esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Current AI-Supported Effort Estimation  
The history of AI-supported effort estimation is decades old. Since AI was always a field of moving targets, and AI itself is a term that redefines itself every few years, AI-supported tools and methods from the 1990s would not be classified as AI at all today (e.g., decision trees, analogy-recommended systems, case-based reasoning, etc.). There  
Figure 1: Positioning tasks that can be assisted by the LLM-based chatbots [19].  
are, however, some exceptions. One such example from 1996 [21] tried to employ artificial neural networks in estimating software development effort and the potential of case-based reasoning for development estimation. Using their simulated dataset, they were rather unsuccessful, with a prediction accuracy of less than 40%.  
Later, AI-supported tools and methods employed primarily conventional machine learning models for effort estimation. An example study, reliable machine learning models for estimating effective software development efforts [22], compares the performance of eight different models for effort estimation, which were also not really successful in terms of estimation accuracy, which, in the best cases, reached up to 50% compared to conventional effort assessment methods.  
The last five years systematic review [23] presents applications of AI techniques for software effort estimation. The authors compare and evaluate the performance of diverse machine learning models, including artificial neural network, support vector machine, linear regression, random forest, and other techniques. The proposed AI-based framework is claimed to hold the potential to enhance project planning and resource allocation.  
In Ref. [24], research was performed, where the authors employed AI to predict an employee's workload from their reported work hours (having a decades long record on their reported working hours). So, the AI-based prediction in this research was on predicting actual effort, not the effort needed to perform a particular task.  
Another review [18], which was based on 11 primary studies, was focused on data-driven effort estimation techniques of agile user stories. They identified several possible techniques and algorithms that are employed to enhance effort estimation, among them: Bayesian network, decision trees, support vector machines, long short-term memory and recurrent highway network, stacked autoencoders, variational autoencoders, denoising autoencoders, stacked denoising autoencoders, gradient boosted trees, k-nearest neighbours, artificial neural network, sridge regression, linear regression, naive Bayes, random forest, logistic model tree, and j48 decision tree, fuzzy based model and stepwise regression.
## 4 Simulation Results and Analysis  
### Simulation Setup  
In this section, a series of simulations are conducted to evaluate the performance comparisons of the proposed DRL-based algorithm and benchmark. MILP model is compared as the accurate solution. A random distribution algorithm is chosen as a benchmark for comparisons. For the simulation environment setup, the proposed MILP model and DRL-based algorithm are programmed on a computer of Intel(R) Core (TM) i7-10710U CPU @ 1.10 GHz, 1.61 GHz, and 16.0 GB RAM, utilizing respective IBM CPLEX 12.8.0, MATLAB, and Python. In the simulations, we choose two test network scenarios of a small network and a large network, as the AI services vary from 20 to 220. We assume that each computational resource supplier has two edge data centers and that each AI user has two service requests. In addition, simulation parameters for the DRL-based algorithm are listed in Table 4, including neural network parameters, discount factor, exploration rate, and other hyperparameters.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Number of CNN layers & 2 \\ \hline Filters of CNN & 5 \\ \hline Kernel size of CNN & 2 \\ \hline Strides of CNN & 1 \\ \hline Number of training iterations & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters for DRL-based algorithm.  
Figure 2: The flowchart of DRL-based dynamic computational resource scheduling algorithm.
## 4 Experiments
### Ablation study  
In order to demonstrate the effectiveness of multi-scale input, multi-scale side output and the dual attention block, we conducted the following ablation experiments. Our proposed method is an enhanced UNet++ network; therefore, UNet++ [21] is the most fundamental baseline model. We replace 2D transposed convolution with 2D bilinear up-sampling operation in UNet++ [21], and called the modified UNet++ as Backbone. Additionally, we add dual attention block to Backbone(remove input\({}^{(1)}\), input\({}^{(2)}\), input\({}^{(3)}\), input\({}^{(4)}\), side\(\_\)output\({}^{(1)}\), side\(\_\)output\({}^{(2)}\) and side\(\_\)output\({}^{(3)}\) from MDAN-UNet-32, as shown in Figure 1 while others remain unchanged), and denote it as Backbone+Attention Block. We remove multi-scale side output and multi-scale label (remove side\(\_\)output\({}^{(1)}\), side\(\_\)output\({}^{(2)}\), side\(\_\)output\({}^{(3)}\), and let \(L^{(0)}\) as total loss shown in Figure 1) from MDAN-UNet-32 while others remain unchanged, and denote it as Backbone+Attention Block+Multi-input.  
As shown in Table 5, we observe that Backbone shows a slight improvement in performance in comparison of UNet++ [21], and that Backbone+Attention Block shows better performance than Backbone except for the average of thickness error(TE), which verifies the effectiveness of the dual attention block. We also observe that Backbone+Attention Block+Multi-input shows better performance than Backbone+Attention Block in all aspects, so the multi-scale input could improve the performance. In addition, Backbone+Attention Block+Multi-input yields a result of 0.883 in average dice score for expert 1 annotation, the same as MDAN-UNet-32 (Backbone+Attention Block+Multi-input+Multi-scale side output and Multi-scale label) yields, but performs poorly in other aspects. In other words, multi-scale side output and multi-scale label could further improve the performance. From the above analysis, we can draw a conclusion that multi-scale input, multi-scale side output and the dual attention block improve the performance, respectively.
# Review of Code Similarity and Plagiarism Detection Research Studies
## 2 Code Clones: Definition and Types
### Type III Code Clone  
Among the code clone types that consider text similarity, type III code clones are the type that allows the greatest variation. This type of code clone considers the overall similarity of code snippets and allows sentences to be changed, added, or deleted. Consider the following code snippet:  
if (i >= j) {  s = t + 1;  t = i - j;  } else  s = t + j;  
The following code snippet is a type III code clone of the previous code snippet:  
if (i >= j) {  s = t + 1;  u = v + t; //added.  t = i - j;  } else  s = t + j;  
Although a new sentence has been added as the third line, the two code snippets show sufficient similarity, considering that the latter snippet has only six lines of code in total.  
The detection of type III code clones is based on the similarity of the overall code, and thus, code fragments that, at first glance, appear to have many differences are also identified as code clones. Consider the following code snippets:  
Code snippets 1 and 2 in Figure 1 show algorithms that traverse a tree depth-first and make a list of the labels of its nodes. If we simply compare the code snippets, the two look very different, except for some matching words. However, upon closer inspection, it can be seen that except for the addition of sb.append(\",\"); in code snippet 2, only the type or identifier was changed. Therefore, it is determined that of these two code snippets, one is a type III code clone of the other. If the third line of code snippet 2 had no added statements, then it could also be determined that of these two code snippets, one is a type II code clone of the other.  
Code fragments are considered type III code clones if there is sufficient similarity between the actual code fragments, even if there are significant changes. Therefore, when the goal is specifically to detect this type of code clone, it is possible to find similar codes that exhibit differences beyond the various changes that occur during code development. The detection of type III code clones can also determine whether part of the code has been divided into detailed pieces and copied. Therefore, many code clone detection studies have aimed at detecting type III code clones.
# Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol
###### Abstract  
Traffic systems have been built as a result of recent technological advancements. In application, dependable communication technology is essential to link any system needs. VANET technology is used to communicate data about intelligent traffic lights, which are focused on infrastructure during traffic accidents and mechanisms to reduce traffic congestion. To ensure reliable data transfer in VANET, appropriate routing protocols must be used. This research aims to improve data transmission in VANET's implemented in intelligent traffic lights. This study investigates the capability of combining the DSDV routing protocol with the routing protocol AODV to boost AODV on an OMNET++ simulator utilizing the 802.11p wireless standard. According to the simulation results obtained by analyzing the delay parameters, network QoS, and throughput on each protocol, the DSDV-AODV routing protocol performs better in three scenarios compared to QoS, delay, and throughput parameters in every scenario that uses network topology adapted to the conditions on the road intersections. The topology with 50 fixed + 50 mobile nodes yields the best results, with 0.00062 s delay parameters, a network QoS of 640 bits/s, and a throughput of 629.437 bits/s. Aside from the poor results on the network QoS parameters, the addition of mobile nodes to the topology influences both the results of delay and throughput metrics.  
DSDV-AODV; VANET; V2X; QoS; UGV 2023  
1
## 3 Experimental Results and Analysis
### Microhardness Analysis  
Figure 4a illustrates the variations in surface microhardness of samples subjected to laser cleaning at diverse energy densities. To ensure the reliability of the data, the testing  
Figure 3: Surface of 20 steel cleaned with different laser parameters: (**a**) change in oxygen content, (**b**) change in surface roughness. (**c**) X-ray diffraction pattern.  
methodology depicted in Figure 4b was employed with the final microhardness value representing an average derived from four distinct test points. The findings reveal that laser cleaning at various energy densities consistently enhances the surface hardness of the material. This increase in microhardness can be attributed to the fact that the hardness of the loose metal oxides, initially present on the surface, is lower compared to that of the substrate material. Consequently, as the laser progressively removes the corrosion layer, the material's microhardness continually increases. The peak microhardness value observed was 142.98 HV, achieved at a laser energy density of 4.26 J/cm\({}^{2}\). Beyond this point, further increments in laser energy density resulted in negligible changes in the material's microhardness.
## 4 Anomaly Detection
### Isolation Forest  
An isolation forest is an integrated learning approach based on a decision tree model. Its underlying principles possess similarities with those of the random forest algorithm; however, it distinguishes itself by employing a fully randomized process to generate isolated binary trees. This algorithm is highly regarded for its robustness with regard to noise, its low computational complexity, its ability to handle extensive datasets, and its proficiency with high-dimensional feature spaces. Consequently, it is exceptionally well suited for anomaly detection, particularly when applied to features extracted from the random convolution kernels of a CNN. By randomly partitioning the dataset, this algorithm can separate anomalous points using a few cuts, whereas normal points require more cuts to be distinguished. In an isolated tree, normal samples typically reside deeper within the tree structure, while abnormal samples tend to reside closer to the tree root. The tree depth corresponds to the number of partitions performed. The core of the algorithm lies in the construction of an isolation tree, which is structured into leaf, inner, and root nodes, as depicted in Figure 8.  
The isolation forest algorithm initially generates \(n\) isolation trees, which are then assembled into an isolation forest model. Subsequently, an anomaly score is computed for each data sample. The algorithm accomplishes the anomaly detection task primarily based on the path length of the data points. This method is well known for its simplicity and efficiency. Figure 8 illustrates that \"triangle-shaped\" samples have the shortest path lengths,  
Figure 7: Optimal hyperplane for OCSVM.  
making them more likely to be classified as anomalies. Anomaly scores are calculated as follows [14]:  
\[c(n)\ =\ 2H(n-1)-\frac{2(n-1)}{n} \tag{12}\]  
where \(c(n)\) is the average path length of the \(n\) isolation trees, and \(H(i)\) is the harmonic number, approximated as \(\ln(i)+0.5772156649\). The anomaly score function is expressed as follows:  
\[s(x_{i},n)\ =\ 2^{-\frac{E[h(x_{i})]}{c(n)}} \tag{13}\]  
where \(s(x_{i},n)\) represents the anomaly score of sample \(x_{i}\), \(h(x_{i})\) denotes the path length of sample \(x_{i}\), and \(E[h(x_{i})]\) is the expectation of the path length of \(x_{i}\) for all isolation trees.  
Furthermore, the relationship between \(E[h(x_{i})]\) and \(s(x_{i},n)\) was obtained, as shown in Figure 9. There are three cases:  
\[\begin{array}{l}E[h(x_{i})]\to 0,s(x_{i},n)\to 1;\\ E[h(x_{i})]\to n-1,s(x_{i},n)\to 0;\\ E[h(x_{i})]\to c(n),s(x_{i},n)\to 0.5\end{array} \tag{14}\]  
The sample was considered abnormal if the anomaly score was close to 1 and it was considered normal if the anomaly score was \(<\)0.5.
## 5 Conclusions  
In this paper, we discussed the need for new strategies that can efficiently coordinate and manage the abundant resources available in VANETs and proposed an intelligent approach that can achieve this goal in a very flexible way. The proposed integrated fuzzy-based system decides the resources that vehicles should use when set under different circumstances. These circumstances include the condition of the network created among vehicles, which is represented by the QoS in the network, its longevity, its size, and the currently available resource, together with the application requirements, such as their complexity and time-sensitivity. We evaluated the proposed approach by computer simulations. From the simulation results, we conclude the following.  
* Higher QoS values are achieved for a moderate number of beacon messages broadcasted, which increases the possibility of vehicles being categorized as potentially helpful neighbors.  
Figure 10: Simulation results for FS-CFELS.  
* When a neighbor vehicle offers only a small amount of resources, it is considered less capable of helping, regardless of the quality of communication.
* In a dense environment, moderate complex data can be processed in the edge only if there are many potentially helpful neighbors in the vicinity.
* Time-sensitive applications are run either in edge or fog layer and never in the cloud.
* With the increase of data complexity less data is processed in the edge layer even if vehicles stay connected to the same potentially helpful neighbors for a long time.  
In the future, we would like to improve IFS-CMR by considering parameters that characterize the fog layer and implementing the proposed approach in a testbed in order to demonstrate its accuracy.  
Conceptualization, E.Q. and K.B.; software, E.Q. and P.A.; data curation, K.B.; validation, M.I., K.M. and L.B.; writing, E.Q. and K.B.; supervision, L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Decoding, Class Assignation  
Based on the encoding section, Section 3.3, for each binary learner, \(g_{i}\), \(i=1,\ldots,c\) and its two superclasses \(\mathbb{R}\)\(\mathrm{y}\)\(\mathrm{N}\), there are two sets of predicted values, \(g_{i}(\mathbb{R})\) and \(g_{i}(\mathbb{N})\), that should be close to \(-1\) and \(1\), respectively. The distribution of these predicted values is calculated using the univariate kernel density [61]. Fixing the probabilities of \(\gamma_{i}\) and \(\delta_{i}\) (that can be different for each \(g_{i}\)), the distributions previously fitted are used to compute critical values \(CV_{i}(\mathbb{R})\) and \(CV_{i}(\mathbb{N})\) and with the conditions established in Equation (8).  
\[\begin{array}{l}P\{\hat{y}_{i}\in\ g_{i}(\mathbb{R})\ |\hat{y}_{i}\leq\ CV_{i}( \mathbb{R})\}=\gamma_{i}\\ P\{\hat{y}_{i}\in\ f_{i}(\mathbb{N})\ |\hat{y}_{i}\leq\ CV_{i}(\mathbb{N})\}=\delta_{i}, \end{array} \tag{8}\]  
where \(P\) stands for probability. Notice that the definitions in Equation (8) imply that \(\gamma_{i}\) would be a large value close to one, whereas \(\delta_{i}\) would be close to zero. In this application, \(\gamma_{i}=0.99\) and \(\delta_{i}=0.01\) for all \(g_{i}\), \(i=1,\ldots c\), have been fixed.  
For each EMG signal in the PLS-box, if \(\hat{y}_{i}\leq\ CV_{i}(\mathbb{R})\), then the \(i\)-th coordinate on the decoding vector will be \(-1\), and if \(\hat{y}_{i}>CV_{i}(\mathbb{N})\), the coordinate will be \(+1\). For each binary learner, the objects whose predicted values are between the corresponding critical values present two possibilities: (i) if \(CV_{i}(\mathbb{R})<CV_{i}(\mathbb{N})\), the signal will not be assigned, neither +1 nor \(-1\), and (ii) when \(CV_{i}(\mathbb{R})>CV_{i}(\mathbb{N})\), the signal is assigned to both +1 and \(-1\) (intersection). Finally, the signal is inside the \(i\)-th class model if the codeword of the \(i\)-th class in \(\mathbf{M}\) is one of the decoding vectors related to it. To summarize, the sEMG signal could be assigned to none, one, or more classes or gestures. In our example, see Figure 1, the purpose is to obtain the \(\mathbf{\hat{L}}_{TR}\) and \(\mathbf{\hat{L}}_{TS}\) label matrix from the \(\mathbf{\widehat{Y}}_{TR}\) and \(\mathbf{\widehat{Y}}_{TS}\) predicted codes. However, based on the premise that any sample measured can only belong to one class, because it is not possible to perform two gestures at the same time, it is possible to obtain not valid results and _errors_ when one sample belongs to none, two, or more classes. For this reason, predicted labels \(\mathbf{\hat{L}}_{TR}\) and \(\mathbf{\hat{L}}_{TS}\) will have one more possible class (\(K+1\)) than the original (\(K\)) classes in label matrix \(\mathbf{L}\).
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Methods
### Pre-Processing
#### 2.4.1 Channel Exclusion Criteria  
We first evaluated exclusion criteria of channels on the raw and relative optical density data with three different methods: Visual inspection (VI, as described in the Introduction (Figure 1)), coefficients of variation (CV, e.g., as used in [48]) and PHOEBE [9].  
\"Coefficients of variation\" (CV) is an example of automated evaluation method of the signal in order to exclude channels. CV is given in percentage by CV (%) = 100 \(\times\) standard deviation (data)/mean (data). \"Data\" in this case is the raw optical transmission data, because otherwise the mean is close to zero. Channels above a certain threshold (CV \(>\) 7.5%, as used in [48]) indicate unphysiological noise and are excluded from further processing.  
PHOEBE [9] is a method, which evaluates the similarity of both wavelengths, as well as the power spectra, to determine cardiac power. PHOEBE outputs these measures separately (SCI_status, Pwr_status) as well as a summary of both combined (Complete_status). For this study, PHOEBE was used with the following default hardcoded parameters: Butterworth filter with cut-off frequencies of 0.8 (\"fcut_min\") to 1.95 Hz (\"fcut_max\"); window time (window length in which calculations are done) was set to 3 s (\"SCIWindow\" = 3); the scalp coupling index (SCI [49]) threshold (\"threshold\") was set to \(\geq\)0.7 and the power matrix threshold or peak value of power (\"spectral_threshold\") was set to \(\geq\)0.1. We accepted the channel when 80% of the channel was evaluated as sufficient under the complete_status (MATLAB code for exclusion: Exclude = find (mean (Complete_status) \(<\) 0.8)). PHOEBE is used on the optical density data but using it on the raw data did not make a difference (data not shown). We continued with PHOEBE, as it was the most conservative method (see Results).
## 3 Results and Discussion
### Imbalance of Network Voltage Values between Phases
The same happens between days 581-585, 838-839 and 946-949 when there was also a failure in inverter 1, and therefore, this inverter stopped registering the grid voltage value. It is therefore important, when using inverter data to perform the analysis being carried out in this work, to know how the inverters are measuring, to be able to interpret the results correctly. If the inverter, when not working, does not measure the grid voltage, it is not feasible to assess the potential imbalance that could occur at such times of inverter failure. Therefore, it would be interesting for manufacturers to establish in the inverter configuration the option of continuing to measure the grid voltage in cases where the inverter fails and does not continue to inject current into the grid. If the colour scale is again modified so that the smaller imbalance values can be better visualised, the results shown in Figure 28b are obtained. The main imbalances were observed in the first and last hours of production, because of the lack of synchronisation between the inverters at the switch-on and switch-off times. During the rest of the day, there are continuous fluctuations in the voltage values between the phases, but always of lesser magnitude. In this case, it can be verified that the imbalances of the voltage values do not have a direct relation to the imbalances in the output of the different phases, which is shown in Figure 29. In this plant, there is a string with shading, which in the early hours of the day, especially in the winter months, leads to an imbalance in the output of the three inverters. However, this does not have a direct consequence on the voltage imbalance values. Even without PV installations, there may already be imbalance due to unequal impedances and loads across the phases [9]. Despite these apparent imbalances, it has been verified that in this installation, 99% of the time periods in which there are measurement records during the three years analysed, the voltage imbalance measured in the three phases by the inverters was no greater than 1 V. It should be remembered that this installation is small, and also injects into one of the most resilient grids of those analysed in this study, which means that the voltage increase experienced with the increase in production was small, and that the voltage values of the three phases were not affected. Figure 28: Grid voltage imbalance on phase 1, measured by inverter 1 in PV installation 6 (**a**) and these grid voltage imbalance values detailing smaller deviations (**b**). Figure 29: PV production imbalance on phase 1 respect the production on the three phases, measured by inverter 1 in PV installation 6.