esa-sceva/satcom-chunk-collection huggingface dataset:
# Void Avoiding Opportunistic Routing Protocols for Underwater Wireless Sensor Networks: A Survey
## 2 Routing Protocols  
In general, the underwater sensor nodes are deployed in the area of interest following one of the underwater network architectures (i.e., 1, 2, 3, or 4 dimensional UWSNs, which are presented in many papers such as [16; 26; 32; 33; 34; 35]). The deployed underwater sensor nodes must be organized in such a way that they cover the entire area of interest in order to gather the data whenever an event occurs. Routing protocols are responsible for discovering and maintaining transmission routes. Thus, a route between sensor nodes and the sink needs to be established for effective and reliable data transmission. Routing is the backbone of any network. The sensor nodes can communicate with the sink(s) either by: (1) direct link, where the data packets can be sent directly from the source node towards the sink(s). or (2) through a multi-hop path where the data packets are forwarded by the relay nodes until they reach the sink(s). However, multi-hop communication suffers from the complexity of establishing a route, which has effects on network performance such as capacity, reliability, and efficiency.
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 2 Hardware Architecture
### Processing Units  
Every sensor produces a high amount of data that needs to be processed, and processing units can also be an expensive part of an autonomous vehicle. For this reason, the minimum amount of CPU and GPU power required to run all the algorithms in real time must be determined. In the proposed platform, we divide the computing between three different computers, allowing us to execute multiple processes in parallel, thus distributing and equalizing the computing load. Each processing unit has different capabilities, which have been selected specifically for the tasks to be executed.  
**Control computer:** This computer manages the communication with the car systems via the CAN bus. Additionally, it is responsible for executing the control and path planning algorithms that allow the vehicle to move autonomously. It is based on a fast CPU that is capable of executing the required operations in real-time.  
**Localization and mapping computer:** This computer is responsible for processing the huge amount of data provided by the three LiDARs and generating the LiDAR odometry and mapping. Furthermore, it also performs all other localization tasks involving the GNSS and the IMU sensors. Since most of the LiDAR processing algorithms are only implemented by the CPU and are not available with GPU acceleration, this computer is only equipped with a fast processor and 16 GB of RAM memory, which is sufficient to process all data at the sensor rate.  
**Perception computer:** The perception computer is responsible for executing all the deep learning algorithms used for obstacle classification and scene understanding. Thus, the computing capabilities of this computer are more specific, and this computer is therefore equipped with a high-end NVIDIA Titan-XP GPU with 12 GB of graphic memory, which allows the vehicle to execute all the required algorithms. Finally, this computer is also connected to a screen inside the car, which acts as an interface with the user.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 4 Results and Discussion
### C-Scan Detection of the Cu/Al Bimetallic Specim  
During the experiment, the spot diameter of the excitation laser was set to 1 mm, and the excitation frequency of the excitation laser was set to 20 Hz. The spot diameter of the detection laser was about 0.2 mm, and the power was set to 0.9 W. The scanning step was set to 0.5 mm. The signal waveforms of laser ultrasonic experiment in the Cu/Al bimetallic specimen are shown in Figure 11, where LS-SL denotes mode conversion waves from composite interface, 2S denotes reflected transverse waves from delamination defect, and 2S\({}_{\text{b}}\) denotes reflected transverse waves from the bottom of the specimen. After 1.3 us,  
Figure 10: Variation of displacement of ultrasonic waves in different modes after interacting with defects of different sizes. (**a**) W = 6 mm, LS-SL, (**b**) W = 6 mm, 2S, (**c**) W = 6 mm, 2S\({}_{\text{b}}\), (**d**) W = 2 mm, LS-SL, (**e**) W = 2 mm, 2S, and (**f**) W = 2 mm, 2S\({}_{\text{b}}\).  
Figure 9: The schematic of scanning laser source method.  
the amplitude of the laser ultrasonic signal in the defective region was significantly higher than that in the non-defective region. The ultrasonic signal waveforms obtained in the experiment are in good agreement with the numerical simulation waveforms in Figure 7b.  
In Figure 12, the C-scan imaging of the delamination in the Cu/Al bimetallic specimen based on reflected transverse waves (2S) from the composite interface was realized. Figure 13 shows the variations of amplitude at the position of the white dotted line in Figure 12. The fabricated delamination can be clearly seen in the C-scan image, where delamination defects are labeled as w2, w4, w6, w8, and w10. The position and width of the delamination defects in the C-scan image are consistent with the T-shaped grooves machined by wire-electrode cutting in the specimen in Figure 3.  
Figure 11: Experimental signals of laser ultrasound in the Cu/Al bimetallic specimen.  
Figure 12: The C-scan image based on reflected transverse waves (2S) from the composite interface.
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 3 Results and Discussion
### Build Prediction Model  
The 461 sets of sample data in the study area were divided by 70% and 30% into a training dataset and a test dataset, respectively. The TOC content prediction model based  
Figure 4: Principal component analysis gravel diagram. The contribution rate of PC1 is the highest at 51.98%. The contribution rate of PC2 is 14.96%. The contribution rate of PC3 is 7.47%. The contribution rate of PC4 is 4.72%. The contribution rates of PC5 and PC6 are 1.91% and 0.48%, respectively.  
Figure 5: Ranking of feature contributions to principal components. (**a**) Ranking of feature contributions to PC1; (**b**) ranking of feature contributions to PC2; (**c**) ranking of feature contributions to PC3; (**d**) ranking of feature contributions to PC4.  
on BPNN, GBDT, and XGBoost algorithms was established by treating the results of PCA as input values and the TOC content of core analysis as output values. The implementation process is shown in Figure 6. Due to the hyperparameters of each model requiring adjustment, and the adjustment process being speculative, the PSO algorithm and fivefold cross-validation were adopted to determine the best combination of hyperparameters for different models to reduce randomness in the selection process, avoid overfitting, and enhance prediction performance. The results of setting model hyperparameters are shown in Table 2. The BPNN prediction model consists of one input layer, three hidden layers, and one output layer. The hidden layer structure is (64, 64, 32), and the activation function is the \(Relu\) function. In the GBDT and XGBoost prediction models, the weak learners amount to 190 and 180, the learning rate is 0.07 and 0.08, and the maximum depth of the decision tree is 4 and 5.
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Trust Models
#### 3.2.2 Trust Parameters
This parameter indicates the degree of mutual interest between the trustee and trustor. In general, the higher the Col between the two nodes, the better the interaction between them and the more trustworthy they are deemed to be [37]. In [48], the authors calculated the community-based trust characteristics of the trustee relative to the trustor at time t.
* _Mutuality and Centrality (MC)_--MC represents the position of the trustee relative to the trustor in the network. The larger the MC is, the more similar the social relationship between them, the more interactions between them, and the higher the degree of trust [37]. * _Confidence_--Confidence indicates the accuracy of the probability estimate. It can save network resources when the credibility of direct trust is greater than the set threshold, and a node is considered as a trusted node without calculating the recommended credibility [29]. * _Packet Delivery Ratio (PDR)_--PDR refers to the contact degree between the trustee and the principal, which is generally defined as the packet forwarding rate between nodes. PDR is generally considered a very important parameter in establishing a trust model, calculating the trust value and identifying malicious vehicles [26; 50; 51]. * _Similarity_--This parameter is used to measure the similarity in content and service between any two vehicles. Generally expressed as the Euclidean distance, the authors of [31] used the concept of similarity to calculate the similarity between the vehicle information in the infrastructure trust table and the information sent to the vehicle. * _Familiarity_--Familiarity indicates the familiarity between the trustor and the trustee, which is a parameter with a high utilization rate. This parameter is used to measure the interaction frequency between the trustee and the trustor. The higher the interaction frequency, the more information can be obtained from the other party, and the more favorable it is to gain higher trust [25]. * _End-to-End Delay (E2 ED)_--This parameter is related to the QoS of trust management, which describes the total delay caused by sharing data packets generated by legitimate vehicles with neighboring vehicles. Of course, the smaller the value of E2 ED, the more reasonably the trust mechanism is designed [41].
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 4 Simulations, Experiments and Results Analysis
### Field Experiment
Note also that the error levels were significantly large in the field experiment, but it did not influence the result much because the flight height was relatively low, and it did not cause a divergence error. Despite the difference, the test results roughly showed the trajectory optimization performance on the whole. The UAV tended to follow the target as quickly as possible and as near as possible to the target, which essentially attempts to minimize the target localization uncertainty. To validate the localization performance, the actual positions of the car were measured using the RTK-GPS. We repeated the experiment 10 times to compute the RMSE, and the results are shown in Figure 15. In the figures, the localization results of the referenced methods are also presented. From the results, we can see that for the scenario with the minimum zero velocity constraint, the method proposed in this paper was slightly better than the second referenced method, and the EKF-based method was also slightly worse than the other two methods, which was, interestingly, different from the simulation scenario. As the target had a slow speed, the EKF-based method can track and localize it robustly, which is different from the simulation scenarios where the target has a fast speed, which is likely to cause a divergence error. However, for the scenario with a nonzero minimum velocity constraint, the localization performance was obviously better than the referenced methods, which coincided with simulation results. Through the field  
Figure 14: Relative trajectory result for the scenario with a minimum velocity of 5 m/s. Figure 13: Trajectory optimization results. (**a**) Trajectory with minimum velocity of 0 m/s; (**b**) trajectory with minimum velocity of 5 m/s. experiments, we confirmed that the proposed method outperformed the existing similar methods, and are also applicable to engineering applications.
# Arabic Gloss WSD Using BERT
## 1 Introduction
Knowledge-based WSD methods usually utilize language resources such as knowledge-graphs and dictionaries to solve the WSD problem. Knowledge-based WSD can be further subcategorized into graph-based approaches and gloss-based approaches [16]. Graph-based approaches utilize language knowledge-graphs, such as WordNet, to represent the sense by its surrounding tokens in that graph. Gloss-based approaches measure the semantic similarity or the overlap between the context of the target word and the dictionary definition of that word (gloss of the word). Gloss-based methods require context-gloss datasets. In this paper, we explore utilizing pretrained BERT models to better represent gloss and context information in gloss-based Arabic WSDs. Fortunately, there are pretrained BERT models available for Arabic [9; 10]. We utilized these pretrained models to build two gloss-based WSD models. The first model uses the pretrained BERT models as a feature extractor without fine-tuning BERT layers to generate a contextual word embedding of the target word in its context. We also used it to generate a sentence vector representation of the gloss sentence. These representations' vectors were then fed to a trainable dense layer to perform supervised WSD. In the second model, we fine-tuned BERT layers by training them with a _sentence pair classification_ objective. Our experiments showed that we outperform many of the state-of-the-art Arabic knowledge-based WSDs. The evaluation of WSD models suffers from a lack of standard benchmark datasets [16]. Each author tests on a different and small (around 100 ambiguous words) dataset, which makes it difficult to compare results. We attempt to fix this issue by creating a publicly available benchmark dataset. We hope that such a dataset can help ease evaluating and comparing different WSD techniques. The rest of the paper is organized as follows. In Section 2, we discuss the related work. Section 3 introduces the context-gloss benchmark. The background is presented in Section 4. Section 5 describes the proposed models. In Sections 6-8, we present our experimental setup, results, and discussion. Finally, we conclude our paper in Section 9.
## 6 Discussion and Conclusions  
### Discussion  
This study presents the first systematic evaluation of in-context learning for Tarifit machine translation, establishing baseline performance metrics and identifying optimal ICL configurations for this underrepresented Berber language. Our key contributions include: (1) demonstration that linguistic proximity significantly enhances ICL performance, with Arabic translations outperforming French and English by substantial margins; (2) identification of k = 8 as the optimal shot count and similarity-based selection as the most effective demonstration strategy; (3) comprehensive error analysis revealing systematic challenges in morphological processing and cultural preservation; and (4) validation of chrF as a more appropriate evaluation metric than BLEU for morphologically complex languages.  
Our findings have immediate practical implications for developing translation tools for the 5 million Tarifit speakers worldwide, particularly in multilingual contexts where Arabic-Tarifit translation can serve as a bridge for accessing digital resources. The demonstrated effectiveness of ICL approaches, despite performance limitations, provides a viable path forward for developing NLP tools for other low-resource Berber languages, circumventing the data scarcity challenges that have historically limited computational linguistics research in this language family.  
The realistic performance benchmarks established in this study (20.2 BLEU for Arabic, 14.8 for French, 10.9 for English using GPT-4) provide a foundation for future research and set appropriate expectations for practical deployment. While these scores indicate that significant challenges remain, particularly in morphological processing and cross-linguistic transfer, they represent substantial progress for a language with virtually no prior computational resources.
## 3 Experiments and Results
### New Combinations of Signals Using Four Common Methods
#### 3.3.4 PCA Using New Combination of Signals  
In this subsection, the results of PCA are summarized in Tables 12 and 13. For illustration, Channels 8 and 12 with the ELM classifier on the left and right brain hemispheres are selected. Normal sub-band signals and new signals (FB I with J) are used for investigation. The results with more information are described below.  
Tables 12 and 13 show the results of PCA, in which computational operations (sorting) were performed to reduce the features. But no features were reduced for classification. Table 12 reports the results of Channels 8 and 12 on the left and right hemispheres of the brain with the ELM classifier with normal signals (filter banks).  
Finally, the average accuracy for Channels 8 and 12 increased by 17% and 18%, respectively, when comparing new combinations of signals to normal filter banks. For Channels 8 and 12, FB 1 and 5 achieved the highest average accuracy for the subjects.
# Bluetooth Low Energy Mesh Networks: A Survey
## 5 BLE Mesh Networks: Proprietary Solutions
### CSRmesh  
Cambridge Silicon Radio (CSR) developed CSRmesh, a proprietary protocol that operates on top of Bluetooth 4.0 and subsequent, which allows messages to be forwarded across BLE devices in a mesh topology [19].  
CSRmesh uses flooding over advertising channels for end-to-end communication. A flat model is used, whereby all devices have the same hierarchical level. Flooding is controlled by using a Time To Live (TTL) mechanism and by preventing rebroadcast of the same packet more than once [47]. A CSRmesh network can in theory comprise up to 64,000 devices. Messages can have individual or group recipients. CSR offers CSR101\(\times\) modules, which support CSRmesh and the CSRmesh Development Kit, which provides a set of assessment tools and software development for CSRmesh.  
Among the proprietary BLE mesh network solutions, CSRmesh appears to be the most popular one, as witnessed by the amount of academic work that is based on this solution [47; 48; 49].  
The authors in [47] evaluated a BLE mesh network composed of 10 CSRmesh nodes in a building. Performance metrics such as single-hop and multi-hop PDR were analyzed experimentally.  
Another CSRmesh case study was presented in [48], although, unlike the prior, simulation results were provided in addition to experimental results. The authors evaluated empirically different network scenarios with 19 CSRmesh modules. The maximum PDR was found to be up to 90% for short distances between sender and receiver. The authors showed the benefits of mesh networking to increase PDR over relatively long distances. They also identified a PDR trade-off that depends on the sender packet rate, wherein PDR is maximized for a sender rate of four packet/s.  
The same work included the simulation of a 500-node network, whereby nodes were spatially located following a uniform grid pattern and each node had nine direct neighbors. The authors showed that network size did not have a significant effect on overall end-to-end PDR for the considered node spatial distribution. However, since such node distribution is not usual in realistic mesh network deployments, the authors also evaluated a randomly distributed mesh network, in which single-node failure and bottleneck problems were found.  
A further academic case study based on CSRmesh can be found in [49]. The authors first evaluate CSRmesh and then improve basic CSRmesh mechanisms by developing two different solutions; Individual Mesh and Collaborative Mesh. Nodes of the first type only transmit their own data and do not relay packets from other nodes, while nodes of the latter type transmit their own packets and also relay the packets received from other nodes. In addition, a new packet format was designed. These improved mechanisms are compared by the authors with the following two approaches; a Bluetooth 4.0, non-mesh solution and a mesh solution using basic CSRmesh functionality.
# Deep Learning for Neuromuscular Control of Vocal Source for Voice Production
## 2 Materials and Methods
### LeTalker
#### 2.2.1 Three-Mass Vocal Fold Model  
Anatomically, each vocal fold is subdivided into five layers, as shown in Figure 2A. Computationally, the five layers are grouped either into two-layer schema (body (epithelium, superficial, and intermediate layers)-cover (deep layer and TA muscle)) or three-layer schema (mucosa (epithelium and superficial layers), vocal ligament (intermediate and deep layers), and TA muscle). In the _LeTalker_, the vocal fold dynamics were modeled using the two-layer schema and the three-layer schema was used to compute the fiber stress.  
The three-mass model shown in Figure 2B is a lumped element approximation of the body-cover layer schema of the vocal folds. The two vocal folds were assumed to be symmetric and have the same movement. Male vocal fold dimensions of 1.6 cm length (\(\mathrm{L_{0}}\)), 0.8 cm thickness (\(\mathrm{T_{0}}\)), and 0.8 cm depth (\(\mathrm{D_{0}}\)) were considered. Two masses (\(m_{u}\) and \(m_{l}\)) were used to model the cover layer, and the third mass (\(m_{b}\)) simulated the effect of the body layer. The two masses of the cover layer were connected using a coupling spring. The coupling spring (\(k_{c}\)) simulates the shear forces in the cover layer. Each of the cover layer masses was connected to the body layer mass using nonlinear springs (\(k_{u}\) and \(k_{l}\)) and damping elements (\(d_{u}\) and \(d_{l}\)). The two springs simulate the cover layer's stiffness and the effective coupling stiffness between the cover and body layers. The body mass was connected to the rigid thyroid cartilage boundary using a spring (\(k_{b}\)), which represent the effective stiffness of the body, and a damping element (\(d_{b}\)). The equations of motion for the three masses were given in [31].  
Figure 2: (**A**) The layered structure of the right vocal fold, (**B**) three-mass model approximation of the body-cover schema of the vocal folds.