esa-sceva/satcom-chunk-collection huggingface dataset:
# Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks
## 5 Conclusions  
In this paper, we proposed the Collision Avoidance Geographic P2P-RPL to achieve low routing overheads with short convergence time and energy-efficiency for indoor IR-UWB-based networks. Collision Avoidance Geographic P2P-RPL makes use of the estimated location knowledge of the individual node in cooperative multi-hop IR-UWB self-localization networks. The proposed algorithm achieved energy-efficient P2P data delivery without reducing the networking reliability with a short convergence time.  
The proposed algorithm utilizes standard RPL control messages and is compatible with the existing RPL implementations. We implemented Collision Avoidance Geographic P2P-RPL on Contiki OS and provide the experimental results using the Cooja network simulator under various network conditions. Our experimental results show that restricting the network search space for route discovery to the location-constrained forwarding zone significantly reduced the routing control packet overhead and energy consumption.  
As the average node degree increased, the positioning accuracy increased, and the routing control packet overhead performance was improved. From various simulation results, the proposed algorithm showed more robust operation characteristics for P2P traffic while supporting a low routing overhead and short convergence time simultaneously. In future work, we aim to extend our work to a 3D environment and consider node mobility to reflect real-life environments more accurately.  
Conceptualization, Y.C.; methodology, Y.C.; validation, Y.C.; formal analysis, Y.C. and Y.K.; investigation, Y.C., J.J. and J.P.; resources, Y.C. and J.J.; data curation, Y.C.; writing-original draft preparation, Y.C. and J.J.; writing-review and editing Y.C. and Y.K.; visualization, Y.C.; supervision, Y.K.; project administration, Y.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Graduate School of Konkuk University on 14 June 2021.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
This paper was supported by Korea Institute for Advancement of Technology(KIAT) grant funded by the Korea Government (MOTIE) (N0002428, The Competency Development Program for Industry Specialist).  
The authors declare no conflict of interest.  
Figure 14: The control packet overhead versus the number of active source-destination pairs.
## 3 Materials and Methods
### Comparison and Selection of the Appropriate Forecasting Method  
In conclusion, by comparing the above methods, the difference in forecasts can be observed (in relation to the principles of each method). Given that targeted demand consists of three basic factors: level, trend, and seasonal factor, the Holt-Winters Exponential Smoothing Method constitutes the optimal method. For example, based on training data (2006-2019) for the period 2020-2032, Figure 7 shows a linear increase--forecast (green), while the actual data for 2020 and 2021 (orange) show a difference in relation to the obtained forecast. A decrease in demand in 2020 and 2021 was caused by the COVID-19 pandemic.  
Exponential smoothing methods (especially the Holt-Winters method) are widely used in airport traffic demand forecasting due to their specific advantages. They can adapt quickly to changes because they respond effectively to seasonal and short-term fluctuations in demand. They are simple to implement and do not require complex data or modeling. A major advantage is that these methods give more weight to recent data, which allows for more accurate traffic demand forecasts, giving current trends more influence. These methods do not require the collection of large amounts of data and can provide good results even with limited historical data.  
Other forecasting methods, such as ARIMA models, have certain disadvantages such as the limited ability to capture nonlinear patterns because ARIMA is based on linear dependencies and can have problems with complex seasonal and nonlinear trends; data often need to be transformed for the model to be effective, which can lead to loss of information. Also, forecasting with machine learning models requires a large amount of high-quality data for reliable forecasts, significant computer processing power, and training time. Due to these key shortcomings, such methods were not considered for use in this case of traffic demand forecasting at the case-study airport.  
Considering the analysis of forecasting methods, the Holt-Winters Exponential Smoothing Method was selected as optimal for this research, as it performs better than the other analyzed methods. However, the Holt-Winters Exponential Smoothing Method also comes with several limitations. It assumes that trends will persist linearly, making it less suitable for datasets with nonlinear or shifting trends. Additionally, it struggles with multiple or irregular seasonal patterns and is highly sensitive to sudden changes, anomalies, or outliers, which can distort forecasts. Furthermore, the model adapts slowly to abrupt shifts in data patterns, such as those caused by financial crises or the COVID-19 pandemic. For these reasons, a historical data period was selected that excluded such anomalies, focusing on the post-financial crisis and pre-COVID-19 timeframe.  
Figure 7: Example of forecasting using the Holt–Winters Exponential Smoothing Method.
## 3 Empirical Results and Analysis
### Establishment of Prediction Equations of Peak Ground Motion
#### 3.1.1 PGA Prediction Equation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **A + \(\sigma\)** & **B + \(\sigma\)** & **C + \(\sigma\)** & **Adjusted R-Square** \\ \hline East–West PGA & 5.488 \(\pm\) 0.525 & 0 \(\pm\) 0.00109 & \(-1.813\)\(\pm\) 0.307 & 0.994 \\ North–South PGA & 5.704 \(\pm\) 0.564 & 0 \(\pm\) 0.00118 & \(-1.912\)\(\pm\) 0.330 & 0.994 \\ Horizontal PGA & 5.617 \(\pm\) 0.385 & 0 \(\pm\) 0.000797 & \(-1.870\)\(\pm\) 0.225 & 0.994 \\ Vertical PGA & 5.212 \(\pm\) 0.563 & 0 \(\pm\) 0.00146 & \(-1.788\)\(\pm\) 0.342 & 0.994 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coefficients of PGA prediction equations.
## 2 Adaptability of FDP to PV Station Access
### Influence of Asymmetric Faults on Action Criteria in PV Station  
Since the control strategy of the inverter makes the negative sequence component of the short-circuit current zero, when a fault occurs, the fault current contains only the zero sequence component and the positive sequence component. And because the photovoltaic transformer shown in Figure 1 often uses Y/\(\Delta\) wiring, the zero-sequence network is open-circuited and can be ignored, leaving only the positive-sequence network. When asymmetric faults occur, due to the weak feed characteristics on the PV side, the fault current on the PV side has little influence on the differential current when the power grid is strong. In this case, the sensitivity of FDP will be reduced, but the possibility of failure to operate will be small. However, when the power grid is weak, the fault current on the PV side has a great influence on the differential current and braking current, and the traditional differential protection will also reduce the sensitivity, and the phase angle difference is obtuse, so the possibility of failure to operate is high [9]. Figures 3 and 4 show the phase angle between PV side current and grid side current when AB-phase fault occurs in strong power grid and weak power grid, respectively.  
Figure 2: The current on the PV side during AB-phase fault.
## 4 Developing SNEP Model in AVISPA
### Simplification of SNEP Protocols
#### 4.1.2 Replacing MAC with a Hash Function  
In SNEP, the keyed MAC function is used for message authentication. AVISPA provides the hash function, which might be also used as the keyed hash function by adding the symmetric key in the hash calculation. Thus, instead of using \(MAC(K^{\prime}_{AB},M)\), we use h(K^ab.M) in AVISPA models where h() is a hash function and K^ab.M means the concatenation of K^ab and M.
# Reconstructing Nerve Structures from Unorganized Points
## 4 Materials and Methods
### Initializing the Algorithm and the Clusters
In practical terms, for all the analyzed points surrounding point X, the first portion of the expression in Equation (7), \(\left\|\frac{\bigtriangledown f(X)}{f(X)}\right\|\), remains constant with each new point selection. This observation underscores that the measure \(S_{xy}\) and the rationale behind defining the successor are solely influenced by the value of \(\cos\theta_{xy}\). The proper choice of the area \(G(X)\) is of great importance for the method's initialization. The key parameter that affects the size of this area is \(r\) from (1). This parameter should be based on the points' density. Based on their experience, the authors suggest that it should be estimated as:  
\[r=k\times\frac{max(X_{max}-X_{min},Y_{max}-Y_{min})}{N/2}, \tag{8}\]  
where \((X_{max},X_{min})\) and \((Y_{max},Y_{min})\) are pairs of maximum and minimum values of all the points from the input set by the \(x\)-axis and \(y\)-axis, respectively, and \(N\) is the total number of points in the input set. The parameter \(k\) is calculated as:  
\[k= \begin{cases}4,&\text{if }\frac{N}{neverDist}<200\\ 5,&\text{if }200\leq\frac{N}{neverDist}<240\\ 6,&\text{otherwise}\end{cases}  \tag{9}\]  
where \(averDist\) is defined as:  
\[averDist=\frac{sum(minDist)}{N} \tag{10}\]  
and where \(minDist\) is an array, each element of which contains the distance from one point to its closest point in the set. While the valley-seeking clustering method is highly applicable in many cases, it is not always able to successfully follow the dynamics of the trajectory. The problem it could encounter is a sudden change in the direction while forming the point order, as exemplified in Figure 5. To address this issue, we employed the classic valley-seeking method exclusively during the cluster initialization phase, specifically when determining the first two points of the cluster. This initial step defines the direction in which the cluster will be constructed, i.e., in which the next point will be searched for. Figure 5: Unexpected and undesired shifts in the clustering direction, resulting from the use of the classic valley-seeking approach.
## 5 Results
### Results Obtained in NYU_V2  
Table 8 shows our results for different subsampling factors in the upsampling task. We compare the results obtained by our proposal with different models available in the literature regarding the completion error (\(MSE\)).  
In Figure 17, we observe that our proposal (in blue) outperforms the model Total Generalized Variation [10], Filtering using static and dynamic guidance [28], and Bicubic interpolation for upsampling factors of \(8\times\) and \(16\times\). The JBU and Ham models [28] perform better than ours.  
In Figure 18, we show quantitative and qualitative results obtained by the proposal on the images already presented above in Figure 9. Depth maps were downsampled by a factor of eight.  
Figure 18 shows color frames and their corresponding interpolated depth maps. Figure 18g shows the completed depth map of the downsampled depth map. Similar results are presented in Figure 18h,i.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Error 4\(\times\)** & **Error 8\(\times\)** & **Error 16\(\times\)** \\  & **cm** & **cm** & **cm** \\ \hline Proposed model & 8.31 & 9.38 & 17.56 \\ Proposed model—anisotropic & 8.31 & 9.35 & 17.16 \\ metric & & & \\ Bicubic & 8.46 & 14.22 & 22.32 \\ TGV [10] & 6.98 & 11.23 & 28.13 \\ Ham [28] & 5.27 & 12.31 & 19.24 \\ JBU & 4.07 & 8.39 & 13.25 \\ Park [2] & 5.21 & 9.56 & 18.10 \\ DJF [29] & 3.54 & 6.20 & 10.21 \\ \hline \hline \end{tabular}  
We plot the data in Table 8 in Figure 17.  
\(MSE\) Comparison between different models
## 4 Empirical Insights from Sectorial and Regional Studies
### AI-Driven Business Model Innovation for Sustainability: Evidence from U.S. SMEs  
[PERSON] et al. [3] examine how AI-driven business model innovation (AIDBMI) enables small- and medium-sized enterprises (SMEs) in the United States to transition toward carbon-neutral operations. Drawing on data from 326 SMEs and analyzed using structural equation modelling partial least squares structural equation modeling (PLS-SEM), the study finds that AI significantly facilitates the integration of sustainable business practices by enhancing both technological (e.g., green technology innovation) and strategic (e.g., strategic intent and architecture) enablers. Key technological factors include the deployment of AI in optimizing energy use, managing renewable energy integration, and supporting circular supply chains--mapped directly to the TOE-Technological domain. Organizationally, the success of AIDBMI depends on the firm's innovation capability, alignment of sustainability goals, and internal structures--corresponding to TOE-Organizational and DOI-Compatibility dimensions. Strategic enablers such as sustainability-focused leadership and employee engagement emerged as critical success factors.  
Quantitatively, the model explains 82.8% of the variance in carbon-neutral business performance, and shows that AIDBMI mediates the impact of green innovation capability and strategic architecture on sustainability outcomes. The study reinforces this paper's challenge-solution framework by validating how AI supports environmental and economic performance through innovation in business models. It also highlights government policy as a moderating environmental factor, emphasizing the importance of external support for SMEs aiming for net-zero goals.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Media and Entertainment  
Drones are used for aerial photography and videography in films, news coverage, and sports events. They can capture unique angles and perspectives that would be difficult or impossible to achieve with traditional cameras [25].
## 5 Conclusions  
This research proposed an efficient and lightweight OCR-based text detection and recognition methodology for Urdu written in Nastaleeq font. The proposed solution is a multi-layer hybrid CNN-RNN architecture for text recognition in combination with CCA and LSTM for text detection. In comparison to existing techniques, this architecture offers a promising accuracy of 97.47%. In the future, the model could be tested for font-free cursive text.  
Conceptualization, M.U., M.Z. and F.D.; methodology, M.U., M.Z. and F.D.; formal analysis, S.A., M.U. and M.Z.; investigation, M.U., M.Z., S.A. and M.S.B.; supervision, M.U. and M.Z.; writing--original draft preparation, M.U. and M.Z.; writing--review and editing, M.U., M.Z., F.D., S.A., M.S.B., M.H. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data is publicly available at [[https://tukl.seecs.nust.edu.pk/downloads.html](https://tukl.seecs.nust.edu.pk/downloads.html)]([https://tukl.seecs.nust.edu.pk/downloads.html](https://tukl.seecs.nust.edu.pk/downloads.html)) (accessed on 10 January 2022).  
The authors declare no conflict of interest.  
Code is available on request ([EMAIL_ADDRESS]).  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCA & Connected component analysis \\ LSTM & Long Short-Term Memory \\ OCR & Optical Character Recognition \\ CNN & Convolutional Neural Networks \\ RNN & Recurrent Neural Network \\ OTF & Open type format \\ UPTI & Urdu printed text line images \\ CLE & Center of language engineering \\ DCT & Discrete cosine transform \\ ANN & Artificial neural networks \\ OTF & Open type format \\ HMM & Hidden Markov Model \\ CTC & Connectionist temporal classification \\ MD-RNN & Multi-dimensional Recurrent Neural Networks \\ CPN & Character proposal network \\ ML & Machine learning \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Percentage Accuracy**} \\ \hline [PERSON] et al. [16] & 96.71\% \\ [PERSON] et al. [19] & 93.39\% \\ [PERSON] et al. [21] & 96.72\% \\ [PERSON] et al. [18] & 80.34\% \\
**Proposed Method** & **97.47\%** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy comparison.
## 4 Results and Discussion
### Final Design  
In order to isolate the effect of the magnetic field, different magnetic-shielding techniques have been applied. For static fields, the most important mechanism is magnetic flux shunting, using a high permeability magnetic material [14; 15], as shown in Figure 12.  
Based on the second design, the final design is proposed to reduce the magnetic flux leakage as much as possible. A 2 mm thin plate made of SUS 430 is attached to the plate. The mechanical material properties and the B-H curve are shown in Table 1 and Figure 3, respectively. The final design modeling is shown in Figure 13.  
Figure 11: Results of the second design: (**a**) magnetic flux leakage; (**b**) displacement; (**c**) impedance.  
Figure 12: Magnetic shielding: (**a**) without shielding; (**b**) with shielding.  
Figure 13: Final design modeling.  
A comparison of the magnetic flux line between the second design and the final design is shown in Figure 14. The permanent magnet generates the magnetic fluxes, which are passing through the magnetic circuit and the air. Most of the magnetic fluxes are concentrated on the yoke due to its high magnetic permeability, however, there are many magnetic fluxes that cannot return to the magnetic circuit, which lead to high magnetic flux leakage. When the high magnetic permeability plate (SUS 430) is added under the yoke, it creates the new magnetic circuit to make magnetic fluxes in the air return to the yoke.  
Compared to the second design (3.01 N/A), the final design caused a 3.07% increase in the force factor due to the added plate. Furthermore, the magnetic flux leakage decreased by 93.07%, from 643.7 to 44.6 G, with a slight increase in the force factor, as shown in Table 6.  
The analysis results of the final design were verified based on the experimental results, which include the magnetic flux leakage, displacement, and impedance, as shown in Figure 15. There is only a 1.36% difference in the magnetic flux leakage between the analysis and experimental results. The final design result shows a magnetic flux leakage of lower than 50 G.  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Force Factor [N/A]** & **Magnetic Flux Leakage [G]** \\ \hline Prototype & 3.12 & 643.7 \\ The final design & 3.11 (\(-\)0.44\%) & 44.6 (\(-\)93.07\%) \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of analysis results between the prototype and second design.  
Figure 14: Magnetic flux line: (**a**) second design; (**b**) final design.  
Figure 15: _Cont._
## 3 Materials and Methods
### Datasets
#### 3.4.2 Synthetic Data Generation  
The quality, quantity, and scene diversity of data impacts the base performance of deep learning-based models. Although we adopted a domain adaptation scheme, it is impossible to train a model with only a few data. Therefore, we propose a method to generate data synthetically and use the outputs in training.  
* Synthetic Visible Light Images: Acquiring images and annotating objects are labor-consuming tasks. Although the small target detection datasets in the visible light spectrum are relatively abundant, in such scenarios as aerial surveillance and distant target detection, it is very difficult to generate a dataset for a specific scenario. Therefore, we used Blender to generate synthetic visible-light object detection datasets. Using 3D Google Map data, we simulated real-world terrain and buildings via 3D mesh. The target object images were fused in random locations with automatic annotations of object class and location. Here, 3D models of vehicles were used to obtain more than 20,000 images.
* Synthetic Infrared Images: Capturing infrared images requires professional equipment, making the acquision process very difficult. Although long-wave infrared range (LWIR) images are relatively simple to obtain, obtaining infrared images from other spectra, such as the mid-range infrared range (MWIR), is not an easy task. Therefore, we used the infrared signature simulation software MuSES to render MWIR synthetic images. After giving the material information and environmental conditions to the 3D object modeling software, we placed the object on a grid and rendered corresponding infrared images at each azimuth and elevation angle. The distance between the sensor and object ranged from 20 m to 3 km. Car and tank models were used to obtain object renderings. Additionally, real-world MWIR images were captured using the FLIR A8580 Compact MWIR Camera. A total of 6 scenes comprised of 3000 images were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Image Count** \\ \hline MS COCO [8] & 3231 \\ FLIR ADAS [47] & 3086 \\ VEDAI [48] & 2538 \\ DOTA [49] & 1680 \\ Generated Visible Light & 21,225 \\ Generated Infrared & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of used images in each dataset. Images containing small-sized objects in human- and vehicle-related categories are sampled for training.  
acquired. The simulated infrared target images were fused to these infrared images, yielding a synthetic MWIR dataset.
## 4 Emergency Data Handling
### IFS for Active Slot Period  
Figure 5 shows the sample case where a node transmits its emergency data during d-TP and u-TP. As previous section including Figures 3 and 4, all data have to be delayed for a short IFS time before transmission according to the overall timeframe (d-TP, u-TP, and CP), except the Sync period. Thus, it clearly provided QoS because the emergency data could be sent by preempting the time moment of IFS in overall TP and CP, prior to other transmissions.  
Figure 4: Transmission example of CP (Contention Period) in the Active subframe.  
In the left side of Figure 5, the emergency data transmission of Node_Emer can be earlier started with short IFS (SIFS) than PC during IFS in d-TP, so the emergency data can be prioritized to be transmitted even though the time slot was not assigned to Node_Emer. In this case, the PC should perform CSMA/CA to guarantee the reliability and QoS of the emergency transmission if it is existed. This process could be identically operated in CP. Although the nodes should consume energy due to carrier sense in CP, this period is seldom allocated in terms of overall superframe.  
Meanwhile, the right side of Figure 5 shows how the emergency data could be transmitted within u-TP. Contrary to the case of d-TP, general data transmission in u-TP is relatively significant because this period is the main stem in the healthcare system. Hence, the proposed MAC protocol allowed for the emergency data to be sent only when the idle slot is detected, for conserving energy of the nodes from carrier sense. We believe that the average delay of emergency data transmissions is not critical because not all nodes are using all assigned slots.  
Even though TP (including d-TP and u-TP) and CP are existed for normal data and special control packet transmissions, the emergency data can be concurrently transmitted with high priority with maintaining normal data transmission function. The only weak point is that a node consecutively transmits massive emergency data. However, this situation could be easily addressed by conducting fragmentation if the required time is greater than a time slot, as shown in the right side of Figure 5. Furthermore, we considered that PC should accept an emergency data from a node even though it was not registered to PC before.