esa-sceva/satcom-chunk-collection huggingface dataset:
## Problem Description
### Introduction to Unit Commitment  
Unit Commitment problems are typically used to determine which units must be involved in dispatch operation at specific time periods. Constraints requiring consideration include the number of units, load capacity, startup cost, spinning reserve, and ramp rate. The goal is a safe UC system with minimal total cost that satisfies the aforementioned constraints.  
The power generating units can be categorized into base-load, Intermediate-load, and peak-load according to their characteristics, explained as follows:  
* Base-load Unit: The base-load units can operate stably for a long time, featuring low cost of variation and high rated capacity. Examples include nuclear and coal-fired generating units.
* Peak-load Unit: The peak-load units can be started up and shut down immediately to fill the power gap at the peak time in a timely manner, such as fuel-fired and gas-fired generating units.  
* Intermediate-load Unit: The Intermediate-load units, such as liquefied natural gas-combined cycle units, feature power generation costs and operational characteristics that are between those of base-load and peak-load units.
# Translating Workflow Nets to Process Trees: An Algorithmic Approach
## 6 Discussion
### Reducibility of WF-Nets  
Thus far, we have considered various system net based patterns that we reduce into a corresponding process tree notation. We have shown that, if the algorithm returns a WF-net with a specific structure, its label captures a process tree describing the same language as the original WF-net. However, it remains an open question what class of WF-nets are guaranteed to result in a process tree.  
Since the basic operators considered in this paper all correspond to _free-choice WF-nets_, i.e., WF-nets s.t., \(\forall p\in P\left(|p\bullet|=1\vee\bullet(p\bullet)=\{p\}\right)\) (a place either has one outgoing arc, or it is the sole incoming arc of all transitions it connects to). Hence, intuitively, we suspect that the class of free choice nets always yields a corresponding process tree.  
However, consider Figure 19, in which we depict a free-choice WF-net, which the proposed algorithm is not able to reduce.  
Observe that the model consists of two concurrent branches, i.e., enabled by transition \(t_{1}\). However, execution of transition \(t_{5}\) is conditional to execution of \(t_{2}\), i.e., firing both \(t_{3}\) and \(t_{2}\) enables transition \(t_{5}\). One can look at this type of condition, i.e., induced by place \(p_{7}\), as a _non-local_ behavioral relation. There is interaction between members of the \"upper part\" and the \"lower part\" of the concurrent construct. Such a type of interaction cannot be modeled using a tree-based process modeling formalism.  
Based on the previous example, we conclude that any class extending free-choice Petri nets might represent various WF-nets that cannot be reduced by the algorithm proposed. The notion of _block-structured_ WF-nets seems to be an adequate subclass of free-choice WF-nets that can always be reduced by the model, i.e., they are often used interchangeable with process trees. However, an exact structural definition of the aforesaid class of WF-nets does not exist in the literature (yet). For example, in [20], an informal description of block-structured WF-nets is proposed: _\"A workflow net is block structured if for every place or transitions with multiple outgoing arcs, there is a corresponding place or transition with multiple incoming arcs. The parts of the net between the outgoing and incoming arcs form regions, and no arcs can exist between regions, i.e., the regions have a single entry and a single exit.\"_ However, transforming this description into a formal, graph-theoretical property is not trivial for cyclic models.  
Figure 19: A Free-Choice WF-net for which the algorithm cannot find a corresponding process tree.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 5 System Model
### Phase 3  
WSNs are composed of multiple clusters of nodes interconnected with each other. Each cluster has a cluster head, which communicates between the layers of the network and with other nodes. This cluster head is shown with a zoomed out view in our system model. Labeled datasets are used in the learning phase.  
In the third phase, the classifiers that are used for classification, i.e., SVM, CNN, SGD, MLP, RF, and PNN, are deployed in the cluster head. These techniques use observation vectors to formulate a decision function. Since, the algorithm used for deploying the decision function in the cluster head is composed of an uncomplicated implementation, the process is not computationally expensive. After deployment of the decision function, data are classified into two classes. If the result is positive, the data belong to the first class (normal case), otherwise data are considered as an abnormal case (faulty case).
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 3 Problem Formulation of Channel Estimation with Colored Noise
To this end, minimizing the MSE cost function in the presence of colored noise over the DTS \(\mathbf{S}\) for a given training phase duration \(T_{\text{s}}\) and training power \(\rho_{\text{s}}\) in the DL MaMi systems equates to the optimization problem defined in (20)  
\[\underset{\mathbf{S}}{\text{minimise}}\ \text{tr}\Big{(}\Big{(}\mathbf{R}_{ \text{h}}^{-1}+\mathbf{S}\mathbf{R}_{\text{n}}^{-1}\mathbf{S}^{\text{H}}\Big{)} ^{-1}\Big{)} \tag{20}\] \[f_{\mathrm{h}_{k}}(\mathbf{h}_{k})=\frac{\exp\{-\mathbf{h}_{k}^{\mathrm{H}} \mathbf{R}_{\mathrm{h}}^{-1}\mathbf{h}_{k}\}}{\det(\pi\mathbf{R}_{\mathrm{h}})}. \tag{9}\]0  
where \(E_{\text{s}}\) is the total energy constraint during the training transition phase, which is given as \(E_{\text{s}}=\rho_{\text{s}}T_{\text{s}}\). The following section investigates the optimum DTS design in the presence of colored noise.
## 4 Experimental Results
### Experimental Results 1  
In the first set of experimental results, the UNET architecture was implemented and several loss functions were employed, including binary cross-entropy with logit loss (BCEWithLogitsLoss), mean squared error loss (MSELoss), Huber loss, and L1 loss. Regarding the optimizers, the stochastic gradient descent (SGD) algorithm and the ADAM algorithm were chosen, both known for their efficiency in adjusting model parameters. Data augmentation was used with random horizontal and vertical flips of the images.  
Before the training phase, the input images were resized to 256 \(\times\) 256 pixels, necessitating an adaptation of the UNET architecture to accommodate these new dimensions. The primary objective of this set of experimental results was to analyze the performance of the UNET architecture under different training configurations, specifically evaluating its behavior when performing 5, 10, 20, and 50 epochs of training.  
Adjustments were made to the loss functions and optimizers to determine their impact on the model's performance.  
For the evaluation of the results, the Intersection over Union metric was used in the test dataset. The results obtained from this first set of experimental results are presented in Table 1.  
The loss function curve for every sample of 50 epoch is shown in Figure 6.  
An example of a segmented image is shown in Figure 7.
# Dynamic Group Recommendation Based on the Attention Mechanism
## 4 Experiments and Analysis
### Overall Performance Comparison (RQ2)  
In this section, we compared the AMGR model with the baselines. Table 1 shows the values of HR and NDCG in different methods at k = 5. Table 2 shows the values of HR and NDCG in different methods at k = 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**User**} & \multicolumn{2}{c}{**Group**} \\ \cline{2-5}  & **HR@5** & **NDCG@5** & **HR@5** & **NDCG@5** \\ \hline Popularity & 0.3725 & 0.2654 & 0.3494 & 0.2375 \\ COM & - & - & 0.4890 & 0.3310 \\ AVG (NCF) & - & - & 0.4793 & 0.3365 \\ LM (NCF) & - & - & 0.4703 & 0.3335 \\ MP (NCF) & - & - & 0.4564 & 0.3257 \\ MGR & 0.5047 & 0.3762 & 0.4981 & 0.3419 \\ AMGR & **0.5218** & **0.3803** & **0.4993** & **0.3502** \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison baseline at k = 5. Best results are in bold.  
Figure 6: (a) Effectiveness of group recommendation (HR@10) and (b) the effectiveness of group recommendation (NDCG@10).  
Now we compared the performance of the AMGR model with the above methods. Note that the COM model is the group recommendation and cannot be recommended for a single user, so the table data is empty. From Tables 1 and 2, it can be seen that the method based on NCF was better than the COM model and the popularity model because of the superiority of the neural network. In addition, when k = {5,10} of our model, HR and NDCG were higher than other models, which verified that the AMGR model was superior to the above method. Specifically, the attention network we used dynamically allocated the weight of members in the group and aggregated preferences according to different items, which could effectively achieve user recommendation and group recommendation, and the model recommendation effect had certain advantages.
## 5 Conclusions  
This paper presents an innovative application of precast technology in the construction of reinforced masonry shear walls, by which the special cleaning-hole blocks can be eliminated in the masonry structures. Quasi-static tests were conducted to evaluate the seismic performance of the newly-developed precast RMSWs. The inelastic behavior of each specimen was investigated based on the cracking patterns, hysteretic curves, lateral load capacity, ductility, stiffness degradation, and energy dissipation capacity. From the experimental results and analysis, the following conclusions can be made.  
1. Precast RMSWs have better load capacity than traditional RMSWs due to the elimination of the cleaning-hole blocks at the bottom layer, which leads to improved integrity and compressive strength of the masonry.
2. Precast RMSWs that failed in the flexural mode had a favorable deformation capacity and an acceptable average displacement ductility value of 4.9 corresponding to 15% strength degradation, which was slightly smaller than the 5.2 for traditional RMSWs.
3. The normalized stiffness of traditional and precast test walls degraded with a similar power law as the displacement increased. The wall stiffness decreased at a rapid rate at early elasto-plastic stage, but at large displacement, the stiffness declined gradually.
4. The traditional and precast RMSWs exhibited a great energy dissipation capacity in flexural failure mode. The hysteresis energy of RMSWs increased significantly with the increasing displacement, and the relationship between the normalized energy dissipation at yield and the ratio of the post-yield displacement to the yield displacement was almost linear for each specimen up to six times the yield displacement.  
Figure 15: Energy dissipation of the specimens: (**a**) Energy dissipation; (**b**) normalized energy dissipation.  
Axial load significantly affects the flexural performance of both traditional and precast RMSWs. Higher values of the axial compression ratio lead to the increase of lateral load capacity. However, more brittle behavior associated with a reduction of displacement ductility occurs with increasing axial load.  
In summary, this paper experimentally studied the seismic performance of precast RBMWs failing in flexure, proving that the precast RMSWs have comparable seismic performance compared to traditional walls, and have the potential to be used in seismic regions. Further research will be concentrated on the potential comprehensive advantage of the precast RMSWs in practical application.  
Formal analysis, W.X. and X.Y.; Methodology, F.W.; Writing--original draft, W.X.; Writing--review and editing, F.W.  
This research received no external funding.  
The present work was financially supported by the National Key R&D Plan-China (Grant No. 2016 YFC0701502-3).  
The authors declare no conflict of interest.
# Evaluating the Effect of Intensity Standardisation
## 2 Materials and Methods
### Considered Intensity Standardisation Techniques  
#### 2.3.1 z-Score  
The z-score is a popular normalisation method that linearly transforms intensities to have zero mean and unit standard deviation. Given an input image \(I\), the process consists of subtracting the mean intensity value from each voxel and dividing the result by the standard deviation value as follows,  
\[I_{z-score}=\frac{I-\mu}{\sigma}, \tag{1}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of intensities within the intracranial volume, respectively.  
Figure 2: Incorporating intensity standardisation in the FSL-SIENA pipeline. Standardisation takes place before or after registering input volumes to the MNI space. The inputs correspond to the baseline and follow-up T1-w scans. The FSL-SIENA pipeline consists of skull stripping, spatial normalisation, tissue segmentation, edge displacement analysis and brain volume change reporting.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 2 Materials and Methods
### Keypoint Extraction Techniques  
#### 2.2.1 Keypoint Extraction  
Keypoint extraction in point clouds is crucial for pose estimation, point cloud registration, and 3D reconstruction, as it reduces data volume and enhances processing efficiency. The ISS algorithm [30] ensures rotational invariance by constructing covariance matrices to characterize local geometric distributions, performing robustly against density variations and noise. Harris-3D [31] extends 2D gradient analysis to 3D normal vector fields, though its cross-view stability hinges on normal estimation accuracy. SIFT-3D [32] builds a 3D Hessian matrix based on differential geometry for high repeatability, yet with high computational complexity. Engineering solutions, like clustering-curvature fusion [33] and cross-dimensional mapping systems [34], enable efficient feature extraction in specific scenarios, but balancing generalization capability and computational efficiency remains challenging. Developing dynamic feature detection frameworks adaptive to computing resources emerges as a promising research direction.  
Figure 1: The overview of RSCS6D.
## 4 Discussion
### Limitations and Strengths  
There are several study limitations. First, the sample of athletes involved comprised youth basketball players. Therefore, the results obtained can only be generalized to similar samples of participants. Furthermore, a larger sample size (>40 or 50 subjects) would have been needed to further understand the impact of PM and VE on the R-CoD test. However, it is not easy to obtain that number of such well-trained young players and recruit them in a short-term randomized experimental design. Another possible limitation is the type of VE and the standardization of the VE protocol. Although we tried to standardize this procedure, there is no doubt that certain differences in the VE could appear between participants. Finally, this study observed performance variables and did not measure psychophysiological response variables (i.e., blood lactate, heart rate, rating of perceived exertion), which will certainly allow us to discuss the obtained results more profoundly.  
On the other hand, this is one of the rare investigations where VE and PM are simultaneously examined and compared, considering their effectiveness. Additionally, the quality of the observed participants (well-trained youth basketball players) and the sport-specific nature of the observed performance are important strengths of the investigation. Therefore, although not being the final word on the topic, we believe that the results of this study will improve the knowledge in this field and initiate further research.
## 3 Research Results and Discussion
### Measurement of the Shaft Sleeve Pressing and Removal Forces
Hence, during joining, adhesive bonds arise, which generate friction forces requiring the use of considerable force. The small shaft sleeve removal force after the first fatigue cycle is due to the presence of wear products between the surfaces. These products prevent the formation of adhesive bonds during joint disassembly. The next joint assembly, before the start of the second fatigue cycle, requires the use of the maximum pressing force of 2370 kN. That force has a value smaller than the pressing force during the first assembly and higher than the removal force. This situation is due to the fact that wear products occurring between the mating surfaces were partly removed, which permits the re-formation of adhesive bonds. In addition to the shearing of surface irregularities, these bonds may also cause plastic deformations to arise on the surfaces joined together. The higher value of the shaft sleeve removal force after the second fatigue cycle In relation to the previous values is due to surface smoothing. Wear products migrating during assembly and fatigue tests fill the microirregularities on both surfaces and create areas with very low roughness. This is where the need to use a greater removal force comes from. The maximum values of the forces at the successive stages of wear tests can be explained in a similar way.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 4 Problem Formulation and Solution  
### Problem Formulation  
The research goal of this paper is to maximize the residual energy of the UAV through joint optimization, so it is necessary to maximize the energy acquisition and minimize the energy consumption of the UAV. The energy acquired and consumed by the UAV is related to the choice of its computing mode, wireless communication and system resource allocation. Therefore, for the WPT-MEC with multiple UAVs, the residual energy maximization problem in each time block can be mathematically formalized as problem \(P1\):  
\[P1:\underset{a,b,f_{j},f_{i},t_{i}\geq U_{0},U_{1}}{\max}\sum_{t_{i}\in U}E_{ n}-\sum_{i\in U_{0}}\sum_{j\in U_{1}}(E_{j}^{loc}+E_{j}^{off})\]  
\[s.t.S1:\frac{R_{i}^{loc}O}{T}\leq\ f_{i}\ \leq\min[f_{\max}\,\ (\frac{\mu\ P\ c_{i}\ a\ T}{k_{i}})^{ \frac{1}{3}}],\ i\in U_{0}\]  
\[S2:\ 0\ \leq\ t_{i}\leq T,\ i\in U_{0}\]  
\[S3:E_{i}-E_{i}^{loc}\geq 0,\ i\in U_{0}\]  
\[S4:\ a+\sum_{j\in U_{1}}b_{j}\leq 1,\ j\in U_{1} \tag{10}\]\[S5:\ 0\leq\ a\ \leq 1\]  
\[S6:\ 0\leq\ b_{j}\ \leq 1,\ j\in U_{1}\]  
\[S7:E_{j}-E_{j}^{off}\geq 0,\ j\in U_{1}\]  
\[S8:U_{0}\subseteq U,U_{1}=U\backslash U_{0}\]  
Problem \(P1\) describes the optimization objective of this paper in detail and lists the relevant constraints. \(S1\) denotes the CPU computing speed constraint in the UAV. \(S2\) denotes the computing time constraint in the local computing mode. \(S3\) denotes the energy consumption constraint in the local computing mode. \(S4\), \(S5\) and \(S6\) are system energy transmission and offloading time constraints in TDMA mode. \(S7\) denotes the UAV energy consumption constraint in offload computing mode. \(S8\) denotes a mutual exclusion constraint between the two computing modes.
## 3 ML Application in O-RAN
### Satellite NTN  
Interference management in satellite networks involves reducing interference between multiple satellites and traditional cellular communication systems, which can lead to degraded signal quality and decreased network performance. O-RAN can be used for interference management in satellite networks by leveraging its cutting-edge functionalities. One way to use O-RAN for interference management in satellite networks is by using a dynamic spectrum access techniques to dynamically allocate and manage spectrum resources based on real-time network conditions. This involves monitoring network parameters, such as channel quality and interference levels, and dynamically reconfiguring the network to avoid interference and optimize network performance. Another approach is to use machine learning algorithms to analyze network data and make intelligent decisions on interference management. For example, machine learning algorithms can be trained to identify patterns in network data that indicate interference and automatically adjust network parameters to mitigate interference. Finally, O-RAN can also be used to implement beamforming techniques to improve signal quality and reduce interference in satellite networks. Beamforming involves adjusting the phase and amplitude of transmitted signals to create directional beams focused on specific network areas. By optimizing beamforming parameters based on network conditions, interference can be minimized and network performance improved. Additionally, the radio spectrum is a finite and highly sought-after resource; therefore, spectrum sharing aims to help resolve this issue by creating regulatory frameworks and developing wireless technologies to share spectrum bands between heterogeneous users. The authors in [55] proposed O-RAN with machine learning for 5G/XG and closed-loop feedback via sensing can reduce harmful interference between heterogeneous 5G and Low Earth Orbit (LEO) satellite communication systems.
## 12 Conclusions and Future Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline Schemes & Features & AchievedParameters & Trade-Offs \\ \hline \multirow{4}{*}{L2-ABF [6,7]} & Geographic routing with & \multirow{2}{*}{Avoidance of empty regions} & Maximum probability of void \\  & layer-by-layer angle-based & flooding for data & results has increased the & nodes and energy consumption \\  & & communication underwater & performance of the network & for each iteration of angle \\ \hline \multirow{4}{*}{TANVEER} & Geographic and opportunistic & \multirow{2}{*}{Increased PDR and throughput} & \multirow{2}{*}{Observed high end-to-end delay} \\  & routing scheme using & & of the network by bypass the & to maximum coverage area \\ \cline{1-1}  & three-angle adjustment and & & empty nodes/regions & \\ \hline \multirow{4}{*}{LBA-TANVEER} & Layer-based adjustment with & \multirow{2}{*}{Improved network topology} & Not enough control, due to the \\  & data collision avoidance & & and performance with & dynamic nature of the \\ \cline{1-1}  & mechanism & adjustment of nodes & environment but accurate \\ \cline{1-1}  & & & detection of empty nodes is \\ \cline{1-1}  & & & possible \\ \hline \multirow{2}{*}{DPD-TANVEER} & Using path change mechanism & \multirow{2}{*}{Improved PDR, throughput and} & Due to path change, a long \\ \cline{1-1}  & by bypassing the empty regions & & fraction of empty regions & propagation delay occurred is \\ \hline \multirow{2}{*}{BIN-TANVEER} & Works with novel watchman & \multirow{2}{*}{Improved PDR and try to} & \multirow{2}{*}{Energy consumption is high} \\ \cline{1-1}  & nodes using watchman-based & & decrease PLR & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall performance trade-offs. **Funding:** This research was funded by the AGH University of Science and Technology, grant No. 16.16.120.773. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors acknowledge the support from the, Najran University, Kingdom of Saudi Arabia, University of Okara, University of Sahiwal, and COMSATS University Islamabad Pakistan, Including Lahore and Sahiwal Campuses. **Conflicts of Interest:** The authors declare that there is no conflict of interest.