esa-sceva/satcom-chunk-collection huggingface dataset:
# Toughened Vinyl Ester Resin Reinforced with Natural Flax Fabrics
## 2 Materials and Methods
### Composite Characterization Methods  
Tensile, flexural, and SEM analyses were performed using the same methods as for the resin. The interfacial shear stress of composite materials is known as the interlaminar shear strength, and it may be determined using the three-point bend test (ASTM D 2344). At the midplane, shear failure will manifest as delamination. The interfacial shear stress is given by Equation (7):  
\[F_{ILSS}=\frac{0.75\cdot P_{max}}{b\cdot t} \tag{7}\]  
where \(F_{ILSS}\) is the interlaminar shear strength (or three-point short beam strength) (MPa), \(P_{max}\) is the maximum load determined during the test, \(b\) is the width, and \(t\) is the thickness. According to ASTM D 2344, the ratio between the support span length and the specimen thickness should be 4:1 to produce pure interlaminar shear failure.  
To determine the densities of the resins analyzed in this study, a pycnometer was employed. This method relies on the known volume of the pycnometer, the temperature, and the density of the distilled water used to fill the instrument. By conducting a series of weight measurements, the volume of the sample can be calculated. In Equation (8), \(m_{1}\) represents the mass of the pycnometer, including the capillary cap, when it is filled with distilled water; m\({}_{2}\) denotes the mass of the system, which includes the sample placed adjacent to the pycnometer, and \(m_{3}\) signifies the mass of the system when it contains the sample and is filled with water.  
\[\rho=\rho_{H_{2}O}\,\frac{m_{2}-m_{1}}{m_{2}-m_{3}} \tag{8}\]  
In Equation (8), the difference between (\(m_{2}\)) and (\(m_{1}\)) provides the exact weight of the sample, while the difference between (\(m_{3}\)) and (\(m_{2}\)) gives the weight of the water that occupies the volume left by the sample inside the pycnometer. Knowing the sample's weight and the density of water (\(1\) g/cm\({}^{3}\)), the density of the sample can be derived.  
Water absorption measures the amount of water a material absorbs under specific conditions. Factors influencing water absorption include the type of plastic, additives, temperature, and exposure duration. In this test, flax fiber-reinforced composite samples were immersed in a beaker of water at room temperature for varying periods. The water volume was \(8\) mL/cm\({}^{2}\). At predetermined intervals, the specimens were removed, gently wiped, and weighed. The percentage of water absorption was then calculated using a specific formula. This test was conducted in accordance with ISO 62.  
\[W_{up}(\%)=\frac{\left(W_{f}-W_{i}\right)}{W_{i}} \tag{9}\]  
In Equation (9), \(W_{i}\) is the initial weight of the composite before immersion in water and \(W_{i}\) is the weight of the sample after being immersed in water.
## 5 Discussions and Contributions
### Generalizability of This Study to Other Low-Resource Languages  
Although our study primarily focused on Twi, the techniques we employed--cross-lingual learning, federated learning, and dynamic dataset aggregation--are theoretically generalizable to other low-resource languages. Cross-lingual learning has already proven successful in transferring knowledge from high-resource languages (like English) to low-resource languages across various domains, thereby enabling better representation of linguistic structures through multilingual models. Several studies have demonstrated how large-scale multilingual models, such as mBART and mT5, can effectively transfer knowledge across languages, improving performance in low-resource settings [43]. Additionally, [PERSON] et al. (2020) showed that multilingual models trained on high-resource languages can substantially enhance the translation performance of languages with limited data [44].  
The key advantage of our approach lies in its ability to adapt the training process by dynamically adjusting the weight assigned to datasets during the fine-tuning phase. This flexibility allows for a scalable solution that can accommodate multiple languages with varying resource levels, further enhancing translation performance for other low-resource languages. Previous studies have explored similar approaches, where language-specific weights were adjusted during training to optimize model performance, particularly in multilingual settings. For example, [PERSON] et al. (2021) investigated methods to optimize neural machine translation (NMT) systems for African languages, addressing the challenges of balancing multiple datasets [42].  
Moreover, the use of federated-like training methodologies presents a unique opportunity for extending this approach to other low-resource languages. Federated learning, with its focus on decentralizing data processing, eliminates the need for direct access to sensitive or scarce datasets, which is particularly important for low-resource languages where data privacy is often a concern. One study demonstrated that federated learning can effectively combine diverse datasets without compromising privacy, thus enabling the use of multilingual data for low-resource languages without exposing sensitive information [45]. By incorporating dynamic dataset aggregation, our model can prioritize the most valuable data for the low-resource language, ensuring high performance even with limited training data. This adaptability, supported by theoretical evidence from existing research, suggests that our framework could be successfully applied to a wide range of low-resource languages, such as those from different African, Indigenous, and minority language communities that face similar challenges of data scarcity.
# End-to-End Performance Evaluation of MEC Deployments in 5G Scenarios
## 3 Co-Simulation Based E2E Evaluation Framework
### CoFluent  
Intel(r) CoFluent(tm) studio [14] is a modeling and simulation tool for optimizing, analyzing, and predicting the performance of complex systems. CoFluent allows one to model and simulate the interactions among both hardware and software elements, thus speeding up the deployment of complex systems. It simulates computing and communication cost with high accuracy and high efficiency, thus it can be effectively used to measure and evaluate the latency and throughput of data flows coming to and leaving from the MEC system. Within this work, CoFluent models a MEC environment, including the User Plane Functions (UPFs) and the MEC app within a MEC host. A high-level view of the latter is shown in Figure 4, and is composed of two main elements: the UPF and the MEC app. Data arrive at and leave the MEC host through the UPF element, which is further divided into two sub-elements, respectively one for the uplink (UL) and one for the downlink (DL) directions. The processing time \(T_{x}\) required by a packet of size \(S\), traversing the UPF in direction \(x\) is \(T_{x}=S/B_{x}\), where \(B_{x}\) is the data bandwidth of the UPF in the \(x\) direction. Packets processed by the UPF are forwarded to a MEC app, which implements the service required by the user and produces a set of data packets as a response. These packets are forwarded back to the UPF, which processes them and sends them back to the RAN. The communication path between the UPF and the MEC app is modeled through two packet queues, one per direction, which have a configurable latency of traversal. The behavior and structure of the MEC app can be customized to model the required service. In Section 4 we will describe the model of a MEC-based infotainment application.
## 5 Implementation and Experiment Results
### Phase 2: Anomaly Detection Phase  
The basic idea of the anomaly detection system depends on the user's actions on the system. When the system compares the user's current behavior to the database of the user's behavior, the system should allow the user to keep working if there is only a small deviation in the user's current behavior; otherwise, the system must log out the user and require static authentication of the user before the user can continue working. In this phase, the dataset was split into 80% as genuine actions and 20% as impostor actions. The 20% of impostor actions were selected from the other users. To verify the feasibility of the proposed algorithms, we separately applied each of the four classifiers to three scenarios: a single mouse movement action (Scenario A), a single point-and-click action (Scenario B), and a set of mouse movement and point-and-click actions (Scenario C). The results showed that the highest accuracy was achieved also in Scenario A (a single mouse movement action) with KNN ACC: 98.2%, DT ACC: 92.2%, RF ACC: 98.0%, and CNN ACC: 98.5%. Individually, the CNN classifier in Scenario A obtained the highest accuracy, recall, precision, and F1-score, with ACC: 98.5%, recall: 97.3%, precision: 97.1%, and F1-score: 95.7%. Tables 6-8 show all the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **KNN** & **DT** & **RF** & **CNN** \\ \hline Accuracy & 82.7 & 83.3 & 96.1 & 95.2 \\ Recall & 80.2 & 82.7 & 95.3 & 95.4 \\ Precision & 81.1 & 81.5 & 93.7 & 94.2 \\ F1-score & 82.5 & 82.6 & 93.9 & 95.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Phase 1: Continuous authentication results for Scenario B: single point-and-click action.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **KNN** & **DT** & **RF** & **CNN** \\ \hline Accuracy & 96.7 & 94.2 & 97.2 & 96.9 \\ Recall & 91.2 & 90.2 & 97.1 & 94.4 \\ Precision & 93.1 & 93.7 & 93.3 & 92.2 \\ F1-score & 95.5 & 93.6 & 92.9 & 90.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Phase 1: Continuous authentication results for Scenario C: set of mouse movement and point-and-click actions.
## 1 Introduction
[PERSON] _et al._ provided in [20] an overview of existing routing protocols that support data fusion in wireless sensor networks. They categorized the algorithms as routing-driven, coding-driven and fusion-driven, depending on their design principles. [PERSON] _et al._ in [21] overviewed fault tolerant routing techniques in WSNs, classifying them into two main schemes: retransmission based and replication based. It should be noted that clustering is an elegant method for grouping sensor nodes, meanwhile making data aggregation feasible and more efficient. An example of this method would be the aforementioned LEACH. The authors in [22,23] classified the hierarchical protocols according to the objectives, the desired cluster properties and the clustering process. Again the papers reviewed the general protocols for WSNs, but not differentiating them for large-scale scenarios or not. In fact, all the papers summarized and analyzed the routing protocols with different requirements, for instance to prolong the network lifetime, to balance energy consumption, to reduce overall network overhead _etc._ based on the large deployment of the sensor nodes. To the best of our knowledge, the work presented in this paper is the first attempt at a comprehensive survey with focus on the scalability of the routing protocols. Hence, in this paper we will give an insight into the hierarchical protocols designed especially for large-scale WSNs and compare their advantages and disadvantages in metrics like message complexity, memory requirement, cluster formation and maintenance, data aggregation, energy consumption, network lifetime, end-to-end delay _etc._ for extending network scale. We categorize them according to their design objective as control overhead reduction, energy consumption mitigation and energy balance, with the goal of increasing energy efficiency. In this paper we present a survey of recent advances in routing protocols for large-scale WSNs, our aim is to provide a full understanding of research challenges in the emerging protocols. The rest of the paper is organized as follows: in Section 2, a detailed analysis of currently innovative protocols for large-scale WSNs is presented, with the objective of highlighting the critical factors influencing protocol design. Section 3 summarizes the characteristics of these protocols and compares them and we present the related open issues for the hierarchical routing protocol design. Finally, we conclude with final remarks in Section 4.
## 3 Results
### Fracture Characteristics  
Figure 10 illustrates the fractographs of the ADC12 alloys with various Y contents. The phases marked Point 1 are the coarse primary Si phases, according to [PERSON] et al. [26]. In Figure 10a, aside from a few stretching holes, the fracture surfaces of the ADC12 alloys without Y addition are mainly covered by cleavage plane. Furthermore, a clear crack in the coarse primary Si phases, as shown in Figure 10a, was observed. These phenomena indicated a typical characteristic of brittle fracture. It was generally accepted that this was due to the rupture of primary Si cracks that originated in the rupture and propagated along the primary Si/matrix interfaces immediately. Subsequently, the neighboring cracks linked and ultimately caused the fracture of the material [26; 27; 28].  
Figure 10c reveals that the fracture surface of ADC12 alloys with 0.2 wt% Y addition also has some cleavage planes and stretching holes. Meanwhile, aggregated dimples appeared on the fracture face and the proportion of the cleavage plane decreased. Hence, the type of this fracture was a ductile failure. Therefore, an appropriate amount of Y addition has an active effect on the fracture properties of the alloy.
## 3 Results
### Effects of FF on Serum C-P, Insulin, and Glucose Levels  
Serum C-P, insulin, and glucose levels were assessed to investigate the improvement of IR in HFD-induced obese mice. Serum C-P levels in the HFD group exhibited a significant increase to 5.51 \(\pm\) 0.62 ng/mL compared to the NFD group at 3.67 \(\pm\) 0.10 ng/mL. Notably, significant decreases to 3.68 \(\pm\) 0.13 ng/mL in the GTE group and to 3.81 \(\pm\) 0.37 ng/mL in the FF 250 group were observed compared to the HFD group (Figure 5A). In addition, serum insulin levels in the HFD group showed a significant increase of 2.48 \(\pm\) 0.33 ng/mL compared to the NFD group at 0.83 \(\pm\) 0.09 ng/mL. However, the FF administration groups showed dose-dependent decreases to 1.83 \(\pm\) 0.27 ng/mL (FF 50), 1.59 \(\pm\) 0.33 ng/mL (FF 125), and 1.41 \(\pm\) 0.28 ng/mL (FF 250) (Figure 5B). Moreover, the glucose concentration was significantly lower in the FF 250 group at 163.5 \(\pm\) 6.5 mg/dL compared to the HFD group at 216.5 \(\pm\) 12.1 mg/dL (Figure 5C). These findings indicate the potential of FF supplementation in improving IR in HFD-induced mice.  
Figure 4: Effects of fermented fig (FF) on (**A**) histopathology and (**B**) steatosis score in HFD-fed obese mice. NFD, mice treated with only DW; HFD, mice treated with HFD and DW; GTE, mice treated with HFD and green tea extract (GTE) 50 mg/kg; FF, mice treated with HFD and FF (50, 125, and 250 mg/kg). The results are expressed as mean \(\pm\) SEM (\(n\) = 8). \({}^{\rm a<}\) The different variables were significantly different at \(p\)\(<\) 0.05 among the groups, as determined by [PERSON]’s analysis.
# Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data
## 4 Evaluation and Limitations
### Counting Results
#### 4.2.2 Traffic on a Bridge  
In this section the counting and classification system analyses traffic on the Confederation Bridge in the East of Canada. Basically, the specifications of the recorded area are very simple: There are two driving directions, each of them with a single lane. The camera position is on top of the bridge recording traffic driving to and away from the camera. The road is in the middle of the scene. However, the scene itself mostly consists of the surrounding sea. The horizontal area of the bridge covered by the camera is great, hence cars can be seen from afar as little dots becoming bigger when approaching. The same holds true the other way around from the other driving direction.  
As shown in Figure 12, the driving paths can be built up successfully for the relevant request intervals. The greater the interval is the bigger gets the covered distance from one frame to the other. Hence, the driving paths also increase in size.  
Due to the fact that the detection and classification framework has its difficulties detecting small objects the biggest request interval possible is 500 ms. With intervals greater than that in combination with the high driving speed on the bridge it is not possible for the detection framework to detect the same object multiple times. This fact is also related to the big difficulties while counting. As depicted in Table 6 the error loss for a request interval of 500 ms is 67.1%. A successful counting of vehicles requires a proper tracking of those. However, the driving speed is too high to allow the framework to process them several times. Decreasing the request interval to 250 ms decreases the error rate to 51.1%. The corresponding counting values are shown in Table 7. The poor performance regarding the vehicle speed and view angle can be concluded by the initialization times of the prediction model. For 250 ms it is 59 s and 3 min 39 s for the request interval of 500 ms.  
Figure 12: Prediction model visualizing the driving paths on the Confederation Bridge in Canada. (**a**,**b**) depict request intervals of 250 ms and 500 ms respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Actual**} \\ \hline  & & **Truck** & **Car** & **Motorbike** & **Lost** & **Sum** \\ \hline \multirow{3}{*}{Counted} & Truck & 0 & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Car & 1 & 41 & 0 & 0 & 42 \\ \cline{2-6}  & Motorbike & 0 & 0 & 0 & 0 & 0 \\ \cline{2-6}  & Lost & 0 & 32 & 0 & 0 & 32 \\ \hline \multirow{2}{*}{Counted} & Sum & 1 & 73 & 0 & 0 & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Counting results per vehicle type from a toll station with 1500 ms request interval.
# 25 Years of Bluetooth Technology
## 2 Bluetooth Technologies
### Bluetooth Mesh
#### 2.3.3 Applications of Bluetooth Mesh  
Bluetooth mesh networks are deployed in several industrial environments which implement IoT technologies. Bluetooth mesh uses include lighting control, asset tracking, environmental monitoring, beaconing for location services, and so on. The mesh network essentially simplifies the implementation of a multi-function network that can enable massive scaling without changes to the network infrastructure. For example, a single Bluetooth mesh network can support several services simultaneously.  
Bluetooth mesh is also useful when low latency requirements exist. Silicon Labs benchmarks [12] demonstrate that Bluetooth mesh can provide a latency less than 10 milliseconds per hop with a single packet payload that is up to 11 bytes of data in size. Silicon Labs benchmarks also demonstrate Bluetooth mesh networks with a high number of nodes are capable of performing well. In a 240-node network test, with proper relay selection mechanism in place, 99% reliability was achieved by the mesh network. Furthermore, 98% of the packets arriving at the nodes had a latency lower than 60 milliseconds [13]. Table 2 presents a summary of the major improvements in each Bluetooth version.
## 6 Experimental Results and Analysis
### Ablation Experiment
#### 6.1.1 The \(\eta\) Hyperparameter in \(L_{ssim}\)  
The hyperparameter \(\eta\) in Equation (14) is used to control the strength of the structural similarity loss function. To fully evaluate the influence of this parameter, we set different \(\eta\) values in the ablation experiments and assess the result of the proposed method on the test dataset. The experimental results, presented in detail in Tables 2 and 3, reveal the performance of the model for different values of \(\eta\) about metrics \(PSNR\) and \(MSSIM\). It needs to be emphasized that this ablation experiment was conducted under the speckle noise model (Equation (1)), where the standard deviation \(\delta\) of the speckle noise was set to a series of different values (0.1, 0.2, 0.3, 0.4, 0.5 and 0.6), aiming to comprehensively explore the denoising effect of the model at kinds of noise classes. To comprehensively evaluate the denoising effect of the model under different noise levels.  
The findings of this study reveal that varying values of the hyperparameter \(\eta\) significantly influence the model's performance on the \(PSNR\) and \(MSSIM\) metrics. Adjustment of \(\eta\) within an appropriate range can augment the denoising efficacy of the model. Nonetheless, values of \(\eta\) that are either too high or too flat may result in a diminished denoising effect. The optimal \(PSNR\) is attained when the standard deviation \(\delta\) is set at 0.5 and the hyperparameter \(\eta\) is configured to 1. At an \(\eta\) value of 1.5, the model achieves the highest mean structural similarity index. However, an upward trend in noise levels leads to a degradation in the model's denoising capabilities. This study highlights the importance of considering different noise levels in designing and optimizing denoising models. The incorporation of the \(SSIM\), with its hyperparameter \(\eta\), and its tuning, results in discernible enhancements in denoising performance. These metrics the substantial contribution of image structural similarity to the advancement of ultrasound image denoising outcomes.