esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
###### Abstract  
With the intensification of energy consumption, how to make rational and efficient use of wind energy has been studied all over the world. The construction of facilities to obtain wind energy requires an accurate assessment of the wind characteristics of the local terrain. In order to study the wind characteristics on an island in Southeast China, a 1:1300 terrain model is established, and the characteristics of mean wind and fluctuating wind are studied by numerical simulation and wind tunnel test. The results show that wind speed is affected by the incoming wind direction and local terrain. Wind speed on windward slopes and flat areas with no obstructions is higher, and wind speed oneward slopes and valleys is lower. Then, the wind attack angle of each measuring point is mainly in the range of \(-10^{\circ}\)-\(10^{\circ}\), which is much higher than that in flat areas. The positive and negative wind attack angles are controlled by the incoming wind direction, and the size is closely related to the local terrain. As for pulsation characteristics, the disturbance of the inflow determines the turbulence intensity. The incoming wind direction mainly affects the turbulence intensity on the hillside, while the turbulence intensity in the valley and flat area is controlled by the local terrain. In addition, the fluctuating wind speed power spectra on the island is more consistent with the von Karman spectrum, which is quite different from the Kaimal spectrum. The bandwidth on hillsides and valleys will not change with the change in inflow, but for flat areas, the bandwidth is greatly affected by the inflow direction.  
wind energy; island; numerical simulation; wind tunnel test; wind characteristics +
Footnote †: journal: Applied Sciences
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Automatic Generation of Streams
#### 4.1.1 Algorithm for Generation of Abrupt Drifts  
Abrupt concept drift is a type of drift that occurs suddenly, resulting in a significant shift in the underlying data distribution. In the context of our binary data streams, an abrupt concept drift might involve a sudden replacement of the target patterns, where a pattern was previously dominant. This type of abrupt concept drift can pose significant challenges for learning algorithms, which rely on a stable data distribution to operate effectively [22].  
In Algorithm 1, used for generating streams with abrupt drift, the variables of duration and amplitude with a random number of bits are inputted. Additionally, the start and end of each drift are randomly generated. When the start bit is reached, a 0-1 bit change is made, simulating the start of an abrupt drift.
## 4 Model Result
### Results Evaluation of the Employed Models  
Model evaluation of the ensemble model with individual algorithms was also conducted using statistical analysis through various error indicators. It can be seen in Table 13 that individual models of DT and MLPNN exhibited higher errors, as compared to their ensemble approaches. This means that ensemble methods not only provide accurate predictions but also help narrow the error range between anticipated and actual findings.
## 5 Results and Discussion  
Following the case study, it was found that it was feasible to implement the proposed system in real-life situations, to provide functionalities for real-time monitoring and decision support for roadside parking activities. On the one hand, property companies can evaluate the severity of double-parking and view the real-time roadside situation from the back office. During the case study, 100 samples of parking gaps were examined for accuracy of parking gap estimation, compared with the actual measured parking gap. The accuracy comparison between estimated and actual parking gaps is shown in Figure 19. A paired sample t-test was applied to examine the difference between estimated and actual parking gaps, and the null hypothesis was to assume that the mean difference was zero. It was found that statistical significance for the mean difference was achieved with a \(p\)-value of 0.02; thus, the measurement of parking gaps using the CVROSS can be considered feasible. The average and  
Figure 17: Overview of the interface design for information visualization.  
Figure 18: Main dashboard of the CVROSS.  
maximum errors for parking gap estimation were 1.47 m and 3.60 m, respectively. Moreover, the accuracy of estimation of the time of stay was also assessed using 50 samples to compare estimated and actual times of stay, as shown in Figure 20. A paired sample t-test was also applied to examine the difference between estimated and actual times of stay, and the null hypothesis was to assume the mean difference was zero. It was found that statistical significance for the mean difference was achieved with a \(p\)-value of 0.015; thus, the estimation of time of stay can be considered feasible. The average and maximum errors in the estimation of time of stay were 1.70 h and 4.64 h, respectively. On the other hand, truckers and drivers can make use of the proposed system to understand the specific roadside situation. In the next section, the performance of CVROSS is assessed by conducting a comparative analysis--before and after adopting the proposed system. Any advantages and contributions are discussed accordingly.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Medical Imaging Techniques for BC Diagnosis
#### 2.1.2 Ultrasound  
Compared to the previous medical imaging technique, ultrasound images are monochrome, with low resolution and can effectively differentiate cysts from solid masses, whereas mammography cannot. However, the malignant areas in ultrasound imaging are visualized as irregular shapes with blurred edges. The presence of speckle noise,an underlying property of the ultrasound imaging technique, also downgrades the images' performance as it reduces their resolution and contrast [7].
## 3 The Safety of the Dual Architecture  
### The Safety Integrity against Random Failures  
Figure 1 shows the block diagram of a dual architecture. The principle of operation is provided by the composite fail-safe technique in case of failure. Channel A and channel B operate independently of the same function, and their results are compared with each other. The Equipment Under Control (EUC) command is only provided if the results of both channels are identical. If the results do not match, the output of the dual architecture  
must be safe for the EUC (this process is called fault negation). The condition required for such a solution is that channels A and B are independent of each other (the occurrence of a fault in one channel does not affect the probability of the occurrence of a fault in the other channel).  
If the above assumptions are met, then the probability of a dangerous failure of the dual architecture can be calculated according to the relation:  
\[P_{D}=P_{A}\cdot P_{B} \tag{1}\]  
where \(P_{A}\) is the probability of failure of channel A and \(P_{B}\) is the probability of failure of channel B.  
If we assume that they are electronic systems, then for these systems, the distribution of failures can be described by an exponential distribution.  
\[P_{D}(t)=\left(1-e^{-\lambda_{A}t}\right)\cdot\left(1-e^{-\lambda_{B}t}\right) \tag{2}\]  
where \(\lambda_{A}\) is the failure rate of channel A and \(\lambda_{B}\) is the failure rate of channel B. \(t\) is the time of fault detection and negation.  
To evaluate the SIL of the dual architecture, it is necessary to know the intensity of its dangerous failures. Then  
\[\lambda_{D}(t)=\frac{\frac{dP_{D}(t)}{dt}}{1-P_{D}(t)}. \tag{3}\]  
Assuming that \(\lambda\cdot t\ll 1\), a simplified relation can be derived from Equations (2) and (3).  
\[\lambda_{D}(t)=2\cdot\lambda_{A}\cdot\lambda_{B}\cdot t \tag{4}\]
# A Novel Bi-Dual Inference Approach for Detecting Six-Element Emotions
## 3 Bi-Dual Inference for Detecting Emotions
### Bi-Dual Inference Construction
```
1:Date \(D_{x}\) and \(D_{y}\); Optimizers \(LM_{A}\) and \(LM_{B}\); Hyper-parameter \(\alpha\),\(\beta\); Beam search size K; Lagrange parameters \(\lambda_{xy}\) and \(\lambda_{yx}\);
2:\(D_{x}\) :A set of short texts with emotional labels
3:\(D_{y}\) :A set of emotion labels
4:Loss function of the model \(\ell_{dual}\big{(}M_{x},M_{y},F_{q},G_{v}\big{)}\)
5:repeat
6:Training \(f_{0}\) and \(g_{0}\)
7:Iteration number accumulation \(t=t+1\)
8:Get a random minibatch of m pairs \(\big{\{}\big{(}x_{j},y_{j}\big{)}\big{\}}_{j=1}^{m}\)
9:Calculate the model gradient according to Equations (16) and (17)
10:Update the parameters of \(f_{0}\) and \(g_{0}\) and return to get \(f_{0}\), \(g_{0}\)
11:\(\theta_{xy}\gets LM_{A}\big{(}\theta_{xy},G_{f}\big{)}\), \(\theta_{yx}\gets LM_{B}\big{(}\theta_{yx},G_{g}\big{)}\)
12:Training \(f_{i}\) and \(g_{j}\)
13:for\(i=0;i<N-1;i++\)do
14:Initialise \(\ell_{f}\) and \(\ell_{g}\) according to Equations (18) and (19), then calculate the \(f_{i}\) and \(g_{j}\) according to Equations (10) and (11)
15:\(f_{i}(x)=arg\min_{y^{\prime}\in Y}\big{\{}\alpha\ell_{f}(x,y^{\prime})+(1- \alpha)\ell_{g}(x,y^{\prime})\big{\}}\)
16:\(g_{j}(y)=arg\min_{x^{\prime}\in X}\big{\{}\beta\ell_{f}(x^{\prime},y)+(1- \beta)\ell_{g}(x^{\prime},y)\big{\}}\)
17:Following Equations (20) and (21), Calculate the \(F_{q}\) and \(G_{v}\)
18:Update the parameters and return the value of the model
19:\(\ell_{dual}\leftarrow\ell_{dual}\big{(}M_{x},M_{y},F_{q},G_{v}\big{)}\)
20:endfor
21:until function convergence
```  
**Algorithm 1** Bi-dual inference algorithm
## 3 Results
### Network Meta-Analysis  
Blood glucose results were reported in 34 studies and used for the network meta-analysis. The SMDs associated with significant glycemic attenuation were greatest at \(-\)1.18 (95%CI \(-\)1.72 \(-\)0.64) for every 30 min interruption and were \(-\)0.89 (95%CI \(-\)1.52 \(-\)0.27) for every 20 min interruption (Figure 2), while other interruptions did not significantly lower blood glucose. A total of 27 studies provided usable results regarding insulin changes. The SMDs associated with significant insulin lowering were greatest at \(-\)0.98 (95%CI \(-\)1.36 \(-\)0.60) for every 30 min interruption, \(-\)0.94 (95%CI \(-\)1.41 \(-\)0.46) for every 20 min interruption, and \(-\)0.89 (95%CI \(-\)1.51 \(-\)0.27) for a 30 min exercise bout (Figure 2), while other interruptions (every 60 min interruption and 60 min exercise bout) did not significantly lower the insulin level. Eleven studies provided usable results for triacylglycerol measurement. However, the SMDs for every 20 min, 30 min, and 60 min interruption and a 30 min bout were insignificant (Figure 2). Compared to the overall sample, analyses that excluded people with T2 DM found that SMDs (every 20 min and 30 min interruption) were smaller, suggesting that every 20 min and 30 min interruption had greater benefits for blood glucose in T2 DM, with glucose attenuation also seen following 60 min interruption. Moreover, every 60 min interruption was found to have a significant beneficial effect on glucose in the sample. The results are presented in Figure 3.  
An inconsistency test based on the network analysis found no significant global inconsistency for glucose (\(p\) = 0.995), insulin (\(p\) = 0.997), and triacylglycerol (\(p\) = 0.73); the same was seen in analyses that excluded people with T2 DM of glucose (\(p\) = 0.48) and insulin (\(p\) = 0.12). In the local inconsistency test, 4 loops out of 15, 1 loop out of 5, and 1 loop out of 3 demonstrated significant differences for glucose, insulin, and triacylglycerol, respectively. Inconsistent testing of the node-splitting model indicated that all comparisons between direct and indirect estimates were consistent for all outcomes. The detailed results for inconsistency are shown in Supplementary File S1 along with network plots for comparisons of different intervention effects on glucose, insulin, and triacylglycerol. The funnel plots for insulin and triacylglycerol are symmetrical, while the funnel plots for glucose show a slight asymmetry (Supplementary File S1).
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from a Random Topology
#### 4.2.2 The Impact on Average End-to-End Delay  
The average end-to-end delay under different numbers of flows in the random topology is investigated in Figure 14(a). The average end-to-end delay can show the effectiveness of these three protocols on a single route. Not surprisingly, CCAR had a smaller delay than for DCAR and COPE + AODV because our scheme can select a better route while considering the state of coding nodes, which significantly influences the delay.  
Moreover, Figure 14(b) presents the average end-to-end delay with different packet loss rates in 10 flows network. Similar to Figure 13(b), we can see that the proposed CCAR scheme had the best robust performance with average end-to-end delay. When the packet loss rate was at a high level, DCAR declined fastest and was close to COPE + AODV. This is because DCAR is sensitive to the packet loss rate. The primary coding conditions in DCAR had a poor effect in a high packet loss rate scenario.  
Figure 14: The impact on total throughput in the random network.
## 4 Discussion
### Comparative Analysis with Similar Studies  
This study primarily focused on examining natural variations in forest health using quantitative remote sensing products, such as LAI, FVC, and CPP. The analysis of these indicators consistently revealed a decline in both the extent of vegetation cover and the overall quality of the forest ecosystem in the Lancang-Mekong region from 2010 to 2020, which is consistent with previous research findings [3; 24]. However, it is important to note that deforestation in the Lancang-Mekong region is largely attributed to human activities [35; 36; 37]. Therefore, future efforts should emphasize the need to incorporate assessments of human activities in order to effectively evaluate forest health.  
In this study, EQI, derived from the ecosystem quality assessment standard by the Ministry of Ecology and the Environment of China [19], was calculated using LAI, FVC, and GPP remote sensing products. The indicator EQI provides a comprehensive reflection of the overall state of vegetation and ecosystems, surpassing the limitations of single parameters such as LAI, FVC, or GPP [2; 3; 16; 17]. However, for the complex interactions among the hydrology, geography, and human activities, additional comprehensive evaluation  
Figure 10: Statistics for EQI in 2020 and statistical histogram of KEQI from 2010 to 2020 at different elevations for each country over the forest ecosystem in the Lancang–Mekong region. (**a**) EQI and FCI for each country; (**b**) variation in KEQI from 2010 to 2020 at different elevations.  
indices, such as the Ecological Environment Pollution Index (EPI), Ecological Environment Management Index (EMI), and Pressure-State-Response (PSR) [38; 39], should also be considered for a more holistic assessment of forest health.  
Given the importance of forests in maintaining ecological balance, this study focuses on forest types in the Lancang-Mekong region, which comprise over 45% of the total land cover. The results offer valuable insights into forest health in the Lancang-Mekong region over the ten-year period from 2010 to 2020 and explore the relationships between the FCI/elevation and annual average LAI, annual maximum FVC, annual cumulative GPP, EQI, and KEQI. However, croplands and shrublands cover more than 50% of the mainland, while other vegetation types, such as cultivated land and wetlands, also play a significant role in assessing the overall ecological environment [6; 40]. Therefore, conservation efforts should extend beyond forests to protect the rich biodiversity of the region, for example, by preserving wetlands, and promoting sustainable cultivated land-use that minimizes harm to the environment.
# State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System
## 4 Enabling Technologies for Transaction-Based Energy  
These are the infrastructures that would enable energy production, storage and trading/sharing among prosumers. They broadly cover DERs, ESSs and ICT. This section discusses the ESS, DER, communication technologies and the current mode of communication adopted in the literature for energy trading/sharing networks.
## 3 Method
### Part 1: Retrieval Algorithm for Instantaneous TOA Albedo (Level-2)  
#### 3.1.1 Data Pre-Processing  
The \"true isotropic reflectance\" [14], here simply referred to as \"narrowband reflectance\" (\(\rho_{\lambda}\)), is calculated from scaled radiance (\(SR_{\lambda}\)) by applying a normalization using the cosine of the solar zenith angle (Equation (2)).  
\[\rho_{\lambda}=\frac{SR_{\lambda}}{cos(\theta_{0})}. \tag{2}\]  
The PPS probabilistic cloud mask was converted to a binary mask by reclassifying probabilities \(\geq\)50% as overcast and \(<\)50% as clear-sky. PPS Cloud Optical Thickness (COT) was only used if the corresponding PPS CPP quality flag indicated 'good quality' for this pixel. If not, the COT was instead taken from a multi-year climatology of monthly mean COT from CERES EBAF. The Pythagorean wind speed magnitude was calculated from the ERA5 10m U and V wind speed components. ERA5 snow depth was converted to snow cover with the depth-cover relation used by the ECMWF Integrated Forecasting System [38; 39]. Finally, the IGBP land cover class was determined from the IGBP map by means of the nearest neighbor method.  
Different kinds of surface types were derived for the narrowband-to-broadband conversion (\"NTB surface types\"), for the ADM and albedo model (\"CERES surface types\"), and for the Twilight model (\"TWL surface types\"). For overcast conditions, ERA5-derived snow cover leads to surface type \"fresh snow\", whereas for clear-sky conditions the observation-based \"snow presence flag\" (PPS classical cloud mask) is decisive. The OSI SAF sea ice concentration determines whether the surface type is \"sea ice\", regardless of cloud cover; the sea ice NTB surface types were subdivided according to concentration (cfr leftmost column in Table 3). When it did not concern fresh snow or sea ice, the surface types are mapped according to Table 4.  
Figure 4: Overview of the RSF processing chain.  
It should be noted that all the above mentioned surface types are not scene types, which are only derived in combination with cloud properties (cover, phase, optical thickness) or other subdivisions (e.g., discretized categories of wind speed or snow cover fraction).  
Many of the above mentioned pre-processing steps were only performed if required for that particular pixel. For instance, wind speed was only calculated for clear-sky ocean pixels, because it is only useful as a determination criterion for the clear-sky ocean scene types.
## 2 Materials and Methods
### Data Set Generation  
#### 2.1.1 Data Set Collection and Digitization of the CRC Data Set  
Formalin-fixed, paraffin-embedded tissue blocks and histological slides, in combination with clinical data and results of expert pathologist evaluation, were obtained from the archives of the Institute of Pathology at the Medical University of Graz, Austria. These samples were part of a previously studied patient group published by [PERSON] et al. [12].  
The study was conducted in compliance with the Declaration of Helsinki and approved by the local ethics committee in Graz (decision 18-199 ex 06/07). We also used parts of this data set in a previous publication on tumor budding in colorectal carcinoma CRC [13]. From the originally described 381 patients [12], 177 cases for which we have received histological slides are included in this study. For these 177 cases 643 slides have been digitalized by the Tissue Bank of the National Center for Tumor Diseases (NCT) Heidelberg, Germany in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University. For the whole slide image (WSI) generation, an Aperio AT2 scanner (Leica Biosystems, Nussloch, Germany) has been used. In total, from the included 177 cases, 643.svs files have been generated. The range of slides included in the study per case was from 1 to 4 slides per case. This variability is due to different tumor sampling per case. For some cases, only one representative part of the tumor has been processed; for others, major parts of the tumor have been embedded, which have been considered as being representative. This embedding and inclusion strategy leads, on the one hand, to as much representative tumor tissue per case as possible, avoiding, on the other hand, duplication of tumor parts. In combination with an inherent anonymized list of patients, we included information like budding and nodal status.In this work, the mentioned nodal status Nx describes the status assessed by a pathologist commonly referred to as pNx. According to at the time of the cohort selection used TNM classification (7 th edition), the nodal status was defined as N0 for cases without any lymph node metastasis, N1 for one up to three affected regional lymph nodes and N2 for more than three affected regional lymph nodes [14]. However, for some predictions the target nodal status was reduced to N\(-\) or nodal positive/pN1-2 (N+). For the data set used here, there is an expert consensus-based budding score per case originally published by [PERSON] et al. in 2015 [12] and subsequently used in several other publications [13; 15; 16]. The budding status was defined based on [PERSON]'s model as B0 for cases without any budding foci, B1 for one up to four budding foci, B2 for five up to nine budding foci, B3 for 10-19 budding foci and B4 for more than 20 budding foci [17]. As this evaluation method is presently considered outdated, tumor budding was not scored according to the current guidelines.
## 2 Data and Methods
### Reduced Major Axis Regression Analyses  
We examined the relationship between flux tower GPP and MODIS products with reduced major axis regression analysis, which minimizes the sum of the product of deviations from the model along both the \(x\) and \(y\) axes [66], both at the short-term and at the annual time scale. The correlation was evaluated with the coefficient of determination \(R^{2}\). The \(p\)-value testing the null hypothesis that the slope of the regression line is equal to zero was calculated using two-tailed t-tests.
## 4 Limitations, Challenges, and Prospects
### Quantum at Infancy  
As much as quantum mechanisms promise an optimized effectiveness and efficiency toward IIoTsec against present and future quantum attacks, it is still at the infant stage as most of the studies on it are at the theoretical stage. As a result, both the quantum resources and quantum hardware are currently limited, and even the limited available resources and hardware are financially expensive, making their use exclusive. When a simulator is freely accessible, like Qiskit, obtainable qubits are limited. In addition, the expertise in quantum use, skills, and knowledge are still below the expected threshold, especially in Africa. Thus, the research community keeps advancing the concept by rolling several media, so that interested researchers might become skilled in leveraging quantum security advancement. Moreover, owing to the sensitivity of security-tailored techniques, standardization of the technique must be prioritized. Surprisingly, the standardization of QCL algorithms is yet to be concluded [4].