esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### FM Core Competence: Project Management  
Another essential core skill in FM is Project Management (PM). Projects can vary in scope, complexity, duration and financial risk. According to IFMA, sub-competencies of PM include planning and design, execution and delivery, and evaluation.  
Most of the articles concerning the PM [73; 74; 75; 76; 77; 78; 79] deal with the topics of real-time tracking of personnel, materials and equipment to enhance the security, safety, quality control, logistics and productivity monitoring. To do this, BIM and Radio Frequency IDentification (RFID) are the most used technologies [73; 74; 76; 77; 78] to implement localisation of people and objects. The proposed systems have a reliable accuracy rate, and RFID localisation systems have great potential in practical applications and could improve resource allocation efficiency and decrease human errors. Instead, [PERSON] et al. (2017) [75] developed a tracking system based on the integration of Bluetooth Low Energy (BLE)technology, motion sensors and BIM. This integration aims to achieve more accurate tracking that reduces and compensates for the sensors' errors.  
On the other hand, [PERSON] et al. (2020) [80] proposed a method that uses BIM interoperability and wireless sensors to monitor concrete maturity and control the concrete formwork process. BIM allows for the calculation of formwork removal time based on the maturity and strength data collected from sensors inside the concrete. This system will allow the concrete placement process to be continuously monitored and controlled and the curing time before formwork removal to be reduced, thus affecting construction management and project controls by (a) reducing the time required to complete the work, (b) avoiding project delays and (c) lowering unnecessary formwork rental expenses.  
In conclusion, the main technological challenges found are related to the location and coverage of the sensors network and the signal strength of the router/hotspot. A significant problem that could arise is the stability of networks for communicating information.  
Future research may include improvement of these systems and platforms by incorporating more functions related to the PM sub-competencies and productive analysis (e.g., future workforce estimation or a deep investigation of impacts on the total cost and time of a construction project resulting from the use of BIM-WSN integration).
## 8 Implementation and Performance Evaluation
### Comparative Analysis with Other Protocols
#### 8.3.2 Performance Comparison  
Table 4 shows estimated operation counts (field operations) for protocols at 160-bit security:  
The performance values reported in Table 4 are based on the estimated number of core elliptic curve operations: scalar multiplications (SM), point additions (PA), and point halvings (PH), when applicable. These values were derived from each protocol's operational structure as described in their respective specifications, with the same cost model. The figures reflect protocol-level efficiency, assuming fixed curve parameters and excluding certificate validation or transport overhead.
## 4 TSN Integration with Edge Computing for Enhanced Reliability
### Single Application Instance Handling End Device Capability
#### 4.2.1 Architectural Aspects of the Single (Emulated) Application Handling  
Due to the emulation, the TSN functions (FRER, talker/listener) should be virtualized, and the TSN FRER and cloud redundancy should be handled in a coordinated way. The proposed architecture for the TSN FRER integration into the (edge) cloud environment can be seen in Figure 11.  
The main architectural principle is to separate the management of the application instances and the TSN functions, so separate application and TSN clusters are defined within the edge cloud domain. In the TSN cluster, the talker/listener and FRER functions  
Figure 10: Single, emulated application handling scenario.  
are configured by the TSN controller entities (centralized user configuration--CUC, CNC), while in the application cluster, the life-cycle management of the application instances, is handled by the cloud orchestrator (e.g., Kubernetes). The main reason behind this separation is to minimize the unwanted interference between the clusters if any type of event (e.g., a failure) occurs in one of them. For example, if an event (e.g., failure) impacts a TSN function, no actions are needed to perform in the application cluster. If an application instance fails and is re-deployed to another node, then only the connection between the new instance and the corresponding TSN function must be configured.  
Another architectural principle is to enable separated TSN-FRER and TSN talker/listener virtualized components, since it provides that the emulation of a single application instance towards the device could be supported by special features of the TSN talker/listener entities, such as IP/MAC address translation. As part of the TSN IWF, the TSN cluster may contain additional support functions for emulating a single application instance (details in Section 4.2.2).  
As shown in Figure 12, the architecture enables flexible deployment of TSN IWF components and application instances in the edge domain. One option could be when the TSN talker/listener and FRER functions are deployed in a single pod/node. In the second option, TSN talker/listener and FRER functions are deployed on different nodes. A third option could be that the TSN FRER function is deployed on the network interface card (NIC) of a node as a virtual switch in order to ensure faster data processing. Furthermore, the separation of the TSN talker/listener and FRER functionality enables that only the talker/listener functionality is moved to the virtualized domain, and the TSN FRER functionality is provided by the DC GWs--this is shown as option 4.
## 4 Results and Discussion
### Comparative Experiments of Different Models on our Own Dataset  
#### 4.1.1 Our Own Dataset  
In this section, the images of wildlife were acquired by infrared camera in the Saihanwula National Nature Reserve in Inner Mongolia from 2010 to 2019 [22]. Thousands of wildlife monitoring images were obtained and a sample library of wildlife monitoring images was established. The monitoring images consist of 24-bit RGB true color images with a resolution of \(2560\times 1920\). In this paper, five common terrestrial protected species [23] were selected, including Red Deer, Goral, Roe Deer, Lynx, and Badger. Table 2 shows the image numbers for the five species in the image sample library. Figure 5 shows a sample of wildlife monitoring images.  
Figure 5: Wildlife monitoring image: (**a**) Red deer; (**b**) Red deer; (**c**) Goral; (**d**) Badger.  
Figure 5a shows the monitoring image of the red deer, in which the individual target is complete and its outline is clear. However, high-quality images are few in the dataset, as most images have various defects, such as obstructions (Figure 5b), indistinguishable backgrounds (Figure 5c), and targets being too far away (Figure 5d). When such images are artificially marked, subjectivity will affect the accuracy of feature extraction, affecting the characteristic learning ability of the model.
## 3 Experiments and Results
### Method Validation  
In order to verify the improved method developed based on the conventional optical flow method, a MATLAB program is written in accordance with the principle of this improved method. When calculating the optical flow between each pair of adjacent frames, for each feature corner point, the increment \(dx\) along the \(x\)-axis and the increment \(dy\) along the \(y\)-axis are computed. Then, the above Formula (7) is applied to determine the corresponding included angle. Feature points with an included angle smaller than the set threshold are removed. The thresholds are set with the standard deviation \(\sigma\), which is calculated from the arrow angles of all detected feature corner points, as a common factor. Specifically, the thresholds are set as 1\(\times\)\(\sigma\), 0.5\(\times\)\(\sigma\), and 0.3\(\times\)\(\sigma\) respectively. The feature corner points eliminated by setting these different thresholds are illustrated in Figure 6.  
In Figure 6, a total of 223 feature angular points are detected. Table 1 presents all the retained feature points and the number of deleted ones when different thresholds are set. The data in the table indicate that when the threshold is set relatively large, fewer feature points with insufficient accuracy are removed. Conversely, as the threshold is set smaller, more angular feature points are eliminated, and the removal effect is more significant. This results in retaining more feature points for which the computed data are more accurate [30].  
Figure 5: Layout of the experimental equipment.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 4 Active Device Technology S-Parameter Extraction  
The selection of the appropriate process technology is crucial, taking into account the unique operating requirements of the intended application. In order to accomplish a successful design, it is necessary to pick a mix of active devices (such as diodes and transistors) and passive devices (such as inductors and capacitors), using a well-defined process technology. This selection should be made to satisfy the design goals and expected performance level of the system. At lower frequencies, the active device components are characterised using impedance (\(Z\)) and admittance (\(Y\)) parameters, defined as:  
\[Z=\frac{V}{I},\quad Y=\frac{1}{Z}=\frac{I}{V} \tag{26}\]  
These parameters provide a linear connection between the input and output voltages and currents. However, in the context of microwave frequencies, power measurements are conducted using scattering parameters (\(S\)-parameters), which analyse the distribution of a signal across different ports of the microwave network. The reflection and transmission properties of a two-port network are given as:  
\[S_{11}=\frac{V_{1}^{-}}{V_{1}^{+}},\quad S_{21}=\frac{V_{2}^{-}}{V_{1}^{+}}, \quad S_{12}=\frac{V_{1}^{-}}{V_{2}^{+}},\quad S_{22}=\frac{V_{2}^{-}}{V_{2}^{ +}} \tag{27}\]  
where \(V_{i}^{+}\) and \(V_{i}^{-}\) represent the incident and reflected voltage waves at port \(i\).  
The insertion loss of transmission lines decreases when using substrates with higher resistivity, as the impact of substrate losses and the quality factor of matching networks are key performance indicators.
## 4 Results and Discussion
### Effects of Water Depth on Gas Chamber Pressure and PDR  
Figure 7 shows the time dependence of the gas chamber pressure and the PDR of different water depths (10 mm, 20 mm, and 30 mm). The equivalent diameters of different defect sizes were evaluated to be 0.349, 0.331, and 0.352 mm, respectively. Taking 10 mm water depth as an example, the measured pressure decreased from 81.43 kPa to 1.23 kPa after 47.7 s. The measured PDR also decreased from 3.80 kPa/s to 0.02 kPa/s after 48.5 s. The pressure can be described as a quadratic function of time and may be presented by the following relationship:  
\[P=79.6956-3.0362t+0.0297t^{2},\ R^{2}=0.9998 \tag{19}\]  
Figure 7: The effect of water depth on the pressure and the PDR. (**a**) \(H\) = 10 mm; (**b**) \(H\) = 20 mm; (**c**) \(H\) = 30 mm. Reaction conditions: defect size = 0.35 mm, gas chamber initial pressure = 80 kPa, and gas chamber volume = 365.59 mL.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(V_{g}\) (mL) & 365.59 & 690.72 & 1015.85 \\ \hline \(r\) & Pressure & 0.999 & 0.999 & 1.000 \\  & PDR & 0.981 & 0.986 & 0.987 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation between measured data and predicted values with different chamber volumes.  
Furthermore, along with the water depth, the change in the required time for the measured pressure to decrease to zero was insignificant: 47.7 s, 60.0 s, and 43.3 s, respectively. Similarly, the required time for the measured PDR to decrease to zero was also roughly the same. The PDR values in the initial stages also had a similar level. This may be because the hydrostatic pressure value that limited the water depth produced, compared with applied pressure, played a limited role in bubble formation and gas flow.  
Similarly, for each water depth, the simulated curves had the same trend as that of the measured pressure and the measured PDR. The correlation between the two curves is demonstrated in Table 4. Values of Pearson's correlation coefficient \(r\) of two indexes under three different water depths were beyond 0.98, which proved the effectiveness of the calculation model in this study.
## 2 Materials and Methods
### Learning Models  
Since we intended to explore different types of ML models, the predictive classifying models used in this study are from three different families: linear, nonlinear, and ensemble. The linear classifiers used include the Logistic Regression, Elastic Net, and Linear SVMmodels, while the nonlinear classifier includes the SVM algorithm with an RBF kernel. Of the ensemble models, the Random Forest and the XGBoost were implemented.  
Logistic Regression is a classification model that employs the sigmoid function as a cost function in order to return a probability value that can be mapped to discrete classes [33]. It is one of the simplest ML algorithms and is easy to implement, interpret, and very efficient to train. Elastic Net regression is a penalized linear regression model that imposes a linear combination of regularization penalties to the loss function during training. These norm regularizations include both the L1 and L2 penalties, which are based on the sum of the absolute coefficient values and the sum of the squared coefficient values, respectively [34].  
SVM is a discriminative classifier that transforms the original feature space into a higher-dimensional space based on a user-defined kernel function and then finds support vectors to maximize the margin separating the classes. New unlabeled samples are classified according to the side of the hyperplane they lie on [35]. Linear SVMs can only find a decision boundary to classify linearly separable features. On the other hand, when the dataset is separable by a nonlinear boundary, SVM uses nonlinear kernel functions, such as the RBF, to overcome the curse of dimensionality and properly transform the feature space. SVMs are powerful yet flexible supervised ML algorithms that are used in a variety of applications, such as the diagnosis and prognosis of cancer and other diseases [36; 37; 38; 39; 40].  
Random Forest classifier is a bagging-type ensemble method of decision trees that trains several trees in parallel and aggregates the decisions of individual trees to reach the final prediction. This classifier tends to outperform most other classification methods in terms of accuracy, variance, and bias, without overfitting issues [41]. XGBoost is a decision-tree-based ensemble algorithm that uses a gradient boosting framework and has been widely used in lung cancer studies [4; 42; 43; 44]. This classifier has been shown to yield superior predictive results using less computing resources in the shortest amount of time compared to other models due to its parallel processing, tree-pruning, sparse data handling, and regularization to present overfitting [45].
## 3 Prediction and Future of EVs in the UK
### Mathematical Formulation of Models
#### 3.3.3 XGBoost Model  
XGBoost (Extreme Gradient Boosting) is a machine learning method that builds an ensemble of regression trees to predict continuous outcomes, such as EV sales in this study. A regression tree is a type of decision tree designed to predict numeric values rather than categories. These trees are trained one after another, and each new tree learns from the residuals made by the previous ones. This sequential training process forms what is called a boosting framework.  
The final prediction at time \(t\), denoted by \(\hat{y}_{t}\), is calculated as the sum of outputs from \(M\) regression trees:  
\[\hat{y}_{t}=\sum_{m=1}^{M}f_{\text{m}}(x_{\text{t}})\,\ f_{\text{m}}\ \in\ \mathbb{F} \tag{5}\]  
where:  
\(\hat{y}_{t}\): are the predicted EV sales at time \(t\);  
\(f_{\text{m}}\): is the mmm-th regression tree;  
\(x_{\text{t}}\): is the feature vector at time \(t\) (e.g., year, battery price, GDP, and chargers);  
\(M\): is the number of trees;  
\(\mathbb{F}\): denotes the space of all possible regression trees.  
Each function \(f_{\text{m}}\) in the XGBoost model represents a regression tree that takes a set of input features \(x_{\text{t}}\) and produces a predicted EV sales value. The model builds these trees one at a time, with each new tree aiming to correct the errors made by the previous ones.  
XGBoost is trained by minimizing a total objective function (\(\mathcal{L}\)), shown in Equation (6). This objective has two parts: (1) a loss function \(l(y_{t},\hat{y}_{t})\) that measures the difference between  
\begin{table}
\begin{tabular}{c c c c} \hline  & **MAPE (\%)** & **RMSE (k Unit)** & **R\({}^{2}\)** \\ \hline Prophet & 17.85 & 21,570 & 0.99 \\ \hline SARIMAX & 33.27 & 27,990 & 0.98 \\ \hline XGBoost & 0.01 & 10 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison between the accuracy of the three prediction models (2015–2024).  
the predicted and actual values; and (2) a regularization term \(\Omega(f_{m})\) that penalizes the complexity of each tree and helps the model generalize well to new data.  
\[\mathbf{\mathcal{L}}=\sum_{t=1}^{T}l(y_{t},\hat{y}_{t}\,)+\sum_{m=1}^{M}\Omega\;(f_{m }\,) \tag{6}\]  
The formulation provided in Equation (6) ensures that each tree incrementally improves the model's accuracy while maintaining generalization. During training, XGBoost uses gradient descent to minimize this objective. Each new tree is built to approximate the negative gradient of the loss function, allowing the model to correct its previous errors efficiently.  
Although XGBoost is not a traditional time series model, it is well-suited for analyzing historical patterns in EV sales. Its flexible structure allows it to capture complex nonlinear interactions between time-based and external variables. In addition to producing accurate predictions, XGBoost excels at diagnostic analysis by ranking feature importance, modeling variable interactions, and revealing latent drivers of EV adoption.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experimental Results
#### 4.3.1 Layer-Freezing Performance Analysis  
Figure 9 presents a comparison of layer-freezing performance across four models, mT5, mBART, GPT-2, and GPT-Neo, evaluated on Swahili data. The comparison is made between fine-tuned and RAG models, showing how freezing layers affects model performance for different techniques. The graph clearly demonstrates that the RAG models consistently outperformed their fine-tuned counterparts. For example, mT5 with RAG exhibited a substantial improvement in performance as more layers were frozen, suggesting that RAG is particularly effective in enhancing translation and contextual accuracy for Swahili tasks. For mBART, the performance increase with RAG was more moderate, indicating that while RAG does provide some improvements, the gains are less significant than those seen with mT5. GPT-2, however, showed a marked improvement with RAG, outperforming its fine-tuned version in all frozen layers. This reflects GPT-2's capability to benefit significantly from the retrieval mechanism, which provides additional context during response generation. GPT-Neo showed a more stable and gradual improvement with RAG, suggesting that while it does not outperform GPT-2, RAG still enhances its performance when freezing layers. This pattern of improvement across all models suggests that RAG's retrieval mechanism adds significant value to Swahili NLP tasks, particularly when multiple layers are frozen to optimize computational resources. This analysis underscores the advantages of using RAG for Swahili NLP, where freezing layers is an effective strategy to reduce computational costs while still improving model performance. The findings support the idea that Retrieval-Augmented Generation is a strong approach for enhancing low-resource language tasks, making it more efficient and contextually aware [52].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model + Approach** & **Translation Accuracy (\%)** & **Summarization Coherence (\%)** & **Question Answering Accuracy (\%)** \\ \hline mT5 (Fine-Tune) & 83.45 & 82.12 & 80.67 \\ mT5 (RAG) & 88.34 & 87.12 & 85.67 \\ GPT-2 (Fine-Tune) & 75.23 & 74.12 & 72.34 \\ GPT-2 (RAG) & 79.45 & 77.89 & 75.23 \\ mBART (Fine-Tune) & 80.12 & 79.45 & 77.23 \\ mBART (RAG) & 83.56 & 82.34 & 80.45 \\ GPT-Neo (Fine-Tune) & 72.89 & 71.12 & 69.45 \\ GPT-Neo (RAG) & 76.45 & 74.78 & 72.12 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Adaptability of fine-tuned and RAG-enhanced models across Swahili NLP tasks.
# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
## 2 Related Work
### Results  
#### 4.5.1 Morphological Segmentation Comparison  
The experimental results comparing unsupervised and supervised morphological segmentation are shown in Table 4. This paper uses recall (R), precision (P), and F1-score to compare the performance of the models. The next row of the experimental results for MMSeg, FEMSeg, and FEMSeg-CRF models in Table 4 represents the results of a 10-fold cross-validation experiment (in line: 4, 8, 14). The experimental results show that the method proposed in this paper has certain improvements in both unsupervised and supervised approaches. In the unsupervised experiments, compared with the mainstream unsupervised segmentation models BPE and Morfessor, the F1-score of MMSeg increased by 29.64%, 11.59%, and 14.75%, 8.08% on the test sets of Uyghur and Kazakh, respectively. In the supervised segmentation, the experimental results reveal the following: (1) Models can generally be divided into two categories based on whether they incorporate a CRF layer. Adding a CRF layer can strengthen the model's constraints on the predicted labels, ensuring the final output is valid. For example, BiLSTM (91.51%, 90.97%) versus BiLSTM-CRF (91.92%, 91.29%), and FEMSeg (92.04%, 92.34%) versus FEMSeg-CRF (92.69%, 92.84%). (2) The FEMSeg and FEMSeg-CRF models show significant improvements in both languages. The F1-score of FEMSeg reaches 92.04% and 92.34% on the test sets of Uyghur and Kazakh, respectively, which is an improvement of 1.57% and 2.43% compared with the BiGRU model, and an improvement of 0.53% and1.37% compared with the BiLSTM model. The F1-score of FEMSeg-CRF reaches 92.69% and 92.84% on the test sets of Uyghur and Kazakh, respectively, which is an improvement of 51.25% and 50.94% compared with the HMM model, an improvement of 4.23% and 12.60% compared with the CRF model, an improvement of 0.77% and 1.55% compared with the BiLSTM-CRF model, and an improvement of 1.28% and 1.75% compared with the BiLSTM-ATT-CRF model. On the test sets of both languages, FEMSeg and FEMSeg-CRF, compared to other models, can learn character features and the relevant relationships between characters and their contexts in different dimensions, thereby improving the accuracy of morphological segmentation. In the case of the BiLSTM and BiLSTM-ATT-CRF models, BiLSTM-ATT-CRF shows a slight decrease in performance compared to BiLSTM on the Uyghur test