esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this paper, the power and cost constraints inherent to UAV platforms are rigorously considered. By utilizing the RK 3566-embedded platform, which has significantly lower power consumption and cost compared to the Nvidia Jetson Nano, efficient object detection is achieved that not only meets but exceeds the performance of state-of-the-art methods like FastestDet and YOLO-FastestV2 in both detection precision and speed. This advancement significantly pushes the boundaries of practical UAV applications, enabling more accessible and effective deployment of UAV systems in real-time scenarios. Specifically, a memory-efficient lightweight object-detection algorithm designed for embedded UAV platforms is presented. The proposed model, based on the FasterNet-16 backbone and PConv convolution, significantly reduces memory bandwidth requirements compared to traditional DSC, enabling more efficient utilization of limited computational resources in embedded devices and resulting in faster inference speeds. Through extensive experiments, it is demonstrated that the model outperforms current state-of-the-art lightweight detection models, such as FastestDet, in terms of inference speed, while maintaining competitive detection accuracy.  
Despite these strengths, one of the main limitations of the proposed model is its lower detection precision compared to more complex algorithms running on higher-performance hardware. This trade-off is a direct consequence of the focus on creating a lightweight model that can operate effectively within the constraints of low-power embedded systems. As a result, while the model achieves satisfactory performance in many scenarios, it may not be as precise as more computationally intensive models in detecting smaller or more obscure objects, particularly in complex or cluttered environments.
# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 3 Proposed Approach
### Second Phase
In a simplified way, LIME works by first generating random perturbations (fake observations) around the instance to be explained (original instance). Secondly, it calculates a similarity distance between the perturbations and original instance (e.g., Euclidean distance). Thirdly, it gets the predictions for the perturbations. Followed by picking a set of perturbations with better similarity scores and calculate weights (distance scaled to \([0,1]\)  
Figure 4: Visualising LRP heatmaps for local (individual (TP (**a**) and TN (**b**))) and global (all TP (**c**) and TN (**d**)) samples in the Telecom Churn testing set. Feature sequence of (**a**,**c**) is same to (**b**,**d**). Figure 3: Structure for the LRP [41]. that represent the importance of these perturbations in respect to the original instance. Once perturbations, predictions, and weights have been calculated, it builds a weighted linear regression model, where the coefficients of this simple model will help to explain how changes in the explanatory variables affect the classification outcome for the instance that wishes to be explained. LIME focuses on training local surrogate models to explain individual predictions. Hence, it can be applied to any DL model. However, in terms of time complexity, it is simpler than SHAP, but yet requires sometime to train. **Shapley Additive Explanations (SHAP):** SHAP is an approach based on game theory to explain the output of any (but mainly traditional) ML models [33]. It uses Shapley values from game theory to give a different perspective to interpret black box models by connecting the optimal credit allocation with local explanations and their related extensions. This technique works as follows: To get the importance of feature \(X_{i}\), it first takes all subsets of features \(S\), other than \(X_{i}\). It then computes the effect of the output predictions after adding \(X_{i}\) to all the subsets previously extracted. Finally, it combines all the contributions to compute the marginal contribution of the feature. To avoid recalculation of these subsets, SHAP does not retrain the model with the feature in question left out. It just replaces it with the average value of the feature and generates the predictions. The features which are strongly influential to the model output from the input values are shown. Typically, these influential features are shown in red and other less influential features in blue. This provides a useful clear explanation for simpler models. It is currently (as of this document date) limited to application to traditional ML models due to its time complexity. In this work, we used kernel SHAP. Whilst a line of investigation could focus on other types of SHAP in order to change the time complexity, we note that is well documented in general that SHAP is a time-intensive technique.
## 5 IETF ROLL Working Group
### Leveraging Upon CoAP to Realize the IoT
#### 6.3.4 Communication with Sleepy Nodes  
Sleepy Nodes are network nodes that sleep most of the time in order to save energy and thus achieve longer battery life times. The base CoAP standard assumes that the communication layers below the application provide support functions for sleeping nodes. Adding better support for sleepy nodes at the application layer might be able to further reduce the power requirements of these nodes. This support is currently a very active subject of discussion in the CoRE working group; this is apparent from the relatively high number (at least seven) of individual Internet Drafts in the group that try to address this issue in one way or another.  
The base CoAP provides minimal support for sleepy nodes by supporting caching in intermediaries. Resources from a sleepy node may be available from a caching proxy (if previously retrieved) even though the node is asleep. This support is enhanced by using the observe option and thus allowing sleepy nodes to update caching intermediaries according to their own schedule.  
Most of these proposals try to achieve better support for sleepy nodes either by extending the functionality of the intermediaries or by extending the CoAP observe option or by a combination of both. For example, [119] proposes to store the actual resource representations in a special type of RD called the Mirror Server. Clients can then fetch the resource from the Mirror Server regardless of the state of the sleepy server. One the other hand, by using the conditional observe option as proposed in [120], the nodes may be allowed to sleep even longer. Similarly the approach of [121] is to introduce storing of sleep characteristics in the RD. Clients can then query the RD to learn the sleep status of the sleepy node before attempting communications. Both [120] and [121] include using/extending the observe option as part of their overall approaches.  
A related patent [122], describes inserting sleep information into a header option or into a payload of an application layer message. Whereas the application layer message may be conveyed in HTTP or CoAP.
## 4 HFAJAYA for the Multi-Constraint OPF Problems
### OPF without Stochastic Renewable Energy
#### 4.1.5 Case 5  
The voltage profile is the essential metric for determining the quality of a system's service. The voltage profile can be improved by lowering the load bus voltage's divergence from unity. A viable solution with undesired voltage deviations is obtained when only a cost-based goal function is considered. As a result, a multiple objective OPF problem to concurrently minimize voltage deviations (\(VD\)) and fuel cost is presented, which may be described as follows.  
\[J_{5}=\sum_{i=1}^{NG}\Bigl{(}a_{i}+b_{i}P_{Gi}+c_{i}P_{Gi}^{2}\Bigr{)}+\lambda v *\sum_{i=1}^{NPQ}|V_{i}-1.0| \tag{31}\]  
where the value of factor \(\lambda v\) was selected as 100 [57].  
In Table 7, the minimal fuel cost (USD/h) and _V.D._ (p.u.) of the FA and HFAJAYA algorithms are examined, and the findings are compared to those described in the current works. The table's findings demonstrate the strength of HFAJAYA, which successfully found appropriate solutions for this situation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & **Fuel Cost (USD/h)** & **Emission (t/h)** &
\begin{tabular}{c} **Power Losses** \\ **(MW)** \\ \end{tabular} & **V.D. (p.u.)** \\ \hline FA & 859.8325 & 0.2289 & 4.5398 & 0.9330 \\ \hline HFAJAYA & **859.0165** & 0.2289 & 4.5293 & 0.9327 \\ \hline SF-DE [57] & 859.1458 & 0.2289 & **4.5245** & 0.92731 \\ \hline MSA [9] & 859.1915 & 0.2289 & 4.5404 & 0.92852 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The obtained optimal results in the current works for Case 4.
## 3 Heterogeneous Parallel Implementation
### Heterogeneous Parallelization Implementation Based on OpenMP, MPI, Pthreads, and OpenCL  
The computational resources used in this study are derived from the domestically developed advanced supercomputing platform \"Dongfang\" supercomputer system. This system employs a \"CPU + DCU\" heterogeneous architecture and consists of thousands of nodes. Each node is equipped with a domestically developed 32-core processor, 128 GB memory, and 4 DCU accelerator cards. Each DCU is equipped with 16 GB memory, a bandwidth of 1 TB/s, and a mounted shared storage system.  
Figure 5 illustrates the heterogeneous parallel framework employed in this study. In the SLURM job script, the \"-map-by node\" option is used to assign each process to a single compute node, ensuring that each process can utilize the computing resources of four DCUs. The MPI_Init_thread function is used to initialize the MPI runtime environment and enable multi-threaded parallelism. Additionally, the MPI_Type_contiguous function is utilized to define a contiguous data type for the nine directions of particle distribution functions that require communication. The MPI_Type_commit function is then used to commit the custom data type, simplifying data transmission and communication operations. Next, the necessary components, such as the OpenCL context and command queues, are created. In this framework, a strategy of CPU-DCU collaborative computing is adopted to avoid CPU idle time during DCU computation. Initially, the entire grid dataset is initialized using OpenMP. Then, the grid is divided into five parts, with four DCUs computing four parts of the grid, while the remaining part is assigned to the CPU for computation, as depicted in Figure 5(a). Pthread is used to create 4 threads, each invoking one DCU for computation. The remaining 28 threads divide the last part of the grid into 4 rows and 7 columns, with each thread responsible for computing a small portion of the grid, as shown in Figure 5(b). Finally, MPI_Send and MPI_Recv functions are employed to perform inter-node communication over boundary grids, completing one iteration cycle. The algorithm terminates when the error converges to \(1\times 10^{-10}\), and the flow field information is outputted to a file.
# A Comparative Study on the Laser Welding of Ti6 Al4V Alloy Sheets in Flat and Horizontal Positions
## 5 Conclusions  
The following conclusions have been drawn from work carried out:  
1. For flat welds, face undercut was larger than root undercut; for horizontal welds, the undercut on the left side (top side during welding) side was larger than that on the right side (bottom side during welding).
2. The excess penetration was greater than the excess weld metal for the flat welds, while for the horizontal welds, the excess penetration was smaller than the excess weld metal. For the same laser welding parameters, the horizontal welds were wider than the flat welds.
3. The horizontal welding position resulted in higher weld metal porosity contents than the flat welding position, because there were fewer routes for pores to escape from the weld pool during horizontal welding. The pores were located in the center plane of the flat welds, and above the center plane of the horizontal welds.
4. In the welds investigated, the undercuts did not show an association with the fracture positions nor the strengths in static tensile testing, although excessive porosity in laser welds did lead to significant decreases in their strength and specific elongation.
5. Compared with a horizontal welding position, the flat welding position led to better weld formation, less porosity, and higher tensile strength. For both flat and horizontal welding positions, it is recommended to use higher laser powers and welding speeds to reduce weld porosity and improve the mechanical properties of laser welds in Ti6 Al4V alloys.  
This work has been financially supported by the National Natural Science Foundation of China (www.nsfc.gov.cn, No. U1537205 and No. 51675303) and the Tsinghua University Initiative Scientific Research Program (No. 2014Z05093).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 11: Fractographs of the horizontal weld (**a**) and the flat weld (**b**) made with higher heat input (2.2 kW, 8 mm/s).
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 8 Other NAD+-Dependent Enzymes in Cancer
### NAPRT in Cancer  
High levels of NAPRT and NAMPT have been found in ovarian, prostate and pancreatic cancers [63,95,96], while in glioblastoma, neuroblastoma, chondrosarcoma, leukaemia and gastric and colon cancers, low levels of NAMPT and low levels of NAPRT have been found [63,97,98,100,101]. Comparing the pattern of expression of the NAPRT and NAMPT genes in human tumour cells with nontumor cells, there is high variability in the levels of NAPRT mRNA and protein in tumour cells [97,101]. This high expression variability depends on the tissue of origin. Thus, tumours from tissues with high NAPRT expression will have higher NAPRT amplification, and conversely, tumours from tissues with low NAPRT expression will be dependent on NAMPT activity [96,100,102].  
NAPRT-negative tumours usually present with mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2) and protein phosphatase 1D (PPM1D). IDH1/2 mutants use NADPH to convert \(\alpha\)-ketoglutate to D-2-hydroxyglutarate (D-2 HG), which acts as an oncometabolite inhibiting NAPRT expression by the hypermethylation of its promoter [98,103,104]. PPM1D is an oncogene that is involved in the DNA damage response and checkpoints of the cell cycle. PPM1D mutants also lead to the inhibition of the NAPRT promoter by the methylation of CpG islands [101,105]. The inhibition of the Preiss-Handler pathway by NAPRT forces tumour cells to rely entirely on the salvage route. Therefore, NAMPT is usually overexpressed in these tumours since their cells rely on its activity to meet the demands for NAD+ [100,104]. In fact, cells with IDH1/2 and PPM1D mutations are more sensitive to treatment with NAMPT inhibitors due to the total depletion of NAD+ [103,104,105]. In tumours with high levels of NAPRT, tumour cells may have a better supply of NAD+, which allows for the activation of NAD+-dependent enzymes, including PARP1, which are required to protect against DNA damage and oxidative stress [106,107]. Overexpression of NAPRT may contribute to resistance to treatment with NAMPT inhibitors or alkylating agents that cause DNA damage. In vivo, xenografts have been shown to be less sensitive to the NAMPT inhibitor FK866 if NAPRT is overexpressed, whereas they are more sensitive when NAPRT is inhibited [100,106]. NAPRT can be inhibited by compounds such as 2-hydroxynicotinic acid (2-HNA). 2-HNA is an analogue of nicotinic acid that competitively blocks the enzyme [108,109]. It has been observed that this compound is capable of potentiating the cytotoxic effect of the NAMPT inhibitor FK866. Therefore, the ability to predict the response to NAMPT inhibitor treatments by low levels of NAPRT depends on the type of cancer [100,106].
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 3 Beamforming-Based Power Estimation Methods
### RPCA-Based Denoising Approach  
Since the derivation of Equation (6) neglects the influence of ambient noise, the measurement error becomes significant when the beamforming output SNR is low. Consequently, denoising methods can be applied first to suppress the ambient noise before estimating the radiated noise power. The Robust Principal Component Analysis (RPCA)method can be used to estimate the signal covariance matrix and the noise covariance matrix simultaneously. The optimization formulation can be expressed as [31]  
\[\begin{array}{ll}\min\limits_{\mathbf{R}_{s},\mathbf{R}_{n}}&\left\|\mathbf{R}_{s}\right\|_ {*}+\beta\left\|\mathbf{R}_{n}\right\|_{1}\\ \text{subject to}&\mathbf{R}_{s}+\mathbf{R}_{n}=\mathbf{R}_{x}\\ &\mathbf{R}_{s}=\mathbf{R}_{s}^{\text{H}}\geq 0\\ &\mathbf{R}_{n}=\mathbf{R}_{n}^{\text{H}}\geq 0,\end{array} \tag{14}\]  
where \(\left\|*\right\|_{*}\) and \(\left\|*\right\|_{1}\) denote the nuclear norm and the \(l_{1}\) norm, respectively, \(\beta\) denotes the regularization parameter, and \(\geq 0\) means the matrix is a positive semi-definite matrix. Then substituting the estimated signal covariance matrix into Equation (11), the radiated noise power estimate is expressed as  
\[\sigma_{s\,RPCA}^{2}=r_{m_{0}}^{2}\mathbf{w}_{NFBF}^{\text{H}}\mathbf{R}_{s}\mathbf{w}_{ NFBF}. \tag{15}\]
## 2 Calculation Model and Method
### Space-Time Neutron Diffusion Model  
While the three-dimensional full core multi-physics modeling of nuclear reactor is an effective way to obtain the key information of the reactor core with high precision, the required data storage space is very huge and the running time is quite long. In a few special calculations like performing the reactor control system design, transient safety analysis or load following study, the detailed radial information of the reactor core is not necessarily needed due to the radially symmetrical layout of fuel assemblies and control rod banks [27]. Thus, a one-dimensional neutron diffusion model in the axial direction will be more preferable when the calculation precision is not reduced.
## 1 Introduction
Some aspects have been stressed in those works, however: that students show favorable attitudes towards AR and that its utilization enhances motivation towards learning [32; 33]; that it helps create a constructivist learning context [16; 34]; that it favors an active teaching environment [35]; that it reduces the cognitive load to which students are subjected in learning tasks [36; 37]; that students have a high degree of acceptance as well as positive attitudes in relation to AR technology [38; 39; 40; 41; 42; 43]; that it increases satisfaction levels amongst both students and teachers [24; 44; 45; 46]; that it improves students' spatial skills and orientation [47; 48]; and finally, that its use enhances academic performance [26; 30; 49]. Additionally, it deserves to be highlighted that experiments are already being carried out for its incorporation into e-learning-based learning initiatives [31; 50], as well as into others where students act as learning object producers [51; 52]. In any case, we must bear in mind that the incorporation of AR into teaching faces a number of obstacles: this is a new technology; no development-focused educational experiments have been carried out yet; no learning resources and objects produced in AR format exist; teachers have a poor training; teachers need to develop positive attitudes towards AR incorporation into educational practice; no educational experiments have so far been performed in the development of AR-based learning objects; screens do not offer a suitable observation size; there are no conceptual frameworks which can help us find innovative practices in AR implementation; support centers for teachers need to be created so that they will find it easier to produce AR-based objects, as well as their maintenance in servers; a base/basic technology must be available for its observation by students; and an information overload clearly exists [28; 52]. Concerning the last obstacle mentioned above, some precautions should be adopted when designing AR objects. Modules must have short and direct contents and be flexible as well as straightforward. They have to reflect an accessible and tolerant approach with a multimedia design, oriented to the student's action and involvement. Tablets and smartphones do not constitute the goal of the educational process, and therefore, the methodology must focus on practicality and interactivity and adapt to the diversity of devices where it can be observed [53]. This paper shows an experiment carried out within a university context, additionally providing a detailed description of the AR products generated. This serves as the starting point to explain the educational research performed, where the Instructional Material Motivational Survey (IMMS) developed by [PERSON] was used as the assessment tool [4]. The paper finishes with a summary of the results obtained as well as the corresponding conclusions drawn from them.
## 2 Related Work  
Recently the use of the blockchain for the development of P2P resource trading models in SMPs has attracted huge attention [1]. Several approaches, such as those in [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29], were proposed to realize participants' anonymity and transactional data privacy in SMPs. An account mapping technique was used in [21] to prevent attackers from directly accessing transactional data. In this method, the characteristics of both active and inactive users' accounts can be effectively obscured by creating multiple accounts. Similarly, in [22], it was proposed to protect the privacy of participants by dividing them into various groups. Then, a miner is chosen among the group members to collect data from the group and add them into the community's private blockchain. This process protects the participant's data from leaking to other participants in the network. However, recent works [1; 2; 11] have shown that, using a detailed analysis process [11], multiple public addresses and accounts can be connected to infer and expose the identity of the participants in blockchain-based networks. One such method is known as blockchain analysis, where transactions, addresses and entity graphs are used to expose the true identitites of blockchain users [1].  
Some techniques [6; 8; 30] use elliptic curve-based security mechanisms, such as ECC and the ECDSA, to preserve the privacy of participants and enable them to anonymously trade with each other. These schemes protect participants against attacks such as address reuse, Sybil, inferential and collusive attacks. However, the techniques presented in [6; 8] can be subjected to 51 percent attacks, high latency due to a long block generation time and other mining issues. In another approach [7], a participant, such as a resource buyer, uses digital currencies to generate multiple accounts and preserve the mapping information to avoid tracking. It also involves the use of a Merkle tree [12] to create a large pool of key pairs and achieve high anonymity. However, this approach may be prone to a blockchain analysis attack, and the implementation of Merkle trees in a blockchain-based network, such as the Ethereum network, can be computationally costly, which can slow down the execution of trading transactions. Therefore, there is a need to come up with a more scalable solution that will provide user anonymity, untraceability and transactional data privacy in the resource trading environment of SMPs. Table 1 summarizes some of the most recent and relevant research conducted in this domain.