esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using SPSS version 20 (IBM SPSS STATISTICS 20). Data were categorized as nominal or quantitative variables. Nominal variables were expressed as numbers or percentages. Quantitative variables were expressed by mean \(\pm\) standard deviation or median and percentiles (\(25;75\%\)), whenever appropriate. A Kolmogorov-Smirnov test was used in order to test for normality of distribution of quantitative variables. Differences between the mean or median between two groups were analyzed using the \(t\)-test, or Mann-Whitney test when appropriate. Differences between three groups with normally distributed variables were analyzed using general linear model test with Bonferroni post-hoc processing. Receiver-operating characteristic (ROC) curves were constructed, and areas under curve were calculated. Sensitivities and specificities were determined for the ability to identify worsening PAH children. A \(p\)-value of <0.05 was considered statistically significant.
# Candidate Waveforms for ARoF in Beyond 5G
## 3 Candidate Modulation Formats for MmWave Over UDWDM-PONs
### Orthogonal Frequency Division Multiplexing (OFDM)  
OFDM is a popular multi-carrier waveform format developed for RF systems and applied in LTE downlink. It can significantly increase the data rate in bandwidth-constrained channels with high spectral efficiency and allowing efficient MIMO integration. Furthermore, the robustness of OFDM to either phase noise as to time-selective channels depends on the spacing of its subcarriers [24]. Therefore, by using the scalable numerology and the subcarrier spacing, OFDM can be robust to almost any channel condition imposed by the scenario and by other requirements.  
On the other hand, the basic OFDM suffers high OOB emissions so multiple techniques have been proposed to reduce them. These techniques are classified into two main categories: (1) windowed-OFDM and (2) filtered-OFDM [12]. The weight overlap and add based OFDM (WOLA-OFDM) is the windowed technique implemented in LTE [25]. This technique greatly reduces the OOB emissions without including high complexity. The WOLA-OFDM OOB emission level decreases with the length of CP since this length determines the length of the window. An asymmetric window may be used instead of well-known symmetric windows for reduction of the cyclic prefix by 30 % [25] and, therefore, to reduce overhead. This technique suppresses OOB emission but makes the system more susceptible to channel induced ISI and ICI.  
* SR \(\rightarrow\) subcarrier. \(\mathrm{FL}\rightarrow\) filter. \(\mathrm{SB}\rightarrow\) sub-band. \(\mathrm{IQ}\rightarrow\) in-phase and quadrature. \(\mathrm{PM}\rightarrow\) pulse mapping.  
PS \(\rightarrow\) pulse shaping.  
\end{table}
Table 1: Overview of the waveform schemes and their signal generation processes.  
Figure 3: Simple analog radio over fiber (ARoF) scheme.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 4 Modeling the Performance of Bluetooth Low Energy for Opportunistic Sensor Data Collection
### Advertisement-Based Approach
#### 4.1.2 Maximum Amount of Collected Data per Contact Interval  
We next model the expected maximum amount of data, denoted \(E[L_{data\_adv}]\), that the mobile entity may collect from the sensor node within a contact interval in the advertisement-based approach.  
Let \(T_{contact}\) be the duration of the contact interval. Let \(L_{adv\_non}\) and \(L_{adv\_payload}\) denote the maximum physical layer size of and the total amount of data that may be transported in a non-connectable advertising packet, respectively. Let \(b\) denote the Bit Error Rate (BER) of the channel. Then, \(E[L_{data\_adv}]\) can be computed as follows:  
\[E\{L_{data\_adv}\}=\frac{T_{contact}}{advInterval+E\{advDelay\}}\cdot L_{adv \_payload}\cdot(1-b)^{L_{adv\_nan}} \tag{6}\]  
Figure 5: Illustration of variables involved in the calculation of the average current consumption in the advertisement-based approach (\(I_{avg\_adv}\)). ‘Adv Event’ refers to an advertising event.
# Enhanced Upward Translations for Systems with Clusters
## 4 Numerical Results for Typical Kernels of BIE
### Computational Prerequisites
Thus for them, the maximal _relative_ errors are studied using the Euclidian norm of the vectors. We consider a cluster comprised of sources with unit intensity each. Using equal intensities is the most unfavourable for the modified translation, because there is no leading source with intensity much greater than that of the remaining sources. The number of sources, comprising a cluster, is 30 in 2D and 50 in 3D. Their centres are distributed almost uniformly by using the sunflower spiral (e.g., [32]). For generality, the radius \(r_{cl}\) of the cluster is defined by the ratio \(r_{cl}/R_{e}\), where the radius \(R_{e}\) of the ES is the same as taken above for leaf cells in 2D and 3D. The ratio \(r_{cl}/R_{e}\) is changed from 0.01 (sometimes from 0.07) to the maximal value 0.12 for which the cluster diameter \(2r_{cl}\) approximately equals to the thickness \(h=0.25R_{e}\) of the layer (Figure 1), where errors of conventional translation, as established in [24], are greatest. The cluster center \(y_{clp}\) is located at the \(x_{1}\) axis, so that its coordinates are \((x_{1},0)\) in 2D and \((x_{1},0,0)\) in 3D with \(x_{1}\geq 0\). The normal to the ES at its point closest to the cluster center is \((1,\ 0)\) in 2D and \((1,\ 0,\ 0)\) in 3D. For kernels \(G_{ny}(x,y)\) and \(H_{nx,ny}(x,y)\), depending on the normal \(n_{y}\) at a source point, the vector \(n_{y}\) is taken in the most unfavourable direction which is orthogonal to the normal at the ES. Thus for these kernels, \(n_{y}=(0,\ 1)\) in 2D, and \(n_{y}=(0,\ 1,\ 0)\) in 3D; the same direction is taken for each source comprising a cluster. The distance \(R_{e}-\left|y_{clp}\right|\) from the cluster to the ES is \(R_{e}-x_{1}\). Again it is defined by the ratio \(\varepsilon=\left(R_{e}-\left|y_{clp}\right|\right)/R_{e}\). To see how the errors of conventional and modified translations decrease with the distance, the calculations were performed for various ratios, which changed from 0.02 (sometimes from 0.1) to 0.2. The maximal and average errors of conventional and modified translations are found by the comparison of exact and translated far fields of the cluster at each point of the control surface. The exact far field is calculated in accordance with (2) by summing fields of sources comprising the cluster. The far field, corresponding to the conventional translations, is calculated by using Equations (3) and (4). The far field, corresponding to the modified translations, is calculated by means of (6)-(9) by employing the algorithm summarised in Appendix A.
# A Comprehensive Survey for Deep-Learning-Based
## 3 Abnormal Detection with Images over Short Distances
### Visible Light
#### 3.1.1 Self-Blast Glass Insulator Location
The orientation angle detection and binary shape prior knowledge (OAD-BSPK) algorithm is applied in this step. The second step is deep feature extraction. An AlexNet [78] network pre-trained on ImageNet is employed to extract multi-patch features so as to obtain features with more rotation invariance. Finally, instead of directly using softmax, they use a one-vs.-all support vector machine (SVM) with RBF kernel to recognize insulator status. With the emergence of new network architectures in image classification and object detection tasks, more methods have been proposed for insulator inspection. [PERSON] et al. [61] proposed an end-to-end real-time method of self-blast glass insulator location based on faster R-CNN and U-net [68]. As an accurate and fast object detection approach, faster R-CNN is employed to localize the insulator string. After object detection, the detected insulator sting is cropped from the raw image. By this means, the low signal-to-noise ratio (SNR) of the original image is enhanced. Next, the segmented images are subjected to pixel binary classification through U-net so as to identify the broken parts. Adapted from the faster R-CNN, the mask R-CNN further develops its architecture and can realize object instance segmentation on the pixel level. [PERSON] et al. [69] proposed a mask region convolutional neural network based on mask R-CNN to detect insulator self-shattering. This approach can locate fault insulators while simultaneously distinguishing between normal and self-blast insulators. In comparison with [PERSON]'s method, a mask region convolutional neural network can achieve better performance. The performance of deep learning methods depends crucially on the scale of the dataset. To tackle the scarcity problem of defect images in a real inspection environment, [PERSON] et al. [70] introduced a four-operation data augmentation method, which consists of affine transformation, insulator segmentation and background fusion, Gaussian blur, and brightness transformation. A cascaded CNN architecture is proposed to detect power line insulator defects. Following the two-level strategy mentioned above, the cascading network uses a CNN based on a region proposal network to locate the insulator position first and then detect the defect position on the insulator.
## 3 Offense and Defense Game Model
### Offensive-Defensive Strategies  
According to the model established in this paper, offensive and defensive strategies are formulated, as shown in the following four points.  
1. Both nodes and edges can be attacked in this model. The costs of attacking and defending each node and edge are the same.
2. The game is a complete information static game. Both offenders and defenders have full information about the network topology graph.
3. The game is played for one round and there is only one player in each role of the game, and both players act simultaneously.
4. In each round of the offense and defense game, each node and edge can only be attacked once. When an offense on a node is successful, both the node and its connected edges are deleted. When an offense on an edge is successful, only that edge is deleted. \(Q=N_{e}+N_{v}\) is the total number of offensive and defensive resources. \(Q_{A}\) represents the total number of resources that the offender can use for offense. \(Q_{D}\) represents the total number of resources that the defender can use for defense. \(Q_{A}\) and \(Q_{D}\) are less than or equal to \(Q\).  
\(S_{A}\) and \(S_{D}\) represent the set of strategies for offense and defense, respectively. \(|S_{A}|\) and \(|S_{D}|\) represent the respective number of strategies that are calculated by permuting \(C_{Q}^{Q_{A}}\) and \(C_{Q}^{Q_{D}}\). The calculation is shown in Equation (16).  
\[\left\{\begin{array}{l}M=|S_{A}|=C_{Q}^{Q_{A}}\\ N=|S_{D}|=C_{Q}^{Q_{D}}\end{array}\right. \tag{16}\]  
Define one of the offensive strategies as \(s_{a}=[s_{a1},s_{a2},\ldots,s_{aQ_{A}}]\), and one of the defensive strategies as \(s_{d}=[s_{d1},s_{d2},\ldots,s_{dQ_{D}}]\). Assuming that node \(V_{i}\) is attacked, then \(s_{ai}=1\); otherwise, \(s_{ai}=0\). Similarly, if node \(V_{i}\) is defended, \(s_{ai}=1\); otherwise, \(s_{di}=0\). For this, \(AV\) is a set to represent the state of each point being attacked. As shown in Equation (17), \(AV_{i}=1\) represents that the \(i\)-th node is successfully attacked; otherwise, \(AV_{i}=0\).  
\[\left\{\begin{array}{l}A_{V_{i}}=1,\ \ s_{ai}=1\&s_{ai}=1\&s_{di}=0\\ A_{V_{i}}=0,\ \ \ (s_{ai}=1\&s_{di}=1)\ \|\ s_{ai}=0\end{array}\right. \tag{17}\]  
Whether the edge is successfully attacked is also the same as the above method of the node. When the game is complete, the network topology diagram at this point is defined as \(G^{{}^{\prime}}\), and the maximum connectivity of this network topology diagram at this time is \(G^{{}^{\prime}}_{max}\).
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 2 UAVs Overview
### UAV Networks and Communications  
As it has been made clear previously, there are many types and uses for UAVs. This can be attributed to the components present and many vital systems, their communication and networking with other UAVs, navigation capabilities, and the software interactions in between these systems.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 3 SINR Coverage Probability and Area Spectral Efficiency
### SINR Coverage Probability
#### 3.1.1 \(\zeta=\frac{2}{1+p}\)  
Where \(p\) Is a Positive Integer  
The derived expression of NLOS coverage probability for the special case of \(\zeta=\frac{2}{1+p}\) is given below:  
\[P_{c,NLOS}(T)=2\pi\lambda\int_{0}^{\infty}r\exp\left(\sum\limits_{q=0}^{p+1} \lambda\alpha_{q}\left(Ta_{\mathbb{P}_{k}}\right)r^{\frac{2q}{p+1}}\right)dr \tag{12}\]  
where,  
\[\alpha_{q}\left(Ta_{\mathbb{P}_{k}}\right)=\begin{cases}\sum\limits_{k=1}^{4} \frac{\pi(p+1)!}{q!^{kp-q+1}}b_{k}Li_{(p-q+1)}\big{(}-Ta_{\mathbb{P}_{k}}\big{)} &0\leq q\leq p\\ -\pi&q=p+1\end{cases} \tag{13}\]  
\(L_{i}(k)\) denotes the \(k\)th order polylogarithmic function given by:  
\[L_{i(p-q+1)}\big{(}-Ta_{\mathbb{P}_{k}}\big{)}=-Ta_{\mathbb{P}_{k}}\int_{0}^{ \infty}\ \left(\frac{-1}{e^{c}+Ta_{\mathbb{P}_{k}}}\right)(c)^{p-q}dc \tag{14}\]  
Proof.: The proof is given in Appendix A.2.
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
## 2 Test Specim and Methods
### Generation of Head Checks in a 1:1 Wheel-Rail Test Setup
Figure 2: Images of the Rayleigh wave (R-wave) transducers: manual attachment of the Rayleigh wave transducer (**a**), design of a holder that fixes the transducer with constant pressure to the rail (**b**). With the transducer holder shown in Figure 2b, the sending and the receiving transducers were placed on the rail as sketched in Figure 3. The sending transducer remained at the left bottom of the rail head (position HBL), whereas the receiving transducer was placed at different positions along the desired wave path (HL = head left, HR = head right, HBR = head bottom right, WR = web right). Note that in this sketch the corner cracks (head checks, see Figure 1) are on the left gauge corner of the rail. Consequently, the surface waves recorded at positions HR, HBR and WR correspond to surface waves that interacted with the head checks. Additionally, the amplitude of the surface wave at the left rail head (HL) before the damage location was measured and used to normalize the data at all sensing locations. The corresponding measurement procedure for a specific damage state took several minutes for each measurement. The main time factor was the careful placement of the comb transducers for sending and receiving. For a future setup closer to the desired in-track wayside condition monitoring, smaller SAW transducers will be fixed on positions where they are not endangered by the passing wheels (see Concluding Remarks). The data was cleaned from unwanted noise by applying a narrow band pass filter with a center frequency of 1 MHz and a width of 0.2 MHz. For each sensing location the peak of the R-wave in the expected time window of wave arrival was extracted. The signal attenuation was computed with reference to the surface wave amplitude at the specific sensor locations (HR, HBR, WR) measured on the undamaged rail specimen.
## 2 Conventional Uplink NOMA
### Channel Estimation Errors
The SINR values of the two real signals in a QPSK-modulated symbol with the channel estimation phase error \(\Delta\theta_{m}\) using the conventional scheme, denoted as \(\gamma_{conventional,1}(\Delta\theta_{m})\) and \(\gamma_{conventional,2}(\Delta\theta_{m})\), can be written as:  
\[\gamma_{conventional,1}(\Delta\theta_{m})=\frac{L^{2}P_{m}}{\rho_{L}\sum\limits_{ k=1,k\
eq m}^{K}P_{k}+L\sigma^{2}}2\cos^{2}(\pi/4+\Delta\theta_{m}) \tag{14}\]  
and:  
\[\gamma_{conventional,2}(\Delta\theta_{m})=\frac{L^{2}P_{m}}{\rho_{L}\sum\limits_{ k=1,k\
eq m}^{K}P_{k}+L\sigma^{2}}2\sin^{2}(\pi/4+\Delta\theta_{m}). \tag{15}\]  
Hence, the average BER before channel decoding using the conventional scheme, denoted as \(p_{conventional}(\Delta\theta_{m})\), can be expressed as follows:  
\[p_{conventional}(\Delta\theta_{m}) \tag{16}\] \[=\tfrac{1}{2}Q\Big{(}\sqrt{2\gamma_{\max}}\cos\big{(}\tfrac{\pi}{ 4}+\Delta\theta_{m}\big{)}\big{)}\ +\tfrac{1}{2}Q\big{(}\sqrt{2\gamma_{\max}}\sin\big{(}\tfrac{\pi}{4}+\Delta \theta_{m}\big{)}\big{)}\]  
when \(\pi/4\leq|\Delta\theta_{m}|<\pi/2\), one of the two signals is inverted and Equation (16) still holds. Therefore, Equation (16) can be used for \(|\Delta\theta_{m}|<\pi/2\).
## 5 Experiments and Results
### Surrogate Training  
For training the proposed predictors, we calculated the aforementioned training dataset. We propose the Mean Square Error (MSE) as a metric to evaluate the predictors' accuracy. It is calculated as shown in Equation (5), where \(n\) is the number of data points, \(Y_{i}\) are the observed values (from ARGoS), and \(\widehat{Y_{i}}\) are the estimated values (from predictors).  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(Y_{i}-\widehat{Y_{i}})^{2} \tag{5}\]  
Table 2 shows the results of the training of the six predictors using surrogate models. The number of observations is lower when the number of UAVs is higher, as collisions are more likely to happen, increasing the number of invalid parameter values. GP using neural networks as the kernel function (\(gp\_nn\)) showed the most accurate results in terms of the MSE. ANN as a predictor showed an accuracy comparable with the rest of the GPs, although they were not the most competitive results. Note that the GP using a linear kernel did not converge for swarms of five UAVs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**\# UAVs** & **\# Scenarios** & **Dimensions** & **D\({}_{\text{CENTRE}}\)** \\ \hline
5 & 100 & 30 \(\times\) 30 \(\times\) 30 & 3 m \\
10 & 100 & 30 \(\times\) 30 \(\times\) 30 & 3 m \\
15 & 100 & 30 \(\times\) 30 \(\times\) 30 & 4 m \\
30 & 100 & 30 \(\times\) 30 \(\times\) 30 & 5 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the five proposed case studies.  
Table 3 shows the elapsed training times for each predictor. It can be seen that GP models are quite fast compared to the ANN (61.3 times faster on average). All models exhibit that their training speed depends on the size of the training dataset, as expected.  
Taking into account the training times plus their accuracy during the training stage, GP models look promising as surrogate models for the simulations of UAV swarm formations. In the next section, we test all the calculated predictors on a number of unseen scenarios and configurations to address their accuracy beyond the training dataset.
## 4 Materials and Methods
### Formatting of Mathematical Components  
LLMs represent a major step beyond earlier statistical language models, which formed the basis of early computational linguistics [38]. The emergence of transformers, along with extensive training corpora and pre-training techniques, significantly enhanced models' capabilities [39]. These models often contain hundreds of billions of parameters that are optimized during training on vast corpora, enabling sophisticated language generation. As the volume of textual data continues to grow, LLMs have driven major advances across NLP tasks. They have been applied in areas such as code generation, medical text processing, information retrieval, classification, and summarization [40; 41; 42]. LLMs generate output by capturing contextual dependencies through multi-layer transformer architectures. User input is first tokenized, then embedded into vector form, and passed through stacked transformer blocks. The final layer produces task-specific predictions (as shown in Figure 5).  
Models such as XLNet, RoBERTa, DistilBERT, ELECTRA, ALBERT, and Pegasus are basically based on the transformer architecture presented in Figure 5[44; 45; 46]. XLNet employs permutation-based training over the transformer architecture, improving its ability to capture word dependencies in longer phrases [47; 48]. RoBERTa provides performance gains by a longer and optimized training process based on BERT architecture but with more data [49; 50]. DistilBERT is faster and uses less resources with its compact structure. It achieves this by reducing the model size while preserving performance [51; 52; 53; 54]. ELECTRA differs from BERT by using a replaced token detection task, allowing for more efficient pre-training. With ELECTRA, a high performance can be achieved even with small models [55; 56; 57]. ALBERT employs factorized embeddings and cross-layer parameter sharing, improving the resource efficiency in deep models [58; 59; 60]. Similarly, the Pegasus model developed by Google [4] demonstrates significant performance improvements in text summarization compared to traditional models. Its pre-training strategy is particularly effective in handling long and complex input texts.  
Despite their capabilities, LLMs are often criticized as black-box systems that lack access to explicit structural features, focusing primarily on conceptual content. Furthermore, their high computational demands and long processing times remain a barrier to widespread deployment. To address these limitations, this study introduces a MIS-based framework that extracts salient structural information from sentence graphs, enhancing both the efficiency and summary quality.  
Figure 5: Generalized flow diagram of LLMs. Adapted from [43].
## 8 Vulnerability Assessment of NRF to API Injection Tests
### OAI API Injection Tests  
Starting from the API schema imported through the appropriate plugin **(OpenAPI **Parser**), an automatic scan was initiated for each service.The requests sent through Burp Suite are reported in Table 4.  
The testing of each API did not reveal any significant security vulnerabilities, and there were no instances of data breaches or disruptions. However, the tests did uncover some noteworthy points:  
* PUT-NF Instance ID (Document): Not all responses adhere to the standard JSON output format. Errors such as \"request method does not exist,\" Java Errors, and header  
Figure 4: Imported schema for NRF NF management.  
errors were observed. In some cases, these errors provided overly informative details, including information about the library and programming language used, which could be valuable to potential attackers.
* OPTIONS-NF Instances (Store): This particular API was found to be unimplemented, as every request simply yielded the response \"Do some magic\".
* GET-NF Instances (Store): Requests containing SQL commands within the parameters were not rejected but instead accepted and ignored. Ideally, any requests that deviate from the specifications outlined in the schema should be rejected. For instance, if a parameter is declared as an integer but passed as a string, the entire API request should be rejected. Additionally, it was possible to input any value in optional parameters (such as the \"limit\" parameter in \"nf-instances?nf-type&=\"limit=\"), and the API would still return a valid response.  
In summary, the tests highlighted the following relevant issues:  
* **Response Inconsistencies:** Some responses from the API did not conform to the standard JSON output format, revealing errors in request methods and providing excessive information about the implementation, which could be exploited by attackers.
* **Lack of Input Sanitization:** The APIs lacked proper input sanitization, enabling the acceptance of invalid parameters and SQL commands. This could lead to security vulnerabilities like SQL injection.
* **Invalid Method Handling:** Inconsistent handling of HTTP methods was observed. For instance, changing the method from GET to POST in certain APIs caused a crash of the NRF, leading to a potential DoS vulnerability.
* **Unimplemented APIs:** Some API services were not properly implemented and returned generic error messages or \"magic\" responses.