esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Feature Engineering  
Features are described as signals that encode information from raw data facilitating machine learning algorithms to classify an unknown object or estimate an unknown value [20]. Feature engineering is the process of selecting, manipulating, and converting raw data into meaningful features that can be used in ML algorithms. The rescue data we used were quite complex and had no attribute directly referring to neurological diseases. That is why we went through multiple filtering processes to finalize our features. A three-fold filtering method was used to select the features:  
1. At the first stage, we collected empirical knowledge from past research, and consulted with medical professionals regarding neurological complications. From this study, we have sorted out 105 features pertaining to neurological complications. Henceme of those features were not directly present in the dataset but rather created using one hot encoding
2. Next step was to review the data trend of selected 105 features separately for neuralgic and non-neuralgic patients. In our observation, 39 features were found showing significant differences between patients and non-patients
3. Recursive Feature Elimination with Cross Validation (RFECV) technique was applied at the last step to the features selected at the second step. RFECV identifies the most relevant and impactful features and removes those with minimal impact. Both random forest and gradient boosting algorithms were applied with RFECV to select the final features  
Our filtering process concluded with only five features which are GCS score, Presence of trauma, brain injury, head discomfort and consciousness disorder. The other features including health vitals were not found to make any significant impact on the detection process of neuralgic complications. Because the values of the vitals for neuralgic patients  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nr.** & **Diseases** \\ \hline
1 & Transient ischemic attack \\ \hline
2 & Intracranial bleeding \\ \hline
3 & Subarachnoid hemorrhage \\ \hline
4 & Seizure \\ \hline
5 & Febrile seizure \\ \hline
6 & Status epilepticus \\ \hline
7 & Apoplexy \\ \hline
8 & Intercostal neuralgia \\ \hline
9 & Retrograde amnesia (after trauma) \\ \hline
10 & Retrograde amnesia (without trauma) \\ \hline
11 & Neurogenic shock \\ \hline \hline \end{tabular}
\end{table}
Table 1: Diseases under neurological complication.  
were quite similar to patients with other complications. In Figure 3, multiple bar plots of health vitals are drawn between patient and non-patient to prove our claim.
## 3 Review of Model Evaluation  
A limitation of many simulation models is an unknown degree of accuracy in model outputs [3; 155]. Before any simulation model can be used to answer research questions and infer conclusions, it is necessary to evaluate the model [3; 9; 155]. Evaluation against experimental performance data given the same initial conditions can ensure that the model is a sufficiently accurate representation of the activity and mechanical system being modeled [9; 35; 60; 118]. Researchers should be aware that this only confirms that the model can generate a movement pattern to recreate the recorded performance and does not guarantee that any subsequently optimized movement solution is possible by the individual [30].  
Model outputs have been evaluated against real performances (by the same individual being modeled) using optimization algorithms to minimize a score or cost function, often a weighted mean of differences (e.g., kinetic and kinematic parameters that the model should match) [4]. This process enables quantification of errors and/or the effects of any inherent assumptions, both of which must be considered when analyzing model outputs to inform future research, scientific knowledge, or applied practice. Forward-dynamics simulation models have been considered satisfactory representations of the modeled sporting movement when the evaluated simulation score or cost is less than 10% [4; 9; 156] or ideally less than 5% [10; 11; 13; 35]. Accepted differences have typically been greater when including kinetics within the cost function compared to when kinematics alone have been considered [10; 60].  
When deriving the cost function and a satisfactory magnitude of difference, the intended application of the model should be considered [4]. Just as with statistical inference in experimental sports biomechanics studies [157], all parameters and methodologies used in a theoretical approach should be justified. To ensure the validity of the evaluation process, future researchers may look to pre-register, or at least specify within the study, the cost functions to be used and the criteria required for successful evaluation [158]. This will reduce the possibility for the modeling equivalent of \"\(p\)-hacking\" [159; 160], where cost functions or criteria are adjusted until \"satisfactory\" scores are achieved.  
Theoretical optimum individual-specific movement solutions and performance outcomes should also be evaluated anecdotally to ensure the performances are within bounds deemed realistic [13]. This process should also seek to prevent extrapolating the model beyond the range of situations in which it has been evaluated and in which accuracy of the model constraints are known [4; 30]. For this purpose, it has been suggested to evaluate the model using experimental data from an elite performer capable of producing close to optimal performances [4] and ideally using different experimental trials to the ones any parameters may have been determined from [156].
# Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
## 3 Computation Model
### Quantum Advantage  
Our quantum implementation of the QPULBA generates the unitary \(U\) in the standard circuit formulation in OpenQASM for Qiskit, based on the provided parameters. This is shown in Figure 7. In this section, we will present the method to use \(U\) to estimate algorithmic metrics.  
The unitary \(U\) typically takes in a set of qubits representing the description number (alternatively, programs, transition table, or finite state machines). For estimating algorithmic metrics, we want to enumerate over all possible programs, thus, these qubits are put into an equal superposition as part of the initialization process. The tape qubit register is initialized to the all-zero (blank) state. The initial state qubit register is initialized to state 0 of the automata. The other qubits required for the QPULBA mechanism are not required for further discussion. These include, read, write, move, tape head, computation history and ancilla qubits.  
Five cases of applying \(U\) for experimental algorithmic information theory (EAIT) are discussed here. The Wolfram Physics Project [16] correlates causal invariance with the action principle in physics and algorithmic probability in information theory. It stresses that knowing the rules (programs) of an automata does not mean it is possible to jump ahead in time without equal amount of computation. This is called computational irreducibility. However, there are some pockets of reducible computation which allows us to predict the mechanics based on the laws of physics. Akin to this concept, the quantum advantage of using QPULBA for EAIT cannot be in the time complexity of a single execution as it takes the same scaling of resources of quantum and classical gates to run a automata for a specified number of cycles. The quantum advantage must be extracted by losing information about the full quantum state vector such that some global statistical property is extracted efficiently via the quantum method. This provides an advantage with respect to classical exhaustive enumeration. Thus, the space of program-output relations is one of the best candidate problem for demonstrating speedup using quantum search.
## 3 The Proposed Algorithm
### Merging Period  
The merging phase aims to merge subclusters that are structurally close and of similar density. In order to obtain actual clusters, this paper proposes a novel similarity metric called inter-cluster interaction degree based on the degree of interaction of the shared nearest neighbors between subclusters. The proposed similarity metric starts from the structural similarity between subclusters and considers the shared nearest neighbors between neighboring subclusters. The neighboring subclusters with maximum similarity are iteratively merged by evaluating the interaction degree of the shared nearest neighbors between the clusters until the desired number of clusters is reached. The merging phase  
Figure 4: The final segmentation result obtained by taking the intersection of all dimension segmentation results.  
consists of two parts: (1) Calculate the inter-cluster interaction degree. (2) Iteratively merge subclusters with maximum similarity.  
```
0: Dataset \(X\), Dimensions \(D\)
0: Final Subclusters \(Sub\_final\),\(N\)_Noses
1: Initialize \(S=[X]\), \(Sub\_final=[\ ]\), \(peak\_points=[\ ]\), \(N\)_Noses =[\ ]
2:for each point \(i\in[1,D]\)do
3: Calculate noises from the \(i\)-\(th\) dimension of \(S\) according to Equations (1) and (2)
4: Identify _noises_ in the \(i\)-\(th\) dimension and remove them from \(S\)
5: Append _noises_ to the _Noises_
6:endfor
7:for each point \(i\in[1,D]\)do
8: Extract the \(i\)-\(th\) dimension point from \(S\)
9: Calculate the kernel density estimation (KDE) for the \(i\)-\(th\) dimension point according to Equation (3)
10: Find all peak points and append the peak points to the \(peak\_points\)
11:endfor
12: Initialize \(subclusters=[\ ]\)
13:for each \(peak\_point\) P \(\in peak\_points\)do
14: Initialize \(subcluster\) for \(p=[\ ]\)
15:for each point \(i\in[1,D]\)do
16: Use Kd-Tree to search for the nearest points to peak point \(p\) in the \(i\)-\(th\) dimension
17: Assign the nearest points to the \(subcluster\) for \(p\)
18:endfor
19: Append the \(subcluster\) for \(p\) to the \(subclusters\)
20:endfor
21:while\(subclusters>0\)do
22:\(Sub\_final=\) first \(subcluster\) in \(subclusters\)
23:for each subcluster \(sc\in subclusters\)do
24:\(Sub\_final=\) intersection of \(Sub\_final\) and \(sc\)
25:endfor
26:endwhile
27:return\(Sub\_final\),\(Nosie\)
```  
**Algorithm 1** Noise detection and partition strategy based on dimensional density peaks
## 3 Results
### Participants' Knowledge and Skills Assessment  
Results of participants performance in the three assessments was compared to the university's stipulated pass mark of 50% in Table 3. In the 2017 pre-intervention assessment, only the knowledge components were assessed. Participants' mean scores were significantly above the pass mark of 50%, except in neonatal and child care, where the mean score was significantly below 50% (43%, \(p\) = 0.016). The overall average score of the participants was above 50%. In the 2017 and 2019 post intervention assessments, participants' mean scores were significantly above the 50% pass mark except in the 2019 infant growth assessment skills section where the mean score was insignificantly above the pass mark (\(p\) = 0.187). The mean score difference between the MCH components and the pass mark of 50% was higher the 2017 assessment. The difference in participants' mean scores in the three assessments are compared in Table 4. The one-way repeated-measures ANOVA showed that there was a significant difference in participants mean knowledge and skills scores across the three assessments (Wilks' Lambda = 0.31, F (2, 45) = 51.0, \(p\) = 0.0005, multivariate partial eta squared = 0.694). A post hoc pairwise comparison using the Bonferroni correction was used in the analysis. Statistically significant (\(p\) < 0.05) increases in participants mean scores was observed between the 2017 pre- and post intervention assessments for all knowledge components evaluated in the study. The biggest increase was observed in neonatal and child care (33.2%) which had the least baseline mean score, while the smallest increase was observed in maternal and antenatal care (13.4%) which had the highest baseline mean score. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Mean Score \% for Each Component in the Three Assessments** & **Mean Difference \% between 2017 Pre- and Post Intervention Assessment (\(p\)-Value)** & **Mean Difference \% between 2017 Pre- and Post Intervention Assessment (\(p\)-Value)** & **Mean Difference \% between 2017 Pre- and Post Intervention Assessment (\(p\)-Value)** \\ \hline Reproductive and sexual health & 56.9 \({}^{1}\), 73.3 \({}^{2}\), 70.7 \({}^{3}\) & \(-\)16.4 (0.000) & 2.6 (0.283) & \(-\)13.8 (0.000) \\ \hline Maternal and antenatal care & 61.0 \({}^{1}\), 79.3 \({}^{2}\), 60.5 \({}^{3}\) & \(-\)13.4 (0.030) & 18.8 (0.000) & 0.5 (0.350) \\ \hline Neonatal and child care & 43.0 \({}^{1}\), 76.1 \({}^{2}\), 65.4 \({}^{3}\) & \(-\)33.2 (0.000) & 10.6 (0.000) & \(-\)22.5 (0.000) \\ \hline Infant growth assessment skills & 74.2 \({}^{2}\), 55.1 \({}^{3}\) & & 19.1 (0.000) & \\ \hline Overall average score & 53.6 \({}^{1}\), 75.7 \({}^{2}\), 63.0 \({}^{3}\) & \(-\)16.3 (0.000) & 12.8 (0.000) & \(-\)3.5 (0.151) \\ \hline \end{tabular} \({}^{1}\) 2017 Pre-intervention assessment. \({}^{2}\) 2017 Post intervention assessment. \({}^{3}\) 2019 Post intervention assessment.  
\end{table} 
Table 4: Comparison of participants performance between assessments (N = 47).
## 6 Conclusions  
This research investigated SQM variation profiles as critical requirements for the theoretical performance evaluation of the SQM approach for multipath detection. Ratio, Delta and Double-Delta metrics and different tracking and signaling schemes were investigated and different actual scenarios and comprehensive analyses were provided. Comparing the proposed profiles with the corresponding multipath error envelopes provides useful information to analyze the sensitivity and effectiveness of different SQM metrics for detecting multipath. The extracted results for the basic Ratio, Delta and Double-Delta metrics showed that although SQM is sensitive to medium and long-delay multipath, its effectiveness varies based on the tracking strategy and signaling scheme. Therefore, for a given scenario, effective utilization of SQM approach requires the joint analysis of both SQM sensitivity and effectiveness as proposed in this research. In short-delay multipath scenarios (especially for multipath delays less than 10 m), due to low sensitivity of SQM metrics, the multipath effect on the pseudorange measurements remains undetected. In this case, narrowing the monitoring correlators can improve the sensitivity of the SQM metrics. In all cases, a lower Signal-to-Multipath Ratio (SMR) results in higher SQM sensitivity and effectiveness, as expected. For a specific level of SMR, higher C/N\({}_{0}\) increases SQM sensitivity by reducing the nominal variance of SQM metrics and lowering detection threshold. Actual measurements in static and kinematic multipath environments revealed the effectiveness of the proposed detection strategy. It was observed that a medium multipath error (e.g., multipath with less than 3 m ranging error) is buried under SQM metric noise and remains undetected. However, for those measurements strongly affected by multipath (for example when multipath error is in the order of 5 m or more), the SQM metrics are sensitive. Besides the aforementioned results, the underlying goal was to introduce the methodology of the SQM profiles in characterization and performance evaluation of SQM metrics defined for GNSS multipath detection.  
**Acknowledgments:** This research was funded by a University of Calgary Eyes High Doctoral Recruitment Scholarship held by the first author and an iCORE/ Alberta Innovates grant held by the third author.  
**Author Contributions:** The first author conceived, conducted and wrote the paper with continuous advice from the second and third author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 3 Proposed Method
### Two-Phase Post-Training  
The parameters \(\mathbf{E}_{L_{T}}\), \(\theta_{ITL_{in}}\), and \(\theta_{ITL_{out}}\) are randomly initialized and learned during the post-training phase. The noise introduced by this randomness can negatively impact the tuned parameters from \(L_{S}\). To prevent this, we split the post-training into two phases, similar to gradual unfreezing [50; 51]. In the first phase, the parameters copied from the \(L_{S}\) model are frozen, and only the \(L_{T}\) embeddings and ITLs are learned using the training examples in \(L_{T}\). This is analogous of the method used for zero-shot transfer learning in the multilingual PLM literature, where only the parameters responsible in learning \(L_{T}\) are updated.  
Phase two of our proposed method proceeds further and completely adapts the language model to \(L_{T}\). This is achieved by unfreezing the parameters from \(L_{S}\) and fine-tuning the entire model using data in \(L_{T}\). Here, it is assumed that the randomness and the resulting noise is minimized in the first phase. Each phase inherits the training objective from \(L_{S}\) training, and the model is optimized until convergence using the unlabeled data in \(L_{T}\).
## 2 Materials and Methods
### NPP Model  
We selected Moderate Resolution Imaging Spectroradiometer (MODIS) productivity model to simulate cropland ecosystem NPP in the alpine region of the southwest China from 1981 to 2014. This model coupled a remote sensing model with an ecosystem process model and has been widely applied in regional and global scale studies [36]. Its calculation process ([[https://modis-land.gsfc.nasa.gov/pdf/MOD17C61](https://modis-land.gsfc.nasa.gov/pdf/MOD17C61) UsersGuideV11 Mar112021.pdf]([https://modis-land.gsfc.nasa.gov/pdf/MOD17C61](https://modis-land.gsfc.nasa.gov/pdf/MOD17C61) UsersGuideV11 Mar112021.pdf), accessed 1 March 2019) can be divided into three steps: First, the monthly gross primary productivity (GPP) and net photosynthesis (PSNnet) products are simulated using remotely sensed fraction of photosynthetically absorbed radiation (FPAR), incident solar radiation, minimum temperature, and daylight average vapor pressure deficit data. Second, annual maintenance respiration (MR) and growth respiration (GR) are calculated using annual maximum leaf mass and temperature. Third, annual NPP can be computed as annual sum of monthly PSNnet minus annual MR and annual GR.
## 4 Results
### Fine-Tuning with Synthetic Datasets  
The models were then fine-tuned with the synthetic datasets generated for this study. The performance of models fine-tuned with the baseline dataset (general English) was compared against those fine-tuned with region-specific datasets (US, UK, India) using both general and detailed prompts. This comparison addresses our research question on how region-specific synthetic datasets affect model performance.  
The performance results presented in Figures 2 and 3 provide insights into our research question (RQ3). These results demonstrate that the highest classification performance is achieved when models are fine-tuned using regionally detailed synthetic datasets. The hypothesis is that the diversity and richness of expressions present in these datasets enable the models to generalize better, allowing them to correctly predict more complex and nuanced unseen cases.  
The BART model fine-tuned with the regionally detailed datasets consistently outperforms other configurations, achieving the highest accuracy and F-1 score across all experiments. This suggests that the pre-trained BART-large-MULTI model, which has been specifically optimized for natural language inference, benefits significantly from fine-tuning on domain-specific data, further enhancing its capability to handle nuanced classification tasks.  
In the case of the BERT-based models, there is a clearer progression in performance from zero-shot classification to the best-performing fine-tuned configuration. This progres  
Figure 3: Classification performance for BART-based models in their different variations. Metrics are based on the weighted averages.  
Figure 2: Classification performance for BERT-based models in their different variations. Metrics are based on the weighted averages.  
sion highlights the model's need for domain adaptation to achieve optimal results. This can be attributed to the fact that the BERT model, being a general-purpose pre-trained language model, requires fine-tuning to specialize in emotion classification tasks. In contrast, the selected BART model exhibits relatively strong zero-shot performance due to its pre-training for language inference tasks, which likely aligns more closely with the requirements of emotion classification.  
These findings underscore the importance of both pre-training objectives and fine-tuning datasets in optimizing model performance for emotion detection tasks, particularly in scenarios involving complex and diverse data distributions.
## 2 Materials and Methods  
### Study Area Description  
This study was conducted in Vulindlela, a communal area located in Kwa-Zulu Natal Province, on the east coast of South Africa (Figure 1). The study area is predominantly rugged, with mountainous at an altitude range of 1273-1412 above sea level [59; 60; 61]. The soils are generally loam in the valley sides, and coarse-grained in the upper slopes due to rocky surfaces [61]. The mean annual rainfall of the area is 979 mm. The maximum monthly rainfall occurs in January [62]. The annual minimum and maximum mean temperatures are 10.3 \({}^{\circ}\)C and 21.9 \({}^{\circ}\)C, respectively [12]. The driest month with the lowest temperatures is July. The study area is located in a subtropical climate, characterized by a standard Southern Africa bimodal climate. The wet season occurs from September to March and the dry season from April to August [63; 64]. The study area is mainly a mesic subtropical grassland biome. Mesic grasslands in South Africa are characterized by _Sorghum bicolour_, _Aristida junciformis_, _Eragrostis tentifolia_, _Alloteropsis semialata_, _Tristachya leucothrix_, _Themeda triandra_, _Paspulum urvillei_, _Panicum maximum_, and _Setaria smacelate_, amongst other grass species [65]. However, the dominant grass species in Vulindela is _Aristida junciformis_. The optimal growing period of these species occurs from October to April [66]. Vulindela is a good example of a predominantly rural community in which land is managed using the communal ownership approach [67]. The primary source of livelihood in Vulindela is livestock farming, and communities depend on grasslands as grazing grounds for their livestock. This highlights the need to provide an efficient and cost-effective strategy for managing this natural resource.  
Figure 1: Location of the study area.
## 3 Results
The opposite consideration can be drawn for damage magnitude. Figure 5: Regression plot related to \(a_{freq}=0.001\) Hz and \(a_{rot}=0.001^{\circ}\). RMSE: root-mean-squared error. Figure 6: Root-mean-squared errors for the accuracy of the frequency in the range 0.001 Hz to 1 Hz and the accuracy of the rotation in the range 0.001\({}^{\circ}\) to 1\({}^{\circ}\). In (**a**) the root-mean-squared error, in meters, related to damage location is proposed. In (**b**) the root-mean-squared error related to damage magnitude is reported. It should be mentioned that considering datasets without any errors, i.e., \(a_{freq}=0\) and \(a_{rot}=0\), the misclassification error drops to 2%, the root-mean-squared errors related to damage location and magnitude are 0.47 m and 0.001, respectively.