esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 3 RPL-Cga
### Optimizing Weighting Factors of Routing Metrics
\(h^{\prime}\) is the newly-generated population individuals after adding chaotic disturbance. \(\alpha\in[0,1]\), the adjustment factor, is determined by Equation (15); the corresponding graph is shown in Figure 2.  
\[\alpha=\left\{ \begin{array}{rl}\exp[-\frac{k^{2}}{2\alpha}],&2\leq k\leq 40 \\ \frac{(2k-1)}{k^{2}},&k>40\end{array} \right. \tag{15}\]  
In Equation (15), \(k\) is the number of iteration and \(\delta=20\). Through Equation (15), \(h\) changes greatly in the beginning search phase, which needs a bigger \(\alpha\) value. With the search going on, \(h\) approaches the optimum solution gradually; so, for the sake of searching within a diminutive area close to the optimum solution, a smaller \(\alpha\) is required. Step 7: judging termination conditions  
RPL-CGA stops operating if it meets Equation (16) or the iteration number reaches 100. Otherwise, it goes to step 4. \[\left|\overline{Fitness(a)}-\overline{Fitness(a)}_{\max}\right|\leq\varepsilon \tag{16}\]  
In Equation (16), \(Fitness(a)_{\max}\) and \(\overline{Fitness(a)}\) indicate the maximum value of fitness function and the average value of all the fitness functions respectively. \(\varepsilon=10^{-5}\)(a small positive number). Step 8: preferred parents selection  
After getting the optimization solution \(\mathbf{h_{sj}}=(h_{s1},h_{s2},\cdot\cdot\cdot,h_{sm})\) through the abovementioned chaotic genetic algorithm, RPL-CGA lets \(\mathbf{a}=(a_{1},\ a_{2},\cdot\cdot\cdot,a_{m})=\mathbf{h_{sj}}\), brings \(\mathbf{a}\) into Equation (11), and gets the fitness function values of each candidate parent. Then, the candidate parent with the maximum fitness function values will be chosen as the preferred parent, i.e., it brings \(\mathbf{a}\) into Equation (9) and gets the comprehensive evaluation function values \(F(i)\) (\(i=1,2,\cdot\cdot\cdot,n\)) of each candidate parent. Then, the node with the minimum comprehensive evaluation function value among all the candidate parents will be chosen as the preferred parent.
## Materials and Methods
### Spectroradiometric Measurements with the Dimona Aircraft  
On two days (DOY 176 and 183), the research aircraft ECO-Dimona from Metair AG (Menzingen, Switzerland) was flying on straight legs at approximately 250 m above the study site [32]. The flight pattern aimed at aligning most of the legs parallel and perpendicular to the largest extent of the main fields (Figure 1). The crossing of both legs was above the main fields of winter wheat on DOY 176 (Figure 1B) and of sugar beet on DOY 183 (Figure 1A). The aircraft flew over the winter wheat field 46 times, between 11:15 and 15:15 UTC and over the sugar beet field 68 times between 7:30 and 10:30 UTC in the morning and 11:30 to 15:30 UTC in the afternoon.  
A calibrated field spectrometer (FieldSpec Pro, ASD Inc., Boulder, CO, USA) was mounted in one of the under-wing pods of the DIMONA research aircraft. Radiance measurements were captured in Nadir orientation with a 1\({}^{\circ}\) fiber optic over the wavelength range between 350 nm and 1050 nm with a FWHM of 3 nm. The light beam was integrated for 130 ms. Three spectra were averaged to increase SNR. The relatively small field of view in combination with the relatively slow and low flying aircraft resulted in a surface area of 4 m \(\times\) 20 m represented in one averaged spectrum. The instrument was operated in continuous mode, and spectra were collected with approximately 2 Hz. A trigger signal by the FieldSpec was used to record the exact time of each radiance measurement in the central data acquisition system of the aircraft and to capture a video image (640 \(\times\) 480 pixels, 12-bit, gray values) using an industrial video camera (Flea, Point Grey Research, Vancouver, BC, Canada; with a 25 mm Cosmicar/Pentax lens) having a 10.5 degree field of view.  
The spatial position of the radiance measurement was computed from the position and orientation of the aircraft (logged by a TANS vector phase sensitive GPS system blended with a 3-axis accelerometer; [32]), the height above ground, and a digital elevation model. We evaluated the geometric accuracy using numerous characteristic scenes from video images and obtained an actual geometric error of less than 5 m.  
We compensated atmospheric disturbances present in the at-sensor radiance measurements and calculated hemispherical-conical reflectance factors (HCRF) using atmospheric parameters (i.e., surface irradiance, atmospheric transmittance, path scattered radiance, spherical albedo) calculated with the radiative transfer model MODTRAN5 [33]. The obtained atmospheric parameters were spectrally resampled based on the sensor characteristics.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 6 Trusted Execution Environment (TEE) Support and Feasibility
### ARM TrustZone with OP-TEE  
The ARM architecture supports large page mappings via MMU configuration. However, OP-TEE, the commonly used secure-world OS, defaults to 4 KB pages. Configuring huge pages in OP-TEE requires kernel-level modification and system-level support.  
More fundamentally, ARM's prefetch instruction (PRFM) operates as a non-coherent cache hint. It does not trigger inter-core coherence traffic or cause cache state transitions that are visible across cores. As a result, issuing PRFM from one core does not influence the cache state of that memory line in another core's private cache. This behavior contrasts with x86 prefetching instructions like PREFETCHW, which can induce cross-core coherence transitions (e.g., to the modified or owned states), making them observable via timing differences.  
Because ARM prefetching does not generate observable coherence activity, it cannot be leveraged to modulate shared cache line states in a way detectable by a receiver on another core [31]. Therefore, under the standard configurations, ARM TrustZone cannot support our cache-coherence-based covert channel design.
## 3 Results
### Workshop Discussion and Survey
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Medication** & **Lifestyle** & **Mixed Group** \\ \hline
1. Did you learn something new that you had not heard about before? & 1/8: Nothing at all & 0/7: Nothing at all & 0/9: Nothing at all \\  & 1/8: Maybe & 0/7: Maybe & 0/9: Maybe \\  & 2/8: A little & 1/7: A little & 3/9: A little \\  & 3/8: A few things & 5/7: A few things & 6/9: A few things \\  & 1/8: A lot & 1/7: A lot & 0/9: A lot \\ \hline  & 2/8: Diet & & \\  & 3/8: Exercise & 5/7: Diet & \\  & 3/8: Stress & 6/7: Exercise & \\  & management & 0/7: Stress & 6/9: Diet \\  & 1/8: Medications & management & 9/9: Exercise \\
2. Which topics were most interesting to you? & 3/8: Management of symptoms & 0/7: Medications & 2/9: Stress \\  & 3/8: Other (heart disease, how to keep people on track, mental health components of diabetes) & 3/7: Management of \\  & 3/8: Stress & 1/7: Other (changing of mindset, motivation to change behavior) & \\ \hline
3. Did you hear something you would like to try? & 1/8: Yes & 4/7: Yes & 5/9: Yes \\  & 3/8: No & 2/7: No & 2/9: No \\  & 4/8: I don’t know & 1/7: I don’t know & 2/9: I don’t know \\ \hline
4. How do you feel about your condition, following this conversation? & 7/8: I feel better & 6/7: I feel better & 6/9: I feel better \\  & 0/8: I feel worse & 0/7: I feel worse & 1/9: I feel worse \\  & 1/8: I don’t know & 1/7: I don’t know & 2/9: I don’t know \\ \hline
5. Would you recommend this workshop? & 4/8: Yes & 7/7: Yes & 8/9: Yes \\  & 3/8: No & 0/7: No & 0/9: No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the workshop survey obtained from six workshops in the three patient groups. patients interested in stress management was 3 out of 8 in the medication group, 2 out of 9 in the mixed group, and 0 in the lifestyle group. Noticeably, themes related to behavior change and motivation emerged in the lifestyle group only. Regarding question 4, most of the patients reported that they felt better (i.e., 7 out of 8 in the medication group, 6 out of 7 in the lifestyle group, and 6 out of 9 in the mixed group), whereas only 1 patient (from the mixed group) was feeling worse. It is worth noting that the patient who selected 'I feel worse' in question 4 also reported limited learning in questions 1 and 3. In response to question 5, some patients provided additional comments, highlighting further differences between the groups. None of the patients in the mixed and lifestyle groups answered 'No' to the question 'Would you recommend this workshop? ', whereas 3 out of 8 patients in the medication group answered 'No'. Patients from the mixed and lifestyle groups were satisfied with the format and found the session useful and enjoyable, also providing suggestions for improvement (e.g., increasing the number of sessions, expanding the size of the group to add richness and breadth to the discussion). Patients in the medication group who were not happy with the format complained about the session format and recommended a reduction in duration (e.g., to below 1 h).
## 2 Related Work
### Spread Spectrum Challenges in NB-IoT Systems  
It is expected that the ever-expanding number of connected NB-IoT devices will generate a massive volume of data, which has diverse requirements including network lifetime (\(\geq\)10 years [26]), latency (\(<\)4 ms [27]), reliability (SNR \(\geq-\)12.6 dB [28]), and throughput(up to 26 kbps in downlink and 66 kbps in uplink [29]). Some of these devices will be dedicated to real-time IoT applications  
Figure 1: Overlay versus underlay spectrum sharing solutions.  
such as remote surgery etc. These specific devices will, therefore, have very low latency requirements which for a given available spectral efficiency accessible to them at a given instant of time, will require a larger bandwidth. These types of applications are the most likely to lose connectivity in a cell as more and more devices join the network. Despite this challenge, it is important to emphasize that the NB-IoT remains the choice of preference for enhanced network scalability [30]. This is because, unlike the unlicensed IoT technologies (Long Range (LoRa), SigFox etc.), it has the benefit of freely leveraging the cellular spread spectrum technology. When considered against other IoT technologies such as the LTE-M technologies, the NB-IoT is more flexible since it can operate in 2G, 3G, 4G, and 5G bands. This eliminates the need for a gateway, which saves costs in the long run. The competitive advantage of NB-IoT technology over the other IoT technologies in terms of achieving network scalability has been emphasized in the previous paragraph. However, it is important to note that the use of the spread spectrum sharing approach for NB-IoT systems has challenges of its own. A key issue with the use of spread spectrum techniques is how they afford protection against interfering signals with finite (limited) power [31]. As more and more NB-IoT devices and cellular UEs attempt to simultaneously access the uplink NB-IoT channel, the likelihood of their power as distributed across the channel to overlap increases and eventually results in interference. Hence, one key design aspect when it comes to the spread spectrum algorithm lies in the random sequence generation approach. The challenge for the random sequence generation approach is to simultaneously achieve repeatability, to pass all statistical tests of randomness, to achieve insensitivity to the seed values and finally to ensure that the finite period after which the sequence repeats itself is much longer than the amount of random number needed at a given time as the network grows (as the number of connected NB-IoT devices increases). Therefore, the present paper proposes an Adaptive Frequency Hoping Spread Spectrum technique capable dynamically adjusting the random sequence number taking into account the amount of remaining energy of the NB-IoT nodes in the network.
## 3 Optimized Visual Geometry Group Model
### Testing  
During the testing stage, there is a trained CNN and an input image. Testing is classified into three steps. First, the test image is also resized to \(150\times 150\) pixels without any preprocessing. Then, the trained network is applied densely over the resized test image in a manner similar to that reported in the literature [28]. Finally, the class score map is spatially averaged and sum-pooled to obtain a fixed-size vector of class scores for the test image. After these steps, the network can predict the most likely class of a test image. As shown in Figure 4, this model can predict the likelihood of the classification of the input image. Corresponding to the three different types of classes, the prediction results for this image are as follows: The probability of Normal is 0.59%, Level Misalignment is 1.76%, and Porosity is 97.65%. Therefore, the model is confident that the image belongs to Porosity and less confident about the other two classes. Thus, the results of the classification are more reliable. In industrial production processes, operators can increase or decrease the percentage of classification results according to product requirements. For example, the result of this image classification will be adopted only if the probability of a certain category exceeds 90%. Otherwise, this image will be output as an unrecognizable classification, and then the manual inspection procedure will proceed. In fact, the failure to identify a part that has a defect and mistakenly classifying a part as Normal are the most serious issues of concern in factories, and a very low fault positive rate is often required. The lower the rate, the fewer the defect types that are not detected. This testing method provides the possibility of achieving a zero-fault positive rate (equivalent to a false positive rate in machine learning), which is 0.16% in our case.
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 4 Experiment
### Analysis and Comparison of Algorithm Effects

Table 5: Statistical results of IGD values obtained by NSGA-II, MOEA/D, NSGA-III, LMEAplots of the populations obtained by various algorithms on the DTLZ6 test instance. The horizontal axis represents different objective dimensions, while the vertical axis indicates the objective values of individuals in the current objective dimension, effectively reflecting the distribution of the solution set within the population. The figure illustrates that the proposed method outperforms other comparison algorithms in terms of both convergence and diversity, achieving a greater number of solutions on the Pareto front. This confirms the effectiveness of the dual-population cooperative co-evolution framework based on decision variable classification proposed in this paper. Figure 2: Population parallel coordinate map obtained by NSGA-II, MOAE/D, LMEA, NSGA-III, S3 CMAES, FDV, and DVA-TPCEA algorithms on DTLZ6-10 test example. To visually demonstrate the performance differences between the DVA-TPCEA and LMEA algorithms, Figures 3 and 4 present the distribution of the final solutions obtained by the two algorithms on the 10-objective DTLZ1 and WFG4 test problems. From Figure 3, it can be observed that the DVA-TPCEA algorithm is capable of obtaining solutions that are distributed across the entire range of the Pareto front, indicating better convergence and diversity. In contrast, the LMEA algorithm fails to cover the entire Pareto front, exhibiting a relatively weaker performance. Figure 4 further illustrates that the solutions obtained by LMEA are missing in certain areas of the problem boundaries, whereas the DVA-TPCEA algorithm demonstrates good convergence across the entire Pareto front. In summary, the DVA-TPCEA algorithm not only shows advantages in IGD value results but also exhibits better uniformity in the distribution of population individuals, confirming its effectiveness and applicability in multi-objective optimization problems.
## 3 Results and Validation
### Correlation between Landslides and Influencing Factors Based on SWARA
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Influencing** & **Classes** & **No. of Pixels** & **No. of Landslide** & **SWARA Weight** \\ \hline \multirow{8}{*}{**Slope angle (degree)**} & \multirow{2}{*}{**Slope** angle (degree)**} & \multirow{2}{*}{**Classes**} & \multirow{2}{*}{**No. of Pixels**} & \multirow{2}{*}{**No. of Landslide**} & \multirow{2}{*}{**SWARA Weight**} \\ \cline{3-3}  & \(<\)20.0 & 2656308 & 0 & 0.01 \\  & 20.0–24.6 & 460317 & 116 & 0.11 \\  & 24.6–29.2 & 312393 & 95 & 0.14 \\  & 29.2–33.8 & 188594 & 46 & 0.11 \\  & 33.8–38.4 & 101643 & 35 & 0.16 \\  & 38.4–43.0 & 46703 & 16 & 0.16 \\  & 43.0–47.6 & 18080 & 4 & 0.10 \\  & \(>\)47.6 & 7394 & 3 & 0.19 \\ \hline \multirow{8}{*}{**Aspect**} & Flat & 1200 & 0 & 0.00 \\  & North & 504881 & 12 & 0.03 \\  & Northeast & 433690 & 14 & 0.04 \\  & East & 479731 & 40 & 0.13 \\  & Southeast & 482847 & 70 & 0.23 \\  & South & 487601 & 97 & 0.30 \\  & Southwest & 431966 & 45 & 0.16 \\  & West & 480314 & 22 & 0.07 \\  & Northwest & 489202 & 15 & 0.04 \\ \hline \multirow{8}{*}{**Altitude (m)**} & 132–282 & 739706 & 4 & 0.01 \\  & 282–432 & 1504606 & 75 & 0.06 \\  & 432–582 & 867013 & 148 & 0.22 \\  & 582–732 & 442338 & 39 & 0.11 \\  & 732–882 & 174022 & 35 & 0.26 \\  & 882–1032 & 55289 & 14 & 0.33 \\  & \(>\)1032 & 8458 & 0 & 0.00 \\ \hline \multirow{8}{*}{**Plan curvature**} & \(<\)–0.001 & 1791191 & 146 & 0.43 \\  & (\(-\)0.001)–(0.001) & 171530 & 0 & 0.06 \\  & \(>\)0.001 & 1828711 & 169 & 0.51 \\ \hline \multirow{8}{*}{**Profile curvature**} & \(<\)–0.001 & 1835414 & 184 & 0.55 \\  & (\(-\)0.001)–(0.001) & 33142 & 0 & 0.06 \\  & \(>\)0.001 & 1922876 & 131 & 0.39 \\ \hline \multirow{8}{*}{**SPI**} & \multirow{2}{*}{0–50} & 2957565 & 221 & 0.10 \\  & 50–100 & 372535 & 48 & 0.16 \\  & 100–200 & 224014 & 25 & 0.14 \\  & 200–400 & 129200 & 10 & 0.11 \\  & 400–800 & 66033 & 8 & 0.15 \\  & 800–1000 & 12254 & 3 & 0.32 \\  & \(>\)1000 & 29831 & 0 & 0.01 \\ \hline \multirow{8}{*}{**STI**} & \multirow{2}{*}{0–10} & 2366364 & 90 & 0.04 \\  & 10–20 & 797385 & 129 & 0.16 \\  & 20–30 & 300375 & 43 & 0.15 \\  & 30–40 & 129124 & 22 & 0.17 \\  & 40–50 & 67080 & 9 & 0.14 \\  & 50–60 & 39877 & 7 & 0.17 \\  & \(>\)60 & 91227 & 15 & 0.16 \\ \hline \multirow{8}{*}{**TWI**} & \multirow{2}{*}{5–7} & \multirow{2}{*}{1708346} & \multirow{2}{*}{98} & \multirow{2}{*}{0.23} \\  & 3–5 & 1400515 & 200 & 0.61 \\ \cline{1-1}  & 5–7 & 1708346 & 98 & 0.23 \\ \cline{1-1}  & 7–9 & 514871 & 17 & 0.14 \\ \cline{1-1}  & \(>\)9 & 167256 & 0 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spatial relationship between landslides and influencing factors by the step-wise weight assessment ratio analysis (SWARA) algorithm.
# Propagation Property of an Astigmatic sin-Gaussian Beam in a Strongly Nonlocal Nonlinear Media
###### Abstract  
Based on the Snyder and Mitchell model, a closed-form propagation expression of astigmatic sin-Gaussian beams through strongly nonlocal nonlinear media (SNNM) is derived. The evolutions of the intensity distributions and the corresponding wave front dislocations are discussed analytically and numerically. It is generally proved that the light field distribution varies periodically with the propagation distance. Furthermore, it is demonstrated that the astigmatism and edge dislocation nested in the initial sin-Gaussian beams greatly influence the pattern configurations and phase singularities during propagation. In particular, it is found that, when the beam parameters are properly selected, a vortex beam with perfect doughnut-shaped profile can be obtained for astigmatic sin-Gaussian beams with two-lobe pattern propagating in SNNM.  
n pacs: 1
## 3 SAM-Based Approach to Improve Panoramic Image Segmentation in Indoor Construction Environments
### Object Extract
#### 3.1.2 Updating Input Images and Point Labels  
Despite parameter adjustments, our experimental results indicated that iterating SAM on the same input image increased the likelihood of redundant object detections. Since duplicate detections lead to unnecessary computational overhead, updating the input image and prompt labels for each iteration was essential. Following Algorithm 1, the input image was updated by masking the background predicted in the previous iteration (i.e., multiplying the original image by the inverted cumulative foreground mask).  
```
Input: Input image \(\mathbf{I}\) of size \(H\times W\), Cumulative mask \(\mathbf{M}_{\textit{cusum}}\) Output: Next input image \(\mathbf{I}_{\textit{next}}\)
1: Generate object masks: \(M\leftarrow\) SamAutomaticMaskGenerator(\(\mathbf{I}\))
2: Sort masks by stability score: \(M\leftarrow\) Sort(\(M\), stability score)
3: Initialize sum mask \(\mathbf{M}_{\textit{sum}}\leftarrow\mathbf{0}_{H\times W}\)
4:for each mask \(\mathbf{M}_{\textit{seq}}\in M\)do
5: Update current mask: \(\mathbf{M}_{\textit{sum}}\leftarrow\mathbf{M}_{\textit{sum}}\)+ \(\mathbf{M}_{\textit{seq}}\)
6:endfor
7: Update cumulative mask: \(\mathbf{M}_{\textit{cusum}}\leftarrow\mathbf{M}_{\textit{cusum}}\)+ \(\mathbf{M}_{\textit{sum}}\)
8: Update input image by masking detected areas: \(\mathbf{I}_{\textit{next}}\leftarrow\mathbf{I}\times(1-\text{BinaryMask}( \mathbf{M}_{\textit{cusum}}))\)
9:Return\(\mathbf{I}_{\textit{next}}\)
```  
**Algorithm 1**Iterative input image update with cumulative mask  
As the input image was modified, the corresponding prompt labels (point grid labels) also required updating. According to Algorithm 2, points located within the foreground region (i.e., the cumulative foreground mask) were assigned a value of 0, ensuring that objects would not be detected at those points.  
```
Input: Cumulative mask \(\mathbf{M}_{\textit{cusum}}\) of size \(H\times W\), Input points \(\mathbf{P}=(w_{i},h_{i})\) Output: Updated point labels \(\mathbf{L}\)
1:for each point \((w,h)\in\mathbf{P}\) satisfying Valid do
2: Update label: \(\mathbf{L}[w,h]\gets 1-\mathbf{M}_{\textit{cusum}}[h,w]\)
3: (If the \(\mathbf{M}_{\textit{cusum}}\) value is 1, set label to 0; otherwise, set it to 1)
4:endfor
5:Return\(\mathbf{L}\)
```  
**Algorithm 2**Input point label update with cumulative mask  
Figure 4 presents a schematic representation of the iterative update process applied to the input image and point labels as they progress through SAM to subsequent stages. Using the input image and point labels, SAM detects objects and accumulates the detected object masks to generate a foreground mask. Subsequently, Algorithm 1 and Algorithm 2 are sequentially applied to the input image and the accumulated foreground mask, respectively, to update them accordingly. The updated input image and point labels are then reprocessed by SAM, enabling the detection of different objects in subsequent iterations.
# Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth
## 3 Antenna Prototype Fabrication and Measurement  
Having obtained promising broadband and high gain simulation results, an antenna prototype was fabricated and then tested for experimental validation. The measured antenna parameters were \(S_{11}\), radiation patterns, and realized gain. They were measured by inserting the antenna prototype into an in-house fabricated seven-layer brain phantom.