esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Video Transmission and Visualization
#### 4.5.1 Video Streaming  
Several methods and streaming protocols were considered for the transmission of live video to the HoloLens, including Real-Time Messaging Protocol (RTMP), Real-time streaming protocol (RTSP), sending frames as individual messages through Kafka, and direct connection via network sockets.  
RTMP is a flash-based video streaming protocol routing the video stream through a server and supported natively by the DJI SDK. However, RTMP induces high latency, ranging from one to several seconds, depending on the the network conditions and server location and capabilities. This makes RTMP a good choice for non-reactive video watching (e.g., for a spectator) but unsuitable for remote control. RTSP and HLS (Http Live Streaming) exhibit similar behavior, and hence they were discarded for use in the presented solution.  
Sending video frames through Kafka was implemented in an effort to simplify the architecture by routing all data through the same hub (Kafka). Two flavors were tested: sending encoded frames directly from the UAV interface app to the HoloLens and decoding the frames first on Android and then forwarding the decoded data to the HoloLens. Naturally, the first flavor minimizes bandwidth requirements, while the second minimizes the processing strain on the HoloLens. On a local (Wi-Fi) network, bandwidth is less of a concern, hence latency was reduced when streaming decoded frames. However, the frame rate has proved to be too low (at about 11 FPS) in both cases. This is attributed to the HoloLens using a REST API to connect to Kafka (as there are no clients available for UWP), which induces an overhead for each message request. Therefore, this approach was also discarded.  
The solution finally selected made use of Network Sockets. This type of connection is Point-to-Point, meaning that once the connection is established, no additional overhead is required for the flow of data. With this implementation, in contrast to the two previous methods, there is no intermediate server between the drone and the HoloLens. The Android device running the UAV interface app acts as a server with the HoloLens being the client. To test this approach, we connected the drone and the HoloLens to alleviate network bottlenecks. We measured the Android-to-HoloLens latency below 100 ms and the frame rate exactly 30 FPS, which is the native frame rate of the drone's camera. Since both requirements were met, this method is the most appropriate for piloting the drone through the live video feed from the camera.
# Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
## 4 Inverse Kinematics
### Inverse Kinematics Simulation and Joint Failure Scenario  
In comparison with the DRL algorithm, we modeled the problem of the end effector of a robotic manipulator moving from its initial position to a drawer, grasping it, and pulling it open. In the scenario where one of the joints is malfunctioning, the robot deviates from the calculated trajectory. The desired end-effector motion is formulated as the path connecting three key points--the initial position (\(p_{i}\)), the drawer position (\(p_{d}\)), and the pulled-out position (\(p_{p}\))--ignoring the internal motion of grippers. This setup is compatible with the motion of the robot in the simulation environment.  
The robot's parameters are shown in Table 1. The robot's base is located at the coordinates of \(X=0\) and \(Y=0\). The end effector moves through the initial position, the drawer position, and the pulled-out position, sequentially. The positions and Euler angles representing the configuration of the end effector at each point are listed in Table 2. The end-effector trajectory was solved using the iterative Newton-Raphson numerical method with a step size of 0.01 and a convergence tolerance of \(1\times 10^{-4}\).  
To demonstrate the impact of a joint malfunction, we simulated the scenario where one of the joints was fixed (the third joint), i.e., it was not actuated during the task. This is equivalent to constraining the corresponding joint angle to a constant value. We then analyzed the reachability and performance of the robot in completing the task of opening a drawer. The kinematic analysis was performed by solving the inverse kinematics and evaluating whether the end effector could reach the desired position and complete the expected trajectory.
## 2 Related Works
### Direct Privacy Protection  
The direct privacy protection approach aims to directly hide crucial transaction information and is primarily based on zero-knowledge proof technology. The critical aspect of this approach is that when the prover proves the correctness of a message to the verifier, they do not need to disclose specific content. As a result, the verifier cannot obtain any unnecessary information, thereby achieving the goal of protecting transaction privacy.  
Zcash, proposed by [PERSON] et al. [27], is a typical example of applying the non-interactive zero-knowledge proof. When proving the validity of transactions, Zcash does not reveal any private information to the relevant verifiers, thus ensuring strong privacy. Although Zcash achieves good anonymity, the generation of initialized parameters requires the participation of trusted parties, making it inherently weakly centralized. Furthermore, the efficiency and throughput of proof generation in Zcash are not ideal. To address these issues, [PERSON] et al. [28] designed the Bulletproof protocol, which uses short proof and does not rely on the trusted setup. It effectively improves the speed of proof generation through secure multiparty computation. [PERSON] et al. [29] applied the Bulletproof protocol to data privacy protection in energy trading scenarios. They used the Bulletproof protocol to provide range proofs for transaction amounts, hiding the specific transaction amounts. Additionally, to address the issue of the authenticity of auditing receipts, they combined zero-knowledge proof and the ElGamal encryption technique, enabling regulators to reliably monitor transaction privacy data without revealing commitments. As the number of network nodes increases, the data volume corresponding to computing proofs in the Bulletproof protocol also increases, implying a higher cost of storing this data in the blockchain and posing new challenges.
# Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery
###### Abstract  
Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwanoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.  
deep learning; image segmentation; brain tumors; radiosurgery; magnetic resonance imaging 2021
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 3 Proposed CAFT Method
### Extracting Features of Images  
Features of the input image are extracted using a convolution network. The feature encoder network applies to both images \(I\)1 and \(I\)2, and maps input images to dense feature maps with low resolution. Encoder \(g_{\theta}\) extracts features at 1/8 resolution.  
\[\mathrm{g}_{\theta}=\mathbb{R}^{H\times W\times 3}\rightarrow\ \mathbb{R}^{H/8\times W/8\times D} \tag{2}\]  
In Equation (2), the resolution changes, where D = 256 is set. The feature encoder consists of a total of six residual blocks: two at 1/2 resolution, two at 1/4 resolution, and two at 1/8 resolution, as shown in Figure 9.  
The input image feature map is divided into two 32 channels (\(x_{0^{\prime}},x_{0^{\prime\prime}}\)) to calculate the cross-stage. The number of convolutions in the cross-stage block is a hyper-parameter determined by the designer or user. In the proposed technique, the purpose of the cross-stage block at the encoder stage is to balance the overall amount of computation with preserving the spatial information of meaningful pixels as much as possible. Therefore, in this paper, the convolution in the cross-stage block of the encoder is fixed at k = 2, and the output of each layer is represented as Equation (3).  
\[\begin{array}{ll}x_{1}=W_{k}*[x_{0^{\prime\prime}}]\\ x_{2}=W_{T}*[x_{0^{\prime\prime}},x_{1}]\\ x_{ll}=W_{ll}*[x_{0},x_{1},x_{2},x_{0^{\prime\prime}}]\end{array},\quad\begin{array}{ll}W _{1}^{\prime}=f(W_{k},g_{0^{\prime\prime}},g_{1},g_{2},\cdots,g_{k-1})\\ W_{2}=f(W_{T},g_{0^{\prime\prime}},g_{1},g_{2},\cdots,g_{k})\\ W_{ll}=f(W_{ll},g_{0^{\prime}},g_{1},g_{2},\cdots,g_{k^{\prime\prime}})\end{array} \tag{3}\]  
Figure 9: Feature and context encoder structure.  
As can be seen from Equation (3), the proposed technique has small parameters while maintaining gradient information compared to CSPNet. Figure 10 shows the update block structure in the optical flow estimation. Additionally, the context network extracts features only from the first input image \(I_{1}\). The structure of the context network \(h_{\theta}\) is the same as the feature extraction network. The feature network \(g_{\theta}\) and context network \(h_{\theta}\) constitute the first step and are performed only once.
## 4 Result and Evaluation
\begin{table}
\begin{tabular}{c|c c c} \hline \hline
**RMS**Method** & **Ideal Daltonization** & **Proposed Method** & **Existing Method** \\ \hline Group A & 95.65 & 109.59 & 138.46 \\ Group B & 118.77 & 120.48 & 154.36 \\ Group C & 58.29 & 128.13 & 103.07 \\ Group D & 71.22 & 94.78 & 105.88 \\ Group E & 144.20 & 172.05 & 175.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RMS results for each image; a smaller RMS represents a bigger contrast. Figure 10: Pictures in odd rows from left to right are: images captured by the scene camera, \(I_{S}\); the ideal compensation results, \(I_{T}\), by applying the Daltonization process to \(I_{S}\); the result using our proposed method; and the result using the existing method [13]; pictures in even rows are from the deuteranopia simulation following the same order of methods as given above.
## Appendix A  
In order to present the VMD algorithm, we introduce an application example on a simple signal. The artificial signal is consist of three sine signals, which is defined as:  
\[f(t)=\cos(2\pi f_{1}t)+0.25\cos(2\pi f_{2}t)+0.16\cos(2\pi f_{3}t) \tag{1}\]  
where \(f_{1}=3\) Hz, \(f_{2}=12\) Hz, \(f_{3}=28\) Hz, and the sample frequency is 1000 Hz, sampling time is 1 s. It is obvious that the artificial signal can be decomposed into three modes. As it mentioned above, the decomposition results depend on the mode number, which is defined as K. Therefore, we select K as 3. The decomposition result is presented in Figure 11. Observed from Figure 11, the artificial signal is decomposed effectively, where three modes are clearly separated.  
Then we would like to discuss the value choice of K. If K is too small (K = 2), the signal is underbinning. Then the decomposed results are shown in Figure A2. It is apparent that each mode is either shared by neighboring modes.  
If K is too small (K = 4), the signal is overbinning. Then the decomposed results are shown in Figure A3. It seems that each mode is separated clearly as pure sine signal. However, compared with Figure A3a,b, the frequencies of two modes are same, which is like mode duplication.  
Based on what discussed above, we can see that the VMD algorithm is able to decomposed signal in an adaptive method. As same as a classical shortcoming of many segmentation algorithms, it is vital to preset the number of clusters in initial stage. For the VMD algorithm, the choice of K is important. Moreover, the parameter \(\alpha\) of the data-fidelity constraint, which influences the tightness of the band-limits, is also important in the decomposition. The details of the VMD parameters can be referred in [19].
## 2 Materials and Methods  
### Technical Discussions on Related Works  
As discussed earlier, the importance of WMAC and wireless routing protocols is seriously considered in many recent research works. Predominantly, the research initiatives focused on medium control, channel allotment, routing efficiency and energy constraints to ensure fault-protective sensor nodes during independent data transmissions. Additionally, the guarantee to increase network stability and lifespan is the most essential quality for well-organized WSNs. Among the unexpected environmental conditions and network uncertainties, the assurance of successful data communication can be attained through a deep technical survey and a newly created solution. The following sections of this proposed review article discuss the details of WMAC concerns and routing concerns to achieve energy-controlled lifetime enhancement routines.  
On the whole, the MAC protocol basically has two variants, such as the carrier sense multiple access with collision detection approach (CSMA/CD) and the carrier sense multiple access with collision avoidance approach (CSMA/CA), used on wired networks and wireless networks, respectively. Under the CSMA/CD approach, the network interface card (NIC) of any node supervises the availability of the other node's activity on the wired channel (collision) to start data transmission. The detection of collision at the NIC delays the data transmission of a particular node. On the other hand, the CSMA/CA approach monitors the wireless channel for a random duration to prevent collision during data transmission. In addition to MAC layer functions, logical link control (LLC) functions are operated in a data link layer for multiplexing, de-multiplexing and network layer interfacing services. In any event, the WMAC (CSMA/CA), LLC and wireless routing functions consume much more energy in WSNs.  
In this case, routing protocols execute neighbor discovery functions (route requests and route replies), routing table organizations, link establishment functions and data routing functions continuously. Particularly, neighbor discovery jobs consume crucial amounts of energy in the idle listening state. From these discussions, this article finds energy optimization challenges through multi-layer network protocols to assure the lifetime of the WSN.
## 4 Analysis and Discussion of Experimental Results
### Result Analysis
#### 4.3.5 Comparative Analysis of Evaluated Values for Different Recommendation List Lengths  
To assess the performance of job recommendation methods across different sample sizes and recommendation list lengths, this paper expands the experimental sample size and generates recommendation lists of different lengths. The experimental dataset used for this comparative analysis comprises 110 targeted graduates, 1200 graduated students, and 1027 job positions. It encompasses various specializations within the management disciplines, including business management, accounting, financial management, marketing, and tourism management.  
By examining Figure 10, Figure 14, and Table 5, it is evident that the assessed values of AR@5 for the five recommended methods exhibited a decline with the augmentation of data. The most substantial decrease occurred in the case of JRSR, dropping from 3.743 to 3.462, which represents a 7.5% reduction. As the length of the recommendation lists increases for the five methods, their AR evaluations correspondingly decrease. In Figure 14, it is evident that the assessed value of AR@20 significantly exceeds that of AR@5, with the MS value-added reaching its peak at 0.301 and the TF-C value-added registering its lowest at 0.114. These findings indicate that both the size of the experimental data sample and the length of the recommendation list directly impact the recommendation satisfaction rate.  
By examining Figure 11, Figure 15, and Table 6, it is apparent that as the sample size of the experimental data expands, the assessed values of NDCG@5 for the five recommended methods decrease. The most substantial decrease is observed in MS, where it is 0.089, while the smallest decrease is in RSCF, where it is 0.03. Moreover, with the increase in the length of the recommended list, there is a decrease in its NDCG assessment. The most significant decrease is observed for GERS at 12.81%, while the smallest decrease is for MS at 4.9%. These findings indicate that both the size of the experimental data sample and the length of the recommendation list directly impact the normalized discounted cumulative gain assessment value.  
Figure 14: Analysis of AR evaluation values for different recommendation list lengths.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & AR@5 & AR@10 & AR@15 & AR@20 \\ \hline TF-C & 2.532 & 2.528 & 2.471 & 2.418 \\ MS & 2.862 & 2.959 & 2.762 & 2.561 \\ GERS & 3.158 & 3.269 & 3.136 & 2.948 \\ RSCF & 3.258 & 3.163 & 3.216 & 3.117 \\ JRSR & 3.462 & 3.298 & 3.272 & 3.189 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AR assessment values for different recommended list lengths.
###### Abstract  
Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.  
ontology; semantic similarity; transport infrastructure; back propagation neural network; spotted hyena optimizer
## 4 Conclusions  
15 wt % B4C-Al and 20 wt % B4C-Al composites were prepared using a powder metallurgy process. Microstructure and mechanical properties of Al-B4C composites were studied and a brief summary of the results is given below:  
1. The yield strength of the 15 wt % and 20 wt % B4C-Al composites prepared by the high-energy ball milling process is 184 MPa and 213 MPa, respectively, which are \(\sim\)70% and \(\sim\)97% higher than those of the pure Al samples prepared at the same condition. The tensile strength is 281 MPa and 306 MPa, respectively, which is \(\sim\)47% and \(\sim\)60% higher than the pure Al samples. On the contrary, the yield strength and tensile strength of the composites prepared by the low-energy ball milling process increases slightly with the B4C particles reinforcement.
2. Microstructural, fracture surface analysis and neutron radiography indicated that composites prepared by high-energy ball milling have a homogeneous distribution of B4C particles in the Al matrix and tight adhesions between the reinforced particles and the matrix, which give rise to better load transfer from the matrix to the reinforcement and dramatically enhance the yield and tensile strength of Al-B4C composites.
3. High-energy ball milling is an effective process to prevent the clustering of the reinforcement particles in the matrix even if the matrix-to-reinforcement particle size ratio was nearly 10. The following pressing, sintering, hot-extrusion, and hot-rolling P/M processes are favorable to gain good adhesions between the reinforced particles and the matrix, which remarkably enhances the mechanical properties of the Al-B4C composites.  
This work is supported by the foundation of China Academy of Engineering Physics (JMZD 200903). The authors acknowledge [PERSON], [PERSON], [PERSON], and [PERSON] for SEM, XRD, and mechanical property analysis. Moreover they are grateful to [PERSON] and [PERSON] for the neutron radiography analysis, and [PERSON], [PERSON], [PERSON], and [PERSON] for the modification of the paper.  
[PERSON] designed and performed the experiment, analyzed the data, and wrote the paper; [PERSON] and [PERSON] performed the experiment together; [PERSON] contributed analysis of the data; [PERSON] and [PERSON] conceived the experiments.  
The authors declare no conflict of interest.