esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Characteristics and Cell Adhesion Behaviors of the Anodized Biomedical Stainless Steel
## 1 Introduction  
Type 316L biomedical stainless steel (BSS) has been used widely in orthopedic implants and bone plates due to its lower cost, higher fracture toughness, superior manufacture properties, accepted biocompatibility, and having the most anti-corrosion properties when in direct contact with biologicalfluid [1; 2; 3]. Especially in the anti-corrosion performance, it is critical that a surgical implant is not susceptible to corrosion when implanted inside the human body, in order to avoid the chances of infection happening. Unfortunately, if biomedical grade titanium is exposed to hydrogen, nitrogen and oxygen, it is easily contaminated, which may affect the corrosion process of the metal and may impair its use in certain medical procedures [4; 5].  
Despite the 316L BSS bone plates and implants having proved their long-term clinical success, some situations, such as placing in bone beds with poor quality and quantity, should be improved to integrate faster [6; 7]. In addition, BSSs have several disadvantages for use in fracture care such as allergic reactions to the nickel element and a higher modulus of elasticity that causes stress shielding and bone loss, etc. [4; 8; 9; 10; 11]. To improve these disadvantages of BSSs and then enhance their surface properties and biocompatibility, various surface modification approaches were employed to alter the surface performances of BSSs [12; 13; 14; 15; 16]. [PERSON] et al. [17] have indicated that the formation of a porous oxide layer on the surface of the 316L BSS implants can be utilized in the morphological fixation of the implants to bone through bony ingrowth into the porous microstructure.  
Based on the improvement of 316L BSS implants, an electrochemical anodizing process was adopted as a surface treatment method to modify the 316L BSS in this study. Previous studies by our research team have reported that the anodized titanium with a hierarchical porous (micro and nanoporous) surface possessed great potential to enhance cell adhesion ability [18; 19]. Accordingly, the potential anodizing method could be utilized to fabricate a promising hierarchical porous surface on the 316L BSS implant to promote biocompatibility. [PERSON] et al. [20] also indicated that the porous oxide surface played a vital role in improving the stress transfer between implant, cortical bone, and cancellous bone for preventing stress-shielding formation. In addition, the modified implant with a porous oxide surface was considered to have outstanding hemocompatibility, biocompatibility, and osseointegration [20; 21; 22]. In order to realize the surface properties of the anodized 316L BSS, the surface characteristics and cell adhesion behaviors of the 316L BSS with and without anodizing modification were analyzed in this study. The research results could offer new scientific information in the biomedical fields.
# Study on Contamination with Some Mycotoxins in Maize and Maize-Derived Foods
## 4 Discussion

Table 6: _Cont._mycotoxins in food in the EU (367 notifications), also detected in dried figs from Turkey (58 notifications) and followed by groundnuts from the United States (29 notifications) [64]. The formation of mycotoxins is a composite process influenced by a multitude of factors, mainly environmental conditions under the influence of climate change. Comparing the results of this study with previous research, for deoxynivalenol, the incidence in the analyzed samples is 25% for maize, while in Hungary the incidence of deoxynivalenol is 86% [33]; in Belgium, the incidence in the studies carried out varies from 64.7% to 92.3% or 100% [34]. Studies carried out in Germany and Switzerland show a deoxynivalenol incidence of 100%, while in China the incidence is 65.9% [35; 36; 37]. For the analyzed products derived from maize, the incidence of deoxynivalenol was 20%, while other studies show an incidence of 40% for maize flakes and 42.9% for maize flour in Serbia, and 100% for pastry products in Brazil [32; 38]. The maximum value identified for deoxynivalenol in this maize study is 152.2 ug/kg, well below the maximum values identified in other studies, while it is 9843.3 ug/kg in China and 5322.4 ug/kg in Belgium [34; 37]. In products derived from maize, deoxynivalenol had a maximum of 63.99 ug/kg; other studies indicate a maximum of 931 ug/kg in Serbia and 1720 ug/kg in Brazil [32; 38]. In the tested samples of maize, the incidence for zeanelenone was 8%. In previous studies on maize, zeanelenone had an incidence of 0% in Serbia [45], 13.6% and 94% in China [48], 33.34% in Egypt [47], 40.7%-64.8% in Belgium [34], 41% in Hungary [33] and more than 90% in Germany and Brazil [35; 46]. The average value of zeanelenone in the Romanian sample was 3.44 ug/kg. Other studies have identified lower average values, such as 2.39 ug/kg or higher in Egypt and 175.5 ug/kg in Belgium [34; 47]. The highest zeanelenone content in a tested sample was 18.565 ug/kg, while other studies indicate maximum values of 3613 ug/kg [35]. For fumonisins, the incidence in this study was 70% for maize and 25% for products derived from maize. In previous studies, fumonisi incidences ranged from 2.5% in Belgium to 100% in Serbia, Italy, Spain and Canada [34; 45; 58; 59; 60]. The average fumonisi value in the tested samples of maize was 154.55 ug/kg. Other studies have identified an average for fumonisins from 1.5 ug/kg to 10,900 ug/kg [34; 58]. For the analyzed products derived from maize, the average value of fumonisins was 37.68 ug/kg. Other studies have identified an average of fumonisins between 14.2 ug/kg and 104.1 ug/kg in corn flakes [59; 61].
## 2 Literature Review  
### Semantic Segmentation  
Semantic segmentation is the segmentation of object targets in an image based on predefined semantic categories, providing consistent semantic information. Different information expressed by each object in the image is obtained through the texture features, scene information, and other higher-order semantic features of the image itself [16]. Traditional point cloud segmentation algorithms usually use basic features, such as geometric features, and the color information of point clouds to design feature describers, and then establish feature filters based on these feature describers for segmentation [17]. Currently, more and more research is applying convolutional neural networks to point cloud semantic segmentation; typical examples include PointNet [18], PointNet++ [19], etc. The transformer network utilizes a self-attention mechanism to learn the correlation between different positions in a sequence [20]. Through the similarity between point cloud features and natural language features, transformer and its optimization models have been proven to present strong learning abilities in point cloud classification and segmentation tasks [21]. Transformer has been widely used for point cloud data segmentation, such as plant point clouds [22], autonomous driving point clouds [23], etc. In addition, a large number of studies have been conducted to optimize transformers, such as SAT3D [24], Mask3D [25], etc.  
However, due to the complexity of indoor environments, semantic segmentation in indoor scenes remains a challenging task. To date, the methods for indoor point cloud data segmentation focus on large-scale planar structures, ignoring other sharp structures and details, which lead to a decrease in the accuracy of a scene's reconstruction [26]. At the same time, the large volume of point cloud data created by the complexity of indoor scenes causes segmentation algorithms to consume a lot of time [27]. Numerous studies have been conducted on the accuracy and efficiency of point cloud segmentation in indoor scenes [28; 29]. The research improves the transformer model and achieves the rapid and high-precision segmentation of indoor large-scale point cloud data through attention and feature enhancement modules.
## 1 Introduction
In the processing of image contrast, the adaptive piecewise self-order enhancement model proposed in this paper has a remarkable effect. The image color scale information is completely corrected, the target contrast is clear, the peak signal-to-noise ratio (PSNR) of the image before and after correction reaches more than 145, and the structural similarity of the image before and after correction can reach more than 95%. 2. By adding a spatial pyramid pooling module to the algorithmic feature extraction network, the feature information under different sense field sizes can be fused, and the fused feature maps contain different levels of semantic information, which is helpful to improve the abstract expression of features. 3. An attention mechanism is incorporated in the network, which can adaptively adjust the weights of each channel, enabling the network to focus more on important information and suppress irrelevant information in the channel dimension, increasing detection accuracy while ensuring an almost constant training time and detection speed. 4. The FPN feature pyramid fusion structure is improved, and a fusion mode is added to the original basis to realize a bidirectional connection, enhance the effective fusion of shallow and deep information, improve the utilization of multiscale features, and enrich the information in the feature map. 5. Finally, compared with the original algorithm, the improved YOLOv4-tiny target detection algorithm shows some improvement in the average precision of detection. The _mAP_ reaches 98.85%.
# Computational Approaches for Grocery Home Delivery Services
## 2 Related Work
### Slotting and Pricing  
[PERSON] et al. [24] discuss how proven revenue management concepts can be translated to AHD services. The authors differentiate into _static_ methods, i.e., forecast-based methods that are applied offline before the actual orders come in, and _dynamic_ methods, i.e., order based methods that are applied in real-time as new demand comes in. Moreover, capacity allocation or \"slotting\" (which time slots are made available to which customers), and pricing (using delivery fees to manage customer demand), are distinguished. Hence, this results in the following four categories of demand management: (i) differentiated (static) slotting: Defining the collection of delivery time windows based on geographical regions or the preferences of customer groups. Hence, the concentration of customer orders in a given area can be increased by limiting the availability of delivery options, see references [25; 26]. (ii) differentiated (static) pricing: Differentiating between different delivery options (on a tactical level) offered to customers by charging different delivery fees. Offering off-peak time discounts or peak-time premiums allows to smoothen the demand over the day, see reference [27]. (iii) dynamic slotting: Deciding which delivery time slots to offer an incoming customer based on the currently available capacity. More sophisticated approaches may hide delivery time slots from unprofitable customers in order to reserve capacity for highly profitable future customers (that are predicted to arrive later on), see references [28; 29; 30; 21]. (iv) dynamic pricing: Allows for finer levels of gradation of incentives than (dynamic) slotting. Offering price incentives can be used to increase the attractiveness of time slots during which the order can be delivered more efficiently, see references [31; 32; 19; 33].  
In contrast to the presented works in this subsection, we investigate the acceptance of new customer requests in terms of improving the chances of finding feasible insertions (given an incomplete delivery schedule) rather than developing new acceptance criteria for improving revenue management.
## 3 Materials and Methodology
### Data Source  
Official maritime accident investigation reports are well recognized as a reliable source that can obtain objective and comprehensive information about the maritime accidents [36]. To ensure the authority and timeliness of maritime accident investigation reports, a total of 555 maritime accidents that occurred between 2011 and 2020 were collected from the official website of seven countries' official maritime investigation authorities, which recorded maritime accidents that occurred in national waters, including China Maritime Safety Administration (MSA, Beijing, China), Marine Accident Investigation Branch (MAIB, Southampton, UK), Swedish Accident Investigation Board (SHK, Stockholm, Sweden), Australian Transport Safety Bureau (ATSB, Canberra, Australia), United States National Transportation Safety Board (NTSB, Washington, DC, USA), Japan Transport Safety Board (JTSB, Tokyo, Japan) and Korean Maritime Safety Tribunal (KMST, Sejong City, Republic of Korea). The collected reports contain the five most frequently occurring types of maritime accidents, which are collision, grounding, contact, sinking, and fire/explosion. In order to ensure that the maritime accident reports involved seafarers' unsafe acts, this study initially screened the collected maritime accident reports and removed the accidents caused exclusively by objective factors such as environmental factors and ship factors. Finally, 476 maritime accident reports were obtained for further analysis, and the basic statistical information of the accident reports is shown in Table 1.  
[PERSON] et al. [37] developed a classification analysis framework for seafarers' unsafe acts. With the help of the framework, this study extracted 31 seafarers' unsafe acts and 38 influencing factors from the 476 accident reports. Table 2 provides the descriptive statistics of the seafarers' unsafe acts and their influencing factors. Figure 1: The process of risk interaction analysis based on the AR-CN method.
# Interface Shear Performance between Porous Polyurethane Mixture and Asphalt Sublayer
###### Abstract  
This paper aims to study interface shear performance between porous polyurethane mixture (PPM) and asphalt mixture with different adhesive materials. Polyurethane, epoxy resin, and SBS (styrene-butadiene-styrene) modified asphalt were selected as adhesive materials to fabricate composite specimens. The interface shear strength and shear fatigue life of composite specimen was measured using inclined shear test. The research results emphasizes that it is necessary to apply adhesive material to the interface between PPM and asphalt mixture, since the untreated interface shear strength is smaller than the ones between two asphalt mixtures. The interface shear strength is affected by the thickness of adhesive layer, temperature, and freezing-thaw condition. In general, the greatest interface shear strength was achieved by using epoxy resin followed by polyurethane and then SBS modified asphalt at 25 \({}^{\circ}\)C as the adhesive layer thickness is the same. However, the interface shear strengths of composite specimen with three adhesive materials are similar to each other at high and low temperatures (60 \({}^{\circ}\)C and \(-18\) \({}^{\circ}\)C) or after freezing-thaw cycles. On the other hand, the composite specimen with epoxy resin as adhesive material has the longest fatigue life; while the SBS modified asphalt has the least fatigue life at 25 \({}^{\circ}\)C. The research findings can help select the appropriate adhesive materials and increase the durability and service life of pavement when PPM is used as road surface layer for safety and noise reduction.  
Keywords: polyurethane; asphalt mixture; epoxy resin; adhesive layer; shear strength; inclined shear test +
Footnote †: journal: Applied Mechanics and Physics Letters
## 5 Case Study and Analysis
### Performance Analysis of Algorithm
#### 5.3.2 Efficiency Analysis  
In this section, we tested the performance of our proposed algorithm, taking three classical datasets designed by [PERSON] (R101, C101, and RC101) and each with 25 and 100 customers, respectively. Among them, the customer locations of R101 examples were generated randomly, while that of C101 were relatively concentrated, and that of RC101 were uniform. Without loss of generality, about 10% of customers were within priority one generated randomly. The types of products for customers were also generated randomly. The basic GA and the TSHM were applied to solve the benchmarks 10 times, the best results obtained by GA and TSHM are shown in Table 5.  
Table 5 shows that the approximate optimal values of the six examples obtained by TSHM were smaller than those of GA. Specifically, the average approximate optimal value for the six examples was less than 22.09% (4813.22 vs. 6177.74). The results demonstrate that the TSHM algorithm was superior to the basic GA.  
In addition, we also solved the problem with 25, 50, and 100 nodes using CPLEX 12.5. The computational time was 8.32 seconds and 31.23 seconds for 25 and 50 customers, respectively. However, when there were 100 customers, the CPLEX 12.5 could not solve the problem to obtain the optimal solution within 3600 s. The computational time was within several seconds when using our proposed algorithm to obtain approximately optimal solutions.  
Figure 5: Stability analysis of TSHM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instance** & **GA** & **TSHM** & **GAP (\%)** \\ \hline R101\_25.R & 2298.21 & 2256.29 & 1.82\% \\ R101\_100.R & 8965.31 & 7020.73 & 21.69\% \\ C101\_25.R & 1845.35 & 1801.38 & 2.38\% \\ C101\_100.R & 7968.58 & 6362.1 & 20.16\% \\ RC101\_25.R & 2754.25 & 2438.56 & 11.46\% \\ RC101\_100.R & 13234.74 & 9000.26 & 32.00\% \\ Mean & 6177.74 & 4813.22 & 22.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results obtained by GA and TSHM.
# CNN-Based Illumination Estimation with Semantic Information
## 1 Introduction
In the fully connected layer method, each convolutional layer gets the input of all the features combined as a result of output in an earlier layer and each convolutional layer relies on local spatial coherence with a small receptive field. On the other hand, the fully connected layers have several well-known vital problems and incur incredibly high computational cost. Motivated to solve these problems, the proposed CNN method uses the residual network to improve the estimation accuracy and reduce expensive computational cost. In addition, the proposed network employs a pooling mechanism to reduce estimation ambiguities as in previous studies [18; 22]. With patch processing and semantic pooling together, the proposed network is able to distinguish between useful data and noisy data during training and evaluating. In the proposed network, semantic pooling designed to extract local semantic information from the original image is performed to form a mask and the resulting image turns out a weighted map. By enabling the network to learn the semantic information in the local region and remove noisy data, the proposed color constancy approach becomes more robust to estimation ambiguities. In addition, the proposed network features end-to-end training, direct processing of arbitrary-sized images and faster computation. To the best of our knowledge, the proposed approach is the first study to investigate and use the residual network-based CNNs to achieve color constancy. In particular, the novelty of this approach is the use of the residual network, mainly distinct from its conventional CNN-based counterparts. The residual network allows the proposed architecture to predict scene illuminant on the local region, as opposed to many previous approaches where features are extracted from the entire image to obtain statistics and estimate the overall illuminant. In addition, the dilated convolution of the residual network is designed to handle multiscale appearance, contributing to efficiency. While there are only a few methods proposed to estimate spatially varying illuminants, the proposed approach has significance and the potential to advance CNN-based illumination estimation accuracy. The experimental results demonstrate that the proposed network stays ahead of other state-of-the-art techniques in predicting illumination and is less likely to cause large errors in estimation as the conventional methods. Moreover, the proposed scheme is further applicable to solving other computer vision problems because of its strength of aggregating local estimation to determine global estimation.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 3 Adversarial Attack Taxonomy
### Attacker's Knowledge  
One of the dimensions of threat model classification is the level of information and knowledge accessible to adversaries concerning the ML model. Attack knowledge can be classified according to the following levels:  
* Full knowledge: This refers to white-box attacks, where the attacker possesses complete awareness of the target ML system's information. This means that the adversary possesses complete and unrestricted access to the training dataset, ML model architecture, and its hyper-parameters as well as the feature learning. This is generally not feasible in most real adversarial attacks. However, the purpose of studying them is to assess the vulnerability of the target ML system to all possible cases and scenarios.
* Partial knowledge: Referring to gray-box attacks, where the attacker possesses partial information of the target ML system's inner workings. This means that the adversary may have limited access to the feature representations, training dataset, and learning algorithm's parameters. Using partial information, the attacker can create a practical strategy to deceive the ML model.
* No knowledge: This corresponds to black-box attacks, where the attacker is entirely unaware of the architecture and parameters of the target model. The adversary relies solely on his capability to query the target ML system by inputting the chosen data and monitoring corresponding results. These attacks are considered the most practical  
Figure 5: Adversarial attack taxonomy.  
because they operate under the assumption that the attacker can only leverage system interfaces that are readily accessible for typical use.
## 2 Materials and Methods  
### Materials  
Soil. The soil was collected from the Gold Coast area, Australia. The grain size distribution of soil was obtained using hydrometer and sieve analysis tests according to the ASTM D422-63 [17] and shown in Figure 1a. Based on the ASTM D4318-17 [18], the plastic and liquid limits and plasticity index for the given soil were 26.9, 38, and 11.1, respectively. The soil was classified as low plasticity silt according to the Unified Soil Classification System, ASTM D2487-17 [19]. The specific gravity of the soil was 2.77 according to the ASTM D854-14 [20]. The standard proctor compaction test in accordance with ASTM D698-12 [21] was performed, and a maximum dry density of 1.72 g/cm\({}^{3}\) and corresponding optimum water content of 21.7% were obtained. The mineral compositions of the soil were obtained employing the X-ray diffraction (XRD) measurements using GBC Scientific, MMA diffractometer equipped with a Cu-K\(\alpha\) anode operated at 28.5 kV and 35 mA employing a 2D array detector to minimize fluorescent backgrounds and an automated knife edge to allow simultaneous measurements at low and high angles. Diffraction patterns were recorded by continuous scans from 10 to 80\({}^{\circ}\) 20, with a step size of 0.02\({}^{\circ}\) at a scan rate of 0.560 s per step and a scan time of 35 min, as shown in Figure 1b. The XRD measurements revealed that soil samples were mainly composed of quartz with additional inclusions of kaolinite and calcite.  
The energy dispersive X-ray spectroscopy (EDS) measurements acquired using TES-CAN MIRA scanning electron microscope (SEM) equipped with an 80 mm\({}^{2}\) ThermoFisher Scientific UltraDry EDS detector were performed to obtain the elemental composition of the soil. The EDS elemental analysis was performed using Pathfinder Microanalysis(r) software using the point mapping function, and the EDS patterns are shown in Figure 1c, confirming the silicon and oxygen as the main elements of soil particles.  
Biopolymer. Xanthan gum (XG) was obtained from Glentham Life Sciences, UK. The used XG was a powder with pH in water (6-8) and viscosity of 1000-1800 mPa.s (1% in 1% KCl).