Residual Shear Strength  
After the Y-type perfobond rib shear connector experiences repeated loads, the ultimate shear strength may decrease. The reduction of shear strength may depend on the repeated number and the intensity of the repeated loads. Table 10 shows the comparisons of the ultimate shear strengths before and after the repeated loads. The reduction ratios of ultimate shear strengths were calculated based on the results of the monotonic loading tests on the fresh specimens without any preceding loadings as well as the specimens loaded with repeated loadings. The reduction ratio depends on the history of the preceding loads. In Table 11, under the load types 1-3, which include 1460 kN loads (65% of the ultimate shear strength), the reduction ratio ranges from 4.1% to 7.1%. However, the reduction ratios are much smaller with the load types 4 and 5, in which the highest load intensity is 1235 kN (55% of the ultimate shear strength). The load types 4 and 5 have minimum 5 times higher numbers of repeats (maximum 30 times). It is concluded that the intensity of repeated loads has a higher effect on the reduction ratio than the number of repeats.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen (Monotonic)** & \(\mathbf{P_{u}(A)}\) & **Specimen (Repeated)** & **Load Type** & \(\mathbf{P_{u}(B)}\) & **Ratio (B/A)** \\ \hline SD400-D16 & 2228.5 & SD400-D16-1R & Type 1 & 2136.4 & 0.959 \\  & & SD400-D16-2R & Type 2 & 2125.4 & 0.954 \\  & & SD400-D16-3R & Type 4 & 2188.5 & 0.982 \\ \hline SD400-D19 & 2470.1 & SD400-D19-1R & Type 3 & 2309.6 & 0.935 \\  & & SD400-D19-2R & Type 4 & 2408.9 & 0.975 \\  & & SD400-D19-3R & Type 5 & 2418.4 & 0.979 \\ \hline SD500-D16 & 2267.1 & SD500-D16-1R & Type 3 & 2114.1 & 0.933 \\  & & SD500-D16-2R & Type 4 & 2224.2 & 0.981 \\  & & SD500-D16-3R & Type 5 & 2200.5 & 0.971 \\ \hline SD500-D19 & 2477.4 & SD500-D19-1R & Type 3 & 2273.6 & 0.918 \\  & & SD500-D19-2R & Type 4 & 2402.5 & 0.970 \\ \hline SD500-D22 & 2635.6 & SD500-D22-1R & Type 5 & 2510.7 & 0.953 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Shear resistance degradation by repeated loads.  
Figure 10: Cumulative residual slip curves according to load cycles.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
###### Abstract  
In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.  
TinyML; Edge AI; IoT; IoT data engineering; IoT Big Data management; IoT systems +
Footnote †: journal: Future Internet
# Resilience in the Internet of Medical Things: A Review and Case Study
## 1 Introduction
It includes technology such as machine learning (ML), software-defined networking (SDN), and microservices architecture (MSA) (see Section 2 for more details about these technologies). In IoMT networks, machine learning (ML) algorithms are used to predict the faults and failures through anomaly detection in advance so that the packet transmission in the IoMT can continue during the partial downtime with an emergency backup or alternate routing [3]. Software-defined networking (SDN) can control and manage the data traffic through an alternate traffic route during failure situations [4]. To avoid complete failure or downtime in the IoMT networks, a single centralized system can be replaced by a decentralized multi-functional system by using the microservices architecture (MSA) [5]. The above-mentioned technologies play an important role in making data transmission resilient; however, it is difficult to conclude that any single technology is sufficient to counter all the existing issues and can transmit the data in an emergency during network failure with the minimal required resource (see Section 3). Ref. [6] reviews the effectiveness of different works in the literature when these technologies are used in combination to provide resilience in the IoMT networks (see Section 4). It also addresses the interoperability and conflicting issues when these technologies are used together (see Section 5).
## 4 Results and Discussion
### Next-Generation Sequencing Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Taxonomy** & **C5a** & **C5b** & **C5c** \\ \hline _Aeronomas_ & 2.13\% & 0.14\% & 0.07\% \\ _Alcantivorax_ & 0.72\% & 0.95\% & 0.46\% \\ _Arobacter_ & 1.67\% & 0.13\% & 1.12\% \\ _Candidatus Nitrotoga_ & 0.00\% & 0.74\% & 0.33\% \\ _ldiomarina_ & 0.20\% & 0.08\% & 0.84\% \\ _Limobacter_ & 1.57\% & 0.00\% & 2.18\% \\ _Magnetospira_ & 0.18\% & 0.73\% & 0.31\% \\ _Marinobacter_ & 0.11\% & 0.36\% & 0.98\% \\ _Marinobacterium_ & 0.05\% & 0.00\% & 1.29\% \\ _Marinomonas_ & 1.23\% & 0.00\% & 0.30\% \\ _Nitrosomonas_ & 0.00\% & 3.43\% & 0.64\% \\ _Nitrospring_ & 0.06\% & 1.91\% & 0.36\% \\ _Ocanobacter_ & 0.12\% & 3.82\% & 0.46\% \\ _Parvibaccumulation_ & 0.28\% & 0.42\% & 1.19\% \\ _Pseudohongiella_ & 0.85\% & 2.72\% & 0.85\% \\ _Pseudomonas_ & 58.95\% & 52.18\% & 47.22\% \\ _Rehaibacterium_ & 0.17\% & 7.38\% & 2.29\% \\ _Rheinheimer_ & 2.50\% & 0.00\% & 0.55\% \\ _Rosevarius_ & 0.04\% & 0.78\% & 0.30\% \\ _Shevanella_ & 7.39\% & 0.07\% & 0.72\% \\ _Splingobium_ & 1.60\% & 0.00\% & 1.06\% \\ _Splingomonas_ & 0.92\% & 0.11\% & 0.44\% \\ _Thibacillus_ & 0.00\% & 0.19\% & 1.34\% \\ _Vibrio_ & 0.00\% & 0.03\% & 5.77\% \\ \hline \end{tabular}
\end{table} 
Table 1: Bacterial genera detected in groundwater samples collected in C5a-c (relative abundance >0.7%). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Taxonomy** & **Aerobic** & **Facultative Aerobic** & **Anaerobic** & **Facultative Anaerobic** & **Halophili/ Halotolerant** & **Sulfate Reduction** \\ \hline _Alcantivorax_ & + & & & + & \\ _Aroobacter_ & & + & & + & \\ _Desulfitiglans_ & & + & + & + & + \\ _Desulfovibrio_ & & + & & + & + \\ _Marinobacter_ & & + & & + & \\ _Marinomonas_ & + & & & + & \\ _Ocanobacter_ & + & & & + & \\ _Pseudomonas_ & + & + & & + & \\ _Shevanella_ & + & & + & + & + \\ _Vibrio_ & + & & + & + & \\ \hline \end{tabular}
\end{table} 
Table 2: Metabolic characteristics of selected bacterial genera detected in groundwater samples collected in C5a–c.
## 3 Simulation Results and Discussion
### Reconfiguration without DGs  
#### 3.1.1 Standard IEEE 33-Bus Distribution System  
The initial configuration of the standard IEEE 33-bus system is shown in Figure 3 followed by the performance results in Table 1. The initial active power loss of the system is 202.69 kW and the minimum voltage node is 18 with 0.9131 pu. After reconfiguration based on SFS [30], SSOE [3], and MSSOE, the power loss for all algorithms is reduced to 139.55 kW with a minimum voltage of 0.9378 pu. Regarding time consumption, MSSOE has shown the fastest speed but we notice that it is not much different (i.e., 0.0166 s) for 33 bus system (0.2496 and 0.2330 s). The voltage profiles of the base case and after the reconfiguration are provided in Figure 4. As seen in the figure, the voltage profile of the system is improved with reconfiguration.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Items** & **Base Case** & **SFS [30]** & **SSOE [4]** & **MSSOE** \\ \hline Tie-switches & 33,34,35,36,37 & 7,9,14,32,37 & 7,9,14,32,37 & 7,9,14,32,37 \\ Power loss (\(kW\)) & 202.69 & 139.55 & 139.55 & 139.55 \\ Loss reduction (\%) & N/A & 31.15 & 31.15 & 31.15 \\ V\({}_{\min}\) (pu) & 0.9131 & 0.9378 & 0.9378 & 0.9378 \\ Voltage node & 18 & 32 & 32 & 32 \\ Computation time (s) & N/A & N/A & 0.2496 & 0.2330 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation result of reconfiguration for IEEE 33-bus without the integration of distributed generations (DGs).  
Figure 3: Single-line diagram of IEEE 33-bus distribution system.
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 2 Multi-Sampling Period Data Fusion in the Distribution System  
The traditional distribution system shown in Figure 1 uses the telemetry function of the SCADA to measure and transmit measurement data. The function mainly measures the three-phase current (\(I_{\varphi_{\phi}}^{m}\)) and branch power (\(P_{Bi_{\phi}}^{m}\), \(Q_{Bi_{\phi}}^{m}\)), the power injection (\(P_{Li_{\phi}}^{m}\), \(Q_{Li_{\phi}}^{m}\)) and the three-phase voltage (\(V_{\varphi_{\phi}}^{m}\)), while the PMUs mainly measure the voltage phasor (\(\dot{V}_{r_{\phi}}^{m}\)) and some or all the connected branch current phasor (\(I_{r_{\phi}}^{m}\)). \(\phi\) represents a phase in A, B or C.  
As mentioned above, the SCADA data acquisition period is generally 2-5 s, and the PMU data acquisition period is generally 30-40 ms. If only using the SCADA measurement data, the state estimation interval time is at least 2 s, and the PMU measurement data during the SCADA data acquisition period will not participate in the state estimation, resulting in a waste of PMU data, which are more accurate and have a higher refresh rate. In this paper, the interval time of SCADA data acquisition is \(T_{SCADA}\), while the interval time of PMU data acquisition is \(T_{PMU}\). Besides, \(T_{SCADA}\) is divided into \(n\) portions, and \(P\) is the PMU data acquisition time during the SCADA data acquisition period.  
According to the data acquisition time, the state estimator switches between the robust estimator and the linear estimator to fast track states. As shown in Figure 2, at the SCADA data acquisition time, the SCADA and PMU measurements are combined in the robust estimation with pseudo-measurements, which can filter the bad data in measurements and prepare for the next state estimation. In the distribution system, the observability is guaranteed by pseudo-measurements which are mainly obtained from load forecasting based on AMI measurements [32; 33]. Due to a large number of load buses in the distribution system, there is a certain error in the load forecasting, so that the obtained pseudo-measurements have a low precision. At a non-SCADA data acquisition time, namely the PMU data acquisition time \(P\), the LSE is carried out based on estimation results of the previous estimation time and the PMU measurement data, which guarantees the system observability and makes the estimation results continue to update during the SCADA data acquisition period. Besides, the estimation accuracy of PMU installation buses can be close to the PMU measurement accuracy through the high-frequency refresh, including the bad data of the PMU measurements, so that combining the robust estimation with a linear estimation can ensure the estimation accuracy and track the distribution system status in real-time.  
Figure 1: The radial distribution system.  
Figure 2: Measurement data fusion diagram of SCADA and PMU.
###### Abstract  
A gap waveguide (GW)-based 4 \(\times\) 4 dual-band, dual-circularly polarized antenna array is proposed. The antenna is composed of two stacked layers which are responsible for right-handed circularly polarized (RHCP) radiation in K-band and left-handed circularly polarized (LHCP) radiation in Ka-band. Each stacked layer consists of a GW power distribution network to excite 16 radiating units from a single input. A waffle grid is mounted on top of the array structure to increase effective aperture areas and reduce grating lobes. A prototype was fabricated using stereo lithography appearance (SLA) 3D printing and metallization technology, which significantly decreased the fabrication cost and complexity. Measurement results for the antenna prototype demonstrate the operating bandwidths of 19.91-20.52 GHz in K-band and 28.26-29.19 GHz in Ka-band, over which the reflection coefficients of \(<\)\(-\)10 dB and axial ratios of \(<\)3 dB are achieved. The prototype provides peak gains of 20.1 and 20.3 dB with total efficiencies of \(>\)90% in the two respective bands. The combination of dual-band, dual-circularly polarized capability, high gain, high efficiency, lightweight, low cost and compact size renders the proposed design a potential candidate for commercial millimeter wave communication applications.  
3D printing technology; antenna array; dual-band dual-circularly polarized; gap waveguide (GW) +
Footnote †: journal: Applied  
Article  
Three-Dimensional Printed, Dual-Band, Dual-Circularly Polarized Antenna Array Using Gap Waveguide Technology.
## 3 KA-DDPG-Based Direct Torque Distribution Method  
### Framework of KA-DDPG  
The KA-DDPG algorithm is, mainly, composed of the original DDPG algorithm and knowledge-assisted methods. The considered knowledge-assisted methods include a low-fidelity controller that generates criteria actions and an evaluation method that generates guiding rewards. In this study, the low-fidelity controller is a simplified and inaccurate torque distribution method for skid-steering vehicles, which may be replaced by a torque distribution method based on optimal control theory. The evaluation method is an estimation value function, based on the ideal torque distribution feature, which was constructed through expert knowledge. The framework of KA-DDPG is shown in Figure 4.  
After receiving a state \(s\), the agent generates an action through its own policy, named the agent action \(a_{a}\). The low-fidelity controller also generates an action based on the state \(s\), named the criteria action \(a_{c}\). The action that the agent actually performs in the environment is called the execution action \(a_{e}\), which is produced by adjustment of the agent action \(a_{a}\), with respect to the criteria action \(a_{c}\). The reward that the agent gathers from the environment is called the observed reward \(R_{o}\), while the reward gathered from the evaluation method is called the guiding reward \(R_{g}\). The updating reward \(R_{u}\) is generated by combining the observation reward \(R_{o}\) and the guiding reward \(R_{g}\). Through continuous interaction with the environment, the agent can quickly learn to make reasonable actions in the direction of high reward, with the help of knowledge-assisted learning methods. The help of knowledge-assisted learning methods is, gradually, eliminated over the training period.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 5 Material and Methods
### Network Update Based on the CQI Threshold  
While the \"optimized tree\" approach substitutes links as soon as others with a better CQI are found, the \"less cost\" approach, on the other hand, keeps links as long as the CQI value is positive. A new approach based on a CQI threshold can be used to minimize the number of network updates, as described in Algorithm 2. If a neighbor in the same Gc group offers a better CQI, we should check if this CQI is at least higher than a certain CQI threshold before executing the handover and generating a new network topology update. The CQI threshold-based approach could certainly reduce the number of substitution operations and therefore network updates. However, simulations must be carried out to assess the impact of such an approach on the average CQI.  
```
For each Gc group of UEi do ifCQI_Value(New_neighbor) -CQI_Value(Old_neighbor) <= CQI_threshold then keep Connection with Old_neighbor else Connect with New_neighbor Disconnect from Old_neighbor  Report the modification to the ProSe Application Server number_of_updates ++ end if
```  
**Algorithm 2** Network update according to the CQI threshold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CQI** & **Modulation and Coding Scheme** & **Min. Rate D2D (kbps)** \\ \hline
1 & QPSK & 28.00 \\ \hline
2 & QPSK & 37.33 \\ \hline
3 & QPSK & 56.00 \\ \hline
4 & QPSK & 112.00 \\ \hline
5 & QPSK & 168.00 \\ \hline
6 & QPSK & 201.60 \\ \hline
7 & 16-QAM & 224.00 \\ \hline
8 & 16-QAM & 336.00 \\ \hline
9 & 16-QAM & 403.20 \\ \hline
10 & 64-QAM & 504.00 \\ \hline
11 & 64-QAM & 504.00 \\ \hline
12 & 64-QAM & 604.80 \\ \hline
13 & 64-QAM & 756.00 \\ \hline
14 & 64-QAM & 840.00 \\ \hline
15 & 64-QAM & 924.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CQI-MCS mapping for D2D direct communication.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 3 Results
### Results of Spherical Deformation
Velocity contours for the presented cases (Figure 15) are shown for the cross-section of the fluid film (the left-hand side is an axis; the right-hand side is an opening); for purposes of the figure, the fluid film has been exaggerated 50 times. The contours show more significant differences between the cases. Maximal fluid velocity for higher frequency case is significantly lower (2.72 m/s for 5 kHz, 0.58 m/s for 25 kHz), which is evidence of the limited airflow through the gap at high oscillation frequency. Figure 13: The amplitude of the modelled sample motion for excitation of the membrane of (**a**) 0.5 μm, (**b**) 1 μm, and (**c**) 1.5 μm. The higher frequency forces the use of the Navier-Stokes equations rather than simplified Reynolds equations. The reasoning is described by [PERSON] et al. [19]. Authors proved that using a criterion of Helmholtz number, i.e., a ratio of time of travel of acoustic wave through the film by oscillation period. For high-frequency cases, the solutions of the Reynolds equation were proven to be inaccurate.