esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 6 Evaluation
### Network Scalability  
In the case where the number of network nodes was increased from 2000 to 16,000, the variation in block broadcast time to the entire network for BLOCK-ICN and the bitcoin network was as shown in Figure 19. The horizontal axis represents the network size, while the vertical axis represents the average time for a single block to be broadcast to the entire network. Based on experimental results under the conditions of a 100% ICN node ratio, a unicast neighbor node ratio of 1 for ICN nodes, and miners located at domain gateway nodes, it can be observed that as the blockchain network size increased, both the BLOCK-ICN and bitcoin networks experienced an increase in block broadcast time. However, for the same network size, the block broadcast time for BLOCK-ICN was smaller than that of the bitcoin network. For example, when the network size was 16,000, the block broadcast time for the bitcoin network was 16,899.3 ms, while for BLOCK-ICN, it was 2385.4 ms, requiring less time for block broadcast. Furthermore, with an increased network size, BLOCK-ICN exhibited smaller variations in block broadcast time. For instance, when the network size increased from 6000 to 16,000, the block broadcast time for the bitcoin network required 3621.8 ms, while for BLOCK-ICN it was 640.1 ms. Therefore, it can be concluded that BLOCK-ICN had a better network scalability performance compared to the bitcoin network.  
As illustrated in Figure 20, we evaluated the broadcast delay of BLOCK-ICN under an extreme scenario with 100,000 network nodes, maintaining an ICN node ratio of 1%. The broadcast delays in different network topologies were compared, and the results are further contrasted with those in Figure 21, which presents the case for 16,000 nodes.  
In Figure 21, compared to the Bitcoin network, BLOCK-ICN reduced the broadcast delay for propagating blockchain data to 90% and 50% of the network by 25% and 33.2%, respectively.  
Figure 17: The broadcast time of ICN nodes acting as relay nodes under different ICN ratios.  
Figure 18: The broadcast time of relay nodes and non-relay nodes under different broadcast coverage rates.  
Under the 100,000-node network scenario, BLOCK-ICN achieved improvements of 32% and 46% in broadcast delay reduction for the same propagation thresholds. Notably, this improvement was also applicable to Kademlia-based network topologies.  
By evaluating broadcast coverage as a function of delay under an extreme network size (100,000 nodes), the results demonstrate the scalability of BLOCK-ICN across varying network sizes, reinforcing its ability to maintain efficient communication performance as network scale increases.
###### Abstract
We model and quantitatively compare the reliability benefits that can be obtained in TSN networks when using time redundancy. More specifically, we compare the reliability when using TSN with no space redundancy, when using approach A of PTRF and when using approach B. To that aim, we carry out a parametric sensitivity analysis that allows us to quantify how several dependability-related aspects of PTRF affect the reliability in the presence of temporary faults. The remainder of the article is structured as follows. Section 2 presents an overview of works related to this one and Section 3 presents a description of the TSN standards that are relevant to understanding this work. Section 4 provides an overview of the PTRF mechanism, focusing on those aspects that are key to carrying out our reliability analysis. Section 5 thoroughly describes the most relevant aspects of the PRISM models developed during this work, including the reliability metric, the fault model, the modelling assumptions, the modelling strategy and the model testing; Section 6 presents the parameters that can be tuned in our models and the values we have used for our analysis and Section 7 show the results of our sensitivity analysis of the reliability achievable by TSN and PTRF. Finally, Section 8 summarises the article and highlights the most important conclusions reached with this work.
## 2 Experimental System and Data Processing
### Data Processing  
This paper mainly discusses the removal of CO, PM, and NO\({}_{x}\) by activated carbon. The removal efficiencies of the three pollutants were calculated by the following Equation:  
\[\eta=\frac{C_{\text{in}}-C_{\text{out}}}{C_{\text{in}}}\times 100\% \tag{1}\]  
here, \(\eta\) is the removal efficiency of pollutants, %; \(C_{\text{in}}\) is the pollutant concentration of the reactor inlet, \(\mu\)L/L or mg/Nm\({}^{3}\); and \(C_{\text{out}}\) is the pollutant concentration of the reactor outlet, \(\mu\)L/L or mg/Nm\({}^{3}\).  
The NO\({}_{x}\) in diesel exhaust mainly includes NO and NO\({}_{2}\), of which NO accounts for more than 90%, and NO\({}_{2}\) generally does not exceed 10%. The flue gas analyzer Testo 350 used in this paper can measure the volume fraction of NO and NO\({}_{2}\), and the sum of them is the volume fraction of NO\({}_{x}\). In order to get the urea flow, it was necessary to first calculate the molar flow of NO\({}_{x}\), as shown in Equation (2):  
\[q_{mol}^{NOx}=\frac{q_{E}\times v_{NOx}}{22.4\times 1000} \tag{2}\]  
Here, \(q_{mol}^{NOx}\) is the molar flow of NO\({}_{x}\), mol/h; \(q_{E}\) is the volume flow of flue gas, Nm\({}^{3}\)/h; and \(v_{NOx}\) is the volume fraction of NO\({}_{x}\), \(\mu\)L/L.  
To avoid the ammonia slip, the molar ratio of NH\({}_{3}\):NO\({}_{x}\) is generally 1 in practical applications, which gives Equation (3):  
\[q_{mol}^{NH_{3}}=q_{mol}^{NOx} \tag{3}\]  
here, \(q_{mol}^{NH_{3}}\) is the molar flow of NH\({}_{3}\), mol/h.  
The urea solution generally needs three stages to produce NH\({}_{3}\), including evaporation, pyrolysis, and hydrolysis, which can be simplified to Equation (4).  
\[CO(NH_{2})_{2}+H_{2}O\longrightarrow 2 NH_{3}+CO_{2} \tag{4}\]  
Therefore, the volume flow of urea with a mass concentration of 32.5% could be expressed as Equation (5).  
\[q_{V}^{CO(NH_{2})_{2}}=\frac{1000\times q_{mol}^{NH_{3}}\times M_{CO(NH_{2})_{ 2}}}{2\times 32.5\%\times\rho_{CO(NH_{2})_{2}}} \tag{5}\]  
here, \(q_{V}^{CO(NH_{2})_{2}}\) is the volume flow of urea, ml/h; \(M_{CO(NH_{2})_{2}}\) is the molar mass of urea, 60 g/mol; and \(\rho_{CO(NH_{2})_{2}}\) is the density of the urea solution with a mass fraction of 32.5%, taking 1090 g/L.
## 3 Methodology
### NLP Pipeline  
The NLP pipeline to identify topics started with text tokenization followed by text normalization, text cleaning, vectorization, and an ML algorithm known as topic modeling [37]. Text tokenization converted raw text into individual word fragments called tokens. Text normalization reduced tokens to their base or root form with the aim of minimizing the number of variations that can serve as unique features in the topic modeling process. Text normalizers can be stemmers or lemmatizers. The former truncates the ends of words to a root form, whereas the latter employs a more sophisticated method to identify a base form that accounts for the morphological structure of a word. Therefore, stemmers can overgeneralize words, whereas lemmatizers can retain semantic accuracy at the expense of requiring more computation. Vectorizers represent the entire corpus as a matrix of values that reflect the importance of words in a document. Each vectorizer has an option to exclude tokens that are too common or too rare. Common thresholds for \"too common\" and \"too rare\" are 95% and 5% of the documents, respectively. A vectorizer builds a predetermined size vocabulary of unique tokens in the corpus. The vectorizer output is a sparse matrix of dimensions (\(N\), \(M\)), where \(N\) is the number of documents in the corpus (rows), and \(M\) is the size of the vocabulary (columns). Hence, each entry in the matrix stores the importance of a token (column) within a document (row). Table 2 summarizes the variety of NLP algorithms available for each part of the NLP pipeline, including their advantages and disadvantages. [PERSON] et al. (2019) provides  
Figure 2: Word cloud of the corpus cleaned by SME classification. further in-depth descriptions of these algorithms, which are currently state-of-the-art and mature [37]. The algorithms listed are available in software libraries such as the natural language tool kit (NLTK), scikit-learn (sklearn), and regular expression operations (re) supported by the Python programming language. The NLP libraries constrain tokenizers and normalizers to operate as compatible pairs. For example, the SpaCy tokenizer works only with the SpaCy lemmatizer, but not with any of the other normalizer algorithms.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 1 Introduction  
The biophysical cover of Earth's surfaces--land cover--has been declared as one of Essential Climate Variables [1]. Accurate knowledge about land cover is indeed key to environmental research, to the monitoring of the effects of climate change, to resources management, as well as to assist in disaster prevention. Accurate and up-to-date land cover maps are critical as both inputs to modeling systems--e.g., flood and fire spread models--and decision tools to inform public policy makers [2].  
State-of-the-art approaches to producing accurate land cover maps use supervised classification of satellite images [3]. This makes it possible for maps to be reproducible and to be automatically produced at a global scale while reaching high levels of accuracy [4]. Figure 1 displays an example of such a map. Latest satellite constellations are capable of acquiring Satellite Image Time Series (SITS) with high spectral, spatial and temporal resolutions. For instance, the two Sentinel-2 satellites provide, since March 2017, worldwide images every five days, freely distributed, within 13 spectral bands at spatial resolutions varying from 10 to 60 m [5].  
These new high-resolution SITS constitute an incredible source of data for land cover mapping, especially for vegetation and crop mapping [6; 7], at regional and continental scales [4; 8]. They also question the choice of the classification algorithms: although traditional algorithms showed good performance for SITS classification [3], they do not explicitly use the temporal relationship between acquisitions. Conversely, deep learning methods are appealing for this task [9], because they are capable of automatically extracting useful representations, as demonstrated both in the machine learning community [10] and in remote sensing [11]. The recent availability of long SITS on large areas calls for new studies to evaluate if (and how) SITS classification task can benefit from deep learning techniques.  
In the following, we first review the state-of-the-art approaches used for SITS classification. Then, we describe the most recent applications of deep learning to remote sensing data, with focus on time series classification. Finally, we present the motivations and contributions of this work.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 3 Experiment Result and Analysis
### Real-Time Experiment
The specific reasons still need further analysis and more experiments. We can infer that the positioning accuracy would be improved once this problem was solved. The error of the CAPS velocity estimation in the static and kinematic modes are given in Figure 13. We find a similar phenomenon with the positioning error, i.e., that the error in the east-west component is much smaller than that in the other two components. This phenomenon mainly resulted from the current distribution of the CAPS constellation. The error RMS of the velocity estimation in the east-west, north-south and vertical components are about 0.01, 0.013 and 0.026 m/s during the static period, while they are about 0.08, 0.051 and 0.051 m/s during the kinematic period, respectively. Figure 11: Time series of the positioning error in the east (**left**), north (**middle**) and up (**right**) components for the real-time CAPS relative positioning based on the carrier-phase in kinematic mode with a sampling rate of 4 Hz. Figure 10: Time series of the positioning error in the east (**left**), north (**middle**) and up (**right**) components for the real-time CAPS relative positioning based on the carrier-phase in static mode with a sampling rate of 4 Hz. In addition, it should be noted that the error of the reference velocity based on the NovAtel terminal has also been absorbed in the RMS calculation, which may make the accuracy of the CAPS velocity estimation in kinematic mode looks slightly worse.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
###### Abstract  
Freshwater is becoming scarce worldwide with the rapidly growing population, developing industries, burgeoning agriculture, and increasing consumption. Assessment of ecosystem services has been regarded as a promising way to reconcile the increasing demand and depleting natural resources. In this paper, we proposed a multidimensional assessment framework for evaluating water provisioning ecosystem services by integrating multi-source remote sensing products. We applied the multidimensional framework to assess lake water ecosystem services in the state of Minnesota, US. We found that: (1) the water provisioning ecosystem services degraded during 1998-2018 from three assessment perspectives; (2) the output, efficiency, and trend indices have stable distribution and various spatial clustering patterns from 1998 to 2018; (3) high-level efficiency depends on high-level output, and low-level output relates to low-level efficiency; (4) Western Minnesota, including Northwest, West Central, and Southwest, degraded more severely than other zones in water provisioning services; (5) human activities impact water provisioning services in Minnesota more than climate changes. These findings can benefit policymakers by identifying the priorities for better protection, conservation, and restoration of lake ecosystems. Our multidimensional assessment framework can be adapted to evaluate ecosystem services in other regions.  
multidimensional assessment; water provisioning ecosystem services; lake water storage; surface water; remote sensing; Google Earth Engine
## 4 Discussion
### Harvest Index--The Relationship between Winter Wheat Grain Production and GPP\({}_{\text{VPM}}\)  
The \"harvest index\" (HI) is used in crop production research in different ways: the ratio of crop grain production over (1) aboveground biomass (AGB), namely HI\({}_{\text{AGB}}\)[28], (2) net primary production (NPP), namely HI\({}_{\text{NPP}}\)[27; 29], and (3) gross primary production (GPP), namely HI\({}_{\text{GPP}}\)[33; 26]. These three His are different but related to each other, as GPP, NPP, and AGB are closely related. GPP represents the total carbon fixed by photosynthesis during the growing season. NPP is part of GPP, deducted by autotrophic respiration, and is often estimated as a sum of aboveground biomass and belowground biomass at harvest time. AGB is the total biomass allocated to the leaves, stems, branches, and seeds. The grain production (yield) is part of AGB at harvest.  
HI\({}_{\text{AGB}}\) derived from field-level studies showed a large variability in previous studies [28; 55], which is contributed to the seed variety and growing condition. HI\({}_{\text{NPP}}\) is usually calculated from HI\({}_{\text{AGB}}\) and the root-shoot ratio at the field scale. The root-shoot ratio for wheat ranges from 0.19-0.21 [28; 56]. For HI\({}_{\text{GPP}}\), [PERSON] et al. [57] and [PERSON] et al. [26] reported a value of 0.29-0.37 based on modeled GPP and yield data at a regional scale. Our results showed a median HI\({}_{\text{GPP}}\) of 0.30 from 2008-2018, and it fluctuated over counties and years (Figure 5). This variation of the GPP-yield relationship (HI\({}_{\text{GPP}}\)) could result from environmental factors, crop management, and crop varieties (genomics). For example, winter wheat grain production in the Southern Great Plains is relatively low and more variable than for other regions, because some winter wheat fields are used as pasture for cattle grazing. The rate of abandonment could vary in some extreme climate years, as adverse weather reduces wheat yield. In addition, fertilizer application, tillage, and row spacing could also affect the yield both spatially and temporally [58]. Additional studies are needed to quantify the responses of GPP and grain production under extreme climate events.
## 6 Experimental Results and Discussion
### L1 Loss  
L1 loss, sometimes referred to as the mean absolute error (MAE) or L1 norm loss, is a commonly employed loss function in the fields of machine learning and optimization, specifically in regression problems. This metric measures the absolute disparities between the anticipated values \(\hat{y}_{i}\) and the actual target values \(y_{i}\) for a total of \(n\) data points. L1 loss is calculated by taking the absolute difference between the predicted value (\(\hat{y}_{i}\)) and the actual value (\(y_{i}\)) for each data point, and then averaging these differences across the total number of data points (\(n\)). The inclusion of the absolute value in this formula ensures that both overestimates and underestimates have an equal impact on the loss. L1 loss exhibits lower sensitivity to outliers in comparison to L2 loss (mean squared error), rendering it appropriate for situations where the influence of outliers on the total loss should be constrained. Figure 5 represents L1 training loss and Figure 6 represents L1 validation loss.  
The L1 loss formula is given by  
\[L1\_Loss=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}| \tag{19}\]  
\begin{table}
\begin{tabular}{l c} \hline
**Loss Type** & **Minimum Value** \\ \hline L1 training loss & 0.2264 \\ L1 validation loss & 0.2231 \\ Training quantile loss & 0.1686 \\ Validation quantile loss & 0.1636 \\ MSE training & 1.4728 \\ MSE validation & 1.5025 \\ ADE test loss & 4.14 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of experimental results (minimum values).  
Figure 5: L1 loss variation across training epochs: tracking the model’s performance.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 4 Methodology
### Bridge Test
1: \(\alpha\leftarrow ComputeAngleX_{s}\)
2: \(P_{e}\leftarrow ComputeTestPoint(X_{s},\alpha+\alpha_{o},d)\)
3: \(P_{f}\leftarrow ComputeTestPoint(X_{s},\alpha-\alpha_{o},d)\)
4: \(\text{if}\text{testPass}\leftarrow\)IsBlocked(\(P_{e},X_{s}\)) XOR IsBlocked(\(P_{f},X_{s}\)) then
5: returnTestPass
6: else
7: returnfalse
8: endif
```  
**Algorithm 2** Bridge Test  
The functions ComputeAngle and ComputeTestPoint have been defined, where the TestPoint function is used to generate test points. ComputeAngle: This function calculates the angle between the line from the nearest node to the current sample (\(X_{s}\)) and the horizontal direction. The resulting angle is used as a basis for calculating the test points in subsequent steps. ComputeTestPoint: This function takes the result of ComputeAngle and adds and subtracts a given angleOffset (\(\alpha_{o}\)) to it to produce two new angles. Using these two new angles and a given distance (\(d\)), i.e., the distance from the sampling point to the test point, the two preset test points are calculated. IsBlocked: This function checks if the path from the sample point (\(X_{s}\)) to both test points is blocked by obstacles. This is handled by performing a logical XOR operation on the results of the obstacle detection at both test points, i.e., if only one test point's path is blocked by an obstacle, then the test is considered to have passed (testPass is true). This indicates that the sampling point successfully passed the Bridge Test. If the paths to both test points are blocked or neither of them are blocked, then the sampling point is considered to have failed the Bridge Test and the function will return false. In the fourth step of the pseudo-code, the returned result of the IsBlocked function is used to determine whether the test point passes the Bridge Test, and the result of testPass is returned in the fifth step. Figure 2: Bridge Test theory: (**a**) Generate additional sampling points near the sampling points at angle \(\theta\) (**b**) Connect and detect any obstacles between the newly generated two paths.
# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Synthesis of the Selected Works
#### 3.8.8 Pediatrics  
Liver tumor removal, principally hepatoblastoma, in children is not a standard procedure; however, it represents the 0.2% of the number of operations in pediatric malignancies [124]. Doctors in the area are currently discussing the best approach for its surgical treatment. Consequently, researchers have focused their attention on developing technology-based solutions [125]. One of the works in this area is [PERSON] et al., where they used CA technology to plan complex liver tumor operations [95]. They used a commercial assistant software called LiverAnalyzer, created by the Fraunhofer MEVIS. LiverAnalyzer is used to process data from cross-sectional CT images. This analysis provides individualized anatomical information of the liver, including tumors, vascular structures, and remnants of the liver after the task. Sixty-three children were evaluated for this CA surgery, and results pointed out that CA systems could play an essential role in the decision-making process of hepatoblastoma surgery.  
Another area in pediatrics that presents advances in simulators is fetal therapy. One of the most severe treatments performed in this area is twin-twin transfusion syndrome (TTTS). TTTS is a severe complication that is usually treated using fetoscopic laser surgery. Its success rate is 60% to 70% to save both twins, and 80% to 90% to save at least one twin [126]. [PERSON] et al. developed a high-realistic simulator to let surgeons practice fetoscopic laser surgery [96]. The system was built by adapting the simulator described in the work of [PERSON] et al. [127]. In this simulator, a realistic monochorionic twin placenta model and twin fetuses models were inserted. Additionally, a silicone interface at the top of the model was used to imitate the abdominal wall. All these modifications let the simulator provide a real-oriented architecture. The Delphi consensus was used as evaluation metrics, where two independent observers assessed the process. Results found in this research can point out that this life-like environment improves the performance of the procedure in a standardized model.