esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Sensitivity of RSM and PRE to Aridity Levels  
In this study, we observed that the enhancing effects on GPP varied across different ecosystem types. Integrating these findings with the data presented in Figure 6, it was evident that the rates of increase in \(SA_{GPP}\) in response to rising \(SA_{RSM}\) differed among various \(SA_{PRE}\) categories, with corresponding variations in saturation levels. Specifically, in hyperarid regions, the parameter \(\Delta SA_{GPP}(SA_{RSM}\,|\,SA_{PRE})\) exhibited the highest mean value (0.577), followed by arid regions (0.54) and semi-arid areas (0.42), whereas semi-arid to semi-humid regions showed the lowest mean value (0.409). The parameter \(\Delta SA_{GPP}(SA_{RSM}\,|\,SA_{PRE})\) demonstrated a trend of increasing mean values with escalating aridity, suggesting that the dominant influence of RSM on GPP intensifies under more arid conditions. Conversely, the parameter \(\Delta SA_{GPP}(SA_{PRE}\,|\,SA_{RSM})\) displayed progressively higher mean values across hyperarid, arid and semi-arid zones, indicating that PRE's effect on GPP becomes more pronounced in less arid environments. Notably, this parameter attained its lowest mean value in semi-arid to semi-humid regions, which may be attributed to reduced GPP variability within these transitional climatic zones. As illustrated in Figure 8c, the semi-arid-to-semi-humid region exhibited the lowest mean rate of GPP increase, with this rate declining progressively as aridity decreased (i.e., with higher \(SAI\) values). In contrast, the hyperarid region showed the highest mean GPP increase rate, which consistently diminished with decreasing aridity. Interestingly, both arid and semi-arid regions demonstrated increasing GPP growth rates with rising \(SAI\) values, suggesting that GPP within these ecosystems is better adapted to the \(SAI\) conditions characteristic of arid and semi-arid environments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Index** & **Influence** & **ASA** & **GLS** & **F** & **BOSVL** & **C** & **W** \\ \hline \multirow{2}{*}{\(\Delta SA_{GPP}(SA_{RSM}\,|\,SA_{PRE})\)} & Positive & 78.12 & 79.19 & 74.69 & 77.91 & 76.74 & 75.54 \\  & Negative & 21.88 & 20.81 & 25.31 & 22.09 & 23.26 & 24.46 \\ \hline \multirow{2}{*}{\(\Delta SA_{GPP}(SA_{PRE}\,|\,SA_{RSM})\)} & Positive & 65.53 & 66.28 & 62.22 & 64.48 & 64.23 & 61.16 \\  & Negative & 34.47 & 33.72 & 37.78 & 35.52 & 35.77 & 38.84 \\ \hline \multirow{2}{*}{\(\Delta SA_{GPP}(SA_{RSM}\,|\,SA_{PRE})-\Delta SA_{GPP}(SA_{PRE}\,|\,SA_{RSM})\)} & Positive & 62.13 & 61.24 & 65.11 & 62.97 & 61.79 & 66.97 \\  & Negative & 37.87 & 38.76 & 34.89 & 37.21 & 38.21 & 33.03 \\ \hline \end{tabular} Note: ASA, arid and semi-arid areas of China; GLS, grasslands; F, forest; BOSVL, barren or sparsely vegetated lands; C, croplands; W, wetlands. The abbreviations are the same in the following tables and figures.  
\end{table}
Table 1: The effects of standardized anomalies of RSM and PRE on GPP-standardized anomalies under different conditions across various vegetation types.
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Host Identity Protocol (HIP)  
HIP [19] is an end host-based network protocol that uses cryptographic keys as host identifiers. The HIP was not designed initially to enable mobility and multihoming: rather, the primary aim was to provide more secure communication between hosts. However, as it uses an identifier-locator split mechanism, later documents have proposed mobility [20] and multihoming [21] approaches that make use of HIP. These became part of its architectural standard documentation in July 2021 [22].  
The HIP introduces the Host Identifier (HI), a cryptographic public key representing the identity of a host. It also introduces the Host Identifier Tag (HIT), an operational representation of the HI that is only 128 bits, being much smaller than current commonly used public key sizes, which can be 2048 bits or 4096 bits.  
In HIP, the use of IP addresses still remains; the IP address is used as a locator, with the full 128 bits of the IPv6 address bound to each interface that is used. The HIP host initiating communication, the Initiator, starts a HIP Base Exchange (BEX) by sending a message to the peer, the Responder. This message initiates a handshake using a Diffie-Hellman key exchange for a session key. Once the handshake is complete, an HIP association, a shared state between two HIP hosts, is created. The binding of an HI to a Fully Qualified Domain Name (FQDN) is done using the Domain Name System (DNS). As the IP address no longer serves any part in host identification, the socket has to be re-written for HIP, requiring a new Application Programming Interface (API) for applications [23]. As it is a host-based solution, networking components such as routers will not be able to participate, thus site multihoming and mobility cannot be achieved, nor is traffic engineering based on a HIT possible. To enable host mobility, a rendezvous mechanism is needed [24], which involves additional network infrastructure entities.  
Overall, HIP mandates the use of secure identities, requires additional network entities, and also requires application changes--none of which are required with our approach.
## 5 Results and Discussion  
The novelty of the research presented in this paper is the adaptation of the original mode of operation of the ISOMAX system to various modes for different building energy systems. Heat/cooling sources and other system components have been added that, when used in conjunction with the building control system, optimize the mode of operation and provide alternatives to buildings with fossil fuel-based heating/cooling systems that are energy safe and reliable. Partial results of our research have been published in several scientific articles and are also part of three utility models (UM SK 5749 Y1 [20], UM SK 5729 Y1 [22], UM SK 5725 Y1 [23]) and one European patent (EP 2 572 057 B1 [24]).  
Figure 15: Chamber of pooled air distributor and air collector for ventilation with heat recovery with installed water/air exchanger. 1—heat recovery heat exchanger pipe-in-pipe; 2—supply of treated air to the building; 3—air exhaust from the building; 4—chamber of the distributor and collector of ventilation air, 5—liquid heat exchanger of water-air type; 6—the heat transfer medium’s supply and return; 7—supply pipes of treated air on the first floor.  
The new scientific results and the novelty of the research can be summarized as follows:  
* In Section 4.1, we analyzed the innovative operation method of combined building-energy systems of buildings;
* In Section 4.2, we defined and proposed the innovative operation method and developed the wiring diagram of combined building-energy systems of buildings;
* In Section 4.3, we presented the block diagram of thermal barrier temperature control in active thermal protection mode for heating and cooling, the principle of temperature control in heat/cold recovery mode, and the retrofit of the ventilation system with tube-in-tube heat recovery;
* In Section 4.4, for the pipe-in-pipe heat recovery ventilation system, we proposed the installation of a water/air heat exchanger in the air distributor and air collector chamber, which will heat or cool the supply air to the rooms of the building as required. Alternatively, this heat exchanger can be installed in the supply duct of the air handling unit.
# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 6 Results and Discussion
### Comparison of Segmented Speech Using Varying LM  
Performance comparison of manually and automatically segmented test speeches by using different LMs having a varied size and using the open and closed vocabulary technique was made. Figure 3a,b show mainly the effect of the changing LM using open and closed vocabularies on the manually and automatically segmented speeches. While increasing the size of training text for the development of the LM from 5 k to 10.6 k sentences, the performance of ASR is also increased. When they are compared in terms of their WER, the systems achieved closer results on the Mseg and A-I test sets. However, the performance of the systems on Mseg and A-II was different. In addition, using closed vocabulary in the LM showed a major decrease in the WER when it is compared to using an open vocabulary.  
The language models' quality was evaluated first in terms of their perplexity [14]. The perplexity for LMs obtained using 5000, 10,000 and 10,602 sentences were 6233.63, 4217.63, and 1486.44, respectively. In addition to the perplexity of the LM's, we also computed the OOV rate and vocabulary size for the training text of 5000, 10,000, and 10,600 sentences. Table 3 shows the OOV rates of the LM that was trained with different training (5000, 10,000, 10,600) sentences. The result shows that as the size of the vocabulary increases, the OOV  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Mseg** & **A-I** & **A-II** \\ \hline \multirow{4}{*}{\(1\)} & & **A-  
rate decreases and the better the LM is. Therefore, the size of the vocabulary increases the performance of ASR. This is because ASR/LVCSR works with a phonetic dictionary. If these words are not in the lexicon, then the word is considered as out-of-vocabulary (OOV), which is one of the main sources of error in automatic speech recognition. Therefore, as the OOV rate increases WER also increases.
## 4 Results and Discussions
### CO\({}_{2}\) Condensation and Shock Wave Interaction  
In Figures 5h,i,k,l, and 6c,d one can observe the appearance of condensation at the same time as the formation of the throat shock. The condensation progresses along the entire diverging section of the diverging part with the shock wave. Indeed, condensation appears due to the correlation of the drop in pressure and temperature slightly below the \"dew line\". The dew line is the right branch of the phase envelope that marks the transition boundary from vapor to liquid (i.e., appearance or disappearance of condensation), as depicted in Figure 1.  
After the shock has left the nozzle, the diverging section is completely filled with a cloud of liquid. The visual confirmation of the condensation is also shown in Figure 7, where the upper part of the image is taken from the experiments of [PERSON] et al. [11] and the lower part is from the numerical simulation of case 1 when the fully supersonic regime is reached. This figure is from the simulation with 95% CO\({}_{2}\) and 5% N\({}_{2}\). Two main observations in this figure are that (1) the starting point of the condensation is at the throat, which is very well reproduced in the simulations, and (2) the condensation front at the throat is somewhat diffused in a very similar way as it appears in the experimental image. Condensation is present throughout the second half of the nozzle. However, a layer with lower condensation can be seen near the walls in Figure 7, in both the numerical and experimental images. This wall boundary layer seems to be due to the higher specified wall temperature, at least in the simulations.
# Accuracy, Time, and Comfort of Different Intraoral Scanners: An In Vivo Comparison Study
## 2 Materials and Methods
### Intraoral Scanner Specifications  
The 3 tested IOSs were TRIOS Color(r) (T), iTero Element 5D(r) (E), and iTero Lumina(r) (Figure 1).  
The intraoral scanner TRIOS Color(r) (3 Shape, Copenhagen, Denmark) provides ultrafast colored imaging based on confocal microscopy principles: it captures single pictures, stitched together into a three-dimensional network. This scanner does not require any powder application and has a scanning depth of 17 mm [15].  
The iTero Element 5D(r) (Align Technology, Santa Clara, CA, USA) is a powder-free intraoral scanner that utilizes parallel confocal imaging technology and point-and-stitch reconstruction to make accurate digital imprints. The scanning depth is about 15 mm [16].  
The iTero Lumina(r) (Align Technology, Santa Clara, CA, USA) uses an innovative technology called \"multi-direct capture\" rather than using confocal imaging. The scanning depth has been increased from 15 mm to 25 mm. It also does not require powder application [17].
## 5 Discussion  
### Technology Usage Trends in Reviewed Studies  
The key findings of the studies framed in Tables 1 and 2 were derived from 58 papers from the review methodology. It can be observed from our analysis of the screening results that the GIS appeared in the highest frequency of papers, amounting to 94.8% of papers employing this technology. In contrast, Landsat exhibited applications in 91% of the studies. This finding can be characterized as the most underlined basic need for technologies within remote sensing and geographical information systems. While the GIS has been broadly used to manage, process, and visualize data in a spatial format, Landsat delivers consistent, long-term satellite imagery crucial to environmental and land-cover changes over long periods.  
Almost one-third of the review papers employed Sentinel-2 (36.21%) and Ground Truth Data (36.21%). This reliance on Ground Truth Data shows that most studies rely on legwork and empirical observations for model validations or adjust satellite-derived data to ensure the accuracy of their remotely sensed outcomes.  
Moreover, Sentinel-1, a radar-based satellite, was used at a low rate of 5.17%, which is indicative of more specialized applications. Sentinel-1 is mainly utilized when other optical satellites, such as Sentinel-2 or Landsat, cannot view the Earth, for instance, during overcast conditions or at night. The limited usage of Sentinel-1 reveals that these conditions have not prevailed in this dataset, and optical data from other satellites can serve most of the requirements of different research studies.  
The modest percentage of 15.52% reveals the emerging but still developing trend in remote sensing applications using AI/Machine Learning/Neural Networks. The data analysis comprised sophisticated algorithms that were intricate but not pervasive in implementation in the dataset. This indicates a transition phase in which conventional remote sensing methods remain dominant while AI tools are becoming increasingly popular.  
However, the least applied was with UAV/Drone technology accounting for less than 2% of the instances, which may be due to the relatively high costs, limited spatial coverage, or logistical challenges in flying drones compared to satellite-based systems that have broader coverage and may provide more reliable data over time.
## 2 Method
### Method for Generating the Probability Distribution of Design Heating Loads  
Then, it is necessary to obtain the probability distribution of the design load in a rural residential building under the influence of the indoor temperature uncertainty. Based on the stochastic model of indoor temperature proposed in Section 2.1, the stochastic simulation of design heating load can be carried out for each room in a rural residential building, and then the probability distribution of the design heating load for the building can be determined, and the specific technical route is shown in Figure 1. The method is divided into the following four steps:  
1. The random indoor temperature scenarios for each room were generated by Monte Carlo sampling based on the stochastic model in Section 2.1, and the outdoor design conditions are a design day on which the outdoor temperature is fixed within 24 h and this fixed value is equal to the design dry-bulb temperature provided in the standard. Based on these, the 24 h loads of different rooms were simulated with the help of EnergyPlus software [26]. The results were as follows: \[Q_{1,t},\ Q_{2,t},\ \cdots,\ Q_{n,t}\] (5) where \(Q_{n,t}\) denotes the heating load of the \(n\)th room in a rural residential building at moment \(t\), \(t=1,\ 2,\ \cdots,\ 24\).
2. The hourly load results for each room were superimposed to obtain the hourly load results for the building, and its daily peak value was used as the design load sample. The specific formula of the design load sample was as follows: \[Q_{t}=Q_{1,t}+Q_{2,t}+\cdots+Q_{n,t}\] (6)\[Q_{d}=\text{Max}\{Q_{t}|\text{t}=1,2,\cdots,24\} \tag{7}\]  
where \(Q_{t}\) denotes the heating load of the building at moment \(t\), and \(Q_{d}\) denotes the design load sample.
3. The rate of change in the mean value of the design load samples \(S(N)\) was taken as an indicator parameter to determine whether the probabilistic characteristics of the building heating load were stable or not. The minimum number of simulations \(N_{min}\) was determined by using \(S(N)<0.2\%\) as a criterion for simulation convergence [22]. S(N) was calculated by the following equation: \[S(N)=\frac{\left|Q_{d,mean}(N+\Delta N)-Q_{d,mean}(N)\right|}{Q_{d,mean}(N)}\] (8) where \(Q_{d,mean}(N)\) denotes the mean value of all design load samples in the previous \(N\) simulations; \(N\) denotes the number of simulations; and \(S(N)\) denotes the rate of change in the mean value of the design load samples.
4. The probability distribution of all design load samples at the minimum number of simulations was counted to characterize the uncertainty of design heating loads in the rural residential building.
## 2 Materials and Methods
### Experimental Set-Up  
Ten smallholder farms were selected for the experiment, and at each farm, four tillage treatments, namely planting basins (Basins), tine ripping (Ripper), single (CP), and double (DP) conventional ploughing, were established. Each farm had four experimental plots, and each tillage treatment was randomly assigned to a plot. Each tillage plot measured 30 m \(\times\) 10 m, and the plots established in the 2006/07 season were maintained for use in the 2007/08 growing season. Planting basins were dug at 0.9 m \(\times\) 0.6 m spacing using a hand hoe, and each basin measured 0.15 m (length) \(\times\) 0.15 m (width) \(\times\) 0.15 m (depth). Ripped furrows were opened at 0.9 m inter-row spacing using a ZimPlow ripper tine attached to the beam of an animal-drawn mouldboard plough. The inter-row spacing used in the basin and ripping treatments followed recommendations for semi-arid areas when maize is grown on smallholder farms [38]. The first conventional ploughing for the DP treatment was carried out in October each year. The second ploughing was performed at the same time as the CP treatment after receiving at least 20-30 mm of rainfall in November-December each year. In all experimental plots, crop residues were removed soon after harvesting and fed to livestock during the dry season as practiced by the farmers in the study communities.  
Surface runoff was measured at four farms during the 2006/07 and 2007/08 growing seasons. Runoff plots measuring 10 m \(\times\) 10 m were established in each tillage treatment (Supplementary Figure S1). Each farm had four runoff plots corresponding to the four tillage treatments. The boundaries of the runoff plots were constructed using a heat-resistant black polythene plastic that prevented water flow from other parts of the field onto the runoff plot. At the downslope side of each runoff plot, a triangular surface of dimensions 10 m \(\times\) 1.7 m was used to receive runoff water from the plot and lead it into a 200 L drum through a polythene plastic gutter. Within each runoff plot, maize was planted at 0.9 m \(\times\) 0.3 m, which is the recommended spacing for semi-arid environments, giving a plant population of 37,000 plants ha\({}^{-1}\).
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
###### Abstract  
In this paper, the potential of visuo-haptic simulators to help engineering students to understand the nature of electric forces between different electric charge distributions is addressed. Three visuo-haptic simulators were designed to perceive the attractive-repulsive behavior as well as the dependence on distance of electrical forces for: (a) point charge, (b) line charge, and (c) plane charge. Design elements were incorporated to improve the 3D perception of the simulators. A sample of N = 111 engineering students practiced with the simulators: 87 enrolled in an Electricity and Magnetism course and 24 enrolled in a more advanced Electromagnetic Fields course. Pre-test and Post-test were applied before and after working with the simulators and average learning gains were obtained. \(t\)-tests were performed to determine the statistical significance of the results. Significant learning gains were obtained for the comprehension of the force dependence in the case of line charge and plane charge, but not for the point charge, due to the fact that most students started with very high Pre-test scores in this last case. These results suggest that the use of visuo-haptic simulators may help students to better identify the dependence of electric forces on distance. It was also observed that the potential effect of improving the recognition of electric interactions was higher among students with lower previous familiarity with these topics, as compared to more advanced students. Through exit surveys, it was found that the students liked very much the haptic activity and that it sparked their interest in learning new physical concepts.  
Keywords:visuo-haptic simulation; higher education; education innovation; learning gains; engineering education; physics and engineering +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Overall Detection Performance Evaluation
Additionally, the variance of the detection range at the 90\({}^{\circ}\) azimuth angle is generally more minor than at the 270\({}^{\circ}\) azimuth angle, indicating more stable data at the 90\({}^{\circ}\) azimuth. In DBS mode, the detection efficiency remains close to 100% from 0 to approximately 1 km, after which it begins to decrease linearly. Beyond 3 km, the decline slows, with distances corresponding to detection efficiencies of 80% and 40% at 1.8 km and 2.7 km, respectively. A comparison reveals that the rate of detection efficiency attenuation in DBS mode is more rapid than in PPI mode, reflecting the more pronounced vertical inhomo \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Month** & **Aug** & **Sep** & **Oct** & **Nov** & **Dec** & **Jan** & **Feb** & **Mar** & **Apr** \\ \hline Variance at 90\({}^{\circ}\) & 0.1 & 0.3 & 0.2 & 0.2 & 0.1 & 0.1 & 0.3 & 0.3 & 1.1 \\ Variance at 270\({}^{\circ}\) & 0.3 & 0.5 & 0.2 & 0.1 & 0.1 & 0.1 & 0.2 & 0.5 & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monthly variance of wind direction at 90\({}^{\circ}\) and 270\({}^{\circ}\). Figure 2: Detection efficiency curves for PPI scanning from August 2023 to April 2024. The PPI mode at 4\({}^{\circ}\) and 6\({}^{\circ}\) elevation angles and DBS scanning are represented by red, blue, and black lines, respectively. The 270\({}^{\circ}\) and 90\({}^{\circ}\) azimuth angles in PPI mode are indicated by solid and dashed lines, respectively. geneity of meteorological factors, particularly aerosol particles. The detection efficiency changes quickly when the laser beam passes vertically through the boundary layer. This is consistent with the conclusion that PPI mode, with its lower elevation angle, exhibits higher detection efficiency.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 2 Related Work
#### 3.1.4 Node Moving Distance  
The moving distance of the node means the length of the node moves from moment \(t\) to moment \(t+\Delta t\), which can be denoted by \(\Delta D_{s}\). Since the movement of the node in a short period of time approximates a uniform motion, so the distance can be expressed by the displacement formula of the physics:  
\[\Delta D_{s}=D_{s_{t+\Delta t}}-D_{s_{t}}=\left(\frac{v_{t+\Delta t}+v_{t}}{2 }\right)\times\Delta t \tag{17}\]  
where \(D_{s_{t}}\) represents the total moving distance of node in \(t\) time. The \(D_{s_{t+\Delta t}}\) represents the total moving distance of node in \(t+\Delta t\) time.  
\[D_{s_{t+\Delta t}}=D_{s_{t}}+\frac{1}{4}\times\Big{[}\theta(v_{t+\Delta t}+v_{t })+(1-\theta)(\eta_{t+\Delta t}+\eta_{t})+\sqrt{1-\theta^{2}}(\zeta_{t+\Delta t }+\zeta_{t})\Big{]}\Delta t^{2} \tag{18}\]