esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Correction Method of Internal Joint Operation Curve Based on Unsteady Flow
### Design and Validation of the Model  
#### 2.3.1 Mesh Division  
A diagram of the meshed geometry is shown in Figure 4. The compressor model consists of six parts, which are the inlet duct, impeller rotation domain, wheel back clearance, diffuser, separate compressor volute, and outlet transition. Among each fluid domain of the compressor, the structural mesh is used for the inlet duct, the outlet transition, and the impeller rotation domain, and the other parts use the unstructured mesh. Table 3 shows the number of elements for the compressor in each domain.  
The turbine model consists of six parts, which are the inlet duct, separate turbine volute, impeller rotation domain, wheel back clearance, outlet transition, and outlet duct. Among each fluid domain of the turbine, the structured mesh is used for the inlet duct, the outlet duct, and the impeller rotation domain, and the other parts use the unstructured mesh. Table 4 shows the number of elements for the turbine in each domain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fluid Domain** & **Mesh Type** & **Element Number** \\ \hline Inlet duct & Structured mesh & 73,219 \\ Separate Turbine Volute & Unstructured mesh & 620,189 \\ Impeller single-blade channel & Structured mesh & 203,038 \\ Wheel back clearance & Unstructured mesh & 242,283 \\ Outlet transition & Unstructured mesh & 527,878 \\ Outlet duct & Structured mesh & 169,575 \\ Total elements & & 3,866,562 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The elements for the turbine model.  
Figure 4: (**a**) Original compressor whole-stage fluid domain; (**b**) original turbine whole-stage fluid domain.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fluid Domain** & **Mesh Type** & **Element Number** \\ \hline Inlet duct & Structured mesh & 71,995 \\ Impeller single-blade channel & Structured mesh & 239,868 \\ Wheel back clearance & Unstructured mesh & 334,446 \\ Diffuser & Unstructured mesh & 406,381 \\ Separate compressor volute & Unstructured mesh & 515,346 \\ Outlet transition & Structured mesh & 110,449 \\ Total elements & & 3,357,561 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The elements for the compressor model.  
In a turbocharger model, the higher the number of elements, the better the computational accuracy. However, the larger the number of elements, the more computational resources are required, and a balance between simulation accuracy and computational speed is needed. Therefore, it is necessary to verify the independence of the number of elements in the turbocharger model. The results show that when the mesh number of the compressor reaches 3,358,000, the performance curve of the compressor is gently distributed and has met the simulation requirements, as shown in Figure 5a. When the turbine grid number reaches 3,867,000, the performance curve of the turbine is basically level, and the grid number has met the simulation requirements, as shown in Figure 5b.
## 4 Experimental Results
### Sample Scenario #2  
The sample video was made on a sunny day with excellent conditions for vehicle detection. We present two views based on the direction of cars to and from the camera. Figure 12 depicts the first frames of the input videos to be analyzed.  
The results of the analysis with respect to general information are shown in Table 5, and specific information is shown in Table 6. Both videos last a relatively long time with  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**View** & **Object Detection Model** & **Average Confidence Score** & **Direction of Cars** & **Count of Cars** & **Real Count of Cars** & **Accuracy [in \%]** \\ \hline Front & YOLOv4 & 97.99 & to back & 19 & 21 & 90 \\ Rear & YOLOv4 & 98.31 & to front & 13 & 14 & 93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specific information of the Sample scenario #1.  
Figure 12: First frames of the Sample scenario #2. (**a**) Front view of Sample scenario #2; (**b**) rear view of Sample scenario #2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Object Detection Model** & **Count of Cars without Using** & **Count of Cars Using** & **Distinction** \\  & **Intersecting Area** & **Intersecting Area** & **[in \%]** \\ \hline Faster R-CNN MobileNetV3-Large & \multirow{2}{*}{8} & \multirow{2}{*}{10} & \multirow{2}{*}{20} \\
320 FFN & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of the object tracking algorithm.  
a low traffic density. This factor causes high performance. Road character determines the optimal area of the ROI, which occupies only 7.55% of the total area. The rear view occupies 11.24% of the total area. Excellent visibility contributed to a high confidence score, which reaches 98.84%, and the direction of cars is detected correctly, i.e., down. Vehicles are counted with accuracy that reaches almost 100%. In this case, only one car is not detected, which seems to be a van and occupies a larger area.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Multi-Hop Extension  
Although the proposed method is designed for one-hop clusters, it can also be applied to multi-hop networks. When two clusters overlap each other, the nodes belong to both clusters can only joint the communication of one cluster, which will affect the communication of the other if they send out packets. So the simplest application of the proposed method to the multi-hop network is to stop the transmission of a cluster if the communication of any other overlapping clusters will affect it. This scheme can be summarized as:  
1. When the nodes that belong to multiple clusters are idle, all clusters can communicate independently.
2. If a node that belongs to multiple clusters has data to send to a destination node in one cluster, it must wait for the nodes in other clusters to be idle and then joins a transmission cycle in the destination cluster. The nodes in the other clusters stop trying to communicate when they receive the RTS packet from the overlapping node and wait until it finishes sending the data packet, or timeout.
3. If a node that belongs to multiple clusters receives an RTS packet from one cluster, it needs to check the state of the other clusters. The CTS packet can only be sent out when all the other clusters are idle. The nodes in the other clusters stop trying to communicate when they receive this CTS packet and wait until they receive the ACK/NACK packet sent by the overlapping node.  
This scheme does not need to synchronize the overlapping clusters, which is difficult to implement in underwater environments. However, because the overlapping clusters cannot work concurrently when there are nodes belonging to multiple clusters needing to join the communication of one cluster, the transmission efficiency in the multi-hop situation is not as high as in the one-hop situation. More sophisticated synchronization or using multi-channel may improve the performance of the proposed method in multi-hop environments. These technologies are beyond the scope of this paper.
# Security, Privacy, and Reliability in Digital Healthcare Systems Using Blockchain
## 5 Proposed Algorithms
### Authentication Mechanism  
We proposed our own authentication method where we used a ring signature mechanism. An entity to be authenticated needs to prove its claimed attributes by declaring that it is eligible and its corresponding private signature key. (1) Unilateral authentication: Only one pass is needed in unilateral authentication, where only one of the two communicating entities is authenticated by others. A simplified authentication mechanism is shown in Figure 4. In the unilateral authentication mechanism, the authentication process is initiated by the claimant \(e_{i}\), and it is authenticated by the verifier \(e_{j}\). The form of \(Token_{i,j}\) is: \(Token_{i,j}=N_{i}||ID_{i}||T\) ext\(||s_{ki}(N_{i}||ID_{i}||T_{ext})\), where \(s_{k,j}(X)\) indicates the signing on the message \(X\) using the private signature key \(s_{ki}\) of claimant \(e_{i}\). \(N_{i}\) is a non-repeating random number that is used to prevent valid authentication information from being accepted at a later time. \(T_{ext}\) is not a necessary data field for authentication, but it can be added for other purposes. Claimant \(e_{i}\) initiates the authentication process by sending \(Token_{i,j}\) to the verifier \(e_{j}\). Upon receiving \(Token_{i,j}\), the verifier \(e_{j}\) first ensures that it possesses a valid public key of the claimant \(e_{i}\). Then, the verifier \(e_{j}\) verifies \(Token_{i,j}\) by generating a signature on the unsigned message through a further comparison with the signature received in \(Token_{i,j}\). Figure 6: Proposed structure and flow of transactions through our proposed topology. (2) Mutual authentication: When two participants are mutually authenticated by each other, one more inverse pass is involved, as described in the algorithm and the mathematical equation. The form of \(Token_{j,i}\) is: \(Token_{j,i}=N_{j}\mid\mid ID_{j}\mid\mid\text{Text}\mid ss_{k,j}\) (\(N_{j}\mid\mid ID_{j}\mid\mid\text{Text}\)). After \(e_{i}\) is authenticated by \(e_{j}\), the two parties exchange their roles with each other, which means that \(e_{i}\) becomes the verifier and \(e_{j}\) becomes the claimant. \(e_{j}\) initializes another round of the authentication process and sends \(Token_{j,i}\) to the verifier \(e_{i}\). Upon receiving \(Token_{j,i}\), the verifier \(e_{i}\) first ensures that it posses a valid public key from \(e_{j}\). Then, the verifier \(e_{i}\) verifies \(Token_{j,i}\) by generating a signature on the unsigned message and further compares it with the signature received in \(Token_{j,i}\). ```
1:\(I\gets 100\)
2:if\(C_{ID}\gets Valid\)then
3: Add \(CID\leftarrow\)\(Blockchain\)
4:else
5: Add(\(BN\), \(CID\))
6: Grant access (\(CID\), \(UName\), \(PK\))
7:endif
```  
**Algorithm 1**Admin Node  
```
1:Input: \(ID\) and key requested from \(N-admin\)
2:Output: Get access to \(PHL\) transactions
3:Initialization: \(PHL\) should be a valid node. \(PHL\) can read/write/grant/revoke \(EHR\) records.
## 8 Conclusions and Future Directions  
In this paper, we discussed a novel approach to deal with the challenge of power consumption issues arises due to the high value of hyper-period in real-time IoT systems. We proposed an adaptive strategy for finding the optimal threshold. The optimal threshold dynamically adjusts according to IoT resources and their profiles. The proposed algorithm uses the optimal threshold to find a task set with a hyper-period below this threshold. It allows tasks only to admit if it contributes to a lower value of the hyper-period than the optimal threshold. Other approaches like Harmonization and static threshold is very popular, but they are not scalable for a dynamic context of Real-time IoT in a sense that they are not catering for IoT resource profiles and, moreover, allow a specific set of period values. The significance of the proposed approach is highlighted with a series of experiments and comparisons with its counterparts in terms of acceptance ratio, overhead and response time and the results have indicated significant improvements regarding CPU clock cycles and thus the power consumption. Moreover, within the optimal threshold, the input tasks can have any natural number of period value that is considered to be crucial in the design of tasks for more dynamic and advance contexts like IoT and Industry 4.0. As a future work, it is worth considering applying the approach on more advanced scheduling algorithms designed explicitly for IoT systems in a realistic IoT environment and highlight the impact of any natural period value on the scalability of these IoT applications.  
[PERSON] conceived the idea for this paper, designed the experiments and wrote the paper; [PERSON] assisted in algorithms implementation; [PERSON] assisted in IoT resource profiling formulation. [PERSON] assisted in Model design and simulation; [PERSON]-[PERSON], K.K. and D.K. conceived the overall idea of IoT scheduling under constrained environment, proof-read the manuscript and supervised the work.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00501, Development of Self-learnable common IoT SW engine), and this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2015R1D1A1A01060493).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 15: Performance Comparison of RM and EDF algorithms on Raspberry Pi-based Embedded IoT Device using various approaches. (**a**) performance of RM algorithm—Existing Approaches vs. Proposed Approach; (**b**) performance of EDF algorithm—Existing Approaches vs. Proposed Approach.
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## 2 Materials and Methods
### Modeling the User Clustering Process as a Game
Definition of the game:  
* the \(ap_{i}\), \(i\)\(\in\)\(\{1,\ldots,K\}\) AP agents are the players;
* \(A_{i,j}=\{(P_{1},P_{1}),(P_{1},P_{2}),\ldots,(P_{N},P_{N})\}\) is the finite set of the actions of the tuple of players \((ap_{i},ap_{j})\), i.e., each group of neighbor player should adjust their transmit power that influences the user terminals to connect or not to the specific AP. An action profile \(\mathbf{a}\) = \((a_{1},a_{2},\ldots\ a_{K})\) is defined, where each element \(a_{i}\) of the vector represents one action for each tuple of players, and each action profile corresponds to an arrangement **s** of APs and users;
* the payoff or the utility function \(u_{ij}\) of a group of players in this case is \(u_{ij}=\left|L_{i}(t)-C_{i}(t)\right|+\left|\left[L_{j}(t)-C_{j}(t)\right]\right|\), i.e., the expected difference between the total load and the sum of APs' capacity. The steps necessary to implement the cooperative clustering process are described by Algorithm 7. ```
estimate the difference between the demand and capacity for each group of two neighbor Aps. ```
1:for i = 1 to N do
2:for j = i+1 to N do
3: estimate \(\left|\frac{L(t)-C_{i}(t)}{C_{i}(t)}\right|\) and \(\left|\frac{L(t)-C_{i}(t)}{C_{j}(t)}\right|\) using the average packet delay or the lengths of the transmit queues. Check if the APs are load balanced. 4:if\(\left|\frac{L_{i}(t)-C_{i}(t)}{C_{i}(t)}\right|>\left|\frac{L_{i}(t)-C_{i}(t)}{C_{ i}(t)}\right|\). Increase the transmit power \(P_{i}\) of AP\({}_{i}\) and decrease the transmit power \(P_{j}\) of AP\({}_{j}\). 5:else Decrease the transmit power \(P_{i}\) of AP\({}_{i}\) and increase the transmit power \(P_{j}\) of AP\({}_{j}\). 6:endif
7:endfor j
8:endfor i
9: Each user checks the strengths of the received signal from each AP and chooses the AP from which the received signal has the highest power. ```  
**Algorithm 7** Cooperative user clustering mechanism
# Diagnosis by SAM Linked to Machine Vision Systems in Olive Fitting Machines
## 4 Discussion
### Future Work  
The following lines of research are planned to enable full real-time and closed-loop deployment of the system:  
Operator feedback and active ejection: The current vision module can already display, in real time, the share of olives travelling in boat or pivoted positions. Showing these metrics on the HMI enables operators to fine-tune brush height or conveyor speed until the misalignment rate is minimized, thereby closing a manual feedback loop. A lightweight, quantized version of SAM (\(\geq\)50 fps) could also drive an array of air nozzles located upstream of the pitting knives: any mis-oriented olive would be blown back onto the in-feed conveyor for a second pass, further reducing the fraction of off-axis fruit without interrupting line throughput.  
Digital-twin integration: The same orientation data can be streamed to a digital-twin layer that supervises the pitting line and automatically adjusts variables, such as brush height or belt speed. A pertinent precedent is the framework developed for an electrical submersible pump by [PERSON] et al. [42], where continuous sensor streams are synchronized with an electromechanical model to enable predictive control. Adapting this architecture to a table olive pitter would allow the image-based orientation signals reported here to update PLC set-points in real time, closing the feedback loop and enabling data-driven optimization.  
Real-time hybrid architectures: The current pipeline validates SAM offline, yet factory floor deployment demands inference latencies below 20 ms per frame. Two complementary strategies will therefore be pursued:  
1. Cascaded SAM \(\rightarrow\) lightweight model: Full-size SAM will be executed once on \(\sim\)5 k representative frames to generate high-quality masks that are then used to distil a compact network (e.g., a MobileNet-V3 encoder with a three-level FPN decoder). A Dice + IoU distillation loss will transfer SAM's spatial priors, while TensorRT INT8 quantization is expected to reduce model size to \(<\)12 MB and runtime to \(\approx\)8 ms on an NVIDIA Jetson Orin-NX.
2. FastSAM/MobileSAM variants: Recent works, such as FastSAM [43] and MobileSAM [22], replace the heavy ViT encoder with a YOLO-N segmentation head or a ShuffleViT backbone, achieving 50-120 fps on a laptop GPU. Both variants will be benchmarked on olive images, fine-tuning only the prompt encoder (points + box) to keep the annotation cost negligible.  
A hybrid schedule--FastSAM on every frame, full SAM every 30 s for self-supervised drift correction--will be evaluated to maintain IoU \(\geq\) 0.94 while meeting the 50 fps throughput of the pitting line.
## 4 Discussion and Conclusions
### Future Work
; writing--review and editing, C.F.C. ; visualization, C.-A.H. and L.M.I. ; supervision, L.M.I. and A.S.-U. ; project administration, L.M.I. and A.S.-U. ; funding acquisition, L.M.I. and A.S.-U. All authors have read and agreed to the published version of the manuscript. The research leading to these results has received funding from the EEA Grants 592 2014-2021, under Project contract no. 33/2021. This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI-UEFISCDI, project number PN-III-P2-2.1-PED-2019-2434, within PNCDI III. Not applicable. The data has been acquired as part of the projects acknowledged in the manuscript, and cannot be made public, considering GDPR regulations and the content of the informed consent signed by the patients. The concepts and information presented in this paper are based on research results that are not commercially available. Future commercial availability cannot be guaranteed. The following abbreviations are used in the manuscript: \begin{tabular}{l l} CVD & Cardiovascular disease \\ CAD & Coronary artery disease \\ XA & X-ray coronary Angiography \\ OCT & Optical coherence tomography \\ PCI & Percutaneous coronary intervention \\ FFR & Fractional flow reserve \\ CABG & Coronary artery bypass graft \\ CFG & Computational fluid dynamics \\ CCTA & Coronary computed tomography angiography \\ ML & Machine Learning \\ IVUS & Intravascular ultrasound \\ DNN & Deep neural network \\ DL & Deep learning \\ ANN & Artificial neural network \\ CNN & Convolutional neural network \\ RNN & Recurrent neural network \\ FSL & Few-shot learning \\ ReLU & Rectified linear unit \\ GRU & Gated recurrent unit \\ MLD & Minimum lumen diameter \\ \%DS & Percentage diameter stenosis \\ NPV & Negative predictive value \\ PPV & Positive predictive value \\ MAE & Mean absolute error \\ ME & Mean error \\ MSE & Mean squared error \\ LAD & Left Anterior Descending artery \\ LCx & Left Circumflex artery \\ RCA & Right Coronary Artery \\ Arch. & Architecture \\ Corr. & Correlation \\ TP & True positive \\ \end{tabular} \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Input Features** & **Output Features** & **Activation** & **Regularization** \\ \hline FC & 2048 & 1024 & ReLU & Dropout \\ FC & 1024 & 1 & Sigmoid & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The bidirectional GRU that was added on top of the architecture that is presented in Table 3, used for CNN + RNN approach. The layer that is highlighted in purple is only used for the regression and the classification approach (not for the FSL approach). For the regression approach, we used no activation function and the activation function that is highlighted in green is used for the classification approach (not for the FSL approach).
## 4 Simulation Analysis and Measurement Verification
### Analysis of Balise I/O Characteristics
Subsequently, the characteristics in water debris are predicted using an electrical model, which indicated that the uplink FSK signal attenuation underwater is approximately 0.62 dB greater than in free space. Finally, the balise is measured in water debris, and the actual results were compared with the calculated expectations. When the balise operates in water debris, the current decreases to approximately 86.8 mA due to signal attenuation. The theoretical model predicting the performance calculates a value of 86.9 mA in water debris, which closely aligns with the measured value of 86.8 mA. The minimal difference between the predicted and actual values validates the accuracy of the theoretical model and demonstrates an excellent correlation between the projections and practical observations. The experimental results closely aligned with the calculated predictions.
# Design and Test of a Single-Row Harvesting and Cutting Integrated Handheld Garlic Harvester
## 2 Materials and Methods
### Orthogonal Analysis Method
#### 2.4.5 Regression Modeling and Significance Testing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Square** & **Df** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 0.0382 & 14 & 0.0027 & 34.04 & \(<\)0.0001 & significant \\ A: Conveying speed & 0.0173 & 1 & 0.0173 & 215.97 & \(<\)0.0001 & \\ B: Tool speed & 0.0000 & 1 & 0.0000 & 0.1755 & 0.6816 & \\ C: Groundbreaking angle & 0.0001 & 1 & 0.0001 & 0.6491 & 0.4339 & \\ D: Forward & 0.0092 & 1 & 0.0092 & 114.48 & \(<\)0.0001 & \\ AB & 0.0000 & 1 & 0.0000 & 0.0000 & 1.0000 & \\ AC & 0.0000 & 1 & 0.0000 & 0.0000 & 0.4800 & \\ AD & 0.0000 & 1 & 0.0000 & 0.0000 & 0.4800 & \\ BC & \(2.25\times 10^{-6}\) & 1 & \(2.25\times 10^{-6}\) & 8.39 & 0.8694 & \\ BD & 0.0000 & 1 & 0.0000 & 0.0000 & 0.5855 & \\ CD & 0.0007 & 1 & 0.0007 & 0.0000 & 0.0104 & \\ A\({}^{2}\) & 0.0018 & 1 & 0.0018 & 0.3185 & 0.0003 & \\ B\({}^{2}\) & 0.0006 & 1 & 0.0006 & 1.64 & 0.0149 & \\ C\({}^{2}\) & 0.0103 & 1 & 0.0103 & 1.64 & \(<\)0.0001 & \\ D\({}^{2}\) & 0.0008 & 1 & 0.0008 & 0.3185 & 0.0079 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regression equation analysis of variance for garlic injury rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Square** & **Df** & **Mean Square** & **F-Value** & \(p\)**-Value** & \\ \hline Model & 0.0488 & 14 & 0.0488 & 21.96 & \(<\)0.0001 & significant \\ A: Conveying speed & 0.0280 & 1 & 0.0280 & 176.65 & \(<\)0.0001 & \\ B: Tool speed & 0.0118 & 1 & 0.0118 & 74.63 & \(<\)0.0001 & \\ C: Groundbreaking angle & 0.0069 & 1 & 0.0069 & 43.25 & \(<\)0.0001 & \\ D: Forward & 8.333 \(\times\) 10\({}^{-6}\) & 1 & 8.3 \(\times\) 10\({}^{-6}\) & 0.0525 & 0.8221 & \\ AB & 0.0000 & 1 & 0.0000 & 0.0000 & 1.0000 & \\ AC & 0.0000 & 1 & 0.0000 & 0.0000 & 1.0000 & \\ AD & 0.0000 & 1 & 0.0000 & 0.0000 & 1.0000 & \\ BC & 0.0013 & 1 & 0.0000 & 8.39 & 0.011 effect on the net harvesting rate. The BC interaction term \(p<0.05\), indicating a significant effect, proves that there is an interactive effect of the tool speed and breaking angle on the missed digging rate.