esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Routing Protocols
#### 3.2.4 Optimized Link State Routing Protocol (OLSR)  
The optimized link state routing protocol (OLSR) [7, 9] is a table-driven protocol. Routes are continuously stored and updated in tables. Therefore, whenever a route is needed, the protocol presents the route immediately without any initial delay. In order to reduce the overhead of packet transmission, candidate nodes, called multipoint relays (MPRs), are selected and responsible for forwarding broadcast packets during the flooding process. OLSR performs hop-by-hop routing, where each node uses its most recent routing information to route packets. The MPR selection is done in such a way that it covers all of the nodes that are two hops away. A node senses and selects its MPRs with HELLO messages. HELLO messages are sent at a regular time interval. MPR selection is signaled through the HELLO messages. The topology control (TC) messages broadcast a subset of the topology information to enable each node to build a (partial) network topology and determine routes based on [PERSON]'s shortest path algorithm, for example [9, 41].
## 5 Experiments and Results
### Evaluation Metrics  
To comprehensively evaluate the classification performance of our proposed ResNet-TF model and baseline systems, we adopt four widely used metrics in multi-class audio classification as follows: accuracy, precision, recall, and F1-score. These metrics enable a balanced assessment across overall correctness, sensitivity to true positives, and robustness against false predictions. Let TP, FP, FN, and TN represent the numbers of true positives, false positives, false negatives, and true negatives, respectively. The metrics are defined as follows:  
1. Accuracy = (TP + TN)/(TP + TN + FP + FN), measures the overall proportion of correctly predicted samples.
2. Precision = TP/(TP + FP), reflects the proportion of correctly predicted positive samples among all predicted positives.
3. Recall = TP/(TP + FN), Indicates how many of the actual positive samples were correctly identified.
4. F1-score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall), the harmonic mean of precision and recall, offering a balance between them.  
These metrics a0072e calculated on a per-class basis and then averaged (macro average) to account for class imbalance and reflect the model's robustness across all sound types.
# SoK: Delegated Security in the Internet of Things
## 2 Application Domains of Security Delegation
### Example  
Identity-less, asynchronous, and decentralised delegation for IoT based on blockchain technology is presented in [23]. The solution consists of brokers (RC system), buyers (delegate), and a resource manager acting as a third party that offers specific services and resources. All components interact via the blockchain network with smart contracts (see Figure 5). During the preparatory phase, the resource manager registers the offered resources on the blockchain network. The broker has the right to access these resources and can also delegate these rights to the buyer on demand. The buyer requests access rights to the desired resource by triggering a transaction to a delegation smart contract. If the transaction is valid, a new event in the blockchain is generated, and the buyer can request the resource directly. After the first request, the buyer is verified, and only if the verification is successful, the buyer obtains full access to the resource. In comparison with other existing approaches that are not based on blockchain, the described system is asynchronous, decentralised, monotonic, and auditable.
# A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation
## 5 Discussion and Perspective  
Arbitrarily tunable OAM has excited a great diversity of interest, because of a variety of emerging applications, but its creation still remains a tremendous challenge. We review the concepts of general OAM, which extends the OAM carried by the scalar vortex modes (classical OAM mode and the modes on the orbital PS) and the OAM carried by the azimuthally varying polarized vector modes (the modes on the higher-order PS and hybrid-order PS). In summary, due to unique characteristics, tunable OAM beams have been the subject of much interest for a variety of fundamental research studies and modern applications. There are mainly two types of methods to generate those tunable OAM beams, free-space and fiber generating methods. Each method has its own advantages and disadvantages. Free-space generating methods have advantages in terms of flexible design and easy manipulation, but active optical spatial phase modulators are expensive and may introduce additional electronic noise. Meanwhile, the volumes of spatial devices are usually large. Compared with free-space generation methods, the fiber-based generation methods have the advantages of the miniaturization and low insertion loss. However, the challenge is robustness because they are basically the combination of fibers and mode converters. The fiber is vulnerable to external influences and some methods involve alignment operation. It must be mentioned that choosing which method to generate tunable OAM modes depends more on its application scenarios. For example, for a transfer of ultrashort pulses, free-space solutions have essential advantages because of avoiding frequency chirping and pulse lengthening. For medical endoscopy, the fiber-based generation method is obviously a better way. Despite this, along with the efforts of researchers all around the world, we may see an increasing number of applications based on tunable OAM beams in the future. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adjustable** & **Reference** & **Combination Modes** & **Adjusting Method** & \begin{tabular}{c} **The Type of** \\ **Tunable OAM** \\ \end{tabular}  \\ \hline \multirow{6}{*}{Relative Phase (\(\varphi\))} & [63] & \(\text{LP}_{1\text{{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ \tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{         }}}}}}}}}}}}}}}}\)) & Stress the fiber by a pair of flat slabs & Orbital PS \\  & [61] & \(\text{LP}_{1\text{{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ \tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\left. }}}}}}}}}}}}}}}}}}\)) and \(\text{LP}_{1\text{{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ \tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\left. }}}}}}}}}}}}}}}}}}}}\) \\  & [26, 27] & \(\text{LP}_{1\text{{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{ \tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\tiny{\leftleft.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 4 Analog-Digital Receiver Design
### Iterative Analog-Digital Equalizer
#### 4.1.2 Analog Feed-Forward Equalizer Design  
To optimize the analog part of the equalizer, we consider an iterative procedure that at step \(r\) selects the column \(r\) of matrix \(\mathbf{W}_{a}^{(i)}\) from the dictionary \(\mathbf{A}_{rx}\in\mathbb{C}^{N_{n}\times N_{d}N_{ny}U}\) given by \(\mathbf{A}_{rx}=\left[\mathbf{A}_{rx,1},\ldots,\mathbf{A}_  
because there only are \(N_{CB}=N_{cl}N_{ny}U\) possible vectors in the dictionary, among which will be selected the \(N_{rx}^{RF}\) vectors to build \(\mathbf{W}_{a,N_{rx}^{RF}}^{(i)}=[\mathbf{w}_{a,r}^{(i)}]_{1\leq r\leq N_{rx}^{ RF}}\).  
The pseudo-code for the proposed hybrid fully iterative receiver is presented in Algorithm 1, where initially it is assumed \(\boldsymbol{\Psi}^{(0)}=\mathbf{0}_{U}\), since in the first iteration we do not have any estimates. For \(\boldsymbol{\Psi}^{(0)}=\mathbf{0}_{U}\), the iterative digital part of the equalizer reduces to the standard MMSE equalizer. The algorithm can be summarized as follows. We have an outer loop with \(i_{max}\) iterations and an inner loop with \(N_{rx}^{RF}\) steps. Let us consider iteration \(i\) of the outer loop. For this iteration the non-normalized full-digital equalizer for iteration \(i\) is computed and the residue matrix is set to the trivial value \(\overline{\mathbf{W}}_{\text{res},k,0}^{(i)}=\overline{\mathbf{W}}_{fd,k} ^{(i-1)}\overline{\mathbf{R}}_{k}^{(i-1)}\), i.e., \(\mathbf{W}_{ad,k,0}^{(i)}=0\). Next, in the inner loop, we select the best column from the dictionary for the RF chain \(r\) to build the analog part of feedforward hybrid equalizer and compute the digital part according to (24). Then, the residue matrix is updated and the previous steps are repeated for \(r=1,\ldots,N_{rx}^{RF}\), i.e., until the full analog and digital parts of the feedforward hybrid equalizer matrix are obtained. After that, we compute the digital feedback matrix, and estimate the transmitted data symbols. Finally, we update \(\hat{\mathbf{c}}_{k}^{(i)}\) and \(\boldsymbol{\Psi}^{(i)}\), and all these step are repeated until the maximum number of iterations is reached in the outer loop.  
```
1:for\(i=1,\ldots,i_{max}\)do
2: Compute\(\overline{\mathbf{W}}_{fd,k}^{(i)}\) accordingly to (20)
3:\(\overline{\mathbf{W}}_{\text{res},k,0}^{(i)}=\overline{\mathbf{W}}_{fd,k}^{( i)}\overline{\mathbf{R}}_{k}^{(i-1)}\)
4:\(\mathbf{W}_{a,0}^{(i)}\) = Empty Matrix
5:for\(r=1,\ldots,N_{rx}^{RF}\)do
6:
7:
8:
9:
10:
11:
12:endfor
13:
14:endfor
15:
16:endfor
```  
**Algorithm 1** The proposed fully iterative hybrid space-frequency multi-user algorithm for broadband mmWave mMIMO systems
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 3 Results and Discussion
### Visual Assessment of Classification Results  
Once optimal parameters were determined, the RF and SVM classifiers were applied to the different case combinations. Prior to quantitative accuracy assessment, the visual assessment of classification results was first conducted. When the RF and SVM classification results were compared for different combinations of input images and kernel sizes, the RF classifier showed misclassifications at some parcels in the southeastern parts of the study area, but significant differences in classification patterns were not observed. Figure 5 shows some classification results using the SVM classifier. When three spectral bands of the August image were used for classification, misclassification and noise effects by isolated pixels were the greatest in visual inspection of classification results. Confusion between highland Kimchi cabbage and cabbage was most common, as shown in Figure 5b, mainly due to their similar spectral characteristics in August (this is further discussed in Section 3.5). When texture features were combined with spectral information for the case using the August image only, the number of misclassified and isolated pixels decreased, but some misclassified pixels were still shown (Figure 5c). Using multi-temporal images greatly reduced misclassified pixels within each parcel, except for some around the parcel boundaries (Figure 5d). As expected, the use of texture features as additional information with multi-temporal spectral information showed the best agreement with the ground-truth data from visual inspection (Figure 5e), indicating the necessity of time-series images and texture features for crop classification.  
The impacts of texture features generated by different kernel sizes on the classification results were also visually compared. The classified patterns were significantly affected by kernel size. When a very small kernel size, such as GK3, was used to extract texture features, the classification result was very similar to the case with spectral information only. As the kernel size increased, the noise effect was greatly alleviated. When multi-temporal images were used for classification, however, the combination of texture features with multi-temporal spectral information was less affected by the change in kernel  
Figure 4: Cross-validation accuracy of SVM classifiers through a grid search. The case with the best accuracy is underlined: (**a**) using a single August image with VNIR and GK31 texture features; and (**b**) using six UAV images with VNIR and GK31 texture features.  
size. The increase in kernel size resulted in the reduction of isolated noise patterns, but the difference was subtle compared to the case using the August UAV image.
# Safe Path Planning of Mobile Robot Based on Improved A* Algorithm in Complex Terrains
## A* Algorithm
### Algorithm Procedures  
The focus of A* algorithm is to expand outward from the initial state \(S\), compute the evaluation function value of each neighboring state, and select the state which has the minimum evaluation function value as the next traversal state. It then repeats the expanding process until the goal state \(G\) is traversed.  
The evaluation function of each state \(X\) is defined as follows  
\[f(X)=g(X)+h(X), \tag{1}\]  
where \(f(X)\) denotes the estimated path cost from \(S\) through \(X\) to \(G\), \(g(X)\) denotes the actual path cost from \(S\) to \(X\), and \(h(X)\) is the heuristic function which denotes the estimated path cost from \(X\) to \(G\). The heuristic function is generally expressed as Manhattan distance, diagonal distance, or Euclidean distance. This article adopts diagonal distance as the estimated path cost.  
A* algorithm maintains an _OPEN_ list of states for expansion. The _OPEN_ list is used to store states waiting to be traversed. Every state \(X\) except \(S\) has a hidden arrow pointing to an adjacent state \(Y\) denoted by \(p(X)=Y\). A* algorithm uses the arrows to represent the path from \(X\) to \(Y\). Every state \(X\) has an associated tag \(t(X)\) such that \(t(X)=new\) if \(X\) has never been on the _OPEN_ list, \(t(X)=open\) if \(X\) is currently on the _OPEN_ list, and \(t(X)=closed\) if \(X\) is no longer on the _OPEN_ list. The cost of moving from \(X\) to a neighboring state \(Y\) is a positive number given by the _moving cost_ function \(c(X,Y)\). The procedure of A* algorithm is given in Algorithm 1.  
```
1 for each state \(X\) in the environment model:
2\(t(X)=new\);
3 Initialize the _OPEN_ list to be empty; Place state \(S\) on the _OPEN_ list; \(t(S)=open\); \(g(S)=0\);
4 while\(t(G)\
eq closed\):
5 Find the state, \(X\), on the _OPEN_ list with minimum estimated path cost;
6 for each neighbor \(Y\) of \(X\):
7 if \(Y\) is not in a forbidden region
8 if\(t(Y)=new\)
9\(p(Y)=X;g(Y)=g(X)+c(X,Y);f(Y)=g(Y)+h(Y)\); Place \(Y\) on the _OPEN_ list; \(t(Y)=open\);
10 else if\(t(Y)=open\)
11 if\(g(X)+c(X,Y)+h(Y)<f(Y)\)
12\(g(Y)=g(X)+c(X,Y);f(Y)=g(Y)+h(Y);p(Y)=X\);
13 Delete \(X\) from the _OPEN_ list; \(t(X)=closed\);
14 if\(OPEN=NULL\)
15 return NO-PATH;
16 The optimal path from \(S\) to \(G\) is obtained.
```  
**Algorithm 1**A*(S, G)
## 4 Results and Discussion
### Analytical Signal Technique  
Analytical signal is a well-known gradient enhancement that is connected to magnetic fields using derivatives. [PERSON] et al. [21] stated that the first analytical signal amplitude can be calculated using three orthogonal gradients of the total magnetic field through the expression:  
\[|A(x,y)|=\sqrt{\left(\left(\frac{\delta m^{2}}{\delta x}\right)+\left(\frac{ \delta m^{2}}{\delta y}\right)+\left(\frac{\delta m^{2}}{\delta z}\right)\right)}\]  
where \(A(x,y)\) is the amplitude of the analytical signal at \((x,y)\), and m is the observed magnetic anomaly at \((x,y)\). While this function is not a measurable parameter, it is extremely useful in terms of interpretation because it is totally independent of magnetization and the direction of the Earth's field [26]. As a result, all bodies with the same shape have the same analytical signal. Figure 8 illustrates the three analytical signal profiles (P1-P1\(\backslash\), P2-P2\(\backslash\) and P3-P3\(\backslash\)) above the map reduced to magnetic pole. The first analytical signal profile P1-P1\(\backslash\) (Figure 9) is about 9065 m long and cuts the western portion of the study area in a NNE-SSW direction.  
Figure 7: Residual airborne magnetic component of the RTP map of Homrit Waggat area.  
The depths of the basement surface range from 0 to 2000 m. The second analytical signal profile P2-P2\({}^{\searrow}\) (Figure 10) is about 10,570 m long and cuts the eastern portion of the study area in a NE-SW direction. The depths of the basement surface along this profile range from 0 m to 2300 m. The third analytical signal profile P3-P3\({}^{\searrow}\) (Figure 11) is about 15,120 m long and cuts the central portion of the study area in a NW-SE direction. The depths of the basement surface along this profile range from 0 m to 2300 m.  
Figure 8: RTP aeromagnetic anomaly map of the studied area showing the profiles used for application of analytical signal.  
Figure 9: Analytical signal technique for aero magnetic profile (P1) of Homrit Waggat area, Central Eastern Desert, Egypt.
## 1 Introduction
### Literature Review
[73] stated that the absolute of the SHAP values can be extrapolated to be used as a feature selection mechanism, while providing a human-compatible explanation for the predictions of ML algorithms. However, they reported that the kernel SHAP approach takes a quadratic amount of time in both the dimensionality and size of the dataset, making the computation unfeasible even for moderate datasets, but they concluded that this problem could be decreased by selecting features based on correlation before feeding the SHAP algorithm. [PERSON] et al. [74] developed a design framework of an XAI-based feature selection method that evaluates the performance of a ML model with a reduced number of features, selected depending on their SHAP values, with respect to the original one, which is trained considering all the features. They highlighted that the use of XAI for feature selection may make the task more accessible for intermediate data scientists and might thereby contribute to data democratisation, facilitating the exchange of information between ML developers and domain experts to enhance the effectiveness of feature selection. This kind of algorithm was employed by [PERSON] et al. [75] in a ML model for visual human action recognition and by [PERSON] et al. [76] for a ML model predicting generalised anxiety disorder among women. They both reported a high improvement in the computation time of their models after feature selection, proving the validity of that method. A further development was proposed by [PERSON] et al. [77] who developed a feature selection algorithm that was based on the SHAP values for a binary classification problem, which does not need a user-defined threshold on the importance values or the number of important features. There are reports in the literature about the application of the SHAP method to explain the outcomes of ML classifying algorithms for fault detection in rotating machinery. The SHAP algorithm was applied to supervised learning in the works by [PERSON] et al. [9], who explained the decisions of a kNN classifier for a small-sized ball bearing fault diagnosis, highlighting the most relevant features and concluding that the presented model had a better generalisation capability than the standard algorithm, making it perform a bearing fault diagnosis under different configurations and working conditions. [PERSON] et al. [78] explained that the prediction of deep neural networks applied to the fault diagnosis of rotating systems through the analysis of vibration data. On the other hand, [PERSON] et al. [49] explained the outputs of an unsupervised machine learning algorithm for fault detection in rotating machinery with gears and employed the Shapley values in a fault diagnosis to find the root causes of the faults.
###### Abstract  
Oil temperature plays a crucial role in hydraulic closed-circuit systems (HCS), and conventional thermal equilibrium models and coupled simulation models face challenges in terms of accuracy, efficiency, and cost when calculating oil temperature. This study introduces an innovative HCS oil temperature precise prediction model and oil refilling parameter optimization method. The initial sample space was determined through a Sobol sensitivity analysis and improved Latin hypercube sampling, leading to the development of a combinatorial agent model (CAM) suitable for oil temperature prediction with superior accuracy and stability compared to other methods. Based on CAM, the optimal oil refilling flow rates under various operational conditions are computed. To validate the efficacy of the theoretical analysis, an HCS experiment platform was established. The data indicates that the temperature prediction error range of the CAM model falls between 0.30 \({}^{\circ}\)C and 1.05 \({}^{\circ}\)C, and optimizing the oil refilling flow rate can effectively enhance system efficiency while ensuring that the oil temperature remains within permissible limits. The research methodology and findings are applicable in engineering practice and can be extended to optimize the design of other hydraulic systems.  
hydraulic closed-circuit system; accurate oil temperature prediction model; combinatorial agent model; sensitivity analysis; oil refilling parameters optimization
# Flow-Induced Fatigue Damage of Large Francis Turbines Under Multiple Operating Loads
ing edge of Francis runner blades in their study. [PERSON] et al. [23] analyzed the failure of a Francis runner and found that the damage was caused by a stress concentration at the T-joint between the blades and the crown, which was due to a combination of cavitation damage and high cycle fatigue when the machine was operated at low partial loads. Based on field measurements and simulations, [PERSON] et al. [24] studied the unsteady flow-induced stress of a high-head Francis runner. The results showed that the maximum flow-induced stress of the runner is closely related to factors including the pressure pulsation level, the speed of the pump-turbine unit, the number of guide vanes, and the number of runner blades. Some of the cracks in the Francis turbine runner blades were caused by turbine operating conditions that deviated from the design conditions [25]. A careful study revealed that the cracking of Francis runner blades can be caused by unevenness of the weld surface and accelerated by hydrogen embrittlement of the weld filler material [26]. Flow-induced resonance under off-design conditions and high stresses in Francis runners can also lead to cracks in runner blades [27]. In order to avoid cracks on the turbine blades, the flow-induced dynamic behavior of a large prototype Francis turbine unit is analyzed in this investigation. The current research on Francis turbines has focused on analyzing the flow and structural characteristics of the Francis runner using CFD and finite element method methods (FEMs) with only one blade of the turbine and only at rated operation conditions. This study adopts a full 3D fluid-structure interaction (FSI) approach in conjunction with CFD and FEMs to analyze the flow-induced structural behavior of the entire Franics turbine at various load conditions. The calculation approach is carefully verified by measurements [28]. First, the geometric models for the fluid domain and structure domain of the Francis turbine unit are established. Then, the flow dynamics analyses are carried out under three representative loads, including low partial load (LPL), corresponding to 1/3 of the full load; high partial load (HPL), corresponding to 2/3 of the full load; and full load (FL). Next, with the calculated pressure loads, the flow-induced stresses of the turbine runner at different loads are analyzed accordingly. Finally, the root causes of runner fatigue cracks are discussed according to the calculation results. The feasible optimization design and operation and maintenance measures are also provided.