esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data
## 2 Benchmark Components
### Realistic Counterfactuals
The requirement to generate points that are realistic highlights the need to define a particular input space where each of the features (and their combination) are conceptually genuine in relation. We call this subspace the realistic space, and will formalize this next. Consider a set of \(m\) features \(\{a_{1},a_{2},...,a_{m}\}\). The realistic space (**R**) is defined as a subspace \(\textbf{R}\subseteq\mathbb{R}^{m}\), which represents all realistic features' combinations. A dataset (**D**) is (or should be, depending on potential data quality issues such as a negative age) a subset of the realistic space (\(\textbf{D}\subseteq\textbf{R}\)). If we get a point (**x**) in the dataset (\(\textbf{x}\in\textbf{D}\)) with class \(y\), and a model \(M\) which transforms the point into a specific class (\(\hat{y}\)), we can define a counterfactual as a close-boundary point (**c**) where the model outputs a different class: \(M(\textbf{c})=\hat{y}\), \(y\
eq\hat{y}\). The realistic space can span the entire real space (\(\textbf{R}=\mathbb{R}^{m}\)) when there are no constraints and the features are free to vary. In most real world application settings, however, some constraints will be imposed to keep the counterfactuals realistic. We, therefore, argue that, for real-life applications, especially when involving end-users requiring explanations that make sense, a counterfactual point should belong to the realistic space (\(\textbf{c}\in\textbf{R}\)). This preference is additionally implied by the already mentioned fact that an unrealistic counterfactual is not supported by any data observation (as all training points are, expectedly, inside the realistic space). To have a better understanding of how the realistic space can be defined by constraints, we can categorize these into two types of constraints: univariate and multivariate. Univariate constraints define the requirements a single feature must follow (for example, age larger than zero), while multivariate constraints describe how a set of variables, dependent on each other, must change (for example, the one-hot encoding rule). This categorization is additionally motivated by the fact that many algorithms treat these requirements differently. For example, ALIBIC, DiCE, MACE, and SynAS can manually restrict the range of certain features, while ALIBIC and DiCE also use, respectively, autoencoders and statistical approaches to generate counterfactuals that are more consistent with the data manifold (thereby, hopefully, respecting both univariate and multivariate constraints). It is also worth noting that, in some situations, we can have both kinds of constraints, like with one-hot encoding: to ensure binary values (univariate) and single activation (multivariate).
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Malicious Hosts  
To simulate the described attacks in our network model, we need to arbitrarily select random hosts as malicious hosts, attempting both worm and TCP-SYN flood attacks. Randomness is challenging to characterize mathematically because of process unpredictability, but it is a critical feature of nature and crucial for many scientific applications [37]. Inaccuracies or failures in the theoretical modeling of such processes, e.g., from adversarial attacks, restrict random number generators' reliability through weaknesses that are difficult to detect and control. We use a randomness generation model verified by Bell inequality violation from [38] in Equation (1) to obtain designated attack hosts for our network model.  
\[Z=\sum_{i,j}(-1)^{ij}[P(x=y|ij)-P(x\
eq y|ij)] \tag{1}\]  
where \(P(x=y|ij)\) represents the probability of \(x=y\) if sets \((x,y)\) are selected and \(P(x\
eq y|ij)\) is analogously defined. Therefore, \(Z\) becomes the summation outcome that quantifies the contrast in probabilities over certain network behavior conditions, and we use \(i\) and \(j\) as summation indices when iterating over the network model. \(x\) and \(y\) denote variables within the probability functions \(P\), where these could represent specific network states, behaviors, or outcomes being analyzed.
## 1 Introduction
Second, real-time adaptability: the model's ability to integrate real-time data and update decision-making processes makes it particularly suitable for the dynamic and fast-changing environments of HSRSs. Third, scalability: the PF-3 WD model can be scaled to accommodate the large size and complexity of HSRSs, ensuring that it remains effective even in highly complex scenarios. The main contributions of this work are as follows: first, it combined the advantages of Pythagorean fuzzy sets (PFSs) in representing fuzzy evaluation information to propose a PF-3 WD decision framework for evaluating the emergency evacuation capacity of HSRSs, verifying its effectiveness and reliability. Second, it addressed the counter intuitive situations and the neglect of practical semantics in membership and non-membership degrees in existing similarity measures of PFSs by proposing a new closeness measure. Third, the effectiveness of the new closeness measure was demonstrated through comparisons with existing similarity measures. The rest of this paper is organized as follows. Section 2 introduces some related work on emergency evacuation evaluation of stations, the PFS and PF-3 WD methods. Considering decision-makers' risk appetite, the PF-3 WD model is put forward to evaluate the emergency evacuation capacity of HSRSs in Section 3. Then, an example is studied to illustrate the effectiveness of the developed method in Section 4. Finally, some conclusions are presented in Section 5.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 1 Introduction
In particular, a link adaptation scheme considering channel quality was combined with a resource allocation algorithm that derived the duration of the interbeacon intervals and transmission periods of sensors [27]. However, those studies did not consider the improvement of the PHY throughput. In other words, more network throughput than that of the current SmartBAN PHY cannot be achieved. Furthermore, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video [14; 15; 16]. Specifically, it is not possible to satisfy these desired data rates and latencies when transmitting high-quality audio or video data, since the maximum data rate of the SmartBAN PHY can only reach 1 Mbit per second. Thus, it is necessary to improve the SmartBAN PHY to enable streaming transmission of this type of information. To address this issue, this research investigates the following approaches for the purpose of improving the data rate of the SmartBAN PHY. The first one is applying multilevel phase shift keying (PSK) modulation, and the second one is increasing the symbol rate. These approaches do not significantly change the specifications of the SmartBAN. In addition, these methods are also expected to improve the data rate while suppressing the energy consumption because their implementation is not complicated. It is confirmed that a sufficient link budget, receiver sensitivity and fade margin can be obtained even when those methods are applied from the numerical results obtained by the mathematical model and computer simulation assuming several channel conditions. In addition, the numerical results show that those types of technology are necessary for high-quality audio or video transmission in the SmartBAN. The study on whether the SmartBAN can be used for industrial use is novel because it has not been considered in previous studies. In addition, this research contributes to the fact that SmartBAN can be effectively used not only for medical and healthcare applications but also for other applications by amending the PHY. The remainder of this paper is organized as follows. In Section 2, the SmartBAN PHY and MAC layers are summarized. In Section 3, the system model proposed in the research is introduced. The numerical results of the performance evaluation are provided in Section 4. Conclusions and suggestions for future research are presented in Section 5.
## 3 Experimental Results
### Ductility and Energy Dissipation  
The ultimate deformation of the strengthened W1S, W2, and W3 was 113%, 109%, and 96% higher than that of the unstrengthened W1, and the area of the hysteretic loops of the strengthened W1S, W2, and W3 was 68%, 120%, and 50% higher than that of the unstrengthened W1, as shown in Figure 10.  
Figure 8: In-plane shear resistance of the walls: (**a**) Skeleton curves; (**b**) resistance.  
Figure 9: Stiffness degradation: (**a**) Stiffness degradation during the loading procedures; (**b**) stiffness at typical states.  
The results indicate that the strengthened walls dissipated more energy before failure. The UHPC layers improved the energy dissipation ability of the masonry walls, whether the wall was in good condition as the W2 or had been severely damaged as the failed W1.  
Although the hysteretic loops of the strengthened walls surrounded more area, they were not plump. Therefore, the increase of energy dissipation mainly resulted from the increase of resistance, and the strengthened walls' energy dissipation ability was not as favorable as their in-plane shear resistance. This can be illustrated by Figure 11, in which the energy dissipation coefficient equals the area of the shaded part ABCD divided by the sum of the area of the triangle OBE and triangle ODF. The enhancement of energy dissipation had been reversed by the premature bed-joint slide failure, in which the strengthening layers had not taken effect sufficiently.
## 3 Results and Discussion  
### Cr(VI) Adsorption Experiments  
#### 3.1.1 Stirring Speed  
The stirring speed applied to the carbon solution may have a key role in the adsorption process due to modifying the mass gradient of adsorbate from the solution to the adsorbent interface [30]. Thus, different experiments were carried out to study the influence of this variable on the Cr(VI) adsorption process. An activated carbon dosage of 0.5 g\(\cdot\)L\({}^{-1}\) was added into Cr(VI) solutions of 0.01 g\(\cdot\)L\({}^{-1}\) (resulting pH 4) at 20 \({}^{\circ}\)C. Figure 1 exhibits the adsorption percentage of Cr(VI) after a contact time of 5 h versus the stirring speed. It can be observed that the adsorption process was influenced by stirring speed. Cr(VI) adsorption percentage increased with the stirring speed from 250 to 1500 min\({}^{-1}\). However, using higher stirring speeds decreased the adsorption percentage. Thus, these results indicate that the thickness of the aqueous boundary layer at 1000 min\({}^{-1}\) was lowest and leads to a higher adsorption percentage. The decrease in the adsorption of Cr(VI) at higher stirring speeds could be due to the formation of local equilibria, which hinders the adsorption process [31; 32]. According to these results, a stirring speed of 1000 min\({}^{-1}\) was used in the subsequent experiments.
## 3 Materials and Methods
### Summary of the Attestation Algorithm  
The steps of the attestation procedure have been summarized in Algorithm 1. Accordingly, its input consists of the attested subsystem \(s\in\mathcal{S}\), the known behavioral scenarios (\(\mathcal{B}^{s}\)), and the new behavioral scenarios \(\mathcal{B}^{s}\) that are computed for attestation purposes. Note that, for initialization purposes, the impact matrices for \(\mathcal{B}^{s}\) are computed according to the previous equations. Next, the attestation of the new behavioral scenarios \(\mathcal{B}^{s}\) can be performed. According to Algorithm 1, the steps are performed for each \(b^{\prime}\in\mathcal{B}^{s}\) and each \(s\in\mathcal{S}\). Consequently, the more sub-systems and behavioral scenarios are attested, the procedure becomes more time-intensive. Note that the algorithm also summarizes the Level 1 and Level 2 fusion. Here, \(m^{1}\) and \(m^{2}\) are used to denote Level 1 and Level 2 fusion, respectively.  
```
Input:\(s\in\mathcal{S}\) (the attested subsystem), \(\mathcal{B}^{s}\) (known behavioral scenarios); \(\mathcal{B}^{s}\) (new behavioral scenarios) Output: The result of Level-2 fusion FunctionAttestProcess(\(\mathcal{S},\mathcal{B}^{s}\)): foreach\(b^{\prime}\in\mathcal{B}^{s}\)do \(C(y^{s},b^{\prime})\leftarrow\frac{1}{n}\sum\limits_{t\in T^{ 0}}\frac{(Y_{s}^{b}(t)-\tilde{Y}_{

+++ ==WARNING: Truncated because of repetitions==
s}^{b})(Y_{s}^{0}(t)-\tilde{Y}_{s}^{0})}{ \tilde{Y}_{s}^{0}\tilde{Y}_{s}^{b}}\); \(R_{y^{\prime}b^{\prime}}\leftarrow\{C(y^{s},b)|M^{-}(b^{\prime})\leq M(b)\leq M ^{+}(b^{\prime}),\forall b\in\mathcal{B}^{s}\}\); \(\mathbf{C}_{x}\leftarrow\frac{1}{k}\sum_{b\in\mathcal{B}^{s}}M(b)\); \(\mathbf{C}_{y}\leftarrow\frac{1}{k}\sum_{b\in\mathcal{B}^{s}}C(y^{s},b)\); \(\mathbf{C}\leftarrow(\mathbf{C}_{x},\mathbf{C}_{y})\); for\(i\gets 1\) to \(N\)do \(m_{i}^{1}(t)\leftarrow\mathcal{F}(\mathbf{C},\mathcal{B}^{s},b^{\prime}), \forall H\in\Theta\); \(m_{i-1}^{1}(H)\gets m_{i}(H)\oplus m_{i-1}(H),\forall H\in\Theta\); // Level 1 fusion  end for \(m^{2}(H)=m^{2}(H)\oplus m_{1,2,\ldots,N}^{1}(H),\forall H\in\Theta\); // Level 2 fusion  end for return \(m^{2}\);
```  
**Algorithm 1**Attestation procedure including Level 1 and Level 2 fusion
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 2 Basic Knowledge and Literature Review
### Multi-Population Optimization Method
#### 2.4.2 Factors of Multi-Population Optimization Method  
One important factor concerns how to divide the population; specifically, how many sub-populations there should be and the size of each sub-population. Too many sub-populations may waste the limited computation resources because of population migration, while too few sub-populations may cause the advantage of multi-population optimization not to be significant. The size of sub-population is closely related to specific optimization algorithms. At present, the appropriate number and size of sub-populations are obtained through simulation experiments.  
The other factor is the migration strategy, which specifically involves three elements--the migration mode, the trigger condition and the individuals participating in migration.  
* Migration mode: As shown in Figure 5, there are two migration modes. The first one, as shown in Figure 5a, is circular migration, in which sub-populations transmit individual information in one direction. This mode is simple to implement and requires no extra computational cost, but it cannot guarantee the quality of population fusion. The second one, as shown in Figure 5b, is interaction migration, in which each sub-population chooses some neighbors to exchange individual information with. This mode is more flexible to maintain population diversity, but it increases additional computational costs.
* Trigger condition: Population migration can maintain population diversity, and the more frequent the migration is, the better the accuracy of the solution will be, but the time of calculation increases accordingly. Therefore, a compromise between the accuracy of the solution and the calculation time is needed. Typically, the trigger condition is set to a certain iteration interval. Events are also used as trigger conditions; sub-populations migrate once a sub-population falls into a local optimum, for example.
* Individuals participating in migration: This element involves two issues, the migration rate and individual selection. The former issue concerns how many individuals participate in the migration, and the latter issue concerns which individuals participate in the migration. These two problems are difficult to solve through theoretical derivation. At present, the experience gained through a large number of experiments is that the best individual of a sub-population is sent to another sub-population and replaces its worst individual.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 5 BPMN24 IoT-A Holistic Framework for IoT-Driven BPs
### Architecture
In detail:
* We extend the process engine with an _IoT Element Parser_, which parses all IoT-specific information (e.g., endpoints; defined condition, i.e., for the IoT start event; and device information) from the exported XML file and processes it for its further use by the engine. * The _IoT Execution Orchestrator_ receives the IoT-specific information during process execution and orchestrates the execution of the IoT artifacts and events. * The _IoT Subscription Manager_ allows the process instances to subscribe and unsubscribe to IoT devices as well as to integrate the real-world data provided by the IoT devices and cleansed by the middleware. * The _IoT-driven Business Expression Evaluator_ accepts IoT data and expressions (e.g., light barrier is interrupted) and evaluates them based on the received sensor data. * _IoT Process Monitoring_ enables the monitoring of IoT-driven BPs using a similar visualization as the IoT Process Modeler. In particular, IoT Process Monitoring receives execution events and displays them in the process monitoring view. * _IoT-driven Process Log_ may be any persistent storage (e.g., PostgreSQL) recording the process information and IoT device data during the process execution. Due to the modular and flexible structure of the architecture, it becomes possible to add components or to alter existing ones. Note that this enables a broad support of different technologies. For example, IoT devices may be integrated regardless of the underlying technologies (e.g., MQTT, CoAP, Zigbee, LoRa and LoRaWAN) on which they rely.
###### Abstract  
In industrial robotic grasping tasks, traditional 3D point cloud registration and pose estimation methods often struggle with low efficiency and limited accuracy in stacked and cluttered environments. To address these challenges, this paper proposes a grasp pose estimation algorithm that integrates 2D object detection based on YOLOv8-URE with 3D point cloud registration. In the detection stage, the method enhances object feature perception and localization by optimizing the receptive field structure and introducing attention mechanisms. It also employs an efficient multi-scale feature fusion strategy to improve bounding box regression accuracy. During point cloud processing, target centers predicted by the detector guide rapid segmentation, followed by robust registration techniques to estimate precise object poses. Experimental results demonstrate that YOLOv8-URE improves detection accuracy by 9.21% compared to YOLOv8n, reduces registration time by 60.5%, and significantly increases grasp success rates, proving its reliability and effectiveness in industrial scenarios.  
Keywords:industrial robotic grasping; YOLOv8-URE; 2D visual detection; information fusion; stacked-scene grasping +
Footnote †: journal: Applied sciences
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
## 4 Experimental and Simulation Results
### Estimation of Parameters of the Laboratory Crane System
The first sensor employs a mechanical approach, tracking the angular displacement of the load with a lever connected to a potentiometer. The second sensor utilizes an optical method, assessing the sway angle with a marker and an infrared camera. The mechanical sensor was used to test other algorithms but was not employed in the algorithm proposed in this paper. Instead, it was used to calibrate the camera, which was used to document the results of the sway measurements. Figure 12: The amplitude Bode’s diagram of modeled sway dynamics \(G_{\Phi}\) and identified crane sway dynamics \(\overline{G}_{\Phi}\). Figure 13: The phase Bode’s diagram of modeled sway dynamics \(\Phi\) and identified crane sway dynamics \(\overline{\Phi}\). Resonant frequency of 2.85 rad/s corresponds to the length of the rope from which the cargo hangs. Another spike occurs at 7 rad/s, corresponding to the lever of the mechanical sensor, which has a length of 20 cm. To assess the discrete model's accuracy used in the proposed control algorithm realized by using ZOH discretization, which is commonly used in control applications, in Equations (9) and (13), an analysis was made in the frequency domain using standard metrics: MSE (Mean Square Error), RMSE (Root Mean Square Error), bandwidth error, cross-over frequency error of amplitude, and phase frequency responses. The MSE and RMSE values for amplitude are 1.9 \(\times\) 10\({}^{-5}\) and 1.7 \(\times\) 10\({}^{-2}\); for frequency responses, they are 7\({}^{\circ}\) and (3\({}^{\circ}\))0.5, respectively. While, the error bandwidth and cross-over frequency are 2.7 \(\times\) 10\({}^{-3}\) rad/s and 0.71526 rad/s, respectively. All the above suggests that the discrete system has good enough accuracy in representing the continuous system's amplitude and frequency and can be used for this type of application.
## 4 Mapping Algorithm
### Training Approach
This way, when facing new virtual requests, the policy network can automatically make effective decisions based on its learned experience. Algorithm 1 demonstrates the specific training process. ```
Input: Epoch limit \(epoch\_num\); Learning rate \(\eta\); differentiated QoS VNRs set \(U_{VNRs}\); Output: Parameters that have been trained in policy network;
1: Initialize all the parameters in policy network;
2:while\(i<epoch\_num\)do
3: flag=0;
4:for\(\text{rep}\in U_{VNRs}\)do
5:for\(\text{node}\in\text{rep}\)do
6: get Matrix \(M_{f}\) by formula 18;
7: get probability by formula 20; //Get the probability distribution;
8: select host node; //Select a node to act as the host;
9: calculate Gradient;
10:endfor
11:ifisMapped(\(\forall\) node \(\in\) rep) then
12: get Link mapping by Breadth First Search;
13:endif
14:ifisMapped(\(\forall\) node \(\in\) rep, \(\forall\) link \(\in\) rep) then
15: get reward by formula 21 //Determine the revenue-to-cost ratio;
16: multiplyGradient(reward, \(\eta\)) //Determine the final gradients;
17:else
18: reset Gradient;
19:endif
20:++flag;
21:ifflag attain the batch magnitude then
22: apply gradients to parameters;
23: flag=0;
24:endif
25:endfor
26:++i;
27:endwhile
```  
**Algorithm 1** Algorithm for training models.