esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Preprocessing Hybrid Level Set Model for Optic Disc Segmentation in Fundus Images
## 4 Experiment
### Evaluation Criteria  
This paper both subjectively and objectively evaluates the segmentation results. The subjective evaluation was FSE and was conducted by clinicians to effectively evaluate whether the segmentation results were clinically meaningful. The value of IoU was used for the objective evaluation, which is expressed by Equation (14). \(area_{result}\) and \(area_{GT}\) are the area of segmentation result and the GT, respectively.  
\[\text{IoU}=\frac{area_{result}\cap area_{GT}}{area_{result}\cup area_{GT}} \tag{14}\]  
IoU represents the overlapping ratio between the segmentation result and GT. A higher IoU value means a better segmentation result. However, there is a problem in objective evaluation methods such as IoU. When the surroundings of the OD are under- or oversegmented, as shown in Figure 21, the Figure 21a is an example of overall oversegmentation, the Figure 21b is an example of partial oversegmentation, the IoU value is higher in Figure 21a, but the segmentation result is too large. This can be regarded as OD boundaries not being found at all. Furthermore, this result greatly impacts subsequent diagnosis, such as glaucoma misdiagnosis through the area ratio of OD to OC. Due to the above problem in IoU, the following subjective evaluation method was designed.  
First, subjective evaluation criteria were established; then, the clinicians scored each OD segmentation result according to the criteria. As shown in Figure 22, the OD is divided into four parts: superior, nasal, inferior, and temporal. Each part is 90\({}^{\circ}\). Depending on the opinion of clinicians, one point is given if each part was segmented accurately enough. The specific evaluation criteria are shown in Table 2. Since the evaluation method divided OD into four parts for evaluation, it is called the FSE.  
Figure 21: Limitations of IoU evaluation methods, (green contour: GT, red contour: hypothetical segmentation results).  
Figure 20: An example of the TMUEH dataset: (left to right) original fundus image and the GT of the OD boundary.  
The FSE proposed in this paper evaluates the OD segmentation results from another perspective. For example, for the two segmentation results of the same image in Figure 21, In Figure 21a, better scores could be obtained regardless of IoU, dice value [36], or mean square error [56], while only 0 or 1 could be obtained in FSE. In Figure 21b, the score was lower when using IoU, but higher scores could be obtained when using FSE. In other words, objective evaluation methods such as IoU focus more on the whole OD, while the FSE proposed in this paper focuses more on each part of the OD.
# Factors Affecting the Stability of Loess Landslides: A Review
## 2 Relevant Measured Parameters
### Matrix Suction  
Several interdependent parameters influence the stability of loess slopes, and by manipulating related parameters, it is possible to establish a basis for analyzing their stability. Certain factors that have received limited attention in previous studies have been increasingly emphasized by researchers, including the factor of matrix suction. Unsaturated zones located above the water table can generate matrix suction that improves soil shear strength and, consequently, slope stability. The suction stresses of the loess samples show different characteristics. The magnitude of suction stress is approximately linearly related to matrix suction in the high saturation range [28]. The failure of the slope occurs when the volumetric water content and the matrix suction reach their maximum and minimum values, respectively [29]. Both the magnitude and rate of reduced substrate suction increase with rainfall intensity [30]. By installing water content sensors and matrix suction sensors connected to data loggers and integrating them with data processing software, it is possible to quantitatively characterize slope deformation and damage [79]. Short-term irrigation reduces toposil matrix suction, while prolonged low-intensity irrigation has a greater effect on slope stability than a single high-intensity irrigation event [70]. In addition, loess volume damage and shear deformation due to wetting are determined by matrix suction, stress levels, and hydraulic pathways [80].  
Figure 4: Mechanism of recurring irrigation-induced loess landslides in Heifangtai loess highland, Gansu, China. The disruption of the hydraulic equilibrium of a loess landslide indirectly changes the irrigation location by directly altering its geometry through the first landslide. Reprinted/adapted with permission from Ref. [74]. 2018, Elsevier.
## 1 Introduction
### Reference PD Pulses and Pulsating Noise Trains  
For the realisation of the PD functionality tests of PD analysers, the following insulation defects representative of HVAC and HVDC grids are considered: (1) Related to GIS: (1-1) moving particles, (1-2) surface on gas, (1-3) protrusion, (1-4) floating potential and (1-5) cavity in a spacer; (2) Related to cable systems: (2-1) cavity and (2-2) internal surface, (3) Related to AIS: (3-1) surface, (3-2) corona, (3-3) floating potential, and (4) Related to converters: internal discharges in a semiconductor junction. The time trend of these PD sources has been investigated to collect representative PD pulse trains in a database. This database of PD trains acquired in laboratory test cells provides a traceable reference of insulation defects for HVAC and HVDC grids, useful to evaluate the efficiency of PD analysers. The insulation defects were generated in test cells during more than 10 months in an ageing process under HV stress so that the PD activity associated with each one was reproduced in a controlled manner. In this way, the defects can be properly defined and measured under low background noise conditions. For each insulation defect under AC stress, 50 PD pulse trains were chosen from five time periods uniformly distributed in the aging process, 10 PD pulse trains every 2 months of aging. In the selection process of the 10 reference PD pulse trains for each insulation defect, four expert PD technicians were involved in the defects recognition through their PRPD pattern. Only the pulse trains whose PRPD patterns were correctly classified by the four expert technicians were selected. A representative PRPD pattern of the 10 reference pulse trains is shown in Table 1 for each defect. For insulation defects under DC stress, no expert technicians were available, for this reason, the criterion applied to choose the reference 10 PD pulse trains was to select those whose main PD parameters (number of pulses per train, \(m\), mean charge value, \(q\), and accumulated charge value, \(q_{a}\)) were as close as possible to their average values, shown in Table 2. It is recommended that other research institutes develop further databases of these types of defects to promote international comparisons, this will lead to international traceability of reference PD event trains associated with insulation defects. Part of the work developed in the European project [9] works towards this goal.  
\begin{table}
\begin{tabular}{l l} \hline
**Grid Part** & **Representative Defect** & **PRPD Pattern** \\ \hline \multirow{4}{*}{GIS} & Moving particles in SF6 \\  & Surface in SF6 \\  & Protrusion in SF6 \\  & Floating potential in SF6 \\  & Cavity in a spacer \\ \hline \end{tabular}
\end{table}
Table 1: Representative PRPD patterns in GIS, Cable systems, and AIS for the PD recognition test.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
###### Abstract  
High-quality processes, characterized by low defect rates, typically exhibit an exponential distribution for time-between-events (TBE) data. To effectively monitor TBE data, one-sided exponential Adaptive Exponentially Weighted Moving Average (AEWMA) schemes are introduced. However, the run length (RL) distribution varies with the magnitude of the process mean shift, rendering the median run length (MRL) a more pertinent performance metric. This paper investigates the RL properties of one-sided exponential AEWMA schemes using a Markov chain method. An optimal design procedure based on MRL is developed to enhance the one-sided exponential AEWMA scheme. Comparative analyses reveal that the one-sided exponential AEWMA scheme provides better balanced protection against both minor and major shifts in the process mean compared to EWMA-type and Shewhart schemes. Finally, two practical case studies illustrate the application of AEWMA schemes in manufacturing.  
Keywords:EWMA; adaptive scheme; exponential distribution; median run length; optimal design _Article_
# IPv6 Convergence for IoT Cyber-Physical Systems
## 2 Background
### 5G Ecosystem Context  
In July 2016, the 5G Public Private Partnership (5 GPP) Architecture Working Group published a whitepaper regarding their view of 5G architecture [23]. They depicted a 5G ecosystem where infrastructure and network function layers are two distinct separate entities. In July 2016, it was reported that Verizon became the first US carrier to complete its own 5G specification [24; 25]. As part of this, Verizon's 5G Network and Signaling Working Group published a 5G packet convergence protocol where they detail the services provided to upper layers comprising data transfer and radio resource allocation supporting a maximum protocol data unit (PDU) size of 65528 octets [26]. In a previous work, we also described how 5G also represents convergence of wireless technologies also [27].  
It is intended that these PDUs transport IP packets [28] using a structure comprising the physical layer (PHY), medium access control (MAC), radio link control (RLC), and a packet data convergence protocol (PDCP) layer. The IP connectivity connection point is at the PDCP layer as depicted in Figure 4. 5G is intended to be a flat IP network and this is one of the key features needed make 5G acceptable for broad adoption and support for internet of the future technologies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**IP** & **Non-IP** \\ \hline IEEE 802.3 (Ethernet) & TIA/RS-485 \\ IEEE 802.11 af/ah & IEEE 802.15.4 (without 6 LoWPAN) \\ IEEE 802.15.4g & LoRAWAN \\ LTE Rel 12+/NB-IoT (transport block size dependent) & RPMA \\  & SigFox \\  & MIoTy \\ \hline \hline \end{tabular}
\end{table}
Table 1: IP and non-IP classification  
Figure 4: 5G architecture stack outline comprising a physical layer (PHY), medium access control (MAC) layer, radio link control (RLC) layer and a packet data convergence protocol (PDCP) layer. IPv6 packets would flow between the networking layer and the Packet Data Convergence Protocol layer, which we depict in red in this diagram.  
Opportunities exist to extend the 5G network function layer vision where interconnectivity, interoperability and in-network computation requirements present critical research challenges specifically in the massive machine-type communications (mMTC) and critical machine-type communications (uMTC) areas. As depicted in Figure 1, our view of 5G is that IPv6/6 LoWPAN over PDCP will also be the common convergence layer between the underlying heterogeneous compute and communications technologies and the applications and services of the internet of the future (IoTF).  
CPS and advanced IoT networks are trending towards a heterogeneous network future also. This introduces a new set of technical challenges, particularly when the network may be dynamic and volatile in nature. We now outline some of the key research focusing on these aspects.
## 2 Materials and Methods
### Loss Functions  
In order to measure the performance of continuous variable predictions, a set of evaluation metrics were used as loss functions so that the DL-based model's performance could be evaluated, and hence its weights updated. A cost function compares a model's predictions and the actual data; in a regression model, the function computes the distances  
Figure 2: Data division, using a 70:10:20 split into training, validation (V) and testing (test) datasets, for each treadmill velocity, based on random sampling of subjects proportional to each dataset.  
between reference and predicted values [27]. The objective is to minimize the evaluation metrics, as they measure the differences between the reference values and their predictions.  
The first loss function used was the MAE, which represents the average absolute of the differences between predicted values and observed values, calculated as shown in Equation (1), where the difference between the reference value \(y_{i}\) and the predicted value \(\hat{y}_{i}\) is summed over \(n\) testing samples. MAE follows the L1-norm of normalization or the Manhattan norm, which measures the distance between two points by summing the absolute differences between measurements in all dimensions [28].  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{1}\]  
Another metric used was the MSE, which represents the average of squares of the differences between predicted values and observed values, calculated as shown in Equation (2), where the squared difference between the reference value \(y_{i}\) and the predicted value \(\hat{y}_{i}\) is summed over \(n\) testing samples. MSE follows the L2-norm of normalization or the Euclidean norm, which measures the Euclidean distance between two points in a given vector space; this distance is significantly affected by outliers when compared to the L1-norm, which takes the absolute value of differences between two points as \(\epsilon^{2}>\epsilon\)[28].  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{2}\]  
Finally, MSLE measures the ratio between predicted and observed values; it is quite different from the other metrics because it measures the percent of difference and thus is better for extreme values [28]; it is calculated as shown in Equation (3), where the squared difference between the logarithm of the reference value \(y_{i}\), and the predicted value \(\hat{y}_{i}\) is summed over \(n\) testing samples--regularized using the log function on predictions and reference values. The \(+1\) operation removes errors due to log being a 0-sensitive function. In addition, negative values are not possible in this metric (one of the reasons why all the data was scaled using MinMaxScaler).  
\[MSLE=\frac{1}{n}\sum_{i=1}^{n}(\log(y_{i}+1)-\log(\hat{y}_{i}+1))^{2} \tag{3}\]
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 5 Performance Evaluation
### Synthetic Datasets  
This section reports our evaluation results from applying the proposed method to synthetic datasets. First, Section 5.1.1 discusses the effect of dimensionality. Second, Section 5.1.2 discusses the effect of cardinality. Finally, Section 5.1.3 discusses the effect of skyband size. Following the common methodology in the literature, we employ correlated, anti-correlated, and independent (uniform) datasets [1].
## 4 Data Analysis Framework Application
### Comparison with CNN and ANN  
However, to showcase the significance of computational time, the same data set has been trained by Convolutional Neural Network (CNN). For this, light networks have been utilized to minimize the computational cost as much as possible. The architecture of CNN has been adopted according to [29], as shown in Figure 17. The dataset includes 14,166 images of scalograms corresponding to 2361 images for each class, so the training dataset embraces 11,333 images and 2833 images for the test. In addition, the obtained matrix of features of black projection has been processed directly into a CNN and a feed-forward Neural Network model instead of images. The architecture of ANN is with the input with 128 neurons, two hidden layers of 8 neurons, and an output of size 6 corresponding to the wave scenarios.  
Figure 16: Comparison of computational error and time for different max-pooling size.  
According to Figure 16, the new setup allows to reach 92.66% validation accuracy only within 4.66 s of training time, which is achieved with a normal laptop computational power with processor 11 th Gen Intel Core i5-1145G7 @ 2.6 GHz with installed memory of 16.0 GB. This becomes more significant when it compares with the DL model performance for the same dataset on the images, as reflected in Table 4. To further improve the computational resources, the same dataset and network were trained on a system with a single GPU of NVIDIA GeForce GTX 1060 6.0 GB, processor Xeon E5-1650 v4 @3.6 GHz with installed RAM 16.0 GB. All the training was run on MATLAB 2024a deep learning package. Although the validation accuracy of CNN outperforms the new approach, the computation time and resources are out of comparison, and the new approach has resulted in a tremendous reduction in training time. Additionally, the engineered features and the matrix of black projection for the whole data have been processed directly to the mentioned ANN and CNN. The obtained results are given in Table 4, where the engineered features have shown a significant improvement in computational costs for CNN and ANN on a normal computational resource, yet SVR takes the lead by far. This result demonstrates the significant computational efficiency of the current innovative data analysis in reducing training time for real world, with a large amount of data that can be the crux and fruit of the current work.
## 3 Biohydrogen Production via Dark Fermentation (DF)
### Biochemistry of Dark Fermentation  
Under anaerobic condition, the fermentation (metabolic) process occurs to regenerate the cell's energy currency (ATP). The tricarboxylic acid cycle is also blocked under this condition. When reduced metabolic end products (e.g., alcohol and acids) formed, fermentation will dispose of the excess cellular reductant. Similarly, the cellular redox potential is maintained by the production of hydrogen that acts as a reduced metabolic product as well.  
For the fermentation process, carbohydrates are the preferred carbon source that contains mainly glucose, which can predominantly increase acetic and butyric acids along with hydrogen gas. Hydrolysis will convert complex organic polymers to glucose. Glucose will then produce pyruvate to generate ATP via the glycolytic pathway. Subsequently, pyruvate may be involved in the formation of hydrogen in two different biochemical reactions [77].  
Pyruvate will be oxidized to acetyl coenzyme A (acetyl-CoA) in _Clostridia_[78] as obligate anaerobes and thermophilic bacteria [79] by pyruvate-ferredoxin oxidoreductaseutase [80]. Next, acetyl-CoA will be converted to acetyl phosphate, along with the production of ATP and acetate. Reduction of ferredoxin (Fd) is required for the oxidation of pyruvate to acetyl-CoA. [Fe-Fe]-hydrogenase will oxidize the reduced Fd and catalyzes H\({}_{2}\) formation. The overall reaction is shown in the equations below.  
\[\text{Pyruvate}+\text{CoA}+2\text{Fd}(\text{ox})\rightarrow\text{Acetyl}- \text{coA}+2\text{Fd}(\text{red})\text{CO}_{2} \tag{1}\]  
\[2\text{H}^{+}+\text{Fd}(\text{red})\rightarrow\text{H}_{2}+\text{Fd}(\text{ ox}) \tag{2}\]  
When pyruvate is oxidized to acetate as the sole metabolic end product, four moles of hydrogen per mole of glucose is formed [81]. However, when pyruvate is oxidized to butyrate, only two moles of hydrogen produced per mole of glucose. Therefore, in the mixed acid pathway, a higher acetate to butyrate ratio is critical for higher hydrogen production [82]. Overall biochemical reaction with acetic and butyric acid as metabolic end products is shown in the next equations, respectively.  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}+2\text{H}_{2}\text{O}\to 2\text{CH}_{3} \text{COOH}+2\text{CO}_{2}+4\text{H}_{2} \tag{3}\]  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}\rightarrow\text{CH}_{3}\text{CH}_{2} \text{CH}_{2}\text{COOH}+2\text{CO}_{2}+2\text{H}_{2} \tag{4}\]
Sensors_**2010**, _10_, 9564-9580; doi:10.3390/s101109564  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Agenda-Based Routing Protocol in Delay Tolerant Mobile Sensor Networks**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,* and [PERSON] 1**  
School of Computer Science and Engineering, University of Electronic Science and Technology of Chengdu, 610054, China; E-Mails: [EMAIL_ADDRESS] (X.-M.W.); [EMAIL_ADDRESS] (J.-Q.Z.); [EMAIL_ADDRESS] (H.-G.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 10 September 2010; in revised form: 14 October 2010 / Accepted: 15 October 2010 / Published: 28 October 2010_  
**Abstract:** Routing in delay tolerant mobile sensor networks (DTMSNs) is challenging due to the networks' intermittent connectivity. Most existing routing protocols for DTMSNs use simplistic random mobility models for algorithm design and performance evaluation. In the real world, however, due to the unique characteristics of human mobility, currently existing random mobility models may not work well in environments where mobile sensor units are carried (such as DTMSNs). Taking a person's social activities into consideration, in this paper, we seek to improve DTMSN routing in terms of social structure and propose an agenda based routing protocol (ARP). In ARP, humans are classified based on their agendas and data transmission is made according to sensor nodes' transmission rankings. The effectiveness of ARP is demonstrated through comprehensive simulation studies.  
**Keywords:** DTMSN; social; mobility model; routing