esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of Educational Games in Programming Assignments: SQL Island as a Case Study
## 4 Results
### Positive Aspects of the Game  
In the open-ended question on the positive aspects of SQL Island, 35 out of the 56 participants (62.5%) responded. Their responses were analyzed qualitatively and qualitatively using content analysis, and the results are summarized in Table 6. More information about students' perceptions of the advantages of the game is presented in the paragraphs that follow using their own words translated from Greek.  
Learning through the game is considered to be one of the most positive advantages of SQL Island. The game _\"helps in comprehending SQL in practice\"_ and also _\"improving skills in SQL\"_ in the case of having prior knowledge. The game is _\"more pleasant than reading a book or notes\"_ and _\"more entertaining than reading slides\"_. As one of the students stated, the game seems to promote _\"learning in a creative and interactive way, has a satisfying level of difficulty that makes a student work and search more, an unprecedented way of learning that raises ones interest\"_. We have to note, however, that this is actually the definition of serious games or, more specifically, educational games.  
One of the constituent elements of an educational game is its scenario, as noted in established design frameworks for serious and educational games [3, 29, 30, 31]. SQL Island has a simple scenario but yet _\"it is an interesting game, with an interesting plot\"_. The interesting story, in combination with the _\"easy and comprehensible rules\"_, _\"the easy and comprehensible challenges and the use of appropriate examples\"_, result in an entertaining game. When considering the educational aspects of the game, the tips provided during the challenges are an important feature and provide the necessary degree of scaffolding [33], according to nearly 20% of the students. The game _\"provides background material each time that a challenge requires using a new SQL statement\"_, as well as _\"guidance in wrong excerpts of code\"_.  
The aforementioned aspects of SQL Island were the most frequent positive aspects mentioned by students. Other features that are actually proposed in serious games design frameworks [3, 29, 30, 31, 32, 33] were also highlighted by students: _\"pleasant graphics\"_ and _\"nice colors that attract the interest and deter a potential attention distraction\"_; _\"the interactivity of the game creates an intrigue and persistence for achieving your goal and finishing the game\"_; _\"the difficulty of the challenges is gradual and this makes the game more accessible and leaves you space to adopt to its requirements for completing it\"_. All these features make the game appropriate for novices.
## 5 Simulation Results and Analysis  
In this section, we evaluate the performance of our proposed access and handover strategy. In the second step, four attributes are used to evaluate the candidate satellites, including SINR, the number of available channels of the satellite, remaining time of satellite coverage, and delay that will exist when connecting the satellite. For comparison, we used the following scheme:  
* SINR-max: The decision-making scheme based on SINR. The satellite with the largest SINR will be choosen.
* TOPSIS: TOPSIS scheme without weight setting, which means the weights of all attributes are equal.
* Entropy-TOPSIS: The TOPSIS scheme using the entropy weight method.
* Importance-TOPSIS: The scheme proposed in this paper.  
This paper assumes that new calls are generated by independent Poisson arrival distribution, and the duration follows a negative exponential distribution with an average call duration of 600 s. The user makes a decision every 10 s after the call is generated. The simulation time is 4 h.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global 'OCO-2' SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05\({}^{\circ}\), 8-day) over the period 2000-2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R\({}^{2}\) = 0.79, root mean squared error (RMSE) = 0.07 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)). The model was then used to estimate SIF for each 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1\({}^{\circ}\)), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R\({}^{2}\) = 0.73, \(p\) < 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.  
solar-induced chlorophyll fluorescence; Orbiting Carbon Observatory-2; Moderate Resolution Imaging Spectroradiometer; gross primary productivity; photosynthesis; machine learning; data-driven approach; carbon cycle; trend; benchmarking; FLUXNET +
Footnote †: journal: Remote Sens. **2019**, _11_, 517; doi:10.3390/rs11050517
## 3 Results and Discussion
### Effect of Operating Conditions
#### 3.2.3 Effect of Rectisol Operating Temperature  
As indicated in Figures 3 and 5, the SN of syngas produced from gasification is so low that it cannot satisfy the requirement of methanol synthesis. Therefore, a syngas post-treatment process like Rectisol is needed to adjust its composition. Figure 6 shows the effect of Rectisol temperature on syngas composition, SN, and methanol yield. As is well known, the solubility of methanol to absorb CO\({}_{2}\) gradually decreases while the Rectisol temperature increases [41]. When the Rectisol temperature decreases from 40 \({}^{\circ}\)C to -40 \({}^{\circ}\)C, the content of CO\({}_{2}\) remaining in the syngas decreases from 6.175 kmol to 0.013 kmol. As a result, the SN continues to increase from 1.34 to 2.36 with the decrease of the Rectisol temperature. As Figure 7 indicated, when the temperature of Rectisol was -20 \({}^{\circ}\)C, the SN value was close to 2. At this point, the methanol yield reached the maximum of 7.99 kmol. When the Rectisol temperature was below -20 \({}^{\circ}\)C, the methanol yield decreased dramatically along with the decrease of Rectisol temperature. This is caused by the low content of CO\({}_{2}\) in syngas to react with H  
Figure 4: Effect of pyrolysis oil moisture content on syngas composition.  
Figure 5: Effect of pyrolysis oil moisture content on SN.  
to synthesize methanol. When the Rectisol temperature is higher than \(-20\)\({}^{\circ}\)C, the methanol yield is also reduced due to excessive CO\({}_{2}\) in syngas that cannot satisfy SN requirement.
# An Open Data Approach for Estimating Vegetation Gross Primary Production at Fine Spatial Resolution
## 3 Results
### The Acquisition of Landsat Data Affects GPP Estimation  
The data acquisition of Landsat NDVI strongly affected the fine-spatial-resolution GPP modeling accuracy. Figure 8A shows that the proportions of cloud-free NDVI observations in the total data acquisition ranged from 26% to 43% over all nine ecosystem types. The DBF, SHR, and WET had higher proportions (>40%) of cloud-free NDVI observations than the other vegetation types. A low proportion of cloud-free NDVI was found in CRO and EBF, and therefore, more than 56% of the periods of the NDVI values were reconstructed. In each vegetation type, the RPE in the GPPL based on observed NDVI (GPPL0) and the GPPL based on the reconstructed NDVI from short-term gap-filling (GPPL1) were similar (Figure 8B), ranging from -5% (ENF) to 22% (CRO). Conversely, the RPE in the GPPL based on reconstructed NDVI from long-term gap-filling (GPPL2) was higher than the RPE of the GPPL0. Apart from the EBF, the lowest absolute RPE was 5% (DBF) and the highest was 49%, in the CRO. The differences between the GPPL0 and the GPPL2 were much greater (>30%) in the SHR and CRO than in the other vegetation types. The R2 was much higher in the GPPL0 than in the GPPL2 in most vegetation types (Figure 8C). Meanwhile, the RMSE was similar among the GPPL0, GPPL1, and GPPL2 (Figure 8D). This is because the mean value of the GPP was higher in the GPPL0 when it was under clear sky, and the incident PAR was much higher than the GPPL2 when it was under cloudy sky. Thus, the higher RMSE in the GPPL0 was not linked to higher uncertainties in the GPPL0 compared to the GPPL2. Generally, our results showed that the GPPL0 was less uncertain than the GPPL2 for most of the vegetation types.  
Figure 8: \(Cont\).
# Nematic Liquid Crystal Composite Materials for DC and RF Switching
## 3 Electrical Conductivity of Liquid Crystal and Carbon Nanotube Composites
### Review on Conductivity Behavior of LC-CNT Dispersions
The hysteresis gap is the largest when a CNT doping concentration is close to the critical concentration of percolation transition as shown in Figure 5b. It is evident that temperature strongly influences the spatial arrangement (orientation of the director) and percolation characteristics of LC composites. The optical transmission measurement of a LC-0.05 wt.% CNT system done by [PERSON] et al. is also in accord with this [41]. They reported that the CNT's contribution to the total absorption and scattering loss is a measure of the CNT's structural ordering. Although there is a 50% loss in transmission after incorporating the CNT, the transiton in  
Figure 4: The electrical conductivity (\(\sigma\)) of a carbon nanotube-doped liquid crystal (LC-CNT) composite versus the weight fraction (wt.%) of carbon nanotubes (CNTs) [39]. Figure 5: The electrical conductivity (\(\sigma\)) versus temperature (here, S-N = Solid-to-Nematic transition and N-I = Nematic-to-Isotropic transition); (**a**) C = 1 wt.%, \(f\) = 1 kHz, U = 1 V. The inset shows thet conductivity and temperature evolution with time t; (**b**) The hysteresis behavior of an LC-CNT composite electrical conductivity in a heating–cooling cycle at different CNT doping concentrations [39]. optical transmission-temperature characteristics indicates a strong effect of the temperature on the structural ordering of the system. In the nematic phase, CNTs orient themselves parallel to the LC director. On the other hand, the ordering of CNTs is insignificant for the isotropic phase. According to [PERSON] et al., electrical conductivity shows a transient behavior at elevated temperatures for EBBA and MWCNT composites at a percolation threshold value of 0.1% [PERSON] et al. (2016). Conductivity goes up over time with a dominant effect in the first hour after the sample is heated to the desired temperature. The increase in conductivity over time is a function of the incubation temperature. For example, if the sample is heated at 363 K (above T\({}_{\text{NI}}\)), the electrical conductivity increases 15 times within 8 h of operation. This happens due the spatial aggregation of CNTs with time, and it is very prominent in the vicinity of percolation threshold [PERSON] et al. (2016).
## 4 Discussion
### Nomenclature  
The following symbols are used consistently throughout the paper. Any additional symbols are defined in the text as they are introduced.  
\begin{tabular}{l l} \(x,y\) & Eastward and northward distances in Cartesian coordinates \\ \(z\) & Height above mean-sea-level \\ \(\lambda\) & Longitude \\ \(\varphi\) & Latitude \\ \(t\) & Time \\ \(d/dt\) & Total (material) derivative \\ \(u\) & Eastward velocity component (=\(dx/dt\)) \\ \(v\) & Northward velocity component (=\(dy/dt\)) \\ \(\mathbf{i}_{\lambda}\) & Unit vector along the longitude \\ \(\mathbf{i}_{\varphi}\) & Unit vector along the latitude \\ \(\mathbf{V}\) & Horizontal velocity vector \\ \(p\) & Pressure \\ \(p_{0}\) & Surface pressure \\ \(\omega\) & Vertical velocity in isobaric coordinates (=\(dp/dt\)) \\ \(f\) & Coriolis parameter (\(f=2\Omega\sin\varphi\), where \(\Omega=7.28\times 10^{-5}\)\(s^{-1}\) is the angular speed of \\  & rotation of the earth) \\ \(\beta\) & Variation of the Coriolis parameter with latitude (=\(df/dy\)) \\ \(g\) & Acceleration of gravity \\ \(c_{p}\) & Specific heat of dry air at constant pressure \\ \(R\) & Gas constant for dry air \\ \(R_{\varphi}\) & Gas constant for water vapor \\ \(L\) & Specific latent heat of vaporization of water \\ \(e_{\mathrm{s}}\) & Saturation water pressure \\ \(T\) & Temperature \\ \(\theta\) & Potential temperature \\ \(q\) & Specific humidity \\ \(\Gamma\) & Lapse rate of temperature (=-\(\partial T/\partial z\)) \\ \(\Gamma_{d}\) & Dry adiabatic lapse rate \\ \(\Phi\) & Geopotential (=\(g\)z) \\ \(g\) & Acceleration due to gravity \\ \(N\) & Buoyancy (Brunt-Vaisala) frequency \\ \(S_{p}\) & Static stability measure in pressure coordinates \\ \(L_{\varphi}\) & Wavelength \\ \(k\) & Wavenumber \\ \(\
abla\) & Nabla operator \\ \(L_{R}\) & Rossby radius of deformation \\ \hline \((\,)\) & Time average \\ \((\,)^{\prime}\) & Departure from time mean \\ \([\,]\) & Zonal average \\ \((\,)^{\prime}\) & Deviation from zonal average \\ \([\,]\) & Vertical average \\ \end{tabular}
## 2 Materials and Methods
### Preliminary Analysis and Optimisation of the Extraction Method
#### 2.1.4 Evaluation Criteria  
The statistical analysis performed to evaluate the FMxEM methodology that provides the best match between SOS, POS and EOS from observed values (i.e., GPP) and satellite-derived values (i.e., kNDVI) was conducted using four statistical indicators: the coefficient of determination (R\({}^{2}\)), the mean absolute error (MAE), Akaike's information criterion (AIC) and root-mean-square error (RMSE).  
The four indices are calculated as follows:  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(y_{i-}\underline{y}_{i}\right)^{2}}{\sum_{i= 1}^{n}\left(y_{i-}\hat{y}_{i}\right)^{2}} \tag{8}\]  
\[MAE=\frac{\sum_{i=1}^{n}\left|y_{i-}\hat{y}_{i}\right|}{n} \tag{9}\]  
\[AIC=2k-2 ln\ln\left(\hat{L}\right) \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{11}\]  
where n represents the number of observations, \(y_{i}\) the observed value, \(\underline{y}_{i}\) the mean observed value, \(\hat{y}_{i}\) the simulated value, \(k\) the number of estimated parameter and \(\hat{L}\) the maximum value of the likelihood function. In order to exclude outlier points, FM\(\times\)EM approaches were evaluated by excluding years in which the difference between observed and simulated phenological dates was higher than 70 days. The percentage of points used out of the total was reported as \(pp\) (point percentage).
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
## 2 System Architecture
### Systolic Array-Based Computing Strategy
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **PE0** & **FIFO0** & **PE1** & **FIFO1** & **PE2** & **FIFO2** \\ \hline t0 & i\_row0 * w\_row0 + b\_row0 & psum00 & & & \\ t1 & i\_row1 * w\_row0 + b\_row1 & psum10 & i\_row1 * w\_row1 + psum00 & psum01 & \\ t2 & i\_row2 * w\_row0 + b\_row2 & psum20 & i\_row2 * w\_row1 + psum10 & psum11 & i\_row2 * w\_row2 + psum01 & psum02 \\ t3 & & & i\_row3 * w\_row1 + psum20 & psum21 & i\_row3 * w\_row2 + psum11 & psum12 \\ t4 & & & & i\_row4 * w\_row2 + psum21 & psum22 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Computation of PEs and the results stored in FIFOs. Figure 4: Dataflow of a PE performing 1D convolution. Figure 5: Processing sequence of the systolic array in the convolution. to the second channel, while the bias values are changed to the results of the first channel, i.e., p_row0-p_row2. Similar to Equation 1, the computing process of p_row0 ch2 can be decomposed into:  
\[ \begin{split}\text{psum00}_{\text{ch2}}=\text{i}\_{\text{row0}_{ \text{ch2}}}*\text{w}\_{\text{row0}_{\text{ch2}}}+\text{p}\_{\text{row0}},\\ \text{psum01}_{\text{ch2}}=\text{i}\_{\text{row1}_{\text{ch2}}}* \text{w}\_{\text{row1}_{\text{ch2}}}+\text{psum00}_{\text{ch2}},\\ \text{p}\_{\text{row0}_{\text{ch2}}}=\text{psum02}_{\text{ch2}}= \text{i}\_{\text{row2}_{\text{ch2}}}*\text{w}\_{\text{row2}_{\text{ch2}}}+ \text{psum01}_{\text{ch2}}\end{split}  \tag{2}\]  
where i_row0 ch2-i_row2 ch2 and the w_row0 ch2-w_row2 ch2 represent the image rows and weight rows of the second channel, respectively, and psum00(01, 02)ch2 denotes the psum produced by the convolution of i_row0(1, 2)ch2 and w_row0(1, 2)ch2. The reconfigured systolic array is shown in Figure 6a. The image rows broadcasted to PEs and the weight rows corresponding to each PE are changed to the second channel. Note that the source of bias values is changed to the FIFO2 which stores the psum rows produced by the first channel convolution. The computing process sequence of the second channel is shown in Figure 6b, which is the same as the first channel, except that the values processed are changed; thus, the control of PEs is consistent. For the case of convolution with multi-channels, the above computing rules make all other channels available and the output feature map rows are stored in FIFO2 until all channels are completed.
## 4 Sparse Cost Functions
### Tree Structure
Then, the marginals are  
\[P_{k}(\mathbf{\mathcal{K}}\odot\mathbf{\Phi})=\phi^{k}\odot\gamma_{k}\odot\beta^{ \odot_{\mathbb{C}_{k}}},\quad k\in[K],\]  
where for any subset \(\mathcal{U}\subseteq\mathcal{C}_{k}\), we define  
\[\mathbb{R}^{n_{k}}\
i\beta^{\odot_{U}}\coloneqq \begin{cases}\mathbf{1}_{n_{k}}& \text{if }\mathcal{U}=\emptyset,\\ \bigodot_{t\in\mathcal{U}}\beta_{t}&\text{otherwise.}\end{cases} \]  
The resulting procedure is summarized in Algorithm 2. ```
1:Input: Tree \(T(\mathcal{V},\mathcal{E})\) with leaves \(\mathcal{L}\) and root \(1\in\mathcal{V}=[K]\), initialization \((\phi^{k})^{(0)}\), \(k\in[K]\), parameters \(\eta\), \(\delta>0\)
2:Initialize \(r\gets 0\)
3:for\(k=K,\ldots,2\)do
4:\(\beta_{k}^{(0)}\coloneqq \begin{cases}\mathbf{\mathcal{K}}^{(p(k),k)}\left(\phi^ {k}\right)^{(0)}&\text{if }k\in\mathcal{L},\\ \mathbf{\mathcal{K}}^{(p(k),k)}\left(\left(\phi^{k}\right)^{(0)}\odot\left(\beta^ {\odot_{\mathbb{C}_{k}}}\right)^{(0)}\right)&\text{otherwise}\end{cases} \)
5:endfor
6:do
7:for\(k=1,\ldots,K\)do
8:\(\gamma_{k}^{(r)}\coloneqq \begin{cases}\mathbf{1}&\text{if }k=1,\\ \left(\mathbf{\mathcal{K}}^{(p(k),k)}\right)^{\intercal}\left(\left(\phi^{p(k)} \right)^{(r+1)}\odot\gamma_{p(k)}^{(r)}\odot\left(\beta^{\odot_{\mathbb{C}_{ p(k)}\setminus\{k\}}}\right)^{(r)}\right)&\text{otherwise}\end{cases} \)
9: Compute the dual vector \(\left(\phi^{k}\right)^{(r+1)}\coloneqq\mu^{k}\odot\left(\gamma_{k}^{(r)} \odot\left(\beta^{\odot_{\mathbb{C}_{k}}}\right)^{(r)}\right)\)
10:endfor
11:for\(k=K,\ldots,2\)do
12: Compute \(\beta_{k}^{(r+1)}\) according to step 4. 13:endfor
14: Set \(\mathcal{S}^{(r)}\coloneqq\eta\left(\sum_{k=1}^{K}\left(\mu^{k}\right)^{ \intercal}\log\left(\phi^{k}\right)^{(r)}-\left(\mu^{1}\oslash\left(\phi^{1} \right)^{(r+1)}\right)^{\intercal}\left(\phi^{1}\right)^{(r)}\right)\)
15: Increment \(r\gets r+1\)
16:while\(|\mathcal{S}^{(r)}-\mathcal{S}^{(r-1)}|\geq\delta\)
17:return optimal plan \(\hat{\mathbf{\Pi}}=\mathbf{\mathcal{K}}\odot\hat{\mathbf{\Phi}}\), where \(\hat{\mathbf{\Phi}}=\bigotimes_{k\in[K]}\left(\phi^{k}\right)^{(r)}\)
```  
**Algorithm 2** Sinkhorn algorithm for tree structure
## 3 Research Methodology
### Paper Selection, Data Extraction, and Reliability of Relevance Assessments  
The initial selection of papers involved a systematic screening of titles for relevance to the inclusion criteria. To assess the consistency of relevance assessments among three independent reviewers, Fleiss' Kappa was calculated [32]. Fleiss' Kappa is a statistical measure of agreement for categorical data that extends Cohen's Kappa to multiple raters, providing a robust metric for assessing inter-rater reliability. In this study, each paper was rated as \"completely relevant\" (2), \"somehow relevant\" (1), or \"not relevant\" (0) by three reviewers. Papers scoring a total of 6 were automatically advanced to the next stage, while those scoring less than 3 were excluded. Papers with scores between 3 and 5 underwent further review. The calculated Fleiss' Kappa score was 0.90, indicating a high level of agreement among the reviewers. According to conventional interpretation scales for Kappa values, this score suggests an \"almost perfect\" agreement. This high agreement demonstrates that the reviewers consistently assessed the relevance of the papers, validating the reliability of the relevance categorization process (Figure 1, Table 1)  
Selected papers were then subjected to detailed examination. Data extraction was conducted using structured spreadsheets to capture key findings, methodologies, and techniques from each study. Terminologies were standardized to ensure consistency and mitigate selective reporting bias. This iterative process allowed for a comprehensive synthesis of findings.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Database** & **Initial Results (All Fields)** & **Title-Based Results** \\ \hline ACM Digital Library & 3859 & 35 \\ ScienceDirect & 7185 & 101 \\ PubMed & 470 & 175 \\ Scopus & 38,549 & 39 \\ Total & 50,063 & 350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of paper selection process.
## 3 Results and Discussion
### CNN Model Training: Single-Lead Results  
The CNN model was trained using the residuals between the reconstructed signals obtained from the autoencoder model and the original signal values. The training results showed an accuracy of 0.74 for the 12 single-lead test datasets from 100 subjects. The test results showed an accuracy of 0.74, a precision of 0.7, a recall of 0.48, an F1-score of 0.58, and an AUC of 0.69.  
Table 1 presents the detailed performance metrics for each single-lead prediction model. The accuracy, AUC, F1-score, precision, and recall were evaluated for each of the 12 single leads. The results indicate that while there are differences in performance metrics among the leads, the variations are minimal, and thus, no significant differences were observed.