esa-sceva/satcom-chunk-collection huggingface dataset:
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 2 Background Research Models  
### The Technology Acceptance Model  
In 1985 [PERSON] developed The Technology Acceptance Model (TAM) [7] as a contribution to the field of Management Information Systems (MIS). Since its origin, the model has been a significant step forward in the research area because it enhances the design and development of information systems and represents a practical testing tool to assess systems acceptance in ex-ante use scenarios. Its applicability and validity in a wide range of contexts make it the most utilized theoretical model in Information Systems (IS) [15; 16]. Figure 1 depicts the model.  
To uncover the reasons behind computer-based technology adoption, [PERSON] adapted the Theory of Reasoned Act (TRA) [17] and the Theory of Planned Behavior [18] to the technology context. Under the TRA theory, [PERSON] et al. identified two factors that significantly influence the user's intention: the attitude toward the behavior and the subjective norms. On the other hand, in the TPB theory [PERSON] stated that additionally to the factors comprised by the TRA, the adoption of a given behavior is influenced by the ease or difficulty in the implementation. Generally speaking, those two complementary theories formulate that there is a sequence of causal relationships among what individuals believe about using a system and the actual use. The authors state that users' beliefs influence the attitude, which in turn modifies the intention to use or not a given technology. The intention is the primary determinant of actual use. On this basis, [PERSON] [7] identified two main beliefs, namely Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), which are defined as [19]:  
_PU is \"The degree to which the person believes that using the particular system would enhance her/his job performance.\"_  
On the other hand,  
_PEOU is \"The degree to which the person believes that using the particular system would be free of effort.\"_  
According to the TAM [20], the two main determinants, PU and PEOU, are affected by external and context-dependent factors as shown in Figure 1. In this sense, the TAM's consolidation has been a multi-stage process [21], and multiple extensions have been developed according to the research field. TAM 2 and TAM 3 are the prevailing and more comprehensive extensions. The TAM 2 by [PERSON] and [PERSON] [22] focuses on the factors influencing PU and some mediating variables, while TAM 3 unveils the aspects accountable for PEOU [23]. [PERSON] and [PERSON] [24] elucidated that there were different types of TAM's applications and classified them in four groups [25] based on whether the analysis focus was: (a) factors predicting PU and PEOU, (b) factors included from other technology acceptance frameworks, (c) variables with potential moderating or controlling effect, and (d) consequent factors which are the attitudes and usage. The present study belongs to the first type.
## 3 Results and Discussion
### Correlation between Soil Bacterial Community and Soil Properties  
Principal coordinate analysis (PCoA) and DistLM was done to analyze the correlation between soil bacterial community and the edaphic factors. The two axes of PCoA (Figure 3) explained 45.4% of the total variation in the bacterial community, and soil factors located at each coordinate are related to soil bacterial communities divided by ordinate or abscissa. Soil factors placed in the abscissa are more correlated with soil bacterial communities compared to those located in the ordinate coordinates because the PC1 variation (27.7%) is higher than the variation of PC2 (17.7%). In other words, soil OM, TN, CEC, Mg and Ca affects soil bacterial clustering more than the other soil factors. The DistLM analysis indicated significant correlation between soil factors and soil bacterial community. Cation exchange capacity (CEC), OM, TN, P\({}_{2}\)O\({}_{5}\), Mg and K were significantly affecting the bacterial community (Table 3). Regarding the sequential tests, the CEC, OM, TN, pH and P\({}_{2}\)O\({}_{5}\) had a more significant effect on the bacterial community compared to other soil factors. The results of Pearson's correlation analysis between soil factors and diversity indices of bacterial community are represented in Table 4. Among the diversity indices, ace, chao and Shannon diversity index were shown to have a significant positive correlation with OM, TN and CEC. The correlation between soil microbial communities and soil properties had been carried out in numerous studies [44; 45]. A study that concentrated on studying the correlation between soil properties and bacterial community in WSG cultivation sites showed that the soil bacterial community is significantly correlated with soil pH, OM, TN and CEC [36]. Soil microorganisms inhabiting the soil have an important relationship with soil quality and productivity such as OM decomposition and nutrient cycling. In addition, the decomposition of OM and nitrogen mineralization in the soil proceeds through a complex interaction of abiotic factors such as soil properties and biotic factors such as microbial population and nutrient demand [46; 47]. Soil microorganisms are an important factor affecting soil fertility [48]. The cation exchangeable capacity (CEC) is an indicator of soil fertility and is involved in improving soil buffer capacity, nutrient holding capacity and supplying nutrients [49]. In general, OM, TN and CEC have a high correlation in the natural vegetation [50]. In the results of this study, the soil bacterial community had a  
Figure 2: The relative abundance of taxonomic composition at the phylum level for bacteria across soil samples, with clustering tree on the left showing the similarities among the soil samples.
# Upgrading of the L-P Band Cryogenic Receiver of the Sardinia Radio Telescope: A Feasibility Study
## 5 Conclusions  
A feasibility study for upgrading the original design of the cryogenic coaxial dual-frequency L-P band receiver of SRT is presented. Although the receiver was used for years in its originally design, with good results, it presented some aspects that could be upgraded in order to improve the performance of the system. In recent years, as technology advances, the generation of radio frequency and microwave signals has grown exponentially. This aspect involves a continuous evolution of the RFI scenario, which represents a very relevant issue for radio telescopes. In particular, the P-band is increasingly populated with signals from the Italian Ministry of Defense (385-395 MHz), the Sardinia emergency department (460 MHz) and the Radio TV broadcasting (470-828 MHz), as well as the signals from mobile communications (1810-1880 MHz), Italian army radars (1310-1370 MHz), GPS systems (L1 band 1575.42 MHz with a bandwidth of 15.345 MHz, L2 band 1227.6 MHz with a bandwidth of 11 MHz and L5 band 1176.45 MHz with a bandwidth of 12.5 MHz) and radio communication links (1620 MHz), which have invaded the L-band. In addition, several self-produced RFIs are generated (both in P- and L-band) by the electronic boards for the remote control of the receiver.  
In order to improve the system performance, in this paper, we presented a feasibility study for some hardware modifications of the receiver front-end. In particular, the new BPF features for the P-band and the L-band channels is proposed. These features are obtained using the Matlab software and they are compared with the features of the previous BPFs installed on the receiver. The results shown that, with new filters with these characteristics, it is possible to reject the unwanted signals with the highest levels of amplitude. In addition, the original electronic control system is replaced with an electronic board named GAIA, which also permits the remote control of the LNAs receiver. This board is characterized from the point of view of RFIs emission, which turns out to be much lower than that of old electronic boards used in the original design of the receiver.  
Conceptualization, A.L. and L.S.; data curation, A.L., L.S. and F.G.; formal analysis, A.L. and L.S.; funding acquisition, A.L.; investigation, A.L., L.S., P.M. and F.P.; methodology, A.L. and L.S.; project administration, A.L.; resources, P.M., F.P. and G.P.V.; software, L.S., F.G. and G.A.; supervision, L.S.; validation, A.L., L.S. and F.G.; visualization, A.L. and L.S.; writing--original draft, L.S.; writing--review and editing, A.L. and L.S. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Comparison between the S21 parameter of the old L-band BPF (red curve), centered at 1540 MHz with a bandwidth of 520 MHz (model 5B120-1540/T520-O/O from K&L) and the new ideal BPF (blue curve). It has a \(-3\) dB bandwidth between 1400 and 1750 MHz.
## 3 Results and Discussion
### Heat Release Rate (\(H_{RR}\))  
The \(H_{RR}\) relates to the fuel conversion efficiency since it shows how much chemical energy is transformed into thermal energy. Figure 4 depicts the \(H_{RR}\) at different engine load conditions as a function of the crankshaft angle.  
Figure 4: _Cont._According to the results, the general pattern in all the load conditions states that ABE 5 + HHO features the highest heat release from the fuel blends, followed by gasoline, ABE 5, ABE 10 + HHO, and ABE 10. It can be observed that a higher engine load promotes the increase of heat release in the combustion chamber, which implies extreme conditions and maximum chemical energy conversion. The average reduction of the \(H_{RR}\) for the ABE 5 and ABE 10 was 3.5% and 6.78%, respectively, compared to gasoline as the baseline fuel. The latter can be explained considering the higher viscosity of these blends that promote a slower combustion process, thus limiting heat release.  
HHO enrichment increases heat release in all the blends, which implies integrating gaseous fuel mitigates the cooling effect associated with utilizing oxygenates (ABE). The higher calorific value and subsequent enhancement in the laminar flame speed resulting from HHO doping can be mentioned as contributors to the enhanced behavior in the heat release that offset the decrement experienced by ABE blends when compared to pure gasoline. It is worth mentioning that the fuel chemical structure supports the enhanced behavior from HHO doping since both hydrogen and oxygen coexist in the air/fuel mixture, whereas gasoline consists of hydrocarbon molecules [41]. Therefore, the gaseous fuel incorporation promotes an improved combustion performance due to the direct interaction of the diatomic molecules that suppress the ignition delay. In this sense, HHO doping also fosters the massive bond-breaking trend of the gasoline molecules, thus facilitating the heat release rate, the laminar flame speed, and subsequently improving combustion efficiency.
## 3 Framework of Dynamic-DSO
### Direct Visual Odometry
#### 3.1.2 Back-End  
The back-end performs a continuous sliding window optimization which usually contains 5-7 keyframes. The full photometric error \(E_{photo}\) is optimized using the Gauss-Newton method. The calculation of \(E_{photo}\) is described as follows:  
For a single point \(p\) in the reference frame \(I_{i}\), the photometric error when it is projected onto the target frame \(I_{j}\) is:  
\[E_{pj}=\sum_{p\in N_{p}}w_{p}\left\|(I_{j}[p^{\prime}]-b_{j})-\frac{t_{j}e^{j_ {j}}}{t_{i}e^{a_{i}}}(I_{i}[p]-b_{i})\right\|_{\gamma} \tag{2}\]where \(p^{\prime}\) is the projection of point \(p\) on frame \(I_{j}\), \(t_{i}\) and \(t_{j}\) are the exposure time for \(I_{i}\) and \(I_{j}\), \(\|.\|_{\gamma}\), represents the Huber norm, \(a_{i}a_{j}\),\(p_{i}\),\(b_{j}\) are brightness transfer function parameters, \(N_{p}\) is the residual pattern with eight surrounding neighbors described in [11], \(w_{p}\) is the weighting factor given by following:  
\[w_{p}=\frac{c}{c^{2}+\|\
abla I_{i}(p)\|_{2}^{2}} \tag{3}\]  
where \(c\) is the camera intrinsics.  
The projection process can be described as:  
\[p^{\prime}=\prod_{c}(R\prod_{c}^{-1}(p,d_{p})+t) \tag{4}\] \[\left[\begin{array}{cc}R&t\\ 0&1\end{array}\right]:=T_{j}T_{i}^{-1},\]  
where \(d_{p}\) is the point's inverse depth of point \(p\), \(T_{i}\) and \(T_{j}\) are the poses of the involved frames \(I_{i}\) and \(I_{j}\).  
The full photometric error \(E_{photo}\) over all keyframes and points can be given by:  
\[E_{photo}=\sum_{i\in F}\sum_{p\in P_{i}}\sum_{j\in obs(p)}E_{pj} \tag{5}\]  
where \(F\) is the set of all keyframes in the sliding window, \(P_{i}\) is the set of all the points in keyframe \(I_{i}obs(p)\) is the set of the keyframes in which the point \(p\) is visible.  
In order to reduce the impact of dynamic scenes, Dynamic-DSO modifies the photometric error model by using the prior information of dynamic objects obtained from the sematic images of all keyframes. It is described in Section 4.4 in detail.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 3 System Design and Implementation
### Packet Loss Rate Transmission  
(1) Transmission Packet Loss Rate: For real-time flows, the PLR is a key performance indicator. The number of packets lost or deleted during transmission must be kept low  
Figure 11: Water Quality Data Collection.  
Figure 10: Mini ship tool.  
Figure 9: LoRa and Water Multi-sensor: (**a**) The total connection diagram of with LoRa module of EK-S76 SXB; (**b**) Multi-parameter Water Quality Sensor.  
because these data flows are promised certain data speeds for smooth and flawless transmission. The PLR can be calculated as follows in a transmission interval:  
\[PLR=\frac{N^{t}x-N^{t}x}{N^{t}x}\times 100 \tag{2}\]  
where \(N^{t}x\) and \(N^{t}x\) represent the total number of packets sent and received, respectively. In LTE-Sim, it could be accomplished quickly by collecting all of the real-time packet sizes that are transmitted and received, then calculating the PLR using one of the evaluation tools available in the simulator. We compared two parameters, which are the central frequency and the amount of data sent, based on the same distance between the gateway and the logging points. There are two key points need to emphasize to optimize PLR. The first one is to increase the numbers of gateway or 3 dBi antenna would be actually reduced the PLR. The second one is to reduce the packet size.  
Radio frequency (RF) test equipment is frequently prohibitively expensive, and test engineers may lack RF knowledge. This leads to a lack of verification of the RF performance in a device. As a result, devices end up exhibiting poor range characteristics that are not circumvent able as the issue lies in the underlying design. In order to verify the receiving ability, a LoRa's receiving testing was conducted. The hardware includes the following: (1) a LoRa(r) Evaluation Board LM-130H1 AEB, which is a built-in switchable protocol (LoRaWAN(tm)/M.O.S.T.) device based on the GlobalSat LM-130H1 module, and (2) a dual channel professional LoRa Gateway (LG-S201H EVB), which can operate independently, as shown in Figure 12.
# Feasibility Assessment of Acid Gas Injection in an Iranian Offshore Aquifer
## 8 Conclusions  
The research focused on a specific area in the Persian Gulf, about 100 km from Iran's coast and 120 km from Bushehr city. Due to limited permeability data, an empirical correlation was used to incorporate permeability information into the simulation for both carbonate and dolomite formations. According to this correlation, the estimated average permeability for the upper part of the Surmeh formation was approximately 0.1 mD.  
Geomechanical Earth Modeling (GEM) was employed to assess pore pressure and fracture pressure (3800 and 6100 psi, respectively), which are crucial factors for evaluating the suitability of the formation for CO\({}_{2}\) storage. Variations in caprock thickness were identified as significant considerations affecting the containment and long-term stability of injection operations.  
Understanding capillary trapping phenomena is essential, and the study analyzed two simulation models. In the first scenario, a homogeneous gas plume rapidly reached the reservoir's top and spread laterally. In the second scenario, which was heterogeneous, the plume was wider with a non-uniform gas saturation distribution as only a fraction of the gas reached the top. These findings emphasized the significant impact of different rock types and hysteresis in capillary curves on trapping processes and the dynamic behavior of the gas plume.  
The Surmeh formation is known for its low porosity and permeability, making it a challenging reservoir with a permeability of only 1 mD. This limits the maximum achievable injection rate to 7 MMSCFD, while the required injection rate is much higher, at 180 MMSCFD. Due to these limitations, it is important to consider formations with higher porosity and permeability values for efficient gas injection. An ideal scenario would involve a porosity of 12% and permeability of 100 mD, allowing high-flow gas injection without fracturing the formation, which would require two injection wells, each with a capacity of 90 MMSCFD.  
In summary, this study utilized available data to model a range of permeabilities (0.1 to 1 mD) and simulated gas injection into the Surmeh formation. The results highlighted the challenges posed by the formation's low porosity and permeability and emphasized the importance of considering formations with higher porosity and permeability for efficient injection operations.  
Conceptualization, O.F. and A.S.; Methodology, M.C., O.F. and A.S.; Petrel 2015, O.F. and A.S.; Validation, O.F.; Formal analysis, [PERSON] ([PERSON]); Investigation, [PERSON] ([PERSON]); Data curation, M.C. and A.S.; Writing--original draft, [PERSON] ([PERSON]); Writing--review & editing, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Land cover classification data have a very important practical application value, and long time series land cover classification datasets are of great significance studying environmental changes, urban changes, land resource surveys, hydrology and ecology. At present, the starting point of continuous land cover classification products for many years is mostly after the year 2000, and there is a lack of long-term continuously annual land cover classification products before 2000. In this study, a long time series classification data extraction model is established using a bidirectional long-term and short-term memory network (Bi-LSTM). In the model, quantitative remote sensing products combined with DEM, nighttime lighting data, and latitude and longitude elevation data were used. We applied this model in China and obtained China's 1982-2017 0.05\({}^{\circ}\) land cover classification product. The accuracy assessment results of the test data show that the overall accuracy is 84.2% and that the accuracies of wetland, water, glacier, tundra, city and bare soil reach 92.1%, 92.0%, 94.3%, 94.6% and 92.4%, respectively. For the first time, this study used a variety of long time series data, especially quantitative remote sensing products, for the classification of features. At the same time, it also acquired long time series land cover classification products, including those from the year 2000. This study provides new ideas for the establishment of higher-resolution long time series land cover classification products.  
time series; land cover classification; Bi-LSTM; quantitative remote sensing 2019 11 1639; doi:10.3390/rs11141639
# Energy-Efficient Spatial Query-Centric Geographic Routing Protocol in Wireless Sensor Networks
## 3 QTGrid Routing Protocol
### Cluster Setup  
Figure 1 shows the construction of grids. The monitoring area is initially expanded, and the smallest square G that completely covers the monitoring area and the sink node in the center of the network is defined.  
Quadtree [44; 45] is a data structure that has been widely used in data clustering, computational geometry, and image processing. Quadtree partitioning of a 2D area facilitates the hierarchical spatial indexing of individual sub-regions and provides easy access routes to such sub-regions [11; 12]. This partitioning method was employed in QTGrid to divide the expanded monitoring area. By using the basic idea of the quadtree management scheme, four adjacent level-1 grids formed quad region G. In each level-1 quad region, four adjacent non-overlapping single quads formed a nested level-2 quad. On the basis of this rule, each level-3 quad was also subdivided and so forth. In this manner, the expanded monitoring area G was recursively divided into logical quads.  
As shown in Figure 1, the abovementioned operations were recursive until the entire G is divided into \(4^{Z}(Z\geq 0)\) sub-zones, where Z is the quadtree depth determined by specific applications (e.g., the spatial resolution of sensor data). Assuming that the side length of G was \(L\) and the side length of the level-Z grid was \(l\), Equation (1) is followed.  
\[l=L/2^{Z} \tag{1}\]
# Edge Computing Approach for Vessel Monitoring System
## 3 Proposed Approach
### Analysis of the Speed Profile
Velocity 0 was removed as we did not want to consider when the vessel is completely stopped. Kernel: A Kernel distribution method was applied in the filtered histogram to have the distribution represented in orange in Figure 5. A dictionary with the velocities and the cumulative percentage of velocity was created. This led to a histogram such as the one shown in Figure 6[23]. Figure 5: Kernel distribution filtered data. Figure 6: Filtred histogram with cumulative kernel distribution. Then, a range across quartiles is defined for probability. As in the estimation of confidence intervals, a confidence level is defined here as well, which will be associated with the two-speed limits that correspond to the fishing activity. Once these limits are set, it is possible to compare the new data with the established limits. If the new data is within limits, it is classified as fishing. Otherwise, it is classified as not fishing. Fishing Spots: Using the history of GPS locations by vessel, it is possible to identify whether the vessel is fishing in its regular fishing zone or in a new location. Fishing in different zones may mean two distinct options: either the vessel has changed its type of fishing, or the vessel may be performing some operation outside legality. Figure 7 shows the GPS points of a vessel. Using methods based on clustering, it is possible to identify several zones that are the normal fishing zones of that vessel. When the vessel is outside those zones, an alert should occur. Using the fishing velocity range identified in the previous steps, we get the GPS points of the vessel within that range. Consequently, it is possible to work only with the positions where the vessel was fishing (see Figure 7). The next step is to use a clustering algorithm to define the fishing zones so that we can compare them with the new GPS points.