esa-sceva/satcom-chunk-collection huggingface dataset:
# A New \"Doctor and Patient\" Optimization Algorithm: An Application to Energy Commitment Problem
## 3 Doctor and Patient Optimization (DPO)
### Mathematical Modeling  
The population in DPO are patients who need to be treated by a doctor. This population of patients is specified in Equation (1).  
\[P=\left[\begin{array}{ccccc}P_{1}&\quad\quad p_{1}^{1}&\quad\cdots&\quad \cdots&\quad p_{1}^{m}\\ \vdots&\quad\quad\vdots&\quad\ddots&\quad\vdots&\quad\vdots\\ P_{i}&\quad\quad p_{i}^{1}&\quad\cdots&\quad p_{i}^{d}&\quad\cdots&\quad p_{i }^{m}\\ \vdots&\quad\quad\vdots&\quad\vdots&\quad\ddots&\quad\vdots\\ P_{N}&\quad\quad p_{N}^{1}&\quad\cdots&\quad\cdots&\quad p_{N}^{m}\\ \end{array}\right]. \tag{1}\]  
where \(P\) is the patients population, \(P_{i}\) is the \(i\)th patient, \(p_{i}^{d}\) is the \(d\)th feature of \(i\)th patient, \(N\) is the number of patient (population), and \(m\) is the number of variables.  
This population is treated and updated in three phases. The required information in this process is calculated by Equations (2)-(5).  
\[\textit{dosage}_{i}=2-\frac{F_{i}^{n}}{F_{best}^{n}}, \tag{2}\]  
\[F_{i}^{n}=\frac{fit_{i}-f_{worst}}{\sum_{j=1}^{N}\bigl{(}fit_{j}-f_{worst} \bigr{)}}, \tag{3}\]  
\[f_{worst}=\max(fit)\ \&\ P_{worst}=P(\textit{location}(f_{worst})), \tag{4}\]  
\[f_{best}=\min(fit)\ \&\ P_{best}=P(\textit{location}(f_{best})). \tag{5}\]  
Here, \(\textit{dosage}_{i}\) is the dosage of vaccine or drug for \(i\)th patient, \(F_{i}^{n}\) is the normalized fitness of \(i\)th patient, \(F_{best}^{n}\) is the normalized fitness of best patient, \(f_{worst}\) is the fitness function of worst patient, \(f_{best}\) is the fitness function of best patient, \(P_{worst}\) is the position of worst patient, and \(P_{best}\) is the position of best patient.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 3 Experimental Setup
### Implementation Details  
The DeepLabV3+ model was implemented using MATLAB-2024a, with a backbone of MobileNetV2, pre-trained on the ImageNet dataset. For the underwater light propagation model, a pre-processing step was implemented to simulate the effects of light scattering and absorption in water. The model was trained using the Adam optimizer with a learning rate of 1e-4 and a batch size of 4. A learning rate scheduler was used to reduce the learning rate during training to avoid overfitting. This paper augments the training dataset using random rotations, flipping, and brightness adjustments to increase the model's robustness to various underwater conditions. The model was trained for 60 epochs, with early stopping based on validation loss to prevent overfitting.  
All the experiments were conducted using MATLAB-2024a on a workstation with an Intel i9-13900K CPU, 64 GB RAM, and an NVIDIA RTX 4090 GPU. During inference, the enhanced DeepLabV3+ model achieved an average processing time of 14.7 ms per image, corresponding to 68 FPS, demonstrating its capability for real-time underwater crack inspection.
## 2 Nanofluid Thermal Conductivity
### Effects of Temperature on Nanofluid Thermal Conductivity
The thermal conductivity of water then increases with temperature, unlike other liquids. The Gum Arabic surfactant created new places in which hydrogen bonding could occur, by adding several -OH groups to the mixture. This led to a decrease in the amount of hydrogen bonds between water molecules, creating a critical concentration above which the thermal conductivity of the nanofluid decreased. By the above discussed experimental results, it can be concluded that the thermal conductivity of nanofluids increases with the thermal conductivity of their respective base liquids. Through plotting various nanofluids along with their respective base liquids in Figures 21-23 below, it was observed that the thermal conductivity of nanofluids generally does not increase at the same rate as the thermal conductivity of base liquids. Thus, it can be suggested that the increase in nanofluid thermal conductivity with temperature is not solely a result of the increased thermal conductivity of the base liquid, but is also a result of Brownian motion and micro-convection due to the addition of nanoparticles. The thermal conductivities of various water-based nanofluids, at \(\varphi=2\%\), are shown in Figure 5 as a function of temperature, in comparison to the thermal conductivity of water as a function of temperature. It is observed that the thermal conductivity of nanofluids increases with temperature, however not all nanofluids increase at the same rate. It is observed that for all nanofluids, other than nanodiamond-water nanofluid, the slopes of the nanofluid thermal conductivities as a function of temperature are different from the slope of increase in the thermal conductivity of water. Therefore, it can be suggested that the increase in thermal conductivity with temperature of a nanofluid is not only caused by the base liquid, but by the nanofluid as a whole. The data shown in Figure 14 further reinforces this suggestion, by showing that the thermal conductivities of Al\({}_{2}\)O\({}_{3}\)-Ethylene Glycol nanofluids also did not increase with temperature at the same rate as the base liquid, Ethylene Glycol. Figure 21: Variation of the thermal conductivity of TiO\({}_{2}\)–water nanofluid at various temperatures, as a function of nanoparticle volume concentration, \(\varphi\). Figure 22: Variation of the thermal conductivity of ZnO–water nanofluid at various nanoparticle volume concentrations, as a function of temperature.
## 3 Proposed Method
### Insertion of Source Obstacle into the Target Image  
Once the images \(I_{\text{obst}}\) and \(I_{\text{mask}}\) are created, as explained in Section 3.1, by inclusion of, respectively, the scaled pre-processed source obstacle and scaled obstacle segmentation mask at PoI, while all other pixels are of 0 values, the masking operation is performed to insert the scaled obstacle into target image \(I_{\text{target}}\) as follows:  
\[I_{\text{syn}}(x,y)=\left\{\begin{array}{ll}I_{\text{obst}}(x,y)&\text{if }I_{\text{mask}}(x,y)\
eq 0\\ I_{\text{target}}(x,y)&\text{otherwise}\end{array}\right. \tag{10}\]
# Salespeople Performance Evaluation with Predictive Analytics in B2B
## 6 Conclusions  
In this work, the authors applied a Naive Bayes model to classify salespeople into pre-defined categories provided by the business. The classification is done in 3 classes, being: Not Performing, Good and Outstanding. The classification was achieved based on KPI's like growth volume and percentage, sales variability along the year, opportunities created, customer base line, target achievement among others.  
The dataset is composed by 594 salespeople classified into three categories being these:  
Figure 11: Dashboard for a salesperson classified as Outstanding.  
Figure 10: Dashboard for a salesperson classified as Good.  
* Not Performing: as someone who has no growth, low or no Opportunities created, low target achievement and low growth over the months on one year
* Good: as someone who was able to grow the base line on at least 2 products, have positive growth for at least 7 months, have some opportunities, and a good target achievement
* Outstanding: as someone who was able to grow the base line on more than 2 products, or had an extremely high growth on one product, and have a positive growth along 8 months or more, have a good or high target achievement based on a large base line and high targets  
The dataset used had in the beginning 45 columns. It was then reduced to 11 columns, based on several techniques to clean the data and evaluate the relevance of the columns to classify a salesperson's success. In this process, the authors also identified the most critical factors to evaluate a salesperson's performance based on the data, as Growth amount on all the products, Target achievement on all the products, Growth percentage on all the products, and the Number of Months with Growth above 0.  
The model was evaluated with a confusion matrix and other techniques like True Positives, True Negatives, and F1 score. The results showed an Accuracy (average) of 92.52% for the whole model. For each of the classes in terms of precision, Not Performing has 90%, Good 87%, and Outstanding 100%. The F1 scores for Not Performing were 94%, for good 86%, and Outstanding 80%.  
The accuracy results in this work are high because the size of the dataset and the variations of data have similar behavior for each of the classes. For instance, a salesperson not performing has in most of the time, low growth, low number of opportunities, and sales above 0 for a small number of months in one year; a good salesperson may have high growth in at least six months over one product; the outstanding salesperson should have growth extremely high for at least one product and growth above 0 for at least eight months.  
This approach, when data is available, can help produce new guidelines that HR with pre-defined rules can use to automate part of the performance appraisal process. It can be applied to other cases and companies, and with DM, start automating the analysis of complex KPI's with relationships between them to generate a classification.
# Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics
## 1 Introduction
### Overview of Proposed Method  
CT images are a valuable diagnostic tool, derived from processing X-ray scans of a patient's body. These images offer crucial organ-related information, thanks to the CT scanner's ability to produce cross-sectional images using rotating X-ray tubes [20]. In our research, we delve into scenarios where diagnostic decisions are predominantly driven by the organ's shape rather than its texture. Our proposed method aims to confuse the target model by intentionally transforming the feature statistics, while leaving its shape unaltered. We leverage a texture reformer [21], a real-time style transfer technique that utilizes adaptive instance normalization (AdaIN) to modify an image's texture. As AdaIN is designed to align the source and target image distributions, we tweak it to intentionally transform the statistics. Our experimental findings affirm that our adversarial samples convincingly passed as genuine in blind tests with physicians. In addition, the experiment demonstrates that our method can be utilized for data augmentation to enhance model generalization. We anticipate that our proposed method can be a potent addition to the tools used for benchmark testing and contribute substantially towards validating automatic CT segmentation systems.  
Figure 1: (a) A standard CT image smoothly interacts with the automatic segmentation system, while (b) a conspicuous noise makes an adversarial sample, introduced by an intruder, readily noticeable to a healthcare practitioner.  
Our contributions are summarized as follows: We present a novel generation of adversarial attacks designed for medical applications, focusing on transforming the feature statistics of the target image, in contrast to traditional noise addition techniques.  
Following our main contribution, our rigorous experiments demonstrate that medical practitioners perceive our adversarial samples as more realistic compared to those generated by existing state-of-the-art methods. Additionally, our technique can be applied for data augmentation, thereby improving model generalization.
# Experimental/Numerical Evaluation of Steel Trapezoidal Corrugated Infill Panels with an Opening
## 3 Results
### Behavior of Specimen 2  
Similar to the first specimen, the infill shear plate behaved elastically during the first four cycles, and buckling of the shear plate was found in cycle 5 with 6 mm displacement and 0.38% drift angle. Distortion of the shear panel was detected first on the left side of the panel under the left opening near the bottom beam and then on the right side of the panel under the right opening near the bottom beam. In cycles 7 and 8, new distortions were observed in the infill plate around the corners of the openings as well as near the top and bottom beams.  
The first tear was monitored on the left side of the panel under the left opening near the bottom beam in cycle 19 with 30 mm displacement and 1.91% drift angle. By increasing displacement, cracks were developed in the welded connections of the infill plate to the beams and columns. During cycle 31 with 50 mm displacement and 3.18% drift angle, four small tears were observed in the infill plate between two openings. Diagonal tears were detected in top corners during cycle 31. Moreover, another tear was detected on the right side of the panel under the right opening during cycle 33 with 50 mm displacement and 3.18% drift angle. Then by continuing the loading, the number of tears, as well as their dimensions, increased. In cycle 39 with 70 mm displacement and 4.45% drift angle, yielding was clearly monitored in the external flange of both columns at their bottom end. The maximum shear force of this specimen, recorded by cycle 43 with 80 mm displacement and 5.12% drift angle, was equal to 390 kN. Similar to the first specimen, during cycles 43-46 with 80 mm displacement and 5.12% drift angle, conditions of the columns at their top end, as well as conditions of the top beam at the points of its connections to the columns, clearly indicated yielding. The loading was terminated by cycle 48 with 100 mm displacement and 6.37% drift angle. Figure 11 shows the second specimen (specimen 2) at the end of loading. As illustrated in Figure 11, diagonal tears in the four corners of two openings were remarkable.  
Figure 12 indicates the specimen without an opening tested by [PERSON] et al. [9] at the end of loading. It is seen in Figures 10-12 that unlike the specimen without an opening where the post-buckling tension field was formed completely, the tension field was formed partially in the specimens with an opening due to the existence of the opening. In the following, the results are considered for three specimens, including two corrugated specimens with an opening tested in this study along with the corrugated specimen without an opening tested by [PERSON] et al. [9].  
Figure 11: Specim 2 at the end of loading.
###### Abstract  
In the context of food and agro-ecological transitions, the development of food applications based on legume flows and plant proteins requires a mastery of grain milling. While wheat grain milling has been extensively studied and is well-mastered, legume grinding and its underlying mechanisms are still poorly understood. The aim of this work is to contribute to the study of the fragmentation mechanisms of pea grains during grinding. Experiments were carried out on hulled yellow pea grains (Kameleon variety) ground under different conditions using a ball mill (MM400, Retsch\({}^{\text{\textregistered}}\), Haan, Germany) or a micro-cylinder mill. The results showed that the grinding of pea grains, regardless of the type of mill, produced powders characterized by particle size distribution curves with a multimodal shape. The curve analysis was performed according to a simplified deconvolution approach, taking into account different particle populations without diameter overlap. Four particle populations of different sizes were identified and correlated with specific mechanisms governing the grinding of yellow split peas. The physical and biochemical properties of the resulting powders were determined. Taking into account the proportions of the four populations within the powders, the results showed a positive correlation between the volume proportions of very fine (0-10 \(\upmu\)m) and fine (10-55 \(\upmu\)m) particles within the powders and the starch damage rate and the specific surface area developed, irrespective of the type of mill.  
pea cotyledons; grinding process; particle populations; fragmentation mechanisms +
Footnote †: journal: Applied Sciences
The experimental results show that the 2 GHz IF signal is up-converted to 18 GHz, and an SFDR of 112.3 dB-Hz\({}^{4/5}\) is obtained. Compared with the traditional CS-DSB modulation scheme, the SFDR is improved by 14.8 dB.
# Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints
## 4 Collision-Avoidance Switch Control Strategy  
This section describes a CASC, and the structure of the CASC is shown in Figure 2. A dynamic calculation of collision risk (DCCR) is introduced to evaluate the risk of collision. Based on the COLREGs, this paper classifies four encountering situations of two vessels: the head-on situation, the left crossing situation, the right crossing situation, and the overtaking situation are considered [32; 33]. More details of the encountering situations can be found in rules 13, 14, and 15 of the COLREGs [34; 35], which are depicted in Figure 3. Based on the DCCR analysis, a switch control strategy is employed. A collision-avoidance controller (CAC) and a prescribed performance controller are designed, and the stability of the control system is proven.
## 3 Experimental Results and Discussion
### Comparison to Existing Systems  
Table 14 gives a comparison of existing plant monitoring systems based on monitoring parameters, field device implementation, monitoring dashboard, monitoring device, and cost. The field device implementation indicates the reliability of actual implementation  
Figure 27: Recorded soil NPK data graphs. Figure 26: Recorded soil moisture and N, P, K data graphs of Plant-2. in outdoor environments, which should consider the casing/box protection and power consumption for battery operation. As shown in the table, most of the existing systems are hardware prototypes, except the work in [16] that uses the commercial product from ADCON. However, the work in [16] only deals with air temperature and humidity, soil temperature, and moisture monitoring. Since most works developed the hardware prototype, comparing the equipment protection in outdoor environments can only be conducted partially. Instead, we compare the power consumption, the monitoring features, and the cost.
## 6 Discussion
### Nb-Ta Mineralization in Lijiapuzi Pegmatite Deposit  
The Nb-Ta mineralization of pegmatite has long been disputed by many scholars worldwide. Magmatic crystallization differentiation was firstly promoted as the main rare-metal mineralization mechanism [50; 51; 52], but later many scholars found that the post-magmatic hydrothermal fluids and the late-stage metasomatism play an important role in the mineralization [53; 54]. Currently, both of the mechanisms are advocated by most geologists [55; 56; 57], especially the post-magmatic supercritical fluid [58; 59] and the albitization [56; 57].  
Field truth and microscopic observation indicate that the Lijiapuzi pegmatites exhibit obvious albitization of microcline and late-stage uranium mineralization superimposition. The Nb-Ta mineralization is intimately related to the albite metasomatism of the intermediate muscovite (lepidolite) -albite zone. The features of major components and trace elements indicate that the Lijiapuzi pegmatite was of crust-derived origin, and the residual rutile fractionated the Nb and Ta in the peraluminous granitic melts and formed in a relatively extensional environment of the post-orogenic tectonic setting, i.e., the Lijiapuzi Nb-Ta deposit was formed by the albitization processes of pegmatite in a post- orogenic collision setting in the eastern margin of NCC was and superimposed by a slightly late-stage uranium mineralization.
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 3 Construction of Vehicle Scheduling Optimization Model for Coal Mine Enterprises
### Improved Sparrow Search Algorithm Design  
(1) Encoding and Decoding  
The MCVS problem refers to the loading sequence of multiple customer vehicles in the warehouse. This paper encodes the vehicle sequence using non-negative integers. Ten customer loading vehicles are taken as the coding plan, and the coding indicates that the loading sequence of the ten vehicles is 1-3-7-10-4-5-8-2-6-9, as shown in Figure 3. The diagram illustrates that the customer's first vehicle, labeled as vehicle number 1, is loaded first in a loading bay, followed by vehicle number 3, and so on.  
(2) Cross Operation  
Based on an adaptive crossover operator, a location-based crossover method is selected. Assuming that there are 10 loading vehicles to be sorted, the specific steps are as follows:  
Step 1: Determine the intersection points of parent \(P_{1}\) and \(P_{2}\). Assume that the intersection points involve vehicles position 2, 4, 5, and 9;  
Step 2: Randomly generate child \(Q_{1}\) and \(Q_{2}\), ensuring that the vehicles at the intersection point in \(Q_{1}\) match those in \(P_{1}\);  
Step 3: Insert the vehicles of \(P_{2}\), excluding the crossing points, into \(Q_{1}\) sequentially. The resulting child \(Q_{1}\) is 2-3-5-10-4-8-1-7-6-9;  
Step 4: Repeat Step 2 and Step 3 for child \(Q_{2}\) to obtain the sequence 3-5-7-8-1-10-4-2-6-9.  
According to the cross-operation process described above, the uniform cross diagram is illustrated in Figure 4.  
(3) Variation Operation  
The reverse variation method is adopted, assuming that the number of loading vehicles is 10. Two integers, \(a=5\) and \(b=7\), are selected from the range [1; 10] to represent the variation position and generate the variation interval. The reverse of the gene interval, (4, 5, 8), results in (8, 5, 4). Consequently, the offspring chromosome sequence becomes 1-3-7-10-8-5-4-2-6-9. The detailed operation is shown in Figure 5.  
Figure 4: Schematic diagram of a uniform crossing.  
Figure 3: Coding diagram.  
The flowchart of the enhanced SSA is depicted in Figure 6.
# The Protection of Data Sharing for Privacy in Financial Vision
## 5 Discussion  
When noise size = 0.1 and 0.3, \(\epsilon\) = 90,964.58 and 284.62, respectively. The value of \(\epsilon\) is so high that there is almost no privacy guarantee. Therefore, we will not discuss the two models. When noise is 0.5, \(\epsilon\) = 39.5, test accuracy = 90.75%, which is about 5% lower than baseline model's test accuracy. When noise is 0.7, \(\epsilon\) = 12.62, test accuracy = 90.45%, which is about 6% lower than the baseline model. When noise is 1, \(\epsilon\) = 5.41, test accuracy = 89.23%, and it is about 7% lower than the baseline model. We can find that even when noise is 1, the accuracy does not decrease too much.  
Financial vision contains sensitive information flagged by experts and could cause significant damage if leaked. Fortunately, differential privacy prevents data leakage. However, the differential privacy mechanism protects data by adding noise to the model, inevitably leading to model accuracy degradation or data distortion problems. The balancing data's readability does not provide a conclusion when we train the differential privacy model. [PERSON] and [PERSON] recommend \(\epsilon\leq 1\) to obtain a privacy guarantee [39]. Nevertheless, using such a small \(\epsilon\) will cause difficulties in data interpretation. A higher \(\epsilon\) is usually selected to balance privacy guarantee and data readability. For example, the \(\epsilon\) standard used by Google and Apple is between 6 and 14 [17; 40]. Similar results are in our experiments. Model accuracy decreases by approximately 6% with \(\epsilon\) = 12.62 and only 7% with \(\epsilon\) = 5.41. The accuracy reduction is tolerable, and the privacy guarantee of the training data is greatly improved.  
On the other hand, there are excessively different results of differential privacy for different data types [25]. The details are in Table 3. In MNIST, compared to the baseline model, when \(\epsilon\) = 2, the accuracy only decreases around 3.3%. However, in CIFAR-10, the accuracy dived from 96.5 to 70% when \(\epsilon\) = 4. In our data, the accuracy of the model of \(\epsilon\) = 5 is 7% lower than the baseline model. Although not as good as MNIST, it is still acceptable.