esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Extraction of Underground Structure  
Formation wave impedance is restricted by two factors: lithology and physical properties. Lithology refers to the characteristics of a rock structure and its components, while physical properties refer to the characteristics of rock porosity, elasticity and fluid in the aquifer. Since the reflection coefficient can reflect the geological elements in the seismic records, we should extract the reflection coefficient series from the seismic records to obtain the geological information, and then use the obtained reflection coefficient series to convert the seismic waveform section into the wave impedance section, so as to comprehensively and accurately display most of the geological information contained in the seismic records [14; 15]. The following figures (Figures 1 and 2) show the relationship between the seismic wavelet, reflection coefficient and wave impedance.  
For non-interference seismic traces, the results of a convolution in the seismic wavelet and reflection coefficient series are shown in Formula (1):  
\[s(t)=\omega(t)*R(t) \tag{1}\]  
where \(\omega(t)\) is the seismic wavelet, representing the reflection coefficient series and seismic trace. According to Formula (1), the geological information contained in the seismic record will only exist in the only two structural element carriers in the seismic wavelet or reflection coefficient [15; 16]. It is impossible to separate every pure instantaneous reflection wavelet from seismic records to study the changes between the top and bottom reflection wavelets in a single target layer to obtain some geological information about the layer. In other words, it is not easy to obtain underground formation information from the seismic wavelet. Therefore, we can only start from another angle: the reflection coefficient series (Figure 2). The reflection coefficient series depends on the wave impedance difference between the upper and lower media at each formation interface, that is, it is determined by the wave impedance in the formation (the product of the wave velocity and density) [17].  
The reflection coefficient of a single interface is expressed by Equation (2):  
\[R=\frac{|\rho_{2}v_{2}-\rho_{1}v_{1}|}{\rho_{2}v_{2}+\rho_{1}v_{1}} \tag{2}\]
## 5 Smart-BEEM For IoT Systems in 5G Scenarios
### Utilising MIMO  
As shown in Table 4, when multiple communication interfaces are available, how to intelligently select among them for data transmission is challenging. Many existing clustering algorithms assume that all the nodes in the network are homogeneous with one single communication interface. With MIMO available in 5G networks, this assumption is no longer held. Existing work lacks flexibility and ignores the diversities of the sensor nodes. To improve existing algorithms, the context that a sensor is in should be considered. For example, a sensor should be able to choose a suitable communication interface according to its transmission requirements and its communication capability.  
A new clustering algorithm that can utilise MIMO feature in 5G environment is required. Based on BEEM, Smart-BEEM is proposed. In this paper, we assume that in the system, all the IoT devices are capable of MIMO. If a node is selected as CH, it will turn on MIMO to receive data from different sources through different communication channels. Then the CHs will compress the received data and transmit the data back to the BS. Except CHs, each sensing node either transmits small volume of data or video stream (In this paper, we use the term--_\"Data\"_ to present small volume of traffic, such as temperature or humidity information; while we use the term--_\"Video\"_ to present large volume of traffic requiring high network bandwidth, such as video stream). A sensing node will only select one communication interface and a CH for transmission through Smart-BEEM algorithm.
## 6 Conclusions and Future Perspectives
In modern-day RT, special consideration is always given to verify and correct the patient setup or to adjust the treatment plan. These are currently accomplished through image guidance using cone-beam computed tomography (CBCT) with a system attached to a linear accelerator [75; 76]. CBCT uses low-energy X-rays and, therefore, it gives rise to the patient-absorbed dose, especially when this imaging process is performed on a daily basis [77; 78]. There is no reported experience related to the radiogenic risks from both therapeutic and imaging doses in rectal cancer survivors. Future work is required to determine the complete patient exposure and the relevant risk for carcinogenesis attributable to rectal carcinoma irradiation. There is also a need for risk assessments from proton therapy, stereotactic body radiotherapy (SBRT), and adaptive RT. Methods of artificial intelligence may improve radiation therapy planning today through the automation of several procedures [79; 80]. It would be of interest to assess the second-cancer risk on the basis of automated treatment planning for rectal carcinoma. While radiotherapy remains a cornerstone of treatment for rectal cancer, the risk of second primary cancers must be carefully weighed, particularly for patients with longer life expectancy. For high-risk patients--such as younger individuals, those with genetic predispositions, or tumors adjacent to critical organs--strategies should be considered to minimize radiation exposure of the adjacent organs-at-risk, including IMRT, VMAT or even adaptive RT, SBRT or proton therapy where available. Regular long-term surveillance for SPCs should be implemented, focusing on bladder, gynecological, and lung cancers based on reported patterns. In low-risk patients, standard 3D-CRT, IMRT or VMAT approaches remain appropriate, balancing oncologic control and toxicity reduction. Shared decision-making with patients regarding treatment modalities, risk profiles, and surveillance plans is essential to optimize outcomes. This research received no external funding. **Institutional Review Board Statement:** Ethics approval was not required for this review study. **Informed Consent Statement:** Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflicts of interest.
# Outlier Detection and Prediction in Evolving Communities
## 2 Related Work
### Community Detection
#### 2.1.2 Dynamic Methods  
When incorporating time into community detection, temporal graphs can be analyzed in a static way as well as online.  
In the first scenario, the usual technique is to segment the network into time-sorted sub-networks, called snapshots, in which community detection takes place independently. Then, interconnections between communities are requested from different snapshots. An example of dynamic clustering and early fusion based on snapshots is [14], which introduced the LabelRankT algorithm. This algorithm performs an initial clustering and then updates nodes only when they are significantly different from their neighbors in terms of the labels between two consecutive snapshots. The main drawback of such methods is the need for advance knowledge of the network's evolution over time, making them unsuitable for real-time analysis. Moreover, determining the optimal snapshot size selection is challenging, as different inputs can result in quite different structures.  
Our work targets online networks with temporal information, where little related research exists. With regard to early fusion methods, in [15] links between social actors were formed based on the use of common keywords, then community and clique detection was applied on the resulting network, where detected overlapping communities could represent important events in real time. Similarly, in [16] an interest social network model was created with links referring to structural connections and similar interests of entities, based on which modularity-driven clustering was performed in a dynamic network to detect overlapping communities.  
Using simultaneous fusion, in [17] a real-world social network incorporating content was used to identify interesting patterns in data which could be used to detect overlapping communities as time evolves. Finally, COTILES, introduced in [6], is an online community detection algorithm based on the TILES algorithm [18], which is extended to form overlapping communities based on both structural and attribute criteria.  
Table 1 includes a summary of the discussed works on community detection, comparing them based on (i) the fusion method for content and structure they select, i.e., early, simultaneous, or late; (ii) whether they detect overlapping or non-overlapping communities; and (iii) for the dynamic methods, whether they rely on snapshots or are online.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Fusion Method** & & \multicolumn{2}{c}{**Communities**} & \multicolumn{2}{c}{**Dynamic**} \\ \hline
**Paper** & **Early** & **Simultaneous** & **Late** & **Overlap** & **Non-Overlap** & **Snapshot** & **Online** \\ \hline
[8; 9; 10] & ✓ & & & & ✓ & & \\ \hline
[11] & ✓ & & & ✓ & & & \\ \hline
[12] & & & ✓ & & ✓ & & \\ \hline
[13] & & & ✓ & & ✓ & & \\ \hline
[14] & ✓ & & & & ✓ & ✓ & \\ \hline
[15] & ✓ & & & ✓ & & ✓ & \\ \hline
[16; 17] & & & ✓ & ✓ & & ✓ & ✓ \\ \hline COTILES & & ✓ & & ✓ & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Community detection techniques.
## 3 Results and Discussion
### Temporal Variation of NH\({}_{3}\)-N and TP in Typical River  
The spatial distribution of NH\({}_{3}\)-N, TN, and TP in the six rivers of Changchun City has been explored from the location of the sampling point and pollution rating in the previous content. To more fully understand their occurrence characteristics, it is necessary to analyze their time distribution. On the basis of the information about NH\({}_{3}\)-N and TP pollution provided in the annual surface water environmental quality report issued by the Changchun Ecological Environment Bureau, compared with the limits in national standard GB383-2002, time analysis of the exceeding standard degree of NH\({}_{3}\)-N and TP qualities in a typical river (Yitong River) was carried out, and the result from 2014 to 2021 is displayed in Figure 6.  
The over-standard multiples of NH\({}_{3}\)-N and TP pollution in Yitong River decreased markedly from 2014 to 2019, as shown in Figure 6; NH\({}_{3}\)-N and TP reduced from 12.7 to 1.6 and 3.0 to 0.13, respectively. In this experiment, NH\({}_{3}\)-N's and TP's exceeding multiples were, separately, 0.918 and 0.246, suggesting that the pollution situation of NH\({}_{3}\)-N and P was gradually developing in a beneficial direction. The main reason for this trend was that the government has actively implemented comprehensive governance work for Yitong River. Yitong River is the mother river of the Changchun people, which was called the largest water in Changyi in ancient times. However, the Yitong River itself gradually declined as the city grew. Fortunately, the advancement of civilization has become more and more rational. In 1997, the people's Government of Changchun City started the management of the upper part of Yitong River, and since then, opened a new chapter for the all-sided treatment of Yitong River. As shown in Figure 6, the pollution degree of NH\({}_{3}\)-N and TP reduced dramatically in 2016. The reason was that a new round of planned treatment projects was established in 2015 and formally implemented in 2016. From the results, the project has achieved great successes.  
Furthermore, when the nutrient load in water is constant, temperature, precipitation, and solar radiation will affect the eutrophication level of water. Thus, both social and cli  
Figure 6: Exceeding multiples of ammonia nitrogen (NH\({}_{3}\)-N) and total phosphorus (TP) in Yitong River of Changchun City from 2014 to 2021.  
mate trends must be taken into account when designing integrated management strategies for surface water.
## 5 Improvement Scheme for the Crayfish Algorithm  
### Introduction to Strategy  
#### 5.1.1 Principles of the Subtraction Averaging Algorithm  
The Subtraction Averaging Algorithm is a technique for optimizing system performance by iteratively adjusting parameters. Its main goal is to improve convergence speed and result accuracy during optimization. The operation process of the algorithm can be divided into the following steps:  
1. Initialization: Define a set of initial parameter values based on preliminary estimates or experience.
2. Difference Calculation: In each iteration, calculate the difference between the current parameter values and the target values. These target values are usually theoretically optimal values or reference values set by external standards.
3. Adjustment: Based on the calculated difference, subtract a proportional amount of the difference from the current parameter values. This proportion is typically a pre-set factor used to control the adjustment magnitude. The subtraction operation moves the parameter values towards the target values.
4. Averaging: Perform averaging on the adjusted values to reduce the sharp fluctuations that may result from single adjustments. By averaging multiple adjusted values, the adjustment process becomes smoother and reduces instability caused by excessive single adjustments.
5. Iteration: Repeat the above steps--difference calculation, adjustment, and averaging--until the parameter values converge to the desired precision level or meet stopping criteria. Stopping criteria may include reaching the maximum number of iterations or the change magnitude falling below a certain threshold.  
The advantages of the Subtraction Averaging Algorithm lie in its simplicity and efficiency. By gradually reducing differences and smoothing adjustments, it effectively improves parameter stability and accuracy, minimizing fluctuations during the convergence process. It is applicable in various fields, including control system design, machine learning model optimization, and data smoothing, making it suitable for scenarios requiring precise adjustment and optimization.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry  
Terpenoids have complex and diverse structures, with various functional groups and active sites, thus exhibiting different biological activities, such as special fragrances, antioxidant, antibacterial, and anticancer effects. Moreover, due to the complexity of terpenoid structures, a single terpenoid compound can play more than one role in a food product. For instance, squalene acts as both an antioxidant and an antimicrobial agent in muffin preservation [87]. In the food industry, terpenoids are highly favored due to their natural source. However, extraction and purification from natural plants or animals is cumbersome and inefficient. Nowadays, an increasing number of microbial hosts have been engineered for the biosynthesis of terpenoids, and some terpenoids have achieved gram-scale production in microorganisms. Here, we provide an overview of several representative terpenoid-producing microbial cell factories that have been used in the food industry (Table 3). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Functions** & **Chemicals** & **Host** & & **Strategy** & **Yield** & **Reference** \\ \hline Flavor and fragrance & \multirow{2}{*}{Linalolol} & _Pantona_ & (1) & Identifying (S)-specific linaolol synthase (LINS) and (R)-specific LINS; Co-expressing them with mutated farnesyl diphosphate synthase & (S)-linalol, 5.60 g/L (R)-linalol, 3.71 g/L (SpA*). & [88] \\ \hline \multirow{4}{*}{Linalolol} & \multirow{4}{*}{Linalol} & \multirow{4}{*}{_Sachmomyces cerevisiae_} & (1) & Semi-rationally designing the substrate binding pocket entrance of & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Myrcene} & \multirow{4}{*}{_Escherichia coli_} & (1) & N-terminal truncation of linaololol dehydrogenase isomerase (LDL)
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 5 _Citrus_ Flavonoid Extraction Techniques
### Non-Conventional Extraction Techniques
#### 5.2.1 Ultrasound Assisted Extraction (UAE)  
Ultrasound-assisted extraction is a new technology that is being used to extract natural products that previously took many hours to extract using traditional methods. Initially, it was employed to preserve food, but in the last decade, it has also been utilized to extract beneficial substances (mainly polyphenols). Because of the simplicity of the method, benefits such as reduced extraction time, increased extract yield, and the use of water as a solvent, which reduces the usage of organic solvents, are documented. Therefore, to avoid unwanted reactions generated by the UAE and maximize the extraction yield, the extraction parameters (e.g., extraction duration, solvent system, and, if possible, U.S. frequency) should be tuned prior to developing the extraction process [58]. [PERSON] et al. 2010, conducted the extraction of _Citrus_ peel flavonoids from _C. sinensis_, _C. lafifolia_, and _C. reticulata_ under optimal ultrasonic conditions of 60 kHz, 40 \({}^{\circ}\)C, for 1 h, using methanol as a solvent [59].
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 4 Reluctance Coil Gun Simulations
### Model Parameters  
In order to compare results with other previous studies, the kicking system simulated is identical to the _Tech United Team_ one described in [11]. However, geometry of our coil gun is very similar to this reference design. Parameters of the model are as follows:  
* Distance from lever axis to plunger touch point: \(R_{1}=13\) cm
* Distance from lever axis to ball touch point: \(R_{2}=24\) cm
* Coils number (each coil as been chosen identical): \(nb_{Cols}=1,2,3,4\)
* Coils length (for each coil): \(L_{Coll}=11.5\,/\,nb_{Cols}\) cm
* Coils number of turns (for each coil): \(N_{Coll}=1000\,/\,nb_{Cols}\) turns
* Coils resistance (for each coil): \(2.5\,/\,nb_{Cols}\Omega\)
* Capacitors number: \(nb_{Capacitors}=nb_{Cols}\)
* Capacitors value (for each capacitor): \(4700\,\,\mu\mathrm{F}/\,nb_{Cols}\)
* Capacitors charge voltage: \(425\) V
* Plunger iron rod diameter: \(D_{Plunger}=25\) mm
* Plunger iron rod length: \(L_{Plunger}=11.5\) cm
* Plunger iron rod mass: \(m_{Plunger}=690\) g
* Plunger extension diameter: \(D_{Ext}=18\) mm
* Plunger extension length: \(L_{Ext}\) cm
* Plunger extension mass: \(m_{Ext}=0.68*L_{Ext}\) (in m)
* Distance from coil to lever: \(D_{Lever}=4\) cm
* Vertical lever mass: \(m_{Lever}=80\) g
* Ball mass: \(m_{Ball}=450\) g
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 4 Geometric Interpretation on Reward Function
With the FIM corresponding to a sensible reward function, the projection will vary as the state of a sensor changes. To highlight the difference between the two statistical reward functions in question, we plot the projection of FID circles in parameter space under the scenario where two fixed targets are observed by the two bearing-only sensors. These circles are centred at a target state with the same radius \(\boldsymbol{\
u}\) in the statistical manifold spanned by the FIM corresponding to the two statistical reward functions, respectively. We examine the changes of FID circles before and after the Sensor 2 move circularly around one target. The FID circles, shown in Figure 3a,b, have changed significantly as Sensor 2 moves circularly around the target on the left. This indicates that the amount of target information which may be observed by the two sensors varies with sensor states. On the other hand, if we plot the FID circles in the statistical manifold spanned by the FIM without off-diagonal elements which correspond to using the trace of FIM as a statistical reward function, as shown in Figure 4a,b, the FID circle centered at the target on the left does not change as the Sensor 2 moves circularly around the target. We emphasize that sensor 2 moves close to the target on the right while keeping the same distance to the target on the left. The FID circles on both left and right targets change in Figure 3 under the Determinant of FIM, but the FID circle on the left remains the same as shown in Figure 4 after the movement of sensor 2 when the Trace of FIM criterion is used. This result indicates that the Fisher information will not be fully explored if trace rather than determinant of the FIM is used as a statistical reward function. The Expected RID performs similarly to the trace of FIM. Figure 3: Illustration of the circles of statistical manifold spanned by FIM for given sensor locations (**a**) sensor 1 is at (1500, 400) and sensor 2 (973, 387); (**b**) sensor 1 is at (1500, 400) and sensor 2 moved to (1219, 544) in a circle around the target on the left. Colorbar value represents the changes of the amount of information gained by the sensor system if the target moves between the center and a manifold circle boundary.
# Chemical Diversity of Mediterranean Seagrasses Volatilome
###### Abstract  
Background/Objectives: Biogenic volatile organic compounds (BVOCs), extensively studied in terrestrial plants with global emissions around 1 PgC yr\({}^{-1}\), are also produced by marine organisms. However, benthic species, especially seagrasses, are understudied despite their global distribution (177,000-600,000 km\({}^{2}\)). This study aims to examine BVOC emissions from key Mediterranean seagrass species (_Cymodaceae nodosa_, _Posidonia oceanica_, _Zostera noltei_, and _Zostera marina_) in marine and coastal lagoon environments. Methods: BVOCs were collected using headspace solid-phase microextraction (HS-SPME) using divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibers and analyzed by gas chromatography-mass spectrometry (GC-MS). Results: An important chemical diversity was found with a total of 92 volatile compounds (61 for _Z. noltei_, 59 for _C. nodosa_, 55 for _P. oceanica_, and 51 for _Z. marina_), from different biosynthetic pathways (e.g., terpenoids, benzenoids, and fatty acid derivatives) and with several types of chemical functions (e.g., alkanes, esters, aldehydes, and ketones) or heteroatoms (e.g., sulfur). No differences in chemical richness or diversity of compounds were observed between species. The four species shared 29 compounds enabling us to establish a specific chemical footprint for Mediterranean marine plants, including compounds like benzaldehyde, benzeneacetaldehyde, 8-heptadecene, henicosane, heptadecane, nonadecane, octadecane, pentadecane, tetradaceane, and tridecanal. PLS-DA and Heatmap show that the four species presented significantly different chemical profiles. The major compounds per species in relative abundance were isopropyl substrate for _C. nodosa_ (25.6%), DMS for _P. oceanica_ (39.3%), pentadecane for _Z. marina_ (42.9%), and heptadecane for _Z. moltei_ (46%). Conclusions: These results highlight the potential of BVOCs' emission from seagrass ecosystems and reveal species-specific chemical markers.  
seagrasses; Mediterranean Sea; biogenic volatile organic compounds (BVOCs); chemical profile screening; HS-SPME/GC-MS 202414705[[https://doi.org/10.3390/metabol14120705](https://doi.org/10.3390/metabol14120705)]([https://doi.org/10.3390/metabol14120705](https://doi.org/10.3390/metabol14120705))