esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We also show the effect of image compression on transmission delay and, of course, execution time. Our main contribution after taking our experimental results and measurements is to develop a statistical model based on our performance metrics which can be used according to the needs of the user. In other words, the user can tailor the system's operation to minimize the execution time or maximize the accuracy of the Edge-Assisted object detector. The exploration of the delay and accuracy trade-off in edge computing has been explored in the past, for example, see [16] and references therein, our work is the first to study this problem in the context of task splitting from servers to end devices. It is noteworthy to point out that this is a new type of offloading architecture, where larger or bigger devices (a server, in this case) splits and outsources their load to many smaller devices (the end nodes, in this case). This paper follows the following structure: In the next section, we present related works regarding task execution and deep learning application aiming at the optimization of an edge computing system. In Section 3, we present the architecture and system evaluation of our testbed. In Section 4, we focus on the design of equations and task allocation policies that were used to get the results. In Section 5, we demonstrate a discussion with results about the performance of our proposed system. We finally conclude this work in the last section acknowledging limitations and proposing future research directions.
# A Review of Training and Guidance Systems in Medical Surgery
## 2 Theoretical Framework
### Guidance Techniques  
A device that provides force feedback is usually implemented to guide users inside virtual tangible environments. In 1998, [PERSON] et al. [31] proposed a virtual professor system. They characterized three ways to guide students in the acquisition process of physical skills:  
1. **Indirect contact**, where an expert and a student grasp a tool in different places, and they do the action together.
2. **Double contact**, where a user holds the tool and an expert grasp his/her hand, and together they proceed to do the task.
3. **Individual contact**, where an expert grasps the tool, and a student holds his hand, and they work together in the activity.  
A wide spectrum of guidance aid exists in virtual learning environments and robot-assisted systems. [PERSON] and [PERSON] proposed a classification of the techniques used in the development of virtual training environments that require force feedback [32]. Five groups were identified:  
1. **Gross Assistance** (GA) uses virtual fixtures (VF), spring-damper systems, or attraction models. They are used to \"guide\" the user to a defined goal by restricting movement. According to [PERSON] et al., GA fell into indirect and individual contact approaches. However, some authors have considered that GA is not very useful in education because it slows the brain's immediate retention [20; 33].
2. **Temporally Separated Assistance** (TAS) systems temporally separate orientation and task forces [34]. TAS are rapidly displayed in the same haptic device to let users think that they control the action. To achieve this, an updating rate of 1 Hz is required, which lets students feel direction clues during the activity.
3. **Spatially Separated Assistance** (SSA) uses two haptic devices. One is used to show the task force, and the other one displays the orientation force. Therefore, according to [PERSON] et al., SSA can be considered a double contact technique. Only the work of [PERSON] et al. can be classified as an SSA system.
4. **Gross Resistance** (GR) is based on the over-training concept, where the users train a task in the presence of an opposing force. Consequently, after taking this force, they can perceive the real environment and efficiently perform the task [35]. Random disturbances, in the form of viscous forces or force fields, are classified as GR guidance.
5. **Shared-Control Proxy** (SCP) techniques are based on a modification to work proposed by [PERSON] and [PERSON] [36], where a second proxy and biased spring-dampers are added. This approach made the shared proxy's position be influenced equally by the expert and the beginner. Recently, authors have started to explore SCP usefulness [37].
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Dictionary Construction
#### 3.3.1 Selection of the First Atom  
For the first atom of the dictionary, we sought the most representative waveform in the QRS complexes database. Indeed, if we intend to construct a sparse model for all the ECGs of all the patients using a single basis signal, then we should choose a waveform which resembles as closely as possible the set of QRS complexes (the most relevant part of the ECGs) in the different patients. To achieve this goal, using the average signals stored in the QRS complexes database, we followed two steps:  
1. Compute a \(Q^{\prime}\times Q^{\prime}\) correlation matrix \(\mathbf{C}\), whose \((Q^{\prime})^{2}\) elements correspond to [PERSON]'s correlation coefficient among each pair of waveforms in the QRS complexes database (in practice, only \(Q^{\prime}(Q^{\prime}-1)/2\) coefficients have to be computed, since \(\rho_{ii}=1\) and \(\rho_{ij}=\rho_{ji}\ \forall i,j\in\{1,\ldots,Q^{\prime}\}\)). \[\rho_{ij}=\frac{C_{ij}}{\sqrt{C_{ii}C_{jj}}}=C_{ij},\] (12) where \(C_{ij}\) denotes the cross-covariance between the \(i\)th and \(j\)th waveforms at lag 0 (i.e., without any time shift), and the last expression (\(\rho_{ij}=C_{ij}\)) is due to the energy normalization described in Section 3.2.3, which implies that \(C_{ii}=0\ \forall i\).
2. Select the waveform with the highest average correlation (in absolute value) with respect to all the other candidate waveforms, i.e., \[\ell_{0}=\arg\max_{i=1,\ldots,Q^{\prime}}\ \sum_{j=1}^{Q^{\prime}}|\rho_{ij}|,\] (13) which corresponds to the most representative waveform of all the candidate waveforms.
## 3 Results and Discussion
### Evaluation of Similarities Among Classification Methods
#### 3.3.3 Similarity Evaluation Based on EI  
For EI, the overall accuracy with NB was 77.40% and the kappa coefficient was 0.57, indicating the highest similarity among the compared classification methods. Additionally, the reclassification accuracy of the SS was 100.0% for NB, Qu, and GI. Notably, the reclassification accuracy of the US was 100.0% across all three classification methods (Table 7). This is likely because the areas of the SS and US in the final suitability map by EI were reclassified at very low rates of 0.4% and 1.4% of the total area, respectively, thus encompassing the reclassification results of the SS and US using the comparison classification methods. Meanwhile, the average value of the reclassification accuracy for the same grade between the different classification methods was the highest, at 86.7%, between EI and NB (Table 7).
###### Abstract  
Marshes represent a unique ecosystem covering a large area of southern Iraq. In a major environmental disaster, the marshes of Iraq were drained, especially during the 1990s. Since then, droughts and the decrease in water imports from the Tigris and Euphrates rivers from Turkey and Iran have prevented them from regaining their former extent. The aim of this research is to extract the values of the normalized difference vegetation index (NDVI) for the period 1977-2017 from Landsat 2 MSS (multispectral scanner), Landsat 8 OLI (operational land imager) and Sentinel 2 MSI (multi-spectral imaging mission) satellite images and use supervised classification to quantify land and water cover change. The results from the two satellites (Landsat 2 and Landsat 8) are compared with Sentinel 2 to determine the best tool for detecting changes in land and water cover. We also assess the potential impacts of climate change through the study of the annual average maximum temperature and precipitation in different areas in the marshes for the period 1981-2016. The NDVI analysis and image classification showed the degradation of vegetation and water bodies in the marshes, as vast areas of natural vegetation and agricultural lands disappeared and were replaced with barren areas. The marshes were influenced by climatic change, including rising temperature and the diminishing amount of precipitation during 1981-2016.  
Iraqi marshes; climate change; satellites data +
Footnote †: journal: Applied Sciences
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 2 Literature Review
### Interference-Based Approaches
The authors in [18] aimed to maximize the LoRaWAN network capacity by optimizing number of EDs for a given SF. The numerical results showed that the proposed method in [18] maximizes the LoRaWAN network capacity up to 700%, as compared to the equal number of EDs per SF strategies.
## 4 Discussion  
### Influence of Scanning Strategy on Surface Topography and Roughness  
The surface topography is the combined result of the roughness caused by selective melting and the specimen deformation in the build direction that accumulates during the SLM build. The deformations depend strongly on the scan strategy, wherefore they were not corrected in calculating the surface roughness. A criterion for evaluation of the scan strategies is obtained by including the deformations. It follows from Table 1 that the surface roughness of the three specimens is high.  
The island scan stripes strategy distributed the deformations more uniformly across the surface, compared to the line scan method, but the arithmetic mean roughness, \(\mathrm{S_{a}}\), increased from 48.4 \(\mathrm{\SIUnitSymbolMicro m}\) in the line scan specimen to 66.4 \(\mathrm{\SIUnitSymbolMicro m}\) and 82.8 \(\mathrm{\SIUnitSymbolMicro m}\) in the island scan stripes and island scan modeled specimens, respectively. For the island scan specimens, the use of six different scan strategies in the island method instead of only PIR removed the direction pattern seen in the deformations, but increased the mean roughness, \(\mathrm{S_{a}}\), further to 82.8 \(\mathrm{\SIUnitSymbolMicro m}\). The more even distribution of deformations was also accompanied with an increase in maximum height, as \(\mathrm{S_{z}}\) increased 6% and 9% by the use of the island scan stripes and island scan modeled strategies, respectively, compared to the line scan strategy. The difference is also clear from the deep (blue) valleys and high (white) peaks in the surface topography displayed in Figures 4a and 3a, compared to Figure 2a.
### Gene Expression Analysis and Terpene Detection in Wheat  
To analyze _TaPS_ gene expression in response to different elicitations, three-week-old wheat seedlings were used for treatment. For pathogen infection, wheat leaves were cut with a scalpel and inoculated with _F. graminearum_ spores (10\({}^{6}\) mL\({}^{-1}\) in 0.1% Tween 20) as described previously [37], and kept at 100% humidity for 48 h, ready for sample collection. UV treatment was conducted with 254 nm UV light to irradiate wheat seedlings with 15 cm distance for 30 min, and treated samples were kept in the dark for 24 h and transferred into the growth chamber with normal growth condition for 48 h, ready for collection. Methyl jasmonate (MeJA) treatment was performed by spraying 50 \(\upmu\)M MeJA on wounded wheat leaves, which were kept at 100% humidity for 48 h and used for RNA extraction. Furthermore, 5 mg/L alamethicin was also used to treat detached wheat leaves for 30 h, which were harvested for later analysis. Wheat leaves were also fed by beet armyworm larvae (third instar) for 30 h and adjacent tissues (~1 cm) were collected for _TaPS_ gene expression analysis. Untreated tissues including leaves, stems and roots were also collected for gene expression analysis. All samples were collected for RNA extraction and gene expression analysis by qRT-PCR. The qRT-PCR analysis was performed on ABI StepOne Plus Real-Time PCR System (Thermo Fisher Scientific) with SYBR green mix (Gangchi Bio). Wheat _Actin_ was used as the endogenous control gene. All primers used are listed in Table S1 with confirmation by amplicon sequencing. Leaves treated by alamethicin were also used for terpene extraction. Five g leaves were ground in liquid N\({}_{2}\) to fine powder and extracted twice with 100 mL hexane. The organic extract was combined and concentrated via rotary evaporation for the GC-MS analysis. \(\beta\)-caryophyllene (10 ng) was added into each sample as the internal standard for \(\beta\)-patchoulene quantification.
## 2 Literature Review
Position-based protocols assume that every node is aware of the location of itself, the location of neighboring nodes and the location of the destination node. With the increasing availability of GPS-equipped vehicles, the position-based protocols are getting more convenient. However, the position-based protocols developed for MANETs may not directly be applied to vehicular environments, due to the unique vehicular network characteristics. There are a number of papers that study packet routing algorithms in VSNs [25; 26; 27; 28; 29; 30; 31]. With the use of geographical location information obtained from GPS devices, studies show that position-based routing (geographic routing) has been identified as a more promising routing paradigm for vehicles [29; 30]. Nonetheless, most existing position-based or topology-based VANET/VSN routing protocols assume that intermediate nodes can be found to set up an end-to-end connection; otherwise, the data packet will be dropped by the protocols. However, finding end-to-end connections sometimes is extremely difficult for a sparse vehicular network. Under this circumstance, topology control schemes may be applied to provide basic levels of system performance. In contrast, the proposed self-organization strategy (HDTC) investigates the design of topology management and information dissemination and considers several important factors, such as the contribution of vehicle mobility, a sensor vehicle joining/leaving a communication group and the routing member selection, in order to keep the dynamic network structure efficient. It applies localized criteria and maintains local dynamic topologies in a fully distributed way. Therefore, the proposed HDTC scheme combines the strength of topology control and the one-hop information exchange between neighboring vehicles to achieve sufficient network reachability under high mobility environments. The comparison of the proposed scheme and other control-based approaches [8; 9] is further discussed in Section 6.
## 5 Conclusions  
We demonstrated a two-color quantum correlation between the down-conversion beams with a telecommunication wavelength at 1.5 \(\upmu\)m and mid-infrared wavelength at 3.3 \(\upmu\)m from an SRO operated above the pump threshold with an MgO:PPLN crystal in the cavity. The noise powers of the signal and idler were measured directly using home-made high-gain broad bandwidth detectors at 3.3 \(\upmu\)m and 1.5 \(\upmu\)m, respectively. The intensity noises of the signal and idler reach the SNL for analysis frequencies above 5 MHz, respectively. The noise power of the intensity difference between two down-conversion beams was measured by the detectors and recorded by an SA. A maximum of 1.8 dB two-color quantum correlation between down-conversion beams at 1.5 and 3.3 \(\upmu\)m was observed. A theoretical model for the two-color quantum correlation between the twin beams was given using a semi-classical approach, and the influence of the analysis frequency and pump parameter on the quantum correlation between the twin beams was discussed theoretically and experimentally. The maximum quantum correlation between the twin beams was observed at an analysis frequency of 5 MHz, which was degraded at the analysis frequencies above 5 MHz due to the limitation of the bandwidth of the SRO cavity and was degraded at the analysis frequencies below 5 MHz due to the excess intensity noise of the pump. The quantum correlation between the twin beams was stronger when the pump power was closer to the pump threshold of the SRO. If the balance homodyne detection system for 3.3 \(\upmu\)m can be developed in the future and a high-quality optical HR coating at 3.3 \(\upmu\)m can be also realized, a two-color CV quantum entanglement at 1.5 and 3.3 \(\upmu\)m can be achieved in the same experimental system. The two-color quantum states at 1.5 and 3.3 \(\upmu\)m have potential applications in high-precision measurements beyond the SNL such as in oil mining, gas pipeline leakage monitoring, industrial process monitoring, biomedicine, and so on.  
Conceptualization, D.N., Y.L., J.F. and K.Z.; investigation, D.N., Y.L. and J.F.; data curation, J.F. and K.Z.; writing--original draft preparation, D.N. and J.F.; writing--review and editing, J.F. and K.Z.; supervision, K.Z.; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key R&D Program of China (No. 2016 YFA0301401).  
Figure 5: Normalized noise power of the intensity difference between the signal and idler at the analysis frequency of 5 MHz as a function of the pump parameter. Curves (i): normalized SNL, curve (ii): theoretical prediction, blue diamonds: experimental data.
# Effects of Climate Conditions before Harvest Date on Edamame Metabolome
## 2 Results
### Correlation Analysis between Climate Conditions and Edamame Metabolome
#### 2.3.2 Compounds That Were Correlated with Sunshine Duration and Average Temperature  
Tables 1 and 2 show compounds that showed a correlation or inverse correlation with the sunshine duration and average temperature, respectively. Interestingly, these values showed different trends depending on the climate conditions and the amount of correlation. For example, some amino acids were found to be correlated with the amount of sunlight accumulated 3 or 5 days before harvest, whereas a high correlation with the average temperature was found with the amount accumulated 1 or 9 days before harvest. On the other hand, regarding the inverse correlation coefficient, a high correlation was observed with the amount accumulated over 5 days after harvest. For example, a positive correlation to Tyr was acquired by an accumulated sunshine duration for 5 days; however, the highest positive correlation of Tyr with the average temperature required accumulation of the average temperature for 9 days. These data show that climate conditions before the harvest date change the amount of some of the compounds in edamame; however, there seems to be a difference in how many days the seed must pass under a given climate condition before a response is observed.  
Figure 2: The amount of alanine in edamame at each harvest day for 6 years.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compounds** & **SD\_1** & **SD\_3** & **SD\_5** & **SD\_7** & **SD\_9** \\ \hline Lys & 0.20 & **0.37** & 0.34 & 0.28 & 0.27 \\ Ala & 0.18 & 0.32 & **0.38** & 0.31 & 0.27 \\ Pro & 0.05 & 0.23 & **0.38** & 0.36 & 0.34 \\ Tyr & 0.13 & 0.25 & **0.34** & 0.25 & 0.23 \\ Hydroxy Pro & 0.10 & 0.22 & **0.26** & 0.24 & 0.24 \\ \hline Fumarate & \(-\)0.33 & \(-\)0.40 & \(\mathbf{-0.49}\) & \(-\)0.48 & \(-\)0.47 \\ GMP & \(-\)0.26 & \(-\)0.30 & \(-\)0.47 & \(-\)0.44 & \(\mathbf{-0.48}\) \\ Citrate & \(-\)0.28 & \(-\)0.30 & \(\mathbf{-0.44}\) & \(-\)0.36 & \(-\)0.31 \\ GDP & \(-\)0.24 & \(-\)0.29 & \(\mathbf{-0.43}\) & \(-\)0.35 & \(-\)0.33 \\ Diethanolamine & \(-\)0.20 & \(-\)0.28 & \(-\)0.33 & \(-\)0.40 & \(\mathbf{-0.43}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Top five compounds that showed a correlation or inverse correlation with sunshine duration (SD). The ranking was determined by the sum of the correlation coefficients from SD_1 to SD_9. Bold numbers indicate the highest correlation or inverse correlation coefficient between SD_1 and SD_9. SD_x means the SD amount accumulated x days before harvest.  
Figure 3: Correlation analysis between climate conditions and edamame metabolome. The color scale is shown at the top right of the figure. Red and blue colors represent higher and lower amounts of each compound and values of each climate condition, respectively. SD: sunshine duration; AT: average temperature; RF: rainfall; AWS: average wind speed; TD: temperature difference. SD_x means the SD amount accumulated x days before harvest.
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 3 Analysis and Modeling of Driving Behavior
### Hyper-Parameters Used in the System Identification Process  
The hyper-parameters of the data pipeline, as well as the parametric assumptions used in the system identification process for modeling the car-following driving task, are given as follows:  
* PWARX model: Input data are range, range rate, KdB, jerk, inverse of time collision, and time headway (\(u_{1},\ u_{2},\ u_{3},\ u_{4},\ u_{5},\ \text{and}\ u_{6}\)). The Model output \(y\) is speed, and the number of data points \(N\) is approximately 4200 with a time step of 0.1 s for each driver. The first-order dynamics is considered as the controller model; thus, the regressor vector \(r_{k}\) is \([y_{k-1}\ u_{1,k-1}\ u_{2,k-1}\ u_{3,k-1}\ u_{4,k-1}\ u_{5,k-1}\ u_{6,k-1}]^{ \intercal}\).  
Figure 5: Definition of variables between ego car and leading car.  
* Feature vector extraction: The constant \(c\) (number of neighboring data points) is chosen as 200. This value was chosen by trial and error based on the data. As a general rule, 10% of the number of data points, \(N\), is a good number for this constant.
* Optimal Number of Sub-models: \(K\), \(P\) and \(n\) (folds) are chosen to be 10, 100 and 3, respectively. These values were chosen intuitively mainly based on the number of data points.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
## 1 Introduction
However, having access to the ETM output is not always possible (e.g., this external port is not included for better and easier impedance matching) and would require an extra ADC to capture the supply envelope after amplification. As mentioned before, low-complexity and robust open-loop solutions are preferred in the UE, since the impact of the DSP consumption is no longer negligible. In this context, as an alternative to the ELC method in [19], an envelope optimization (EOPT) method is proposed in this paper to linearize the ET PA RF output without the need for monitoring the ETM output. Thus, saving the cost of including an extra ADC and a matching transformer (in between the ETM output and the ADC). The proposed EOPT method is also based on the EGMP behavioral model. However, the offline identification procedure to estimate the coefficients of the EGMP model is completely different from [19]. The identification process is inspired by the deep deterministic policy gradient (DDPG) algorithm, a popular algorithm for reinforcement learning [20]. In addition, I-Q DPD based on the generalized memory polynomial (GMP) behavioral model is also considered for ET PA linearization to better highlight the contribution of the proposed envelope optimization method. Experimental results will show a comparison of the proposed EOPT method with both the ELC approach in [19] and the conventional I-Q DPD in terms of linearization performance and computational cost. The remainder of this paper is organized as follows. A detailed explanation of the proposed EOPT approach for wideband ET PAs is given in Section 2, including an in-depth description of the main key subsystems contributing to define the power efficiency and linearity trade-off in ET PAs. The proposed envelope optimization approach is validated on an SoC ET PA board, considering an NR-60 MHz OFDM-like test signal. Thus, experimental results are shown in Section 3, comparing the linearization performance of the proposed EOPT method, which is agnostic of the ETM behavior, with the ELC or I-Q DPD linearization methods. Finally, a conclusion is given in Section 4.