esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 3 Key Aspects of the BSI Technical Guideline with Relevance for Energy Management
### Tariff Use Cases  
The technical guideline defines 13 tariff use cases (\"Tarifanwendungsfall\", TAF) that specify requirements for the provision of data to an EMP and the consumer. Table 1 provides an overview of the TAFs and outlines the theoretically achievable currentness of meter measurements based on the tariff use case descriptions in [7]. Since the technical guideline provides only a name and an outline instead of an actual specification, the table omits TAF 13, which is intended for visualizing measurements for a consumer via the WAN interface. Depending on how this TAF will be defined in the future, it may be very relevant for providing data to a remote EMS.  
Both billing period and registration period are TAF-specific parameters. The billing period is relevant to TAFs 1, 2, 3, 4, 5, 7, and 8, and determines the length of the individual time frames for which measurements and derived data need to be collected. Additionally, for TAFs 3, 4, 7, and 8, the registration period determines the temporal granularity of the (derived) metering data that are collected during a billing period. This means that, if applicable, the EMP receives data for each registration period once the billing period has passed. Except for TAFs 6, 9, and 10, the collected data are sent at the configured time of transmission. On the one hand, the specification does not provide clear restrictions for the time of transmission and the length of the billing period. On the other hand, it  
Figure 1: The processing of meter measurements by a Smart Meter Gateway, based on [7].  
states that the SMGW must support measurement periods of 5 min and that the measurement period is to be understood as the smallest billing-relevant time frame [7] (Footnote 4). This implies that billing periods or registration periods of 5 min must be supported. For the registration period, the SMGW must support a length of at least 15 min.  
An SMGW does not necessarily support all TAFs presented in Table 1. Instead, only the availability of TAFs 1, 2, 6, and 7 is guaranteed [30]. Some tariff use cases do only provide very specific information to the EMP, since they are meant for distinct situations. An example is given by TAF 9, which transmits solely the latest feed-in power. For TAF 9, the technical guideline states the requirement of providing current data, but does not further specify the exact meaning in regards of architectural and technical limitations. It is also important to note that for some tariff use cases the EMP receives only information derived from the measurements in lieu of the actual meter readings. One example is TAF 3, which registers the energy consumption based on the magnitude of power currently consumed. Then, after the billing period, the EMP receives information about the consumption for each load level.
## 3 Results  
Implants with Ti-UniteTM surface underwent peri-implantitis in eight implants (1.6% of the total amount of implants); other causes of implant loss were prosthetic problems (six implants, 1.2% of the total), fractures (four implants, 0.8% of the total), and lack of osteointegration (two implants, 0.2% of the total). Therefore, considering only implant loss, the majority was due to peri-implantitis (40%), followed by prosthetic problems (30%), fractures (20%), and lack of osteointegration (10%). The total number of implants lost was 20 (Table 2).  
Implants with Ossean(r) surfaces underwent peri-implantitis in 15 implants (1.5% of the total amount of implants); other causes of implant loss were prosthetic problems (11 implants, 1.1% of the total), fractures (5 implants, 0.8% of the total), and lack of osteointegration (5 implants, 0.5% of the total). Considering only implant loss, the majority was due to peri-implantitis (40%), followed by prosthetic problems (30%), fractures (15%), and lack of osteointegration (15%). The total amount of implants lost was 36 (Table 3).  
In the Ti-Unite group of implants, two implants underwent peri-implantitis at the fourth year of follow-up, three in the seventh year, and three in the ninth year of follow-up.  
In the Ossean group of implants, three implants underwent peri-implantitis in the fifth year of follow-up, six in the seventh year, four in the eighth year, and two in the tenth year of follow-up.  
Regarding peri-implantitis that occurred in Ti-Unite implants, there was no statistical difference between the mean MBL in the upper arch (3.88 mm) and the mean MBL in the mandible (4 mm) (\(p\) = 0.7795) or between the mean MBL in regenerated bone respect (3.92 mm) and the MBL in pristine bone (3.9 mm) (\(p\) = 0.9574).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Implant Loss** & \
\begin{tabular}{c} **Prosthetic** \\ **Problems** \\ \end{tabular} & **Fractures** &
\begin{tabular}{c} **Lack of** \\ **Osteointegration** \\ \end{tabular} & **Peri-Implantitis** & **Total** \\ \hline Total & 6 & 4 & 2 & 8 & 20 \\ \% of total & 1.2 & 0.8 & 0.4 & 1.6 & 4 \\ \% lost & 30 & 20 & 20 & 40 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ti-Unite implants lost.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Implant Lost** & \
\begin{tabular}{c} **Prosthetic** \\ **Problems** \\ \end{tabular} & **Fractures** &
\begin{tabular}{c} **Lack of** \\ **Osteointegration** \\ \end{tabular} & **Peri-Implantitis** & **Total** \\ \hline Total & 11 & 5 & 5 & 15 & 36 \\ \% of total & 1.1 & 0.5 & 0.5 & 1.5 & 3.6 \\ \% lost & 30 & 15 & 15 & 40 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ossean® implants lost.  
For peri-implantitis in Ossean implants, the statistical analysis conducted between mean MBL in the upper arch (3.63 mm) and mean MBL in the mandible (3.55 mm) showed no statistical differences (\(p\) = 0.6973). The MBL in regenerated bone (3.73 mm) and MBL in pristine bone (3.4 mm) also showed absence of statistical differences (\(p\) = 0.1020).
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(a_{c}\), \(a_{t}\) & Cross-sectional area of the chamber and turbine duct \\ \(b_{g}\) & Generator damping coefficient \\ \(b_{t}\) & Blade span \\ \(f_{r}\) & Radiation memory function \\ \(h_{0}\) & Chamber tube draft \\ \(k_{h}\) & Hydrostatic stiffness \\ \(k_{p}\) & Proportional control gain \\ \(l_{t}\) & Chord length of the turbine rotor blade \\ \(n_{t}\) & Number of turbine rotor blades \\ \(\Delta p_{c}\) & Pressure difference between the chamber and atmosphere \\ \(\Delta p_{t}\) & Pressure drop across the turbine \\ \(\Delta\omega_{t}\) & Small wave angular frequency interval \\ \(r_{t}\) & Turbine rotor mean radius \\ \(t\) & Time \\ \(x\) & Vertical position of the inner water level \\ \(z\) & State vector for \(F_{r}\) \\ \(A\), \(B\), \(C\) & State-space matrices for the approximation of \(F_{r}\) \\ \(C_{a}\), \(C_{t}\) & Input and torque coefficients \\ \(E_{g}\) & Induced generator voltage \\ \(F_{e}\) & Excitation force \\ \(F_{r}\) & Convolution part of the radiation force \\ \(H_{s}\) & Significant wave height \\ \(I_{a}\) & Circuit current \\ \(J\) & Inertia of the turbine and generator rotors \\ \(K_{g}\) & Generator torque proportionality constant \\ \(M\) & Mass of trapped water in the chamber \\ \(N\) & Arbitrary number \\ \(P_{g}\), \(P_{t}\) & Power extraction from the generator and turbine \\ \(P_{pne}\) & Pneumatic incident power \\ \(Q\) & Flow rate \\ \(R_{a}\), \(R_{e}\) & Internal and external loads \\ \(S_{PM}\) & Pierson-Moskowitz's wave energy spectrum \\ \(T_{p}\) & Wave peak period \\ \(V\) & Lyapunov candidate function \\ \(V_{x}\) & Airflow speed at the turbine duct \\ \(\gamma_{r}\),\(e\) & Positive constants \\ \(\eta_{t}\) & Turbine efficiency \\ \(\theta_{n}\) & Initial phase of the wave component of order \(n\) \\ \(\lambda(t)\) & Wave elevation \\ \(\lambda_{n}\) & Amplitude of the wave component of order \(n\) \\ \(\mu(\infty)\) & Added mass at infinite frequency \\ \end{tabular}  
\(\rho_{a}\), \(\rho_{w}\) Air and seawater density  
\(\tau_{g}\), \(\tau_{t}\) Induced torques from the generator and turbine  
\(\phi\), \(\phi_{o}\) Flow coefficient and optimal value at the maximum turbine efficiency  
\(\omega\) Wave angular frequency  
\(\omega_{n}\)\(n^{\prime}\)-th order wave angular frequency  
\(\omega_{l}\) Lowest wave angular frequency  
\(\Omega\) Angular velocity of the turbine rotor  
\(\Omega_{d}\) Desired angular velocity of the turbine rotor  
\(\ast\) Relative value with respect to the ideal case or the maximum value  
- Error value  
- Average value
# Research on Sentiment Classification of Online Travel Review Text
## 3 Case Study
### Data Acquisition and Preprocessing Experiment  
As shown in Table 2, the present research used a crawler program written in Python to obtain four datasets from the TripAdvisor website. Mutianyu_Great_Wall contains review text pertaining to Mutianyu Great Wall, which is the number one attraction on the TripAdvisor website in Beijing, China. It contains a total of 2772 pieces of review data in English published by tourists from January 2016 to December 2019. Wizarding_World_of_Harry_Potter contains comment text pertaining to the Harry Potter Wizarding World Theme Park in Orlando, USA, specifically, 6641 pieces of comment data published by tourists from June 2017 to December 2019 in English. Tower_of_London contains comment text about the Tower of London in the United Kingdom. It contains the data of 4428 comments in English published by tourists from July 2018 to December 2019. Finally, Sydney_Opera_House contains review text pertaining to the Sydney Opera House in Australia. It contains 6776 pieces of comment data in English published by tourists from March 2017 to December 2019.  
Table 3 presents a piece of comment text in the Mutianyu_Great_Wall dataset and its pre-processed results. In the table, the first column is the comment text published by tourists, while the second column is the pre-processed text. As can be seen from the introduction in Section 2.2, the preprocessing operation removes any original comment text content that is unrelated to sentiment classification.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 8 Conclusions  
In this research, we analyzed the current challenges in the performance of 3D detection networks regarding bounding box regression and classification problems. A literature study investigated the current trends in developing 3D object detectors. We analyzed the shortcomings of applying axis-aligned IoU losses to rotated bounding boxes. We studied that IoU losses and their variants suffer drawbacks in edge cases of inclusion and objects with the same centers and aspect ratio, affecting the network convergence. To address  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Network Type** & **3D Pedestrian (IoU = 0.5)** \\  & & **Easy Mod Hard** \\ \hline Point-GNN [45] & LiDAR & 51.92 43.77 40.14 \\ Part-\(A^{2}\)[52] & LiDAR & 53.10 43.35 40.06 \\ ine PointPillars [40] & LiDAR & 51.45 41.92 38.89 \\ F-ConvNet [41] & LiDAR & 52.16 43.38 38.80 \\ MGAF-3 DSSD [42] & LiDAR & 50.65 43.09 39.65 \\ PFYB [55] & Camera + LiDAR & 43.93 36.07 32.86 \\ AVOD-FPN [56] & Camera + LiDAR & 50.46 42.27 39.04 \\ PointPainting [57] & Camera + LiDAR & 50.32 40.97 37.84 \\ Fast-CLOCs [58] & Camera + LiDAR & 52.10 42.72 39.08 \\ Frustum-PointPillars [59] & Camera + LiDAR & 51.22 42.89 39.28 \\ Ours & Camera + LiDAR & **53.62 44.42 40.40** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance evaluation with state-of-the-art detection networks for 3D pedestrian class on KITTI test benchmark set.  
this issue, we proposed an efficient IIoU loss function that estimates the center distance of the x, y, and z axes individually while considering orientation as a 4 th-dimensional parameter. We also carried out a simulation experiment to observe the convergence of loss functions using a synthetic data and loss convergence during the network training phase. Then, the performance of the proposed loss function was explored on the KITTI and nuScenes val datasets using Camera-LiDAR fusion and LiDAR-based 3D networks. Our proposed loss function performs better compared to its counterparts. Performance evaluation was also carried out in the KITTI test set to demonstrate the optimal performance of our approach compared to other state-of-the-art methods. This approach can also be extended to multi-object tracking and object segmentation applications.  
Conceptualization, N.R. and M.E.-S.; methodology, N.R.; software, N.R.; validation, N.R. and M.E.-S.; writing--original draft preparation, N.R.; writing--review and editing, N.R. and M.E.-S.; supervision, M.E.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.  
The authors declare no conflict of interest.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 6 Results
### Probability of Connection Success
For the two configurations that start sending beacons every 4 s, the time they take to connect the whole network is around 3 min, taking into account that the node at the farthest level (node 16) is at least six jumps from the root node. Reducing this connection time will have a great impact on the energy consumption of the nodes. This is because, during the time that they remain scanning, the devices have their radio interface connected persistently, which reduces the autonomy of those nodes that are unable to connect for a long time. Figure 13 shows the energy consumption of the different nodes for each of the configurations. In configurations that use a lower amount of signalling traffic, Figure 13a,b, energy consumption increases greatly, since, although fewer beacons are sent, the nodes spend more time scanning until they are synchronized. For the other two configurations, Figure 13c,d, the energy consumption is much lower during the first 15 min, and their results are quite similar. The small variations in consumption levels are mainly due to the reduced statistical mass. If the number of repetitions in the simulations is greatly increased, then these energy consumption levels should tend to the same value in both configurations, since in both configurations the nodes spend 2 min sending beacons every 4 s, and, from that moment, the configuration with the variable period will change to 16 s as proposed by the recommendations. Figure 12: The connection time for different EB period configurations. By correlating the consumption results of Figure 13 with the connection times of Figure 12, it can be seen how the energy consumption in this first synchronization phase is mainly affected by the scanning phase, when each node maintains its radio in an active state until it receives a synchronization message. This is why the configuration with the fixed beacons every 4 s and the variable beacon transmission configuration obtain similar results for the first 15 min of operation. Once all of the nodes are connected, the configuration that sends beacons with a fixed period will have a higher energy consumption since it will be transmitting 4 times more messages. Figure 14 shows the evolution of energy consumption for the last two configurations during 100 h, in which one of them maintains its fixed period at 4 s and the other changes the period to 16 s.  
Figure 13: The power consumption for different EB period configurations.
## 3 Proposed Low-Latency Compact Baseband System for Wireless VR
### Baseband Processing with Block-Level Interleaved-BCH Codes
As illustrated in Figure 1, the analog serializer finally gets the 16-parallel data stream from OFIFO to make the valid bit stream to media devices, which has the same data-rate as the original input stream shown in Figure 1. Similar to the baseband processing in TX mode, the RX processing also utilizes the double buffering scheme, which means that the next data frame is transferred by using the empty frame buffer during the CC decoding operation, as depicted in Figure 5.  
Combined with the simple component BCH codes as well as the block-level interleaving scheme, the proposed digital baseband processing effectively overcomes the burst-error channels. Figure 6 illustrates how the proposed baseband system corrects a number of burst errors. Basically, we process the data blocks in CC-level orders and transfer them in FS-level orders. As two different ways manage the data blocks by handling the opposite directions to each other, as illustrated in Figure 3, the burst errors that exist in a number of blocks during the wireless transmission are naturally scattered to the different CCs at the baseband processing. If the channel suffers from the misalignment of beams, all the transmitted blocks contain errors for a while, which normally belong to the same FS depicted in Figure 3.0 By rearranging the received FSs with the block-level de-interleaver, however, those erroneous blocks are then placed into different CCs. As each CC can correct up to 18b errors, i.e., a single block, the proposed ECC solution can tolerate up to 60 consecutive error blocks by scattering all the error blocks to the different CC. As a result, the proposed (106,920, 95,040) block-level interleaved-BCH ECC can recover the burst errors of up to 1080 bits during transmitting a data frame of 106,920 bits. Without applying the block-level interleaving scheme, one can use a long BCH code for correcting total 1080 error bits in a user data of 95,040 bits; however, such a long BCH code requires an impractical amount of hardware complexity due to the increased dimension of finite field [30; 31]. It is also possible to apply the RS code as a component code for providing the symbol-level correction [32; 33]; however, the RS code only focuses on the burst-error channel condition. Even though the practical channel condition of the wireless VR system contains lots of burst errors, based on our observation, there still exist a few random errors. Therefore, the RS-based concatenation scheme is naturally inefficient compared to the proposed BCH-based ECC solution. As a result, the proposed baseband processing is attractive to the wireless VR systems in terms of the error-correcting capability as well as the computing complexity. Figure 3.1 Proposed de-interleaving and buffering scheme for seamless error correction.
# Design and SAR Analysis of a Dual Band Wearable Antenna for WLAN Applications
## 3 Results  
### Off-Body Analysis  
#### 3.1.1 Conventional Antenna  
The proposed dual-band wearable antenna operating at 2.5 GHz and 5.2 GHz was designed in CST MWS software with a frequency range of 2 to 5.5 GHz, as shown in Figure 1. Figure 9 shows the return loss of the designed dual band antenna. The resonance frequencies were 2.5 and 5.2 GHz with an input impedance bandwidth of 23.8 and 60.4 MHz, respectively. The proposed antenna had a return loss of \(-\)24.975 and \(-\)28.234 at 2.5 and 5.5 GHz, respectively.  
Figure 10 shows that the proposed antenna is very precisely matched at both resonance frequencies with a voltage standing wave ratio (VSWR \(<\) 1.119).  
The directivity and gain patterns of the designed conventional dual band antenna at 2.5 and 5.2 GHz are shown in Figures 11 and 12, respectively. The maximum directivities at 2.5 and 5.2 GHz are 8.39 and 9.01 dB, respectively. The 3 dB angular widths at 2.5 and 5.2 GHz are 83.4 and 76.9 degrees, respectively. The maximum gains at 2.5 and 5.2 GHz are 8.08 and 8.74 dBi, respectively. A small amount of disturbance is observed in the patterns at the higher resonance frequency. The second reason for this disturbance is the compact size of the proposed antenna. The miniaturization of an antenna is always a matter of compromise between antenna radiation characteristics and its size. Therefore, various slots  
Figure 8: Antenna wrapped around four-layered body parts. (a) Leg of radius 70 mm; (b) arm of radius 60 mm; (c) arm of radius 40 mm.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tissue** & \begin{tabular}{c} **Conductivity** \\ **(2.5 GHz), S/m** \\ \end{tabular} & \begin{tabular}{c} **\(e_{r}\)/2.5 GHz** \\ \end{tabular} & \begin{tabular}{c} **Gmicity** \\ **(5.2 GHz), S/m** \\ \end{tabular} & \
\begin{tabular}{c} **\(e_{r}\)/5.2 GHz** \\ \end{tabular} &
\begin{tabular}{c} **GHz** \\ \end{tabular} \\ \hline Bone & 0.38459 & 11.41 & 1.0101 & 9.946 \\ Muscle & 1.705 & 52.79 & 4.2669 & 49.278 \\ Fat & 0.10235 & 5.28 & 0.2547 & 5.0104 \\ Skin & 1.4407 & 38.06 & 3.2185 & 35.61 \\ \hline \end{tabular}
\end{table}
Table 3: Average EM properties of human tissues at 2.5 GHz and 5.2 GHz.  
The distribution of surface currents in the conventional wearable antenna at 2.5 and 5.2 GHz is shown in Figure 13, which clearly shows the resonant lengths.
## 5 Experimental Results
### Back-EMF  
Dynamometer in Figure 14 provides constant speed control for no-load test of the fabricated motor. Figure 15 are waveforms of phase voltage of U (blue line) and V (purple line) and line to line voltage U to V-phase (green line). Each result was obtained at 6000 r/min and 8000 r/min from power analyzer connected to the terminals of the manufactured motor. Peak values were measured to be 179 V\({}_{\text{peak}}\) for phase voltage and 282 V\({}_{\text{peak}}\) for line to line voltage at 400 Hz (3000 r/min) while at 534 Hz (8000 r/min) 232 V\({}_{\text{peak}}\), 239 V\({}_{\text{peak}}\) for U and V-phase and 372 V\({}_{\text{peak}}\) for line to line are measured. These results are deviated from the results shown in Section 4.2. The reason for this deviation was a gap between the rotor core and interior PM in magnet cavity.  
Given the gap that appears in magnet cavity, FEA simulation was performed again reflecting the gaps. The waveforms at 6000 r/min are shown in Figure 16a. The agreement in back-EMF is observed if noise in measured back-EMF is ignored. Numerically calculated RMS values are, respectively,  
Figure 14: The experimental testbed for no-load and under-load tests.  
Figure 15: Measured back-EMF, blue/purple/green: U/V/U to V: (a) 6000 r/min and (b) 8000 r/min.  
114.57 V\({}_{\rm rms}\) for FEA and 113.33 V\({}_{\rm rms}\) for the measured data. RMS values of fundamental component at each speed reference are presented in Figure 16b. The biggest error was found at 4000 r/min, as 2.56%, and mean error at 1000 to 6000 r/min is 2.0%. THD is, respectively, evaluated to be 7.06% from FEA and 5.99% from the measured data. The whole results seem to have good agreements between the fabricated model and the model of FEA simulation.