esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Proposed Decision-Support Tool
#### 2.2.2 Multi-Criteria Decision Method and Weight Factors
Initially, vector W\({}_{\text{s}}\) is defined as the product of A and W, and subsequently, the consistency vector C\({}_{\text{v}}\) is derived by dividing each cell value of the matrix multiplication result vector by the corresponding priority vector cell as follows:  
\[\{W_{\text{s}}\}=[A]\{W\} \tag{4}\]  
\[\{C_{\text{v}}\}=\{W_{\text{s}}\}\bigg{\{}\frac{1}{W}\bigg{\}} \tag{5}\]  
The eigenvalue of the algebraic system \(\lambda\) is obtained from the average of the elements of the consistency vector C\({}_{\text{v}}\), and it is utilized to calculate the value of CI as follows:  
\[CI=\frac{\lambda-n}{n-1} \tag{6}\]  
Moreover, the CR is calculated as:  
\[CR=\frac{CI}{RI} \tag{7}\]  
where RI (i.e., random index) denotes the CR of a randomly generated matrix, and its value depends on the number of criteria and can be derived from related publications. Finally, when the resulting CR is less than 0.1, the pairwise comparison matrix is consistent; consequently, the AHP methodology is successfully applied without unreasonable pair combinations. More details regarding the theoretical and practical aspects of AHP were provided by [PERSON] et al. [21]. The high functionality and advanced capabilities of GIS allow users to employ Python programming language to create customized scripts and tools that can automate and optimize the workflow in GIS applications, which have been utilized herein. Particularly, a Python script was programmed in order to successfully implement the matrices and relationships of AHP. This script has been integrated into the developed GIS model through the Python site package for analysis and data management, ArcPy.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 4 Services for Final Users
### Market  
All the services aimed at enabling the user to manage a contract and save money are grouped in the Market category. Thanks to real-time metering data, ToU or even RTP can be effective and prepaid contracts can be activated. Retailers could start to offer customized contracts based on the collected measurements and send hourly price signals for helping the customer to identify the cheaper moments. It is worth noting that price signals do not flow through the SM and must be received through a separate channel.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Ground Mobile Sink-Based Frameworks
#### 2.3.2 Integration of Geographic and Hierarchical Routing  
[PERSON] et al. [62] proposed a hierarchical and geographic-based WSN data-collection mechanism IoJHR, with the help of a mobile sink. The main objective of this research was to establish energy-efficient data collection, resulting in an increased WSN lifetime. The network model used in this approach utilizes a virtual grid approach. Each virtual grid consists of a CH and four other sensor nodes. The CH is responsible for disseminating the data to the sink node. The energy level is used to elect the CH from a virtual grid. The number of virtual grids could be determined as follows:  
\[N_{virtualgrids}=\frac{N}{4} \tag{3}\]  
where the number of virtual grids is denoted by \(N_{virtualgrids}\) and \(N\) represents the number of sensor nodes. At the beginning of the data-collection operation, the sink node broadcasts its position in the ROI. During the rest of the operation, all the CHs know the exact position of the sink node as the trajectory of the sink is predefined. In fact, the sink node does not need to send its positional information twice. Figure 5 shows the working mechanism of the IoJHR protocol.  
The control system operates a data-collection mission based on fixed-time scheduling. As all the sensor nodes are aware of the scheduling, the nodes can easily predict the next arrival time of the sink node. To collect data, the mobile sink moves back and forth, based on predefined time scheduling. When a CH finds the sink closer to its position, it simply transfers its data to the sink. This mechanism supports data collection via multiple sinks. However, to enable a new sink in action, the predefined path and collection time should be conveyed to the sensors beforehand. In the case of multiple sink scenarios, CHs are divided into different sinks for data offloading.  
Advantages: This procedure applies techniques to support multiple sink nodes. As a result, the latency for data collection decreases dramatically.  
Limitations: It is assumed that every virtual head (VH) has exactly four nodes. This assumption is not practical for random or unplanned deployment. Even in the case of planned deployment, depending on the spatial necessity for sensing, four nodes may not be in close proximity to each other. In such a situation, if a cluster is about to form  
Figure 5: Working mechanism of the IoJHR protocol.  
consisting of four sensor nodes, energy consumption will increase due to long-distance data transmission. There are no inter-cluster communication mechanisms mentioned in this study.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 6 Conclusions  
This paper presented the RASST, a novel framework for oriented object detection in aerial imagery. By incorporating rotationally aware patch embeddings, adaptive rotational convolutions, and a MSSF module, RASST effectively detects objects at arbitrary orientations and varying scales. Its PGL framework, leveraging RAW and GC losses, enhances the utilization of unlabeled data, achieving superior performance in semi-supervised settings. Evaluations on the DOTA-v1.5 benchmark demonstrated that RASST outperforms SOTA methods in both fully supervised and semi-supervised scenarios, achieving higher mAP scores and showing robustness in complex and densely packed scenes. Despite its strengths, RASST faces challenges related to computational overhead and reliance on fixed confidence thresholds for pseudo-labeling. Testing on more diverse datasets, including those with extreme conditions, would further validate its generalizability. Future research could refine RASST efficiency, incorporate uncertainty modeling for pseudo-labeling, and explore applications in other domains such as maritime surveillance or medical imaging.  
In conclusion, RASST offers significant advancements in oriented object detection, combining robust rotational invariance, effective semi-supervised learning, and adaptable multi-scale feature fusion. It provides a strong foundation for future research and practical applications, inspiring the development of more efficient and scalable solutions in aerial imagery and beyond.  
Methodology, S.M., S.U., A.M.O.Q., and Y.I.C.; Software, S.U., S.M., and A.M.O.Q.; Validation, S.U., S.M., A.M.O.Q., and Y.I.C.; Formal analysis A.M.O.Q., and Y.I.C.; Resources, S.U., S.M., and A.M.O.Q.; Data curation, A.M.O.Q., and Y.I.C.; Writing--original draft, S.M., S.U., A.M.O.Q., and Y.I.C.; Writing--review & editing, S.U., S.M., A.M.O.Q., and Y.I.C.; Supervision, S.U., S.M., and A.M.O.Q.; Project administration, S.M., and Y.I.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All used dataset are available online which open access.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RASST & Rotationally Aware Semi-Supervised Transformer \\ ViT & Vision Transformer \\ ARC & Adaptive Rotational Convolutional \\ mAP & mean Average Precision \\ RAW & Rotation-aware Adaptive Weighting \\ GC & Global Consistency \\ CNN & Convolutional Neural Networks \\ PVT & Pyramid Vision Transformer \\ FPN & Feature Pyramid Networks \\ MSSF & Multi-Scale Feature Fusion \\ PGL & Pseudo-Label Guided Learning \\ EMA & Exponential Moving Average \\ SGD & Stochastic Gradient Descent \\ SOTA & State-of-The-Art \\ \end{tabular}
###### Abstract  
Periodontal regeneration using a barrier membrane can be affected by several factors, including patient-related factors (such as smoking habits), surgical techniques, and type of barrier membrane. Smoking exposure has a negative impact on the periodontitis due to its direct inhibition of gingival fibroblast function. Vitamin C is widely recognized as an antioxidant that can be used to mitigate the detrimental impact of smoking products on periodontal cells. This study aimed to investigate whether vitamin C could improve the proliferation and viability of gingival fibroblasts extracted from smoking and non-smoking donors and then cultured on non-crosslinked (CopiOs Pericardium) and crosslinked (BioMend) collagen membranes. To address this aim, human gingival fibroblasts were extracted from healthy periodontitis of smoker patients (Group 1) and non-smoker patients (Group 2). The cells were cultivated and subsequently subcultured in a growth medium supplemented with the required nutrients. Subsequently, the medium at passage six was supplemented with vitamin C, i.e., at the start of the experiment. An evaluation of cell proliferation and viability was carried out using cell migration assays and AlamarBlue(r) assays for cells grown on BioMend and CopiOs Pericardium collagen membranes. Assessment of the morphology and attachment of gingival fibroblasts to the experimental collagen membranes was conducted using scanning electron microscopy (SEM). The viability and proliferation assessments of hGFs from the migration assay were evaluated using AlamarBlue(r). The results exhibited significant fluorescent intensity of gingival fibroblasts on both membrane groups (BioMend and CopiOs Pericardium) in the smoker group compared to the non-smoker group (\(p<0.05\)), which was interpreted to be the result of hGF metabolic activity and the exclusion of any cytotoxic effects, particularly from vitamin C addition. Vitamin C positively affected cells from the smoker group with statistically significant results in the BioMend group (Wilcoxon signed-rank test of \(p\) value \(<0.05\); \(p=0.028\)). SEM images revealed the crosslinking pattern of the BioMend membrane and the non-crosslinked natural tissue structure of the CopiOs Pericardium membrane, which did not change regardless of whether the cultured smoker or non-smoker hGFs were treated with vitamin C. Small numbers of attached hGFs in membrane matrices in all samples, mainly in the peripheries, were observed. It can be concluded that the addition of vitamin C to collagen membranes in vitro seems to combat the adverse effects of smoking products on gingival fibroblasts.  
human gingival fibroblast; vitamin C; periodontal therapy; smoking; collagen membrane; tissue regeneration 2023  
[PERSON]  
1 Department of Periodontics and Community Dentistry, College of Dentistry King Saud University, Riyadh 12372, Saudi Arabia; [EMAIL_ADDRESS]
## 2 Study Area and Datasets
### Meteorological and Hydrological Data  
The Parameter-elevation Regressions on Independent Slopes Model (PRISM) [29] is a knowledge-based system developed for estimating regional-scale climate elements in physiographically complex landscapes at 4 km spatial resolution, such as precipitation (PPT), maximum temperature (TMAX), minimum temperature (TMIN), and mean temperature (TMEAN) [30]. The Global Land Data Assimilation System (GLDAS) [31] enables a reanalysis of land surface data by combining three land surface models (LSMs): Mosaic, Noah, and the Community Land Model (CLM) [32]. The SM product of GLDAS is known to be very reliable [33], and we used the monthly SM from the Noah land surface model (LSM) in a 0.25\({}^{\circ}\) grid for crop yield modeling.
## 2 High Data Rate Modulator Design Description
### PL Framing  
The input to the PL framing processing is encoded blocks of size \(N\). The modulation order \(m\) selects one of the possible constellations with its respective bit-mapping, as displayed in Figures 7-11.  
Figure 8: 8-PSK.  
Figure 10: 32-APSK.  
Figure 9: 16-APSK.  
In order for the receiver to work properly, it will have to be synchronized to the RF-signal as well as perform channel and frequency offset estimation to compensate for impairments. Thus, a distributed pilot pattern is used for carrier and phase synchronization purposes. Concretely, a total of 240 pilot symbols are distributed within each block of 8100 data symbols, as represented in Figure 12. A codeword section is divided into 15 subsections, where each is composed of a 540 data symbols block and 16 pilot symbols with equal in-phase and quadrature components: \(I=Q=\frac{1}{\sqrt{2}}\).  
Thus, PL signaling insertion creates a 320-symbol header which is divided into 256 frame marker symbols and 64 frame descriptor symbols, as shown in Figure 12. The frame marker is generated by a gold sequence which is used for the start of frame detection and synchronization. The frame descriptor is coded by a linear block code and contains information about the ACM format and pilot presence within the transmitted frame. Algorithm details for generating the frame marker and frame descriptor are defined in [2].  
Physical layer pseudo-randomizer aids the telemetry link to facilitate the receiver acquisition, bit synchronization, and code synchronization. The pseudo-randomization process is applied to both data symbols and pilot symbols in the frame to prevent sending symbols in a predictive way due to pilot insertion. Frame header symbols are not randomized. The details of the randomization process are described in ANNEX C of [2].
# A Study on the Essential and Parkinson's Arm Tremor Classification +
## 4 Methodology
### Extracted Features  
For each acquired acceleration magnitude, 12 features are extracted based on statistical signal characteristics (SSC) proposed in [33]. For extracting these features, the following procedure is utilized:  
1. Division of initial acquired signal in sections (\(N_{s}\)). Given that the total number of samples for each measurement is approximately 1800 (sampling rate: 62.5 Hz and recording time: 30 s) and each section consists of 40 samples, the total number of sections is approximately 45 with no overlaps.
2. Calculation of local maximum for every section (\(\alpha_{n}\)).
3. Calculation of an amplitude change, \(A_{n}\), for every section, defined as the absolute difference of the maximum acceleration of the previous section (\(\alpha_{n-1}\)) from the maximum of the current section (\(\alpha_{n}\)), with \(\alpha_{0}=0\).
4. Calculation of the mean amplitude change. \[\text{mean\_amp}=\sum_{m=1}^{N_{s}}\frac{A_{m}}{N_{s}}\]
5. Calculation of the mean absolute deviation of amplitude changes. \[\text{amp\_dev}=\sum_{m=1}^{N_{s}}\frac{|A_{m-mean\_amp}|}{N_{s}}\]
6. Calculation, for every section, of the period, \(T_{n}\), the difference between the time of the current section's maximum and the previous section's maximum. \[T_{n}=t_{n}-t_{n-1}\]
7. Calculation of the mean period. \[\text{t\_mean}=\sum_{m=1}^{N_{s}}\frac{T_{n}}{N_{s}}\]
8. Calculation of the mean absolute deviation of periods. \[\text{t\_dev}=\sum_{m=1}^{N_{s}}\frac{|T_{n}-t\_mean}{N_{s}}\]
9. Finally, from the 4 extracted parameters, the mean, maximum, and minimum across accelerometer axes of each of the 4 features described above are calculated: * Mean, maximum and minimum value of median value sum of each section's amplitude (mean_amp). * Mean, maximum and minimum value of median value sum of each section's amplitude deviation (amp_dev). * Mean, maximum and minimum value of median value sum of each section's \(T_{n}\) deviation (t_dev).
# Investment Incentives in Competitive Electricity Markets
## 6 Mathematical Formulation
### Six-Bus Power Transmission Network  
The studied power network is depicted in Figure 5, which is composed of two deferentially separated areas (north and south zones) being interconnected by two tie-lines. The parameterization data associated with each unit and all demands' characteristics for this network were adopted from [36]. Accordingly, for the purpose of simplification and optimization, it is assumed that capacities of thetie-lines are limited to 450 MVA under available investment budget assumed to be 50 million euros (EUR). Regarding investment considerations by the rival producers/suppliers, the selection of peak technology only and implementation of all new units on bus 4 only are assumed. It is also assumed that 10% of new capacity units are purchased in a contractual framework where contractual price is assumed to be 33 (EUR/MWh). Thereby, the capacity payment rate can be assumed to be 5 (EUR/MWh), which is applied to all available units. The Forced Outage Rate (FOR) of new and existing units are then assumed to be 0.03 and 0.05, respectively. For simplification, four different scenarios are considered for emulating investment incentive strategies and policy implementation by producers, as follows:  
1. No investment over the planning period with the probability of 10%.
2. Investing 350 MW on the bus 4, with the probability of 50%.
3. Investing 400 MW on the bus 4, with the probability of 25%.
4. Investing 450 MW on the bus 4, with the probability of 15%.  
In order to emulate impacts of different incentivizing policies on overall capacity expansions and economic indices of the market topology, the above four policy models are considered as: Energy Only (EO), Capacity Payment (CP), Firm Contract (FC) and Hybrid (H) (that incorporates both capacity payment and firm contract). The proposed model is solved using Solver CPELX (IBM ILOG CPLEX Solver, 11.0.1, Armonk, NY, USA) for a high performance solver for Mixed Integer Programming (MIP).  
For validation of the simulation model, the static model initialization and parameterization [36] were implemented at first. Conventionally [36], the total profit and total constructed capacity over the planning period have been obtained as 32.2 MUR and 700 MW, respectively. After validation of simulation for the EO market, as the base case of initialization, the model was further extended to study impacts of investment incentives on generation capacity expansion from a dominant producer perspective.  
Figure 5: Parameters of energy generation expansion planning.  
Figure 6 illustrates the location of capacity constructed by the strategic producer as well as kinds of installed capacities. For instance, the total and the base installed capacities in the EO market are 700 MW and 500 MW, respectively, thereby resulting 200 MW investment in the peak technology. In addition, the generation expansion planning results for different market designs are given in Figure 7, which shows the average market prices, the net profit of investor, the Net surplus of strategic producer, rival, and consumers.
## 3 Results and Analysis
### Correlation of ITS and POTS Ratio  
This section discusses the relationship between the TSRs from indirect tensile strength test and pull-off tensile strength test on sample groups using granite filler at both 20% and 30% by volume together with both granite and limestone aggregate. The tensile strength ratio for two methods can be correlated to verify the potential of using POTS as the alternative method for quantifying moisture damage in asphalt mixture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Volume of Fillers**} \\ \hline \multirow{2}{*}{**Type of Aggregate**} & \multicolumn{2}{c}{**20\%**} & \multicolumn{2}{c}{**30\%**} \\ \cline{2-5}  & **Dry Condition** & **Wet Condition** & **Dry Condition** & **Wet Condition** \\  & **(MPa) (\%CV)** & **(MPa) (\%CV)** & **(MPa) (\%CV)** & **(MPa) (\%CV)** \\ \hline
**Granite** & 6.15 (3.8\%) & 2.16 (8.3\%) & 4.52 (2\%) & 1.74 (7.2\%) \\ \hline
**Limestone** & 2 (10\%) & 1.31 (10\%) & 7.25 (5\%) & 2.99 (9\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pull-off tensile strength test (POTS) of dry and wet conditions on asphalt mixtures and different filler contents (i.e., 20% and 30% by volume).  
Figure 8: TSR of POTS test after moisture of asphalt mixtures for all filler types.  
The relationship of the TSR values of the ITS and POTS tests is shown in Figure 9, which illustrates that there is no equality between the two tests, but there is a very strong linear relationship. This might be due to the different concepts for obtaining the strength of ITS and POTS tests. Also, the temperatures and curing time used for POTS test were just in primary stage of study, and further studies optimizing these factors are required. Additionally, results show that the TSR values of the ITS test showed positive and rational relationships with those of the POTS test. A good significant slope relationship can be established between ITS and POTS with R\({}^{2}\) of 0.91 as shown in Figure 9. All samples on ITS test were above the equality line. These results are logical, as the ITS uses the concept of compressive strength, which can yield the lower values of strength than those of POTS, which uses the premise of tensile strength. For the TSR of indirect tensile strength, the prediction model is presented, as shown in Equation (4).  
\[\text{Y}=1.0179\text{X}+0.243;R^{2}=0.9078 \tag{4}\]  
where Y = TSR of the indirect tensile strength test and X = TSR of the pull-off tensile strength test.
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 6 Conclusions  
We investigate extracting text features via graph operations since unstructured text belongs to non-Euclidean data and graphs can better represent such non-Euclidean data. However, recent research indicates that the pruning process may destroy some vital information in the complete tree; hence, the approach taken in this article is to construct a fully linked graph. Furthermore, traditional graphs are built by external toolkits, and they are fixed and cannot be modified. For the graph generated by the graph generator, the parameters of the generator can be optimized by training to make it have a certain generalization ability. Because conventional GCN only acquires local node features and frequently ignores global text features, we adopt a dense connection layer in this research to obtain additional global information. Furthermore, to solve the problem that the model has difficulty extracting overlapping triples, we use the GlobalPointer joint decoding method. The extraction of triples is subdivided into quintuple extraction. The extraction of entity head and entity tail under a specific relationship will be transformed into a form similar to scaled dot-product attention so that the overlapping entities can be identified with multiple specific relationship matrices, and then the overlapping problem can be tackled. The experiment results on the NYT and WebNLG datasets demonstrated the effectiveness of our model. In addition, our experiments show that our model can effectively extract overlapping triples and handle complex sentence multi-relation problems.  
In the future, we will further improve the performance of this model to apply it in knowledge graphs or other fields.  
Conceptualization, J.L.; methodology, J.L.; software, J.L.; validation, J.L., Q.H., D.Z. and S.F.; formal analysis, Q.H. and D.Z.; investigation, J.L.; resources, Q.H. and D.Z.; writing--original draft preparation, J.L.; writing--review and editing, J.L., Q.H. and S.F.; visualization, J.L. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China \"Research on the Evidence Chain Construction from the Analysis of the Investigation Documents (62166006)\", supported by Guizhou Provincial Science and Technology Projects (Guizhou Science Foundation-ZK [2021] General 335).  
Not applicable.  
The study did not report any data.  
There are no conflict to declare.