esa-sceva/satcom-chunk-collection huggingface dataset:
# Visible Light Communication: A System Perspective--Overview and Challenges
## 2 Visible Light Communication System
### Standardisation Efforts
#### 2.1.2 OpenVLC  
[PERSON] et al. have proposed and demonstrated an OpenVLC system, rapid prototyping, flexible and open source VLC system for the research community [35]. It provides an interface between VLC front-end with the embedded Linux platform. The hardware consists of beaglebone black board (BBB), and a transceiver front end consisting of a single LED which can serve both as the transmitter and receiver. A single LED in dual mode is used to reduce design complexity. A software-defined switch operates the transceiver. In transmitter mode (Tx) the LED is connected to the power amplifier, and in the receive mode (Rx) it is connected to a low noise amplifier. OpenVLC has implemented both the time-division duplex and IEEE 802.15.7 protocol that includes software programmability, carrier sensing, TCP/IP interoperability, encoding and decoding, preamble detection and signal sampling. The upgraded version of OpenVLC1.3 improves the data rate from UDP throughput of 100 kbps (in OpenVLC1.2) to 400 kbps without any modifications to the existing hardware [36]. It also reduces physical footprint (as it runs on off the shelf microcontroller) along with memory efficient frame detection technique. Techniques were introduced to reduce noise due to high-frequency components along with the synchronisation issues of the frame receptions. OpenVLC facilitates the research of VLC both for academia and industry.  
In [37] authors have challenged the assumption that light sources are always static and users can expect LOS with many luminaries, and many scenarios together can provide deterministic localisation. The authors observe that these assumptions may not hold for many scenarios. For example, when the nodes consist of a single light and are mobile (e.g., bikes or swarms of robots) the localisation become non-deterministic. A framework is proposed to compute the relative position of objects when nodes are moving freely in all directions. The proposed framework has been implemented with OpenVLC platform. A good error rate of below 5 cm is achieved through simulations.  
In [38] an inexpensive receiver has been designed to cope with the issues arising due to optical noise and the mobility of users. This receiver is based on the OpenVLC platform; photodetectors are utilised to sense optical noise arising from the sun and other sources. The physical and data link layers are modified to adjust the receiver to the detected noise. Experiments were conducted for two nodes trying to maintain a link under different paths (e.g., straight and curved) and illuminations (e.g., night and day). Results validate that the noise sensing with photodetector outperforms LED only design in optical noise and mobility.
## 5 Evaluation Results
### Intersection Passing Rate  
The intersection passing rate is the ratio of the number of vehicles that have passed through the intersection to the number of vehicles that have come in at POIs and started running. It is expressed by:  
\[\text{Intersection passing rate}=\frac{\text{No. of vehicles that have passed through the intersection}}{\text{No. of vehicles that have started running}} \tag{4}\]  
As shown in Figure 10, the intersection passing rate of low-priority vehicles improved in all situations except for the case where the number of vehicles coming in on the priority road was 200, and the number of vehicles coming in on the low-priority road was between 100 and 200. The improvement was prominent in cases where the number of vehicles coming in on the priority road was large, and the number of vehicles coming in on the low-priority road was small. The intersection passing rate improved by 11.28% in the case where 1200 vehicles came in on the priority road and 100 vehicles came in on the low-priority road. As shown in Figure 11, the intersection passing rate improved by 7.80% in the case where the percentage of self-driven vehicles was 60%.  
In contrast, the overall intersection passing rate involving both low-priority and priority vehicles improved only in some limited cases where the intersection passing rate of low-priority vehicles improved. For example, it improved by 0.74% when 800 vehicles came in on the priority road, and 100 vehicles came in on the low-priority road. The impact of the proposed yielding protocol on priority vehicles was minimal.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Simulation time & 1800 s \\ Execution time step & 100 ms \\ Whether the yielding protocol is in operation & Yes and no \\ Percentage of self-driven vehicles & 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 \\ Number of vehicles that came in on the priority road & 100, 200, 300,400, 500, 600, 700 per POI \\ Number of vehicles that came in on the low-priority road & 50, 75, 100 per POI \\ Number of pedestrians & 20 \\ Mobility seed value & 123-132 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation conditions.  
Figure 10: Variation in the intersection passing rate of low-priority vehicles when the number of vehicles that came in were varied. The intersection passing rate was the average for the entire percentage range of self-driven vehicles.
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
### AI-Based UAV Networks
#### 1.1.4 General Applications  
Various research studies are being conducted to explore multiple applications of UAV networks. These include but are not limited to using UAV networks as part of cellular networks, vehicular networks, high-risk areas coverage, and effective use of spectrum. In [11], the authors have explored UAV networks for the performance improvement of VANETs. They proposed routing protocols that not only provide better and more reliable coverage for the vehicles involved but also help to identify malicious vehicles. The simulation results presented are promising and show approx. 7% gain in malicious vehicular detection using UAV networks.
## 3 Results and Discussion
### UV-Vis Absorption and Steady-State Fluorescence Spectroscopy  
The UV-Vis absorption and steady-state fluorescence spectra of pure AFB1 and CdSe/ZnS QDs in aqueous solutions are shown in Figure 1. The absorption and fluorescence maxima of AFB1 are centered at 360 and 435 nm, respectively. The absorption and fluorescence maxima are solvent dependent (in this case, the solvent is methanol). The absorption spectrum of CdSe/ZnS QDs has broad absorption that extends from the ultraviolet to the visible region with absorption edge at 520 nm, while the fluorescence spectra show strong emission with maxima at 545 nm.  
Figure 1 shows that there exists sufficient overlapping of the AFB1 fluorescence spectrum and the CdSe/ZnS QDs absorption spectrum. This is the required condition for FRET between AFB1 and CdSe/ZnS QDs to occur. Here, AFB1 acts as a donor that receives energy from the excitation source and transfers that energy to CdSe/ZnS as an acceptor. The energy transfer results in change of the fluorescence intensity and fluorescence lifetime.  
Figure 1: The absorption and fluorescence spectra of AFB1 and CdSe/ZnS QDs in aqueous solution. The overlap between AFB1 fluorescence and CdSe/ZnS absorption is shown by cross-line region.
## 2 Problem Description and Modeling
### AHP-Based Weighting  
We structured the weighting process using AHP [23] to formalize the prioritization of objectives, The specific implementation steps are as follows:  
Step 1: Expert evaluation and pairwise comparison matrix  
We invited a panel of 10 logistics experts from the distribution center of H University to prioritize the objectives, minimizing the total distance and maximizing the average loading rate, and each expert scored independently using [PERSON]'s 1-9 scale [24]. The details of the specific rating ratio are shown in Appendix A.3. The mean ratings indicate that minimizing examples is considered more critical than maximizing the loading rate, with a consensus ratio of \(w^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text  
directly matching the original pairwise ratio \(a_{12}=2.33\). [PERSON]'s framework does not require a consistency ratio (CR) validation for \(n=2\), as the random index (\(RI\)) is undefined for such cases [27]. In addition, this confirms a perfect consistency. The minor discrepancy (\(CI=-0.001\)) solely reflects controlled numerical simplification for practical implementation.  
Although the precise AHP calculations yielded weights of \(\alpha=0.699\) and \(\beta=0.301\), these were adjusted to 0.7 and 0.3 to harmonize mathematical rigor with practical feasibility. First, H University's logistics team advocated for a simplified \"70-30\" heuristic, reflecting their empirical prioritization of minimizing distance over loading rate. Second, numerical simulations confirmed that rounding introduced negligible deviations (<0.5%) in total distance and loading rate outcomes, ensuring algorithmic robustness. Finally, integer-like coefficients enhance interpretability in industrial settings, avoiding computational complexities associated with fractional weights in real-time routing systems. This adjustment balances precision with usability, maintaining alignment between theoretical methodology and operational realities.
## 3 Results
### MPs Ingestion Evaluation and Mortality  
MPs ingestion was detected in both experimental Groups A and B (Figures 2 and 3, Table 2). Microspheres were found in treatments A4-A5 and B1-B5. Significant differences were observed in MPs ingestion (\(p=0.003\)) when exposed to a concentration of \(10^{4}\) MP/mL compared to other concentrations. The main variations in MPs ingestion between treatment A and B were observed between the exposure time T0, T6, T12 and T24 vs. T48 in the Group A and between T0, T6, T12 and T24 vs. T96 in the Group B.  
Group A showed significant variation in MPs ingestion (\(p<0.001\)) depending on exposure concentration. In particular, the main differences were detected between \(10^{4}\) MPs/mL and all other concentration investigated.  
Mortality rates varied according to exposure time (\(p<0.001\)) (Table 3), with the main differences observed between T168 vs. T0, T168 vs. T12 (\(p=0.014\)). Similar results were observed in the Group B, where MPs abundance resulted to be dependent on exposure concentration (\(p=0.001\)). Even in this case, the mortality rate followed an exposure time-dependent trend (\(p<0.001\)), with a peak of mortality at T96 and T168.  
Figure 2: Ten \(\mu\)m of Polybead® Blue Dyed Microspheres detected in the gut and feces of Group A specimens at T48. (**a**) Treatment A3 (\(10^{2}\) MPs/mL); **(b**) treatment A4 (\(10^{3}\) MPs/mL); (c,d) treatment A5 (\(10^{4}\) MPs/mL). Scale bars: 25 \(\mu\)m (**a**); 50 \(\mu\)m (**b–d**).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Exposure Time**} \\ \hline
**Experimental Groups** & **T0** & **T6** & **T12** & **T24** & **T48** & **T96** & **T168** \\ \hline A0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ A1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ A2 & 0 & 0 & 0 & 0 & 0 & 0 \\ A3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ A4 & 0 & 0 & 0.4 \(\pm\) 0.38 & 2.2 \(\pm\) 1.57 & 2.4 \(\pm\) 1.46 & 4.4 \(\pm\) 1.44 & 4.4 \(\pm\) 1.32 \\ A5 & 0 & 1.4 \(\pm\) 1.16 & 13.2 \(\pm\) 12.4 & 43.6 \(\pm\) 28.7 & 122 \(\pm\) 51.8 & 108.2 \(\pm\) 38.3 & 110.2 \(\pm\) 31.4 \\ B0 & 0 & 0 & 0 & 0 & 0 & 0 \\ B1 & 0 & 0 & 0 & 0.2 \(\pm\) 0.44 & 0.6 \(\pm\) 0.54 & 0.8 \(\pm\) 0.83 \\ B2 & 0 & 0 & 1 \(\pm\) 1 & 1.4 \(\pm\) 0.54 & 2.8 \(\pm\) 1.92 & 2.2 \(\pm\) 1.48 \\ B3 & 0 & 1.4 \(\pm\) 1.94 & 7.4 \(\pm\) 7.66 & 6.4 \(\pm\) 4.39 & 8 \(\pm\) 9.46 & 5.4 \(\pm\) 4.15 \\ B4 & 0 & 22.4 \(\pm\) 29.3 & 23.8 \(\pm\) 14.9 & 72.8 \(\pm\) 28.1 & 85.6 \(\pm\) 9.81 & 75 \(\pm\) 49.4 \\ B5 & 0 & 37.6 \(\pm\) 51.5 & 157.4 \(\pm\) 68.6 & 169.4 \(\pm\) 65.6 & 300.4 \(\pm\) 42 & 306.2 \(\pm\) 49.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ingested microplastics counted for each experimental group (A and B) and exposure time. Data are shown as means \(\pm\) SD (\(n\) = 3).  
Figure 3: Ten \(\upmu\)m of Polybead® Blue Dyed Microspheres detected in the gut and feces of Group B specimens at T48. (a) Treatment B1 (1 MPs/mL); (b) treatment B4 (10\({}^{3}\) MPs/mL); (c,d): treatment B5 (10\({}^{4}\) MPs/mL). Scale bars: 10 \(\upmu\)m (a); 25 \(\upmu\)m (b); 50 \(\upmu\)m (c); 75 \(\upmu\)m (d).
## 4 Performance Evaluation
### Resource Occupancy Ratio  
Figure 15 presents the percentage of resources utilized during the operation of each scheme. WirelessHART, a network based on IEEE 802.15.4e, is characterized by limited bandwidth, making efficient resource management a critical factor for scalability. In the case of [15], the resource occupancy ratio is the highest among the compared schemes due to its assumption that all mobile devices could communicate with every fixed device in the network. This scheme ensures network stability through excessive pre-allocation of resources; however, it results in significant resource inefficiency. On the other hand, Ref. [21] achieves the lowest resource occupancy ratio by allocating resources based on a static network topology, without accounting for the mobility of devices. While this scheme minimizes resource usage, it struggles to adapt to dynamic network changes, which might lead to performance degradation in complex environments. In contrast, the proposed scheme classifies mobility patterns--linear, non-linear, and random--and predicts movement paths to allocate resources accordingly. As a result, it shows a slightly higher resource occupancy ratio than [21], while remaining significantly lower than [15]. Additionally, it achieve high transmission success ratio while avoiding unnecessary resource allocation. In other words, the proposed scheme could achieve both scalability and efficient resource management in dynamic network environments. To summarize the results, Ref. [15] achieve high transmission success rates through extensive resource pre-allocation but suffer from high resource occupancy. In contrast, ref. [21] minimizes resource occupancy but becomes less reliable in dynamically changing network environments. The proposed scheme balances between these trade-offs by predicting mobility patterns and allocating resources efficiently. These findings demonstrate that the proposed scheme achieves both a high transmission success rate and superior scalability compared to existing schemes.  
Figure 14: Packet delivery ratio versus number of mobile devices has been analyzed based on [15; 21] and proposed scheme.  
Figure 15: Resource occupancy ratio is calculated based on [15; 21] and proposed scheme.
## 6 Numerical Example
### Step 3: Publish Each Part Using a Covert Channel  
We recall the maximum MQTT message size: \(l=268,435,456\).  
To be concrete, we consider that the publisher knows 256-bit values for \(n\) and \(a\):  
* Prime modulus \(n=89802601036489635412700864252846407279584917476820871708\) 772821852121126622533
* Exponent \(a=1861836248542572424\) Then, it calculates the length \(L\) for each part: For \(P_{1}\): \[L_{1}=(P_{1}{}^{a}\mod n)\mod l\] \[L_{1}=(45^{a}\mod n)\mod 268,435,456\] Assume \(L_{1}=264313832\). For \(P_{2}\): \[L_{2}=(P_{2}{}^{a}\mod n)\mod l\] \[L_{2}=(211^{a}\mod n)\mod 268,435,456\] Assume \(L_{2}=89686678\). For \(P_{3}\): \[L_{3}=(P_{3}{}^{a}\mod n)\mod l\] \[L_{3}=(139^{a}\mod n)\mod 268,435,456\] Assume \(L_{3}=144660972\). Therefore, the lengths \(L_{1},L_{2},L_{3}\) will be 264313832, 89686678, and 144660972, respectively.
# Two Taylor Algorithms for Computing the Action of the Matrix Exponential on a Vector
## 3 Numerical Experiments
(**b**) Performance profile. (**c**) Ratio of relative errors. (**d**) Lowest and highest relative error rate. (**e**) Polynomial, interpolation and subspace orders. (**f**) Ratio of matrix–vector products. (**g**) Execution time. (**h**) Ratio of execution time. Figure 3: Experimental results for Set 3. (**a**) Normwise relative errors. (**b**) Performance profile. (**c**) Ratio of relative errors. (**d**) Lowest and highest relative error rate. (**e**) Polynomial, interpolation and subspace orders. (**f**) Ratio of matrix–vector products. (**g**) Execution time. (**h**) Ratio of execution time. For each of the methods under evaluation, the normwise relative error \(Er(A,v)\) committed in the computation of the action of the exponential function of a matrix \(A\) on a vector \(v\) was calculated as follows:  
\[Er(A,v)=\frac{\left\|exp(A,v)-\widetilde{exp}(A,v)\right\|_{2}}{\left\|exp(A,v) \right\|_{2}},\]  
where \(exp(A)\) denotes the exact solution and \(\widetilde{exp}(A,v)\) represents the approximate one. Figures 1a-3a present the normwise relative error incurred by each of the seven codes under study. The solid line that appears in them plots the function \(k_{\text{exp}}u\), where \(k_{\text{exp}}\) (or _cond_) means the condition number of the matrix exponential function ([29], Chapter 3) for each matrix and \(u\) represents the unit roundoff in IEEE double precision arithmetic. The matrices were ordered by decreasing value their condition number. It is well-known that the numerical stability of each method is exposed if its relative errors are positioned not far above this solid line, although it is always preferable that they are below. Consequently, these figures reveal that expv is the code that most frequently presents relative errors above the \(k_{\text{exp}}u\) curve, being therefore the least numerically stable of all the codes analysed for the matrices used in the numerical tests. The rest of the codes can be said to offer high numerical stability. The percentage of cases in which expmvtay1 incurred in a normwise relative error less, equal or greater than the other codes is listed in Table 2. As can be appreciated, expmvtay1 always provided a higher percentage of improvement cases than the rest of its competitors, especially over expv followed by expm_newAv, expleja, and, with very similar rates, by expmAvtay and expmv. On the other hand, the gain in accuracy by expmvtay1 over expmvtay2 is not noticeable, and it can be concluded that the latter will also offer a notable improvement in the reliability of the results compared to the rest of the methods. In a very detailed way, Table 3 collects the values corresponding to the minimum and maximum normwise relative error committed by all the functions for the three sets of matrices employed, as well as the mean, median, and standard deviation. While the minimum relative errors incurred by the codes are very similar, it is easy to observe how the maximum relative error and, consequently, the largest values in the mean, median,
## 2 Materials and Methods
### Use Cases for Calculating Periodicity Intensity
#### 2.2.2 Periodicity Intensity in Student Learning Habits  
Many Universities have wellness programs to promote the overall health and well-being of their staff and students [30]. Their benefits lead to improved health literacy, healthier mindsets, healthier relationships with others and positive changes in lifestyles regarding exercise, nutrition and more. In our University, we created and ran an elective undergraduate course on wellness called FLOURISH. This also includes elements of data literacy by showing students how their own personal data, gathered from devices used to measure their sleep, exercise, nutrition, etc., can be used for good as part of students themselves monitoring these wellness factors.  
The wellness course was delivered entirely online via the University's virtual learning environment (VLE) Loop, a variation of Moodle, and was accredited by the University, earning 5 ECTS for students who successfully completed it. For each of the 10 wellness topics, the personal data that students were required to gather on themselves was not shared with the course instructor or with the University. However, as part of their participation in the course and to demonstrate how personal data gathered by the University as part of recording their online activities could also be used for good, each of the 169 students on the course authorised us to download their VLE access logs for all their taught courses for the semester. This included the date, time and URL of the Loop resource for each time that a student accessed any resource on the VLE.  
At the end of the teaching semester, we used the log of all student accesses to all Loop resources on all their courses and generated a single periodicity intensity graph for each student covering all online accesses and shared the graph online privately with them. The resulting graphs provide insight for each student into which period(s) of the preceding taught semester their learning habits, as exemplified by their access to online resources, were more structured and regular. This may help them in reflecting back on their learning throughout the semester. Some sample graphs to illustrate this, as well as further insights, are presented in the Results section (Section 3) of this paper.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 4 Segmenting the Maps and Building the Anchor Sets  
Sketch maps of indoor places cannot directly be used as metric maps due to errors in local scale, non-homogeneous levels of detail, and the approximation of distances and sizes. Instead of using a sketch map as an accurate representation of an environment, a person using a sketch map will focus on the topology and points of interest along the trajectory [25], i.e., places that are distinct from the rest of the environment. However, as demonstrated by [PERSON] et al. [3], using the topology of the environment alone is not enough to find correspondences between sketch and metric maps. Indeed, sketch maps can vary greatly from the metric map and, while the topology of both the sketch and the metric map can be correct, the schematization of the sketch can make it hard to find correct correspondences due to the difference in the level of detail. In our previous work [3], we extracted the topology by computing the Voronoi Diagram of the maps and used a planar graph matching algorithm to find correspondences between the diagrams. We obtained a matching accuracy of 58% on a dataset of sketches and we came to the conclusion that to get better correspondences between a sketch and an equivalent metric map, we would need to consider more than just the topology of the environment represented by the Voronoi diagram.  
URSIM takes a different approach. URSIM takes as inputs the sketch and metric maps, segmented into regions using a given map segmentation algorithm (see Section 4.1), and finds the most unique regions in each map, as presented in Sections 4.2 and 4.3. Section 5.1 presents how URSIM then builds region descriptors using those unique regions, the topology of the map, and attributes of the regions. Finally, correspondences between the regions are found using graph matching, as described in Section 5.2.