esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
MC_TQR used an algorithm that additionally distributed and transmitted the data while using the trust and QoS metrics and, therefore, demonstrated the capacity to effectively transfer a larger amount of data than other schemes. Figure 7 shows the average end-to-end delay based on the percentage of malicious nodes. As the percentage of malicious nodes increased, the average end-to-end delay increased for all the schemes. Considering a tactical ad-hoc network environment, even for no malicious nodes, the system may have a 40-60 ms delay because it set the data usage higher than the link bandwidth. The results of the proposed MC_TQR protocol demonstrated that the delay did not exceed 60 ms even when the percentage of malicious nodes increased to 40%. Therefore, the reliability of the scheme as a reliable routing protocol was proven owing to its observed end-to-end delay being much lower than the 220 to 1000 ms delay required in tactical ad-hoc networks. On the other hand, the results for TQR and AOTDV indicated that they did not satisfy the required delay conditions as the percentage of malicious nodes increased. In the case of TQR, which used a routing protocol that considered both reliability and QoS, a delay still occurred when only one path was used for transmission. The AOMDV protocol continued transmitting data through a path even after it was exposed to a DoS attack, and the end-to-end delay increased significantly as the percentage of malicious nodes increased. Figure 6: Average packet delivery ratio plotted against the percentage of malicious nodes. Figure 8 shows the average throughput based on the percentage of malicious nodes. As the percentage of malicious nodes increased, the throughput decreased in all schemes. TQR, AOTDV, and AOMDV reported a throughput of approximately 1500-2000 kb/s and displayed a significant decrease when the percentage of malicious nodes increased. This anomaly made it difficult to achieve reliable communication in tactical wireless networks, which require guaranteed reliability and transmission urgency of mission-critical data. The proposed MC_TQR protocol reported a processing throughput of approximately 2800 kb/s even when the malicious node percentage increased to 40%, which verified that it could stably process mission-critical data.
## 5 Results
Positive values represent better results for BIN-NLCEE compared to traditional classifiers, and negative values represent worse results. For the breast dataset, the fitness difference varied from 0.46 (SVM (S) to 6.23 (Naive Bayes (_B_)). As can be seen, big differences were obtained for the diabetes dataset, which varied from 9.48 (obtained by SVM _S_) to 16.49 (obtained by IBK _B_). For the Parkinson's dataset, the fitness difference varied from 1.01 (IBK (_S_)) to 23.16 (SVM (_B_)). For the mammography dataset, the fitness difference varied from 1.96 (J48 (_S_)) to 10.29 (IBK (_B_)). Finally, for the COVID-19 dataset, the fitness difference varied from 6.89 (NB (_S_)) to 15.37 (SVM (_B_)). BIN-NLCEE had higher fitness values according to the results shown previously, and it is important to understand deeply the reasons for that. Figures 4-13 show the convergence comparison between the BIN-NLCEE, NLCEE, and CEE for all five datasets. A random seed was chosen (seed 27) to compare the convergence difference in execution between the three methods. As can be seen, BIN-NLCEE achieves better convergence rates in the breast-w, diabetes, and Parkinson's datasets. Also, for the COVID-19 dataset, BIN-NLCEE obtained a better convergence rate for the tested_positive class. We believe that this can be explained due to the binary representation. CEE and NLCEE use integer and real values in weight and value fields of the gene and the mutation operator work, adding or subtracting a value on the field. On the other hand, BIN-NLCEE works by changing the bit value, and this can make the exploration of the search space more efficient, avoiding local minima. Figure 5: Convergence comparison for the breast-w dataset—B class. Figure 6: Convergence comparison for the diabetes dataset—tested_negative class. Figure 7: Convergence comparison for the diabetes dataset—tested_positive class. Figure 8: Convergence comparison for the Parkinson’s dataset—healthy class. Figure 10: Convergence comparison for the mammography dataset—benign class. Figure 9: Convergence comparison for the Parkinson’s dataset—PD class. Figure 11: Convergence comparison for mammography dataset—malignant class. Figure 12: tested_negative class. Figure 13: tested_positive class. With this approach, the BIN-NLCEE generates several rules for each dataset. In this way, it is possible to choose among several possibilities those rules with the highest fitness value for testing.
## 2 System Model
### Channel Model  
The channel between each of the \(L\) optical transmitters and \(N\) photodiodes of the receiver follows the Lambertian radiation model. The optical power radiated by an LED \(l\) at a distance \(d\) is given by the Lambertian radiation model, which is defined as  
\[\phi_{d}=P_{\text{opt(tx)}}\cdot\frac{m+1}{2\pi d^{2}}\cos^{m}(\alpha)\, \tag{3}\]  
where \(P_{\text{opt}}\) is the optical power of the LED, \(\alpha\) is the radiation angle, \(d\) is the distance between the optical transmitter and receiver, and \(m\) is the Lambertian index defined as \(m=\frac{\ln(2)}{\ln(\cos(\alpha_{1/2}))}\), where \(\alpha_{1/2}\) is the radiation semi-angle. Notice that the optical power emitted by the LED is subject to an electrical power consumption given by \(P_{\text{elec}}=\frac{P_{\text{opt}}}{\eta}\), where \(\eta\) is the LED efficiency, i.e., the amount of electrical power that is converted into optical power. The electrical power consumption in (3) is given by \(P_{\text{elec}}=V_{\text{LED}}\cdot I_{\text{LED}}\)where \(V_{\text{LED}}\) and \(I_{\text{LED}}\) correspond to the average voltage and current of the LED, which define the polarization point.  
The received power at photodiode \(n\) from a specific LED can be written as  
\[P_{l}^{[n]}=\phi_{d}\cdot A_{p}\cos(\beta_{l}^{[n]})\Pi\left(\frac{\beta_{l}^{[n ]}}{\Psi}\right)\;, \tag{4}\]  
where \(A_{p}\) is the area of detection of the photodiode, \(\beta_{l,n}\) is the incidence angle between LED \(l\) and photodiode \(n\), \(\Psi\) is the field of view (FoV) of each photodiode, which is assumed as the same for all of them for the sake of clarity, and  
\[\Pi(x)=\begin{cases}0&x>1\\ 1&x\leq 1\;,\end{cases} \tag{5}\]  
is the step function that relates the incidence angle with the FoV of the photodiode.
## 5 Results and Discussion
### Future Directions  
The field of TOC prediction, critical for shale gas exploration, is undergoing a transformative shift due to machine learning (ML)-driven methodologies. Among these, Random Forest Regression (RFR) remains a cornerstone, particularly due to its inherent capability to process high-dimensional, nonlinear petrophysical datasets that often exhibit collinearity. However, to enhance the precision and robustness of next-generation TOC estimation, several key improvements in RFR implementations are required. These enhancements aim to address challenges posed by reservoir heterogeneity, complexities in overmature shales, and computational inefficiencies, thereby enabling more accurate and scalable models for large-scale basin analysis. (1) A significant challenge in TOC prediction lies in accurately modeling overmature shales, where organic maturation exhibits nonlinear and intricate behaviors. Conventional data-driven approaches often fail to capture subtle organic maturation dynamics that play pivotal roles in shale gas reservoirs. Future research should explore the integration of physics-based ML models into learning frameworks, embedding geochemical and thermal maturation processes. By incorporating mechanistic knowledge of organic transformation--such as kinetic reaction rates and temperature-dependent maturation stages--into model architectures, ML algorithms can achieve higher accuracy in predicting TOC within overmature and highly heterogeneous shale formations. (2) A critical limitation of current RFR models is their inability to quantify uncertainties in TOC predictions, particularly in data-scar environments. Future advancements should focus on integrating uncertainty-aware ensembles that leverage Bayesian inference to quantify prediction confidence. By embedding Bayesian frameworks, models can incorporate prior  
Figure 10: Comparison of predicted vs. measured TOC content across different models.  
Figure 9: TOC prediction results for Well Z201.  
knowledge and generate predictive distributions rather than deterministic outputs. This capability is indispensable during the exploration phase of shale gas development, where data availability is often limited.  
Addressing these two advancements--physics-informed ML architectures and uncertainty-aware Bayesian ensembles--will enable researchers to overcome challenges posed by geological complexity and data scarcity. These innovations hold transformative potential for improving the accuracy and efficiency of TOC workflows, making them indispensable for large-scale shale gas exploration. While promising, their realization demands interdisciplinary collaboration among geoscientists, data scientists, and quantum computing experts to tackle associated technical barriers.
## 5 A New Approach for Selection and Validation of Workflows
### Workflows  
The starting point for all our service compositions is a ranked list of multimedia workflows. Different from general purpose workflow management systems, our workflows are very specific and only require a small subset of the possibilities of general purpose workflow languages.  
Consider our example of media streaming. It consists of fetching, adapting and delivering the content data to the user. Each step, or role (see Figure 4) manipulates a certain aspect of the content stream, e.g., it changes the encoding of the stream from the content providers high quality mpeg format to a low bandwidth codec used by mobile phones, like h.264.  
In our architecture, the multimedia workflows are represented using a straightforward action-like model, consisting of roles and data objects. Data objects model the changing of properties of multimedia content after each step. During the design process we considered using a reasoning system to derive a workflow from a set of attributes of the source content data and the given requested target format. Since we are facing real-time constraints, a less computational heavy approach was chosen. For practical reasons we decided to create a set of suitable template workflows for those multimedia  
Figure 4: Role example: Transcoding a stream from one format to another.  
processes in our scenarios, which will then be selected based on the tasks required, e.g., if the source and destination formats differ in size and encoding, a workflow template reflecting the necessary transformation will be selected. Still, a simple domain ontology is needed to define the types of adaptations and allow enough reasoning about service capabilities and interoperability, as well as the initial workflow selection.  
Workflows can have different goals, e.g., can be optimized for quality, network efficiency or costs. As a result the system ends up with a ranked list of suitable workflows.  
Figure 3 shows an example adaptation workflow with steps for scaling, transcoding and bit-depth reduction to adapt the source content object for the user's display device. Starting on the left with a high resolution source stream, each service processes the data. Our sample workflow starts with a scaling role to reduce the size. The transcoding step decodes the stream and re-encodes it using a different codec, while the last step is a service specialized in adapting the color information for the limited capabilities of the user's display device.
However, these performance gains are often achieved through optimization formulations that use binary and integer variables, which increase computational complexity. In real-time applications such as autonomous driving or smart traffic management, the increased complexity of resource optimization due to interference and poor user clustering can lead to delays, impacting system performance and safety. The time complexity of optimization problems becomes crucial because delays in solving them could degrade the responsiveness of the system. In particular, the high complexity of resource optimization can become a significant hurdle in real-time applications [3]. If the optimization process is computationally expensive due to the use of binary or integer variables, it may not be fast enough to meet real-time requirements. Therefore, a thorough investigation of the impact of binary and integer variables on time complexity is essential. This will help ensure that the optimization process remains efficient without introducing significant delays, which would hinder the effectiveness of NOMA in real-time environments.
## 3 Results
### AIGC Input-Output Classification
#### 3.3.4 Text to Code and Code to Text  
In the world of software development, the task of manually writing or replicating code patterns can be a time-consuming process. However, there is an innovative solution known as text to code and code to text, which offers significant benefits. Text to code enables us to generate entire source code for specific business problems, resolve issues within existing code by providing suggestions for corrections, while code to text empowers us to generate documentations for the code. Moreover, these cutting-edge advancements leverage generative AI techniques, allowing us to effortlessly complete simple functions. Notably, GitHub has recently launched GitHub Copilot, which utilizes Open AI Codex [116]. It allows for tasks such as generating repetitive code patterns or even entire functions by simply providing natural language comments in the editor. Figure 15 demonstrates the generation of source code using the textual prompt. Another noteworthy model is CodeBERT [114], which is based on a bimodal transformer [1] technique. CodeBERT supports natural-language (NL)-to-programming-language (PL) applications, enabling tasks such as code search, code documentation (code to text), and code review. Additionally, CodeBERT [114] has been further pretrained for CodeReviewer [167], CodeEx-ecutor [168](for tracking execution traces), GraphCodeBERT [169] (for code refinement and translation between programming languages), and UniXcoder [170] (for code generation  
Figure 14: Prompt is “Sprouts in the shape of text ’Imagen’ coming out of a fairytale book”. (Image was obtained from Imagen.)tasks). CODEGEN [50] is an autoregressive-transformer-based large language model (LLM) that specializes in program synthesis based on input and expected output or even natural language descriptions. CodeTS [115], on the other hand, is a pre-trained encoder-decoder transformer model capable of tasks such as code understanding, code generation, and converting source code between programming languages. Additionally, AlphaCode [35], another encoder-decoder transformer-based model, excels in understanding algorithms and generating competition-level source code for given problem statements. This model has been effectively evaluated on the Codeforces platform.
## 2 System Model
### Cache Model  
The primary objective of this work was to serve the rigorous computation demand in the vicinity of the UE by caching the computation-intensive services. The service is considered as the abstraction of applications, namely, video streaming, social gaming, navigation, and AR and VR, hosted by the E-APs or cloud and requested by the UE. To run a specific service, it is necessary to cache the related data, which includes required libraries and databases, on the service provider (smart devices, E-APs, or cloud computing). We consider each E-AP \(l\) to have the storage capacity \(S_{l}\), which can be utilized to store the maximum number of services [14]. We will assume that a set is denoted as \(Q\), which encompasses various computing services. These services are indexed using the set \(Q=1,2,\ldots,Q\). At the time slot \(t\), the controller E-AP \(l\), which decides whether the service \(q\in Q\) should cache or not, can be represented as the binary variable \(C_{q,l}^{t}\in\{0,1\}\). However, insufficient cache storage capability limits the number of services at each E-AP \(l\), which can be described as  
\[\sum_{q\in Q}C_{q,l}^{t}s_{q}\leq S_{l},\forall l,\forall t, \tag{1}\]  
where \(s_{q}\) denotes the occupied storage space in E-AP \(l\) for caching the service \(q\) and the maximum storage capacity of E-AP\(l\) is represented by \(S_{l}\). Since the services considered are stored distributively among the E-APs in the shared environment, we strictly follow the non-duplication policy as follows:  
\[\sum_{l\in n}\sum_{q\in Q}C_{q,l}^{t}s_{q}\leq 1,\forall q,\forall l, \tag{2}\]where \(C_{g,l}^{t}s_{q}\leq 1\) shows that only one service \(q\) can be cached in one E-AP \(l\) in the E-APs group \(n\).  
\[c_{f,l}=\begin{cases}1&\text{if the file $f$ cached in E-AP $l$}\\ 0&\text{otherwise}\end{cases} \tag{3}\]
## 4 Theoretical Approach and Discussion
### Comparison of the Theoretical and Experimental Moment Capacity  
The calculated theoretical moment capacity for each batch, calculated from Equation 6, is compared in Table 9 with the experimental moment capacities for each of the corresponding batch specimens from the experimental results discussed above. In addition, a comparison is made between Equation 3 and 4 to calculate \(f_{ult}\). The \(k_{f}\) value for FF4, FF6, and FF8 is determined as the average plus the standard deviation of the ratios \(f_{R1}/f_{L}\) from Table 8, resulting in \(k_{f}=0.38\), 0.40, and 0.65 for FF4, FF6, and FF8, respectively. \(M_{u,tho,1}\) is determined using Equation 3 and \(M_{u,tho,2}\) is determined using Equation 4. The ratio of the experimental to theoretical moment \(M_{u,exp}/M_{u,tho}\) capacities shows that, although conservative, the modified stress distribution model is a fairer predictor for the moment capacity when compared to the model proposed in the _fib_ Model Code [34]. The moment capacity prediction for FF8, and to a lesser extent FF4, is slightly conservative but is not conservative for FF6. Nevertheless, FF6 yields a very precise result compared to FF4 and FF8. While comparing the proposed model and the _fib_ Model Code [34], it is evident that the proposed model is more accurate in terms of predicting the moment capacity of twisted-bundle macro-synthetic fiber reinforced SCC.  
Figure 10: (**a**) Modified stress-crack opening constitutive law, (**b**) Stress and forced distribution at ultimate moment based on modified model.  
Assuming three mixtures with 4, 6, and 8 kg/m\({}^{3}\) of fibers and similar split tensile properties, a theoretical comparison can be made among them to compare their theoretical moment capacity. Providing that the split tensile strength of all the concrete mixture is 4 MPa, the theoretical section moments are 1.78, 1.88, and 3.05 kN.m, respectively. Hence, it can be concluded that the FF8 mixture has the highest moment-carrying capacity.
## 6 Discussion  
The sparsity makes the semantics of words and phrases not fully learned, which in turn harms the performance of NLP tasks [16; 17; 22; 25]. To reduce the sparsity, researchers have designed effective algorithms to split long words into short fragments. These algorithms include BPE and WordPiece [1; 2; 20]. There is also a study pointing out that using character-level models in Chinese NLP tasks can achieve better performance [16]. In this article, we use the dictionary to estimate the embeddings of low-frequency words. In extrinsic tasks, we obtain better performance by using word embeddings estimated by our proposed method to replace the original low-frequency words' embeddings (estimated by Word2 Vec). This shows that our method can provide higher-quality low-frequency word embedding. However, from the experimental results, we can see that too-high a replacement rate will harm the performance of tasks. In addition, performing such a replacement on a larger dataset will lead to higher performance improvement.  
In this article, we design a new sentence representation model and expect to extract the semantics of explanatory notes more efficiently. Our sentence representation model achieves the best performance in many tasks in both the intrinsic and extrinsic experiments.  
In summary, dealing with OOVs and low-frequency words is one of the challenges in NLP tasks. OOVs and low-frequency words are universal. Therefore, we think that it is very difficult to eliminate the OOV problem. Although the method proposed in this paper reduces the impact of the OOV problem on performance to a certain extent, there are still many problems worthy of further study. In the future, we will conduct in-depth research in the following aspects:  
* Use relationships between the rich nodes in knowledge bases to estimate the embedding of low-frequency words.
* Construct more high-performance sentence representation models to extract semantics from sentences.
* Since there are two different word embedding estimation methods, we will study measures to make two semantics spaces better coupled.
* Use the correspondence between words in multilingual dictionaries to estimate the embeddings of low-resource language words.  
**Author Contributions:** Conceptualization, Y.H.; methodology, X.L.; software, X.L.; validation, C.W., C.Z. and Y.D.; formal analysis, Y.H.; writing, K.Y.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China under Grant No. 61866008, and we are also supported by the Basic and Applied Basic Research Fund of Guangdong Province, China under Grant No. 2019B1515120085. We are also supported by the central government guides for the local science and technology development fund project subsidization (Guike AD20238072).  
**Acknowledgments:** We are grateful to the anonymous reviewers for their insightful suggestions and the editors of Applied Sciences--Basel.  
**Conflicts of Interest:** The authors declare no conflict of interest.