esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 4 Reliability Analysis
### Performance Analysis Before and After Optimization  
Using the same dataset for training, testing, and validation, we compared the performance of the FraSegNet model before and after optimization. Figure 7 and Table 9 illustrate significant enhancements in key performance metrics following optimization. Notably, the optimized FraSegNet model achieved improvements across multiple dimensions: increased accuracy, reduced training time, and faster testing time.  
The optimized model demonstrated a marked increase in accuracy of 3.32%, resulting in more precise fracture extraction outcomes. Furthermore, the enhanced accuracy ensures that the model captures subtle fracture details with greater fidelity.  
Efficiency gains were equally impressive: training time was reduced by approximately 42.3%, while testing time saw a reduction of 50%. These reductions underscore the dual benefits of the optimization process: enhanced model performance coupled with significantly improved computational efficiency.  
In summary, the optimizations not only elevated the model's accuracy but also substantially reduced the required computational resources, demonstrating a balanced enhancement in both performance and efficiency.  
The optimized FraSegNet model is more robust against different levels of noise and lighting changes, as shown in Table 10. In the lighting test, the optimized model keeps higher accuracy and shows better detail and precision than the pre-optimized model. In the noise test, the optimized model handles Gaussian noise better, maintaining high accuracy in extracting nodal fissure edges even with high noise. In contrast, the pre-optimized model sees a large drop in accuracy, with blurred edges in high noise conditions. The generalization ability accuracy is shown in Figure 8.  
To test the model's ability to generalize, both the pre-optimized and optimized FraSegNet models were tested with K-fold cross-validation on three different datasets. The optimized model showed better stability across the datasets while maintaining high accuracy in nodal extraction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Original Image** & **Pre-Optimization Model** & **Post-Optimization Model** \\ \hline Increased Brightness & & & \\ Decreased Brightness & & & \\ Strong Noise Interference & & & \\ Weak Noise Interference & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Robustness comparison.  
Overall, the optimized FraSegNet model has clear improvements in accuracy, robustness, training efficiency, and generalization. It is better at extracting rock joints and fractures with more precision and reliability.
# Deformation of Air Bubbles Near a Plunging Jet Using a Machine Learning Approach
## 2 Material and Methods
### Algorithms
#### 2.3.4 Support Vector Regression  
Starting from a training dataset \(\{(x_{1},y_{1}),\)\((x_{2},y_{2}),\)\(\ldots,\)\((x_{l},y_{l})\}\subset X\times R\), where \(X\) is the space of the input arrays (e.g., \(X\in R^{n}\)). Support Vector Regression (SVR) is a supervised learning algorithm [55] whose purpose is to find a function \(f(x)\) as flat as possible and with a maximum \(\varepsilon\) deviation from the observed target values \(y_{i}\). Therefore, given a linear function in the form:  
\[f(x)=\langle w,x\rangle+b \tag{11}\]  
in which \(w\in X\) and \(b\in R\), the Euclidean norm \(\|w\|^{2}\) must be minimized, and this leads to a constrained convex optimization problem. In most cases, a significant error must be tolerated, so it is necessary to introduce slack variables \(\xi_{\iota}\), \(\xi_{\iota}\)\({}^{*}\) in the constraints. Consequently, the optimization problem can be formulated as follows:  
\[\text{minimize}\ \frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{l}\left(\xi_{i}+\xi _{i}^{*}\right) \tag{12}\] \[\text{subject to}\ \begin{array}{l}y_{i}-\langle w,x_{i} \rangle-b\leq\varepsilon+\xi_{i}\\ \langle w,x_{i}\rangle+b-y_{i}\leq\varepsilon+\xi_{i}^{*}\end{array} \tag{13}\]  
where the function flatness and the tolerated deviations are dependent on the constant \(C>0\).  
The SVR algorithm becomes nonlinear by pre-processing the training instances \(x_{i}\) by means of a function \(\Phi\): \(X\)\(\rightarrow\)\(F\), where \(F\) is some feature space. Since SVR only depends on the dot products between the different instances, a kernel \(k(x_{i},x_{j})=\langle\Phi(x_{i}),\Phi(x_{j})\rangle\) can be used rather than explicitly using the function \(\Phi(\cdot)\).  
In this research, the Pearson VII universal function kernel (PUFK) has been selected:\[k(x_{i},x_{j})=\frac{1}{\left[1+\left(\left(2\sqrt{\left|x_{i}-x_{j}\right|^{2} \sqrt{2^{\left(1/\omega\right)}-1}}\right)/\sigma\right)^{2}\right]^{\omega}} \tag{14}\]  
in which the parameters \(\sigma\) and \(\omega\) control the half-width and the tailing factor of the peak. In this study, the best results have been obtained for \(\sigma=0.4\) and \(\omega=0.4\).
# Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees +
## 4 Illustrative Results
### Quantitative Analysis
For illustrating the OCS + DSCM solution, we consider a destination that is only operating on 1 SC of the DSCM system with a 150 Gb/s capacity. Suppose that the capacity required by this node reduces over time and more nodes are needed to be installed. With the OCS on top of DSCM solution, the constellation can be sliced and more nodes can be added, allowing for topology reconfigurability. For example, instead of a single node, two nodes with a capacity of 62.5 Gb/s can be installed if 32 CPs are assigned to each node. Similarly, 4 nodes can be enabled by 16 CPs with capacity of 25 Gb/s each, as illustrated in Figure 14.  
Figure 13: Efficiency of aggressive and non-aggressive availabilities for P2P (**a**) and P2P + P2 MP (**b**) traffics. Furthermore, if the node's desired capacity is less than the overall capacity of the destination, the capacity sits unused. We can make better use of the destination capacity by adding more nodes and implementing OCS on top of DSCM. Consider the case as shown in Figure 15 where the capacity requested by a destination working on a 150 G SC decreases to 62.5 G. The efficiency of the destination in that case will be 42% (62.5/150). On the other hand, OCS can provide the benefit of adding one more node by performing 32-CP slicing to increase the capacity utilization to 83.33%. Figure 14: Reconfigurability options in OCS + DSCM. Figure 9:0 Efficiency improvement for OCS + DSCM.
## 1 Introduction
In the literature, many MP mitigation techniques are proposed (e.g., [10; 11], just to cite a couple of examples). However, when the goal is to satisfy some integrity requirements, the general approach is to identify the presence of errors in the delay estimation without mitigating its effect, but rather excluding the satellite affected by impairments from the solution. We propose here to adopt a technique of MP detection, which derives from the MP Distance Detector (MPDD) described in [12], and based on the identification of a Linear Adaptive Filter (LAF) able to model the MP effects, as shown in [13; 14]. The new version of MPDD described in this paper introduces a Signal Quality Index (SQI), which is both a metric able to evaluate the goodness of the signal, and a parameter used to improve the performance of the FDE module of RAIM. Note that methods of Signal Quality Monitoring (SQM) are already proposed in literature in the framework of integrity systems. For example in [15; 16] SQM algorithms are proposed to detect anomalous signal distortions by using extra correlators, which are not used to maintain the receiver locked to the incoming signal. The difference here is that the signal quality is used to create an index (SQI), which becomes an integral part of the assisted RAIM. The paper is organised as follows: in Section 2 a brief introduction of the considered GNSS receiver architecture and its working principles is given. In Section 3 the methods of PR calculation are reviewed and the MP impact on DLL outputs and PR measurements is analysed. In Section 4 an overview of the LAF theory used in [12] is given. Section 5 is devoted to the description of the MP detector. Section 6 introduces the concept of SQI. In Section 7 some RAIM and FDE concepts are given and the RB RAIM algorithm is briefly explained. In Section 8 a possible interaction between SQI and RAIM is described and simulation results are given. Section 9 concludes the paper.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 3 Location-Based Deferred Broadcast Scheme
### Deferred Timers in Ad-Hoc NDN  
Timers are effective mechanisms for addressing issues relating to the broadcast wireless medium by delaying the times of transmissions. Typically, a transmission occurs only when a timer expires after a pre-set waiting time value. The timer-based deferred broadcast schemes can achieve the goals of reducing packet losses and bandwidth usage. In our scheme LBDB, two types of timers are used, namely, Forwarding-Priority (FP) timer and Collision-Avoidance (CA) timer. The FP timer determines the transmission priority. A node with higher transmission priority should send with a shorter delay, i.e., a smaller value for the FP timer. The CA timer is to prevent collision. Thus, it is set to a random number so that the neighboring nodes will delay randomly before they transmit.  
For interest flooding, among all nodes that need to rebroadcast an interest, the one with higher forwarding priority will have a shorter waiting timer before transmission. If a node overhears an interest packet that has the same name as its already scheduled transmission, it cancels this transmission for that interest because the interest has been forwarded by another node with a higher forwarding priority than itself. Eventually, only a part of the nodes with higher forwarding priorities actually forward the interest. In some cases different nodes may have same forwarding priority. Then CA timer will add randomness.  
Figure 1 illustrates how deferred timer works in a vehicular network scenario. In this example, the consumer at the east side requests traffic information near the location of the producer at the west side. It broadcasts an interest containing the location in the data name. Car A, B and C all receive the interest from the consumer and decide to forward the interest because they do not have the data  
name. They set up a pending transmission using a deferred timer that can be calculated based on each car's forwarding priority. In this example, car A has the highest forwarding priority thus its deferred timer is the shortest. Therefore, A is the first to actually transmitting interest. Car B and C will receive the interest from A. They will know the interest has been forwarded by some other cars with higher forwarding priority. So they cancel their own pending transmission.
###### Abstract  
(1) Background: For patients irradiated due to prostate cancer, monitoring of bladder volume (BV) is crucial for accuracy of radiation delivery and minimizing exposure to surrounding healthy tissues. Due to the limited imaging capabilities of the CyberKnife system, ultrasound imaging plays a vital role in the monitoring of bladder filling. (2) Methods: A study was carried out in 142 prostate cancer patients treated with the CyberKnife system. Bladder ultrasound (US) was performed before each RTH session to assess real BV (rBV). The double US assessment of rBV was performed by two independent operators or a single operator during 177 and 495 RTH sessions, respectively, giving, in total, 1344 BV assessments. (3) Results: The mean BV in the first and second US assessment was 214.7 mL and 218.1 mL, respectively, while the mean planning bladder volume (pBV) was 340.8 mL. A pBV was significantly higher than an rBV. The mean difference between the US and CT assessment of BV was the smallest in the 100-349 mL group and the largest in the group above 349 mL. The Passing-Bablok regression results confirmed the reliability of the ultrasound (US) measurements. (4) Conclusions: The introduction of US-based BV assessment in patients irradiated due to prostate cancer using CyberKnife seems to be a crucial element in controlling the reproducibility of the treatment plan and should be a standard procedure. The pBV should be within the range of 200-300 mL. The US examination prior to CT scanning for planning is recommended to ensure the optimal range of BV for CT.  
Keywords:CyberKnife; prostate cancer radiotherapy; ultrasound; bladder volume; radiation injury; bladder filling; ultrasound bladder monitoring; radiosurgery +
Footnote †: journal: Applied Sciences
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 4 Calculation of Tire Forces  
At present, the Magic Formula [6; 10; 12; 14; 23], HSRI tire model [7; 24], Duggoff tire model [5; 15] and some other ones have been widely used for the practical application of acquiring vehicle information. When the road adhesion coefficient in the mathematical model is assumed as constant, remarkable model errors occur subsequently. Therefore, an online estimator needs to be adopted to identify road conditions for the use of these semi-empirical models. On the other hand, system model inevitably remains strong nonlinearity even though some considerable simplifications are made. These make it difficult for the design of the online estimator [25; 26]. To address this issue, observer theory is introduced by some researchers. The observer-based method is a good choice for indirectly obtaining the tire forces through vehicle dynamics, where the construction of a semi-empirical tire model is replaced, such as the sliding-mode observers (SMO) designed in [27], and the PID-based observer and adaptive-sliding-mode observer (ASMO) designed in [28]. However, for the observer-based method, the effect of system noise is not taken into account.  
To a certain extent, filtering algorithm can deal with it by establishing the state and measurement equations with the process and measurement noises. From the perspective of design complexity, in this study, a KF-based scheme is proposed to construct the longitudinal and lateral tire force estimators. This scheme takes the tire forces as a part of the states to be estimated without any prior knowledge of the tire-road friction model. The longitudinal force estimator is built by use of a tire rolling dynamic model, and the lateral force estimator is realized by means of a single-track vehicle dynamic model. The advantage of this scheme is that it can not only alleviate the computation burden, but also enhance the robustness of state estimation due to its insensitivity to the change of road conditions.
# Linked Data Triples Enhance Document Relevance Classification
## 5 Learning Model Methodology
### Models with Pre-Trained Word Embeddings  
Firstly, a vanilla model is developed with plain text inputs extended using GloVe (Global Vectors for Word Representation). The \"input text\" section from Figure 3 represents this model's architecture. Given a set of documents from the corpus, each document \(D\) is represented as a bag-of-words w\({}_{1}\), w\({}_{2}\), w\({}_{3}\),..., w\({}_{\text{N}}\). This baseline model is built with a multi-layered feed forward network. The maximum length of the document (\(N\)) is fixed as 1000 tokens and given as input to the embedding layer. Vector representations for each token are extracted from pre-computed 300-dimensional Global Vectors (GloVe). The output from the embedding layer is flattened to form a 1000 \(\times\) 300 sized vector. This is then passed into a series of fully-connected Rectified Linear Unit (\"ReLU\") layers of 512 and 100 neurons, respectively. The output \"Softmax\" layer provides the probability of pre-defined (relevant/irrelevant) classes.  
Multiple models are then created by the addition of semantic rich elements, LDA topics, named entities, and triples, and these elements are concatenated to the baseline \"input text\" model. The final models use the combination of all inputs--plain text, LDA-generated topics, named entities, and extracted topic-entity triples.  
Input topics are represented as the probability distribution of topics for each document. The number of topics is a hyper-parameter of the LDA topic modeler, identified using an optimum coherence score (further explained in Section 6.1 below).  
The length of named entities and triples to input is set to 1000 tokens to be consistent with the baseline \"input text\" model.  
The classifier requires three separate embedding layers to form 300-dimensional vector representations of the text, NEs, and linked data, as shown in Figure 3. These representations are flattened to obtain a single dimension vector. This vector is fed to multiple \"ReLU\" layers (512 and 100 neurons). This produces four 100-unit feature vectors from four different inputs. All these vectors are joined together to form a 400 \(\times\) 1 feature vector. This is used for classification by connecting to a fully-connected \"softmax\" layer with output neurons. These neurons represent the predicted probability of the classes.  
Figure 3: A deep learning architecture for the classification of news-feeds using raw text, topics, named entities, and triples.
# A Novel Integrated Gliding and Flapping Propulsion Biomimetic Mana-Ray Robot
## 4 Discussion  
Most of the prototypes collected in the literature are proof-of-principle prototypes, and there are few specific practical engineering applications. The prototype in this paper has a load of as much as 20 kg, and it also can take on a mission in a harsh underwater environment. Compared with the prototype we designed, Festo's BOSS Manta Ray robot [25] has pump propulsion in addition to pectoral fin propulsion, which is faster. Still, its deep-sea pressure bearing capacity and load capacity are smaller. The prototype developed in our present work could withstand a pressure of 10 Mpa. Compared with the underwater gliders, such as the manta ray-like glider [29], it has an advantage in sliding, but the prototype in this article has a shape design mimicking the real manta ray creature and has good hydrodynamic swimming performance.  
There are also some limitations of the bionic manta ray robot compared with the existing prototypes.  
1. The accuracy of the heading control based on the centroid mechanism is not high, which affects the gliding speed.
2. The material of the flexible flapping wing is a skeleton plus fiber skin, leading to considerably more resistance compared with real fish during flutter propulsion.
3. The flapping propulsion model can achieve a highly maneuverable movement. The motor's power is limited.
4. We located the buoyancy-adjustment system in the head of the prototype; the buoyancy changed with the alteration of the location of the centroid, which would affect the stability of the control system.  
We applied the fuzzy controller combined with CPG to adjust the robot's attitude. However, the parameters must be optimized to improve the control quality in future work. The structure also should be optimized to improve the dynamic stability of the robot. Further, the motor driving mode and the pectoral materials could be changed to alter the hydrodynamic performance and increase propulsion efficiency.
## 4 Discussion
### Assessment of Scraping Depth in Rock-Ice Avalanches  
The measured base friction angles and flow depth were applied to the frictional model in the shear constitutive model [33; 34] to describe the scraping effect of debris flows. This approach was used to assess the applicability of the model for calculating the scouring depth of rock-ice avalanches. The sliding force generated by the debris flow is computed using the following equation:  
\[S=W_{d}\cos\beta\tan\varphi^{\prime} \tag{1}\]  
The shear resistance of the deposited soil layer is determined by the Mohr-Coulomb criterion:  
\[\tau=\sigma\tan\varphi=W\cos\beta\tan\varphi \tag{2}\]  
The scraping depth of the debris flow is then calculated using:  
\[Z_{b}=\frac{\gamma_{d}Z_{d}(\tan\varphi-\tan\varphi^{\prime})}{\gamma_{b}\tan\varphi} \tag{3}\]  
where \(\beta\) is the angle between the sliding surface and the horizontal plane, \(\gamma_{d}\) is the bulk density of the debris flow, \(\gamma_{b}\) is the bulk density of the deposited material, \(Z_{d}\) is the thickness of the debris flow, \(\varphi\) is the internal friction angle of the deposited soil, \(\varphi^{\prime}\) is the equivalent friction angle between the debris flow and the substrate. The gravitational calculation formula is: \(W_{d}=\gamma_{d}Z_{d}\).  
While the calculated scraping g depths show a deviation within \(\pm 15\%\) of the experimental values, analysis of Figure 11a reveals that when the ice content is below \(80\%\), both experimental and calculated scouring depths exhibit a clear increasing trend as ice content rises. However, when the ice content exceeds \(80\%\), the calculated scraping depth continues to increase, while the experimental scouring depth starts to decrease. A comparison with existing research on rock-ice avalanches indicates that the influence of ice content on the dynamics and impact characteristics of rock-ice avalanches reaches a certain threshold [7; 27]. The present experiments, however, do not provide a definitive explanation for this phenomenon. As such, the current model shows considerable limitations in its application to the scraping effect of rock-ice avalanches. Future studies should focus on investigating the underlying causes of this behavior and refining the model to improve its accuracy in predicting the scraping effect.
## 2 Numerical Methodology
### Turbulence Simulation Methods
#### 2.1.2 Construction of the Hybrid Length Scale  
ZDES in this study is implemented based on a structured multi-block grid, achieved by defining different grid blocks to use different simulation methods in the same flow field. In a flow field composed of N grid blocks, for the nth grid block (\(0<n<N\)), when \(\mathit{ides}_{n}\ =\ 0\), RANS is employed in that region; when \(\mathit{ides}_{n}\ =\ 1\), DES is used in that region. The hybrid length scale can be expressed as follows:  
\[\overset{\sim}{l}_{\text{ZDES},n}\ =\ (1-\mathit{ides}_{n})\times\mathit{l}_{ \text{SST}}+\mathit{ides}_{n}\times\overset{\text{IOHI}}{\text{DES}} \tag{10}\]  
The selection of the hybrid length scale \(\overset{\text{IOHI}}{\text{DES}}\) is specified by \(\mathit{imode}\). When \(\mathit{imode}_{n}\ =\ 1\), it corresponds to the separation of Type I flow; when \(\mathit{imode}_{n}\ =\ 2\), it corresponds to the separation of Type II flow. Therefore, it can be further written as follows:  
\[\overset{\sim}{l}_{\text{ZDES},n}\ =\ \left\{\begin{array}{l}l_{\text{SST}}, \text{if }\mathit{ides}_{n}\ =\ 0\\ \overset{\text{I}}{\text{DES}}\ =\ \min(\mathit{l}_{\text{SST}}, \mathit{C}_{\text{DES}}\overset{\text{I}}{\text{DES}}),\text{ if }\mathit{ides}_{n}\ =\ 1,\mathit{imode}_{n}\ =\ 1\\ \overset{\text{II}}{\text{DES}}\ =\ \mathit{l}_{\text{SST}}-f_{d}\max(0, \mathit{l}_{\text{SST}}-\mathit{C}_{\text{DES}}\overset{\text{II}}{\text{DES }}),\text{ if }\mathit{ides}_{n}\ =\ 1,\mathit{imode}_{n}\ =\ 2\end{array}\right. \tag{11}\]  
where \(\mathit{C}_{\text{DES}}\) is 0.78 and \(f_{d}\) is the delay function defined in DES. ZDES redefines a new sub-grid scale, and the following section will provide a detailed introduction to it.
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Information  
Railway industry is also currently challenged by the improvement of the experience of the passengers and the management of the freight. On the one hand, passengers usually demand better train punctuality, precise scheduling information and improved on-board entertainment. On the other hand, logistics often require cost-effective solutions that include the whole monitoring of the freight. Thus, taking the characteristics of the information into consideration, two types of targets have to be distinguished: passenger and freight.