esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of a Synchronization System for Distributed Inverters Conceived for FPGA Devices
## 4 Conclusions  
In this paper, a new control method for parallel-connected inverter systems has been presented and discussed.  
The proposed method has been designed in a model-based fashion and simulated in a Simulink environment. The model is designed to be implemented on an FPGA, through a HDL coder--this technique allows one to reduce time to market and improve flexibility.  
Each FPGA controls its inverter, a transmission line modelled as an impedance and the load, and the same circuit is instantiated on all system devices, enhancing modularity and scalability. Since a little phase delay or difference in the clock causes delays in the control response that generates the circulating current, a non-ideal clock for the FPGAs has been considered, and, in particular, a displacement from the nominal frequency of 0.1% has been simulated.  
The simulations performed with a different number of inverters in parallel (up to six) demonstrate the ability of the control designed to reduce the circulating current and so reduce the risk of inverter damage. These results have been obtained without the necessity of any communication among the inverters that cooperate autonomously, each providing the same output current, unlike other authors that achieved the system control or the load sharing using communications lines or wireless communication among the inverters [34; 35]. It has been demonstrated that the circulating current can be negligible in a system equipped with this control method, especially in a system with two parallel-connected inverters  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Inverters** & **fPWM** & **IFRMS** & **Frequency Variation** \\
**in the System** & **[kHz]** & **[fA]** & **[\%]** \\ \hline
2 & 50.668 & 0 & 1.38 \\ \hline
3 & 50.500 & 15.910 & 1.00 \\ \hline
4 & 51.365 & 46.502 & 2.73 \\ \hline
5 & 51.720 & 16.956 & 3.44 \\ \hline
6 & 51.017 & 78.200 & 2.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Inverters’ configurations.  
where the circulating current is equal to zero. Furthermore, when system control in a steady-state condition has been achieved with a modular and redundant structure, then, new inverters can be added without intervening on other parts of the system.  
Conceptualization, V.B., I.D.M.; methodology, V.B. and I.D.M.; validation, L.S., V.B.; formal analysis, L.S. and V.B.; investigation, L.S. and V.B.; data curation, L.S. and V.B.; writing--original draft preparation, L.S. and V.B. and I.D.M.; writing--review and editing, L.S. and V.B. and I.D.M.; visualization, L.S. and V.B. and I.D.M.; supervision, V.B. and I.D.M.; project administration, I.D.M.; funding acquisition, I.D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available on request from the authors.  
The authors declare no conflict of interest.
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
## 1 Introduction
The heterogeneous wireless network architecture has been developed to operate in the unlicensed band where inter and intra-network interferences could be a critical problem. This proposed architecture introduces a new inter network cooperative medium access control (MAC) layer-based signaling protocol to mitigate the above interference problem and to improve the throughput of an M2M area network based on short range wireless networking standards. The objective of the paper is to present new directions on the low-cost wide area network design for M2M and IoT applications using unlicensed band standards. The paper structure is as follows: Section 2 briefly reviews typical smart city IoT and M2M communication requirements. Section 3 reviews M2M communication network architectures and requirements; Section 4 discusses M2M area network design issues and reviews the IEEE 802.15.4 and IEEE 802.11 networking standards for the area network design; Section 5 presents a new IPv6 Low power Wireless Personal Area Network 6 LoWPAN) and IEEE 802.11 standards-based heterogeneous M2M area network architecture where the coexistence problem is mitigated by using a cooperative MAC protocol; Section 6 presents extensive simulation results and performance analysis obtained from a custom OPNET (Optimized Network Engineering Tool)-based simulation model; and conclusions are drawn in Section 7.
## 3 Layer Thickness Determination
### Forward and Inverse Operator  
As discussed in the preceding section, the thermal properties needed for the calculation of the surface temperature of a coating system with \(n\) layers can be summarized with the vector  
\[\mathbf{p}:=(\alpha_{1},\ldots,\alpha_{n},e_{1},\ldots,e_{n+1})^{T}\in\mathbb{ R}_{+}^{2n+1}. \tag{22}\]  
For \(m\in\mathbb{N}\) and frequencies \(\mathbf{\omega}:=(\omega_{1},\ldots,\omega_{m})^{T}\), define the forward operator  
\[F_{n}\colon\mathbb{R}_{+}^{n}\to\mathbb{R}^{m},\quad(L_{1},\ldots,L_{n})^{T}= :\mathbf{L}\mapsto\mathbf{\varphi}_{\tilde{T}}, \tag{23}\]  
which maps \(\mathbf{L}\in\mathbb{R}_{+}^{n}\) to vector of phase angles \(\mathbf{\varphi}_{\tilde{T}}\in\mathbb{R}^{m}\), cf. Equation (21). Note, that in (23) the dependencies on \(\mathbf{p}\) and \(\mathbf{\omega}\) were dropped since these are kept fixed, but it is sometimes practicable to use the extended notations,  
\[\mathbf{\varphi}_{\tilde{T}}=F_{n}(\mathbf{L})=F_{n}(\mathbf{L},\mathbf{p})=F_{n}(\mathbf{L}, \mathbf{p},\mathbf{\omega}). \tag{24}\]  
For the determination of \((L_{1},\ldots,L_{n})\), assume that an infrared camera measures the temperature response of such a system for every frequency. After some post-processing of the signals (cf. [5; 20; 21]), the phase angle data  
\[\mathbf{\varphi}_{\tilde{T},\textit{meas}}\in\mathbb{R}^{m}\]  
are given.  
The inverse operator is defined by  
\[G_{n}\colon\mathbb{R}^{m}\to\mathbb{R}^{n}_{+}\]  
\[G_{n}(\mathbf{\varphi}_{\mathbf{\hat{T}},meas})\coloneqq\operatorname*{arg\,min}_{\mathbf{L} \in\mathbb{R}^{n}_{+}}||F_{n}(\mathbf{L})-\mathbf{\varphi}_{\mathbf{\hat{T}},meas}||_{2}^{2}, \tag{25}\]  
which maps the phase angle data to the layer thickness vector by minimizing a nonlinear least-squares functional. Here, \(\|\cdot\|_{2}\) denotes the Euclidean norm.
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 1 Introduction
[14] reported that the relationship between the matrix strain and the surface-attached fiber strain is governed by the effectiveness of shear transfer through the adhesive and polymeric coating. It should be noted that in the abovementioned previous studies, the strain transfer analysis theories are based on the models of surface pasted or embedded FBG sensors. This paper proposes an innovative strain transfer model for a clamped FBG sensor. The axial strain distribution principle of the optical fiber in consideration of the gauge length and interlayers is developed. The theoretical analysis results are verified by experimental tensile tests.
## 2 Materials and Methods
### Analysis Methods
#### 2.2.2 Sen's Slope Estimator and Mann-Kendall Test  
[PERSON]'s slope [34] and the Mann-Kendall test [35; 36] were used for their robustness as non-parametric methods. These methods were used to effectively detect trends in time-series data without requiring specific distributional assumptions [37; 38; 39]. Thus, [PERSON]'s slope trend analysis method was used to examine the temporal dynamics of CUE, while the significance of the observed trends was rigorously assessed using the Mann-Kendall test.  
Sen's slope estimator is calculated as follows:  
\[slope=Median\Bigg{[}\frac{\big{(}x_{j}-x_{i}\big{)}}{(j-i)}\Bigg{]},\ \forall j>i\]  
where \(x_{i}\) and \(x_{j}\) represent sequential values at times \(i\) and \(j\), respectively. An upward trend is denoted by a positive slope (>0), whereas a downward trend is signified by a negative slope (<0) [40].  
The calculation of the Mann-Kendall test statistic is as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i-1}^{n}Sgn\big{(}x_{j}-x_{i}\big{)}\]  
\[Sgn\big{(}x_{j}-x_{i}\big{)}=\left\{\begin{array}{ll}1&x_{j}-x_{i}>0\\ 0&x_{j}-x_{i}=0\\ -1&x_{j}-x_{i}<0\end{array}\right.\]  
where \(x_{i}\) and \(x_{j}\) denote sequential values at times \(i\) and \(j\), respectively, and \(n\) represents the dataset's record length, the number of years.  
\[Z=\left\{\begin{array}{ll}\frac{S-1}{\sqrt{VAR(S)}}&S>0\\ 0&S=0\\ \frac{S+1}{\sqrt{VAR(S)}}&S<0\end{array}\right.\]  
\[VAR(S)=\frac{\big{[}n(n-1)(2n+5)-\sum_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+5)\big{]} }{18}\]  
where \(n\) represents the total number of values in the sequence, \(m\) signifies the count of repeated values within the sequence and denotes the number of repeated data values in the \(i\)th group. A positive value of \(Z\) suggests an increasing trend, whereas a negative value of \(Z\) suggests a decreasing trend. The significance level is ascertained based on the absolute value of \(Z\)[40; 41].  
Based on the results derived from [PERSON]'s slope trend combined with the Mann-Kendall significance test, the observed change trends were categorized into six distinct classifications: extremely significant increase (slope > 0,\(|\)S\(|\) > 2.58), significant increase (slope > 0,\(|\)S\(|\) < 2.58), non-significant increase (slope > 0,\(|\)S\(|\) < 1.96), non-significant decrease (slope < 0,\(|\)S\(|\) < 1.96), significant decrease (slope < 0,\(|\)S\(|\) < 2.58), and extremely significant decrease (slope < 0,\(|\)S\(|\) > 2.58). Here, slope denotes the calculated trend slope, and \(|\)S\(|\) represents the absolute value of the Mann-Kendall test statistic S.
# Survivability Prediction of Open Source Software with Polynomial Regression
###### Abstract  
In modern software development, OSS (Open Source Software) has become a crucial element. However, if OSS have few contributors and are lacking in maintenance activities, such as bug fixes, are used, it can lead to significant costs and resource allocation due to maintenance discontinuation. Since OSS are developed by a diverse group of contributors, the consistency of their involvement may vary, making continuous support and maintenance unpredictable. Therefore, it is necessary to identify the status of each OSS to avoid increased maintenance costs when selecting OSS for use. To address these issues, we use polynomial regression to predict trends in bug-fixing activities and evaluate the survivability of OSS accordingly. We predict the trend of bug-fixing activities in OSS, using factors such as popularity, number of contributors, and code complexity. A lower trend value indicates more vigorous activity. In this paper, we conduct data collection and extraction, generating model, and model testing and evaluation to predict survivability using these data. After collecting data through various tools, the models of different degrees are generated using K-fold cross-validation. The model with the highest performance is selected based on the RMSE (Root Mean Squared Error) and RSE (Residual Standard Error). Then, the chosen model is employed to predict the survivability of OSS and compare it with actual outcomes. This method is experimented with on OSS used in the KakaoTalk commercial messenger app. As a result, several OSS are predicted to have low survivability, and among them, five are analyzed. It is observed that, in reality, activities such as delayed or discontinued release updates occurred. These findings can support OSS users in selecting OSS for cost-saving purposes and alert OSS administrators to the need for solutions to ensure project survival.  
open source software; survivability; software maintainability; polynomial regression; GitHub +
Footnote †: journal: Applied Sciences
# Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
## 3 Related Work
### Cross Entropy Loss  
Semi-supervised acoustic model training can be formulated in terms of a cross entropy loss minimization problem for a given \(L\) number of labeled data \((\mathbf{X}_{l},\mathbf{Y}_{l})=\{(\mathbf{x}_{i},y_{i})\}_{1}^{L}\) and \(U\) number of unlabeled data \(\mathbf{X}_{u}=\{\mathbf{x}_{L+i}\}_{1}^{U}\) as follows:  
\[\mathcal{J}(\theta)=\frac{1}{L+U}\sum_{i=1}^{L+U}\mathcal{J}_{i}(\theta) \tag{15}\]  
Here, the cross entropy loss \(\mathcal{J}_{i}(\theta)\) measures the difference in probability distributions between the predicted labels and the ground truth labels for the \(i\)th input feature \(\mathbf{x}_{i}\) as follows:  
\[\mathcal{J}_{i}(\theta)=-\sum_{k=1}^{C}t_{k}(y_{i})\log P_{\theta}(y_{i}=k| \mathbf{x}_{i})\]  
where \(C\) is the number of classes or output states, \(t_{k}(y_{i})\) is the distribution of the ground truth labels \(y_{i}\), and \(P_{\theta}(y_{i}=k|\mathbf{x}_{i})\) is the distribution of the predicted labels.
# Forecasting the Impacts of Prescribed Fires
## 2 Methodology
### Burn Activity Forecasting  
To conduct safe and effective burns, the fuels must be dry enough to catch fire but not so dry that they burn uncontrollably. Similarly, the winds must be strong enough to spread the fire over the land to be treated but not strong enough to carry the flames beyond the perimeter. Based on these guidelines, the weather forecast can be used to predict PB activity, at least on a burn/no-burn decision level.  
In Georgia, according to the Prescribed Burning Act of 1992, a permit is required for the PB of woods, lands, marshes, or other flammable vegetation. The Georgia Forestry Commission (GFC) is the agency in charge of issuing permits and keeping permit records. The burn season for most sites in GA is from 1 October to 30 April of the next year. A burn ban goes into effect in 54 counties during the ozone season (1 May-30 September). We obtained permit record data from GFC for the years 2010-2016. Considering the burn season restriction, we focused our analysis on the first four months of each calendar year. The database contains the date and time, location and area of the burn, along with other information such as the purpose of the burn and the contact information of the burner. While some permit records have coordinates of the burn location, non-standard addresses that cannot be readily georeferenced are also common, but all records contain the county of the burn location; therefore, the spatial resolution of our analysis was set at the county level. GFC makes available daily weather forecast as well as observed weather data in an archive for fire danger ratings on its website. We downloaded the data for the 2010-2016 period at 18 fire weather stations in Georgia from this website. Information on how to download the data can be found in Appendix A.  
A preliminary analysis of the data revealed that weather plays an important role in the decision of the burner [11]. In general, burns were not attempted on rainy days, days following large rain events or during prolonged dry periods. Surface wind speeds were generally between 5 and 15 mph on the days when most burns were conducted. These findings as well as a weather forecast can be used as a first indicator of upcoming burn activity. Analysis has also shown that the amount of acreage treated by fire increases when weather conditions are more favorable for conducting safe and effective burns. This trend along with historic burning patterns employed by a list of likely burners can be used to predict the location and size of the burns. The more precise the prediction of PB activity and emissions, the more accurate the fire impact forecasting will be.
## 2 Materials and Methods
### Interrater and Intrarater Reliability  
Interrater and intrarater reliability were evaluated by selecting seven casts randomly. Intrarater reliability was completed by a primary investigator by manually measuring dental casts for all target disorders and summing the points awarded for the total DI scores twice, separated by a 2-week interval. Digital models corresponding to the same seven casts were measured and scored twice by the primary investigator using Insignia and Dolphin Imaging software for cephalometric tracing. Interrater reliability was achieved with a second examiner (R.J.S.V.). The same seven casts were sent to the second examiner, who manually measured all target disorders and computed the total DI scores digitally using the same software mentioned above.  
After the reliability testing, the primary investigator measured 28 sets of the initial study models manually and digitally according to the same guidelines as described in the initial reliability testing. The accuracy of the digital DI scores was compared with the manual scores from the plaster casts.
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 5 Future Directions  
Diabetic retinopathy is a topic in which a large amount of research is happening lately. In this section, we would like to present some possible directions for future work related to DR segmentation and DR lesion detection.  
* The number of studies which performed optic disc/fovea segmentation are relatively few in the reviewed literature. Hence, more research studies are needed in this area.
* Similarly, in the case of DR lesion detection, more studies need to be performed for HM/SE detection.
* Due to the serious complications that can result from proliferative DR, it is desirable to detect DR at an early stage. Hence, it will be beneficial to have more studies which focus on non-proliferative DR lesions.
* Security is of prime importance for any proposed method for DR segmentation/lesion detection. Hence, studies like the recent one by the authors in [104] are required. They have used adversarial training and feature fusion for DR detection.
* DR patients are at the risk of developing other conditions like glaucoma. Hence, more studies related to such conditions similar to the latest study by the authors in [105] are needed. They performed glaucoma detection by using a novel deep CNN.
# Design and Experimental Evaluation of an Electrorheological
## 2 Design and Characterization of the Haptic Module
### Design and Working Principle of Miniature ER Actuator
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Value** & **Description** & **Value** \\ \hline Initial spring electrode gap & 1.3 mm & Membrane thickness (PDMS) & 0.43 mm \\ Spring electrode radius & 4.7 mm & Membrane thickness (VHB) & 0.08 mm \\ Fixed electrode gap & 1.8 mm & Membrane radius & 7.5 mm \\ Fixed inner electrode radius & 7.5 mm & Compensation chamber width & 2.34 mm \\ Fixed outer electrode radius & 11 mm & Viscosity of GER fluid & 0.060 Pa s \\ Maximum indentation depth & 1 mm & GER fluid yield stress at 5 kV/mm & 80 kPa \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions and values of the actuator’s components. Figure 1: (**a**) Form factor of the ER fluid-based haptic actuator. Cross-section view of the working principle of the cylindrical haptic actuator (**b**) pre-press and (**c**) mid-press. Reproduced with permission [56]. Copyright 2020, IOP. However, prior work only evaluated the actuator as an open-loop device; the actuator provided haptic output, but without any sense of the provided input. Thus, the focus of this article is to develop a combined actuation and sensing module to provide closed-loop haptic feedback. By closing the loop, the actuator may provide controllable haptic feedback with respect to depth, such as emulating the click of a button at a specific pressed depth. Therefore, the module may provide more granular feedback, resulting in a more capable interface.
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.3 Virtual Staining  
Virtual staining is an innovative and rapidly advancing task within computational pathology. GANs are employed to simulate histological staining on unstained tissue images, such as those acquired via fluorescence or autofluorescence microscopy [98]. This approach aims to replace traditional, labor-intensive, and costly staining protocols with automated computational transformations, enabling faster and more cost-effective tissue analysis [99].  
A range of GAN architectures has been proposed for this task, each showing varying degrees of success. For example, [PERSON] et al. [74] used a combination of CycleGAN and E-CUT architectures, reporting a FID of 50.91 and KID of 0.2451, demonstrating a moderately effective simulation of staining. Similarly, [PERSON], [PERSON] and [PERSON] (2024) introduced GramGAN, obtaining FID = 52.086, KID = 5.815, and CSS = 0.714, suggesting a reasonable approximation of stain distribution with somewhat higher KID values.  
A noteworthy contribution is from [PERSON] et al. [58], who proposed the CSTN model for both stain normalization and virtual staining, but reported a high FID of 101.37, indicating room for improvement in image fidelity and realism. [PERSON] et al. [72] introduced HistoStarGAN, a domain-adaptive method that generalizes across multiple stain types. Their model reached FID = 56.0 and SSIM = 0.81 across seven domains, indicating solid performance and robustness, although with slightly reduced structural similarity compared to state-of-the-art models.  
On the other hand, [PERSON] et al. [85] explored the application of CycleGAN for virtual staining on an NVIDIA Tesla V100 GPU. They reported CWSSIM = 0.880 \(\pm\) 0.170 and SNR = 1.132, indicating strong structural consistency and acceptable signal quality. In a more advanced approach, [PERSON] et al. [84] developed a method combining Pix2 Pix and CUT, achieving FID = 11.16 on colorectal datasets and FID = 38.52 on breast cancer data.