esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parameterization by Number of Colors
### Trees  
In Section 4.2, we discussed algorithms for Minimum Rainbow Subgraph parameterized with \((\Delta,p)\) and \((\Delta,k)\). Now we present an algorithm for trees that does not depend on the maximum vertex degree \(\Delta\), but only on the number of colors \(p\) (or, using Equation (3), the maximum solution order \(k\)).  
**Theorem 5**: _When the input graph is a tree, Minimum Rainbow Subgraph can be solved in \(O(2^{p}\cdot np^{3}\log^{2}(np))\) time._  
We root the tree arbitrarily at a vertex \(r\) and use dynamic programming bottom-up from the leaves, utilizing fast subset convolution (Lemma 2) to get a speedup.  
We fill in a table \(T[v,C]\) for each \(v\in V\) and each subset of colors \(C\subseteq\{1,\ldots,p\}\). The idea is that \(T[v,C]\) holds the minimum number of vertices needed to cover the colors in \(C\) using only vertices from the subtree rooted at \(v\). We can then find the value of the overall solution in \(T[r,\{1,\ldots,p\}]\), and the vertex set realizing this can be found using standard dynamic programming traceback.  
We will need some additional tables. Let \(v_{1},\ldots,v_{\deg(v)}\) be the children of a vertex \(v\in V\). Then \(T_{j}[v,C]\) holds the minimum number of vertices needed to cover the colors in \(C\) using only \(v\) and the vertices in the subtrees rooted at \(v_{1}\) to \(v_{j}\). Further, let \(T^{*}[v,C]\) and \(T^{*}_{j}[v,C]\) be the versions of \(T[v,C]\) and \(T_{j}[v,C]\), respectively, where \(v\) is required to be in the cover. Clearly, we can equate \(T[v,C]=T_{\deg(v)}[v,C]\) and \(T^{*}[v,C]=T^{*}_{\deg(v)}[v,C]\).  
First, we initialize \(T\) and \(T^{*}\) for each leaf \(v\) with \(T[v,\emptyset]=0\) and \(T^{*}[v,\emptyset]=1\) and \(T[v,C]=T^{*}[v,C]=\infty\) for \(C\
eq\emptyset\). Then we use the following recurrences for each non-leaf \(v\) and each \(C\subseteq\{1,\ldots,p\}\) and \(2\leq j\leq\deg(v)\):\[T_{1}^{*}[v,C] =1+\min\begin{cases}T^{*}[v_{1},C\setminus\{\chi(\{v,v_{1}\})\}]\\ T[v_{1},C]\end{cases} \tag{7}\] \[T_{1}[v,C] =\min\begin{cases}T_{1}^{*}[v,C]\\ T[v_{1},C]\end{cases}\] (8) \[T_{j}^{*}[v,C] =\min\begin{cases}\min_{C^{\prime}\subseteq C\setminus\{\chi(\{v,v _{j}\})\}}(T_{j-1}^{*}[v,C^{\prime}]+T^{*}[v_{j},C\setminus(C^{\prime}\cup\{ \chi(\{v,v_{j}\})\})])\\ \min_{C^{\prime}\subseteq C}(T_{j-1}^{*}[v,C^{\prime}]+T[v_{j},C\setminus C^{ \prime}])\end{cases}\] (9) \[T_{j}[v,C] =\min\begin{cases}T_{j}^{*}[v,C]\\ \min_{C^{\prime}\subseteq C}(T_{j-1}[v,C^{\prime}]+T[v_{j},C\setminus C^{ \prime}])\end{cases} \tag{10}\]  
Calculating \(T_{1}^{*}[v,C]\) or \(T_{1}[v,C]\) takes \(O(p)\) time per table entry; there are \(O(2^{p}n)\) entries. Calculating \(T_{j}^{*}[v,C]\) or \(T_{j}[v,C]\) for all \(C\subseteq\{1,\ldots,p\}\) at once can be done in \(O(2^{p}\cdot kp^{3}\log^{2}(kp))\) time using fast subset convolution with a running time as provided by Lemma 2 (note that the maximum value that we need to store in table entries is \(k+1\)). Overall, we compute \(O(n)\) convolutions. Thus, the total running time is \(O(p\cdot 2^{p}n+2^{p}\cdot kp^{3}\log^{2}(kp)\cdot n)=O(2^{p}\cdot np^{3}\log^{ 2}(np))\).
## 2 Materials and Methods
### Electronic Nose Evaluation  
The volatile compounds of chicken breast meat were analyzed using an E-Nose 10001 system (developed by the College of Information and Electrical Engineering, China Agricultural University). The E-Nose 10001 electronic nose system mainly consists of the following parts: data acquisition part, data conditioning part, interface circuit part, and computer host. The hardware part includes a gas sensor array, a signal conditioning circuit board, an A/D conversion interface, and a computer, as shown in Figure 1. Previous studies have proven that, after the sensor array optimization and feature optimization, E-Nose 10001 can distinguished pork from different manufacturers, and also the parts and storage conditions. Compared with the PEN3 electronic nose of Airsense, the results of E-Nose 10001 are more accurate.  
The electronic nose was equipped with 16 different metal oxide sensors: TGS824, TGS822, TGS825, TGS880, TGS812, TGS831, TGS813, TGS830, TGS822 TF, TGS2600, TGS2620, TGS2611, TGS2602, TGS2620, TGS2610, TGS2201. Before the measurements were taken, the headspace gases were injected at a flux speed of 3 L/min for 60 s. Then, 5 g of minced chicken breast samples (3 birds per breed, respectively) were placed in a vial at a temperature of 40 \({}^{\circ}\)C. The gases in the headspace of the sample were pumped into a gas sensor chamber at the same speed. The electronic nose measurement interval was 0.05 s. Electronic nose real-time responses to chicken breast samples were recorded with 5 replicates.
## 2 Literature Survey
### Numerical Simulation on Underground Hydrogen Storage  
[PERSON] et al. [26] investigated the storage of hydrogen in the Norne field with numerical reservoir simulation. They considered different scenarios with storage in different zones of the reservoir (gas, oil, and water zones) and used methane as cushion gas. Their results indicated better performance of the underground hydrogen storage (UHS) in the field's gas zone.  
[PERSON] et al. [27] evaluated the impact of molecular diffusion in underground hydrogen storage (UHS) operations through numerical simulation. Their findings suggest that diffusion plays an important role in the transport dynamics of hydrogen in depleted gas reservoirs. Therefore, consideration of diffusion is essential for evaluating the purity of the produced hydrogen, the distribution of hydrogen in the reservoir, and the amount of hydrogen recovery.  
[PERSON] et al. [28] assessed hydrogen storage capacity using the compositional simulator CMG-GEM in two storage sites: a depleted oil reservoir utilized for natural gas storage and a saline aquifer utilized for CO\({}_{2}\) storage. The study compared the cumulative injection of hydrogen as opposed to natural gas and employed an optimization workflow to maximize hydrogen storage capacity. In addition, this study examined the impact of parameters like injection/production duration, soaking time, and pump wells on hydrogen capacity. The findings also delineate the major differences between depleted oil reservoirs and saline aquifers from a numerical simulation point of view. [PERSON] et al. [29] continued the optimization study by taking a closer look into the hydrogen storage in two aquifers and one depleted gas reservoir. [PERSON] et al. [30] applied a stochastic gradient-based method to optimize the trajectories of storage wells to maximize hydrogen storage efficiency in a saline aquifer.  
[PERSON] et al. [31] conducted a simulation study using PetraSim TOUGH2 software (version 2) to evaluate the maximum amount of hydrogen storage in a deep aquifer by constraining the injection to the fracture pressure of the target formation and the capillary entry pressure of the caprock. [PERSON] et al. [32] developed OPERATE-H\({}_{2}\), an integrated tool for optimizing UHS operations. The tool is based on reduced order models (ROMs) from 10,000 samples of numerical simulation runs with variable inputs capturing different operational scenarios. The tool enables the user to evaluate hydrogen withdrawal efficiency, purity, produced water, and hydrogen injectivity.  
In this work, a novel MBE-based framework is proposed. The novel approach is simple but can capture the behavior of real gas mixtures and can be used to estimate hydrogen storage capacity in depleted gas reservoirs. The new approach is verified with a commercial reservoir simulator.
## 5 Results and Discussion
### Comparison of Model Results
#### 5.3.4 Comparative Analysis of Prediction Accuracy of OLS and GWR Model
The citywide passenger flow forecasts for both government and corporate offices and public service stations are relatively stable, illustrating Shenzhen's multi-cluster urban structure, with more balanced office and public service support facilities. Shenzhen has achieved a good balance between urban planning and administrative area development. The above analysis illustrates that, compared with the OLS model, adding the average house price as the solution independent variable and applying the GWR model that takes into account spatial heterogeneity improves the accuracy in station ridership prediction and captures the spatial heterogeneity of the factors affecting the passenger flow at the station more effectively.
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 3 Materials, Methods, and Tools Used by the CloudMonitor Framework
### The LIBDFT Meta-Tool Capabilities
#### 3.3.4 Implementation of LIBDFT Using Tracker and the I/O Interface  
The main component of the LIBDFT library is the Tracker which is responsible for instrumenting a program to insert DIFT logic. The Tracker consists of two components:  
* The Instrumentation Engine (IE).
* The Analysis Routines (AR).  
The Instrumentation Engine (IE) is responsible for examining a program's layout and sequencing to identify which analysis routines should be inserted into the program. LIBDFT depends on Intel Pin's IE to check each instruction for type, category, and length. First, LIBDFT identifies the type of instruction (e.g., move, arithmetic, or logic instruction), then analyzes operands of the instruction (e.g., memory address, register-based or immediate) to decide their category, and finally the instruction's length (e.g., word or double word). After gathering this data, LIBDFT utilizes Intel Pin to insert a suitable analysis routine ahead of each program instruction. This ensures that the instrumentation code is executed for each execution of the instruction sequences.  
The Analysis Routines (AR) comprise the actual code responsible for implementing the DIFT logic for each instrumented instruction. The IE injects the analysis routines before the program creation. Analysis routines are executed more frequently when compared to the instrumentation code. To put it differently, the AR code is inserted to target a particular type of instruction, meaning it will run every time that specific type of instruction is executed. The types of instructions enhanced by the AR can be categorized into the classes outlined in Table 4.  
The Input and Output interface is tasked with processing the transfer of data between the kernel and the various system and application processes via a variety of system calls. The I/O interface contains a table of system call descriptions \"syscall-desc\", that has all 344 calls' information of the Linux system. Moreover, the Kernel stores user-defined call-backs and argument descriptors per system call, whether reading or writing data on memory. During program execution, when a system call is triggered, user-defined call-backs are activated both upon entering the system call (i.e., pre-syscall call-backs) and upon exiting it (i.e., post-syscall call-backs).
# Second-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel
## 2 Description of the Theoretical Model for Non-Isotropic V2V Flat Fading Channel
Conversely, the SB rays have the AoA independent on the AoD, while the DB rays AoD and AoA are independent, respectively. By using the results from prior studies, \(\alpha_{R}^{(m)}=sin^{-1}\left[\Delta_{T}sin(\alpha_{T}^{(m)})\right]\), \(\alpha_{R}^{(m)}=sin^{-1}\left[\Delta_{R}sin(\alpha_{T}^{(m)})\right]\), where \(\Delta_{T}=R_{T}/D\), and \(\Delta_{R}=R_{R}/D\) and this relationship holds for LSS and MSS scenarios only where \(D\gg R_{T},R_{R}\). In order to capture the impact of a high VTD, the SBT and SBR rays predominantly model the stationary scatterers due to their single reflection and proximity to the Tx and Rx with velocities \(v_{T}\) and \(v_{R}\), while the DB rays are mostly associated with high-moving vehicle scatterers with velocities of \(v_{T}^{\prime}\) and \(v_{R}^{\prime}\) in the direction of \(\gamma_{T}\) and \(\gamma_{T}\).
## 3 Results
### Performance of the Robust Optimization Algorithm  
#### 3.2.1 DVH Curves  
The effect of the proposed robust optimization is demonstrated in Figures 4 and 5. Figure 4 illustrates the DVH curves from the nominal and robust planning for cervical brachytherapy, including inverse planning without considering the applicator displacement parameter. The target received a sufficient dose in all plans with improved rectal and sigmoid dose sparing. We found no severe violation of EMBRACE protocol guidance in any DVH curves of the target volume or OAR using the robust planning.  
Figure 3: DVH curve variation of the targets (**a,b**) and OAR (**c–f**) under applicator positional errors with the assumption of shift scenarios. The DVH for the nominal plan setting that does not contain uncertainties is the blue curve; the DVHs for 79 error scenarios containing dose uncertainties are the grey curves.  
In order to show the difference of dose variations between nominal and robust plans, in this case the dose to 98% for target and dose to 2 cc for OAR (with dose variations under applicator-displacement uncertainty) were plotted using colors. The standard deviations  
Figure 4: DVH curves for the nominal (manual) plan, inverse plan, and robust plan in EBRT brachytherapy boost treatment of cervical cancer were plotted using colors indicating HRCTV (Black), IRCTV (Teal), Rectum (Dark Yellow), Sigmoid (Pink), Bladder (Royal) and Bowel (Violet). Graphs (**a,b**) are the magnification of the DVH curves of the target and OAR.  
Figure 5: The bandwidth variation at the critical dose-volume points on the DVH of the targets (HRCTV and IRCTV) and OAR (BLADDER, BOWEL, RECTUM, and SIGMOID) under uncertainty with the applicator-shift scenario assumptions. The dose to 98% for target and dose to 2 cc for OAR were shown using a black square (nominal plan) and a red circle (robust plan) with a standard deviation of dose variation based on error scenarios.  
of robust planning were less than the others in most of the target and OARs. They ranged in standard deviation from 32.72 to \(-\)18.99%.  
Moreover, although both plans satisfied the requirements for the dose coverage of the target and the OAR, the band width of the DVH curves under the uncertainty of the robust plan in all organs was much narrower than its nominal plan.
## 7 Challenges and Future Directions
### Data Processing  
A large amount of connected IoT devices generates enormous data called Big Data. The sensors and actuators on millions of consumer devices are interconnected, interacting, gathering, and transmitting the information. Each device produces or senses a large amount of data that requires further processing. The main problem will be making sure that these different forms of data are handled appropriately and efficiently, especially given the lack of time, computational resources, and processing power. In 2000, the volume of stored data was 80,000 petabytes, whereas it was predicted that by 2020 the volume would be 35 zettabytes. However, only 20% of these are analyzed by traditional methods due to different data structures, and the rest 80% is not taking part in the decision-making purposes. It is assumed that the quantity of data will be increased further with the evaluation of 6G. Data-driven methods can be developed with the help of FL and AI to handle Big Data from large-scale IoT devices.
###### Abstract  
This research introduces a new multibeam survey line model that optimizes geometric relationships to improve the efficiency and accuracy of surveys over complex seabed topographies. Since existing multibeam echosounder systems have limitations in handling complex terrains, this study presents an advanced model to enhance data quality and operational efficiency. By strategically designing survey lines and optimizing coverage strategies, this paper achieves the optimal configuration of survey lines for secondary measurements in marine areas, ensuring high precision and reliability of measurement data. Experimental results show that the new model significantly outperforms existing technologies in terms of effective coverage area and measurement accuracy, with an average coverage rate of over 95%, higher than existing models, and a weighted average overlap rate of 3.18%, greatly improving the economic efficiency of measurements by reducing redundant coverage and minimizing operational costs. These findings confirm the advantages of the new model in practical applications and offer valuable technical support for advancing seabed mapping technology.  
seabed mapping; multibeam echosounder system; spatial geometry; geometric optimization; path planning; planar mapping; curve approximation Article
## 3 Results and Discussion
### Spatial Distribution  
The spatial variability of the As, Cu, Pb, and Zn concentrations within the study area was investigated through the analysis of experimental variograms. The data processing and visualization were performed using ArcGIS v. 10.5. Various theoretical variogram models, including linear, exponential, and spherical functions, were assessed, considering nugget effects. The technical aspects of this modeling are described in detail in classical geostatistical literature, such as [PERSON] et al. [32].  
In the Aconcagua River basin, sampling points with mining activity show high levels of As, Cu, Pb, and Zn, potentially toxic and harmful to human and animal health [33]. Sampling points in areas without mining activity also recorded high concentrations of these elements due to mobility and transport processes. On the other hand, the Casablanca River basin did not show high distribution values for these elements (Figure 2).  
Figure 2 illustrates the spatial distribution modeling of As, Cu, Pb, and Zn in the area where the sampling points are located. In Figure 2, a higher concentration of arsenic is observed in the northeastern part of the Aconcagua River basin, where the mining activities are located, which extracts refined copper and arsenic. In contrast, this concentration is lower in the Casablanca basin. The figure shows that copper has a distribution similar to lead, with higher concentrations towards the north of the Aconcagua River basin, while the values decrease in the Casablanca basin. This is due to the presence of mining activities in the area of higher concentration, while there is no mining activity in the Casablanca basin.  
Figure 2: Spatial distribution of heavy metals As, Cu, Pb, and Zn (mg/kg) for soils of the Aconcagua and Casablanca River basins.  
Regarding zinc, its highest concentrations are found in the northern part of the Aconcagua River basin. In addition, these values have remained stable since 2004, implying that there have been no relevant changes in the sources of discharge into the Aconcagua River.
# Unranking Small Combinations of a Large Set in Co-Lexicographic Order
## 2 Materials and Methods
### Modification of Unranking Algorithm  
Let us change Algorithm 1 by adding a function \(\mathtt{FindK}(r,m)\) for preliminary search for the value of \(k\). This function should calculate the approximate value of \(k\) for solving the inequality (3); however, the obtained value of \(k\) must not be greater than the true value of the solution of this inequality. Thus, we get an algorithm for unranking combinations in colex order (Algorithm 2).  
```
algorithm: UnrankColexNew(\(r\),\(m\)) input: A nonnegative integer \(m\) and a rank \(r\) output: A sequence \(c=(c_{1},c_{2},\ldots,c_{m})\)
1 begin
2 for\(i:=m\) to \(1\)do
3\(k:=\mathtt{FindK}(r,i)\)while\(r\geq\binom{k}{i}\)do\(k:=k+1\)\(c_{i}:=k\)\(r:=r-\binom{k-1}{i}\)
4 end for
5 return\(c\)
6
7 end for
```  
**Algorithm 2**Modification of algorithm for unranking combinations in colex order  
Next, we consider the development of the function \(\mathtt{FindK}(r,m)\) for Algorithm 2 in more detail.  
A binomial coefficient can be presented as  
\[\binom{n}{m}=\frac{n(n-1)(n-2)\cdots(n-m+1)}{m!}. \tag{5}\]  
Using (3) and (5), we get  
\[r<\frac{k(k-1)(k-2)\cdots(k-m+1)}{m!}.\]  
After some transformations, we obtain the following inequality:  
\[\sqrt[n]{k(k-1)(k-2)\cdots(k-m+1)}>\sqrt[n]{r\;m!}. \tag{6}\]  
If we consider the inequality of arithmetic and geometric means [32] for the list of numbers \(k,(k-1),(k-2),\ldots,(k-m+1)\), then we get  
\[\frac{k+(k-1)+(k-2)+\ldots+(k-m+1)}{m}\geq\sqrt[n]{k(k-1)(k-2)\cdots(k-m+1)}.\]  
After some simplifications on the left, we obtain  
\[k-\frac{m-1}{2}\geq\sqrt[n]{k(k-1)(k-2)\cdots(k-m+1)}. \tag{7}\]  
Combining (6) and (7), we get  
\[k-\frac{m-1}{2}>\sqrt[n]{r\;m!}\]or  
\[k>\sqrt[n]{r\ m!}+\frac{m-1}{2}.\]  
Hence,  
\[k\approx\left\lceil\sqrt[n]{r\ m!}+\frac{m-1}{2}\right\rceil. \tag{8}\]  
Thus, applying (8), we can find an approximate value of \(k\) for which the next inequality is true:  
\[r<\binom{k}{m}. \tag{9}\]  
We also use the following [PERSON]'s approximation [33] for calculating the factorial in (8):  
\[n!\approx\sqrt{2\pi n}\ \Big{(}\frac{n}{e}\Big{)}^{n}\ e^{\frac{1}{2n}-\frac{1}{36 \mathsf{m}^{3}}}. \tag{10}\]  
Using the approximate Formula (10) in (8) and making some transformations, we obtain the following approximate formula for \(k\):  
\[k\approx\left\lceil m\ e^{\frac{\log r}{m}+\frac{\log 2\pi m}{2m}+\frac{1}{12m^{2 }}-\frac{1}{36\mathsf{m}^{4}}-1}+\frac{m-1}{2}\right\rceil. \tag{11}\]  
This approximate formula can be used in the preliminary search for the value of \(k\). That is, the Formula (11) can be applied as the basis of the function \(\mathtt{FindK}(r,m)\) (Line 3 in Algorithm 2).
# Biodegradable Contrast Agents for Photoacoustic Imaging
## 5 Polymeric Materials as Biodegradable PA Agents
### Functional Polymers as PA Agents  
Cleavable chemical groups give rise to biodegradability in polymeric contrast agents. Recently, [PERSON] et al. have designed functional nanoparticle agents for PAI (Figure 9) [79]. The semi-conducting polymer core of these particles generated not only PA signals but also heat for photothermal therapy. The core polymer was further tethered with poly(ethylene glycol) methyl ether-\(b\)-poly(lactide-\(co\)-glycolide) (PLGA-\(co\)-PEG), which endowed biocompatibility and water dispersibility (Figure 9C). In particular, the semiconducting polymer, DPPV (Figure 9A), had vinylene groups in the backbone. Thus, the whole chain was fully \(\pi\)-conjugated but the oxidizable nature of the vinylene group allowed degradation of the chain, which is in contrast to DPPT (Figure 9B). As a result, the particles, SPNV (DPPV-based polymer nanoparticle) and SPNT (DPPT-based polymer nanoparticle), showed comparable PAI and photothermal therapy simultaneously (Figure 9D-I) but SPNV was only degraded biologically to monomeric species in the presence of a peroxidase enzyme (myeloperoxidase) reducing the signal. Notwithstanding, the examples of well-designed, biodegradable, functional polymers for PAI are still rare as far as we know. The use of other chemistry, such as continuous  
Figure 8: (**A**) Synthetic procedure for poly(lactide-\(co\)-glycolide) (PLGA) using cyclic dimers of lactic acid and glycolic acid. The resulting copolymer has ester linkages that provide a hydrolytic response in the backbone. (**B**) Chemical structure of poly(aminomethylpiperidine-bis(acryloyl)cystamine) (poly(AMBD-BAC)) containing Pt(II)-based signal transducers and poly(ethylene glycol) side chains. The chemical structures of the side chains are shown below the polymer structure.  
depolymerization [80; 81; 82] and dynamic physicochemical bonds or the formation of reversible and hierarchical structures of polymeric materials [83; 84; 85; 86], would further provide new, biocompatible, degradation pathways of polymer structures without losing PA properties.