esa-sceva/satcom-chunk-collection huggingface dataset:
# BEST--Building Energy-Saving Tool for Sustainable Residential Buildings
## 2 Materials and Methods
The minimum required data and the related simplifying assumptions are as follows: (Table 3):  
The data collection process follows a sequential approach to ensure the completeness and consistency of the information, providing a solid foundation for subsequent simulation phases. The tool BEST guides designers through a series of integrated steps:  
1. Case study identification: Based on a structured database, the tool facilitates the identification of the building's current condition by taking into account its construction period, structural typology, and climatic zone. The tool includes 30 types of vertical perimeter walls and 30 roof configurations representative of the national residential building stock; \begin{table}
\begin{tabular}{c c} \hline \hline
**Data** & **Simplifying Assumptions** \\ \hline Location & Climatic data derived from UNI 10349 standard [38] \\  & Setpoint temperature data, system \\  & operation schedules, occupancy levels, air \\  & \\ Building use type & flow rates, domestic hot water demand, and \\  & internal loads derived from UNI TS 11300-1 \\  & and UNI 10339 [39] standards \\ Thermal envelope characteristics & Only one type of opaque or transparent \\  & surface per orientation is considered \\ Type of thermal generators for & Limited selection options with standardized \\ heating/cooling/DHW and renewable & efficiencies as defined by \\  & UNI TS 11300 standards \\ Installation period of building systems & Efficiency values for subsystems are selected \\  & based on assumed installation periods \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Minimum required data and associated simplifying assumptions adopted in the first module of the BEST tool for preliminary energy analysis. 2. Preliminary energy analysis of opaque envelopes: It provides an overview of potential retrofitting opportunities in terms of energy savings and economic impact, addressing both heating and cooling energy demands;
3. Database of optimized solutions: Leveraging the results of prior Work Package activities, the tool suggests pre-calculated interventions for perimeter walls and for roof (Predominantly prefabricated insulation solutions (5 for the perimeter walls and 21 for the roofs) selected based on comparison parameters such as performance indicators, environmental impact, applicability, and cost, and validated through energy simulations conducted using certified BIM energy software to determine the required insulation material thicknesses);
4. Post-intervention simulation: BEST generates an energy model to evaluate the technical and economic performance of the proposed solutions. It also offers downloadable detailed performance sheets;
5. Prioritization of interventions: The tool ranks the available solutions to identify those with the best cost-benefit ratios.
## 5 Discussion
to classify isolated T2 maps for the progression to symptomatic OA with 75% accuracy [13]. Progression to clinical OA was defined by the development of symptoms as quantified by the WOMAC questionnaire 3 years after baseline evaluation. MRI images and PCA were employed by [PERSON] et al. to predict the progression of KOA using four ML techniques [33]. For KL grade prediction, the best performance was achieved by ANN with AUC = 0.761 and F-measure = 0.714. An MRI-based ML methodology has been also proposed by [PERSON] et al. to prognose tibial cartilage loss via quantification of tibia trabecular bone where a odds ratio of 3.9 (95% confidence interval: 2.4-6.5)was achieved [15]. X-ray combined with pain scores have been utilized by [PERSON] et al. to predict the progression of joint space narrowing (AUC = 0.86 using data from two visits spanning a year) and pain (AUC = 0.95 using data from a single visit) [6]. Similarly, another two studies ([PERSON] et al. [10] and [PERSON] et al. [11]) made use of Xray images along with clinical data to predict KOA progression using either CNN or ML approaches achieving less accurate results. The current paper is the only one employing exclusively clinical non-imaging data and also contributes to the identification of important risk factors from a big pool of available features. The proposed methodology achieved comparable results with studies predicting KL grades progression demonstrating its uniqueness in facilitating prognosis of KOA progression with a less complicated ML methodology (without the need of big imaging data and image-based deep learning networks). Among the limitations of the current study is the relatively large number of features (55) that were finally selected as possible predictors of KOA. The selected features come from almost all feature categories highlighting the necessity of adopting a rigorous data collection process in order to formulate the input feature vector that is needed for the ML training. Moreover, the ML models employed are opaque (black boxes) and therefore they are insufficient to provide explanations on the decisions (inability to explain how a certain output has been drawn). To overcome the aforementioned challenges, it is important for AI developers to build transparency into their algorithms and/or enhance the explainability of existing ML or DL networks.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
## 4 Performance Evaluation  
In this section, we evaluated the performances of the proposed scheme in the NS-3 simulator [25]. MU transmission was not supported by NS-3 when this paper was written. We extended the Wi-Fi modules of NS-3 to support the OFDMA-based MU transmission on the basis of the implementation in [26] and the description in the latest TGax documents.  
An IEEE802.11 ax WLAN was established for evaluation in the following experiments, as shown in Figure 11. There were uplink or downlink user datagram protocol (UDP) streams transmitted between all stations and the Access Point (AP). Consequently, only the transmission path in the WLAN was of concern here.  
In the established WLAN, the bandwidth was 80 MHz, the PHY rate of the data frame was the Modulation and Coding Scheme (MCS) 9, the PHY rate of the control frame was MCS 0, and the number of spatial streams was 4. The packet lifetime in the WLAN was set to 500 ms, and the buffer size was adequate to accommodate the arrived packets. In the OFDMA scheduler, the size of RU is 106-tones, and the number of users at each round is 8. All the RUs are scheduled by AP in the UL MU transmission without adopting the optional UORA. Other settings of the MAC layer are listed in Table 1. The set of BER evaluated in the experiments is \(\{10^{-6},10^{-5},2\times 10^{-5},3\times 10^{-5},4\times 10^{-5},5\times 10^{-5},6 \times 10^{-5},7\times 10^{-5}\}\).  
The evaluation was conducted by comparing the proposed BP-ARQ scheme with the ASR-ARQ [5; 6; 7; 8] and the BAW-ARQ [3; 9] in the existing literature. The error bar in the following figures indicated the standard deviation of the simulation results.  
The performance was compared via throughput, end-to-end delay, and packet loss rate. The throughput indicates the amount of data received by the UDP layer of the recipient per second and is calculated by  
\[Throughput=\frac{num_{recv}\times len_{payload}\times 8}{T_{sim}}, \tag{20}\]  
where \(num_{recv}\) is the number of received packets and \(T_{sim}\) represents the simulation time. The saturated throughput is measured under the saturated condition and the saturated condition is performed by offering excessive load. The end-to-end delay, \(D_{e2e}\), is defined as the time interval between when a packet is generated and when it reaches the UDP layer of the recipient and is given as  
\[D_{e2e}=t_{recv}-t_{generate}, \tag{21}\]  
where \(t_{generate}\) is the moment when a packet is generated and \(t_{recv}\) is the instant of time when it reaches the UDP layer of the recipient. The packet loss rate (PLR) is the ratio between the number of packets that do not reach the destination and the number of offered packets and is derived as  
\[PLR=\frac{num_{lost}}{num_{lost}+num_{recv}}\times 100\%, \tag{22}\]  
where \(num_{lost}\) is the number of packets that do not reach the destination and is counted according to the UDP serial numbers of the received packets.
## 2 Data Details
### Wind Generation vs. Active Wind Applications  
To examine the importance of tax incentives on renewable resource electricity generation in ERCOT, one could pose the question: How much does each active wind application add to wind generation during different hours of the day? An answer to this question is possible by running a simple linear regression with the natural logarithm (LN) of wind generation at each hour serving as the dependent variable and the number of wind applications acting as the independent variable. However, such a regression analysis must be treated with some caution since the data on active wind applications is reported as a total in each year, whereas wind generation is obtained every 15 min.  
The answer to the above question via a simple linear regression analysis may be a rough approximation; nonetheless, we ran a simple regression for Hours 3 AM, 11 AM, and 4 PM. Note that the following regression model does not change by region, since the number of wind applications and the wind generation data are for the ERCOT as a whole, rather than by region. For these reasons, and the others detailed previously, the  
Figure 6: Scatter plot of gross tax savings (USD) vs. wind and solar applications.  
WSapp18 variable is preferable to the actual count of wind and solar applications by region; WSapp18 will be used for the pricing models in Section 3.  
With WGen denoting wind generation and Windapps denoting the number of active wind applications, the simple linear model for providing an answer to the above question is given through:  
\[LN(WGen)=b_{0}+b_{1}Windapps\]  
Regardless of region, for Hours 3 AM, 11 AM, and 4 PM, the values of slope b1 are estimated to be 0.0436, 0.051, and 0.055, respectively, with all \(p\)-values less than 0.0001. That is, each additional active wind application increases wind generation by 4.36%, 5.1%, and 5.5%, respectively, and the results are statistically significant.  
Saving the rigorous pricing analysis for later, this preliminary conclusion offers some directional evidence to the main contention of this paper: tax incentives that increase the number of active wind applications statewide are associated with an increase in wind generation, which, in turn, could lead to a decrease in the wholesale price per MWh. Stated differently, the MOE from wind generation should contribute to more renewable energy over time, due to an increase in tax-incentivized wind generation.  
In principle, one could execute a similar regression model for solar generation, as well. However, (a) the solar generation data are non-stationary; (b) the number of active solar applications is negligible; (c) the total contribution to the MOE from solar power is also negligible, based on the analysis later described in Section 3. For these reasons, the solar applications versus solar generation linear regression analysis, similar to the one above, is omitted.
## 2 Background
### SHARAD Data Products  
#### 2.2.1 Introduction to SHARAD Sensors  
MARSIS, equipped in Mars Express [19], is the first spaceborne ground penetration RADAR (GPR) to map the upper portions of the crust of a solid planet or satellite with an extremely low-frequency electromagnetic wave. Although the demand to investigate the subsurface liquid and solid water in the footprint of Martian geological evaluation has been actively proposed [45], the observation priority of MARSIS was given on the Martian polar deposits, as they may play a crucial role in atmospheric circulation and evolution [46; 47]. In contrast, the SHARAD instrument of the Mars Reconnaissance Orbiter, which was developed through collaboration with the Italian Space Agency and the Jet Propulsion Laboratory, was launched in 2005 and characterized by better vertical resolution but lower penetration capability. Due to such system specifications, the complex substructures of the Martian crust, such as polar deposits, pedestal craters [48], and LDA [28], have been successfully observed employing SHARAD. To understand the system characteristics and operational concepts of SHARAD, see Table 1 and Figure 2, respectively. Since this study aims to extract detailed information on subsurface layering utilizing ML algorithms, SHARAD became the prime data source rather than MARSIS. The potential application to MARSIS data with developed ML algorithms will be explored in a future study.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**System Characteristics** & **SHARAD Specification** \\ \hline Carrier frequency & 20 MHz \\ Vertical resolution (if surface permittivity is 4.0) & 7.5 m * \\ Transmitter power & 10 W \\ Pulse length & 85 \(\mu\)s \\ Pulse repetition frequency & 700/350 Hz \\ Horizontal resolution (along track x cross track) & 0.3–1 km \(\times\) 3–6 km \\ Estimated maximum penetration depth in the \\ scenario of porous (30\%) materials and underneath & 1500 m ** \\ ice filled pore spaces below [20] & \\ \hline \hline \end{tabular}  
* This is ten times higher compared to MARSIS. ** Estimated penetration depth of MARSIS on polar deposit is 3000 m [19].  
\end{table}
Table 1: System characteristics of the SHARAD instrument onboard the MRO satellite.  
Figure 2: Operational concept of SHARAD. Ground resolution is determined from the range-to-surface distance in cross-track way and the Doppler shift of the returned signal along the track.
###### Abstract  
Background: Alternative metrics have been proposed to estimate the impact of research on the academic and social environment. The objective of the current study was to analyze the longitudinal behavior of Altmetric resources related to online engagement in orthodontic research and to explore their correlation with citations over time. Methods: The Dimensions App was searched in December 2019 and December 2021 for published items belonging to orthodontic journals listed in the Journal Citation Reports (JCR) from 2014 to 2018. Items with an AAS (Altmetric Attention Score) equal to or greater than one were selected and screened for data related to authorship and publication. The breakdown of the different Altmetric resources was collected in 2019 and updated in 2021. Citations were retrieved from Web of Science (WOS) and Scopus at the same time interval. Results: The best performing journals were Progress in Orthodontics and the European Journal of Orthodontics at both time points, with a mean AAS per published item of 1.74 and 1.63, respectively, in 2021. The topics with the highest online engagement display a change over time, while the study design remained randomized clinical trials (RCTs) in both observations. Tweets, Facebook posts, and blogs showed a very slight increase over time, while News Outlets, patent data, and policy sources longitudinally showed a significant increase. No or poor correlation was found between altmetrics and citation except for Mendeley reader count. Conclusions: Tweets, Facebook, and Blog mentions can be considered attention trackers. News Outlets, patents, and policy sources are time dependent data. Mendeley reader count, can help to identify the article with a future citation potential.  
almetrics; bibliometric; citations count; Mendeley; social media +
Footnote †: journal: _Sci._
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Light Sensor Data Mining  
As a crucial component of smartphones, light sensors enhance user experience and can also be utilized as part of scene-aware features [26; 27]. Setting aside the effects of light sensor occlusion, we will analyze the relationship between light intensity and scene perception.  
In our experiment, we placed sensors at indoor and outdoor nodes for 24 h and repeated the process 10 times to obtain average values, as illustrated in Figure 9a. The figure shows that outdoor light intensity varies significantly, peaking during the day at up to 100,000 Lux, while at night, it drops to only a few hundred Lux. This fluctuation is quite pronounced. During sunrise and sunset, the difference between indoor and outdoor light intensity becomes less distinct, making it challenging to differentiate between the two environments. However, at other times of the day, the distinction between indoor and outdoor light intensity is much clearer.
## 6 MOFT Tailored for IoT  
Since the adjacent secret for the device is generated by the admin and shared with the centralized server, the admin can generate all the key materials subsequently needed to obtain the group key of all groups in the application. To reduce the overhead on the centralized key management server, the admin device can act as an edge device and take part in the key distribution part. The devices on the left side of the root node can obtain group keys from the centralized server, and the right-side devices can obtain their keys from the admin or vice versa. This will further reduce the overhead on the centralized server. To sum up, even though the group exhibits regular rekeying with membership changes,  
Figure 12: Rekeying frequency comparison of proposed MOFT with OFT [19], Ku & Chen [37], Xu [38] and ROFT [39].  
the suggested MOFT minimizes the communication cost and thus reduces the network traffic to a significant extent. The proposed MOFT also offers lower storage overhead on group member devices. The suggested solution additionally provides collusion resistance using basic cryptographic primitives such as a one-way function and mixing function. The computation overhead is lower than the collusion-deprived enhancements of OFT. All these features make MOFT secure and adaptable to the IoT environment.  
The proposed work using the SDN controller, switches, and routers are simulated using the Mininet [49] simulator installed in an oracle VM. The topology is created with 30 hosts, an SDN controller, and 2 switches. The flow rule is set using python script. The hosts are grouped into three groups with each group consisting of ten hosts. The communication link is set only for the hosts of the same group. This restricts the communication between hosts of different groups.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 2 DTNbox
### Challenges and Countermeasures  
DTNbox, like all synchronization programs, must keep track of the state of synchronizations. To this end it uses an external relational database, based on SQLite. Every synchronization necessarily involves an exchange of information between synchronized nodes, consisting of file updates, commands (e.g., to establish or close a synchronization) and command acknowledgments. The real difference when dealing with a challenged network is that this exchange is much more difficult than usual, because of channel impairments, such as long delays and link intermittency. In challenged networks the time elapsed between sending of a command (e.g., a file update) and corresponding acknowledgment reception can be very long, e.g., in the order of minutes or even hours, instead of a few tens of ms as in Internet. Therefore, it is necessary to reduce DTNbox datitiness to a minimum and to use the DTN architecture and the Bundle Protocol. To this end, DTNbox aggregates commands and file updates in one tar file at regular intervals (in our test every 5 s), and then sends it to the synchronized node in one bundle [8]. This way the overhead is reduced, as commands, acknowledgments and file updates can be multiplexed in one bundle.
## 5 Anti-Rutting Analysis of HMAC Pavement under Continuous Temperature Variation
### Comparison of Putting Resistance between HMAC and Conventional Asphalt Pavement
#### 5.3.2 Comparison of Permanent Pavement Deformation under Heavy Traffic  
Heavy traffic is a main cause for permanent deformation and the rutting distress of asphalt pavement. In order to further study the rutting resistance performance of HMAC pavement, a series of vehicle loads were applied to analyze the deformation of the two types of pavement. The result of this test is shown in Figure 15.  
From Figure 15, it can be seen that as the tire contact pressure increased from 0.7 MPa to 1.5 MPa, the maximum relative deformation in both HMAC pavement and conventional asphalt pavement significantly increased; however, the maximum relative deformation of conventional asphalt pavement increased faster. When the tire contact pressure was 0.7 MPa, the maximum deformation of the conventional asphalt pavement was 7.13 mm higher than that of HMAC, and this value increased to 11.4 mm when the tire contact pressure was 1.5 MPa. Therefore, a heavy load is more harmful to the conventional pavement structure; HMAC pavement can effectively resist road deformation caused by heavy-duty loading and hot weather.  
Figure 14: Comparison of the relative maximum deformation of two types of pavement at different depths.
# Seismic Coherent Noise Removal of Source Array in the NSST Domain
## 2 Theory and Method
### Non-Subsampled Shearlet Transform  
Because Shearlet transform does not have the translational invariance, it is easy to generate the pseudo-Gibbs phenomenon near singular points when it is applied to image fusing and denoising. The NSST with the translational invariance can solve this problem. The discretization process of the NSST is composed of the multi-scale subdivision and the direction localization:  
1. The multi-scale subdivision of the NSST is achieved by the non-subsampled pyramid transform (NSP). The image is decomposed by the \(j\)-level of the non-subsampling filters to obtain \(j+1\) sub-bands of the same size as the original image, including a low-frequency image and \(j\) high-frequency images;
2. The direction localization of the NSST is achieved by the improved shearing filter (SF). The standard shearing filter is realized by the translation operation of the window function in the pseudo-polarization grid; the subsampling operation is required during the execution, so it does not have the translation invariance. However, the NSST maps the standard shearing filter from the pseudo-polarization grid system to the Cartesian coordinate system and then directly completes its operation through the inverse Fourier transform and the two-dimensional convolution. Therefore, NSST can avoid the subsampling operation and meet the translation invariance. Figure 2 shows the multi-scale and multi-direction decomposition process of the NSST.
# High-Power, High-Efficiency GaSb-Based Laser with Compositionally Linearly Graded AlGaAsSb Layer
###### Abstract  
We propose a novel graded AlGaAsSb layer growth method to achieve a super-linear interface by precisely controlling the cell temperature and valve position. Atomically smooth surface and lattice-matched epitaxy was confirmed by AFM and the HRXRD characterization of the graded AlGaAsSb layer sample. With the inserted graded layer between the cladding and waveguide layers, high-power, high-efficiency GaSb-based laser emitters and laser bars were confirmed. The linearly graded interface layer smooths the potential barrier peak between the cladding and waveguide layers, which resulted in a low turn-on voltage of 0.65 V and an ultra-low series resistance of 0.144 \(\Omega\). A maximum continuous-wave output power of 1.8 W was obtained with a high power conversion efficiency of 28% at 1.1 A and 12% at 8 A. A facet-coated laser bar was also fabricated with a record-high CW output power of 18 W. A high internal quantum efficiency of 83 was maintained at 40 \({}^{\circ}\)C, implying improved carrier injection efficiency, which benefits from the built-in electric field of the composition-graded AlGaAsSb layer.  
GaSb-based diode lasers; high power; molecular beam epitaxy +
Footnote †: journal:  
_Applied_  
_sciences_