esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Segmentation Algorithm for Cytoid Macular Edema Based on Omnidirectional Wave Operator
## 2 Methods
### Image Segmentation  
#### 2.3.1 ROI Extraction  
The OCT imaging results of the retina include the retina and choroid. The tomography images of the capillary layer, middle vascular layer, and large vascular layer in the choroid will cause interference in CME segmentation. Therefore, before segmentation, the ROI region is extracted to avoid the influence of choroidal imaging.  
CME is distributed in most layers of the retina. Once the internal and external environment of the retina is determined, the region to be segmented is determined. The calculation time is reduced and the calculation speed can be improved at the same time. The boundary of the ROI is the inner limiting membrane (ILM) and the outer Bruch membrane (OBM), as shown in Figure 2.  
The ROI is judged by calculating the row gray mean distribution by Formula (1).  
\[mean_{gray}(i)=\frac{\sum\limits_{j=1}^{N}I(i,j)}{N},\ i=1,2\ldots M,\ j=1,2 \ldots N \tag{1}\]  
Given an image of size \(M\times N\) pixels, the gray value of each pixel is \(I(i,j)\), where \(i\) is the row coordinate of the pixel and \(j\) is the column coordinate of the pixel. The average row gray value is calculated by using Formula (1).  
For the convenience of observation, the white curve on the left side of Figure 3 indicates the change in the average gray value of each row, and the blue straight line indicates the mean gray value of the whole image. The intersection points A and B of the two curves can divide the image into three parts. From top to bottom are the posterior vitreous, retina, and sclera. The segmentation range of this algorithm is the retina which is the region between points A and B. Using the wave algorithm, the image is segmented from top to bottom to obtain the ILM layer and the OBM layer, that is, the ROI area.  
Figure 3: The result of the ROI.  
Figure 2: The locations of ILM and OBM in retina image.
## 2 Methodology and Model Descriptions  
### Methodology  
In this study, a SAGE computation solver in CONVERGE (Version 2.3.0, Convergent Science, Inc., Madison, WI, USA) codes was selected in order to predict the autoignition controlled by reaction kinetics in a gasoline engine [22; 23]. In addition, a detailed chemical reaction mechanism coupled with the algorithm of adaptive mesh refinement (AMR) was adopted to characterize the multiple combustion regimes such as ignition, premixed and diffusion combustion process.  
As illustrated by [PERSON] [24], a multi-step chemical reaction mechanism of the combustion is described as follows:  
\[\sum_{l=1}^{M}v_{i,r}^{\prime}X_{i}=\sum_{l=1}^{M}v_{i,r}^{\prime\prime}X_{i},\ \ \ \ r=1,2,\ldots,R \tag{1}\]  
where \(v_{i,r}^{\prime}\) and \(v_{i,r}^{\prime\prime}\) are the stoichiometric coefficients for reactants and products, respectively, for species \(i\) and reaction \(r\), \(R\) is the total number of reactions and \(X_{i}\) indicates the corresponding species \(i\).  
The equations of energy conservation and mass are solved for a specified computational cell as follows:  
\[\frac{d[X_{i}]}{dt}=\dot{\omega}_{i} \tag{2}\]  
\[\frac{dT}{dt}=\frac{V\frac{dP}{dt}-\sum_{m}(h_{i}\dot{\omega}_{i})}{\sum_{m}( [X_{m}]\overline{c}_{p,i})} \tag{3}\]  
where \(V\) indicates volume, \(P\) pressure, \(T\) temperature and \(\dot{\omega}_{i}\) net reaction rate of the specified species \(i\). The \(h_{i}\) and \(\overline{c}_{p,i}\) represent the molar specific enthalpy and molar constant-pressure specific heat, respectively.  
In this study, a standard RNG (renormalization group) closure model and an improved two equation eddy viscosity turbulence model based on a generalized RNG closure were adopted in CONVERGE software, respectively. By averaging the equations of motion over infinitesimal bands of small scales and collecting the effect of the scale removal process in a modified viscosity, the RNG method removes the impact of smaller scale structures on the larger ones. This approach has been used to derive the \(k\) and \(\varepsilon\) equation from the N-S (Navier-Stokes) equations, including providing numerical values for the model constants in the transport equations [25]. In addition, a generalized RNG turbulence model based on the local 'dimensionality' of the flow field is also adopted and the detailed description is illustrated in literature [26]. Compared with the standard \(k-\varepsilon\) model, the \(k\)-equation remains the same in the RNG version. Nevertheless, an extra term, \(R\), appears in the \(\varepsilon\)-equation. It is closed from an approximation rather than by a rigorous RNG derivation.
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 7 Conclusions  
In order to improve the accuracy of lane detection, a novel approach based on DROI selection in the safety vision and lane line fitting in a polar coordinate system is proposed in the paper. The characteristics of lane lines on structured roads are analyzed, and the DROI global search method is used to detect the lane lines. Meanwhile, the interpolation and tracking of lane line feature points are carried out. Given the advantages of the polar coordinate equation fitting curve, the least square method is used to fit the lane line in the polar coordinate system. Lastly, the proposed approach is evaluated using the Caltech database, and the accuracy rate is 99.21%. Additionally, the positive detection rate is shown to be 99.07% in complicated real road environments, which verifies the robustness of the proposed method. The accuracy of this method is clearly superior to those of other mainstream methods.  
The approach proposed in this paper has good application prospects in advanced driver assistance systems. In addition, the method of extracting the dynamic region of interest can also be applied in the field of robotics.  
Conceptualization, J.H.; Data curation, Y.S. and J.Z.; Funding acquisition, C.F.; Investigation, Y.S.; Methodology, J.H. and S.X.; Project administration, J.H. and C.F.; Software, S.X.; Validation, S.X. and J.Z.; Visualization, S.X.; Writing - original draft, J.H., S.X. and C.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities under Grant No. 106112016 CDJX2338825) and the National Natural Science Foundation of China under Grant No. 51805055.  
The authors declare no conflict of interest.
## 2 Methodology: Data Pre-Processing and Models  
The data was collected from the Igiugigig, AK (Latitude: 59.3278\({}^{\circ}\) N, Longitude: 155.8947\({}^{\circ}\) W) microgrid from 19 December 2013 to 17 December 2014. The weather conditions and the community power draw reports were temporally aligned and the reporting frequency was standardized to 10 min per reporting interval. The data collection was incomplete for the given time period, and the final data set had multiple periods of missing measurements for either the community power load, the weather conditions or both. Some of the missing weather data was filled in using the readings from other nearby weather stations that reported the environmental conditions on publicly available sites [17]. The incomplete instances in the final data-sets were disqualified and not used to train or assess the models.  
The resulting data set associated each reported community power draw reading with the reported weather data vector that includes temperature, humidity, dew-point, wind speed, wind direction, wind gust speed, conditions, and events. The total number of complete instances is 38, 661 which is equivalent to approximately 268 days worth of usable data.  
Each measurement category (feature) was pre-processed by feature scaling and normalization using \(x_{i}^{{}^{\prime}}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}}\) to map the values of each feature to the range \((0,1)\). The data points without the power draw value were omitted from the data set.  
The data set was split into the training and validation subsets using 80%/20% ratio for the two sets. The training instances were used to build the models while the instances in the validation set were used to assess the models accuracy and were not used to train the models. Treating the training and validation subsets as mutually exclusive reduces the chance of the model over-training and a loss of generalization. The model training terminates if the model cost function is minimized to a desired accuracy or the maximum number of training epochs is reached (Figure 4).  
The model training is performed in epochs and during each epoch multiple batches of data instances are selected from the training set. Training the temporal model to predict the weather conditions defines a data-point in the batch as the current weather condition and a discrete window of past and future measurements. The model uses the past measurements as the inputs to learn the prediction of the current and future weather conditions. The model to predict the power draw defines a batch data-points as a single measurement of community power-draw and the associate weather conditions at that time. The data-points in each batch were selected from the training set at random with uniform distributions with replacement.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
###### Abstract  
Heavy metal is released from many industries into water. Before the industrial wastewater is discharged, the contamination level should be reduced to meet the recommended level as prescribed by the local laws of a country. They may be poisonous or cancerous in origin. Their presence does not only damage people, but also animals and vegetation because of their mobility, toxicity, and non-biodegradability into aquatic ecosystems. The review comprehensively discusses the progress made by various adsorbents such as natural materials, synthetic, agricultural, biopolymers, and commercial for extraction of the metal ions such as Ni\({}^{2+}\), Cu\({}^{2+}\), Pb\({}^{2+}\), Cd\({}^{2+}\), As\({}^{2+}\) and Zn\({}^{2+}\) along with their adsorption mechanisms. The adsorption isotherm indicates the relation between the amount adsorbed by the adsorbent and the concentration. The Freundlich isotherm explains the effective physical adsorption of the solute particle from the solution on the adsorbent and Langmuir isotherm gives an idea about the effect of various factors on the adsorption process. The adsorption kinetics data provide valuable insights into the reaction pathways, the mechanism of the sorption reaction, and solute uptake. The pseudo-first-order and pseudo-second-order models were applied to describe the sorption kinetics. The presented information can be used for the development of bio-based water treatment strategies.  
heavy metal; adsorption; polymeric adsorbents; bioadsorbent; wastewater +
Footnote †: journal: Materials
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 2 Establishment of Heavy-Duty AGV Vehicle Model  
The 20-ton heavy-duty AGV studied in this paper mainly includes the drive module, lifting module, power module, control module, laser radar, and other parts. It is used for intelligent unmanned transportation of liquid ton buckets and small buckets in the factory area. When running, there are both curves and ramps with a maximum slope of 10%. When the liquid in the bucket turns, the load transfer to the outside of the curve occurs. When driving on the ramp, it moves back and forth to make the load difference between the front and rear axles large. Both load transfers affect the stability and safety of the vehicle. The driving module is used to drive the vehicle movement, the lifting module is used to lift the goods, the control module is used to control the vehicle to perform various expected actions, such as trajectory tracking and lifting actions, and the lidar is used to detect the operating environment and map construction. The three-dimensional model of the heavy-duty AGV is shown in Figure 1.  
Common vehicle models mainly include three-degree-of-freedom (3-DOF) bicycle models, as well as five-degree-of-freedom and seven-degree-of-freedom four-wheeler models. The bicycle model is suitable for analyzing the basic handling stability of the vehicle, and the four-wheeler model is suitable for analyzing the dynamic response of the vehicle under high-speed driving and complex pavement conditions. The motion process of heavy-duty AGVs is highly nonlinear. Although the more complex the vehicle model is, the closer the analysis results are to the real motion law, the calculation amount is greatly increased, which reduces the timeliness of the controller. The heavy-duty AGVs running in the factory have low speed and large inertia. The three-degree-of-freedom vehicle model can meet the research needs of this paper while reducing the amount of calculation. Therefore, this paper establishes a three-degree-of-freedom vehicle kinematics model and a linear error model.
## 1 Introduction
### Innovative Contributions
#### 1.1.3 Reliability and Resilience  
Reliability is supported by the presence of a specialized unit of vigilant monitoring mechanisms that examine network behaviors and patterns. In case of any deviations from the norm, alerts are triggered, allowing proactive responses to potential threats or abnormal activities. Following an attack or system crash, the model employs specific strategies to quickly restore the system without significant problems. This involves maintaining data, configurations, and personal files, ensuring a swift return to operational status.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.7 Casein Phosphopeptide-Amorphous Calcium Phosphate (CPP-ACP)  
A recently introduced dentin remineralizing agent derived from milk proteins is known as Recaldent(tm). This product incorporates phosphopeptides and plays a role in retaining and stabilizing amorphous calcium phosphate. CPP-ACP possesses the capability to remineralize initial subsurface enamel lesions [90]. Additionally, it has been asserted that CPP-ACP significantly contributes to both the prevention and treatment of DH. The mechanism by which CPP-ACP operates involves a series of processes that collectively contribute to its beneficial effects on tooth surfaces. Firstly, CPP-ACP enhances the availability of binding sites on the tooth surface for Ca, which results in a reduction of the continuous diffusion of Ca. Moreover, ACP functions as a buffer for free calcium and phosphate ions found in dentin, helping to maintain a state of supersaturation. This state is crucial in preventing demineralization and promoting the remineralization of the tooth's structure [91]. Another reason for the positive outcomes of CPP-ACP is that its application facilitates the formation of a crystalline layer that covers the openings of dentin tubules. This protective layer acts as a barrier against external stimuli, providing additional protection for the tooth surface [92]. However, this layer is not entirely uniform; it exhibits attached irregularities and appears as clusters of structures that fill the interprismatic spaces while partially covering the tubules [76].
###### Abstract  
This study aimed to investigate the relationship between diabetic foot ulcer and depression based on treatment methods employed, as evaluated according to Medicare claims data provided by the Health Insurance Review and Assessment Service (HIRA). Data on diabetic foot patients from January 2011 to December 2016 were collected from the HIRA using codes for diabetic foot and depression disorder. The incidence of depression was analyzed based on patients' demographic variables, and comorbidities were assessed using the Charlson comorbidity index (CCI). The participants were divided into two groups based on the treatment method used: a limb-saving group and an amputation group. The 1-, 3-, and 5-year incidence rates of depression were 10.1%, 20.4%, and 29.5%, respectively, in the limb-saving group and 4.5%, 8.2%, and 11.5%, respectively, in the amputation group. Female sex, the CCI, and the use of limb-saving treatment methods were significant risk factors. It is plausible that depression in diabetic foot patients may be associated with frequent recurrence and chronicity rather than a single intense event. Our findings highlight the need for clinicians to consider the treatment period as a contributor to patient mood disorders when selecting the appropriate course of action in patients.  
diabetic foot; amputation; depression +
Footnote †: journal: Applied Sciences
The results of local management without coordination indicated traffic time reductions of more than 10%, whereas comparison of the results with coordinated management indicated a reduction of up to 5%, confirming better efficiency of EPICS when using it for local optimization. The above literature review shows that the usage of microsimulation tools to test proposed traffic solutions is common in related research. Even though various micro and macrosimulation tools are used to evaluate the proposed optimization and prioritization solutions, VISSIM is notable among the many simulation tools. Many studies refer to the improvement of existing priorities resulting from areawide timing plans by designing signal offsets in a coordinated signal system using PT travel times. These methods are limited to lengthening or shortening phases depending on the announcement of priority vehicles within the time frame provided by the PT timetable. Some of the studies considered only the results of PT improvement, whereas others considered prioritized and non-prioritized traffic. According to case studies mentioned in the literature review, using the network-level optimization module (BALANCE) and the local intersection-level prioritization module (EPICS) it is possible to achieve optimal solutions and reduce the average waiting time by giving priority to PT without major impacts on individual motorized traffic. Even though a study presented in the literature review showed that EPICS has a greater impact on local optimization than on coordinated optimization, this cannot be confirmed due to the lack of available literature. To our knowledge, with the exception of the cases presented in the literature, EPICS has not been used elsewhere and the results presented in this article are based on one of the largest microsimulation models, giving priority to trams, to date. Based on data collected in the City of Zagreb, Croatia, from a traffic network of 49 intersections, a VISSIM microsimulation model was designed. Using EPICS for the priority model and a mathematical model to determine the PT weight factors, the conditional PT priority was conducted for 29 simulated intersections.
## 4 Outline of the Approach
### Process of Concept Alignment for Interoperability in Heterogeneous Sources
#### 4.5.3.1 Element Level Matching Phase  
The element level matching phase provides reasoning on the similarity of the isolated concept element. From this stage we can obtain the relation \(R_{0}\) of every triplet \(<\)\(c_{s}c_{s}R_{0}\)\(>\)\(\forall c_{s}\in C_{s},\forall c_{t}\in C_{T}\) which is a first attempt in finding the semantic mapping relation if we consider only the concept labels without context. The element level matching phase consists in three steps:  
1. Calculates the similarity matrix between \(C_{s}\) and \(C_{T}\) with equation 1. The concepts with the highest similarity scores in the target are considered the best suited candidates for alignment (\(c_{tb}\)).
2. Determines determine the target collection to which the source document is most related, and to which sub collection each concept in the source \(c_{s}\) belong. The most related collection is determined by calculating which target collection has the higher relative frequency of membership of best suited concepts [Equation (4)]: \[r.f.(collecion_{t})=\frac{f(c_{bu})}{count(c_{bu})}\forall i\in DimensionsU\] (4)
3. Determines the relation \(R_{0}\) between \(c_{s}\) and \(c_{T}\) according to the criteria presented in Section 4.5.2 given the similarity results of the similarity matrix calculated in step 1.  
The mapping relations cannot only be determined with the element level matching because from this process we can have the following possible outcomes:  
1. Exactly one concept label of the target has \(sim_{0}=1\), the best suited concept is clearly defined, but it can be the case that the labels are homonymous.
2. More than one concept label of the target has \(sim_{0}=1\) or _equivalent_ relation, so in order to select just one concept in the target for alignment context in the hierarchy must be considered.
3. No concept label of the target is classified as _equivalent_ and the best score is _related_.  
Case 4. No concept label of the target is classified as _equivalent_ and the best score is _independent_.  
When it is the case 3 or 4, no exact match is found and we need to consider context in order to determine new additions of concept, sub collection or collection to the target scheme. For every case in the previous outcomes, reasoning on structure is needed.
# Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
## 2 C-V2X Mode 4  
### Physical Layer  
C-V2X employs Single-Carrier Frequency-Division Multiple Access (SC-FDMA) and can operate on 10 and 20 MHz channels. Each channel is broken down further into subchannels, as well as Resource Blocks (RBs), which are smaller subframes. Each subframe takes up one millisecond (like the Transmission Time Interval). The lowest possible amount of frequency resources that may be allotted to an LTE user is referred to as an RB. It has a frequency range that is 180 kHz broad (12 subcarriers of 15 kHz). A set of RBs that are contained inside the same subframe is what C-V2X refers to as a subchannel. The number of RBs that are allocated to each subchannel is variable. The transmission of data, as well as control information, makes use of subchannels. The information is sent across the wires using Transport Blocks (TBs). A Transmission Block (TB) stores the whole of a packet that is about to be broadcast, such as a beacon or a Cooperative Awareness Message (CAM)/Basic Safety Message (BSM). Transmission of TBs may be accomplished with either QPSK or 16-QAM with turbo coding. Each TB is sent out along with a Sidelink Control Information (SCI), which takes up two RBs in the same subframe and serves as a representation of the signaling overhead for C-V2X Mode 4 communications. The SCI contains a variety of pieces of information, including the modulation and coding scheme that was used in the transmission of the TB, as well as the RBs that were utilized in the transmission of the TB. In order for other cars to be able to receive and interpret the sent TB signal, it is imperative that its reception be accurate. The highest power that may be transmitted is 23 dBm, and the specification states that the receiver's sensitivity power level must be \(-90.4\) dBm [27].