esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a high-mobility MDOF UOWC system based on the off-the-shelf LED is proposed, and the system performance is verified by our experiments. At the transmitting end, the modulation bandwidth of the off-the-shelf LED is extended from 5.03 MHz to 50 MHz using a time-domain hardware pre-equalizer. At the receiving end, the optical signals from different angles are effectively converged through a combined Fresnel lens array. To improve the quality of the received signal, a DSR module is designed after the traditional TIA circuit. After a 100-Mbps NRZ-OOK signal is transmitted through a 1.2-m long water channel, the verification system of \(0^{\circ}\) and \(\pm 45^{\circ}\) incident angles is demonstrated. Using the test results of the eye diagrams and BER curves at the receiving end of the system, the reliability of the MDOF system is verified. The proposed system can be used to improve the flexibility and mobility of the communication system between underwater vehicles and fully utilize the large coverage characteristics of LED light sources, which can also be deployed as part of the real-time ocean observing system to provide an efficient transmission relay link.  
Conceptualization, A.L.; formal analysis, A.L. and R.Z.; methodology, A.L.; software, A.L. and R.Z.; validation, A.L., B.L. and H.Y.; writing--original draft, A.L.; writing--review and editing, H.Y. and B.L.; visualization, A.L. and R.Z.; supervision, A.L.; project administration, A.L. and B.L.; funding acquisition, A.L. and B.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the China Postdoctoral Science Foundation under Grant 2019M651095, the Fundamental Research Funds for the Central Universities under Grant 313202233, the National Key Research and Development Program of China under Grant 2019 YFE0111600, Liaoning Revitalization Talents Program under Grant XLYC2002078, and the Major Key Project of PCL (PCL2021A03-1).  
Not applicable.  
Figure 12: BER performance of the received signals at different DC bias voltages.  
Data Availability Statement: The data presented in this paper are available after contacting the corresponding author.  
The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.
# Interference Suppression in EEG Dipole Source Localization through Reduced-Rank Beamforming
## 2 Materials and Methods  
In terms of the methods, here we introduce our measurement model based on EEG data and we explain the basis under which our proposed brain activity indices are derived from the structure of the GSC. Since much of our evaluation will be conducted in terms to comparisons to other well known activity indices, we review them as well. Before we present the methods in full detail, Table 1 provides a summary of variables and notations used throughout this paper.  
With respect to the materials, in this section we explain the way in which realistic EEG data is simulated. Finally, we briefly describe the database that was used in order to show the applicability of our proposed methods with real EEG data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable/Notation** & **Description** \\ \hline \(q_{l}\) & \(l\)th dipole source \\ \(L\) & number of dipoles \\ \(Q\) & matrix containing all dipole sources \\ \(q_{l,x}(t),q_{l,y}(t),q_{l,z}(t)\) & time-varying magnitudes of \(l\)th dipole’s Cartesian components \\ \(N\) & total number of time samples \\ \(r\) & vector representing a position (Cartesian coordinates) \\ \(\Omega\) & volume of the brain \\ \(r_{l}\) & \(l\)th dipole’s position \\ \(\theta\) & matrix containing all \(L\) dipole’s positions (parameter of interest) \\ \(y_{m}(t)\) & time-varying EEG measurement at the \(m\)th sensor \\ \(M\) & total number of sensors \\ \(Y_{k}\) & matrix with all EEG measurements at the \(k\)th experiment (trial) \\ \(K\) & total number of independent trials \\ \(A_{l}(\tau_{l})\) & \(l\)th lead field matrix associated to the \(l\)th dipole’s position \\ \(A(\theta)\) & matrix comprising the \(L\) lead field matrices as a function of \(\theta\) \\ \(v_{m}(t)\) & measurement noise realization in the \(m\)th sensor at time \(t\) \\ \(\sigma_{v}^{2}\) & variance of measurement noise \\ \(V_{k}\) & matrix with all measurement noise at the \(k\)th trial \\ \(\tilde{Z}\) & indicates a consistent estimate of \(Z\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of variables and notations used in this paper (arranged in appearance order).
## 2 Results and Discussion
### Optimization of QuEChERS
#### 2.3.3 Selection of Salt Addition  
In general, the addition of salt during QuEChERS extraction could separate water from the organic solvent based on the salting-out effect, and these salts generally include anhydrous magnesium sulfate (MgSO\({}_{4}\)), sodium chloride (NaCl), anhydrous sodium citrate (Na\({}_{3}\)Cit), and sodium acetate (NaOAc). In this study, we selected anhydrous MgSO\({}_{4}\) and NaCl as the salting-out agents and different amounts were investigated individually. From Figure 3, we see that the ERs% for all the _Alternaria_ toxins increased as the amount of anhydrous MgSO\({}_{4}\) increased from 0 to 4 g and was significantly unchanged as the amount of NaCl increased from 0 to 1 g. Thus, we concluded that the classical combination of 4 g of anhydrous MgSO\({}_{4}\) and 1 g NaCl in most studies was also adopted in this study.
## 4 Tests and Experiments
### Contacting the Device Disconnected from the Network for a Long Time Period
Identical 1000 seed values were used for each of the combinations, ensuring the consistency of the compared results (the randomly selected transmission moments are the same for variable combinations of algorithms). The probability of packet loss assumed in the simulations changes between experiments in the discrete range {1%, 5%, 15%, 25%}. The values presented in the figures are the average calculated from 1000 individual simulations, namely the average number of packets outside the listening window (Figure 9), the average amount of energy used for listening during a single transmission (Figure 10) and the average length of the listening window (Figure 11). Figure 12 shows the comparison between algorithms (W1) and (W3) combinations with (A1) and (A2) algorithms for a better view on the degree of improvement. The following parameter constant values were used in the algorithms for selecting the window length: \(D_{min}=0.5\); \(A_{min}=3.0\); \(\widehat{B}_{min}=0.0000035\). Figure 9: Comparison of algorithms in terms of the average number of packets outside the listening window. Figure 11: Comparison of algorithms in terms of the average length of the listening window. Figure 10: Comparison of algorithm ms in terms of the average amount of energy used for listening during a single transmission. Figure 12 provides insight into how all the evaluated methods are effective in terms of energy used for listening (the average window length) and the number of packets lost during simulation time. The combination of algorithms W3 (window width adjustment based on the average of the N most recent received transmissions) and A2 (receiver time synchronization based on the statistics from the N most recent received transmissions) outperforms all other methods. Generally, the higher the average number of time points used to calculate the average for algorithm A2 the better, but there is a very small difference between using 6 and 8 packets.
## 3 Results  
Exploring the DA model with low cost, strong versatility, and strict mechanisms has become one of the research hotspots in the field of precision agriculture assimilation. Therefore, by combing the literature, we summarize the mechanism process and evolution trend of the DA model, as shown in Figure 5. The development track of the DA model gradually develops from simplification to refinement, diversification, and integration. From the initial simple forcing method, which is slightly rough due to the accumulation of model errors due to over-reliance on observations, to the innovation of DA model under Bayesian paradigm, such as Markov chain Monte Carlo (MCMC) algorithm, Variational Bayes, and Bayesian coupling, it undoubtedly marks a great improvement in the theoretical framework and a great leap in domain performance. In addition, the development of the DA model continues to expand in the direction of computational efficiency and parallelization, multi-source data fusion and multi-assimilation strategy coupling, model universality enhancement, and so on.  
Figure 5: The development trend and evolution process of DA model. The upper left part represents the Earth observation data (EO), including various types of sensors, and the output is the observed  
values of physical and chemical parameters or the results of pre-processing and inversion. The lower right part represents the crop growth model simulation process (CGM), including initializing various types of parameter inputs, model simulation, and output as crop parameter simulation values. The middle part is the data assimilation model assimilation process (DA), including the core mechanism of different models, assimilation process, and evolution trend.
## 5 DynDLOff: An Offline Algorithm  
To complement our online algorithm, we also present an offline algorithm that knows the cores' initial loads. The offline algorithm DynDLOff contains two phases. Phase I produces an initial assignment where all tasks are data-local and the cores are load balancing, and Phase II refines the initial assignment to lower the overall makespan by gradually increasing the number of data-remote tasks. By using the initial assignment, Phase II easily distinguishes data-remote and data-local tasks. It sets a few deadlines, generates a series of assignments, and selects the best assignment as the algorithm's output. Because our algorithm computes the final assignment incrementally, it efficiently and effectively solves the scheduling problem. In the following, we present the phases in detail.
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Propagation with Line of Sight  
1. Loss in free space. When an electromagnetic wave (EM) propagates in free space, path loss can be calculated using the Friis equation, widely used by microwave link designers [42], which assumes the absence of obstacles in the vicinity [39; 46]: \(\frac{P_{t}}{P_{r}}=\left(\frac{4\pi f/d}{c}\right)^{2}\), where Pr and Pt are the receiver and transmitter power respectively, \(f\) is the frequency of the radiation wave, \(c\), the speed of light in the vacuum and \(d\) is the distance between the transmitter and the receiver [36; 47]. The loss of the path in the free space means that the transceiver antennas, both transmitters and receivers, use communication with LOS, without obstructions or reflections of any kind. However, if the antennas are located close to the ground, the above equation is no longer valid, the reflection of the earth must be taken into account [18]. The power loss is usually expressed in terms of \"path loss\" (PL), defined as: PL = 10 log10(P/P\({}_{r}\)) [48] with P\({}_{t}\) and P\({}_{r}\) as power transmitted and received, respectively. Therefore, PL in free space can be expressed: PLFree-space(dB) = 20 log(f) + 20 log(d) - 147.56, the radiofrequency, \(f\), is expressed in Hz and distance is expressed in meters [49; 50].
2. Two-ray propagation model. When the RF propagates near the ground with LOS, the flat ground wave (PE) propagation model can be used to define the path loss instead of the PLFree-space model. This model includes the effects of the reflection of the rays of the ground and the ray LOS, which is given by the equation: PLPE(dB) = 40 log(d) - 20 log(\(h_{T}\)) - 20 log(\(h_{R}\)), where \(d\) is the distance between the transmitter and receiver antennas in meters, \(h_{T}\) and \(h_{R}\) are the elevations of the transceiver antennas in meters. The separation distance (\(d\)) in this model is much greater than \(h_{T}\) and \(h_{R}\)[45].
## 3 Data and Method
### Workflow of the Developed Methodology
#### 3.2.1 Data Collection  
To obtain the 3D geometry of buildings, it is necessary to acquire data using techniques and methods, such as laser scanning (LiDAR) [12], photogrammetry, traditional surveying methods (total stations, inclinometers and levels), and GPS/GNSS surveying.  
At this stage, it is necessary to define the target data to be obtained and the relevant study area, paying greater attention to the level of detail and precision required. Furthermore, it is necessary to determine the appropriate sensors according to the type of data needed and subsequently integrate them into a coherent dataset.  
Figure 2: Layout of the dormitories (C2–C7 four-level buildings; C19–C22 six-levels buildings; 2—sidewalk, 3–7 relaxation areas).  
Another important aspect is represented by environmental conditions starting with the temperature of the external environment, the radiance, the complexity of the model and the morphology of the land. All these aspects must be taken into account in the study design.  
Figure 4: The influence of different UAS and TLS factors in determining the energy characteristics of a building.  
Figure 3: Workflow of the developed methodology.
## 5 Conclusions  
This paper proposes a non-intrusive sensing technique, which consists of temperature, humidity sensor called DHT22, and an infrared camera named FLIR B8400. In order to improve prediction performance of thermal comfort, individual thermal models for female subject and male subject were developed with SVM and RF based on a six day experimental study conducted in an open-plan office in Shanghai.  
A total of three different feature sets were selected to develop individual thermal models with the labels based on 7-point thermal sensation scale. As a result, all proposed feature sets have achieved much better performances than the baseline of the PMV model. In addition, the model trained with the feature set consisting of indoor air temperature, indoor relative humidity, side face skin temperature and clothing surface temperature, and with linear kernel SVM has achieved the best performance. The precision, recall and f1 scores of the best model was 100% on test data of female subjects and 97.5%, 96.1% and 95% on that of male subjects, respectively. Besides classification, the causal analysis has also shown that clothing surface temperature has a higher correlation coefficient with thermal sensations than side face skin temperature for both subjects, which explains why the individual thermal models with feature sets containing clothing surface temperature have the best performance. Last but not least, the individual difference in thermal comfort indicates the importance of personalized heating and cooling systems.  
Moreover, the proposed sensing system could be improved further by using smaller-sized infrared thermal array such as AMG8833 to realize large-scale real deployment in open-plan office buildings. In future, the personalized cooling system could also be applied to various scenarios besides open-plan offices, such as sleep mode and driving mode to improve thermally comfortable indoor environments and energy performance.  
The following statements should be used conceptualization, S.L.; methodology, S.L. and S.W.; software, S.L. and S.W.; validation, S.L. and W.W.; formal analysis, S.L., S.W. and W.W.; investigation, S.L., S.W. and W.W.; resources, S.L.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, W.W. and E.C.H.; visualization, S.L. and W.W.; supervision, E.C.H.; project administration, S.L. and E.C.H.; funding acquisition, S.L. and E.C.H.  
This research received no external funding.  
Siliang Lu is sponsored by Chinese Scholarship Council and this work is partially supported by CMU Graduate Small Project Help (GuSH) Research Grants. Moreover, many thanks to [PERSON] for providing the test rooms.  
The authors declare no conflict of interest.
## 4 Applications for IoT Technology in Smart Buildings
### Lighting and Shading Control System  
IoT-implemented systems are essential in controlling energy losses with the industry's trending techniques and updated technology in lighting systems. Implementing IoT-based lighting systems that inform consumers when energy usage exceeds a predetermined level within less than 100 s. The IoT devices are connected to control the light and shading of that building using the fuzzy logic method. The IoT system prototype has been developed to handle natural and artificial light inside and around the structure. Prototype studies have shown that it can maintain the desired illuminance even under high variation settings by altering shade and illumination in less than 100 s [88]. Sensor data is transferred into the primary process, which analyzes it, makes decisions, and transmits the resulting orders to the actuator subsystem. The end-user can monitor the energy consumption through HTTP server-enabled smart devices.
## 5 Conclusions
The lowest latitude site (Medellin) has the lowest increase. On the other hand, for higher latitudes, this total useful energy gain takes values up to 53% (Almeria). The longitudinal tilt configuration remains the best-performing when the wind speed is 10.03 (m/s), except for the lowest latitude location (Medellin). At this wind speed, the total useful energy gain decreases to 25% for the highest latitude location (Almeria). Therefore, it can be concluded that secondary system designs with longitudinal tilt angles perform well at locations with latitudes above 10 (\({}^{\circ}\)). These results are very good if the latitude is higher than 24 (\({}^{\circ}\)) and lower than 37 (\({}^{\circ}\)). It can be concluded that the effect of the longitudinal tilt of the secondary system has a positive effect. To highlight the importance of this work, the results obtained in the configuration comparison were used to compare a nonconcentrating photovoltaic system and a low-concentrating photovoltaic system. The following conclusions were drawn: (i) The solar irradiance incident on the photovoltaic cells will be much higher in nonconcentrating photovoltaic technology. This solar irradiance gain is more than 60% with the base configuration and 45% with the longitudinal tilt configuration. (ii) The total useful energy gain at the lowest latitude location (Medellin) is the only location favourable to nonconcentrating photovoltaic systems in the absence of wind. For all other locations, this gain is in favour of low-concentrating photovoltaic systems. It is worth noting that this gain is \(-70\%\) at the highest latitude location (Almeria). (iii) The nonconcentrating photovoltaic system performs better if the wind speed is 10.03 (m/s). With increasing latitude, the total useful energy gain of the nonconcentrating photovoltaic system decreases considerably. Conceptualization, C.L.-S., A.B. and L.B. ; methodology, A.B. and L.B. ; software, C.L.-S. and C.B.-C.; validation, A.B. and L.B. ; investigation, C.L.-S. and C.B.-C.; data curation, C.L.-S. and C.B.-C.; writing--original draft, C.L.-S. and C.B.-C.; writing--review and editing, C.L.-S. and C.B.-C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Author [PERSON] was employed by the company DNV UK Limited, OPEX. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. **Nomenclature**
## 3 Results  
In the recorded measurements, significant differences were noticed between the rear-cross and lead-jab strikes. There are statistically significant differences in the measurements of pressure force (Total GRF) as well as fist and forearm accelerations. Statistically significant differences between the techniques were also observed in measurements of arm acceleration and target contact time. The range of total GRF results ranged from 670.04 N to 1748.19 N, with an average value of 1176.55 N for the lead jab, and from 1082.48 N to 2866.76 N, with an average of 1709.28 N for the rear cross. The time of fist contact with the target averaged 0.03 s for the rear cross and 0.02 s for the lead jab, with the highest recorded values being 0.17 s and 0.15 s, respectively. The lowest values oscillated around 0.01 s. The duration of the strike averaged 0.17 s for the rear cross and 0.18 s for the lead jab, with the smallest speeds being 0.08 s and 0.07 s, respectively, and the highest being 0.20 s for both techniques. The acceleration times for the rear cross averaged 94.33 m/s\({}^{2}\) for the fist, 67.11 m/s\({}^{2}\) for the forearm, and 88.40 m/s\({}^{2}\) for the arm. For the lead jab, these were, respectively, 66.07 m/s\({}^{2}\) for the fist, 41.62 m/s\({}^{2}\) for the forearm, and 81.36 m/s\({}^{2}\) for the arm (Table 1). In the exploration of acceleration patterns across the upper limb from the proximal-to-distal segments, our analysis employed a polynomial trend analysis and visual inspection. Specifically, we assessed acceleration values across the arm (proximal), forearm (middle), and fist (distal) to investigate the hypothesized pattern of decreasing acceleration from the proximal to distal segments, which aligns with biomechanical expectations of movement efficiency and energy transfer in human limbs. Descriptive statistics of the boxplot presented in Figure 5 are presented in Table 1, with the median and range of presented values. The linear regression applied to these data segments revealed a slight negative slope (\(-2.67\)), suggesting a trend where acceleration values decrease from the proximal segment toward the distal segment. However, this observed trend did not reach statistical significance (\(p\) = 0.146), indicating that the data do not provide strong evidence for a consistent proximal-to-distal decrease in acceleration values across the studied segments (Figure 5). In the examination of intersegmental dynamics and their influence on total ground reaction force (Total_GRF) within the context of upper limb movements, our analysis focuses on the acceleration patterns across the distal (fist), middle (forearm), and proximal (arm) segments. Utilizing a binary categorization based on median acceleration values, we delineated combinations of acceleration patterns and investigated their impact on Total_GRF, providing a nuanced understanding of the biomechanical efficiency across the upper limb. The analysis identified the \"1-1-1\" acceleration pattern, representing high acceleration in all three segments (fist, forearm, and arm), as yielding the highest