esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 4 Numerical Examples
### Sioux Falls Network  
The Sioux Falls network, a standard testbed in transportation analysis [41], represents the road infrastructure of Sioux Falls, South Dakota, and is employed in our study to further validate the scalability and computational efficiency of our proposed model. We also conduct a sensitivity analysis on the computing time against the scales of a test network. The test network used here is the well-known Sioux Falls network, which refers to the largest city in the state of South Dakota in the USA. The topology of this network is given in Figure 7.  
We achieve this by partially blocking the OD pairs and links, from 10% to 90%, of the Eastern Massachusetts network. Figure 7(a) illustrates an example in which 50% of the OD pairs and links are blocked in the network. One should note that the blocking of the network is not a random operation. The blocked OD pairs and links are carefully chosen because the connectivity of the network must be guaranteed. The larger the partition is, the larger computational resources needed. Figure 7(b) shows the computing time with respect to the scales of the tested network.  
Figure 6: Computing hours against the number of maintenance road segments in the Eastern Massachusetts network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Maintenance Road Segments** & **Computing Hours** & **Computing Feasibility** \\  & & **in 24 h** \\ \hline
1 & 0.02 & Yes \\
2 & 0.27 & Yes \\
3 & 1.24 & Yes \\
4 & 3.67 & Yes \\
5 & 8.49 & Yes \\
6 & 16.86 & Yes \\
7 & 30.10 & No \\
8 & 49.73 & No \\
9 & 77.44 & No \\
10 & 115.09 & No \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sensitivity analysis results using the Eastern Massachusetts network.  
The above results highlight the importance of considering travel demand fluctuations and the scalability of the model for different network sizes.
# 5G Cross-Border Operation for Connected and Automated Mobility: Challenges and Solutions
## 4 5 GCroCo Solutions
### HD Mapping  
MEC application servers can be used as caches to reduce the download time between the map content provider and the vehicle. The MEC application servers can cache map tiles requested earlier that were downloaded from the map content provider backend in the public cloud. When another vehicle requests the same map tile, the MEC already has it cached and serves the request. This will reduce the data traffic between the MNO backend and the map content provider backend, since data do not have to be transmitted all the way from the public Internet to the vehicle. It is also possible to distribute map tiles from the map content provider backend to the MEC beforehand, given that the MEC is only serving map tiles for a specific area, but it must be ensured that updates are consistent in the MEC and map content provider backend.  
Map changes detected and uploaded by a car will be fused at a geographically close MEC host and then uploaded to the map content provider backend. The most urgent intended receivers for these changes are vehicles close by, and they are likely connected to the same MEC host that received the update. This also allows to quickly verify a local HD map content update, where multiple vehicles update a detected change to the MEC Application Server. The server optionally fuses the information from these different vehicles to more precisely define what has changed.  
The QoS prediction mechanism, as discussed above in Section 4.1 and shown in Figure 4, can be used to enable planning ahead for the best time or place to download a larger part of the HD map, e.g., according to cell utilization in certain areas.  
Figure 4: General principle of network quality of service (QoS) prediction.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 4 Concurrent Wake-Up Protocol Design  
This section presents two prototype network protocols that we developed using concurrent wake-up messages based on beat frequencies. The first subsection introduces a prototype algorithm that can be used to integrate concurrent wake-up messages into existing network protocols, in order to increase their reliability or to decrease the required sending power. The second subsection introduces a prototype wake-up flooding mechanism based on concurrent wake-up messages to achieve a quick and reliable waking up of several sensor nodes.  
Throughout this section, we use the abbreviation WUC for a wake-up call and CWUC for a concurrent wake-up call generated by two senders by exploiting the beat frequency.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 5 Multi-Identity Full Homomorphic Encryption Scheme
### The Decomposition Method
Moreover, the MIBFHE scheme constructed using the decomposition method can directly generate extended ciphertexts that are executable for homomorphic operations without the need to generate new ciphertexts, making the scheme more concise and reducing the operations that users need to perform. We will better explain the decomposition method through the following example. Consider the case of two users, where user \(i=\{1,2\}\). The ciphertext\(\mathbb{Z}_{q}^{n\times m}\) is the fresh ciphertext. Let \(\hat{\mathbf{sk}}_{1,2}=(\mathbf{sk}_{1},\mathbf{sk}_{2})\) be the concatenation of the two keys. Using the decomposition method, the extended ciphertext is constructed in the following two steps:  
1. Construct \(\mathbf{X}_{i}\), such that \(\mathbf{X}_{i}\) satisfies \(\hat{\mathbf{sk}}_{1,2}\cdot\mathbf{X}_{i}=\hat{\mathbf{e}}_{1,2}\in\mathbb{Z }_{Q}^{2m}\);
2. Construct \(\mathbf{Y}_{i}\), such that \(\mathbf{Y}_{i}=msg_{i}\mathbf{F}_{2n}\) and satisfies \(\hat{\mathbf{sk}}_{1,2}\cdot\mathbf{Y}_{i}=msg_{i}\hat{\mathbf{sk}}_{1,2} \mathbf{F}_{2n}\in\mathbb{Z}_{Q}^{2m}\). Compared to traditional ciphertext extension methods, the new ciphertext produced by the decomposition method is no longer just a part of the extended ciphertext. The newly generated ciphertext can directly perform homomorphic operations. The overall scheme does not require generating new ciphertexts, allowing users to execute fewer operations, making the scheme more concise.
## 6 Study Limitations and Conclusions  
To better estimate the nature of lung nodules, a radiologist must identify features that are somehow related to cancer suspiciousness, such as volume, shape, subtlety, solidity, spiculation, and sphericity, among others, which is not always easy. Computer assistance using aided diagnostics can be of real help. Modern methods include advanced machine learning techniques to identify the nature of the nodules, but these systems are dependent on many parameters that have to be manually introduced, limiting the reproducibility of the results [45]. To eliminate these inconveniences, deep learning techniques that use neural engines for machine learning were imagined. They have the advantage of performing end-to-end detection of the most subtle changes during the training process. By having a variable training set, the system can learn invariant features from malignant nodules to obtain better results. Since no features are manually introduced, the software is able to learn, on its own, the correlation between CT findings and cancer using the provided ground truth [3]. Once training is finished, the software is expected to be able to generalize its learning and detect malignant nodules (or patient-level cancer) in new cases that have never been seen before by the system.  
For our study, we chose to create a training database of 150 CT scans of patients who have already been examined or operated on and have a positive cancer diagnosis, with TNM staging ranging from 1A to 3A. Our data has proven to be sufficient to obtain good results for the system training. Our training database is relatively similar to the SPIE-AAPM-NCI LungX database, containing datasets from 60 thoracic CT scans [46], or the ANODE09, an automated nodule detection that contained 55 patients [47].  
Our training series was limited by the fact that we used data from a single thoracic surgery unit. We opted for 150 scans training dataset that was designed for our purpose even if there are also bigger training datasets, such as The Danish Lung Cancer Screening Trial, containing 823 scans [48], or The National Lung Screening Trial (NLST), containing more than 54,000 participants [49]. The number of scans in the datasets has not yet proven to be an insurmountable prerequisite for good results since the literature provides studieswith better performance on fewer scans. [PERSON] and colleagues proposed the HUNT model for lung cancer prediction in the Danish screening trial and had better results than the NLST or the NELSON study criteria [50]. Also, the presented models could be further enhanced with image or signal processing techniques like [51; 52], or other deep learning predictive models see [53].
# Soliton Content of Fiber-Optic Light Pulses
## 4 Methods to Verify Soliton Content
### The Inverse and Direct Scattering Transform
#### 4.1.1 The Method
[69], one finds that solving the dimensionless NLSE:  
\[\frac{\partial q}{\partial z}=\frac{i}{2}\frac{\partial^{2}q}{\partial t^{2}}+ i|q|^{2}q \tag{19}\]  
is equivalent to the integration of the system of equations:  
\[\frac{\partial\Phi}{\partial t}+T\Phi=0\quad, \tag{20}\]  
\[\frac{\partial\Phi}{\partial z}+Z\Phi=0\quad. \tag{21}\]  
\(T\) and \(Z\) are operators (matrices) that determine the temporal and spatial evolution of some auxiliary function:  
\[\Phi=\left( \begin{array}{c}\phi_{1}\\ \phi_{2}\end{array} \right) \tag{22}\]  
where \(\phi_{1}\) and \(\phi_{2}\) are called Jost functions. Explicitly, the operator \(T\) can be written as:  
\[T=\left( \begin{array}{cc}-i\lambda&q\\ -q^{*}&i\lambda\end{array} \right)\quad. \tag{23}\]  
Calculation of the DST can be done by integrating Equation (20) with respect to time with different test values of the eigenvalue candidate \(\lambda=\zeta\). The initial condition can be chosen as:  
\[\Phi(z_{0},t,\zeta)=\left( \begin{array}{c}1\\ 0\end{array} \right)\exp(-i\zeta t)\quad\text{with}\quad t\rightarrow-\infty\quad, \tag{24}\]  
when \(q\) is localized in the sense that for \(t\rightarrow\pm\infty\), \(q\to 0\) at an exponential rate [65; 66]. As a result, one obtains the scattering coefficients \(a(\zeta)\) and \(b(\zeta)\) from \(\Phi(z_{0},t,\zeta)\):  
\[a(z_{0},\zeta) =\phi_{1}(z_{0},t,\zeta)\exp(i\zeta t)\quad\text{with}\quad t \rightarrow\infty\quad, \tag{25}\] \[b(z_{0},\zeta) =\phi_{2}(z_{0},t,\zeta)\exp(-i\zeta t)\quad\text{with}\quad t \rightarrow\infty\quad. \tag{26}\]  
Additionally, the derivatives of \(a\) and \(b\) with respect to \(\zeta\) can also be calculated by extending the integration scheme to include \(\Phi^{\prime}=\partial\Phi/\partial\zeta\). In the scattering spectrum for anomalous dispersion, solitons are present in the form of discrete eigenvalues \(\zeta_{k}\) with \(a(\zeta_{k})=0\) and \(\text{Im}(\zeta_{k})>0\). \(\text{Re}(\zeta_{k})\) represents the frequency, and \(2\,\text{Im}(\zeta_{k})\) corresponds to the amplitude of the soliton; both values are preserved during propagation. The continuous part of the spectrum (radiation) can be calculated from the values of \(a(\zeta)\), with \(\text{Re}(\zeta)=0\). The spectral power density \(|\mathcal{F}|^{2}\) is then obtained as:  
\[|\mathcal{F}|^{2}=-\frac{1}{\pi}\ln(|a(\zeta,z)|)\,. \tag{27}\]
## 3 Results
### Bioactivity in Diatom Biomass with and without Fume  
Bioactivity results reported here represent two cultivation campaigns using two strains of the same species. The cultivations are also differing in the cultivation technique. The first campaign used 600 L PBR's while the latter referred to as \"G300\" herein used a 300,000 L PBR (both PBR's are vertical bubble column type PBR's). These results are the summary and comparison of bioactivity efforts and results. Some results are previously presented, e.g., G300 in [PERSON] et al. (2014) and on 600 L cultivations at different cultivation conditions in [PERSON] et al. (2017). In addition, previously unpublished results are included to further elaborate the bioactivity profile.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**pg TE/g ww** & \begin{tabular}{c} **With Fume** \\ **UB IncI LOD** \\ \end{tabular} & \begin{tabular}{c} **With Fume** \\ **LB Excl LOD** \\ \end{tabular} & \
\begin{tabular}{c} **Without Fume** \\ **UB IncI LOD** \\ \end{tabular} &
\begin{tabular}{c} **Without Fume** \\ **LB Excl LOD** \\ \end{tabular} \\ \hline Sum & & & & \\ PCDD/PCDF * & 0.049 & 0.025 & 0.036 & 0.013 \\ Sum no-PCB ** & 0.007 & 0.007 & 0.003 & 0.003 \\ Sum no-PCB *** & 0.0002 & 0.0001 & 8.9 \(\times\) 10\({}^{-5}\) & 5.3 \(\times\) 10\({}^{-5}\) \\ Total sum TE & 0.057 & 0.033 & 0.040 & 0.016 \\ \hline \end{tabular}
*Sum of PCDD/PCDF includes 2378-TCDD, 12378-PeCDD, 123478-HxCDD, 123678-HxCDD, 123789-HxCDD, 1234678-HxCD, 1234678-HxCDF, 1234678-HxCDF, 1234678-HxCDF, 1234678-HxCDF, 123478-HxCDF, 123478-HxCDF, 123478-HxCDF, 1234789-HxPDC, OCDF: */*** Sum DL-PCB includes PCB-77, -81, -126, -169, -105, -141, -118, -123, -156, -157, -167 and -189.  
\end{table}
Table 11: Concentrations (ng g\({}^{-1}\) ww) of ICES-6 PCBs, various pesticides and metals (ng g\({}^{-1}\) dw) in the diatom fermented with and without fume (n = 2). Data for compounds with no detection in any samples are omitted from the table.  
\begin{table}
\begin{tabular}{c c c} \hline
**ng/g ww** & **With Fume** & **Without Fume** \\ \hline Sum ICES-6 PCB * & 0.07 & 0.02 \\ PeCB & 0.012 & 0.047 \\ HCB & 0.026 & 0.041 \\ \(\alpha\)-HCH & 0.005 & 0.006 \\ \(\beta\)-HCH & 0.003 & 0.002 \\ \(\gamma\)-HCH & 0.010 & 0.007 \\ op\({}^{\prime}\)-DDD & 0.001 & \textless{}0.001 \\ pp-DDD & 0.003 & \textless{}0.001 \\ PFUnDA & 0.008 & 0.016 \\ \hline
**mg/kg dw** & **With Fume** & **Without Fume** \\ \hline Mercury (Hg) & 0.005 & 0.002 \\ Lead (Pb) & 1.32 & 0.16 \\ Cadmium (Cd) & 0.02 & 0.01 \\ Arsenic (As) & 2.58 & 1.72 \\ Selenium (Se) & 0.67 & 0.27 \\ \hline \end{tabular}
*Sum ICES—PCB6 includes PCB-28, -52, -101, -138, -153 and -180.  
\end{table}
Table 10: Concentrations (pg TE/g ww) for dioxins and furans (PCDD/PCDF), non-ortho PCBs (no-PCBs) and mono-ortho PCBs (mo-PCBs) and sum of all three groups (Total sum TE), fermented with and without fume (n = 2). For upper bound (UB), <LOD concentrations are included; and <LOD concentrations are set to zero in lower bound (LB).
## 3 Results
### Field Trials
#### 3.2.2 Visual Inspection of Soil Surfaces  
Figure 7 illustrates the typical surface characteristics of eroded soils as observed during the field trials. Surfaces of the control group (i.e., water-treated and untreated mine soil) and the treated soil samples changed differently over time. Soil surfaces prepared with food processing by-products showed minor indications of wind erosion (i.e., disturbance of soil surfaces, removal of smaller soil particles) after 14 days and 21 days, respectively, without any significant changes in horizontal soil thickness (Figure 7b). By contrast, plots with water-treated and untreated soil formed a conical-shaped depression that expanded slightly in depth over time (Figure 7a). The highest soil losses were observed at the beginning of the study. Almost all test plots indicated wildlife tracks that can be attributed primarily to birds. One of the water-treated plots had to be removed from the field trial after seven days because it was extensively damaged. On day 28, all specimens appeared to have a high moisture content, and their surfaces showed visible traces of raindrops and thus were exposed to obvious rainfall events.  
Figure 7: Photographic images of soil surfaces observed on day 3, 7, 14, 21, and 28 after the dust suppressants were applied: (**a**) untreated mine soil, and (**b**) mine soil treated with palatinose molasses. The size of each plot is 0.7 m \(\times\) 0.4 m.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### Sensor Fusion  
Real-time and accurate knowledge of vehicle position, state and other vehicle parameters such as weight, stability, velocity, etc. are important for vehicle handling and safety and, thus, need to be acquired by the AVs using various sensors [88]. The process of sensor fusion is used to obtain coherent information by combining the data obtained from different sensors [15]. The process allows the synthesis action of raw data obtained from complimentary sources [89; 90]. Therefore, sensor fusion allows the AV to precisely understand its surrounding by combining all the beneficial information obtained from different sensors [91]. The fusion process in AVs is carried out by using different types of algorithms such as Kalman filters and Bayesian filters. The Kalman filter is considered very important for a vehicle to drive independently because it is utilised in different applications such as RADAR tracking, satellite navigation system and visual odometry [92].
## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### Validation and Reproducibility  
In the study, the alignment of modeling results with expert assessments is achieved through a structured validation process:  
* Validation Against Expert Judgments: The calculated results must be systematically compared with evaluations provided by domain experts, leveraging their extensive experience in asset health assessment and management. This comparative process involves identifying and addressing discrepancies to refine the model.
* Accuracy Confirmation: The framework integrates real-time IoT monitoring data with advanced decision-making tools like the Asset Health Index (AHI). This integration will ensure that the predictions are continuously updated based on objective, real-world data, which enhances the reliability of the outputs.
* The data sources and their preprocessing steps.
- The bibliographic sources including algorithms used to calculate the AHI.
- Suggesting specific steps in comparing model results with expert assessments.
# Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review
## 2 Existing Breast Cancer Detection Techniques & Technologies
### Body Image-Based Technology
#### 2.1.2 Mammography  
Mammogram, also called mastography, is a low-dose energy X-ray (ionizing radiation) procedure to produce images (radiographs) of the breast. It can be used to screen or diagnose people who are symptomatic (have symptoms of illness) or asymptomatic (have no symptoms of illness) [39]. Ordinary radiation dose around 0.4 millisieverts (mSv) or 30 peak kilovoltage (kVp) for two views of each breast [40,41]. 2D mammograms only compress the breast and catch images from the front and side. 3D mammography (or called tomosynthesis) produces X-ray images of the breast by taking various views across the breast in an arc. Previous studies reported that the detection is significantly improved when 3D mammography was used with 2D mammography [42]. A mammography expert, often known as a mammographer, performs this process. A mammographer is well-trained to take X-ray images of the breast. The radiologist specializing in reading medical imaging tests will review the mammogram to search for unusual configurations that look different from normal tissues. It can be representing a cancerous tumor, non-cancerous masses or cysts. Breasts are compressed between two firm surfaces to separate overlapping breast tissue and to reduce the thickness of the breast during the procedure. Then an X-ray produces black-and-white picture of breasts that are appear on a computer screen and examined by a radiologist that looks for signs of cancer, as shown in Figures 5 and 6. A mammogram is not suitable for women with denser breast and more likely to have false alarms due to the overlap of normal fibro-glandular tissues [43, 44, 45].  
Figure 4: Samples of Ultrasound breast image [38].  
Figure 5: Samples of Mammogram breast image [46].
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 7 Simulation Results
### Simulation Results of Code Length and Starting Bits  
We used MATLAB to generate 50,000 bit random 0 and 1 sequences, which were encoded by \((15,11)\) BCH code, a 1% error code was added, and the first 7 bits of data were deleted. The code length is first identified in the following section, and the result is shown in Figure 7. Only when the number of matrix columns is 15, 30, 45, 60 and other integer multiples of 15, can the normalized value \(\epsilon\) reach the maximum value. When the code length is 15, the normalized value is 0.1022, which is the first maximum value, and the other length values are all 0. Then, symmetrically, a maximum occurs every 15 code words. Therefore, the corresponding code length can be estimated to be 15.  
Figure 7: The code length recognition of \((15,11)\) BCH code.  
The next step is to recognize the starting point of the code word; \(n_{0}=15\) is known and we calculate the corresponding 15 kinds of shift of \(\varepsilon_{n_{0}d}\), as shown in Figure 8. This can be found when the code word is affected by the error condition, and originally it should only be in a state of 8 shift to the maximum, but in Figure 8, the \(\varepsilon_{n_{0}d}\) is entitled.  
Therefore, a total of 5 shift states from 7 to 13 were selected, and the average number of Q = 30 was taken to calculate \(\varepsilon_{n_{0}d}\) in each state again, as shown in Figure 9. It can be seen from the observation that, only when the shift state is 8 is the corresponding \(\varepsilon_{n_{0}d}\) value the largest, so this is the starting point of the code word, which is consistent with the actual simulation conditions, and the algorithm is valid.
## 3 Method
### Evaluation Matrix  
To evaluate the performance of our model, the benchmark dataset is evaluated using the _Overall Accuracy_ (OA) and the _mean Intersection over Union_ (mIoU) score. OA is the percentage of points that were correctly classified and total number of points, ignoring incorrect classifications. In the semantics segmentation task, _Intersection over Union_ (IoU) is the ratio of the intersection of the pointwise classification results with the ground truth to their union, and mIoU is the class-averaged IoU. Specifically, IoU (Equation (5)) and mIoU (Equation (6)) can be computed as:  
\[\text{IoU}=\frac{TP_{c}}{TP_{c}+FP_{c}+FN_{c}} \tag{5}\]  
\[\text{mIoU}=\frac{1}{C}\ \sum_{c=1}^{c}\frac{TP_{c}}{TP_{c}+FP_{c}+FN_{c}} \tag{6}\]  
where \(TP_{c}\) is the number of true positives for class \(c\), \(FP_{c}\) is the number of false positives for class \(c\), \(FN_{c}\) is the number of false negatives belonging to that class, and \(C\) is the number of classes. Meanwhile, mIoU computes the class-averaged ratio of true positives to the sum of false positives (false alarms), false negatives (misses), and true positives.  
As we can see, IoU and mIoU measures are more informative compared to OA because they take false alarms (incorrect classified points in point clouds) into consideration, whereas OA does not. Therefore, we use both OA and mIoU as evaluation criteria for segmentation results.