esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 1 Introduction  
Bioflocculants are natural product metabolites of microorganisms that have capacity to flocculate various suspended solids including colloidal solids and cells [1; 2]. Chemical flocculants have been extensively applied in various industries owing to their cost-effectiveness and high flocculating efficiency [3]; however, many researchers are investigating the use of bioflocculants as a result of health problems implicated in the use of chemical flocculants. Polyaluminium chloride (PAC) and polyacrylamide are among chemical flocculants that have been used extensively in downstream processing, wastewater treatment, food and fermentation industries [3]. However, polyaluminium salts have been implicated in [PERSON]'s disease [4] and evidence also exists associating polyacrylamide monomers with neurotoxicity and carcinogenicity in humans [5]. Consequently, the use of these chemical flocculants is now a restricted cause of concern. These inevitable disadvantages of chemical flocculants have shifted the focus on investigating a variety of microorganisms for their flocculant-producing potentials [1; 6].  
Unlike chemical flocculants, bioflocculants have additional benefits such as being environmentally friendly, biodegradable, free of the risk of secondary pollution, and nontoxic and harmless to humans,animals and the environment [1; 7; 8]. Bioflocculant-producing microorganisms have been isolated from almost all kinds of different environments such as soil, wastewater, activated sludge and rivers [9; 10; 11; 12]. Recently, biofloculants have also been produced by microorganisms isolated from unusual environments such as sputum [13] and human saliva [14].  
Biofloculants are not only useful in wastewater treatment but have a wide range of applications. For example, biofloculants produced by _Sobacillus silvestries_ W01 and _Paenibacillus_ sp. AM49 were used to harvest marine microalgae _Nannochloropsis oceanica_ and _Chorella vulgaris_, respectively [12; 15]. The practical applications of biofloculants in industries however, have thus far been limited due to their high cost of production as well as high dosage requirements [1]. To overcome this challenge, utilization of a number of cost-effective substrates for bioflocculant production has been investigated [16]. As an example, dairy wastewater was used as a cheap substrate for bioflocculant production by _Klebsiella mobilis_[16]. In addition, a facultative oligotrophic _Klebsiella_ sp. PB12 produced an exopolysaccharide antonutrient-poor medium thus reducing production costs [17]. The need for novel bioflocculants that are thermostable with enhanced bioflocculation efficiency and yield has therefore become apparent.  
In this current study, the effect of culture conditions on biofloculant production by a freshwater bacterium identified as _Bacillus pumilus_ was assessed and the purified biofloculant characterized. From our literature search, no report has been documented on the implication of _Bacillus pumilus_ in the production of bioflocculant.
## 3 Results
### Vertical Activity
Within coastal shelf areas vertical behaviours included (1) multi-day periods during which the shark spent most of each day at a fairly constant depth level, with these constant depth levels varying among days from 20-80 m (Figure 4A), and (2) multi-day periods in which the shark occupied an overall shallower and narrower depth range (0-40 m) and showed an increased vertical activity during the day (Figure 4B). In offshore areas, the depth distribution during the day was generally deeper (80-120 m) compared to coastal habitats (Figure 4C). Nocturnal movements during all periods were characterised by an oscillatory or yo-yo swimming motion, during which the shark repeatedly ascended and less frequently descended and dived back to the respective daytime depth level (Figure 4A-C). Of the 12 sharks that provided data, six (sharks 234, 354, 356, 357, 366, 375) dived to maximum depths of 424-632 m (Table 1). Deep-dives (beyond 200 m) occurred during all months of the year in 4 geographic clusters along the coast: (1) between Brisbane (27.47\({}^{\circ}\) S 153.02\({}^{\circ}\) E) and Fraser Island (25.23\({}^{\circ}\) S 153.13\({}^{\circ}\) E); (2) between Coffs Harbour (30.29\({}^{\circ}\) S 153.11\({}^{\circ}\) E) and Yamba (29.43\({}^{\circ}\) S 153.36\({}^{\circ}\) E); (3) offshore Jervis Bay (35.04\({}^{\circ}\) S 150.74\({}^{\circ}\) E) to the NSW Victoria border (37.50\({}^{\circ}\) S 149.97\({}^{\circ}\) E); (4) along the Tasmanian east coast (Figure S4). Deep-dives were initiated at all times of the day and night, with a peak in the morning between 0700-0800 h (25% of all deep-dives) (Figure S5). Figure 3: Vertical activity analysis based on **recovered tag data**. Coefficient of variation values and associated mean depth occupation for each of the four datasets, displayed over 24 h for the entire tracking period for sharks 180, 356 and 365, 386. The same colour/numeric scale is shared by mean depth (in meters) and coefficient of variation in depth (expressed as a percentage). Dotted lines indicate times of local sunrise and sunset. Figure 4: **Fine-scale diving behaviour. ** Representative archival time-series depth and temperature data from shark 180 (210 cm FL, female). (**A–C**) Each plot on the left displays ten days of diving data collected at 3-s intervals and colour-coded by day. Areas shaded in grey indicate approximate night-time (darker grey) and twilight periods (lighter grey). (**A–C**) Plots on the right display the associated daily temperature records collected at 3-s intervals. Locations of track periods are indicated in the map. The map was generated using the marmap package in R [37]. Full archival time-series depth and temperature data for all four recovered archival tags are available in the supplement (Figure S6).
## 3 Results and Discussion
### Stress Analysis Results  
The support of the platform was input by total weight of \(\sim\)400 kg in the analysis. The stress simulation was divided into roll and pitch motion. Among them, the stress of roll motion simulation was analyzed at platform motion angles of 0\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\). The simulation results showed that the maximum stress was located at the angle of 20\({}^{\circ}\) with a maximum stress of 129.63 MPa. The maximum stress analysis result is shown in Figure 13. The roll motion stress trend is shown in Figure 14.  
For the pitch motion, the platform motion angles were 0\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\). The simulation results showed that the maximum stress was located at the angle of 20\({}^{\circ}\) with a maximum stress of 119.79 MPa. The maximum stress analysis result is shown in Figure 15. The pitch motion stress trend is shown in Figure 16.  
Figure 12: Central rotating mechanism.  
Figure 13: The maximum stress of the roll motion.  
Figure 14: The stress trend chart of the roll motion.  
Figure 15: The maximum stress of the pitch motion.  
According to the analysis results, it can be seen that the multi-axis platform mechanism was safe during movement, the overall stress was within the yield strength 648.5 MPa of material S45C, and the material safety factor N was 2.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 3 Results
### Simulation and Validation  
#### 3.2.1 Logistic Regression Analysis  
Table 2 shows the \(\beta\)-coefficients of the results of logistic regression and auto-logistic regression. The spatial autocorrelation factor (AutoValue) in auto-logistic regression, is the spatial weight value of each LU type calculated according to Equation (3). The results show that the ROC values of auto-logistic regression analysis with AutoValue were all improved: 0.856 for farmland, 0.933 for forestland, 0.934 for grassland, 0.935 for water, and 0.829 for construction land. The ROC values are all greater than 0.8, and forestland,  
grassland, and water reach more than 0.9 indicating a high fitting accuracy and strong explanation ability of the selected driving factors. As shown in Table 2, the main driving factors of farmland are POP, DEM, MAP, GDP, and Town_d. The distribution of forestland is mainly influenced by Town_d, River_d, MAP, MAT, and DEM. It is most influenced by Town_d, followed by MAP. For grassland, the main driving factors affecting its distribution are DEM, MAP, River_d, Slope, and Soil_SOM. Additionally, mainly the locational factors such as River_d and Freeway_d are negatively correlated, and positively correlated with natural factors such as DEM and Slope. The driving factor affecting the water is mainly the DEM, which shows a significant negative correlation. Construction land is mainly influenced by POP, GDP, MAP, DEM, and Town_d. It is most influenced by POP and shows a significant positive correlation.
## 3 Methods  
The methodology comprises three integrated stages: propensity matching based on tensor KL-divergence to align each incident scenario with a statistically comparable normal condition and thus isolate causal effects; training a dual-channel deep counterfactual model that fuses shared station embeddings, graph attention, temporal convolutions and incident-type embeddings to capture both baseline and disruption-driven OD flow dynamics; and applying the trained model to generate spatiotemporal passenger-flow predictions under varied incident types while quantitatively assessing how disruptions propagate through the network over time. This framework combines robust predictive accuracy with causal interpretability to support real-time operational decision-making in urban rail systems.
## 4 PSIFTIS Imaging Experiment Results and Data Processing  
### Polarization Interferometric Image Acquisition and Decoupling  
To complete the processing of system spectrum information, an experimental platform was set up in the laboratory environment. Narrow-band filters with central wavelengths of 3800 nm, 3970 nm, and 4720 nm are adopted, whose specifications are \(\mathcal{O}\)25.4 \(\times\) 1 mm, \(\mathcal{O}\)25.4 \(\times\) 1 mm, and \(\mathcal{O}\)25.4 \(\times\) 0.5 mm, respectively. The half-peak widths are 76 \(\pm\) 10 nm, 40 \(\pm\) 10 nm, and 90 \(\pm\) 10 nm. The images of the filters are shown in the Figure 6a. The target used in the experiment is shown in Figure 6b.  
When the system performs imaging experiments on the target, polarization interference image arrays will be formed on the detector array. Taking 45\({}^{\circ}\) polarization as an example, as shown in Figure 7a, the polarization interference image arrays are segmented according to the above methods, and the results are shown in Figure 7d.  
Figure 6: Materials used in the experiment: (**a**) narrow-band filter; (**b**) target.  
As can be seen from the results, the convolution kernel traversal image in Figure 7b makes a rough location of the edge, and the edge closure is not good. Figure 7c shows the location results of the Canny edge detection method, which indicates that the image edge contour location effect is good and the edge closure is good. Finally, the convolution kernel traversal rough location and Canny edge detection location results are merged to obtain the results shown in Figure 7d.  
Then, the image units after optical path difference matching are registered in optical path order. As shown in Figure 8a, the results of two image units obtained by using the SUSAN corner detection method show that the number of the corner detection was moderate. The results after coarse SIFT feature matching and fine RANSAC feature matching are in Figure 8b, showing that the number of matching lines is moderate and the matching degree is good. After registration of all image units after feature matching, a polarization interference data cube set of each polarization channel is obtained as shown in Figure 8c.
# Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability
## 2 Work Object Analysis and Trajectory Planning  
### Selection of Work Object  
The work object is based on the U-cell of three representative ship types built at shipyards: container ship, LNGC, and VLCC (very large crude-oil carrier) [23]. The proportion of work objects by ship type fluctuates according to global ship ordering trends, and as the number of orders for LNGCs has increased rapidly in the past two years, LNGCs also account for most of the analysis data. A total of 23,457 U-cells were analyzed, of which 90% (21,208) were LNGC, 7% (1537) were container ships, and the remaining 3% (712) were VLCC. The main analytical dimensions of the work object were Longi height and Longi width, and they were analyzed by boxplot for each type of vessel as shown in Figure 2.  
Figure 1: U-cell weld lines created inside the double hull of a ship.  
Table 1 shows the size of Longi by vessel type. The maximum, minimum, and median values of height and width are summarized. LNGC and VLCC had the same median height of 400 mm, and only container ships had a smaller median value of 325. In the case of width, the median value range of the three ship types was 845 to 914. LNGC, which accounted for 90% of the total volume, had a height of 400 mm and a width of 880 mm, so it can be considered a representative size of a U-cell.
## 3 Synergistic Support System of \"LDAGF\"
### Characteristics of the Synergistic Support System of \"LDAGF\"  
According to Figure 6, the synergistic support system is featured by the complementary advantages between the support forms (anchor cables, flat-roof U-shaped steel sheds and advance bolt grouting) and the SR improvement methods (floor breaking and side lowering and advance water injection for the coal body). The synergistic support system was applied to the ESFMCFR in the LL, ZXZ, QD, GB, YY, TT, ZZ and XH coal mines in Huaibei mining area. Based on the effects of its support measures and the results of on-site industrial tests and mine pressure observation, the following three characteristics of the synergistic support system are summarized:  
1. The bearing capacities of different support forms are complementary: Before roadway excavation, advance bolt grouting can effectively ensure the integrity of roadway roof SR with a simple construction process. The installation of flat-roof U-shaped steel sheds and anchor cables soon after roadway excavation not only provides large support resistance but also utilizes the anchor cables so that the SR deformation slowly acts on the steel sheds. Moreover, it can make up for the low resistance of conventional anchor mesh shotcrete support, and realize the complementarity of various support forms in bearing capacity, so as to achieve the best support effect.
2. The synergistic support system and the SR are integrated to play a bearing role: Before roadway excavation, advance water injection and grouting can enhance the plasticity of the coal body while advance grouting can solidify the roof and improve the integrity of the roadway and the head. Compared with anchor bolts, anchor cables can anchor into deep and stable rock strata, transfer the additional load from the shallow SR to the deep SR and make the SR a three-way compression-bearing body by applying a large preload. On this basis, anchor cables are adopted to fix the flat-roof U-shaped steel sheds. Hence, the synergistic support system can realize an omni-directional overall bearing of deep and shallow SR both before and after excavation.
3. The one-time permanent support of SR is realized: It can avoid repeated renovation of roadways and realize one-time permanent support. Underground monitoring reveals that the ESFMCFR supported by the synergistic support system can fully mobilize the bearing capacity of SR and realize ventilation and transportation for a long time without regular renovation, thus saving the roadway support cost to a certain extent and yielding remarkable economic benefits.  
Figure 5: Basic components of the synergistic support system of “LDAGF”.
## 4 Mitigating the Data Protection Risks
### Protection against Reidentification Attacks
#### 4.1.1 Noncentralized Approaches  
Probably the most notable approach, from a data protection engineering perspective, for multiparty settings (i.e., in distributed learning and not in centralized learning), focusing mainly on the risks related to the information that the adversary can extract from the input data, is the so-called federated learning (FL) technique [48, 49]. The underlying idea of FL techniques is to build machine learning models in a distributed way; that is, each entity, with its own data, uses these data to locally train the model without providing these data to any other party and, similarly, without receiving data from other entities for this training. (For example, in a case necessitating an ML model that is based on sensitive health data from hospitals/medical centers, each such organization trains its own local model independently based on its data.) By these means, training data are not shared at all. However, there is a coordinated central aggregate server that receives the models and, based on them, creates a global model which, in turn, is distributed to all the other entities. Therefore, the data sharing that takes place does not involve personal data but only the parameters of the model that is built. However, despite the obvious advantages of FL in terms of privacy, there are still risks remaining; for example, it has been shown that data could be inferred by considering only model weights [50], whilst even local personal data can be reconstructed through inversion of the model gradients sent by the clients to the server [51, 52].  
More generally, privacy-preserving techniques in distributed learning settings enable the training of an ML model using aggregated data while maintaining the privacy of the training datasets. Several research works propose ML techniques consisting of multiple members and based on homomorphic encryption (i.e., advanced encryption that allows mathematical operations on ciphertexts without necessitating decrypting them), while the type of ML used is deep learning (see, e.g., [53, 54]). By these means, taking as input a large amount of encrypted data, the aim is to develop practical and efficient algorithms that handle encrypted data.  
In a similar principle of decentralizing the training model, researchers in [55] proposed a technique called \"Privacy-Preserving Bandits (P2B)\". This technique is mainly focused on systems providing personalized services, e.g., a personalized news service that would learn to recommend news articles based on past articles the user has interacted with. Such recommender systems rely on an individual's data obtained through his/her interactions. The technique provided in [55] is based on the following idea: individual agents run locally on users' devices and are able to contribute useful feedback to other agents through centralized model updates while providing privacy guarantees, namely, differential privacy guarantees.
## 4 Numerical Results
### Results of Parametric Analysis
#### 4.3.4 Influence of Compressive Net-Section Axial Force for Combined Loading  
The effect of a compressive net-section axial force on the CLS of pipes subjected to monotonically-increasing curvature is depicted in Figures 16 and 17. The relationship between the CLS and the compression factor, \(f_{c}\), is represented by the plots in Figure 16 for YPT pipes, with \(D/t\) ratio = 64.08 and YPL = 1.25%. Figure 17 comprises CLS vs. \(f_{c}\) plots for RHT pipes, with \(D/t\) ratio = 41.15 and PLUS ratio = 0.577. Each plot corresponds to a respective level of internal pressure, as indicated by the elements of the graph legends.  
Figure 16: Plots of CLS vs. \(f_{c}\) for YPT DT2 pipes with YPL = 1.25%.  
Figure 14: Plots of CLS vs. PLUS ratio of RHT DT1 pipes for (**a**) uniform axial compression, (**b**) uniform bending, and (**c**) combined loading (\(f_{c}\) = 0.2).  
Figure 15: Plots of CLS vs. PLUS ratio of RHT DT3 pipes for (**a**) uniform axial compression, (**b**) uniform bending, and (**c**) combined loading (\(f_{c}\) = 0.2).  
An inverse relationship is observed between the compression factor and the CLS for both YPT and RHT pipes. Observation of various parametric combinations of the factors investigated in this study shows a strong and general indication that the upper and lower bounds of the CLS for pipes subjected to combined axial compression + bending is coincident with the CLS under uniform bending and the CLS under uniform axial compression, respectively. The observed upper and lower bound phenomenon is portrayed accordingly on the horizontal axes of the plots in Figures 16 and 17. such that a \(f_{c}=0\) represents uniform bending and \(f_{c}=1\) represents uniform axial compression. It can therefore be inferred that for all combinations of investigated parameters, the CLS of both YPT and RHT pipes reduces progressively and somewhat nonlinearly from a state of uniform bending to a state of uniform axial compression as the compressive net-section axial force is increased from 0% to 100% of the limit axial stress.
# Analysis of Machine Learning Techniques for Information Classification in Mobile Applications
## 4 Image and Video Classification Algorithms on Mobile Devices
### EfficientNet  
Convolutional neural networks (ConvNets) are developed with a fixed resource requirement in mind, then scaled up to obtain better accuracy if more resources are available.  
The EfficientNets family of models was designed based on the neural architecture and scaling efficiency of MobileNet and ResNet, achieving better precision and efficiency than their base models. The EfficientNet model, like MobileNet, uses a convolutional neural network (CNN) architecture that improves performance by uniformly balancing and scaling depth, width and resolution using a composite coefficient, limiting those of the convolutional network to fixed portions of the parameters. The composite scaling method intuits that if the input image is larger, the network needs to increase the receptive field by increasing layers and channels to capture detailed patterns from the input image [22].  
The EfficientNet model conforms to the MnasNET search method by adding two important concepts, squeeze and excitation blocks (swish activation function and the SE), making use of inventive residual blocks from MobileNetV2 [32].
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 5 Security Analysis
Insider Attack: In our proposed authentication mechanism, the blockchain stores an abstract of the certificate, rather than the complete certificate information. Even if an insider attacker queries the abstract of the certificate from the blockchain, it is almost impossible for the attacker to obtain the complete certificate information from the abstract, because the hash algorithm used has the anti-collision property. Therefore, our proposed authentication mechanism is fully capable of resisting insider attacks.