esa-sceva/satcom-chunk-collection huggingface dataset:
# Optics Based Label-Free Techniques and Applications in Brain Monitoring
## Abbreviation  
APD Avalanche Photodiode  
AR-PAM Acoustic-resolution PA microscopy  
B-NIRS Broadband NIRS  
BBB Blood-brain barrier  
BBBD Blood-brain barrier disruption  
BCI Brain-computer interface  
BFI Blood flow index  
BMSCs bone mesenchymal stem cells  
CA Cerebral aneurysm  
CBF Cerebral blood flow  
CBFi Cerebral blood flow index  
CBV Cerebral blood volume  
CCD Closed-caption device  
CMRO2 Cerebral oxygen metabolism  
CP_NPs conjugated polymer nanoparticles  
DCS Diffuse correlations spectroscopy  
EEG Electro encephalopathy  
fcPAT functional PAT  
FD-OCT Fourier-domain Optical coherence tomography  
fDCS functional DCS  
FF-OCT full-field Optical coherence tomography  
fMRI functional magnetic resonance imaging  
fNIRS functional near-infrared spectroscopy  
FUS focused ultrasound  
HbO Oxy-hemoglobin  
HbR Decay-hemoglobin  
HbT Total hemoglobin  
ICP Intracranial pressure  
iOCT Integrated optical coherence tomography  
IR Infrared  
LD Laser diode  
LED Light emitting diode  
MEG magnetic encephalopathy  
NIRS Near-infrared spectroscopy  
OCT Optical coherence tomography  
OCTA Optical Coherence Tomography based Angiography  
OMAG Microangiography  
OR-PAM Optical resolution PA microscopy  
oxCCO Oxidation state of cytochrome c oxidase  
PACT Photoacoustic computing tomography  
PAI Photoacoustic imaging  
PD Photo diode  
PET positron emission tomography  
PMTs photomultiplier tubes  
PoC proof of concept  
PZT Piezoelectric Transducer  
SAS Subarachnoid space  
SD-OCT Spectral-domain Optical coherence tomography  
SiPD Silicon PD  
SPECT Single-photon emission computed tomography  
SS-OCT Swept-source Optical coherence tomography  
StO2 Cerebral tissue oxygenation
## 2 Materials and Methods
### Data Modelling  
The proposed framework was based on data inter-calibration and used the nonparametric random forest (RF) machine learning algorithm to impute the missing parts of a UAV orthomosaic.  
The RF, proposed by [34], is an ensemble learning algorithm comprising many decision trees (forest) combined to solve both classification and regression problems. Using a bootstrap sampling strategy (sample with replacement), a random independent draw of predictors' subsets generates the sets of decision trees. The RF regression algorithm has a history of successful use in remote sensing applications [22; 23; 25; 35] and comparable accuracy to other state-of-the-art machine learning algorithms [36], in addition to its simplicity and efficiency and ability to process high-dimensional data, as well as prevent overfitting [37]. In addition, the RF is a well-known regression method with high stability and robustness that provides fast, flexible, and accurate predictive capabilities in which the final prediction value corresponds to the averaged output of all individual decision trees, thus decreasing the variance of the results.  
RF modelling involves a hyperparameter tuning process that maximizes the model's predictive accuracy. We used the 3-fold cross-validation method to evaluate the accuracy estimation in the training dataset during the hyperparameter tuning process, which was conducted to select the optimal values of the hyperparameters: _ntree_ (number of decision trees) and _mtry_ (number of predictor variables randomly sampled at each split). The hyperparameter tuning process for each RF-based imputation model employed the random search method [38] to values ranging from 150 to 300 for each _ntree_. The _mtry_ hyperparameter definition was based on Equation 1--\(\sqrt{p}\), where \(p\) is the number of predictor variables.  
The target variables in the RF-based imputation models were the five spectral bands of the UAV orthomosaic (B, G, R, RE, and NIR) and the NDVI. We considered three sets of predictor variables in the RF-based imputation models: (i) the PS image spectral bands (B, G, R, and NIR) and its NDVI; (ii) the S2 image spectral bands (B, G, R, RE1, RE2, RE3, RE4, NIR, SWIR1, and SWIR2) and its NDVI; and (iii) the combination of the predictor variables in (i) and (ii).  
We assessed the importance of the predictor variables in the RF-based imputation models to identify each model's most important predictor variables. The RF uses the mean square error increase (IncMSE) when a variable is randomly permuted to measure its importance [34]. We normalized the variable importance metric of each model in the 0 to 1 range to obtain the predictors' overall performance for the best RF-based imputation models. Then, we summed the variable importance value of each selected RF model for every predictor variable. Finally, we obtained the relative importance of each predictor variable by normalizing the total variable importance in the range of (0, 100). We calculated all data modelling using the _mlr_ package [39] in the R software.
## 3 Experimental Results and Analysis
### Analysis of Object Detection Results
#### 3.5.1 Comparison of Model Experimental Results
Note: red bounding boxes are generated by the algorithm; green boxes indicate missed detections; yellow boxes indicate false detections. In summary, the detection performance of YOLOv8-URE is similar to that of YOLOv8n when the number of targets is small, but with an increase in the number of targets, the misses and false detection of YOLOv8n rise significantly, especially in blurred scenes. In contrast, YOLOv8-URE exhibits higher accuracy and robustness under higher target density and occlusion conditions, and the overall detection performance is better than YOLOv8n.
## 4 Results
### Drought Spatial Distribution Pattern  
#### 4.2.1 Determining the Optimal Number of Clusters  
The K-means clustering algorithm is highly efficient but choosing the optimal number of clusters is a tricky problem in this study. To determine the optimal number of clusters, different \(k\) values from 2 to 9 were used to partition meteorological stations, and the corresponding quantity evaluation measurements are shown in Table 3. All quantitative measures, except for the Calinsky criterion, indicate that 3 is the optimal number of clusters. In addition, the VRC values exhibit only slight differences between \(k=3\) and \(k=4\), with corresponding values of 30.36 and 30.42, respectively. The drought characteristics heterogeneity test was conducted for all clusters, and the results showed that the drought characteristics for each cluster are homogeneous (Table 4). Hence, 3 is the optimal number of clusters for Hunan Province to partition these meteorological stations.
## 2 Overview of SDN Architecture
### Installation of Flow Table Entries
#### 2.3.2 Proactive Flow Table Allocation  
In contrast to reactive, proactive handles the incoming packet faster because the controller can install forwarding rules in advance before the flow arrival. Incoming flow whose packet headers information matches an existing entry is processed according to the instruction defined in the flow table, hence, the switch does not need to consult the controller to process the packet. For example, in Figure 4 the controller installed \(n\) number of flow entries in advance before the arrival of packets as shown in arrow 1 and 2. In contrast, to the \(n\) number of OpenFlow messages exchanged between the switch to the controller in reactive, the proactive approach reduced the messages. To some extent, the approach had improved the user QoS with less processing load on the controller. A prior study by [PERSON] et al. [30] verified the performance of proactive rule installation, the experimental results outperform reactive approach in terms of throughout (flows per second) with various scenarios. The performance gain was attributed to zero additional flow setup time. Importantly, traffic flow will not be disrupted even when switch loses connection with the controller. Therefore, a proactive method can be preferable in achieving low packet processing delay with an acceptable performance where the flow table storage space is enough. However, it may not give better performance in SDN because of the switch memory constraint. Assuming 200 packets for different flows arrived at the switch flow table at the same time whose flowtable size is less than 200, in such a case, the chances of flowtable overflow are unavoidable. Consequently, it may lead to the removal of flow that belongs to active flow. Future packets meant for active flow, forces switch to trigger more packet-in event to the controller to repeatedly update the flowtable for the same flow entry.  
Figure 4: Example of proactive entry installation.  
The significant traffic increase and small TCAM space gap have remained one of the challenging issues in a proactive approach. This challenge is more critical for large scale networks, for example in the event of failure, a huge number of backup flow entries required besides primary path flow entries to guarantee the survivability of the network state. Pushing extra flow entries can easily overwhelm the TCAM and chances of overflow are unavoidable. Additionally, flow entry update operation is hard with long delay when the flowtable is full to the highest capacity. This is because the time to update entry is proportional to the number of entries in the flowtable. Therefore, more investigation is required to address the proactive flow entries consumption.
## 2 Materials and Methods
### 2D-LC-MS Method  
Figure 1 shows a scheme of the instrument used for rapid N-glycan analysis by the 2D-LC-MS method with on-line immobilized IdeS digestion. To analyze N-glycan analysis at the subunit level (about 25 kDa), a 2D-LC-MS system that included a FabRICATOR-HPLC column (Genovis, Cat# A0-FRC-050, 2.1 mm \(\times\) 50 mm) as the first dimension for IdeS digestion and a BioResolve RP mAb polyphenyl column (Waters, Cat# 186008944, 2.1 mm \(\times\) 50 mm, 2.7 \(\upmu\)m) as the second dimension for separation of subunits hyphenated to Q-TOF MS was utilized.  
Two micrograms of mAb were dispersed into 10 mM PBS buffer (pH 7.4) to obtain a concentration of 1.0 mg/mL. Then, it was centrifuged at 13,000 rpm for 10 min. About 2 \(\upmu\)L of the supernatant was injected into the 2D-LC-MS system for analysis. The first dimension FabRICATOR-HPLC parameters were as below: column temperature was 37 \({}^{\circ}\)C, the mobile phase was pH 7.0 ammonium acetate buffer, with an isocratic elution for 30 min, and the flow rate was 0.05 mL/min. The second dimension RP mAb Polyphenyl used a binary gradient of 0.1% (\(r/v\)) formic acid in water (mobile phase A) and 0.1% (\(v/v\)) formic acid in acetonitrile (mobile phase B). The gradient conditions were as follows: 25% mobile phase B initially for 8 min, increased to 95% mobile phase B in 15 min and a 3 min isocratic hold for regeneration, then decreased to 25% mobile phase B in 0.1 min and held 4 min for re-equilibration. The column temperature was maintained at 75 \({}^{\circ}\)C and the flow rate was sustained at 0.4 mL/min. The heart-cutting mode was used for targeted fractions of on-line IdeS digested samples and then re-injected into the second-dimension column hyphenated Q-TOF MS analysis at the subunit level (Fc/2, about 25 kDa). Data were acquired in positive mode with a mass range of 100-5000 \(m/z\) at a rate of 1 Hz. The fragmentor voltage was set as 225 V. The percentage of different glycans was calculated using MS intensity.  
Figure 1: Scheme of 2D-LC-MS method for rapid N-glycan analysis.
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
###### Abstract
Our approach integrates RL to target this issue specifically. Using RL, the model dynamically adjusts its focus towards these less-represented classes during training, enhancing its sensitivity and specificity in these areas. This targeted focus mitigates the class imbalance problem and improves the model's generalization capabilities across diverse patient datasets. * Innovative reward mechanism in RL for enhanced lung segmentation: We have developed an innovative reward mechanism within our RL strategy tailored for lung segmentation. This mechanism provides a higher reward for correctly identifying typically underrepresented classes. This approach ensures that the model pays greater attention to these infrequent classes, promoting a more equitable and balanced segmentation process. * Utilization of dilated convolution in discriminator network: Our model incorporates a discriminator network that employs dilated convolution layers. This architectural choice allows the network to expand its receptive field without losing resolution, enabling it to better differentiate between real and synthesized images. The ability to focus on more extensive and critical features significantly improves the reconstructed images' visual clarity and detail resolution. * Error correction in generator's loss function for tumor detection: We have integrated an error correction component into the loss function of our generator during the tumor detection phase. This component is specifically designed to fine-tune the detection accuracy and minimize potential errors in the output. By implementing this correction process, the model enhances its reliability and ensures high precision in tumor detection. The remainder of the article is organized as follows: Section 2 discusses the related work, Section 3 details the proposed methodology, Section 4 describes the experiments conducted, and Section 5 provides the conclusion and outlines future research directions.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 1 Introduction
Thus, the deployment location and number of P-SFCs influence the deployment number of idle BVNFs and vice versa. Moreover, since they both preempt resources to enhance reliability, the increase in the number of parallel VNFs will reduce the resources available to deploy BVNFs, and vice versa. However, the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs. Thus, establishing how to optimally deploy P-SFCs and BVNFs to maximize the throughput of user requests while satisfying their delay and reliability requirements is a challenging problem. In this paper, the throughput maximization problem is first formulated as an integer linear programming problem. Due to the high computational complexity, we devise a proactive approximation algorithm to maximize the throughput by trading off the resource preemption of P-SFCs and BVNFs. Then, we evaluate the performance of our proposed algorithm through experimental simulations. Experimental results demonstrate that our proposed algorithm outperforms other counterpart algorithms in terms of delay, reliability, and throughput. The contributions of this paper can be summarized as follows:  
* We propose a new approach to enhance the reliability of P-SFCs and reduce the deployment number of idle BVNFs. Then, we model the dynamics of delay and reliability caused by the deployment of P-SFCS and BVNFs and formulate the throughput maximization problem;
* We design an approximation algorithm to find a near-optimal solution. It defines delay-reduction priority and reliability-enhancement priority to identify the critical VNF in SFC and designs two schemes to deploy P-SFCs and BVNFs and steer user requests traversing these VNF instances;
* To evaluate the effectiveness of our proposed algorithm, we conduct extensive simulation experiments based on real-world datasets from the central business district of Melbourne, Australia. Experimental results show that compared to two benchmark algorithms (see Section 5), our proposed algorithm can significantly decrease the delay and increase throughput while improving resource utilization. The remainder of this paper is organized as follows. Section 2 presents recent works. Section 3 presents the system model and formulates the problem. Section 4 designs an approximate algorithm for the throughput maximization problem. The performance of our proposed algorithms is empirically evaluated in Section 5, and Section 6 concludes the paper.
###### Abstract  
The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R\({}^{2}\) = 0.91 and a root mean square error (RMSE) of 470.90 km\({}^{2}\). The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.  
corn mapping; optical and SAR images; object-based; Sentinel-1 and 2; Google Earth Engine
## 3 Results and Discussion
### Wettability, Chemical, and Physical Properties  
In Figure 3, wettability test and FT-IR results displayed no significant difference between the two membranes. Both PCL-AC and PCL-7 AA/3 AC surfaces were hydrophobic, with average contact angle values of 118\({}^{\circ}\) and 116\({}^{\circ}\), respectively. The chemical composition of the two membranes was the same, with similar dominant peaks characteristic of PCL at 2940, 2864, 1722, and 1240 nm [16]. No distinctive peaks of AC or AA were found for either of the two membranes, demonstrating that the solvents were completely removed. However, a noticeable difference was encountered in terms of physical strength. The PCL-AC membrane could endure up to 12.4 MPa before being torn apart completely. The deformation ratio at break of the PCL-AC membrane was about 167% of its original length. Meanwhile, the PCL-7 AA/3 AC membrane possessed far weaker strength, could endure only 7.8 MPa, and its maximum strain was 60%. Reports have shown that there is a relationship between fiber diameter and tensile strength. In contrast to the PCL-7 AA/3 AC membrane with low tensile strength, the PCL-AC membrane consisted of thicker fibers with fibers point bonding and some fusion of adjacent fibers [17], which increased the fiber interconnectivity and therefore resulted in higher tensile strength [18]. In another study, [PERSON] found that PCL was hydrolyzed and degraded into oligomeric fragments in both alkaline and acidic solutions [19]. Therefore, the presence of the carboxylic group of AA in the solvent could aid in cutting down the long PCL chains into shorter ones [20] with lower molecular weights, and this helps explain the significant decrease in tensile strength of the PCL-AA membranes [21].  
Figure 2: (**A**) Scanning Electron Microscope (SEM) images of electrospun polycaprolactone (PCL) in acetone (AC), (PCL-AC), (a1) and PCL dissolved in acetic acid (AA)/AC (7:3, PCL-7 AA/3 AC) (a2) membranes, (**B**) Average fiber diameter and pore size, and (**C**) Histogram of fiber diameter and pore size distribution of PCL-AC (c1,c2) and PCL-7 AA/3 AC (c3,c4) membranes.
## 2 Materials and Methods
### Data Collection
#### 2.5.1 Establishing a Base Alignment  
Before the measurement can start, an inspection plan must be devised and programmed. This step was completed at the CMM with the accompanying computer running the Zeiss proprietary software Clypos 6.6. The CAD-model is first imported as a STEP file for efficient feature extraction before a base alignment is established to determine the position and rotation of the specimen in space. The base alignment is necessary to teach the CMM where the specimen is located so that it can continue an inspection in automatic mode. In fact, the base alignment lay the foundation for all machine movement in CNC-mode, potentially causing the probe to crash if not correctly defined. The base alignment is typically determined based on the part function and/or the production method using the so-called 3-2-1 principle where a plane (defined by a minimum of three points), a line (minimum two points), and a point is used to define the spatial translation and rotation of a part. However, because the test artifact has no real purpose and the nature of the process inaccuracies are unknown, the following considerations are made regarding which features to utilize: (i) the features should be contained within a single surface/element (i.e., a line stretching across several planes are not recommended); (ii) the features should be located close to the center of the part to counter dimensional accuracies; and (iii) planar features are preferred because they are generally easier to clean and residual powder is more easily detectable. Consequently, the base plate was selected as the plane for base alignment and also defined the origin for the z-axis, a line is drawn on the second plane of HX2, which also defines the origin for the x-axis, and finally, a point is taken on the fourth plane of HX1, which also defines the origin for the y-axis. Figure 15 illustrates the probing points used for the manual alignment conducted at the beginning of every inspection. Because this is a manual task, variation is inevitable, and the red discs of Figure 15 differ in size to reflect the area available for the manual probing. After the manual alignment, the CMM switches to CNC-mode and repeats the alignment before the inspection is automatically completed.  
Figure 14: Fisture design for test artifact. (**a**) Assembled CAD-drawing. (**b**) The fixture with a prototype of the test artifact in place.
# Design and Analysis of Systematic Batched Network Codes
## 4 Triangular Embedding: A Structured Systematic Outer Code Design  
We propose a structured design of consistent outer codes with a general batch size \(M\geq 1\). Our approach is based on the following observation. For a consistent instance found by the random design, DEC\({}_{n_{s}}\) gives an order of the batches such that the \(i\)th batch is solvable if all the previous batches are solved. Our approach, called _triangular embedding_, tries to design ENC\({}_{n_{s}}\) so that the order of the batches for solving is predefined. When \(M=1\), our approach also gives a new design of systematic fountain codes.