esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarimetric Detection of Chemotherapy-Induced Cancer Cell Death
###### Abstract  
Imaging polarimetry is a focus of increasing interest in diagnostic medicine because of its non-destructive nature and its potential to distinguish normal from tumor tissue. However, handling and understanding polarimetric images is not an easy task, and different intermediate steps have been proposed in order to introduce helpful physical magnitudes. In this research, we look for a sensitive polarimetric parameter that allows us to detect cell death when cancer cells are treated with chemotherapy drugs. Experiments in two different myelomonocytic leukemia cell lines, U937 and THP1, are performed in triplicate, finding a highly-significant positive correlation between total diattenuation of samples in transmission configuration, \(D_{T}\), and chemotherapy-induced cell death. The location of the diattenuation enhancement gives some insight into the cell death process. The proposed method can be an objective complement to conventional methodologies based on pure observational microscopy and can be easily implemented in regular microscopes.  
Keywords:polarimetry; light polarization; diattenuation; cancer cell; apoptosis +
Footnote †: journal: Applied Physics Letters
# A 5G-Based VR Application for Efficient Port Management
## 4 Demonstration and Performance Evaluation  
### Materials and Methods  
The experimental material mainly includes physical production of 3D printing materials, VR equipment, wireless network, and 5G environment.  
We choose ABS plastic for 3D printing of the ship body. ABS plastic is generally opaque, with good dimensional stability, electrical properties, wear resistance, chemical resistance, abrasion resistance, molding process, and machining. ABS plastic can better adapt to the changes in various situations in a complex environment. This system uses 5G technology to overcome the latency problem of traditional VR technology. The VR perception is provided by Huawei VR glasses with 70/90 FPS (mobile mode/PC mode), 3K screen resolution and 1058 PPI. The performance of Huawei VR glasses is stable, cost-effective, and adapted to the 5G network. The wireless communication transmission technology adopts HTTPS. Through 5G transmission rate, it can ensure data transmission at high speed and encrypt and decrypt the data to prevent the transmission data from being illegally stolen. The experiments of this system are conducted in Wuhan, which is rich in 5G base stations and can provide a complete and accurate 5G network.  
This experimental VR environment is built-in Unity3D, which simulates the construction of a whole modern port. Various weather conditions can be simulated, and modes such as inspection or teaching can be conducted. In addition, we make cargo ships and car models by 3D printing to create a set of simple physical models. Moreover, we also designed a user experience test and participants were given ten minutes to familiarize themselves with Huawei VR glasses, using simple interactions such as moving the objects in the VR environment, calling out, and canceling the main menu through gestures and voice. After that, detailed instructions on how to use the system will be provided for each user. When the preparation work is completed, users will start to experience the system. The experiment continued about 20 min per person, including the time to familiarize themselves with the device, conduct tests, and fill out questionnaires. Users will control the objects in the VR environment, reach the specified location and carry a certain amount of goods. In the VR environment, we tested a series of data such as data transmission rate, packet loss rate, and latency, and scored the user's experience to verify the feasibility of the system as well as to provide experience and direction for subsequent system development. The data and process obtained from the experiments will be described in more detail in the subsequent chapters.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### Operation of Framework
#### 3.4.2 QoE Normalization  
When comparing algorithm performance in various network environments, there is a limit to using the actual QoE value. When comparing the performance of algorithms operating in a rich network environment and in a poor network environment, the actual QoE is lower in a poor network environment. Therefore, to use the QoE value as a threshold in a changing network environment, it is necessary to normalize the QoE value. QoE normalization is performed using the actual QoE value calculated using the QoE function of Equation (5) and the maximum QoE value obtained from the current network state. The formula for QoE normalization is expressed as Equations (12) and (13).  
\[Q_{max}=\sum_{k=1}^{K}\hat{r}_{k}-\lambda\sum_{k=1}^{K-1}|\hat{r}_{k+1}-\hat{r} _{k}| \tag{12}\]  
\[Q_{nom}=\frac{Q}{Q_{max}}100 \tag{13}\]  
\(Q_{max}\) means the maximum QoE value in the current network state, and \(Q_{nom}\) means the normalized QoE value. Since \(Q_{max}\) means the maximum QoE value only at the current time in the short term, it is calculated using the maximum quality \(\hat{r}_{k}\) of the segment \(k\) that can be requested in the available bandwidth without considering the metric related to playback interruption.
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
###### Abstract  
Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, requiring extensive medical expertise and time. To tackle these issues, researchers have explored the application of artificial intelligence (AI) tools, particularly machine learning techniques such as shallow and deep learning, to enhance the diagnostic process for skin cancer. These tools employ computer algorithms and deep neural networks to identify and categorize skin cancer. However, accurately distinguishing between skin cancer and benign tumors remains challenging necessitating the extraction of pertinent features from image data for classification. This study addresses these challenges by employing Principal Component Analysis (PCA), a dimensionality-reduction approach, to extract relevant features from skin images. Additionally, accurately classifying skin images into malignant and benign categories presents another obstacle. To improve accuracy, the AdaBoost algorithm is utilized, which amalgamates weak classification models into a robust classifier with high accuracy. This research introduces a novel approach to skin cancer diagnosis by integrating Principal Component Analysis (PCA), AdaBoost, and EfficientNet B0, leveraging artificial intelligence (AI) tools. The novelty lies in the combination of these techniques to develop a robust and accurate system for skin cancer classification. The advantage of this approach is its ability to significantly reduce costs, minimize reliance on medical experts, and expedite the diagnostic process. The developed model achieved an accuracy of 93.00% using the DermIS dataset and demonstrated excellent precision, recall, and F1-score values, confirming its ability to correctly classify skin lesions as malignant or benign. Additionally, the model achieved an accuracy of 91.00% using the ISIC dataset, which is widely recognized for its comprehensive collection of annotated dermoscopic images, providing a robust foundation for training and validation. These advancements have the potential to significantly enhance the efficiency and accuracy of skin cancer diagnosis and classification. Ultimately, the integration of AI tools and techniques in skin cancer diagnosis can lead to cost reduction and improved patient outcomes, benefiting both patients and healthcare providers.  
skin cancer diagnosis; machine learning; Convolutional Neural Network (CNN); adaptive boosting algorithm (AdaBoost); principal component analysis (PCA); Support Vector Machine (SVM) +
Footnote †: journal: _Algorithms_
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation  
We first present our evaluation methodology and then demonstrate the fairness achieved without and with ABC.
## 2 Materials and Methods
### Multi-Disciplinary Optimization Method  
In this research, the estimated environmental impacts and mechanical strength of the product of interest were used to construct the required response surface models. Multi-disciplinary and MOO were performed after combining these models. The calculation results would allow designers to collect design information about the product's environmental impacts and mechanical strength simultaneously. The optimization flowchart is shown in Figure 4, and the procedures are explained as follows:  
1. For the environmental impacts, assessment data for products with the same specifications are obtained through the bill of material (BOM) and then LCA software is used for the environmental impact evaluation;
2. To determine the mechanical strength, the mechanical properties are analyzed using CAD software. The corresponding von Mises stress data from step 1 are adopted as the references for the mechanical strength calculation;
3. With the help of the RSM, the data acquired in steps 1 and 2 are used to construct two models, namely the environmental impact function (\(V_{1}(x)\)) and mechanical strength function (\(V_{2}(x)\)). As previously mentioned, regression models can be classified into three types. Therefore, it is necessary to derive and compare models of all three types to select the one with the lowest error rate for the subsequent multi-disciplinary and MOO;
4. First, \(V_{1}(x)\) and \(V_{2}(x)\) are combined into a multi-objective function, \(\mathrm{V}(x)\). Next, the GA is applied for numerical optimization. Yet, because the two functions are different in nature and show opposite trends, normalization is needed before the optimization, as illustrated in Equations (1) and (2). The first equation divides each objective function by its maximum value. In the meantime, Equation (2) first computes theabsolute value of the difference between each function and its minimum value and then divides the absolute value by the difference between the maximum and the minimum. After normalization, \(V_{1}(x)\) and \(V_{2}(x)\) are assigned different weights for optimization. The objective functions are given as Equations (3) and (4), which are weighted equations of Equations (1) and (2), respectively.  
\[\frac{V_{i}(X)}{V_{i(max)}},\ i=1,2 \tag{1}\]  
\[\frac{V_{i}(X)-V_{i(min)}}{V_{i(max)}-V_{i(min)}},\ i=1,2 \tag{2}\]  
\[V(X)=w_{1}\cdot\frac{V_{1}(X)}{V_{1(max)}}+w_{2}\cdot\frac{V_{2}(X)}{V_{2(max) }},\ w_{1}+w_{2}=1 \tag{3}\]  
\[V(X)=w_{1}\cdot\frac{\left|V_{1}(X)-V_{1(min)}\right|}{\left|V_{1(max)}-V_{1( min)}\right|}+w_{2}\cdot\frac{\left|V_{2}(X)-V_{2(min)}\right|}{\left|V_{2(max)}-V_{2( min)}\right|},\ w_{1}+w_{2}=1 \tag{4}\]
## 3 Research Results
### Spatial Dependence Mechanism Underlying CS
#### 3.3.2 Spatial Correlation Between CS and the Topographic Gradient  
A strong and consistent positive relationship exists between the topographic gradient and carbon sequestration (CS) in the study area, leading to a clear stepped pattern in spatial distribution (Figure 9): the region with a low elevation and gentle slope is gradually transitioning to a region with high altitude and good vegetation cover. Low-CS areas such  
Figure 8: Distribution characteristics of CS at different topographic gradients in BRB.  
as urban construction land are often surrounded by high-CS areas. On the whole, the CS in the west and northwest of the study area formed a significant high-high accumulation pattern, indicating that the forest resources in this region are rich and have a positive effect on carbon accumulation. The area with a high altitude and large topography is rich in forest resources, which forms favorable conditions for carbon accumulation. The urban construction land with a relatively low elevation and flat terrain shows a low-low accumulation pattern and faces the risk of gradual shrinking of ecological carbon reserves.  
The low- and high-concentration areas decreased gradually (90 km\({}^{2}\), 19.25 km\({}^{2}\), 30.25 km\({}^{2}\) and 652.5 km\({}^{2}\), respectively), indicating that urban expansion and the intensification of human activities had an impact on the ecosystem. At the same time, the decrease in high-high-aggregation area (34 km\({}^{2}\), 132.25 km\({}^{2}\), 114.5 km\({}^{2}\) and 1502.75 km\({}^{2}\), respectively) reflects the destruction of forest resources and the decline in CS. The low-low aggregation pattern increased from 2000 to 2010, indicating rapid urban expansion during this period. However, with the successive implementation of ecological policies from 2010 to 2020, the low-low aggregation area was reduced, and the ecological environment was partially alleviated. The low-high-aggregation area decreased slightly, showing a declining  
Figure 9: Bivariate LISA cluster map of CS and topographic gradient drivers.  
trend for ecological quality. In areas with a steep terrain, CS is relatively high, while in agricultural areas with a high population density, flat terrain, and vast land, CS declines significantly. There is a correlation mechanism between this distribution and topographic gradient, showing synergistic effects (39.6% in 2000, 34.7% in 2010, 34.9% in 2020) and tradeoff effects (13.7% in 2000, 11.89% in 2010, 11.9% in 2020). However, these effects gradually waned over time, suggesting that the carbon sink capacity of regional ecosystems is declining in general.
## 3 HGV Tracking with Multiple Model Algorithms
### IMM Tracking Algorithm
#### 3.1.3 Model Probability Calculation  
The likelihood of the CKF model is given by the innovation \(\widehat{Y}_{k}^{j}\) and the covariance of the innovation \(S_{k}^{j}\) from  
\[\Lambda_{k}^{j}=\frac{\exp\left[-\frac{1}{2}\left(\widehat{Y}_{k}^{j}\right)^ {T}\left(S_{k}^{j}\right)^{-1}\widehat{Y}_{k}^{j}\right]}{\sqrt{\left(2\pi \right)^{M}\left|S_{k}^{j}\right|}} \tag{23}\]  
where \(M\) is the dimension of the radar measurement; \(\widehat{Y}_{k}^{j}\) is the residual of actual measurement and prediction measurement in CKF; and \(S_{k}^{j}\) is the covariance of \(\widehat{Y}_{k}^{j}\).  
The model probabilities \(\omega_{k}^{j}\) are given by Bayes rule from  
\[\omega_{k}^{j}=\frac{\Lambda_{k}^{j}\sum\limits_{1=1}^{N}\rho_{ij}\omega_{k-1}^{i }}{\sum\limits_{i=1}^{N}\Lambda_{k}^{i}\rho_{ij}\omega_{k-1}^{i}} \tag{24}\]
## 2 Materials and Methods
### Characterization  
The microstructure of composites was characterized by scanning electron microscopy (SEM, Hitachi S-4800, Tokyo, Japan). The phase analysis was evaluated by X-ray diffractometry (TTR-III, Tokyo, Japan). A high-precision density tester (Dahometer, DE-120M, Hangzhou, China) was used to measure the density of samples via the Archimedes method. A Rockwell hardness tester (Huayin HRS-150, Yantai, China) was used to measure the Rockwell hardness scale C (HRC) of samples. Bending strength for each sample was measured by using a three-point bending test machine (DDL 100, Changchun, China), and was calculated by following formula:  
\[\sigma=3 PL/2 bh^{2}, \tag{1}\]  
where \(\sigma\) is the bending strength, MPa; \(P\) is the axial load, N; \(L\) is the length of the sample holder, 24 mm; \(b\) is the width, 5 mm; and \(h\) is the height, 8 mm.  
A grinding ratio measurement device (DHM-2, Changchun, China) was applied to measure the grinding ratio. A schematic diagram of the grinding ratio test is illustrated in Figure 2. SiC grinding wheels with diameter of 100 mm and thickness of 20 mm were used as wear counterparts. The testing parameters involved were: linear velocity 15 m/s, swing frequency 30 min\({}^{-1}\), load 500 g, and grinding time \(\geq\)100 s. The grinding ratio \(Ra\) was calculated by the formula:  
\[Ra=\Delta Mg/\Delta Ms, \tag{2}\]  
where \(\Delta Mg\) is the weight loss of the SiC grinding wheel, and \(\Delta Ms\) is the weight loss of sample.  
Figure 1: The schematic diagram of CDW-196 cryogenic treatment system.
## 2 System Description and Assumptions
### Assumptions  
The main assumptions for this study are listed as below,  
1. The systems are assumed to operate at a steady state, and the off-design performance or dynamic performance is not considered in this study;
2. The variation of kinetic and potential energy is neglected;
3. The pressure losses and heat losses of pipes and heat exchangers are neglected [25];
4. Isentropic efficiencies are assumed for the turbine, pump and compressors [26];
5. The liquid working fluid exiting the condenser (stream A9) and the generator (stream A7) is saturated liquid, while a subcooled degree of 3K is assumed at the outlet of absorber (stream A2) [27];
6. Vapor exiting the generator is assumed to be pure ammonia for the sCO2/ammonia-water AHP system [27,28];
7. A temperature difference or an effectiveness is assumed in each heat exchanger [26].
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion  
### Simulation Setup  
All evaluations are performed using our previously developed 802.11 ah ns-3 module [2], based on ns-3 version 3.23. We consider two IoT scenarios, where sensors periodically monitor the environment and send the resulting data to a server (via the AP). Different sensors may have different monitoring and transmission intervals, which may change over time. The transmission interval of sensors in an IoT network follows a uniform distribution. The ratio between any two sensors' transmission interval in an experiment is never higher than 20. The default PHY and MAC layer parameters used in our simulation are shown in Table 4. Given the low-power nature of battery powered sensors, the PHY layer parameters are configured based on the low-power 802.11 ah radio hardware prototype developed by [PERSON] et al. [15], with a transmission power of 0 dBm, a gain of 0 dBi (for both sensor and AP), and noise figure of 6.8 dB. As Section 5.1 indicates, the data rate and payload size impose an impact on the performance of TAROA. Therefore, two scenarios are considered: (i) high-throughput (HT) using MCS8 with 2 MHz bandwidth (data rate 7.8 Mbps) and payload size 256 bytes; and (ii) low-throughput (LT) using MCS1 with 1 MHz bandwidth (data rate 0.15 Mbps) and payload size 64 bytes. The two scenarios are hence referred to as HT and LT, respectively. For each scenario, TAROA is evaluated both with and without hidden nodes, with the stations randomly placed around the AP in a circle of 50 and 100 m, respectively, for the HT scenario, and 200 and 450 m, respectively, for the LT scenario. Taking into account the IoT scenario we evaluate, in which the traffic of each station is quite low, a small buffer size can be used. Therefore, the size of the stations' transmit queues is configured to be 10 packets. Thus, packets can be dropped during simulation due to buffer overflow. No rate control algorithm (RCA) is used at the MAC layer.  
RAW performance is evaluated in terms of two metrics: throughput, latency and packet loss. Throughput is calculated as the average number of successfully received payload bytes by the AP per second. Latency is defined as the average time between a packet entering the transmit queue of the station and being received by the AP. Packet loss represents the ratio between the number of packets not received by AP, and the number of packets sent by all stations. Each simulation runs 600 s. This simulation time is long enough as each station transmits packets with a certain (predictable) frequency, the steady-state simulation result is achieved after less than 100 s in all experiments. All results are averaged over 10 iterations, with the variability of results over these iterations quantified using the standard deviation (SD).
_Article_  
**Thermal Characteristics of Plastic Film Tension**  
**in Roll-to-Roll Gravure Printed Electronics**  
**[PERSON] \({}^{1,3}\)\({}^{\copyright}\), [PERSON] \({}^{2,*}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China;  
[EMAIL_ADDRESS] (K.H.); [EMAIL_ADDRESS] (K.W.); [EMAIL_ADDRESS] (X.M.)  
\({}^{2}\) Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an 710048, Shaanxi, China  
\({}^{3}\) School of Mechanical Engineering, Henan University of Science and Technology, Luoyang 471003, Henan, China  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +86-159-2972-4880  
Received: 17 January 2018; Accepted: 14 February 2018; Published: 23 February 2018  
**Abstract:** In the printing section of a roll-to-roll gravure printed electronics machine, the plastic film tension is directly associated with the product quality. The temperature distribution of the plastic film in the printing section is non-uniform, because of the higher drying temperature and the lower room temperature. Furthermore, the drying temperature and the room temperature are not constants in industrial production. As the plastic film is sensitive to temperature, the temperature of the plastic film will affects the web tension in the printing section. In this paper, the thermal characteristics of the plastic film tension in roll-to-roll gravure printed electronics are studied in order to help to improve the product quality. First, the tension model including the factor of temperature is derived based on the law of mass conservation. Then, some simulations and experiments are carried out in order to in-depth research the effects of the drying temperature and room temperature based on the relations between system inputs and outputs. The results show that the drying temperature and room temperature have significant influences on the web tension. The research on the thermal characteristics of plastic film tension would benefit the tension control accuracy for further study.  
**Keywords:** printed electronics; tension model; web tension; thermal characteristics; room temperature; drying temperature