esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 4 Proposed CCB-Guided Adaptive Channel Denoising Mechanism
### Training Algorithm for Channel DenoisingNet  
The training of Channel DenoisingNet mainly consists of two parts: encoder1 and decoder1. The training algorithm is shown in Algorithm 3. encoder1 consists of two convolutional layers that extract information from the input features, and each layer applies convolutional filters to capture spatial hierarchies of patterns, such as edges and textures. The mapping procedure is  
\[f_{conv}(\mathbf{x}^{\prime})=\mathbf{W}_{conv}*\mathbf{x}^{\prime}+\mathbf{ b}_{conv}, \tag{13}\]  
where \(\mathbf{W}_{conv}\) and \(\mathbf{b}_{conv}\) are the convolution kernel and bias term, respectively. \(*\) represents the convolution operation. After convolution, a pooling operation reduces the spatial dimensions (height and width) of the feature maps, retaining essential information while downsampling the data. This pooling increases the receptive field, allowing the network to understand larger context from the image.  
The structure of the decoder1 mirror, i.e., encoder1, realizes the precise positioning of the features. decoder1 reconstructs the segmented image according to the coding features, which contain two convolutional layers and an upsampling layer. The upsampling layer consists of a series of upsampling operations (such as deconvolution or transposed convolution) and convolution operations, which are used to gradually increase the size and number of channels of the feature map. This can gradually restore the resolution and retain more details, and the mapping procedure is  
\[\mathbf{x}^{\prime\prime}=f_{deconv}(\mathbf{a})=\mathbf{W}_{deconv}* \mathbf{a}+\mathbf{b}_{deconv}, \tag{14}\]  
where \(\mathbf{W}_{deconv}\) and \(\mathbf{b}_{deconv}\) are the deconvolution kernel and bias term, respectively, and \(\mathbf{a}\) is the output of the previous stage.  
Through the training algorithm for Channel Code-Book MappingNet in Section 3, the model can obtain the different values of \(\mathbf{W}\) and \(\mathbf{b}\) based on \(\theta^{*}\). By choosing the values of \(\mathbf{W}\) and \(\mathbf{b}\), the denoiser can adjust the parameters according to different channel states to adapt to dynamic channels.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 3 Workflow of DevOps and MLOps
### DevOps  
Traditionally, the developers would wait until the release date to pass the newly developed code (patch) to the operations team. The operations team would then foresee that the developed code is deployed with additional infrastructure abstraction, management, and monitoring tasks. In contrast, DevOps aimed at bridging the gap between the two branches: Dev and Ops. It responds to the agile need by combining cultural philosophies, practices, and tools that focus on increasing the delivery of new features in production. It emphasizes communication, collaboration, and integration between Software Developers and Operations team [27]. An example of a DevOps workflow is depicted in Figure 1b. As seen from the Figure, the customer and project manager can redirect the development team on short notice if there are any changes in the specification. The different phases of DevOps can be implemented in a shorter duration such that new features can be deployed rapidly. The prominent actors involved in the DevOps process are also depicted in Figure 1b.  
Figure 1: (a,b) Difference between Waterfall and DevOps software development life cycle. (c) A manual ML Pipeline.  
DevOps has two core practices: Continuous Integration (CI) and Continuous Delivery (CD). Continuous Integration is a software practice that focuses on automating the process of code integration from multiple developers. In this practice, the contributors are encouraged to merge their code into the main repository more frequently. This enables shorter development cycles and improves quality, as flaws are identified very early in the process. The core of this process is a version control system and automated software building and testing process. Continuous Delivery is a practice in which the software is built in a manner that is always in a production-ready state. This ensures that changes could be released on demand quickly and safely. The goal of CD is to get the new features developed to the end user as soon as possible [27; 28].  
There is also another practice known as Continuous Deployment, which is often confused with CD. Continuous deployment is a practice in which every change is deployed in production automatically. However, some organizations have external approval processes for checking what should be released to the user. In such cases, Continuous delivery is considered a must, but Continuous deployment is an option that can be left out.
## 3 Methodology
### Validation Strategy  
The measured WL\({}_{a}\)s are compared to in situ WL\({}_{t}\)s using the historical dataset provided by SAIH, with measurements taken every 15 min and an accuracy of 1 cm. Linear interpolation is applied to estimate WL\({}_{t}\) values between in situ measurements. The performance of the methodology is evaluated based on the error time series, which are analyzed using several statistical indicators: the root mean square error (RMSE), the median absolute deviation (MAD), the bias, and the standard deviation of the errors (SDE).  
The FFSAR methodology used in this study is expected to have an uncertainty of approximately 1 cm, as revealed by an analysis of long-term series of point target measurements using a corner reflector [36], which is a technique typically used in the calibration of altimeters like Sentinel-6. The corner reflector is a metallic object of about 1 square meter in size, which acts as a proxy for a point-like bright scatterer from the perspective of the radar. The obtained uncertainty is primarily influenced by the precision of the geophysical corrections. Since both point target and inland water measurements follow similar procedures, this precision level serves as the theoretical limit for the current study on inland waters. Any degradation exceeding 1 cm observed over natural targets could be attributed to the additional complexity inherent in natural scenarios.
## 4 Conclusions and Future Work  
This article proposes a mathematical model of the interaction of the main mechanisms of the TCP (Slow Start, Additive-Increase/Multiplicative-Decrease) in the form of a queuing system, which describes the process of reliably controlling massive traffic from the SNEIoTD environment to a hub. As part of our model, a semi-Markov process is selected that describes the interaction of these mechanisms, locked to the definition of such a characteristic parameter as the unified speed of information transfer. As a result of recurrent analyses of the aforementioned process, a stationary distribution of an embedded Markov chain was determined. This made it possible to analytically describe the stationary distribution of the original semi-Markov process. An analytical representation of the above-mentioned stationary distribution became the basis for our formalization of an accurate, computationally efficient information technology for calculating the TCP Window Size of both the studied mechanisms and the \(<\)ssthreshs- parameter. The conducted simulation made it possible to determine a number of important regularities in the distributed process of controlling data transfer from the SNEIoTD environment to a hub in the context of using the TCP. Using the example of TCP Westwood+ protocol modification, it has been shown that the results of applying the information technology proposed in Section 2.2 make it possible to increase the stability of targeted data transfer in conditions of an increasing Round-Trip Time. As a promising direction for further research, the authors suggest the expansion of the mathematical apparatus presented in Section 2 according to the analytical description of the optimizing mechanisms specific to different versions of the TCP. Conceptualization, V.K. ; methodology, V.K. ; software, V.K. ; validation, K.G., W.K. and K.P. ; formal analysis, V.K. ; investigation, V.K. ; resources, K.G., W.K. and K.P. ; data curation, K.G., W.K. and K.P. ; writing--original draft preparation, V.K. ; writing--review and editing, V.K. ; visualization, V.K. ; supervision, V.K. ; project administration, V.K. ; funding acquisition, V.K. All authors have read and agreed to the published version of the manuscript. Figure 11: Demonstration of the effect of an applied use of the information technology presented in Section 2.2. **Funding:** This study was funded by the \"Methodology for Increasing the Dependability of Information Systems for Critical Use with a Heterogeneous Wireless Interface\" project, reg. no. 2022/45/P/ST7/03450, of the POLONEZ BIS 2 program, implemented by the National Science Center in Krakow. **Data Availability Statement:** Most data are contained within the article. All the data are available on request subject to restrictions, e.g., privacy or ethics. **Acknowledgments:** The authors are grateful to all colleagues and institutions that contributed to this research and made it possible to publish its results. **Conflicts of Interest:** The authors declare no conflict of interest.
_Remote Sens._**2014**, \(6\), 11391-11419; doi:10.3390/rs61111391  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/renotesensing  
_Article_  
Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case Study for Spain  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Departament de Fisica de la Terra i Termodinamica, Universitat de Valencia, Dr. Moliner, 50, 46100 Burjassot (Valencia), Spain; E-Mails: [PERSON](B.M.);  
[PERSON]S] (F.J.G.-H.); [PERSON]@uv (A.M.); [PERSON]M.A.G.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]; Tel.: +34-96-354-32-56; Fax: +34-963-543-385.  
External Editors: [PERSON] and [PERSON]  
_Received: 22 July 2014; in revised form: 21 October 2014 / Accepted: 24 October 2014 / Published: 14 November 2014_  
**Abstract:** Ecosystem state can be characterized by a set of attributes that are related to the ecosystem functionality, which is a relevant issue in understanding the quality and quantity of ecosystem services and goods, adaptive capacity and resilience to perturbations. This study proposes a major identification of Ecosystem Functional Types (EFTs) in Spain to characterize the patterns of ecosystem functional diversity and status, from several functional attributes as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Albedo. For this purpose, several metrics, related to the spatial variability in seasonal and annual patterns (e.g., relative range), have been derived from remote sensing time series of 1 km MODIS over the period 2000-2009. Moreover, precipitation maps from data provided by the AEMet (Agencia Estatal de Meteorologia) and the corresponding aridity and humidity indices were also included in the analysis. To create the EFTs, the potential of the joint use of [PERSON]'s Self-Organizing Map (SOM) and the _k-means_ clustering algorithm was tested. The EFTs were analyzed using different remote sensing (_i.e._, Gross Primary Production) and climatic variables. The relationship of the EFTs with existing land cover datasets and climatic data were analyzed through a correspondence analysis (CA). The trained SOM have shown feasible in providing a comprehensive view on the functional attributes patterns and a remarkable potential for the quantification of ecosystem function. The results highlight the potential of this technique to delineate ecosystem functional types as well as to monitor the spatial pattern of the ecosystem status as a reference for changes due to human or climate impacts.  
Keywords:Self-Organizing Map (SOM); Ecosystem Functioning Types (EFTs); MODIS; Albedo; NDVI; LST; Precipitation Data +
Footnote †: journal: Journal of the Royal Society Interface
## 3 Results
### PPIA and YWHAZ Expression in Primary Human MSCs  
As previously mentioned in Results Section 3.2, an immortalized human MSC line was used in this study. A question we posed was if the findings were also consistent in primary human MSCs, that still retain replicative senescence and are of a different donor. Therefore, we assessed the expression of YWHAZ versus PPIA in primary human bone marrow-derived MSCs (Figure 6). We found no change in the gene expression after 30 or 60 m/d of ES; therefore, YWHAZ and PPIA were also stable with increasing duration of ES in primary MSCs as well as the Y201 MSC line. This shows that the human immortalized cells used in this study are a highly relevant MSC line, with transferable results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Pearson’s Correlation**} \\  & \multicolumn{3}{c}{**Coefficient (r)**} \\ \hline
**Reference** & \multicolumn{3}{c}{**Seeding Density (Cells/cm\({}^{2}\))**} & \multicolumn{1}{c}{**Average**} & \multicolumn{1}{c}{**Standard Deviation**} \\ \cline{2-5}
**Gene Pair** & **5000** & **10,000** & & \\ \hline PPIA:YWHAZ & 0.979 & 0.994 & 0.987 & 0.0106 \\ \hline PPIA:B2M & 0.990 & 0.942 & 0.966 & 0.0339 \\ \hline YWHAZ:18S & 0.981 & 0.912 & 0.947 & 0.0488 \\ \hline PPIA:GAPDH & 0.932 & 0.957 & 0.945 & 0.0177 \\ \hline YWHAZ:B2M & 0.971 & 0.914 & 0.943 & 0.0403 \\ \hline PPIA:18S & 0.957 & 0.875 & 0.916 & 0.0580 \\ \hline YWHAZ:GAPDH & 0.842 & 0.961 & 0.902 & 0.0841 \\ \hline B2M:GAPDH & 0.923 & 0.822 & 0.873 & 0.0714 \\ \hline
18S:GAPDH & 0.801 & 0.956 & 0.879 & 0.1096 \\ \hline
18S:B2M & 0.930 & 0.68 & 0.805 & 0.1768 \\ \hline GAPDH:ACTB & 0.231 & 0.958 & 0.595 & 0.5141 \\ \hline PPIA:ACTB & \(-\)0.111 & 0.883 & 0.386 & 0.7029 \\ \hline B2M:ACTB & \(-\)0.110 & 0.723 & 0.307 & 0.5890 \\ \hline YWHAZ:ACTB & \(-\)0.271 & 0.869 & 0.299 & 0.8061 \\ \hline
18S:ACTB & \(-\)0.363 & 0.880 & 0.259 & 0.8789 \\ \hline \hline \end{tabular}
\end{table}
Table 3: [PERSON]’s correlation coefficient (r) values for each potential reference gene pair as determined by BestKeeper.
## 2 Methods
### Finite Element Modeling and Analysis
#### 2.2.3 Loading Conditions  
[PERSON] et al. [28] determined the average tendon forces based on tests performed using 12 cadaver arms, set to different wrist positions. The relevant tendon forces were used as a database for our FE analyses for the following tendons: extensor carpi ulnaris (ECU), extensor carpi radialis brevis (ECRB), extensor carpi radialis longus (ECRL), abductor pollicis longus (APL), flexor carpi radialis (FCR), and flexor carpi ulnaris (FCU).  
The forces (Table 1) were applied to the distal surfaces of the carpal bones articulating with the metacarpals (Figure 2c). The tendons' loads were distributed on the surfaces of the bones. Each force was applied in the direction of a vector from the insertion point in the metacarpal relevant bone to the bone in the distal carpal row. The tendon forces reflect a wrist position of 20\({}^{\circ}\) in extension. Unfortunately, the radial deviation angle during the radial-extension position studied in [28] was not measured.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Tendon Force [N]**} \\ \hline \hline \multicolumn{1}{c}{Wrist position} & ECU & ECRB & ECRL & APL & FCR & FCU \\ \hline \hline \multicolumn{1}{c}{Radial-Extension} & 58.2 & 50.5 & 38.4 & 38.2 & 16.0 & 65.7 \\
20\({}^{\circ}\) Extension & 54.2 & 44.1 & 31.8 & 37.6 & 12.5 & 86.8 \\ \hline \hline \multicolumn{1}{c}{ECU = Extensor Carpi Ulnaris} & \multicolumn{3}{c}{APL = Abductor Pollicis Longus} \\ \multicolumn{1}{c}{ECRB = Extensor Carpi Radialis Brevis} & \multicolumn{3}{c}{FCR = Flexor Carpi Radialis} \\ \multicolumn{1}{c}{ECRL = Extensor Carpi Radialis Longus} & \multicolumn{3}{c}{FCU = Extensor Carpi Radialis Longus} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forces applied to the tendons of the two models during the two conditions of extension and radial extension [28]. Note that the total force applied for each wrist position is 267 N.
## 3 Methodology
### CurveNet  
The CurveNet architecture [64] introduces a novel approach for aggregating local features by generating continuous point cloud sequences, referred to as curves, to capture long-range information within point clouds. The model's performance is mainly influenced by two modules: the Curve Grouping Block and the Curve Aggregation Block. The Curve Grouping Block generates continuous point sequences (curves) from the point cloud. To prevent the formation of loops and intersections, a guided walking strategy is employed, which traverses along geometric curves within the point cloud structure, thus capturing the relationships between local points. Specifically, a point is selected as the starting point, and neighboring points are dynamically chosen according to the walking strategy to gradually form the curve. The Curve Aggregation Block integrates the generated curve features back into each point's features. Through this method, local geometric information is effectively captured and integrated, thereby enhancing the representation capability of each point.
# Fingerprint Classification through Standard and Weighted Extreme Learning Machines
## 4 Methodology
### Results Evaluation by the Five-Fold Cross-Validation Scheme  
To assess the quality of the novel technique, we follow a perspective oriented to machine learning known as five-fold cross-validation approach [52]. This scheme results in an unbiased and accurate measurement of the classifier performance due to the training and testing are not developed on fixed parts. To this end, the database is split into five-folds, each one containing 20% of the samples of the database. For each split, the classification model is trained by using the 80% of fingerprints from the rest of the folds, whereas testing is done on the current fold. For each database and classifier, the overall results are reported from averaging five executions. In particular, in order to estimate the optimal hyper-parameters of the ELM (see Section 5.1), we destine the 20% of each training set for validation purposes according to the methodology exposed in [18]. Hence, the validation set is intended to find the ELM hyper-parameters (e.g., the regularization parameter \(C\) and the number of hidden nodes \(N\)) that will maximize its performance. The previous scheme allows a direct comparison with results obtained by the benchmark proposal [3] since the experiments are performed on the same testing sets. Figure 4 depicts the five-fold cross-validation approach proposed in this section for the experimental evaluation, where the validation set is discarded for a better understanding.
## 2 Materials and Methods
### Measurement
#### 3.2.3 Temperature Sweep Test  
Temperature sweep tests were conducted on pre-cut SBS-modified asphalt with different shredded particle sizes. The experimental results are shown in Figure 12.  
In Figure 10a, it can be observed that the complex modulus of SBS-modified asphalt prepared by different shredded particle sizes through mixing was consistently higher than that of the conventional-process asphalt at each scanning temperature. Among the samples, the complex modulus of the asphalt with shredded particles sized 30-18 mesh was the highest, followed by 100-30 mesh, and that for 18-10 mesh was the lowest. The results indicate that the complex modulus was greater when the SBS was in a fibrous and particulate combination state as opposed to a predominantly granular state. This is attributed to the higher intertwining of the fibrous morphology, which led to a more intricate spatial network structure that enhanced its load-bearing capacity at elevated temperatures. Furthermore, in the fibrous and particulate combination state, as the width of the fibrous elements and the diameter of the particulates decreased, the strength of the interwoven spatial network structure diminished.  
In Figure 10b, it is evident that the phase angle of the SBS-modified asphalt prepared with different shredded particle sizes was generally lower than that of the conventional-process asphalt, indicating a higher elastic proportion in the pre-cut SBS-modified asphalt. Among the three different shredded particle sizes prepared through mixing, the asphalt with shredded particles sized 30-18 mesh exhibited the smallest phase angle, followed by 100-30 mesh, and the 18-10 mesh exhibited the largest phase angle. This trend can also be attributed to the fibrous morphology of the SBS. The interlocking nature of the fibrous SBS enhanced the elasticity of the network structure. Additionally, the wider fibrous elements contributed to a stronger network structure, resulting in greater elasticity.
# The Architecture of the Access Protocols of the Global Infocommunication Resources +
## 2 Materials and Methods
### Step II. Data Transfer from the Terminal Device to the Base Station
#### 2.2.1 Mathematical Model  
This section offers a mathematical model of the process of transferring user data blocks at the second step of the scenario under consideration--the transfer from the terminal device to the base station. When developing the mathematical model, the features of the physical and channel level were taken into account (see Figure 2).  
In general, the mathematical model is represented by an expression for the z-transformation of a series of time distribution delays in transmitting user data blocks:  
\[g(z)=g_{1}(z)g_{2}(z), \tag{1}\]  
where \(g_{1}(z)\) and \(g_{2}(z)\) are \(z\)-transformations of time distribution series for delay of transmission of user data blocks on the 1 st and 2 nd phases, respectively.  
Taking into account the above assumptions both phases of transmission are presented in the form of QS \(M^{d}/G^{d}/1\) in discrete time.  
Formulas that specify the form \(g_{1}(z)\) and \(g_{2}(z)\) at each transmission phase have the form:  
\[g_{j}(z)=\frac{(1-\theta_{i})(1-z)f_{j}(z)}{1-p_{j}z-q_{j}zf_{j}(z)},p_{j}=1=q+ j,j=\overline{1,2} \tag{2}\]  
\[q_{1}=\mu QT_{1};q_{2}=(\overline{\Pi_{1}}\mu QDT_{2})/N \tag{3}\]  
where \(\overline{\Pi_{1}}\)--probability that the user data block in the first phase will be delivered in a timely manner, \(\theta_{1}\)--the probability that the transmitting buffer of the terminal device is busy during the first transmission phase (i.e., it has at least one block to transmit); \(\theta_{2}\)--the probability that the transmitting buffer of the network aggregator in the second transmission phase is busy. \(\theta_{1}\) and \(\theta_{2}\) are defined according to the formula:  
\[\theta_{j}=q_{j}(d/dz^{-1})f_{j}(z)\Big{|}_{z=1},\theta_{j}<1,j=\overline{1,2}. \tag{4}\]  
\(f_{j}(z)\)--\(z\)-transformations of service interval distribution series on the \(j\)-th transmission phase (\(j=\overline{1,2}\)), the following parameters based on the TDM application are set:  
\[f_{1}(z)=z^{-ID};f_{2}(z)=z^{-IB}. \tag{5}\]  
The final expressions for \(\theta_{1}\) and \(\theta_{2}\), if we consider (4), may be like:  
\[\theta_{1}=q_{1}ID,\theta_{2}=q_{2}IB. \tag{6}\]  
The mathematical model of the process of transferring user data blocks in the second step of the scenario under consideration--the transfer from the terminal device to the base station--is represented by Equations (1)-(6).