esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
For _A. fischeri_, the EC\({}_{50}\) and their corresponding 95% confidence intervals were computed using the MicrotoxOmni(r) software version V1.18 (Modern Water, Guildford, UK) with a linear model [19]. The EC\({}_{50}\) values with the 95% confidence limits for _D. magna_ immobilisation test were calculated using a Probit analysis [27] using the IBM SPSS 21 software (NY, USA). For the growth inhibition test with _R. subcapitata_ and _L. minor_, and for the chronic assay with _D. magna_, the effect concentrations (EC\({}_{50}\) and EC\({}_{20}\)) and the 95% confidence limits were calculated for each effluent sample (RE, TE1 and TE2) using the nonlinear least squares regression procedure supplied by the software package Statistica 12.0 (StatSoft, Inc., Tulsa, OK, USA). One-way ANOVAs followed by a Dunnett test or Tukey's test were also employed to find out potential significant differences in the endpoints assessed between the control and tested effluent dilutions, or between each other, using the software Sigmaplot version 11.0 (Systat Software, Inc., CA, USA, 2008; [PERSON], 1996). No observed effect concentration (NOEC) and low observed effect concentration (LOEC) values were taken from ANOVA significant results, reported for a significance level of 0.05. Toxicity was expressed as toxic units (TU), where TU = 100/EC\({}_{50}\), to better compare the results.
## 3 Results and Discussion
### Isomeric Ratio  
The isomeric ratio analysis was carried out to understand the sources and fate of the considered PAHs in the environment [46]. In this study, isomeric ratio Ant/(Ant + Phe) and BaA/(BaA + Chry) were plotted against Flu/(Flu + Pyr) and Ind/(Ind + BghiP), respectively, as illustrated in Figure 7. The isomeric ratios were analyzed to investigate the PAH distribution for petrogenic (petroleum, including liquid fossil fuel, vehicles, and crude oil) and/or pyrogenic sources (combustion, including biomass and coal) [18]. The results indicate that the sources of PAHs in agricultural soils are predominantly pyrogenic, arising from various combustion-related activities. Key contributors include the burning of crop residues, which releases PAHs into the soil, and the use of fossil fuel-powered agricultural machinery, such as tractors and harvesters, which emit PAHs through exhaust. Additionally, domestic cooking with biomass in rural areas, along with industrial emissions from nearby factories, further contributes to PAH deposition. The application of ash as soil amendments also introduces pyrogenic PAHs, and vehicle emissions, particularly from diesel engines, can contaminate fields adjacent to roads and highways.  
The isomeric ratio BaA/(BaA + Chry) versus Ind/(Ind + BghiP) suggests that the primary sources of PAHs in rural, research, and local market areas are linked to petroleum combustion, while in coastal and industrial areas, biomass and coal combustion are dominant sources. These findings may be related to mechanized farming practices impacting agricultural soils. Specifically, PAH contamination in coastal soils is likely due to biomass burning, coastal vehicle emissions, and coal-powered plants. In industrial soils, PAHs may arise from biomass burning and coal combustion associated with road construction. These results are consistent with findings from [PERSON] et al. [47].
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 3 Methods
### Time Series Analysis  
Following generation of water fraction maps, we tracked lake and river surface area changes over the late summer season. Due to the presence of clouds and varying swaths and pixel sizes of Planet imagery, an object-based approach was used to track changes in lake and river extent, with each water body treated as an individual object. This approach is well-suited for time series analyses involving different sensors and ground resolutions and additionally limits the impact of potential geolocation error. To do this, we first reapplied the maximum extent buffered polygons to each water fraction map and calculated the total area of pixels within each polygon that are 100% water and greater than 75% water for each image. We also determined the total >50% fractionated water area within each polygon by summing the pixel values for all pixels greater than 50% water. This created a sub-seasonal time series consisting of three different lake areas for each lake observation (>50%, >75%, 100%). We primarily used >75% area for our time series analyses but observed temporal patterns are similar for all three metrics. We analyzed changes in river surface area by examining seven individual river reaches ranging from 2.5 to 3.5 km in length. To better facilitate comparison between reaches, river surface area was converted to effective width (W\({}_{\rm e}\)) [5; 36; 37] by dividing by the length of each river reach. If multiple PlanetScope image acquisitions were acquired on the same day, we assigned the largest observed lake area or river W\({}_{\rm e}\) to that day. Finally, to illustrate large-scale changes in  
Figure 3: Demonstration of classification method: (**a**) the initial adaptive OTSU classification; (**b**) the buffered maximum lake polygons; (**c**) the histogram classification from the buffered lake polygons with land level (_LL_) and water level (_WL_) labeled; (**d**) the final water fraction map generated by applying the land and water levels to the NDWI image.  
inundation extent, we produced pixel-based water fraction change maps between three cloud-free RapidEye images representing early (23 June), mid (17 August) and late (1 October) season.
## 3 Manufacturing Techniques for Wearable Antennas
### Flexible Textile Wearable Antennas  
Textile antenna manufacturing techniques can be divided into the following categories: (**1**) Thin and uniform metallization layers (_i.e._, copper or silver tape, foil) attached to the non-conductive textile fabric [22]; (**2**) the use of conductive textile yarns to weave or knit the conductive patterns of the antenna and then attach or stitch them onto the non-conductive textile substrate [22]; (**3**) the use of conductive textile yarns to embroider the conductive patterns of the antenna on the non-conductive textile substrate [22]; (**4**) Inkjet and screen printed printing on non-conductive textile materials.  
Before moving into the details of each technique it is important to note that different conductive textile materials (_i.e._, copper tape, knitted copper, _etc._) can result in different antenna performance. The effect of different conductive materials on wearable antenna performance is presented in [23]. Six different antennas were fabricated. For all the antennas, the substrate that was used was fleece fabric (thickness = 3.5 mm, \(\varepsilon_{\mathrm{r}}\) = 1.1). The different conductive sections of the antennas were: solid copper tape; knitted copper tape; vertically cut pieces of copper tape; horizontally cut pieces of copper tape; horizontally cut and soldered copper tape; areacon fabric. Conductive tapes and fabrics are both acceptable solutions for the conductive sections [23]. Though, not all copper tape and fabric configurations  
Figure 2: **(a)** 2.5 GHz patch antenna positioned at the left side of the thorax under the chest; **(b)** 2.5 GHz patch antenna positioned on the trunk.  
are appropriate for textile antennas. The conductive fabric is required to have a good conductivity and needs to have a high density of conducting fibers. A principle derived from this study [23] is that there can be discontinuities in the conducting shape from cutting the copper tape as long as these discontinuities are parallel to the surface current (vertically cut copper tape). When discontinuities are perpendicular to the current flow direction there will be reflections of the electromagnetic fields at the interfaces [23]. Additionally the use of different conductive materials (conducting nylon, embroidered conducting thread, conducting paint) as conductive sections and different constructing methods of a textile spiral antenna were presented in [24].
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm
### Particle Representation and Initialization
#### 5.2.2 Determination Optimal Number of Circular Areas  
The proper number of clusters is essential for clustering effectiveness, otherwise the network cannot benefit from clustering advantages. The optimal number of clusters is defined as C. If the value of \(C\) is too large, there will be many circular areas that overlap. If the value of \(C\) is too small, the circular area cannot cover as much of the network environment as possible. In this paper, in order to determine the C value, it is assumed that the clusters distribute the entire network environment evenly by layer, as shown in the Figure 3. There are partial overlaps between the circular areas in the figure. Because the network area cannot be filled with circles, and partial overlap of the circular areas can offset the unfilled network area. \(r\) is the sum of \(2R_{i}\) which is calculated by Equation (11). The radius of the circle of the \(k\)th layer is \(R_{\rm k}\) (assuming that the network area is divided into \(K\) layers). The number of circles of the \(k\)th layer is \(n_{\rm k}\). According to the above conditions, Equation (12) can be obtained.  
\[r=\sum\
olimits_{i=0}^{K}2R_{i} \tag{11}\]  
\[A\approx\sum\
olimits_{k=1}^{K}n_{k}\times\pi R_{k}^{2} \tag{12}\]  
where \(A\) is the area of the whole network environment.  
Thus, the optimal circular area number \(C\) can be obtained by cyclic calculation. The following is the calculation process for calculating the \(C\) value. The algorithm to calculate the optimal circular area number is illustrated in Algorithm 1.  
```
Input: The radius of cluster: \(R=d_{1}/^{\bullet}\) R is initialized to \(d_{1}\)*/. The area of the network: \(A\). The maximum distance between circular area centers and the BS: \(dis_{\max}\). The farthest distance from the base station to the boundary of the circle where \(R\) has been calculated: \(r\)=0/* \(r\) is initialized to 0. \(r\) is the sum of \(2R_{i}\) which are calculated */
```  
**Output:** the optimal number of circular areas: C  
```
1.while A>0 do
2.\(A=A-\pi\Big{[}(2R+r)^{2}-r^{2}\Big{]}\)
3.\(C=C+\frac{\pi(2R+r)^{2}-\pi r^{2}}{\pi R^{2}}\)
4.\(r=r+2R\)
5.\(R=\frac{r(R_{\max}-d_{1})+d_{1}\times dis_{\max}}{dis_{\max}-R_{\max}+d_{1}}\)
6.endwhile
7.\(C=C\times H/^{\bullet}H\) is determined by the location of the BS*/
```  
**Algorithm 1**The calculation process of optimal circular area number \(C\)  
\(H\) is determined by the location of the BS. As shown in Figure 4, \(H\) in (a) where the BS is located on the boundary of the monitoring area is 1/2. \(H\) in (b) where the BS is located in the monitor area is 1. \(H\) in (c) where the BS is located on a vertex of the rectangle network area is 1/4. \(H\) depends on the angle between the two boundaries intersecting the BS.  
Figure 3: Uniform distribution by layer of circular areas.
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 5 Low Time Fragmentation-Oriented TT&C Task-Planning Algorithm  
### Graph Learning-Based Framework  
Section 4 has transformed the TT&C task-planning problem into the problem of solving the independent set in the task-planning conflict graph. Based on this, this section combines deep learning and graph theory to propose a problem-solving framework, as shown in Figure 4. In this framework, the set _IS_ represents the local solution of the independent set problem, which is a subset of set \(V\). The Q evaluation function is obtained through reinforcement learning training.  
As shown in Figure 4, we first construct a set of feasible schemes. On this basis, we construct a task-planning conflict graph and calculate the Q values of all vertices in the graph. Then, according to the Q evaluation function trained by deep learning, we select the vertex with the largest Q value. Additionally, we add this selected vertex to the current local solution _IS_. Afterward, we deleted the vertex from the task-planning conflict graph, and the task-planning conflict graph is updated. Lastly, we repeat the above process until there are no remaining vertices in the task-planning conflict graph. The current local solution _IS_ is the optimal independent set.  
Figure 4: Graph learning-based framework for solving the TT&C task-planning problem.
## 4 Case Study  
### Data Description  
The basic data are the actual bus operation data of the No. 115 bus line provided by the Ganzhou Public Transportation Corporation on 28 March 2022 (Monday), including passenger flow data, on-board real-time GPS data, travel logs, and urban road network structure and coordinates. The data for each bus stops of the No. 115 bus line is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bus Stop Number**} & \multirow{2}{*}{**Bus Stop Name**} & **Longitude** & **Latitude** & **Distance between** \\  & & **Coordinates (E)** & **Coordinates (N)** & **Stations (m)** \\ \hline
1 & Station & 114.96962 & 25.82529 & - \\
2 & Ganzhou Trade Mart & 114.96907 & 25.82199 & 458 \\
3 & Jitai New City District & 114.96069 & 25.81343 & 1104 \\
4 & Ganzhou Technician College & 114.94987 & 25.80440 & 1527 \\
5 & Hydraulic Turbine Factory & 114.93209 & 25.79220 & 1472 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bus stop coordinates and station spacing for No. 115 bus line.  
Figure 8: Two-point crossover operation. The yellow and white blocks respectively represent segments that have been cut down and fused together from the parental generation to the offspring generation.
## 4 Methods
### Cluster-Head Election Phase  
This paper develops a dual cluster-heads method combining the primary and vice cluster-heads, to reduce the communication load of cluster-heads, and extend the network lifetime. The primary cluster-head is selected based on a combination of the node's residual energy and the distance from the node to the clustering center. The vice cluster-head is selected based on two objectives: the node's residual energy, and the distance from the node to the base station.
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 5 Experiments and Results
### Application Tests on Real-Time Pathfinding  
This work is motivated by the need to engage real-time and collision-free pathfinding tasks for agents with a certain safety radius. Such agents often maneuver in dynamic, crowded environments, and have to decide their next move in a limited amount of time. In order to demonstrate the usefulness of our algorithm in such scenarios, two agents (denoted as agent \(X\) and agent \(Y\)) working in a grid map of size \(200\times 200\) (as shown in Figure 12) are simulated. For each round of testing, both of the two agents are assigned the same start and goal cells located on the top right and left bottom of the grid map, respectively. The resulting paths of Agent \(X\) and \(Y\) are respectively shown in Figure 12a,b as light green trajectories; by contrast, the optimal path searched by the classical path planning algorithm, A*, is marked as red trajectories. Agent \(X\) simply adopts LRTA* to directly search and move in the underlying DM, while agent \(Y\) shown in Figure 12b would firstly search out a high-level path from the DM-based subgoal graph, and then engages LRTA* to search and move between the segments of the path. In order to gain the average performance, we run the test of each of the ten pairs of start and goal cells 100 times.  
For agent \(X\), applying LRTA* to engage a real-time search can help the agent successfully avoid collisions with obstacles, but it is very easy for the underlying DM to generate local minima around typical locations such as concaves, narrow channels, long distance barriers, and so on. To the cells  
Figure 11: The average cell visits provided by the three dynamic algorithms.  
Figure 10: The average computation time provided by the four algorithms for updating DMs. The algorithms prefixed with “*” are dynamic algorithms and only update the affected areas.  
within these locations, the occurrence of the local minima results in an increase in errors between the default heuristic values and the actual values. Therefore, LRTA* has to recheck these cells many times to incrementally correct their heuristic values (as shown in line 90, Table 6). Only in this way can the heuristic depressions be gradually filled up and finally drive the agent to escape from the local minima. Unfortunately, such runtime correction incurs meaningless movements (As the regions labeled as A, B, and C in Figure 12a) which are unacceptable to the requirements of engaging reasonable behavior.  
As for agent \(Y\), a high-level path (as the red path shown in both Figure 12a,b, is firstly searched out from the DM-based subgoal graph by executing the algorithms proposed in Table 5 (in order to make the figures clear, we only retain the subgoals and do not draw the direct-h-reachable edges). The resulting path provides a list of sparse and direct-h-reachable waypoints to circumnavigate the collision regions. Based on the path, agent B uses LRTA* at runtime to search and move along the path; therefore, it can efficiently avoid local minima and terminate meaningless movements.
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 6 Simulation Results
### Performance of Algorithm  
For the problem of the UAV offloading strategy in the MEC system presented in this paper, the Coordinate Descent (CD) method randomly gives a set of offloading actions for all UAVs. For the combination of each piece of equipment in offloading mode and local calculation mode in sequence, the maximum residual energy of the UAV is calculated by solving formula \(P2\). After comparison, the computing mode with the largest residual energy in the UAV is selected. When the mode conversion is performed for a device, the offloading action of other devices is fixed. The CD algorithm is equivalent to searching in a two-dimensional space at this time. The dimension is low and the result is good.  
In this section, we evaluate the decision quality of the DOTO algorithm. Firstly, the optimal offloading decision of each time block is found from all \(2^{N}\) offloading actions of \(N\) UAVs. Then, we find the total residual energy of the UAV in the MEC system obtainedwithin 10,000 time blocks under the three modes of the DOTO algorithm, CD algorithm and optimal offloading decision. In Figure 4, we compare the ratio of the results of the DOTO algorithm and the CD algorithm to the results of the optimal offloading decision. The closer the curve in the figure is to 1, the better the algorithm's performance. From the results in Figure 4, it can be seen that when the number of UAVs in the MEC system varies between 5 and 10, the DOTO algorithm performs better than the CD algorithm. When the number of devices increases, the performance of the two modes decreases gradually. However, the trend of performance degradation for the DOTO algorithm is more gradual than that of the CD algorithm. When the number of devices increases, the performance gap between the two algorithms is larger. When there are 10 UAVs, the DOTO algorithm can still obtain 99.9% of the residual energy. However, the CD algorithm can obtain 99.2% of the residual energy because, when the number of devices increases, the possible offloading actions increase exponentially. At this time, the simple two-dimensional search of the CD algorithm is unable to search for the global optimal solution, so the performance of the algorithm becomes increasingly worse. The DOTO algorithm benefits from the powerful fitting effect of the DNN, and even if the number of devices is large, the data sample training is sufficient. Therefore, the performance of the algorithm is relatively better and more stable.
# Bacterial Therapy of Cancer: A Way to the Dustbin of History or to the Medicine of the Future?
## 3 Tumor Microbiome: Peaceful Inhabitants or Malicious Instigators?
It is still unclear whether quantitative or qualitative changes in the taxonomic composition of the bacterial microbiota caused by the harmful effects of the environment lead to the development of the disease, or, conversely, the disease causes a transformation of the microbiota composition, shifting it to the \"unfriendly\", hostile form. However, the long-term colonization of the tissue by the bacterial species that are able to cause local genotoxic (DNA-damaging) stress can lead to cellular transformation and ultimately to cancer development (Figure 2) [7; 45; 46; 47]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Microorganism** & **Cancer Type Associated** & **References** \\ \hline _Helicobacter pylori_ & gastric carcinoma & [42; 43] \\ \hline _Salmonella enterica serovar Typhi_ & gallbladder cancer & [48; 49; 50] \\ \hline _Campylobacter jejuni_ & small intestinal lymphomas, & [51; 52] \\ \hline _Chlamydia psittaci_ & ocular lymphomas & [52; 53] \\ \hline _Mycobacterrium tuberculosis_ & lung cancer & [54] \\ \hline _Chlamydia pneumonia_ & lung cancer, lymphomas & [53] \\ \hline _Fusobacterium nucleatum_ & colorectal cancer & [55; 56] \\ \hline _Bacteroides fragilis_ & colorectal cancer & [57; 58] \\ \hline _Escherichia coli_ & colorectal cancer & [57; 58] \\ \hline _Streptococcus bovis_ & colorectal cancer & [59; 60] \\ \hline _Chlamydia trachomatis_ & cervical cancer, lymphomas & [53; 54; 55; 56; 57; 58; 59; 60; 61] \\ \hline _Porphyomonas gingialis_ & pancreatic and oral cancer & [62; 63] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bacteria that are suspected to be associated with various types of cancer. Figure 1: Bacterial species that are commonly discussed as associated or even being causative agents of cancer, and bacteria that were attempted to be used to produce drugs for the treatment of cancer patients, or at least to improve the well-being of patients.
## 2 Data and Methodology  
The operational product, 2B-GEOPROF, from CloudSat is used as the primary dataset in this study. It contains the basic information about cloud detection at 240 m intervals, generally labeled as the cloud mask profile. The accessorial parameters that have been merged in the product of 2B-GEOPROF and 2C-PRECIP-COLUMN, such as surface type, sea surface temperature, and freezing level, were also used. The data in 2009 are used and only oceanic samples are concerned.