esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic-Aligned Cross-Modal Visual Grounding Network with Transformers
## 4 Experiments
### Datasets  
We conducted experiments on two challenging datasets: the Military Aircraft Dataset [58] ([[https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset](https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset)]([https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset](https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset)), ( accessed on 27 April 2023)) and the FGVC Aircraft Dataset [59] ([[https://www.robots.ox.ac.uk/vgg/data/fGVCircraft/](https://www.robots.ox.ac.uk/vgg/data/fGVCircraft/)]([https://www.robots.ox.ac.uk/vgg/data/fGVCircraft/](https://www.robots.ox.ac.uk/vgg/data/fGVCircraft/)), ( accessed on 27 April 2023)), which are widely used to evaluate fine-grained object detection and visual grounding.  
The **Military Aircraft Dataset (MAD)** is one of the commonly used fine-grained object detection datasets with 40 aircraft types with bounding boxes recorded in the PASCAL VOC format (\(x_{min}\), \(y_{min}\), \(x_{max}\), \(y_{max}\)). It consists of 7177 JPEG images with two resolutions (\(1280\times 1280\), \(640\times 640\)) for \(12,337\) objects collected from the website via a 'Google Images' search, which are manually split into 5365 for training, 1330 for validation, and 482 for testing, respectively. We invited 10 people to manually annotate textual expressions. Before annotation, we selected four discriminative properties (\"engine\", \"propeller\", \"wing\", and \"tail\"), and told the annotators that the text descriptions should include all four properties. Then, the annotators followed the instructions to give detailed descriptions to each object in an image, as Figure 4a shows, where two different aircraft may have different--or the same--text descriptions. As a result, there are a total of 8137 referring expressions, and each image contains one or more expressions with an average length of 30 words for visual grounding.  
The **FGVC Aircraft Dataset (FAD)** organizes aircraft into a four-level hierarchy: model, variant, family, and manufacturer. We use 'variant' as the category label. It contains \(10,000\) JPEG images with two kinds of resolutions (\(1280\times 1280\), \(640\times 640\)) for 100 different aircraft variants; each variant contains 100 images. The train/test set split ratio is around 2:1, resulting in 6667 samples for training, and 3333 samples for testing. For text annotation, we describe the following aircraft features, \"fuselage\", \"wing\", \"tail\", and \"engine\", respectively, as Figure 4b shows. There are a total of \(10,200\) referring expressions, and each expression has an average length of 30 words.  
To support future research, we uploaded our datasets with text annotations to the following website: ([[https://github.com/XuZhang1211/SAC-VGNet](https://github.com/XuZhang1211/SAC-VGNet)]([https://github.com/XuZhang1211/SAC-VGNet](https://github.com/XuZhang1211/SAC-VGNet)), (accessed on 27 April 2023)).
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Unified Control  
Unified control in the CMS framework centralizes all operational aspects, applying to both the integrated and coordinated operations architectures while excluding the adaptive operations architecture, where operational control is shared with an NTN MANO. From the perspective of infrastructure and architecture challenges, this unified control approach effectively addresses the complexities of heterogeneous architectures. It also enables a hybrid management strategy, incorporating satellite-distributed control and centralized coordination. Regarding integrated management challenges, the framework enables mixed scheduling by supporting both centralized and distributed scheduling within the satellites. This flexibility is absent in the adaptive architecture, where MANO follows a strictly centralized paradigm. Additionally, in the integrated and coordinated operations architectures, the presence of a unified control entity improves the seamless integration of network operational aspects into satellite operations. Finally, unified control directly tackles the multi-customer nature of NTN scenarios in addressing operational dynamics challenges by balancing varying priorities within the CMS framework. This ensures efficient resource allocation and service differentiation across diverse user requirements.
## 3 Numerical Results
### System-Level Simulations
Performance close to the digital precoding can be achieved when considering the use of a DPS scheme for \(L_{max}=4\) and a cluster size of three. When compared to the cluster size 1C, with 3C it is observed that the throughput loss is lesser when adopting the hybrid schemes instead of the digital one. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Transmitted Power**} & \multirow{2}{*}{**Cluster Size**} & \multicolumn{2}{c}{**Average Throughput (Mbps)**} \\ \cline{3-4}  & & **MO** & **OO** \\ \hline
10 & 1C & 542.6 & 388.3 \\ \hline
10 & 3C & 725.6 & 648.2 \\ \hline
100 & 1C & 551.6 & 411.6 \\ \hline
100 & 3C & 733.4 & 662.8 \\ \hline
1000 & 1C & 526.3 & 376.3 \\ \hline
1000 & 3C & 722.3 & 665.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of the variation of the transmitted power of the Aps on the system results. Figure 14: Throughput (Gbps) vs. number of subcarriers for different versions of the algorithm (SPS or DPS) as a function of \(L_{\text{max}}\), considering the proposed precoder with \(N_{tx}=256\), \(N_{rx}=4\), \(N_{RF}^{tx}=14\), \(N_{s}=2\) and \(N_{u}=180\) (with LOS component) and 3C. Figure 13: Throughput (Gbps) vs. number of subcarriers for different versions of the algorithm (SPS or DPS) as a function of \(L_{\text{max}}\), considering the proposed precoder with \(N_{tx}=256\), \(N_{rx}=4\), \(N_{RF}^{tx}=14\), \(N_{s}=2\) and \(N_{u}=180\) (with LOS component) and 1C.
## 3 Results and Discussions
### Workability
#### 3.1.4 Influence of SS/SH Ratio  
The slump values of group D mixes (SS/SH = 2.5 and 3.0) and group A mixes (SS/SH = 2.0) were compared to study the influence of SS/SH ratio on workability. The slump values of group A mixes were observed to be higher than the corresponding mixes  
Figure 9: Influence of mix design variables on slump values of GPC mixes.  
of group D (having higher SS/SH ratio). The slump values of D1-S20 (SS/SH = 2.5) and D3-S20 (SS/SH = 3.0) were decreased by 10% and 38% respectively when compared to the corresponding group A mix, A2-S20 (SS/SH = 2.0). Similarly, for D2-S30 (SS/SH = 2.5) and D4-S30 (SS/SH = 3.0), the slump values were decreased by 15% and 40% respectively as compared to A3-S30 (SS/SH = 2.0). It can be observed from Figure 2 that A2-S20 (SS/SH = 2.0) has the highest slump value of 120 mm when compared to D1-S20 (SS/SH = 2.5) and D3-S20 (SS/SH = 3.0) with slump values of 110 mm and 75 mm respectively. Similarly, A3-S30 (SS/SH = 2.0) has the highest slump value of 100 mm as compared to D2-S30 (SS/SH = 2.5) and D4-S30 (SS/SH = 3.0) with slump values of 85 mm and 60 mm respectively. It shows that workability of FS-GPC mixes decreases significantly by increasing the SS/SH ratio. Since sodium silicate has more viscosity than sodium hydroxide in AAS, higher SS/SH ratio led to more viscous AAS which resulted in lower slump values of FS-GPC mixes [28].
## 4 Simulation Experiments
### Experiments Platform  
Our experiments were performed on a computer with an Intel Core i7-8700K CPU (Intel, Santa Clara, CA, USA) and an Nvidia GTX 1650 GPU(Nvidia, Santa Clara, CA, USA). The experiments ran on the Ubuntu system, while the robot-related components made use of the ROS ([[https://www.ros.org/](https://www.ros.org/)]([https://www.ros.org/](https://www.ros.org/)) accessed on 20 December 2022)) and the machine learning-related components made use of the Keras ([[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/)) accessed on 20 December 2022)) based on TensorFlow ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)) accessed on 20 December 2022)). It takes about three hours for a thousand episodes. SO-DRL is a model-free algorithm and we define the UGV/UAV as a prime point for training and testing of the simulation. We used a UAV model instead of a prime point for easy viewing in the simulation. We used an open-source autonomous software platform Prometheus ([[https://github.com/amov-lab/Prometheus](https://github.com/amov-lab/Prometheus)]([https://github.com/amov-lab/Prometheus](https://github.com/amov-lab/Prometheus)) accessed on 20 December 2022)), which supports simulation and experiments with the Gazebo interface.  
For environments during training, we generated obstacles (in Figure 6) in the initial stage and create two training scenarios. The learning rate was set to 0.0001 in the experiments. The work proposed in [46] is a seminal paper on deep reinforcement learning, and two ways are given in the paper for evaluating the performance of reinforcement learning algorithms. One is the average score, which averages all the reward values obtained by the agent, showing how correct the algorithm's action selection is for achieving the purpose. The other is the average Q-value, which averages the actions' Q-values and shows the algorithm's reliability in selecting the correct action. Since the paper [46] is the pioneering work of deep reinforcement learning, most subsequent papers use it as a reference. These two metrics have become the performance evaluation metrics for reinforcement learning research. In this paper, the average reward and the average Q-value are also used as evaluation metrics for the network in training, and we take their average in each episode.  
We trained SO-DRL in the two scenarios, as shown in Figure 6. The learning rate in the experiments was set to 0.0001. The performance metrics for training include the average reward per set, the average q-value per set, and the success rate computed per 50 sets.
## 4 Considerations for the Reliability Analyses of the Structural System
### Stochastic Models for the Basic Variables
However, for the purpose of illustration, Figure 3 shows the model uncertainties of the failure mode at maximum  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Normal Distribution (ND)**} & \multicolumn{3}{c}{**Lognormal Distribution (LND)**} \\ \cline{3-8}
**Failure Mode** & & \multicolumn{3}{c}{**Interval**} & \multicolumn{3}{c}{**Point Estimators**} & \multicolumn{3}{c}{**Interval Estimators (95\%)**} \\ \cline{3-8}
**(Design Equation)** & **Parameter** & **Point Estimators** & **Estimators (B5\%)** & **LND Values** & **LND Values** & **IE Values** & **IE Values** & **Corrected** \\ \hline (i) Inside shear reinf. & \(\mu\) & 1.3927 & [1.3397 ; + \(\infty\)] & 0.3219 & 1.3927 (\(\infty\)) & [0.2841 ; + \(\infty\)] & **1.3478 (\(\infty\))** [0] & 1.3478 (\(\infty\))** [0] \\ zone & \(\sigma\) & 0.1910 & [-\(\infty\); 0.2375] & 0.1362 & 1.0966 (\(\infty\)) & [-\(\infty\); 0.1694] & [-\(\infty\); 0.2302] (\(\phi\)) & 0.2299 (\(\phi\)) \\ (Equation (4)) & CoV & 0.1371 & [-\(\infty\); 0.1773] & 0.4231 & 0.1368 (\(\infty\)) & [-\(\infty\); 0.5962] & [-\(\infty\); 0.1706] (\(\phi\)) & **0.1631** \\ \hline (ii) Outside shear reinf. & \(\mu\) & 1.1072 & [1.0654 ; + \(\infty\)] & 0.0903 & 1.1075 (\(\infty\)) & [0.0514 ; + \(\infty\)] & **1.0713** (\(\infty\))** [0] & 1.0713 \\ zone & \(\sigma\) & 0.1648 & [-\(\infty\); 0.2008] & 0.1536 & 0.1712 (\(\infty\)) & [-\(\infty\); 0.1871] & [-\(\infty\); 0.2023] (\(\phi\)) & 0.1951 (\(\phi\)) \\ (Equation (10)) & CoV & 0.1489 & [-\(\infty\); 0.1885] & 1.7013 & 0.1545 (\(\phi\)) & [-\(\infty\); 3.6442] & [-\(\infty\); 0.1888] (\(\phi\)) & **0.1821** \\ \hline (iii) At max. punching & \(\mu\) & 1.0855 & [1.0657 ; + \(\infty\)] & 0.0769 & 1.0855 (\(\infty\)) & [0.0500 ; + \(\infty\)] & **1.0956** (\(\infty\)) [0] & 1.0596 (\(\phi\)) \\ shear capacity & \(\sigma\) & 0.1095 & [-\(\infty\); 0.1345] & 0.1021 & 0.1111 (\(\phi\)) & [-\(\infty\(failure mode (iii)). All the values attained for the model uncertainties are described in Table 2 and summarised in Table 3.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### 5.4.6 TD-FT Using the GPST Algorithm  
[PERSON] et al. reconstructed TD-FT images using the GPST algorithm which was developed in TD-DOT [171; 211]. From phantom experiments using a four-channel TCSPC system and GPST algorithm, combined DOT and FT provided images of \(\mu_{a}\), \(\mu_{s}{}^{\prime}\), \(N\), and \(\tau\), and hemoglobin images were obtained from \(\mu_{a}\) images at two wavelengths of 780 nm and 830 nm [192].
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 2 Background Theories
### Isomap  
For dimensionality reduction, Isomap algorithm performs nonlinear data embedding through multi-dimensional scaling (MDS), which successfully upholds distances between data points in low-dimensional space [22]. This isometric feature mapping approach exploits global characteristics of geodesic distances of each data point, and computes piece-wise Euclidean distances between neighbor data points. In other words, Isomap inherently creates connecting graphs between a nonlinearly distributed data set based on nearest neighbors from specific data. Using [PERSON]'s algorithm [23] and the Floyd-Warshall algorithm [24; 25], Isomap calculates the shortest distance among the connecting nodes of the graph, so that a high-dimensional data structure iteratively transfers into a reduced dimensional space through embedding processes. The first step of the Isomap algorithm is to identify the \(k\)-nearest neighbors of each point, in which neighborhood relations are represented by a graph G. Each data point is connected to its nearest neighbors by edges. In the second step, the algorithm  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Equation** \\ \hline Standard Deviation & \(\sqrt{E[(x(t)-\mu)^{2}]}\) \\ RMS & \(\sqrt{E[x(t)^{2}]}\) \\ Kurtosis & \(\frac{E[(x-\mu)^{4}]}{(E[(x-\mu)^{2})]}\) \\ Zero-crossing rate & \(\frac{1}{T-1}\sum\limits_{t=1}^{T-1}II\{x(t)x(t-1)<0\}\) \\ Entropy & \(\sum\limits_{i=1}^{n}p_{i}\,\log(p_{i})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical parameters of damage-sensitive feature vector: standard deviation, root mean square (RMS), kurtosis, zero-crossing rate, and entropy.  
Figure 1: Exemplary illustration of mode mixing problem in empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD): (**a**) EMD; and (**b**) EEMD.  
estimates geodesic distance between all pairs of points in the data. The Floyd-Warshall algorithm is employed to find the matrix of the graph containing the shortest path distances between all pairs of points in G. Finally, classical MDS is performed, to create lower dimensional embedding of the data with the shortest path in Euclidean space.
# Using Mechanical Metamaterials in Guitar Top Plates: A Numerical Study
## 3 Results
### Displacement Under Tension  
To quantify the effect of the holes in the stability of the guitar, static load tests were carried out, simulating the tension of the strings. The load was applied to six circular areas with a diameter of 2 mm located in the back surface of the bridge, corresponding to the points where the strings are mounted on a real guitar. Assuming a typical range of tensions of 50-80 N for nylon guitar strings [33], we used a total force of 480 N in the positive \(x\) (vertical) direction.  
These load tests were computed with various choices of hole size for the top plate, with both longitudinal and radial orientation, as well as with the solid top. Figure 3a shows the normal displacement fields for the longitudinal (\(p=1\)) and radial (\(p=1\)) holes together with the original solid top, used as a reference. The soundboard exhibits the expected warped configuration, with a slight bulge behind the bridge and a slight dip in front. This deformation is accentuated with the introduction of the metamaterial, and increases with the hole size. Moreover, the soundboard is more deformed, if slightly, in the case of the radial holes. This is consistent with the results in [17], where the introduction of elliptical holes was found to be correlated with higher effective values of Young's modulus in the direction parallel to the major axis.  
Figure 2: Eigenfrequencies values and percentage variations with respect to the case of the solid top plate. (**a**) Unbraced plates in simply supported boundary conditions, with longitudinal (\(p=1\)) holes and radial (\(p=1\)) holes. (**b**) Braced plates in simply supported boundary conditions, with longitudinal (\(p=1\)) holes and radial (\(p=1\)) holes. (**c**) Complete body, with longitudinal (\(p=1\)) holes and radial (\(p=1\)) holes.  
In order to further assess the capacity of the metamaterial instruments to withstand the tension of the strings, we can also look at the distribution of stresses in the sound-board. Unfortunately, we cannot develop a way to predict failure, as failure theories in anisotropic materials require the definition of a number of constants to be determined experimentally [34].  
Figure 3b shows the von Mises stress for the case of the solid plate, of the longitudinal holes (\(p=1\)), and of the radial holes (\(p=1\)). Contrary to the displacement, the stress distribution between the two hole patterns is clearly different, with the radial holes showing a greater concentration of stresses on the bridge area. This could potentially lead to premature failure of the instrument for the radial hole patterns. The longitudinal holes, on the other hand, albeit having more concentrated stresses than the solid top case, are able to spread the load in a more even way longitudinally by the \"channels\" of solid wood left by the pattern, therefore actually making a stronger top than the radial pattern for an equivalent density. This difference of stiffness for the same equivalent density should imply a higher radiation coefficient, and this is the subject of the next subsection.
## 3 Routing Protocols for UWSN
### Protocols Featuring Energy Balancing  
All the sensor nodes in UWSN are battery powered. So, energy balancing is one of the important factors while determining the route to the sink. Otherwise, it is most likely for some nodes to be overburdened in comparison to others thus discharge faster than other. This create holes in the network creating serious disruption in the network. RE-PBR and EEDBR does the energy balancing which ultimately improve the reliability and network lifetime. These routing schemes can be utilized in applications like natural hazard detection, where the reliability and network life-time is the most critical issue.
###### Abstract  
Intrusions are becoming more complicated with the recent development of adversarial techniques. To boost the detection accuracy of a separate intrusion detector, the collaborative intrusion detection network (CIDN) has thus been developed by allowing intrusion detection system (IDS) nodes to exchange data with each other. Insider attacks are a great threat for such types of collaborative networks, where an attacker has the authorized access within the network. In literature, a challenge-based trust mechanism is effective at identifying malicious nodes by sending challenges. However, such mechanisms are heavily dependent on two assumptions, which would cause CIDNs to be vulnerable to advanced insider attacks in practice. In this work, we investigate the influence of advanced on-off attacks on challenge-based CIDNs, which can respond truthfully to one IDS node but behave maliciously to another IDS node. To evaluate the attack performance, we have conducted two experiments under a simulated and a real CIDN environment. The obtained results demonstrate that our designed attack is able to compromise the robustness of challenge-based CIDNs in practice; that is, some malicious nodes can behave untruthfully without a timely detection.  
intrusion detection; collaborative network; on-off attack; challenge-based mechanism; trust computation and management +
Footnote †: journal: Future Internet
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 5 Performance Evaluation
### Simulation Results
#### 5.2.5 PUSCH Wastage of Legacy CeRA and CeRA-eSP  
In Figure 11, the PUSCH wastage of legacy CeRA and CeRA-eSP for various PUSCH resources and device densities is shown. In Figure 11, it can be seen that CeRA-eSP generated less PUSCH waste than legacy CeRA in all cases when \(N=100,000\). Legacy CeRA inferred more phantom codewords due to high codeword ambiguity with the growth of allocable PUSCH resources. In other words, the BS in legacy CeRA had a larger deducable preamble set than that of CeRA-eSP, so relatively more phantom codewords were generated, which resulted in a waste of PUSCH resources. Figure 11b shows that CeRA-eSP achieved low PUSCH wastage in all cases. In legacy CeRA, as the number of devices increased, the number of deduced phantom codewords increased, and as the number of cases in  
Figure 10: RA success rates for different available PUSCH resources  
Figure 9: (**a**) Successful preamble transmission rate for different numbers of devices and (**b**) Successful random access rate for different numbers of deviceswhich PUSCH resources were allocated to phantom codewords increased, it was confirmed that up to a 100,000 PUSCH was generated during the entire RA. On the other hand, since the proposed CeRA-eSP method dynamically adjusted the preamble codeword set according to the number of active device estimations, it maintained a low number of phantom codewords and as a result, achieved a low PUSCH wastage performance. It is worth noting that CeRA-eSP achieved high PUSCH efficiency in a highly densified network, which consisted of more than \(N=120,000\) devices. This means that CeRA-eSP saved the precious uplink resources of mMTC by improving the efficiency of resources consumed by RA.