esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Biosynthesis of Dextran in Optimized Microbial Processes
### Bioagents Used in the Production Process  
Normally, the synthesis of polysaccharides by plants and production from agricultural products are relatively inexpensive. However, the cultivation of such resources requires favorable climates during a particular season in a year. Furthermore, plant-sourced materials have a vast inconsistency in their availability, purity, and properties. In contrast, the yield and properties of polysaccharides originating from microbial sources can be controlled by optimized parameters set for microbial fermentations. At the same time, microbial synthesis can be performed using economical substrates like by-products and residual materials produced in the agricultural and food industries [66; 67]. Although the dextran is a neutral glucan with complex \(\alpha\)-1, 6 glycosidic linkages between glucose units with branches with \(\alpha\)-1, 2, \(\alpha\)-1, 3, and \(\alpha\)-1, 4 connections [60], nevertheless, the degree and nature of branching at 2, 3, or 4 positions can be manipulated by the selection of an appropriate dextran-producing strain in the synthesis process of dextran.  
Dextran is mainly released extracellularly by fermenting lactic acid bacteria utilizing sucrose as a substrate in synthesis. Enzyme dextran's curds are as a catalyst in the transfer of D-glucopyranosyl residues from sucrose to form dextran [60]. LAB are non-sporge-forming, fermentative, facultatively anaerobic bacteria; products prepared using LAB are considered safe for human consumption. Several LAB strains approved as Generally Recognized As Safe (GRAS) have been given Qualified Presumption of Safety (QPS) status [68], which are widely employed in food production [69]. Characterized as effective probiotics, LAB have been used for the restoration of imbalanced gut microbiota affected in antibiotic-therapy-induced gut dysbiosis [70]. The clinical potential of LAB strains, which are used in fermentation for probiotic food and beverages and in the formulation of symbiotic supplements, has been recognized as a psychobiotic for cognitive treatment through gut-brain signaling [68], and for the biosynthesis of compounds for medical applications [71].  
Due to their health-improving effects in nutrition and functional food and nutraceuticals, certain species of LAB have also been exploited as suitable bioagents for dextran production. Dextran biosynthesis by various strains differs in their glucosidic linkages, degree and type of branching, molecular weight, and physical and chemical characteristics [61]. Dextransucrase can utilize the high binding energy of the glycosidic bond in sucrose to generate the \(\alpha\)-1, 6 linkages of the polymer backbone without using adenosine triphosphate or cofactors. In the presence of dextran-surcase, other natural or synthetic polymers such as lactulosucose, \(\alpha\)-D-glucopyranosyl fluoride, and p-nitrophenyl-\(\alpha\)-D-glucopyranoside can also act as donor substrates to produce dextran [72]. The bulk production of dextran is required to meet its extensive needs in medical and food applications.
## 4 Adaptive OFDM-IM Method  
The proliferation of vehicular network scenarios, coupled with the increasing number of users, intensifies network competition, resulting in scarce spectrum resources. As the number of users continues to rise, the energy consumption due to user mobility in various scenarios must also be taken into account. Building on the previous section, this section further explores the multidimensional requirements for the optimization of vehicular communication systems' performance. Unlike traditional approaches with a single optimization objective, optimizing vehicular network performance requires attention to be paid not only to the error rate but also to the spectral efficiency and energy consumption, ensuring efficient operation under various interference conditions. Therefore, this section integrates three performance indicators: the spectral efficiency, error rate, and energy consumption. We propose a method to adaptively control the OFDM-IM index parameters according to the scenario and user demands, aiming to maximize the efficiency. By referencing fuzzy logic to calculate the weight distribution, we determine the optimal OFDM-IM index parameters in various scenarios, enabling the adaptive control of the index parameters.  
Figure 3 illustrates the overall process of the user demand- and scenario-adaptive OFDM-IM method. First, the channel parameters for the vehicular network scenario are determined, including the reflection factor of NLOS paths and the Rician factor. These parameters are then used to construct the channel formulas for the straight road scenario discussed in the previous section, which are incorporated into the OFDM-IM model to facilitate communication in the vehicular network channel. Subsequently, an energy efficiency maximization formula is established for this scenario, and the user requirements are analyzed to determine the weights of the three factors accordingly. The BER performance under the OFDM-IM model is analyzed to find the minimum signal-to-noise ratio (SNR) at which the OFDM-IM BER surpasses that of traditional OFDM. Using the concept of fuzzy logic, the optimal index parameters of the OFDM-IM model in this scenario are determined to maximize the system's energy efficiency. The optimal number of subcarriers N, the number of active subcarriers K, and the modulation order M at the minimum SNR for this scenario are identified, thereby implementing a method for adaptive control according to the user requirements and the scenario.
###### Abstract  
In the traditional approach for centrality measures, also known as sociocentric, a network node usually requires global knowledge of the network topology in order to evaluate its importance. Therefore, it becomes difficult to deploy such an approach in large-scale or highly dynamic networks. For this reason, another concept known as egocentric has been introduced, which analyses the social environment surrounding individuals (through the ego-network). In other words, this type of network has the benefit of using only locally available knowledge of the topology to evaluate the importance of a node. It is worth emphasizing that in this approach, each network node will have a sub-optimal accuracy. However, such accuracy may be enough for a given purpose, for instance, the vehicle selection mechanism (VSM) that is applied to find, in a distributed fashion, the best-ranked vehicles in the network after each topology change. In order to confirm that egocentric measures can be a viable alternative for implementing a VSM, in particular, a case study was carried out to validate the effectiveness and viability of that mechanism for a distributed information management system. To this end, we used the egocentric betweenness measure as a selection mechanism of the most appropriate vehicle to carry out the tasks of information aggregation and knowledge generation. Based on the analysis of the performance results, it was confirmed that a VSM is extremely useful for VANET applications, and two major contributions of this mechanism can be highlighted: (i) reduction of bandwidth consumption; and (ii) overcoming the issue of highly dynamic topologies. Another contribution of this work is a thorough study by implementing and evaluating how well egocentric betweenness performs in comparison to the socioeconomic measure in VANETs. Evaluation results show that the use of the egocentric betweenness measure in highly dynamic topologies has demonstrated a high degree of similarity compared to the socioeconomic approach.  
ego-networks; sociocentric centrality measures; egocentric centrality measures; egocentric betweenness measure; vehicular ad hoc networks (VANETs); social network analysis (SNA) +
Footnote †: journal:  
0000-0002-4000-0000-0000][PERSON] 1,2,*0000-0002-0002-0002-0002][PERSON] 1  
1 Institute of Computing (IC), University of Campinas (UNICAMP), 1251 Albert Einstein Av., Campinas, SP 13083-970, Brazil; [EMAIL_ADDRESS] (R.I.); [EMAIL_ADDRESS] (E.R.M.M.); [EMAIL_ADDRESS] (L.A.V.)
# Novel Exploit Feature-Map-Based Detection of Adversarial Attacks
## 1 Introduction  
Due to clear advantages in terms of the prediction accuracy of deep learning (DL) models that are presently being applied in a broad variety of disciplines. VGGNet and AlexNet, two of the most popular DL models for computer vision tasks, have a lot of hype because they promise things such as superhuman autonomous driving or health diagnostics [1]. Simultaneously, a drawback of DL is becoming more generally recognized as the opaque nature of its decision-making process. Pre-trained models, commonly called black boxes, make it impossible to understand which components of the input contributed to the output and in what way [2]. The end user may desire an exploit that is simple enough to be easily communicated, but the CNN may be needed to conduct a highly intricate chain of operations in order to accomplish the task. As a consequence, there is a trade-off between how realistic the description is of the internal dynamics of the network and how explainable it is [3].  
Image classification systems are prone to adversarial attacks [4], which add tiny alterations to photos, leading these algorithms to make erroneous predictions. Theseattacks are extremely powerful against even the most successful convolutional network-based systems [5; 6], despite the perturbations being typically unnoticed or regarded as little \"noise\" in the image. The success of adversarial attacks puts real-world convolutional network applications at risk of security breaches. The network performs calculations that are very different from those made by human brains. As provided below, the Figures depict original and attack images with a feature-map and also its attacked feature-map as shown in Figure 1.  
This research describes adversarial attack detection on image classification or prediction algorithms such as AlexNet, VGGNet, and ResNet. It further provides different feature-map detection methods and proposes to exploit a feature-map that works well on individual objects in an image possible to use.  
To summarize, the following are the major contributions: (1) it presents an adversarial attack using the \"Wherefore? What? and How?\" concepts. Researchers can quickly and effectively establish adversarial attack awareness using the proposed method; (2) it discusses the strengths and flaws of the proposed methods to explore each for a specific application; and (3) it also describes different types of attacks on image pixel and its detection methods.
# An Improved Lightweight Dense Pedestrian Detection Algorithm
## Proposed Algorithm
### SloU Loss Function  
#### 3.2.1 Existing Loss Function Analysis  
Although the design of depth models has been extensively researched, the loss function used for bounding box regression is also important in object detection [30]. The original IoU [31] loss can be expressed as the intersection ratio of the actual and predicted frames, as shown below:  
\[\text{IoU}=\frac{\text{A}}{\text{B}},\text{ Loss}_{\text{IoU}}=1-\text{IoU} \tag{1}\]  
where A is the intersection and B is the union. It does not differentiate between the cases where the two frames do not overlap. The GloU [32] loss was then suggested using the following equation:  
\[\text{GIoU}=\text{IoU}-\frac{\text{C}}{\text{D}},\text{ Loss}_{\text{GIoU}}=1-\text{GIoU} \tag{2}\]  
where C is the difference set and D is the minimum circumscribed rectangle. When the prediction frame is inside the real frame and its area is equal, GIoU cannot identify the relative position relationship between the prediction frame and the real frame, which is why DIoU [33] introduces the center distance, which is defined as follows:  
\[\text{DIoU}=\text{IoU}-\frac{\text{d}^{2}}{\text{c}^{2}},\text{ Loss}_{\text{DIoU}}=1-\text{DIoU} \tag{3}\]  
Figure 8: VoV-GSCSP module structure.  
Figure 7: GS bottleneck module structure.  
where d is the distance between the prediction center point of the frame and the real frame, and c is the diagonal distance of the minimum outer rectangle. The distance between the prediction frame and the real frame can be directly approximated by reducing the DIoU loss function, and the convergence speed is quick. DIoU cannot identify the relationship between the prediction frame and the real frame when the prediction frame is inside the real frame and the area of the prediction frame and the center distance are equal.  
CIoU then adds the loss of detection frame scale to DIoU and raises the loss of length and breadth, resulting in a more consistent prediction frame with the real frame.  
The following is the CIoU formula:  
\[\text{CIoU}=\text{IoU}-\frac{\text{d}^{2}}{\text{c}^{2}}-\frac{\text{v}^{2}}{ \left(1-\text{IoU}\right)+\text{v}},\text{ Loss}_{\text{CIoU}}=1-\text{CIoU} \tag{4}\]  
where d is the distance between the predicted box's center point and the real box, c is the diagonal distance of the smallest outside rectangle, and v is the aspect ratio's similarity factor, defined as follows:  
\[\text{v}=\frac{4}{\pi^{2}}\left(\arctan\frac{\text{W}_{\text{b}}}{\text{H}_{ \text{b}}}-\arctan\frac{\text{W}_{\text{p}}}{\text{H}_{\text{p}}}\right)^{2} \tag{5}\]  
where W\({}_{\text{b}}\), H\({}_{\text{b}}\), W\({}_{\text{p}}\), and H\({}_{\text{p}}\) are the true frame width and height, as well as the predicted frame width and height, respectively.
## 3 Results
### Findings from the Case Study Comparison
#### 3.5.4 Ganjam, India--Case Study Findings  
Ganjam is a coastal district in the state of Odisha in East India. Its economy is based on agriculture and the population is concentrated along the Bay of Bengal and the valleys among the main rivers. Ganjam is frequently affected by cyclones and subsequent flooding, which displace large proportions of the population, leaving behind major destruction. The most destructive cyclone hit in 1999, others followed in 2013, 2014 (cyclone Philain), and most recently cyclone Far in 2019.  
Microgrids have been adopted at various levels to reduce the economic losses caused by damaged energy infrastructure and the long-lasting power outages. Disaster responses involve state-led, utility-centred grid transformation and community-led energy projects. In 2014 after Philain hit, the Asian Development Bank offered a loan of USD 100 million to the state government to build cyclone-proof energy infrastructure. While the opportunity for a microgrid was discussed, the utility focused on reinforcing and diversifying the existing infrastructure (underground cabling, pole upgrade, additional grid sub-stations and multiplying power sources to each grid). Other improvements involved grid automation, smart meter uptake and some rooftops solar and solar streetlights [90; 91].  
An example of a community microgrid is Maligao, a village in rural Odisha (50 homes). In 2009, Australian energy company Ekistica in collaboration with Indian NGO Gram Vikas installed a solar microgrid, which was more cost-effective than connecting to the main grid [92]. The project was overseen by a village committee, and monthly payments covered the operation and maintenance undertaken by villagers. By 2013, the village was connected to the main grid, and the microgrid became redundant, oversight failed, and the project derailed. When villagers became dissatisfied with the unreliability of the main grid, Gram Vikas agreed to rebuild the microgrid, this time with a focus on household electricity demand and adjusted tariffs that reflected affordability.  
The regulatory environment in India--the main grid is state-owned but both public and private ownership arrangements occur for generation--allows for a diversity of energy supply options, in particular in regions that are not grid-connected or where electrification is insufficient and unreliable.
## 1 Introduction
### Medical and Surgical Procedures in Space  
Despite the presence of microgravity, both basic life support and advanced cardiac life support are feasible during space travel with some modifications [249; 250]. For example, the recent guidelines for CPR in microgravity recommend specialized techniques for delivering chest compressions [251]. The use of mechanical ventilators, and moderate sedation or general anesthesia in microgravity are also possible but the evidence is extremely limited [252; 253]. In addition, there are several procedures such as endo-tracheal intubation, percutaneous tracheostomy, diagnostic peritoneal lavage, chest tube insertion, and advanced vascular access which have only been studied through artificial stimulation [254; 255].  
Once traditionally \"surgical\" conditions are appropriately diagnosed, the next step is to determine whether these conditions should be managed medically, percutaneousously, or surgically (laparoscopic vs. open procedures) [47; 256]. For example, acute appendicitis or cholecystitis that would historically be managed surgically in terrestrial hospitals, could instead be managed with antibiotics rather than surgery. While the use of antibiotics for these conditions is usually effective on Earth, there remain concerns due to space-induced immune alterations, increased pathogenicity and virulence of microorganisms, and limited resources to \"rescue\" cases of antibiotic failure [39]. In cases of antibiotic failure, one potential minimally invasive option could be ultrasound-guided percutaneous drainage, which has previously been demonstrated to be possible and effective in microgravity [257]. Another potential approach is to focus on the early diagnosis and minimally invasive treatment of appropriate conditions, rather than treating late stage disease. In addition to expediting the patient's post-operative recovery, minimally invasive surgery in space has the added benefit of protecting the cabin environment and the remainder of the crew [258; 259].  
As in all aspects of healthcare delivery in space, the presence of microgravity can complicate even the most basic of procedures. However, based on collective experienceto date, if the patient, operators, and all required equipment are restrained, the flow of surgical procedures remains relatively unchanged compared to the traditional, terrestrial experience [260]. A recent animal study confirmed that it was possible to perform minor surgical procedures (e.g., vessel and wound closures) in microgravity [261]. Similar study during parabolic flight has further confirmed that emergent surgery for the purpose of \"damage control\" in catastrophic scenarios can be conducted in microgravity [262]. As discussed previously, telesurgery may be feasible if the surgery can be performed with an acceptably brief time lag (<200 ms) and if the patient is within a low Earth orbit [263; 264]. However, further research and technological advancements are required for this to come to fruition.
# An Integration Model for Flux Density Distribution Formed by a Heliostat
## Appendix A Location Determination  
In a solar tower system, the given quantities are the altitude angle \(\alpha_{rr}\), the azimuth angle \(\gamma_{rr}\) of the receiver plane, and the normal vector of the receiver plane (\(u_{rr},v_{rr},w_{rn}\)).  
Firstly, we need to calculate the coordinates of the incident ray (\(u_{sun},v_{sun},w_{sun}\)) from the solar altitude angle \(\sigma_{s}\) and azimuth angle \(\gamma_{s}\):  
\[(u_{sun},v_{sun},w_{sun})=(cos\alpha_{s}sin\gamma_{s},-cos\alpha_{s}sin\gamma _{s},sin\alpha_{s}) \tag{1}\]  
Then, the altitude angle \(\alpha_{r}\) and azimuth angle \(\gamma_{r}\) of the central reflected ray can be calculated by the position of the heliostat and the receiver plane:  
\[\alpha_{r}=atan(\frac{Z_{receiver}-Z_{helistat}}{\sqrt{(x_{receiver}-x_{ helistostat})^{2}+(y_{receiver}-y_{helistat})^{2}}}) \tag{2}\]  
\[\gamma_{r}=atan(\frac{x_{receiver}-x_{helistostat}}{y_{receiver}-y_{helistat }}) \tag{3}\]where \((x_{heliostat},y_{heliostat},z_{heliostat})\) and \((x_{receiver},y_{receiver},z_{receiver})\) represent the heliostat center coordinates and receiver center coordinates in the global coordinate system, respectively.  
The altitude angle \(\alpha_{n}\) and azimuth angle \(\gamma_{n}\) of the heliostat normal vector should also be calculated:  
\[\alpha_{n}=atan(\frac{sin\alpha_{s}+sin\alpha_{r}}{\sqrt{cos^{2}\alpha_{s}+cos ^{2}\alpha_{r}+2 cos\alpha_{s}cos\alpha_{r}cos(\gamma_{r}-\gamma_{s})}}) \tag{10}\]  
\[\gamma_{n}=atan(\frac{cos\alpha_{r}sin\gamma_{r}+cos\alpha_{s}sin\gamma_{s}}{ cos\alpha_{r}cos\gamma_{r}+cos\alpha_{s}sin\gamma_{s}}) \tag{11}\]
# Identification of Complicated Lithology with Machine Learning
## 3 Data Selection and Practical Application
### Comprehensive Lithology Identification Analysis  
The random forest results, which had the best predictive performance, were selected to identify lithology and favorable reservoirs. The lithology was classified based on felsic content: less than 25% is dolomite, 25-50% is tufaceous dolomite, 50-75% is dolomitic tuff, and greater than 75% is tuff.avorable reservoirs were identified based on the TOC content data provided in this paper. The specific results are shown in Figure 17. Combined with the NMR movable fluid porosity curve, it can be concluded that the identification of favorable oil-producing layers is relatively accurate.  
As shown in Figure 18, the lithology interpretation log of the Lu 1 well indicates that the XRD-measured felsic content in the core is quite close to the predicted felsic content. This demonstrates that using predicted felsic content for lithology identification yields good results. At a depth of 3175.5 m, the core photograph shows that the lithology is tufaceous dolomite. According to the XRD data, the main minerals present are felsic minerals and dolomite, with felsic content at 43.1% and dolomite content at 54.7%. The random forest algorithm predicted the felsic content to be 47.14%, which is close to the XRD results. At a depth of 3182.65 m, the core photograph shows that the lithology is dolomitic tuff. According to the XRD data, the main minerals present are felsic minerals and dolomite, with felsic content at 57.1% and dolomite content at 39.2%. The random forest algorithm predicted the felsic content to be 60%, which is also close to the XRD results. By comparing the actual core data with the predicted data, it can be concluded that the prediction results are highly accurate.  
According to the XRD analysis, the mineral content is 43.1% felsic and 54.7% dolomite. According to XRD analysis, the mineral content is 57.1% felsic and 39.2% dolomite.  
Figure 16: Three models predicting felsic content diagram.  
Figure 17: Lu 1 well lithology comprehensive interpretation map.  
Figure 18: Lithology interpretation diagram for Well Lu 1.