esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments on the HVAC System
### System Performance and Convergence  
In order to evaluate the robustness of the proposed approach against other optimization methods we simulated our HVAC model running: (i) BB-BC algorithm in MATLAB sw, (ii) BB-BC in FPGA (our approach), (iii) PSO method in MATLAB sw and (iv) GA in MATLAB sw. The simulation is performed for different number of iterations (\(M\) = 100, 200, 500, 1000) and the results are illustrated in Figure 10.  
The BB-BC, PSO, and GA models run in a Core i7-3612 QM@ 2.10 GHz (64-bit) processor. The transient performance measured in seconds is presented in Table 4, while Figure 11 shows the control system action, i.e., system output, control signal, error, and objective function for all optimization methods, assuming population size (\(N\) = 10) and number of iterations (\(M\) = 100). As it can be clearly, the BBBC-FPGA approach is more robust compared to both PSO and GA approaches.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**PID Parameters**} & \multirow{2}{*}{**Os \%**} & \multirow{2}{*}{**Ts**} & \multirow{2}{*}{**Tr**} \\ \cline{2-2} \cline{5-6}  & \(k_{p}\) & \(k_{i}\) & \(k_{d}\) & & & \\ \hline BBBC & 0.205 & 0.063 & 0.046 & 0.505 & 8.632 & 7.708 \\ BBBC-FPGA & 0.236 & 0.076 & 0.020 & 0.505 & 9.498 & 7.959 \\ GA & 0.341 & 0.154 & 0.014 & 0.493 & 23.196 & 14.014 \\ PSO & 0.479 & 0.217 & 0.006 & 0.499 & 15.430 & 9.364 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transient performance comparison.  
Figure 10: Time responses of four optimization methods (BBBC, BBBC-FPGA, PSO, GA) for different number of iterations (**a**, **b**, **c**, and **d** for 100, 200, 500, and 1000 iterations, respectively).  
Figure 12 shows the best values of objective function per iteration for all four optimization algorithms for different number of iterations. In all cases, BBBC and BBBC-FPGA converge faster (locally minima) than GA and PSO, mostly expected from the BB-BC algorithm literature.  
Figure 11: The (**a**) system output, (**b**) control signal, (**c**) error signal, and (**d**) objective function for all four optimization methods.  
Figure 12: _Cont._  
With a new run of all optimization methods, Table 5 and Figure 13 provide more details about the objective function convergence presented on Figure 12 including the number of iterations, the steady state time (SST) and the running time assuming population size (N) equals 10 and number of iterations (\(M\)) equals 100.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 5 Conclusions  
In this paper, we propose a smartphone-based real-time indoor positioning method for providing indoor LBS, in which a particle filter framework combines PDR and BLE-based positioning using the built-in sensors and BLE chip. First, the built-in smartphone accelerometer, magnetometer, and gyroscope are used to provide data measurements and formulate a feasible solution for PDR. Second, a range-free weighted centroid algorithm is proposed to realize BLE-based localization with low computation complexity. Finally, to exploit the complementary strengths of each technology, a fusion framework utilizing a particle filter is proposed to combine PDR and BLE-based methods and provides more stable and accurate positioning results. Experimental results show that our proposed fused positioning method offers more accurate and stable performance in the long run compared with single PDR or BLE-based positioning. The achieved average positioning error is 1.34 m, which is a reduction of 24.16% compared with PDR positioning and 10.60% compared with BLE-based positioning. Moreover, about 95% of the positioning errors are smaller than 1.7 m. The proposed fused positioning method has vast application prospects in indoor navigation, indoor user tracking, and interactive experience for indoor visitors, etc. In future work, we will further expand the proposed approach to the situation when there are map constraints, and the placement of the smartphone is unconstrained.  
Conceptualization, Y.L.; methodology, Z.J., Z.Y. and Y.L.; software, Z.J.; validation, Z.J. and Y.L.; formal analysis, Z.J.; investigation, Z.J., Z.Y., Y.Z. and Y.L.; resources, Y.L.; data curation, Z.J.; writing-original draft preparation, Z.J. and Y.L.; writing-review and editing, Y.Z. and Z.C.; visualization, Z.J. and Y.L.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Zhejiang Province under Grant No. LZ21F020005, National Natural Science Foundation of China under grants Nos. 61772472 and 62271446, Fundamental Research Funds for the Provincial Universities of Zhejiang under grant No. RF-A2019002.  
Not applicable.  
Figure 9: The CDF of the positioning errors with different methods.
## 4 Evaluation
### Replication Techniques and Average Delivery Delay
#### 4.2.2 Performance Evaluation in the Helsinki Scenario  
The Helsinki scenario is simulated under five different rates of message generation in order to analyse how the replication techniques respond to changes in data traffic. The scenario consists of 600 nodes, which include 80 source nodes and 36 destination nodes that are placed in popular locations to represent sensors and gateways, respectively. Each source node generates messages to a randomly chosen destination node at the following rates: (i) 1 message every 3 h; (ii) 1 message every 2 h; (iii) 1 message every hour; (iv) 2 messages every hour; and (v) 3 messages every hour. External messages are also generated at the same rate, with randomly selected nodes as source and destination. The value of \(L\) for fixed-quota and ADS is taken as 4 and other settings remain as that of Section 4.1.2. The results are recorded in Figures 21 and 22--the former showing throughput and average delivery delay, and the latter showing transmission overhead and average energy consumption.  
Figure 21: Throughput and average delivery delay under increasing data traffic in the Helsinki scenario.  
Figure 20: Multiple deliveries of the same message and its impact on average delivery delay.
## 5 Verification of the Numerical Model-Examples
### Reinforced Concrete Girder
Stresses \(\tau_{xy}\) increase about 64% on day 730 compared to the stresses at the time of loading (day 30), while the change in shear stresses \(\tau_{yz}\) is negligible. The highest shear stresses are \(\tau_{xz}\), and their magnitude increases over time. At the end of the observation period, these stresses are up to 24% higher than at the time of loading. Furthermore, the analysis of the stresses in the reinforcement bar at the same moments in time as for concrete is given. Figure 25a clearly shows that just before the external load is applied, only the compressive stresses, as a consequence of shrinkage, occur in the reinforcement. These stresses in the reinforcement cause the appearance of compressive forces in the reinforcement bar which are transferred to the concrete as tensile forces and cause the stress states in the concrete shown in previous Figures 23 and 24. In Figure 25b it can be seen that, immediately after the application of the external load, the stresses in the reinforcement bar change from compressive to tensile. This indicates that the concrete has cracked due to shrinkage and that after loading, all of the load-bearing capacity of the tensile zone was taken over by the reinforcement. Figure 25c shows the state of stresses in the reinforcement bar at the time of the application of the maximum size of the external load, and a considerable increase in stresses is seen compared to those that were present at a smaller load size. It points to the  
opening of new cracks and the expansion of the existing ones. In Figure 25d, it is visible that the propagation of cracks due to concrete creep will cause an increase in the stresses in the reinforcement bar. The maximum stress in the reinforcement bar on day 730 is about 28% higher than it was on day 30 after the application of the full external load. This increase in tensile stresses in the reinforcement bar is shown in Figure 26, where the stress increase at the reinforcement bar point near the middle of the span of the girder, is visible between day 30, after the application of the full external load, and day 730. The figure shows that the increase in these stresses, caused by concrete creep, is greatest in the first 10 days of creeping, while in the later phase of creep it stabilises and the increase in stress is much smaller. So, in the later stages, due to creep, the stresses in the reinforcement bar reach a constant magnitude.
## 3 RMNCE-Based Satellite Selection  
Optimal satellite selection is an important way to realize acceptable accuracy with minimum computation [33]. The tightly coupled algorithm approach has been proven to be an effective method of positioning based on limited satellites, especially in harsh situations [34; 35]. For an embedded tightly coupled system, proper satellite selection, based on indexes to evaluate measurement accuracy directly, plays an important role to guarantee filtering precision with necessary calculations.
## 2 Results
### Identification of QTLs for GPC, Quality Traits, and Yield  
The focus of the present study was to identify QTLs for GPC, grain quality, and yield using an F\({}_{2}\) population of BPT 5204 X JAK-686. QTL analysis was performed using the inclusive composite interval mapping additive (ICIM-ADD) method implemented in the ICIM software ICIM [19]. In total, 19 QTLs were identified for GPC, grain quality, yield, and their contributing traits using ICIM-ADD on 12 rice chromosomes (Table 2; Figure 3).  
Figure 3: Genetic linkage map indicating position of QTLs for grain protein content, grain quality, yield, and yield traits in F\({}_{2}\) population.  
Figure 2: Linkage map generated using ICiMapping software V.4.1 with 103 polymorphic marker.  
Four QTLs, _qPC1.1_ (RM6120-RM3233), _qPC1.2_ (RM562-RM11307), _qPC3.1_ (A05P00597-A05P05283), and _qPC5.2_ (A05P22287-A05P26105), were identified for GP on chromosome 1 and 5, explaining 5.38%, 15.71%, 4.59%, and 3.09% of phenotypic variance (PVE), with LOD scores of 6.01, 4.46, 2.84, and 2.88, respectively (Table 2). Details of previously identified QTLs including their physical location are presented in Table 3. Among the four QTLs for GPC, _qPC1.2_, a major QTL, explained about 15.71% of the PVE (Figure 4). All the four GPC QTLs identified had a positive additive effect, indicating that the trait-enhancing alleles are from JAK-686 (Table 2).  
AC and GC, two important quality traits, have a significant impact on the cooking and eating quality of rice. We identified three QTLs for AC (_qAC3.1_, _qAC6.1_, and _qAC7.1_) on chromosome 3, 6, and 7, respectively. _qAC3.1_ had the highest LOD score of 6.37, and explained approximately 7.15% of the PVE (Table 2). All the three QTLs identified had positive additive effects, indicating that the contributing allele came from JAK-686. We identified five QTLs (_qGC1.1_, _qGC3.1_, _qGC5.1_, _qGC9.1_, and _qGC12.1_) governing GC. Among the five QTLs, _qGC9.1_ and _qGC12.1_, present on chromosomes 9 and 12, respectively, explained about 6.0% of the PVE (Table 2). _qGC9.1_ and _qGC12.1_ had LOD scores of 3.68 and 3.00, respectively (Table 2).  
Additionally, we identified two QTLs for KL: _qk1.1_ and _qk5.1_ on chromosomes 1 and 5, respectively. _qk1.1_ and _qk5.1_, with LOD scores of 2.86 and 4.77, respectively, explaining 9.63% and 4.24% of the PVE. A single QTL was identified each for KB (_qkb8.1_) and L:B (_qlb1.1_) on chromosomes 8 and 1, respectively. _qkb8.1_ and _qlb1.1_, with LOD scores of 3.30 and 2.51, respectively, explained 7.79% and 6.19% of the PVE. We have also identified two QTLs, _qptpp9.1_, and _qptpp12.1_, for PTPP on chromosome 9 and 12, respectively; _qptpp9.1_ and _qptpp12.1_, with LOD scores of 4.34 and 3.55, respectively, explained 5.65% and 5.78% of the PVE. Further, _qggpp8.1_, a QTL for GYPP with an LOD value of 2.57, explained 6.07% of the PVE.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Sensors
#### 3.3.4 Sensors for Water Quality Monitoring  
Water is an invaluable commodity and is necessary for any living being. Smart water management focuses mainly on making water distribution systems more efficient by applying sensors and telemetry for metering and communication [103; 104]. It applies in three broad areas: fresh water, wastewater, and agriculture. Moreover, more holistic perspectives around shared resource systems, such as the water-energy-food nexus are also benefiting from new sensing capacities and smart management systems enabled by digital technologies to provide more sustainable, resource efficient use solutions [97]. The principal usefulness of smart water systems lies in controlling valves and pumps remotely [104] measuring quality [103], pressure, flow, and consumption [105].  
Consumption monitoring includes metering and model applications to describe consumption patterns. Water loss management encompasses leakage detection and localization [105]. For water quality the focus is on measuring, analyzing, and maintaining a set of pre-established parameters. It is an integral real-time management involving stakeholders [106; 107; 108]. In the agriculture, the use of IoT devices is a common way to make irrigation more efficient and effortless [109; 110; 111]. Noise sensors and accelerometers are popular methods to detect leaks in water distribution infrastructure [105; 106].  
The use of electromagnetic and ultrasonic flow meters and sensors for measuring pressure are IoT technologies for water consumption rate analysis [103; 108].  
Sensors used to analyze the quality of the water are mainly applied for physical-chemical parameters such pH, temperature, electrical conductivity and dissolved oxygen [108; 109], also oxidation-reduction potential and turbidity [112; 113; 114], and presence of toxic substances [115]. In some cases, novel probes, such as for residual chlorine [103] or nitrate and nitrite, were implemented (see Figure 7) [112]. Humidity sensors are applied to measure soil moisture and assist in managing the schedule programs of irrigation in agricultural lands [110; 116; 117].
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 3 Materials and Methods
### Qualitative and Semi-Quantitative Analysis of Volatile Compounds  
Hemp volatile compounds were identified by comparing the retention times of the chromatographic peaks with those of authentic reference standards run under the same conditions and by comparing with the literature the linear retention index (_LRI_) relative to a mixture of \(n\)-alkanes (C\({}_{8}\)-C\({}_{40}\)) in \(n\)-hexane loaded onto the DVB-CAR-PDMS fibre and injected under the same conditions. Peak enrichment by co-injection with authentic reference compounds was also carried out. Comparison of the MS-fragmentation pattern of the target analytes with those of pure components was performed. A mass-spectrum database search was performed by using the National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA) mass-spectral database (version 2.0d, 2005). The percentage relative amount of individual components was expressed as percent peak area relative to total peak area.
## 2 Positioning in Cellular Networks
### Range-Based Localization Techniques  
The range-based localization techniques estimate the unknown position of the UE based on range measurements between transmitters and a receiver (or vice versa). These measurements can be obtained by extracting the information contained in the received signal, such as Received Signal Strength (RSS), Time of Arrival (ToA), and Time Difference of Arrival (TDoA) [28]. As shown in Figures 3 and 4, the distance between the UE and a minimum of three base stations (or one base station if the BS antennas are separated wide enough) is required to estimate the BS location by using trilateration [27].  
If there is a misalignment between the user receiver clock and the synchronized transmitter clocks, a common offset (bias) corrupts the measurements, resulting in pseudoranges (a range estimation containing an unknown error). In addition to the unknown user position, this offset needs to be treated as an unknown parameter too. It can be either estimated using the pseudoranges or eliminated by taking differences among the pseudoranges (hence, TDoA). For fully synchronized (ToA) or non-timing-based systems (such as RSS), this type of offset does not exist [28].  
The 3 GPP NR system supports two types of TDoA measurements: (a) the Observed Time Difference of Arrival (OTDoA), also called downlink-TDoA (DL-TDoA), where the ToA is measured in the downlink; (b) uplink-TDoA (UTDoA), based on sounding reference signals (SRS) [29]. One advantage of UTDoA compared to OTDoA is the time-stamping accuracy, which affects the time measurement accuracy. Time-stamping accuracy depends on the clock frequency and clock drift in the hardware. In the case of OTDoA, the ToA is measured at the UE, which typically gives worse performance than measurements at the BS. Since the UE measurements are performed on separate occasions, the time drift between the measurements becomes an additional error source.  
As emphasized in the discussion of the angle-based techniques, in practice, the UE does not have line-of-sight paths to more than one BS, resulting in corrupted ToA measurements. Usually, in non-line-of-sight conditions, the ToA-based solutions have low accuracy.  
Figure 2: The angle of arrival (\(\theta\)) can be calculated with the measurement of the incoming signal’s phase shift between the antennas.
# Special Issue on \"Mapping and Monitoring of Geohazards\"
## 2 Mapping and Monitoring of Geohazards
### Volcanic Hazard  
Volcanic hazards are addressed in the paper by [PERSON] et al. [15]. This study characterises the shoreline changes associated with the volcanic activity in 2018-2019 at Anak Krakatau (Indonesia), spanning a major lateral collapse (that occurred on 22 nd December 2018 [16]) and a period of regrowth through explosive activity. The study highlights the potential for satellite-based automated shoreline mapping to provide databases for monitoring the hazard associated with remote island volcanoes.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 1 Upper-level model
\(L\) represents the length of the control area. **b. ** **Constraints on Signal timing. **  
In the process of signal optimization, the constraints that must be adhered to include the minimum green time constraint, the maximum green time constraint, the phase se quence constraint, and the clearance time constraint. They are respectively represented as follows:  
\[u_{k}^{i}+g_{min}\leq o_{k}^{i},\forall k\in K;i=1,\ldots,N \tag{11}\]  
\[u_{k}^{i}+g_{max}\geq o_{k}^{i},\forall k\in K;i=1,\ldots,N \tag{12}\]  
\[o_{k}^{i}\leq u_{k}^{i+1},\forall k\in K;i=1,\ldots,N \tag{13}\]  
\[u_{k_{m+1}}^{i}\leq o_{k_{m}}^{i}+t_{clear} \tag{14}\]  
where \(t_{clear}\) represents the clearance time required during phase switching. \(g_{min}\) denotes the minimum green time duration, \(g_{max}\) specifies the maximum green time duration. Equations (11) and (12) are the constraints for the minimum and maximum green time duration of a phase. Equation (13) constrains the start and end times of the signal phase. Equation (14) addresses the constraints on the phase clearance time.
# Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features
## 4 Experimental Results and Analysis
### Datasets  
NTU-RGB+D [27] is the largest dataset with 3D Node annotation in relation to human motion recognition tasks. This dataset contains 60 motion categories and a total of 56,000 human motion video clips. All these clips were completed by 40 volunteers in a specific laboratory. It uses three Kinect V2 cameras with the same height but different angles to capture information at the same time, with angles of \(-45^{\circ}\), \(0^{\circ}\), and \(45^{\circ}\). This dataset obtains the position information, marked by 3D nodes on each frame, through the Kinect depth sensor. Each character in each skeleton sequence has 25 nodes, and there are only two characters in each segment at most. The author of NTU-RGB+D proposes to use two indicators: (1) the Cross Subject (X-Sub) evaluation index; in this evaluation index, the dataset is divided into the training set and verification set according to the person's ID. the training set has 40,320 segments and the verification set has 16,560 segments; (2) the Cross View (X-View) evaluation index, in which the evaluation index is divided according to the number of cameras. The information captured by cameras 2 and 3 is divided into training sets, and the information captured by camera 1 is divided into verification machines, including 37,920 segments in the training set and 18,960 segments in the verification set. The evaluation indexes used in this paper follow the above two indexes, and the recognition accuracy of top-1 on the two datasets is given.  
Kinetics [28] is a large human action dataset, which contains 400 action categories. These actions are depicted in videos obtained from YouTube. Each action category has at least 400 video clips, and each video clip includes at least 10 s. These actions include the interaction between people and objects, such as playing musical instruments, as well as interactions between people, such as shaking hands. However, this dataset only provides video without skeleton data. Therefore, in this study we used the OpenPose [29] toolbox to estimate the positions of 18 joints in each frame of the video clips. We selected two people for multiplayer editing according to the average joint confidence. We used their published data to evaluate our model. The dataset was divided into a training set and a verification set, in which the training set included 240,000 fragments and the verification set included 20,000 fragments. According to the evaluation method in Kinetics, we trained the model on the training set and obtained the top-1 and top-5 accuracy in the verification set. In  
Figure 10: Skeleton motion recognition model based on multi-scale deep spatiotemporal features.  
the dynamics skeleton dataset, the node labels were: 0--nose, 1--neck, 2--right shoulder, 3--right elbow, 4--right wrist, 5--left shoulder, 6--left elbow, 7--left wrist, 8--right hip, 9--right knee, 10--right ankle, 11--left hip, 12--left knee, 13--left ankle, 14--right eye, 15--left eye, 16--right ear, and 17--left ear.
## 3 The Establishment and Analysis of BBD Experimental Design and Prediction Model  
### BBD Experimental Design and Grey Relation Analysis  
The RSM is a technique which elucidates the correlation between design variables and response variables via polynomial fitting, aiming to find the optimal solution through experiments or simulations. The RSM is commonly used in experimental design, significantly reducing the number of experiments [19; 20]. The experimental design methods of the response surface methodology mainly encompass central composite design and BBD two distinct types.