esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 4 Results  
Our model was run as shown in Figure 5. When the interest reaches the source, the event packet with the required information returns the requested data to the sink by moving to a neighbor closer to the sink until the sink is reached.  
1. A gradient is formed from the source, identifying the distance from the source (So).
2. A gradient is formed from the sink, identifying the distance from the sink (Si).
3. The sink searches the source using the distance from the source.
4. When the source is reached the source sends the message to the sink by using the distance from the sink.
5. When the sink is reached, the average energy is recorded as well as the run number.
6. The model is run till the energy level is too low to receive or transmit data.  
The energy was compared to our previous model of DD. A graph of the data was generated and is presented in Figure 5.  
Extracts from the data are presented in Table 1. The average energy comparison of DD and AD clearly shows significantly reduced energy loss with adapted diffusion compared to DD. The data presented above indicates that more runs are possible with AD compared to DD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **DD: Average Energy per Node** & **AD: Average Energy per Node** \\ \hline After 10 runs, search 2 energy units, return 4 energy units & 80.1 & 96.4 \\ \hline After 10 runs, search 6 energy units, return 12 energy units & 43.7 @ 9, Fail @ 10 & 90.9 \\ \hline After 20 runs, search 2 energy units, return 4 energy units & 57.9 & 92.4 \\ \hline After 19 runs, search 6 energy units, return 12 energy units & Fail & 81.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average energy usage comparison of DD and AD.  
Figure 5: Average energy of directed diffusion vs. adapted diffusion. Energy to search 2 units, and energy to return 4 units. Energy to search 6 units, and energy to return 12 units. The vertical lines are the spread, calculated as standard deviation of the values recorded in each case. The length of each line indicates the standard deviation observed.
## Nomenclature  
\begin{tabular}{l l l l} \(A\) & area (m\({}^{2}\)) & \(S_{w}\) & net cross-flow area through one baffle window (m\({}^{2}\)) \\ \(B\)o & boiling number & \(S_{m}\) & the cross-flow area at the bundle centerline (m\({}^{2}\)) \\ \(Co\) & convective number & \(F_{shp}\) & ratio of the bypass to the crossflow area \\ \(C_{p}\) & fluid-specific heat at constant pressure (Jkg\({}^{-1}\)K\({}^{-1}\)) & \(p_{p}\) & tube pitch (m) \\ \(d\) & diameter (m) & \(v\) & specific volume (m\({}^{3}\)/kg) \\ \(D_{b}\) & outer baffle diameter (m) & \(w\) & velocity (ms\({}^{-1}\)) \\ \(D_{atl}\) & outer tube limit diameter (m) & \(x\) & vapor quality (-) \\ \(D_{s}\) & shell diameter (m) & \(X_{tt}\) & Martinelli parameter \\ \(f\) & friction factor (-) & \(z\) & tube length (m) \\ \(f_{x}\) & he overall pressure gradient (kg/(m\({}^{2}\) s\({}^{2}\)) & \(T\) & temperature (K) \\ \(Fr\) & Froude number & \(\hat{q}\) & heat flux (Wm\({}^{-1}\)) \\ \(G\) & mass flux (kg m\({}^{-2}\) s\({}^{-1}\)) & \(\dot{V}\) & volumetric flow rate (m\({}^{3}\)s\({}^{-1}\)) \\ \(h\) & specific enthalpy (Jkg\({}^{-1}\)) & \(\O\) & two phase multiplicator \\ \(i_{bg}\) & latent heat of vaporization, (J/kg) & _Greek letters_ & \\ \(j\) & Colburn j-factor & \(\alpha\) & heat transfer coefficient (Wm\({}^{-2}\)K\({}^{-1}\)) \\ \(J_{c}\), \(J_{L}\), \(J_{B}\), \(J_{S}\) & correction factors (Bell-Delaware) & \(\alpha_{i}\) & ideal heat transfer coefficient \\ \(L_{bc}\) & central baffle spacing (mm) & \(\lambda\) & thermal conductivity (Wm\({}^{-1}\)K\({}^{-1}\)) \\ \(L_{bo}\) & outlet baffle spacing (mm) & \(\rho\) & density (kgm\({}^{-3}\)) \\ \(L_{ui}\) & inlet baffle spacing (mm) & \(v\) & kinematic viscosity (m\({}^{2}\)/s) \\ \(L_{c}\) & baffle cut & _Subscripts_ & \\ \(m\) & mass flow rate (kg/s) & wall & tube wall \\ \(n\) & number of tubes & w & cooled water \\ \(N_{b}\) & number of baffle & i & inside \\ \(N_{cw}\) & effective cross-flow rows in the window zone & i & inlet \\ \(p\) & pressure (Pa) & s & shell \\ \(Pr\) & Prandtl number & nb & nucleated boiling \\ \(R\) & correction factor & l & liquid phase \\ \(R_{b}\) & bundle bypass correction factor for pressure drop & o & outside \\ \(Re\) & Reynolds number & out & outlet \\ \(R_{L}\) & baffle leakage correction factor for pressure drop & r & refrigerant \\ \(r_{ss}\) & sealing strip ratio & v & vapor phase \\ \(r_{m}\) & parameter for finding the leakage correction factor & tp & two-phase \\ \(r_{s}\) & parameter for finding the leakage correction factor & & \\ \end{tabular}
## 8 Conclusions
Additionally, its high-resolution capabilities allow for the accurate detection and quantification of small defects or fine details on concrete surfaces, providing comprehensive and detailed evaluations. Moreover, the SDI method is less dependent on human judgment, thus reducing the subjectivity in the final results and ensuring consistent and reliable results regardless of variations among inspectors and the complexity of defects. Overall, these advantages position the developed method as a practical and efficient solution for evaluating concrete surface defects, addressing the challenges associated with various methods and enhancing the assessment process. Further investigation could involve a parametric and comparative study with other current methods. While the current study has provided valuable insights into the effectiveness of the proposed method for evaluating concrete surfaces, future research could focus on extending the application of this method by exploring its applicability to different contexts for meaningful applications in the construction and civil engineering industries and in other areas of application. Conceptualization: F.Z.B., S.E.B., A.C., C.B., T.T.N., E.H.K. and S.C.; methodology: F.Z.B., S.E.B., T.T.N., C.B. and S.C.; software: F.Z.B., S.E.B., A.C., C.B., T.T.N., E.H.K. and S.C.; validation: All authors; formal analysis: All authors; investigation: All authors; resources: F.Z.B., S.E.B., T.T.N. and C.B. ; data curation: All authors; writing--original draft preparation: F.Z.B., S.E.B., A.C. and T.T.N. ; writing--review and editing: F.Z.B. and C.B. ; visualization: All authors; supervision: F.Z.B., S.E.B., A.C., C.B., T.T.N., E.H.K. and S.C.; project administration: All authors;funding acquisition: All authors. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. Conflicts of Interest: The authors declare no conflicts of interest.
###### Abstract  
The traditional LDPC encoding and decoding system is characterized by low throughput and high resource consumption, making it unsuitable for use in cost-efficient, energy-saving sensor networks. Aiming to optimize coding complexity and throughput, this paper proposes a combined design of a novel LDPC code structure and the corresponding overlapping decoding strategies. With regard to structure of LDPC code, a CCSDS-like quasi-cyclic parity check matrix (PCM) with uniform distribution of submatrices is constructed to maximize overlap depth and adapt the parallel decoding. In terms of reception decoding strategies, we use a modified 2-bit Min-Sum algorithm (MSA) that achieves a coding gain of 5 dB at a bit error rate of \(10^{-6}\) compared to an uncoded BPSK, further mitigating resource consumption, and which only incurs a slight loss compared to the standard MSA. Moreover, a shift-register-based memory scheduling strategy is presented to fully utilize the quasi-cyclic characteristic and shorten the read /write latency. With proper overlap scheduling, the time consumption can be reduced by one third per iteration compared to the non-overlap algorithm. Simulation and implementation results demonstrate that our decoder can achieve a throughput up to 7.76 Gbps at a frequency of 156.25 MHz operating eight iterations, with a two-thirds resource consumption saving.  
QC-LDPC; high-speed; 2-bit quantification; decoder; overlap; FPGA 2023 23 7828. [[https://doi.org/10.3390/s23187828](https://doi.org/10.3390/s23187828)]([https://doi.org/10.3390/s23187828](https://doi.org/10.3390/s23187828))
# LUMDE: Light-Weight Unsupervised Monocular Depth Estimation via Knowledge Distillation
## 5 Conclusions  
This work proposed a lightweight unsupervised monocular depth estimation network (LUMDE) achieved by knowledge distillation. The whole framework uses SC-SfMLearner as the teacher net, MobileNet V3 (small) as the backbone of the student net, and further incorporated a pose net and Transformer into the baseline distillation network to optimize the model's performance. Experimental results show that it is possible to significantly reduce the model size and computational complexity, while still retaining SOTA accuracy. Major contributions of this paper include: (1) a suitable student backbone was selected for the compressed depth estimation network; (2) introducing a pose network and Transformer to further improve the performance of the compressed network; (3) SOTA results in self-supervised depth estimation model distillation, i.e., 0.158 on AbsRel error and 0.782 on \(\delta\)1 accuracy; (4) 95.2% computational compression, 77.2% parameters compression, and real-time inference speed.  
Conceptualization, X.D. and N.L.; methodology, W.H.; software, W.H.; validation, W.H., X.D. and Y.C.; formal analysis, W.H.; investigation, W.H.; resources, Y.C.; data curation, Y.C.; writing--original draft preparation, W.H.; writing--review and editing, X.D.; visualization, W.H.; supervision, X.D.; project administration, X.D.; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant 52006137, in part by Shanghai Sailing Program under Grant 19 YF1423400, and in part by China Postdoctoral Science Funding under Grant 2016M600313.  
Not applicable.  
The authors declare no conflict of interest.
# Knee Osteoarthritis Classification Using 3D CNN and MRI
## 2 Materials and Methods
### 3D CNN Model for MRI  
The architecture of the 3D CNN model proposed in this study is shown in Figure 5. The structure was inspired by the work performed by [PERSON] et al. [21]. The most important difference between 3D CNNs and 2D CNNs is that 3D CNNs use 3D convolutional kernels to process a volumetric patch of a scan, while 2D CNNs process a single anatomical plane. The 3D convolutional kernels incorporate information from adjacent slices and are therefore able to extract 3D features, which are not detectable from 2D CNNs. As shown in Figure 5, three stages are included in the proposed 3D CNN model before the final softmax layer. The details of the model are discussed below.  
Stage 1 of the model began with a convolutional layer containing 32 kernels of size \(7\times 7\times 7\) with a stride of \(2\times 2\times 2\). This was followed by batch normalization and an activation layer using the ReLU function. A max-pooling layer was added with a window size of \(2\times 2\times 2\) and a stride of \(2\times 2\times 2\). Finally, a dropout layer was placed before the start of the residual blocks. We used dropout layers in each stage of our 3D CNN model to help reduce overfitting. Each dropout layer used a rate of 0.5, which gives each node a 50% chance of being set to 0.  
The second stage of the model contained a sequence of six residual blocks [28]. Each residual block featured a shortcut connection from the input to the output. There were two types of blocks used in this model as shown in Figure 6. The convolutional block features a convolutional layer in the shortcut path. This layer was used when the input dimensions were changed. The identity block did not have any layers in the shortcut path and was used when the input and output dimensions matched. This stage also ended with a dropout layer.  
The final stage of the model used global max pooling, followed by a fully connected layer of size 1024 and a dropout layer. The last layer of the model uses the softmax function, which outputs the possibilities of the sample belonging to each category.  
The model was implemented in Python using the Keras library with TensorFlow as a backend. The model was trained using a batch size of 15 with early stopping based on validation loss. The Adam optimization function was used with a learning rate 0.001.  
Figure 5: The architecture of the 3D CNN model used in this study. Details of the convolutional block and the identity block are shown in Figure 6.  
Figure 6: Details of the convolutional block and the identity block.  
The training was performed on a high-performance computer with a NVIDIA Tesla V100 32 G GPU.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 1 Introduction
Although there have been attitude determination techniques based on feature matching of observed images in the field of aerial image analyses and unmanned aerial vehicle (UAV) studies (e.g., [11; 12]), our study is different from these in two respects: (1) the proposed method utilizes satellite observation geometry; and (2) it employs robust estimation to allow attitude determination even when many clouds cover land surfaces in an observed image. This paper is guided by data from a particular satellite but this proposed framework is general and applicable to any satellite. In Section 2, we describe two satellite imagery datasets; one from a small satellite UNIFORM-1, whose attitude is to be determined, the other from Landsat-8, which is used as the base-map. Section 3 presents the mathematical basis of our approach based on the satellite observation geometry, and the image processing for land features extraction and matching by SURF, and outlier rejection by robust estimation. Section 4 applies the proposed method to satellite attitude determination and map projection from the real satellite imagery data. In Section 5, we discuss the performance of our proposed method and suggest ways to reduce its computational cost. Section 6 concludes the paper.
## 3 Materials and Methods
### Test Environments  
#### 3.4.1 Augmented Reality Device: HoloLens2  
A variety of AR equipment exists to build an AR environment.  
Among them are Nreal Light (Nreal) and HoloLens2 (Microsoft), which are suitable for development environments. Nreal is lightweight because it is worn on the head for exercises such as running or jumping. HoloLens2 is heavy but high-precision with hand tracking, eye tracking, spatial mapping, and a wide field of view so you can focus on your work without errors. We tested both devices and checked the following:  
1. Mirroring function is needed: Testing required verifying that objects would appear properly as holograms to the user and that they would interact correctly when performing their actions in real-time.
2. It can be worn for more than 10 min: The subjects were required to jump and bend while jogging with the equipment on. In situations where they are out of breath or sweat, they must interact with objects that appear as holograms. The actual location of the hologram and the location coordinates of objects within the preset AR space may differ during gameplay. This can result in incorrect moves or affect the game score.  
The advantage and disadvantage of the Nreal development environment was that it could be played on mobile phones using Nebula. Nreal and your phone have a wired connection. In other words, it was judged to be more suitable for static experiments than dynamic exercise because the mobile phone had to be moved while holding it in the hand or attached to the body. Although it was lighter than HoloLens2, it was difficult to jump and run at the same time.  
Because HoloLens2 needs to communicate continuously, it consumes a lot of battery power and has the disadvantages of being heavier than Nreal and causing sweating in the area that touches the forehead. However, for the above reasons, HoloLens2 was finally selected as the AR equipment using a pilot test and purchased with funds to support this research. HoloLens2 is owned by the Chung-Ang University Sports Research Institute.
## 3 Network Slicing in 5G Networks
### 5G Network Slicing Architecture from the Perspective of Energy Efficiency
The contribution of the NS instance layer to the overall energy consumption of NS is not negligible, since the configuration of the equipment, the amount of exchanged information for the support of user and control plain functions, and the lifecycle management of NS resources directly contribute to the overall energy efficiency of the NS. **The resource layer** is the layer that provides all the essential virtual or physical resources and network functions needed to establish a service of NS instances (Figure 3) [19]. It includes all RAN, core, and cloud network elements, along with their corresponding communication links. The resource layer makes the highest direct contribution to the NS energy consumption and the main energy consumption optimization policies need to be implemented at this layer. The **management and orchestration** (MANO) entity (in Figure 3) is responsible for translating service models and use cases into operational NS(s). The MANO handles dynamic management tasks, including the initiation, termination, and monitoring of an NS and its VNFs. This is necessary for achieving the real-time scaling of NS resources or auto-healing of potential issues during the operation of NS. The **management part of the MANO entity** involves predicting and regulating the usage of resources needed for NS functionality. It encompasses envisioning virtual resources, assigning resources to users including performance or diagnosing issues, and performing tasks related to solving these issues. The **orchestration part of the MANO entity** entails the anticipation and organization of the network resources needed to provide NS services. This includes assigning resources according to the requirements of applications or users, the distribution of workloads, dynamic adjustment of the network to accommodate traffic demands, and performing other comparable functions. Since the MANO entity spans all three layers of the NS architectural framework, it can serve as the main entity for monitoring and controlling the energy consumption of NS. In order to take part in all architectural layers of the NS, most implementation policies related to the optimization of NS energy efficiency can be executed through the MANO entity. Examples that implement NFV at different network levels in the current MANO platforms are ETSI NFV [20], while examples of the practical implementation of MANO controllers that enable the reconfiguration of resources to VNFs on the fly are the open network automation platform (ONAP) [21] or open-source MANO (OSM) [22].
## 3 Results
### Results Using Camera Extrinsic Parameters from Training Setup  
Table 1 shows the results of our evaluation using synthetic depth images from Blender and the same camera extrinsics as in the setup for generating the training data. As a result, in these evaluation frames, only the object pose was randomized. It can be seen that our fine-tuned GQCNN outperformed the original Dex-Net 2.0 pre-trained model significantly.  
It is important to note that none of the predicted grasps from either model resulted in a collision during picking the object. All the failed grasps in our experimental setup can be attributed to the fact that the models failed to predict an appropriate grasp in the given task context. Thus, the predicted grasp resulted in a collision during placing the object. Having a higher success rate for picking the object than placing it is to be expected since out of all the possible grasps for picking the object, only a few will be feasible for placing it as well. As a result, the probability of predicting a grasp that will be successful for placing the object too is lower. The results suggest that fine-tuning a GQCNN on a small number of synthetic grasps labeled with the task-specifics in mind can significantly increase the probability of the GQCNN model predicting a grasp which will be successful for the whole task (both picking and placing the object).  
Given the assembly information (assembly order, objects' relative poses), scene setup, and the 3D models of the objects and the robot, our method can quickly be applied to new objects or new assembly tasks. As a result, the grasp prediction pipeline can be adjusted flexibly for novel scenarios, even within a single day, including scene setup in Blender, fine-tuning dataset generation, and GQCNN model training (especially since the scene, robot and camera setups usually do not change as frequently as the assembly tasks, so they only need to be created once).
## 8 Conclusions  
Wireless communication protocol plays key role for reliable transmission between the sensor nodes and cloud server in IoT. LoRa is one of the prominent and emergent wireless communication that meet the requirements of IoT in terms of minimum power consumption and long-range transmission. In this paper, we implemented the LoRaWAN architecture for the realizing of the real-time filling level of the bins with customized nodes. LoRa-based customized sensor nodes are deployed in the bins for establishing energy efficient nodes for transmitting the collected sensor data to the cloud server. FLoRa simulation based on LoRaWAN is performed for evaluating the energy consumption of the nodes by varying distinct parameters and also evaluated the metrics of LoRa network with plots. Finally, to show real application, an experimental set up is implemented for checking the performance of the proposed LoRa architecture for sensing the level of the bins in real-time scenario and logging the status of each of the bins in the cloud server.  
\begin{table}
\begin{tabular}{p{12.3 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**S.No** & **Parameters** & **Achievements** & & & & \\ \hline
1 & Energy consumption & Energy consumption of nodes are calculated in FLoRa simulation by changing distinct network parameters & Data rate & An increase in SF leads to transmission of a low data rate, so SF 7 is the optimal for sending a large amount of the data. & The sensitivity power is highest for the SF 7 at the 500 kHz. & 500 kHz. & ToA is good at 125 KHz and code rate 1, i.e., 56.58 ms. & 56.58 ms. & 56.58 ms. \\
2 & Data rate & An increase in SF leads to transmission of a low data rate, so SF 7 is the optimal for sending a large amount of the data. & The sensitivity power is highest for the SF 7 at the 500 kHz. & 500 kHz. & 56.58 ms. \\
3 & Sensitivity power & The sensitivity power is highest for the SF 7 at the 500 kHz. & The sensitivity power is highest for the SF 7 at the 500 kHz.  
**Author Contributions:** [PERSON] and [PERSON] made contributions to conception and manuscript writing; [PERSON] and [PERSON] examined and supervised this research and outcomes; [PERSON], [PERSON], [PERSON], and [PERSON] revised and polished the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the Deanship of Scientific Research, Taif University Researchers Supporting Project number (TURSP-2020/98), Taif University, Taif, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.