esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### LTT Item Prescision Emprical Data  
The latent trait theory model was applied in estimating item suggestions on real student data, 2021 dataset Figure 7.  
Figure 5: Formative assessment precision selecting topics in CCT as a function of quiz length, 2019 dataset. A vertical dashed line denotes 95% precision.  
Figure 6: Distribution of students assessed for topic precision for UCB, left, and Elo-bandit, right. Where red <1%, orange 1-50%, dark green 50–99%, and green >99% precision.  
A formative assessment quiz included 80 items on 15 topics and was taken by 106 students. Results confirm observations made using synthetic students. The Elo-bandit is a promising alternative to IRT based tests, achieving almost the same precision 97% with half the test length. The Elo-bandit has significantly better precision finding suitable items than CCT based algorithms. The Elo-bandit algorithm shows better performance on real data compared to simulation. Offering more than twice the precise results for tests longer than 15 questions. The 80% precision was achieved at 15 questions in comparison to 23 questions for simulation, and 90% precision at 23 in contrast to 45 question simulation value. Despite the fact that constants for the algorithm were calibrated using simulated data. A possible explanation is provided in the following discussion section.  
Question difficulties and Pearson coefficient correlation matrix for the 2021 dataset are presented in Figure 8.  
The question difficulties graph shows a percentage of correct answers each item has received. A number of items were answered correctly by less than 25% of students, despite having a guess factor of \(P_{g}\) = 0.25. Colored bars illustrate selected suitable items for a sample strong student: dark green--true positive, red--false positive, yellow--false negative, light green--true negative. The correlation matrix demonstrates low generally correlation even for inter topic questions except for items 64-69. Strongly correlated items  
Figure 8: Question difficulties in 2021 dataset on the left and item correlation matrix on the right.  
Figure 7: Mean algorithm precision estimating personalized suitable items as measured against IRT MLE.  
would improve the Elo-bandit and UCB algorithms' performance. Probing an item from a strongly correlated topic brings more information about other topic items, so fewer questions are needed to identify familiar and unfamiliar subjects for a student.
## 3 Early-Warning Systems
### Thermal Camera (TC)  
The video-monitoring network system of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE), consists of the ENT camera, located in Nicolosi, about 15 km south of the Etna summit craters, and belonging to the Etna video-surveillance system. The ENT camera is equipped with an A40M Thermovision Forward-Looking InfraRed (FLIR Systems) camera, and records in the 7.5 and 13 \(\upmu\)m spectral range, providing a time series of 640 \(\times\) 480-pixel images with a spatial resolution of 1.3 \(\upmu\)rad (few meters) and a thermal sensitivity of 80 mK at 25 \({}^{\circ}\)C. Thermal images of ENT are displayed with a fixed color scale corresponding to the temperature range from \(-\)20 to 80 \({}^{\circ}\)C [34].  
Images recorded by ENT provide the brightness temperature over the whole eruptive episode. Employing the algorithm described in [18], we detected the saturated region evaluating the brightness temperature gradient as a function of a given threshold, to detect the height of the IJR. As a first approximation, the height (H\({}_{\rm{IJR}}\)) of the IJR can be considered as the height of the lava fountain. The exit velocity, \(v_{ex}\) (m/s), of pyroclastic material is derived from the H\({}_{\rm{IJR}}\), starting from the Torricelli relation, assuming that: (i) most of the pyroclasts are sufficiently large, which can be considered as accelerated projectiles confined only in this incandescent jet region, and (ii) atmospheric density variations and drag effects are negligible. Under these assumptions, at each timestep, \(t\), we applied the relation: \(v_{ex}(t)=(2\times g\times{\rm{H}}_{\rm{IJR}}(t))^{0.5}\), where \(g\) (m/s\({}^{2}\)) is the earth gravity acceleration [18].  
The thermal camera-early warning (TC-EW) follows the same flowchart previously described for the IP signal (Figure 3).  
Figure 2: IS-EW flowchart showing the relation among the infrasound parameter (IP) (dimensionless) and corresponding alert levels (EW0 in yellow, EW1 in orange, and EW2 in red) (modified from [6; 15]).  
In this case, from the quiescent activity with \(v_{ex}\) equal to 0 m/s (EW0, yellow box), the beginning of the precursory Strombolian activity, corresponding to EW1 (orange box), was delivered when the \(v_{ex}\) was greater than 0 m/s and lower than 90 m/s. When \(v_{ex}\) exceeded 90 m/s for more than 5 min, we identified the onset of the explosive eruption, EW2 (red box).
# The Effectiveness of Pump Techniques and Compages: A Systematic Review
## 3 Results  
### Overall Results  
3673 studies were identified with the database search. Through other searches, a further 66 studies were added. After removing duplicates and excluding by title and abstract, 106 studies were eligible to be assessed by full-text reading to verify the eligibility for inclusion in this systematic review. Eighty-one articles were excluded for various reasons (Supplement 2), with 25 studies available for qualitative synthesis [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56] (Figure 1).  
Among these studies, 20 analyzed pump techniques [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56], and five concerned pumpages [57; 58; 59; 60; 61]. The included studies were published from 1968 to 2019 and conducted in the USA [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 54; 55; 56], Brazil [57; 58; 60; 61], Italy [51; 52; 59], Egypt [37], Poland [53], and India [42]; the total number of patients who completed the assessments was 1632.  
The treatment techniques used in the studies were osteopathic pump and pompage techniques. In 20 studies, thoracic lymphatic techniques were applied together with other techniques, such as pedal pump, abdominal pump, and sternal pump [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56]. In five studies, pompages were used [57; 58; 59; 60; 61]. The duration of the treatments ranged from one day to seven weeks and the duration of each treatment ranged from two to 50 min. The interventions with which the pump techniques were compared were no treatment [37; 52], light touch [44; 45; 46; 47; 48; 50; 54; 56; 57], conventional rehabilitation [38; 41; 42; 51; 53; 55; 61], vaccination [39; 40] stretching [58], muscle-tension headache program and electotherapy program [59], and educational lectures [60]. Primary outcomes were pain intensity, lung functions, blood count cell, and length of hospitalization. Secondary outcome measures were ROM and psychological condition.
## 3 Experimental Results
### System Performance  
The net electric efficiency and power of the ORC system are presented in Figure 9. The low and, in a few cases, even negative values are due to the low isentropic efficiency of the refrigerant pump working in off-design conditions as mentioned before. Indeed, the present test bench had been initially designed to study the performances of different expansion devices such as the scroll compressor of this study while the net efficiency of the system was not of concern. Thus, the pump was adopted considering its ability to generate a wide range of pressure and flow rates to supply the expander machines at different working conditions.  
Figure 8: Scroll expander filling factor with shaft speed (**left**), and with pressure ratio (PR) (**right**).  
Figure 7: Scroll expander shaft power with flow rate.  
To better understand the destructive impact of the low isentropic efficiency of the pump on the net efficiency of the system, the back-work ratio (BWR) is presented in Figure 10 confirming the disproportionate pump selection for the range of the power generated by the ORC system. BWR increases as the heat source temperature and fluid critical temperature decrease [15]. Furthermore, BWR is estimated to be about four times higher for R134a compared to water [16].  
Therefore, this parameter reveals the non-negligible impact of the pump power consumption in micro-to-small scale power systems for low-grade waste heat recovery. Hence, it is worth noting the gross system electric efficiency as well in Figure 10 to obtain an estimation of the net electric efficiency if a well-sized pump is used.
## 4 Proposed Solution
### Energy Model  
The total energy consumption for a message or data transmission by the SN is the sum of event-specific energy consumption and calculated using Equation (6). The power and timing measurements were performed during the lab experiments, as described in Section 6.3, where \(\rm P_{\rm AC}\), \(\rm P_{\rm RX}\), and \(\rm P_{\rm TX}\) are the power requirements at active, LoRa-receive, and LoRa-transmit events, respectively, and \(\rm T_{\rm AC}\), \(\rm T_{\rm RX}\), and \(\rm T_{\rm TX}\) are the times required for those activities. This total energy requirement is highly dependent on the hardware design of the SN. For complete data transmission using the LDAP, the total energy consumption of a SN can be given by Equation (7), where \(\rm E_{\rm A1}\) is the energy consumption for request and acknowledgment messages (\(\rm T_{\rm TX}=TOA_{\rm min}\)) and \(\rm E_{\rm A2}\) is the maximum energy consumption for a data transfer message (\(\rm T_{\rm TX}=TOA_{\rm max}\)). Similarly, the total energy consumption of the RN, for data transmission through it, can be provided by Equation (8). Therefore, the total WSN energy consumption can be calculated using Equation (9). The average per node energy requirement (\(\rm E_{\rm AW}\)) per cycle can be given by Equation (10), where k is the number of data transmissions through the RN. Energy efficiency (\(\rm\eta\)) for the proposed LDAP can be calculated using Equation (11). From this equation, it is shown that the energy performance of the proposed LDAP depends on the number of hop or repeater nodes used in the WSN. Energy consumption calculation values are given in Table 5, using the experimental values of \(\rm T_{\rm TX}\).  
\[\rm E_{\rm A}=P_{\rm AC}T_{\rm AC}+P_{\rm RX}T_{\rm RX}+P_{\rm TX}T_{\rm TX} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline SF & Spreading Factor & 7 \\ BW & Bandwidth (kHz) & 250 \\ CR & Code Rate & 1 \\ nPR & Preamble length & 8 \\ H & Explicit Header Enable (0: enable) & 1 \\ CRC & Cyclic Redundancy Check Enable & \\  & (1: enable) & 0 \\ D & Low data-rate enable (0: disable) & 0 \\ U\({}_{\rm CH}\) & LoRa channel utilization & 10\% \\  & Time On Air (TOA) at maximum & \\ TOA\({}_{\rm max}\) & payload size of 245 bytes & 197.25 ms \\  & TOA at minimum payload size of & 33.41 ms \\  & 16 bytes & \\ \hline \hline \end{tabular}
\end{table}
Table 4: LoRa parameters with their default and calculated values.  
\[\mathrm{E_{SN}}=\mathrm{E_{A1}}+\mathrm{E_{A2}} \tag{7}\] \[\mathrm{E_{RN}}=4\mathrm{E_{A1}}+2\mathrm{E_{A2}}\] (8) \[\mathrm{E_{WSN}}=\mathrm{N_{max}}\mathrm{E_{SN}}+\mathrm{k}\mathrm{E _{RN}}\] (9) \[\mathrm{E_{AV}}=\mathrm{E_{SN}}+(\mathrm{k/N_{max}})\mathrm{E_{RN}}\] (10) \[\mathrm{\eta}=1-\mathrm{k/N_{max}} \tag{11}\]
# Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus
## 3 Results and Discussion
### Hourly Values
#### 3.2.2 Diurnal Variation of Hourly Global Irradiances  
The daily variation of the average hourly global irradiance for the selected stations is shown in Figure 16. The figure shows that the hourly average global irradiance fluctuates between 400 W/m\({}^{2}\) in January and 950 W/m\({}^{2}\) in July at local noon. Slightly lower values were observed at Agros ( mountainous site). The curves of each month of global irradiances were symmetrical around the local noon. As it can be seen, the curves can be classified in five groups with similarities with respect to their variation. Starting from the top curves, the first group represents the summer months (June, July, and August) and slightly below it is the second with the months of April, May, and September. March and October compose the third group, which is followed by the group of the months of February and November, and the last group is the winter months of January and December.
## 3 Results
### AlpC Containing Ser Presented Enhanced Chaperone Activity  
We also investigated the abilities of these AhpCs to protect citrate synthase (CS) against thermal insult. CS slowly aggregated when kept at 48 \({}^{\circ}\)C as indicated by increased light scattering as a function of time (Figure 10A,B, black lines). In the presence of PaAhpC, the CS aggregation decreased significantly (Figure 10A, blue line). We also tested if the pre-treatment of PaAhpC at 48 \({}^{\circ}\)C by 1 h was able to exert additional protection to CS, but no difference was observed in comparison to the untreated enzyme (Figure 10A, red line). In the case of SeAhpC, the sample without thermal pre-treatment presented a more pronounced protection activity (Figure 10B, blue line) than PaAhpC. Notably, pre-heated SeAhpC (48 \({}^{\circ}\)C/1 h) protected CS with very high efficiency, because only a negligible amount of CS aggregated (Figure 10B, red line). Therefore, our results suggest that AhpCs containing Ser in the catalytic triad are more prone to act as chaperone induced by heat shock stress than their counterparts containing Thr.  
Figure 9: NADPH oxidation assay to assess the effects of thermal inactivation of AhpC enzymes. Aliquots of the PaAhpC (**A**), PaAhpC\({}^{\text{T43S}}\) (**B**), SeAhpC (**C**) and SeAhpC\({}^{\text{S46T}}\) (**D**) were incubated at different temperatures (37 \({}^{\circ}\)C = \(\blacksquare\) blue, 42 \({}^{\circ}\)C = \(\times\) orange, 50 \({}^{\circ}\)C = \(\blacktriangledown\) green and 55 \({}^{\circ}\)C = \(\blacktriangledown\) red) for 1 h. Then the enzymes were added to the reactions containing 6 \(\upmu\)M EcTrx, 0.9 \(\upmu\)M EcTrxR, 150 \(\upmu\)M NADPH, 50 mM HEPES (pH 7.0), 100 \(\upmu\)M DTPA, and 1 mM sodium azide. The reactions were initiated by the addition of 500 \(\upmu\)M H\({}_{2}\)O\({}_{2}\) and absorbance at 340 nm was monitored at 37 \({}^{\circ}\)C, for 5 min. As negative control (\(\bullet\) black), reactions were performed without the addition of the AhpC. The standard deviations are represented by the error bars. All experiments were performed at least three times and yielded similar results using replicates.  
The ability of the typical 2-Cys Prx to act as chaperone (holidase) was already described for several enzymes from eukaryotes [32; 33] and from some bacterial proteins [34; 62; 63]. These enzymes are able to switch from peroxidase to chaperone in response to oxidative stress, by hyperoxidation of C\({}_{\text{P}}\) or by thermal stress. Considering the high resistance of AhpC to C\({}_{\text{P}}\) hyperoxidation, the thermal stress may be the main factor that triggers the chaperone activity, as observed by [PERSON] and coworkers (2018) using _E. coli_ AhpC (Thr-Prx) as model [34]. Here, we observed the same effect for SeAhpC, which is a Ser-Prx. It is tempting to speculate that chaperone activity may be facilitated in these enzymes because they are stabilized as decamers, irrespective to the oxidative state.
## 1 Introduction  
Predicting landslide susceptibility of a region or study area has become a critical necessity with the continuing expansion of urbanizations across hazardous landscapes, increasing soil deterioration and the extensive damages inflicted by landslides [1]. Regional approaches to quantitative predictions have developed following the availability of thematic maps in digital form, including that of interpreted aerial photography and remotely sensed images. Examples of themes related to mass movement processes are expressions of various aspects of the rocks, soil, land cover, land use, soil permeability, groundwater table, and topographic surfaces [2, 3].  
In any application, the themes are hopefully representing the gravity-induced physical context of the process of slope failure. For this reason, the landslides are considered spatially related with the thematic map units or values over the study area of concern. These spatial relationships are established and integrated by mathematical models that transform raw data from specific landslide distribution maps and contextual maps into prediction maps or maps of the likelihood of future landslide occurrence [4].  
Obviously, the detail and accuracy of the digital maps used for modeling must be congruous and the statistical relationships significant to obtain convincing likelihood maps of landslide occurrences that, hopefully, are more informative than what is already known. The term \"convincing\" must reflect the interpretable quality of the integrated likelihood maps as spatiotemporal predictions and the certainty--or conversely, the uncertainty--associated with that quality.  
The applications discussed in this contribution have the purpose of exposing aspects of spatial predictions that are commonly ignored--validation, robustness, and uncertainty of the resulting susceptibility levels expressed as relative ranks. They are the modeled values that make up the prediction patterns.  
Analyzed are a database of landslide occurrences and their spatial context in a study area in northern Italy. The database has been the focus of experiments on comparison of models and analysis of modeling structure. Here, we will focus exclusively on the uncertainties affecting the ranks representing relative predicted levels of susceptibility to active landslides.  
The following section deals with the predictive methodology, the terminology, and the analytical procedures applied. The next section describes the study area, its database for modeling, and previous research on landslide susceptibility. Experimental results follow on uncertainty identification and its relative measures. They consist of modeling prediction, target and uncertainty patterns, their interpretation, and comparison. Conclusions deem that uncertainty assessments are a desirable and necessary endeavor for making spatial prediction modeling a worthwhile practice.
## 4 Discussion  
Lignite substrate can be applied in hydroponic cultivation of cucumber. Studies have confirmed that lignite growing mats at the beginning of their use in plant cultivation have low water holding capacity and low content of readily available water [19]. Bulk density (BD) increased in lignite mats after 51 DAP of cucumber cultivation in comparison to new cultivation mats, which was associated with a decrease in air content in mats. The effect of lowering this parameter was an increase in water holding capacity (WHC) of the substrate and water readily available to plants (Table 1). A similar trend was found when lignite substrate was used twice in hydroponic cultivation of greenhouse cucumber [19]. Water holding capacity and water readily available to plants can significantly affect plant growth and yield [19; 49; 50]. According to a study by [PERSON] et al. and [PERSON] et al. [6; 8] the physical properties of the substrate recommended for most plants are in the following ranges: 20-30 vol% readily available water, \(\geq\)85 vol% total porosity, and 10-30 vol% total water holding capacity. Despite the low physical parameters of lignite mats, such as water holding capacity and readily available water content, no negative effect of this substrate on plant growth and yield was observed. As shown in previous studies, the yield of cucumber grown in reused lignite mats was higher compared to reused mineral wood mats. This may be due to the better conditions for the root system in the reused growing mats [19]. The results for the nutrient extracts from the growing mats (substrate) indicated that the pH of the lignite substrate was similar to that of the mineral wool substrate. However, the EC level was lower in the lignite substrate compared to the mineral wool substrate. The sorption complex of lignite may to some extent reduce the negative effect \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**MW/Control EC**} & \multicolumn{2}{c}{**L/Control EC**} & \multicolumn{2}{c}{**MW/High EC**} & \multicolumn{2}{c}{**L/High EC**} \\ \cline{2-9}  & **R** & **D (\%)** & **R** & **D (\%)** & **R** & **D (\%)** & **R** & **D (\%)** \\ \hline Dry matter & 0.3898 & 15.19 & 0.6469 & 41.85 & 0.4363 & 19.04 & 0.7232 & 52.30 \\ \hline TSS & 0.3545 & 12.57 & 0.6040 & 36.48 & 0.4704 & 22.12 & 0.4704 & 22.13 \\ \hline \(\beta\)-carotene & 0.5359 & 28.72 & 0.2296 & 5.27 & 0.4540 & 20.61 & 0.7142 & 51.01 \\ \hline Lutein & 0.5351 & 28.64 & 0.8411 * & 70.74 & 0.5226 & 27,31 & 0.8211 * & 67.42 \\ \hline Nitrates & 0.4403 & 19.39 & 0.2398 & 5.75 & 0.5504 & 30.29 & 0.4901 & 24.02 \\ \hline \hline \end{tabular}  
* significant at \(p\leq\) 0.05. < 25%—low impact, 25–45%—average impact, 46–100%—high impact.  
\end{table} 
Table 12: Linear correlation coefficient(2) for the relationship between the content of dry matter, TSS, selected bioactive compounds and nitrates (in mg N-NO3/100 g\({}^{-1}\) FW) in cucumber fruit and the physical parameters of the medium (BD, WHC) depending on the type of medium and EC of the medium.
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 3 Fast Connectivity Construction Framework
### Light-Weight Multi-Layer Neural Network  
The designed light-weight multi-layer neural network, denoted by LMK, is based on k numbers of nodes in the input layer, hidden layers, and output layer. The number k indicates the maximum number of the MAC layer links and the hidden layers use minimum numbers of k nodes in LMK. LMK uses the Sigmoid function as the activity function, and then error backpropagation is calculated at the output and distributed back through the network layers. In order to reduce computing overhead, LMK does not exploit the convolutional layer and the SoftMax function. When the input data is prepared, the connectivity context and signal state context are utilized to make the label for training data, and the prepared input batch is normalized. LMK carries out the training of data. During the training, the error against the label is propagated backward. After multiple numbers of epochs, LMK can be ready to receive queries with the trained weight of its own neural network. Then, a query can be conducted in LMK by preparing a data set with real-time  
Figure 2: Fast Connectivity Construction Framework with On-node Training and Query.  
RSS values and current MAC layer links. LMK provides query in the trained NN and decides on connecting paths as the prediction result.  
LMK is composed of multi-layer perceptron (MLP) based on DNN [25]. The MLP is a type of neural network using the backpropagation method and should be pre-trained by a lot of data to calculate the required prediction accuracy [26]. Equation (1) represents the inputs, weights, and bias that are computed for MLP.  
\[f_{j}=\sum_{i=1}^{n}w_{ij}\cdot x_{i}+\beta_{j}, \tag{1}\]  
where \(n\), \(x_{i}\), \(\beta_{j}\), and \(w_{ij}\) show the number of inputs, the input variable of \(i\), bias term, and the connection weight respectively. The Sigmoid function is used as the activation function in the MLP model. The Sigmoid function is described in Equation (2), and the output of the neuron \(j\) can be measured in Equation (3) as follows [19; 25]:  
\[s_{j}(x)=\frac{1}{1+\exp{(-f_{j})}}, \tag{2}\]  
\[y_{i}=f_{j}\cdot\left(\sum_{i=1}^{n}w_{ij}\cdot x_{i}+\beta_{j}\right). \tag{3}\]