esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Different Sampling Time  
We also analyze the angular errors caused by the different sampling time. We set \(L=20\) m, \(\beta=~{}\theta=0^{\circ}\) and \(\phi=45^{\circ}\). A timer is installed in our VUCA system, and it records the sampling time of the whole direction finding progress. The experimenter rotates his body stably and naturally in different velocities for 50 times, and the timer records different sampling times. In the above experiments, the sampling time is between 2 and 4 s most of the time. However, we try to finish the sampling process between 1 and 9 s in this experiment, and then observe the impact of different sampling times on the system accuracy. According to Figure 14b, the results show that when the whole sampling time is less than 2 s, the accuracy of the VUCA system is affected. The reasons for this phenomenon are that when the sampling time is too short, the velocity of the body rotation is changed rapidly, and the experimenter may wobble his body or spin the smartphone in his hand. The sampled acoustic signals is not ideal under this circumstance.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 3 Results
### B2b-RTPPP Time Transfer under Constrained Sky-View
The mean MDEVs of 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\) are 1.07 \(\times\) 10\({}^{-15}\), 1.03 \(\times\) 10\({}^{-15}\), 1.35 \(\times\) 10\({}^{-15}\), and 1.78 \(\times\) 10\({}^{-15}\) at 122,880 s. As the cut-off elevation angle increased, the long-term frequency stability decreased. In addition, the frequency stability of the TLM2-USUD time link outperformed those of other time links at the same averaging time. A possible explanation for this may be that the  
Figure 17: PDOP values of the TLM2 (**top**) and USUD (**bottom**) station.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 2 Background and Related Work
### Belgium Festival Study  
The aim of [10] is to propose suitable routing algorithms to be used at mass events for delivering small messages through opportunistic communication. The researchers used GPS traces recorded at a festival in Belgium to test their ideas in a real-world scenario.  
ONE (Opportunistic Network Environment, [[http://akeranen.github.io/the-one/](http://akeranen.github.io/the-one/)]([http://akeranen.github.io/the-one/](http://akeranen.github.io/the-one/)), accessed on 10 May 2021) was used as a test environment. From 5300 unique devices detected each day of the festival, 1000 were selected in the test scenario. The conclusions obtained by the authors were that Epidemic [11] and binary Spray-and-Wait [4] are the most suitable protocols to be used at mass events. Epidemic is most reliable in an emergency situation, when the network contention and power consumption are not taken into consideration, but Spray-and-Wait outperforms Epidemic in an energy-conscious environment. Both protocols require a small number of control messages, while achieving better values for delivery delay and delivery ratio than more complex protocols (e.g., PRoPHET [12]).
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Network Model
#### 3.2.3 AP Utility  
AP utility is the weighted product of the allocated data transmission time for client \(i\) from AP \(j\), which is related to the number of clients and the effective bandwidths and provides an efficient tradeoff between the client bandwidth and the AP load. It can be expressed by the following equation:  
\[U_{A_{j}}=\prod_{i\in A_{j}}\left(t_{i}\right)^{w_{i}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**STA3’s Choice**} & \multicolumn{4}{c}{**Achievable Throughput (Mbps)**} & \multirow{2}{*}{**System Throughput (Mbps)**} \\ \cline{2-2} \cline{4-6}  & **STA1** & & **STA2** & & **STA3** & **STA4** \\ \hline AP1 & 4.23 (3.56) & 4.23 (3.12) & 4.23 (3.33) & 11.45 (8.33) & 24.14 (18.34) \\ AP2 & 6.21 (4.73) & 6.21 (4.13) & 1.42 (0.88) & 1.42 (0.93) & 15.26 (10.67) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Throughput Loss Caused by Performance Anomaly [19].  
where, \(A_{j}\) denotes the set of clients associated with AP \(j\); \(t_{i}\) denotes the transmission time of client \(i\) obtained actually from AP \(j\), which satisfies the constraints of \(\sum_{t=1}^{n}t_{i}=T_{j}\) and \(b_{i}=\frac{r_{i}t_{i}}{T_{j}}\), where \(b_{i}\) is the bandwidth that client \(i\) obtains in fact from AP \(j\), and \(w_{i}\) is the weight of client \(i\).
## 1 Introduction
It was indicated that when the solar radiation was 850 W/m\({}^{2}\), the air flow rate of the inclined solar chimney and the combined solar chimney were 0.81 m\({}^{3}\)/s and 2.3 m\({}^{3}\)/s, respectively. The results showed that the combined solar chimney had a better ventilation effect than that of an inclined solar chimney. [PERSON] [22] established a mathematical model of a combined solar chimney by an energy balance method. It was found that the ventilation performance was improved with the increase of the width and height of the chimney but an excessive air gap reduced the air flow rate. The optimal air gap was 0.1 m and the optimum inclination angle could be controlled between 30\({}^{\circ}\) and 60\({}^{\circ}\) depending on the situation. A combined solar chimney (CSC) can be utilized for both summer ventilation and winter heating. However, the influence of structural parameters on the ventilation performance of a combined solar chimney is different from that of a single-structured solar chimney. At present, research has mostly focused on the thermal and ventilation properties of a solar chimney as well as the influences of structural and environmental factors on the ventilation effect, especially in a single-structured solar chimney. Few studies have focused on the operation modes and their applicability in different environmental conditions [23]. In this study, numerical models of a combined solar chimney (CSC) were established to research the natural ventilation and space heating effect of four different operation modes; that is, natural ventilation modes in summer, an anti-overheating mode in summer, a space heating mode in winter, and a preheating mode in winter. In order to verify the improvement of the ventilation performance, an inclined-roof solar chimney (IRSC) and a Trombe wall (TW) system were also discussed for comparison. The novelty of this paper lies in the comparative analysis on the ventilation performance of three different structural solar chimneys under particular operation modes that focuses on the ventilation effect and overheating prevention in the summer. The feasibility and applicability of different operating modes under different environment conditions are discussed as an emphasis. The study is significant for promoting the application of a combined solar chimney in practical engineering, providing guidance for the optimal operation of a solar chimney.
## 3 Methods
### Evaluation Metrics  
The six evaluation criteria that we employed in this work were the area under the curve (AUC), \(\kappa\), final_ and F1_scores, hamming loss (HL), and classification accuracy (ACC).  
Figure 5: Structure of interclass learning module.  
ACC measures the proportion of samples the model correctly classifies in its predictions. The formula to calculate ACC is as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}, \tag{8}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) represent the true positive, true negative, false positive, and false negative samples, respectively.  
\(F1\_score\) was calculated by the formula:  
\[F1\_score=\frac{2 TP}{2 TP+FP+FN} \tag{9}\]  
_Final_score_ was calculated by the formula:  
\[Final\_score=\frac{F1\_score+Kappa+AUC}{3} \tag{10}\]  
\(\kappa\) is the degree to which the model's predictions and actual categorization outcomes agree. This statistic can be useful to evaluate how successfully the model classifies minor categories in unbalanced datasets. The confusion matrix, which has a value between \(-1\) and \(1\) and is often greater than \(0\), is used to compute \(\kappa\). The formula is as follows:  
\[\kappa=\frac{p_{0}-p_{e}}{1-p_{e}}, \tag{11}\]  
where \(p_{0}\) is the accuracy of the prediction, and \(p_{e}\) is the sum of the product of the actual and predicted numbers divided by the square of the total number of samples.  
By dividing the total number of labels by the number of elements that are not equal between the two, the Hamming loss metric was used to evaluate the discrepancy between true and predicted labels. The smaller the ratio, the more accurate the model's prediction. The formula is as follows:  
\[HL_{(y,y)}=\frac{1}{n*m}\sum_{i=0}^{n-1}\sum_{j=0}^{m-1}1(\hat{y}_{i,j}\
eq y _{i,j}) \tag{12}\]
## 5 Simulation Setup  
We validated our approach by evaluating its performance using KheperaIII robots on the Webots simulator [25] (Version 8.3.0). The KheperaIII robot [26] was used because it is well known and commonly used in such experiments; it is a differential wheeled robot with dimensions of 13 cm in diameter and 7 cm in height, with a ring of nine infrared (IR) distance sensors, which are used to detect obstacles.  
The controllers of the robots are written in the C programming language, and it includes our proposed approach, DYMO-Auction, as well as a proportional-integral-derivative (PID) controller [27], which controls the robot navigation. The PID controller is a feedback control system that continuously calculates the difference between the desired goal and measured current state. A robot uses the PID controller to navigate to a task's location (desired goal), always computing the error (the difference between the current robot location and task location) and minimizing it. The PID controller uses odometry to estimate the current robot location and utilizes the IR sensor ring to avoid obstacles while driving a robot towards a task's location.  
Initially, we deployed three KheperaIII robots that were assigned quality levels represented by numbers (2, 5, 8) as low, medium, and high quality, respectively. Tasks emerge with a wide range of quality level requirements; we represent these levels from very low to very high-quality level requirements with numbers from 1 to 9, respectively. Robots and tasks are given a unique color based on their quality level as follows: gray for the low-quality level, blue for the medium quality level, and red for the high-quality level. Figure 7 illustrates the schematic diagram of the network for the simulation experiments.  
The robots form a robotic network, and they are responsible for receiving tasks from an external agent and then allocating those tasks among themselves. While the robots work on the assigned tasks, they report their status (quality satisfaction, load, traveled distance) to the external agent, which can then be used to generate the simulation results.  
Table 2 shows the experimental parameters. The simulation starts by deploying the robots uniformly in the area. Then, tasks are generated following exponential inter-arrival times, uniform locations, and quality requirements. A task advertisement message is produced with each newly generated task in the area. A random robot receives the task advertisement message and considers itself the auctioneer for that task; then, the auction process occurs. The task advertisement message is sent by an external agent located outside the team of robots. It includes the details of the task (location and quality level requirement).  
We have tested the proposed approach with different numbers of tasks (12, 24, 36, and 48 tasks). We repeated each experiment 35 times to achieve a 95% confidence level. Video footage of a nine-task experiment is available [28].
## 2 Results and Discussion
### Crystal Structures of the UMP-VPg1 and FUMP-VPg1 Bound to the 3D Polymerase
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Collection** & 3 Dpol-FUMP-VPg1 \\ \hline Beamline & ID29 (ESRF) \\ Resolution (Å) & 74.23–2.30 (2.42–2.30) \\ Space Group & P41212 \\ Cell dimensions & \\ a, b, c (Å) & 93.85 93.85 121.31 \\ \(\alpha\), \(\beta\), \(\gamma\) (\({}^{\circ}\)) & 90 90 90 \\ Rmerge & 0.092 (0.77) \\ I/cI & 12.7 (2.5) \\ Completeness (\%) & 99.9 (100) \\ Multiplicity & 6.6 (6.9) \\ \hline
**Refinement** & \\ \hline Resolution (Å) & 74.23–2.3 \\ No. reflections (total/unique) & 162,185/24,747 \\ R\({}_{\text{work}}\)\(\uparrow\)/R\({}_{\text{free}}\)\(\ddagger\) & 22.88/26.91 \\ No. Atoms/Residues & \\
3 Dpol & 3918 (476) \\ Waters & 148 \\ B-factors (Å2) & \\ All atoms & 27.41 \\
3 Dpol & 27.54 \\ Waters & 26.94 \\ R.m.s. deviations & \\ Bond lengths (Å) & 0.006 \\ Bond angles (\({}^{\circ}\)) & 0.892 \\ Ramachandran plot & \\ Residues in preferred regions & 462 (97.5\%) \\ Residues in allowed regions & 10 (2.1\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data collection and refinement statistics.
###### Abstract  
In this paper, a defected ground-structured antenna with a stub-slot configuration is proposed for future 5G wireless applications. A simple stub-slot configuration is used in the patch antenna to get the dual band frequency response in the 5G mid-band and the upper unlicensed frequency region. Further, a 2-D double period Electronic band gap (EBG) structure has been implemented as a defect in the metallic ground plane to get a wider impedance bandwidth. The size of the slots and their positions are optimized to get a considerably high impedance bandwidth of 12.49% and 4.49% at a passband frequency of 3.532 GHz and 6.835 GHz, respectively. The simulated and measured realized gain and reflection coefficients are in good agreement for both operating bandwidths. The overall antenna structure size is 33.5 mm \(\times\) 33.5 mm. The antenna is fabricated and compared with experimental results. The proposed antenna shows a stable radiation pattern and high realized gain with wide impedance bandwidth using the EBG structure, which are necessary for the requirements of IoT applications offered by 5G technology.  
5G; defected ground structure; electronic band gap; stub-notch configuration Article
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 5 Conclusions  
This paper proposes a reconfigurable FPGA-oriented baseband modulator architecture suitable for 4G/5G scenarios, such as non-contiguous carrier aggregation, parallel multi-access processing in C-RANs, and multiple waveform coexistence. The design was implemented on a Zynq xc7z020 device and supports three waveforms: OFDM, FBMC, and UFMC. It considers three independent, dynamically reconfigurable baseband processing blocks, whose mode of operation and clock frequency can be adapted at run-time through DPR and DFS. The clock frequency adaptation at run-time enables throughput adjustment according to communication demands and improves overall power efficiency (power reductions from 49%-88%) at the cost of a negligible resource and latency overhead (47 us). The aggregate sum of FPGA logic slices used for all modulator configurations plus the slices used for the system static part exceed the available slices on the xc7z020 by 17%. This enhanced resource efficiency is due to DPR hardware virtualization and makes it possible to implement the proposed architecture on a small-form cost-optimized device. The versatility of the current architecture is highlighted by the 32 possible modulation arrangements, when all three independent baseband processing blocks are simultaneously active. With an acceptable worst-case reconfiguration latency of 767 us, the application of DPR also makes the architecture forward-compatible with future modes of operation.  
Conceptualization, M.L.F. and J.C.F.; methodology, M.L.F. and J.C.F.; validation, M.L.F. and J.C.F.; investigation, M.L.F. and J.C.F.; writing, original draft, M.L.F.; writing, review and editing, J.C.F.  
This work is financed by the ERDF (European Regional Development Fund) through the Operational Programme for Competitiveness and Internationalization (COMPETE) 2020 Programme within Project POCI-01-0145-FEDER-006961, by National Funds through a Ph.D. Grant (PD/BD/105860/2014) from the FCT (Fundacao para a Ciencia e a Tecnologia) (Portuguese Foundation for Science and Technology).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
# Recent Advancements in Anaerobic Digestion and Gasification Technology
## 11 Bibliometric Analysis of the Study  
In this section, the data employed were obtained from the Web of Science (WoS) core collection. In total, 207 and 801 results from the WoS the core collection were obtained for anaerobic digestion and biomass gasification, respectively. The choice of WoS was to maintain consistency in the study and avoid any overlap in the data collection. The period considered for the search was set to be 10 years from 2011 to 2021, focusing on journal types, publishers, and countries. Original articles, review papers, and proceeding papers were the document types considered; these are presented in Table 9a,b. This search was carried out to investigate the trend in research on anaerobic digestion and biomass gasification. For the successful analysis of the bibliometric, the keywords criteria used were _\"anaerobic digestion\"_, _\"biogas digester\"_, and _\"biogas\"_ for anaerobic digestion, while for biomass gasification, the keywords used were _\"biomass gasification\"_, _\"gasifier\"_, _and \"syngas\"_.  
Although the entire period set for bibliometric analysis was 2011 to 2021, the authors also considered the recent years from 2017 to 2021. It was observed that there is a fluctuation in the total number of publications in the two research fields, implying that the research in the two fields has not been consistent. Hence, future study is recommended. Regarding the journals, Energy and Fuel are the most active journals in terms of the number of publications in anaerobic digestion and biomass gasification, with 83 and 587, respectively. The Energy and Fuel journals are published by Elsevier. For the anaerobic digestion and biomass gasification research, Elsevier published a total number of 80 and 498 publications (original article, review paper, and proceeding papers). Moreover, it can be deduced that Energy, Fuel and Green Sustainable Science Technology are present and common in research or study. In addition, in terms of publishers, Elsevier, Springer Nature, and Taylor and Francis are known as the popular publishers present in both journals. For countries researching anaerobic digestion and biomass gasification, China is regarded as the leading country (See Table 9a,b). This might be because of the country's interest in funding renewable energy and alternative energy research, especially as it relates to anaerobic digestion for biogas production and biomass gasification for syngas production.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification of Biomass**} & **Moisture Content** & **Volatile Matter** & **Ash Content** & **Heat of Combustion** \\  & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(MJ/kg)** \\ \hline Woody biomass & 4.0–12.0 & 60.0–85.0 & 0–5 & 16.0–20.0 \\ Agricultural and food biomass & 2.0–12.0 & 64.0–85.0 & 0–18 & 15–17 \\ Animal and industrial biomass & 3.0–10.0 & 70–81 & 5–10 & 24–25 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of recommended operating parameters of biomass waste for gasification technology [64].
# Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
## 2 Material and Methodology
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Hyperparameters Tuned** & **Range** & **Optimal Value** \\ \hline \multirow{4}{*}{DTR} & max\_depth & \([1,100]\) & 60 \\  & min\_samples\_split & \([2,10]\) & 2 \\  & min\_samples\_leaf & \([1,4]\) & 1 \\  & max\_features & \([1,13]\) & 6 \\  & n\_estimators & \([1,100]\) & 89 \\  & max\_depth & \([1,100]\) & 50 \\ RFR & min\_samples\_split & \([2,10]\) & 2 \\  & min\_samples\_leaf & \([1,4]\) & 1 \\  & max\_features & \([1,13]\) & 5 \\  & n\_estimators & \([1,100]\) & 61 \\  & max\_depth & \([1,100]\) & 39 \\ ETR & min\_samples\_split & \([2,10]\) & 2 \\  & min\_samples\_leaf & \([1,4]\) & 1 \\  & max\_features & \([1,13]\) & 8 \\  & n\_estimators & \([1,100]\) & 50 \\  & learning\_rate & \([0.01,1]\) & 0.1 \\  & max\_depth & \([1,50]\) & 37 \\ GBR & min\_samples\_split & \([2,10]\) & 2 \\  & min\_samples\_leaf & \([1,4]\) & 1 \\  & max\_features & \([1,13]\) & 7 \\  & n\_estimators & \([1,100]\) & 57 \\  & learning\_rate & \([0.01,1]\) & 0.2 \\ XGBR & max\_depth & \([1,50]\) & 30 \\  & subsample & \([0.01,0.8]\) & 0.76 \\  & colsample\_bytes & \([0.01,0.8]\) & 0.42 \\  & gamma & \([0,20]\) & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameters of models. Figure 5: Box plot of R\({}^{2}\) obtained from different models in 10-fold cross-validation. than other boosting and bagging models. Accordingly, the DTR is not recommended for prediction of the SOG. A further assessment of the performance of a model is through its achieved accuracies using the testing dataset. From Table 8, it is observed that ETR performed slightly better than RFRs. In addition, the computational time was almost half of that of the RFR. Furthermore, the \(R^{2}\) of XGBR and DTR even increased to approximately 96.98% and 96.46%, respectively. Finally, it is important to note that the investigation was executed using a computer with the following specifications: Windows 10 with 64-bit Operating System and x64-based processor, Intel(R) Xeon(R) CPU E3-123 v3 @3.30 GHz processor, and 32.0 GB installed memory (RAM). By far, the computational time of DTR is better than the others after LR. In general, the ETR model has shown better accuracy than the other models. In addition, its computational time is relatively acceptable. To validate the consistency of the ETR model for different ship routes and ship types, it was tested by extracting different ship data as a testing dataset. As shown in Table 9, the performance of the ETR model was consistent for two tankers and three cargo ships with various ship routes, and thus, the ETR model is still valid for predicting the SOG for various ship data.