esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
In this study, we address the problem of lemmatization for a resource-poor but morphologically rich language, namely Urdu. We propose a dictionary lookup approach to solve the problem. We manually developed an extensive dictionary consisting of a word, part of speech (PoS) tags, and lemmas (roots). A total of 25,000 words were selected from two corpora, the Urmono corpus, and the Wikipedia dump. In addition, we also prepared a test dataset containing 8590 words to evaluate the lemmatizer. This test data was collected from different sources and manually annotated. The results were obtained using two different cases. In the first case, words and PoS tags were provided to the system, and we achieved an accuracy of 66.79%. In the second case, only words without PoS tags were provided to the system. The PoS tagger assigned the PoS tags to the given words, and after assigning the PoS tags, lemmas were generated by using the dictionary lookup approach. For the second case, we achieved an accuracy of 76.44%.  
Poor grammatical structuring of the Urdu vocabulary, the improper joining of Urdu characters, the use of extra white spaces, and missing words contribute toward the increased complexity of the Urdu lemmatization task. Therefore, an increase in the dictionary size to improve the accuracy of the Urdu lemmatizer, and the use of a spell checker alongside the lemmatizer will be a part of future work. In addition, adding the English translation to Urdu words and their lemmas in the corpus is also a part of our future work.  
Conceptualization, M.A.; Methodology, S.S.; Formal analysis, S.S. and A.A.; Investigation, S.S.; Writing--original draft, S.S.; Writing--review & editing, M.A. and A.A.; Visualization, A.A.; Supervision, M.A. All authors have read and agreed to the published version of the manuscript.  
These research results were obtained from research commissioned by the National Institute of Information and Communications Technology (NICT), Japan.  
Not applicable.  
The original contributions presented in the study are included in the article; the corpus data can be accessed through our GitHub repository.  
The authors declare no conflict of interest regarding the publication of this research article.
# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 3 Results
### Segmentation Result
In comparison to the sophisticated UNet++ model, CPF-UNet achieves slightly higher segmentation accuracy, as evidenced by marginal improvements in measures like Mean Intersection over Union (MIoU) and Recall, while also providing expedited inference times. This suggests that, in enhancing segmentation precision, CPF-UNet has effectively gained an edge in computational resource utilization efficiency, thereby offering a more competitive solution for segmentation tasks that necessitate real-time feedback or operate within constrained resource environments. In summary, CPF-UNet demonstrates superior performance in balancing both crucial dimensions of segmentation accuracy and computational efficiency.
## 2 Related Work
The goal of any MCDM method is to decide the appropriate weight of each criterion in the model. Paper [28] gives an exhaustive survey of applications of AHP in the field of sustainable development. The main idea behind AHP is to simplify complex decision-making by splitting complex choices into a pairwise comparison of criteria. AHP uses the concept of hierarchy structuring (goal at the highest level, criteria/subcriteria at the intermediate level, and alternatives at the lowest level). At each level, the pairwise comparison represents a PCM (pairwise comparison matrix--judgment matrix). The decision-maker or expert performs weight evaluation for the construction of PCM for comparing a higher-level element to a lower-level element. If, in the end, all elements in the PCM follow the same rule, i.e., we can create a connection between criteria using the transitivity, then the matrix is said to be consistent. By adapting the weights, the aim is to make PCM as close to a consistent matrix as possible. There are multiple consistency indices [29] used to evaluate how \"close\" the PCM is to a consistent matrix and to help guide weight adjustment. Meta-heuristic optimization techniques have received notable attention among researchers and engineers in the last two decades. Several different, including nature-inspired, techniques have been discussed in detail in [30]. As finding the optimal weights for criteria is an optimization problem, these techniques are also of interest to researchers of MCDM techniques. In our proposal, previously described I/O multipliers are used as a quantitative measure of the proposed alternatives extracted from the SWOT analysis. As in previously identified steps of MCDM, we will identify objectives and stakeholders and develop alternatives using the results of the SWOT analysis. A key novelty in our proposal is the usage of I/O multipliers as a quantitative measure of the identified alternatives for criteria weighing.
## 3 Empirical Gramians
### State-Space Empirical Gramians
#### 3.1.2 Empirical Observability Gramian  
The (linear) observability Gramian quantifies how well a change in the state of an underlying linear system is visible in the outputs and is defined as:  
\[W_{O}:=\int_{0}^{\infty}\mathrm{e}^{A^{\intercal}t}\,C^{\intercal}C\,\mathrm{ e}^{At}\,\mathrm{d}t=\int_{0}^{\infty}(\mathrm{e}^{A^{\intercal}t}\,C^{ \intercal})(\mathrm{e}^{A^{\intercal}t}\,C^{\intercal})^{\intercal}\,\mathrm{d}t.\]  
The empirical variant is given by the following definition based on [2; 35].  
**Definition 2** (Empirical Observability Gramian).: _Given non-empty sets \(E_{x}\) and \(S_{x}\), the **empirical observability Gramian**\(\widehat{W}_{O}\in\mathbb{R}^{N\times N}\) is defined as:_  
\[\widehat{W}_{O} =\frac{1}{|S_{x}|}\sum_{l=1}^{|S_{x}|}\frac{1}{d_{l}^{2}}\int_{0} ^{\infty}\Psi^{l}(t)\,\mathrm{d}t,\] \[\Psi^{l}_{ij}(t) =(y^{li}(t)-\bar{y}^{li})^{\intercal}(y^{lj}(t)-\bar{y}^{lj})\in \mathbb{R},\]  
_with the output trajectories \(y^{li}(t)\) for the initial state configurations \(x^{li}_{0}=d_{l}e^{i}+\bar{x}\), \(u(t)=\bar{u}\), and the offsets \(\bar{u}\), \(\bar{x}\), \(\bar{y}^{li}\)._  
For an asymptotically stable linear system, no input and an arithmetic average over time as offset \(\bar{y}=\frac{1}{T}\int_{0}^{T}y(t)\,\mathrm{d}t\), the empirical observability Gramian is equal to the observability Gramian \(\widehat{W}_{O}=W_{O}\)[2]. The numerical computation of this empirical Gramians requires \((|S_{x}|\cdot N)\) trajectories.
## 4 Experiments and Analysis  
### Evaluation Metrics  
This research evaluates the capability of the emotion feature encoding network using two main metrics: F1 score and unweighted accuracy (UA), as described below:  
Figure 4: KAN structure diagram.  
1. F1 Score: The F1 score is a measure that balances both the precision and recall of the model. In the emotion recognition task within second language emotion learning, the F1 score provides an effective measure of the model's overall performance across various emotion categories, ensuring that the model can maintain both a high precision and high recall when dealing with various emotion categories. The formula for the F1 Score is  
\[F1=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{2}\]  
2. Unweighted Accuracy (UA): Unweighted accuracy refers to the average of the model's accuracy across categories, regardless of the quantity of samples in each category. In the task of emotion recognition in second language emotion learning, using unweighted accuracy helps to evaluate the model's performance in recognizing different emotion categories.  
\[Accuracy=\frac{\text{T}_{emotion}}{\text{T}_{emotion}+\text{F}_{emotion}} \tag{3}\]  
where \(T_{emotion}\) is the quantity of samples with a correct emotion prediction and \(F_{emotion}\) is the quantity of samples with an incorrect emotion prediction.  
For the emotion evaluation network, we chose to use the MSE as the main evaluation metric. The MSE refers to the mean of the squared differences between the predicted values and the actual labels. In emotion evaluation, it is utilized to assess the model's performance in emotion score regression.  
These evaluation metrics were chosen to give an overall measure of the model's accuracy, recall, and ability to handle unbalanced data, allowing for an in-depth analysis of the model's performance.
## 3 Results
### Method Validation  
Under the optimum conditions (salt concentration, 25% (\(w/v\)); extraction temperature, 40 \({}^{\circ}\)C; extraction time, 10 min; and desorption temperature, 200 \({}^{\circ}\)C; and desorption time, 3 min), the quantitative performance of the developed method was assessed, and the results were given in Table 1. The calibration curves were prepared using the mixed standard solutions of the OPs at nine concentration levels. The curves were linear in the range of 8-1000 ng L\({}^{-1}\), with a satisfactory determination coefficient (R\({}^{2}\)) of \(>\)0.9971. The limits of detection based on the signal-to-noise ratio (S/N) of three replicates that were 3-80 ng L\({}^{-1}\). The limits of quantification (S/N, 10) were calculated as 8-200 ng L\({}^{-1}\). The intra-day relative standard deviations (RSD\(\%\), n = 3) at the concentration levels of 400 and 1000 ng L\({}^{-1}\) were within the 1.7-13.8\(\%\) range. The inter-day RSD\(\%\) was calculated using fiber in three different days with three replicates on each day equal to 1.7-11.8\(\%\) (C = 400 and 1000 ng mL\({}^{-1}\)). The reusability of the PET/nano-SiO\({}_{2}\) fiber was also evaluated by assessment of its extraction performance in successive adsorption/desorption cycles under the same experimental conditions. After completing each cycle, the adsorbent was washed three times with MeOH and water sequentially. Then, the dried adsorbent was reused for the next run. The results indicated that the fiber can be reused at least 75 times without a significant reduction in efficiency (\(<\)5%). Therefore, the PET/nano-SiO\({}_{2}\) fiber qualified for frequent use in solid-phase extraction-based methods.
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 3 Wind Turbine Blades and Its Protection Methods
### Blade Model for Investigation  
The blade to be inspected was based on an existing model, currently the largest turbine on the market Vestas V164-9.5 MW [9], at present, produced for offshore, although the company is in the process for an onshore model with similar dimensions [27]. For this study, the length of the blades was only considered for the simulation. Although their lightning protection systems are compliant with IEC 61400-24 standards, the exact lightning protection system employed by the blade's manufacturer is not available in the public domain. However, as briefly discussed in Section 3.3, any wind turbine blades should be protected and complied as per methods proposed by IEC 61400-24 standards [7]. For a structure this size, approximately with a tip height around 200 m above sea level, the number of strikes can be estimated considering the lightning density in Europe (between 0.1 and 42 flashes per year per km\({}^{2}\)) [5; 7].  
The height of structure greatly affects the number of flashes predicted on the structure. Based on the regular expected turbine lifetime, what is generally predicted to be 20-25 year, it is very likely that the turbine will be hit at least once during its lifetime. Without any protection, the blade will most likely be destroyed. If the base cost lies between GBP 0.6-0.8 million per MW for an onshore turbine, and generally around 13% of these blades are [28], therefore, the estimated price for losing one blade would be roughly GBP 300,000 on the aforementioned model, not calculating the replacement, transportation, and power outage caused costs. From this, it is clear that wind turbines require adequate protection against lightning strike nevertheless of their location, since even if it is estimated with the lowest density, over the expected lifetime the turbine will be struck at least one or two times.
## 3 Proposed Method
### Operators
#### 3.2.1 Crossover Operation  
The classical uniform crossover is used for the selected feature vector. In this paper, we adapted the recently proposed rand-length crossover for the random variable-length crossover differential evolution algorithm [42] to crossover two discretization schemes. More precisely, offspring lengths are firstly randomly and uniformly selected from the range \([K_{c}^{lower},\min(|x^{\mathcal{L}_{c}}_{1}|+|x^{\mathcal{L}_{c}}_{2}|,K_{ c}^{upper})]\), where \(x^{\mathcal{L}_{c}}_{i}\) indicates the discretization scheme (to be used for the gesture class \(c\)) associated with the solution \(x_{i}\) and \(|.|\) indicates the number of elements in this designated discretization scheme. For the current value of \(i\in[1,\min_{i\in[1,2]}|x^{\mathcal{L}_{c}}_{i}|]\), three cases might occur. When both parent solutions contain a discretization point at the index \(i\), the simulated binary crossover (SBX) is applied to each dimension of the two points. When one of the parent solution discretization scheme is too short, both children inherit from the parent having the longest discretization scheme. Otherwise, a new discretization point is uniformly chosen in the training space for each children solution. All newly created discretization points are randomly assigned to children solution. The pseudo-code of the rand-length crossover for discretization scheme procedure is given in Algorithm 1.  
Since LM-WLCSS penalties are encoded as real-values, the SBX operator is also applied to the decision variable \(P_{c}\). In contrast, SearchMax window lengths are integers; thus, we incorporate the weighted average normally distributed arithmetic crossover (NADX) [54]. It induces a greater diversity than uniform crossover and SBX operators while still proposing values near and between the parents. Despite the length of the backtracking variable having been fixed, the NADX operator could be considered.  
When selecting features, the discretization schemes or LM-WLCSS penalties, and SearchMax window lengths of children solutions are different from those of parent solutions, and their coefficients, \(h_{c}\), of the threshold must be undefined because the resulting LM-WLCSS classifier from the solution is altered.
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Random Forest (RF) Classifier  
The random forest method [81] is an ensemble (bagged) predictor composed of a set of classifications generated by the DT algorithm, called the Classification and Regression Tree (CART). The classifier is trained against randomly sampled (with replacement) data subsets using randomly selected features from a feature vector. For each DT, an input dataset is split into training (referred to as in-bag samples) and validation subsets (referred to as out-of-bag samples) using a 67/33% proportion. This allows for internal cross-validation of derived classification and computation of goodness-of-fit statistics called an out-of-bag (OOB) error, defined as an error rate (number of misclassifications divided by the total number of cases). Decision trees are not pruned and the final classification is computed by majority voting across results acquired from all of the generated trees. The main benefit of the RF classifier over the DT method is insensitivity to overfitting and to some level of noise in a training dataset. On the negative side, RF classifiers may be computationally expensive in the case of large training datasets.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 5 Discussion
### Architecture Comparison
In the C/S architecture, the cost is solely paid by the platform owner who pays the cost of the entire infrastructure. In the Hybrid-I architecture, as some functions (e.g., state distribution) are distributed to user clients, the platform owner's cost can be largely decreased. In the Hybrid-II architecture, the cost depends on who provides cloud resources. Since a Hybrid-II virtual world is collectively run by cloud resources and user devices, the cost in the Hybrid-II architecture will not be larger than the one in the Hybrid-I architecture. In the P2P architecture, the infrastructure cost is fully shared by all users. Thus, application providers barely need to pay for the infrastructure. Game cheating is the main factor leading to the low acceptance of decentralized architectures in the industry [66]. Compared to centralized structures, decentralized structures, due to the lack of a central arbitrator, are more vulnerable to player escaping, network flooding, suppress-correct cheat, etc. than their C/S counterparts, while time cheat, blind opponent, consistency cheat are only possible to P2P games [66]. In P2P architectures, though a few cheatings (e.g., code tampering and event suppressing) can either be prevented or detected through cryptographic measures [104] or mutual verification [107], a more general approach either needs a central server for rule enforcement [106; 147] or lockstep message check which largely increasing communication overhead. In the Hybrid-II architecture, a referee anti-cheating scheme is applied for detecting illegal messages in communication [108]. Nevertheless, a malicious user can still tamper the code of VNs and change the simulation logic which becomes favorable to them and cannot be detected by the scheme. Moreover, the deterministic mapping from object identifiers to the VN address space [144] enables an attacker to guess the content on the controlled device, increasing the attack success rate.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 3 Preliminaries and Problem Definition  
Given a microdata table \(T\) of records with \(d\)+1 attributes, \(AT=\{AT_{1},AT_{2},...,AT_{d},S\}\) and the attribute domains are \(\{D[AT_{1}],D[AT_{2}],...,D[AT_{d}],D[S]\}\). A tuple \(t\in T\) can be expressed as \(t=(t[AT_{1}],t[AT_{2}],...,t[AT_{d}],t[S])\), where \(t[AT_{i}](1\leq i\leq d)\) is the quasi-identifier of \(t\) and \(t[S]\) is the sensitive value of \(t\).  
We assume that a number of other microdata tables \(F=\{F_{1},F_{2},...,F_{n}\}\) are published by different independent publishers whose microdata tables are also defined by the same schema as \(T\) and published using the same anonymization method. This is a reasonable assumption since public and private groups may have a standard method for data distribution. For example, all health care institutions in the USA follow one law, the HIPAA, for their distribution of _d-identified_ data [2].
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 4 Results and Discussion
### Parameter Setting  
In this study, the STA-GCN model was implemented using the PyTorch framework, with the following parameter settings during training: a batch size of 32, the Adam optimizer, RMSE as the loss function, and an initial learning rate of 0.001.  
The output dimension of the input layer was set to 32, and a (3,1) kernel was chosen for the temporal convolution based on previous work.  
The Chebyshev polynomial in the graph convolution layer was set to 3.  
The number of temporal and spatial modules was set to 2, and the model used the previous 12 time windows to predict the next 12 time windows. To prevent overfitting, the model stopped training if the validation loss did not decrease within 10 epochs.
## 2 Minimum Description Length and Bayes Information Criterion  
For what we may term a homogenous modeling framework, the minimum description length, MDL, associated with Shannon entropy [6], will serve us well. However as we will now describe it does not cater for hierarchically embedded segments or clusters. An example of where hierarchical embedding, or nested clusters, come into play can be found in [5].  
Following [PERSON] and [PERSON] [7], we consider a model class, \(\Theta\), and an instantiation of this involving parameters \(\theta\) to be estimated, yielding \(\hat{\theta}\). We have \(\theta\in\mathbb{R}^{k}\) so the parameter space is \(k\)-dimensional. Our observation vectors, of dimension \(m\), and of cardinality \(n\), are defined as: \(X=\{x_{i}|1\leq i\leq n\}\). A model, \(M\), is defined as \(f(X|\theta),\theta\in\Theta\subset\mathbb{R}^{k},X=\{x_{i}|1\leq i\leq n\},x_ {i}\in\mathbb{R}^{m},X\subset\mathbb{R}^{m}\). The maximum likelihood estimator (MLE) of \(\theta\) is \(\hat{\theta}\): \(\hat{\theta}=\text{argmax}_{\theta}f(X|\theta)\).  
Using Shannon information, the description length of \(X\) based on a set of parameter values \(\theta\) is: \(-\log f(X|\theta)\). We need to transmit parameters also (as, for example, in vector quantization). So overall code length is: \(-\log f(X|\theta)+L(\theta)\). If the number of parameters is always the same, then \(L(\theta)\) can be constant. Minimizing \(-\log f(X|\theta)\) over \(\theta\) is the same as maximizing \(f(X|\theta)\), so if \(L(\theta)\) is constant, then MDL (minimum description length) is identical to maximum likelihood, ML.  
The MDL information content of the ML, or equally Bayesian maximum a posteriori (MAP) estimate, is the code length of \(-\log f(X|\hat{\theta})+L(\hat{\theta})\). First, we need to encode the \(k\) coordinates of \(\hat{\theta}\), where \(k\) is the (Euclidean) dimension of the parameter space. Using the uniform encoder for each dimension, the precision of coding is then \(1/\sqrt{n}\) implying that the magnitude of the estimation error is \(1/\sqrt{n}\). So the price to be paid for communicating \(\hat{\theta}\) is \(k\cdot(-\log 1/\sqrt{n})=\frac{k}{2}\log n\) nats [7]. Going beyond the uniform coder is also possible with the same outcome.  
In summary, MDL with simple suppositions here (in other circumstances we could require more than two stages, and consider other coders) is the sum of code lengths for (i) encoding data using a given model; and (ii) transmitting the choice of model. The outcome is minimal \(-\log f(X|\hat{\theta})+\frac{k}{2}\log n\).  
In the Bayesian approach we assign a prior to each model class, and then we use the overall posterior to select the best model. [PERSON]'s Bayesian Information Criterion (BIC), which approximates the Bayes factor of posterior ratios, takes the form of the same penalized likelihood, \(-\log f(X|\hat{\theta})+\frac{k}{2}\log n\), where \(\hat{\theta}=\text{ML}\) or MAP estimate of \(\theta\). See [8] for case studies using BIC.