esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 4 The Technologies for Bridging the Service-Fruition Divide
### Extended Reality  
Extended Reality (XR), which encompasses key terms such as Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR), is believed to drive several killer applications in the 6G era. 5G requirements fall short in supporting such applications, which need ultra-reliable low latency communications and cannot rely on mobile broadband [7]. In particular, XR pervasive applications will depend not only on the networking constraints, but also on the perceptual and sensory ones, which should be aligned with the above (i.e., tolerate delays that are imperceptible to human senses). The data rate is one of the major obstacles as, even now, many of the existing XR applications struggle in moving from wired to wireless, in particular for WAVAR (Wide Area VR, AR, MR) which aims to provide ubiquitous wireless services for XR [22]. WAVAR demand 6G technologies for ultimate application--as opposed to their local counterpart, LAVAR--in terms of data rate requirements (1 Tbps peak rate), which cannot be satisfied by current 5G deployments [23]. If requirements are met, even digital illiterates would be able to interact with a controlled digital world through actions and perceptions that leverage all five senses (haptic, gestures, sound and speech, virtual sight, etc.). Over the past years, XR has been proposed in a variety of use cases connected to teaching activities [24] and aiding people with disorders [25], however, these use cases are typically experimental or extremely localized. It is evident how the potential of overcoming the service-fruition divide may dramatically increase if these technologies become pervasive. Challenges rise now in designing unique metrics capable of capturing both network requirements and the physical experience of users.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 2 Background and Related Work
### Incremental Frequent Itemsets Mining  
The idea of maintenance of association rules and frequent itemsets during database update has been discussed shortly after the first algorithms for FIM appeared. The reason for it is that the updated part is usually much smaller than the full DB, and this fact could be used for faster algorithms. A well-known efficient algorithm for it is \"Fast Update\" (FUP) [15]. It is based on the fact that for an item to be frequent in the updated database (\(\mathcal{D}^{+}\)), it must be frequent in the old DB (or simply \(\mathcal{D}\)), and/or the new added transactions (\(\Delta\)). Table 1 describes the options for an itemset to become frequent or not frequent in \(\mathcal{D}^{+}\) (it is based on the same observation discussed in the previous chapter).  
FUP is working iteratively by mining only new \(\Delta\) by a method similar to Apriori. At the end of each iteration, for each FI, the algorithm decides if it is frequent, not frequent or needs to be counted in the old \(\mathcal{D}\). The transactions being recounted in the old \(\mathcal{D}\) are pruned away as necessary. The survivors are used to create candidates for the next iteration. By performing most of the work only on \(\Delta\), the algorithm achieves better time compared to re-running the full mining algorithm on the whole \(\mathcal{D}^{+}\).  
ARMIDB [7] is based on FUP but tries to minimize scans over the original \(\mathcal{D}\) while still calculating the incremented FI. It tries to use data from the original FI, update them and then uses a technique called \"Look Ahead for Promising Items\" (LAPI). It then scans for candidates in \(\Delta\) only for those items that may be frequent in \(\mathcal{D}^{+}\) (early pruning of candidates that cannot be frequent in \(\mathcal{D}^{+}\) during the scan of \(\Delta\)). However, it is not a distributed algorithm like ours.
## 4 Discussion
### TB Spatial Heterogeneity Comparisons  
The goal of downscaling SMAP TB is to represent the spatial heterogeneity of TB at a finer scale than possible via passive microwave data alone. While we expect to see increased spatial heterogeneity at the 3 km scale compared to the 9 km or 33 km scale, the amount of additional heterogeneity depends on various landscape attributes. To gauge the accuracy of 3 km SMAP/CYGNSS TB spatial heterogeneity, we compare it to other TB datasets with similar spatial resolutions.  
SMAP uses Passive Active L-band Sensor (PALS) flights during their calibration and validation campaigns. While no PALS flights are both spatially and temporally coincident with CYGNSS data, we can still use PALS data as a general comparison. During the SMAPVEX15 campaign in southeastern Arizona between 1 and 18 August 2015, the standard deviation of PALS 1 km TBs within 3 separate 36 km pixels during 7 overpasses varied from about 2-13 K [31]. We calculated the standard deviation of 3 km SMAP/CYGNSS TBs within 33 km boxes covering approximately the same geographic area in Arizona between 1 and 31 August 2018 and found a range of about 0-16.9 K (5 th-95 th percentile). These TB standard deviations are fairly similar. We do not expect an exact match, as we are comparing two different scales (1 km vs. 3 km TBs) over 2 different years.  
Figure 11: **(a)** Uncorrected SMAP/CYGNSS \(\beta\) values. **(b)** Corrected SMAP/CYGNSS \(\beta\) values for 2020. All \(\beta\) values with R \(>-0.4\) were replaced with representative \(\beta\) values, based on the median \(\beta\) values of the MODIS IGBP landcover classes.  
We also compared 3 km SMAP/CYGNSS TB spatial heterogeneity to 3 km SMAP/Sentinel TB spatial heterogeneity. Since CYGNSS retrieves L-band microwave signals and Sentinel retrieves C-band microwave signals, and CYGNSS and Sentinel observations are generally not concurrent, we do not expect an exact match. However, the spatial heterogeneity of different downscaled 3 km SMAP TBs should be similar.  
We calculated the RMSD between 9 km SMAP TBs and the corresponding 3 km SMAP/CYGNSS or SMAP/Sentinel TBs [32] for each SMAP observation with concurrent CYGNSS or Sentinel data between 1 April and 30 June 2018. We then calculated median RMSD TBs for each 9 km grid cell. We only included 9 km grid cells with both SMAP/CYGNSS and SMAP/Sentinel results in our analysis. The RMSD between 3 km SMAP/Sentinel TBs and 9 km SMAP TBs range from 0.8 to 11.7 K (5 th-95 th percentile) with a median of 3.7 K (Figure 12a). The RMSD between 3 km SMAP/CYGNSS TBs and 9 km SMAP TBs range from 0.06 to 12.1 K (5 th-95 th percentile) with a median of 2.7 K (Figure 12b). The lower median RMSD between SMAP/CYGNSS and SMAP TBs is primarily due to near-zero SMAP/CYGNSS \(\beta\) values. When we recalculate SMAP/CYGNSS TBs using landcover class corrected \(\beta\) values (Section 4.3), the RMSD between 9 km SMAP TBs and 3 km SMAP/CYGNSS TBs range from 0.7 to 12.9 K (5 th-95 th percentile) with a median of 3.7 K (Figure 12b). The similarity between SMAP/CYGNSS and SMAP/Sentinel 3 km TB spatial heterogeneity is promising.
# Sources of Extraterrestrial Rare Earth Elements: To the Moon and Beyond
## 5 Lunar Resources
### Other Potential Lunar Resources
#### 5.3.9 Lunar Regolith  
The composition of the lunar regolith represents the results of billions of years of impacts [151; 152; 28; 127; 153]. This has been of specific interest due to its potential application as a construction or manufacturing material, as a chemical consumable, or as a propellant [24; 153; 154] and will likely provide the initial (and major) lunar resource [29]. More recently, lunar regolith has been proposed as a material that could be utilized in 3-D printing technology to build lunar infrastructure (e.g., [154]).
## 4 Primary Findings
### Comparative Analysis of I4.0 Stakeholders
#### 4.2.2 Comparative Analysis of I4.0 Stakeholders: Large Enterprises vs. SMEs  
Comparative analysis of interviews with large enterprises and SMEs is provided in Table 4. Key differences are highlighted and discussed below.  
Firstly, 27% of SMEs do not have any employees with good awareness of I4.0 concepts. Contrarily, just 7% of large enterprises are experiencing such an issue with human resources.  
Secondly, 67% of large enterprises indicated that governmental policies affect their I4.0 transformation. On the other hand, only 22% of small firms noted this. Therefore, governmental influence is not pervasive for small firms. This may imply that the support by governmental digitization programs is known and enjoyed mostly by large enterprises, but not SMEs. Thus, the government should re-evaluate the impact of support programs and then, probably, redesign them.  
Thirdly, 44% of SMEs find costs of I4.0 transformation as a barrier. However, only 17% of large enterprises experienced such restrictions.  
Fourthly, 100% of large enterprises and just 33% of SMEs have 3+ ongoing or completed I4.0 projects. Thus, the I4.0 implementation level in small firms is much lower.
# The DLR FireBIRD Small Satellite Mission: Evaluation of Infrared Data for Wildfire Assessment
## 3 Results and Discussion
(**c**) shows VIIRS active fire data of the Suomi-NPP overpass in closest temporal vicinity to the respective FireBIRD overpass. Background: Stamen/OpenStreetMap shaded terrain map, shown in gray scale. The analysis of the subset as well as the full dataset shows, as expected, that the FireBIRD results are more similar to the reference the closer the overpass times of the sensors are to one another. Several influencing factors have to be considered when interpreting the results of the accuracy analysis. First, the temporal difference in overpass times of the different sensors plays a significant role. This can be seen by the higher concordance of FireBIRD and reference results when only a short time lag (of less than 45 min) lies between the overpasses, opposed to the results when the time lag is not considered (as can be seen on the left-hand and right-hand side of Table 3, respectively). Since the development of the fire front line can be very dynamic, and biomass consumption can happen at a rapid pace, detections at different points in time necessarily show considerable differences. Also, a complete change in atmospheric and near-surface conditions could occur between two overpasses: Clouds could appear or vanish, and smoke plumes may cover areas which have clearly been visible previously. Secondly, the significant differences in spatial resolutions of FireBIRD and the reference datasets represent a further parameter that may distort the significance and validity of the results. Thirdly, due to the rather low accuracy of the FireBIRD geolocation system, all scenes have to be geo-coded and rectified. Since the presented analysis requires an exact spatial matching of FireBIRD and reference data, an inadequate rectification introduces inaccuracies. Finally, the observation angle limits the sensor's possibility to distinguish different features. Detection accuracy generally declines with a higher off-nadir angle, which makes the comparison of observations resulting from differing angles error prone. Figure 7: FireBIRD active fire data (derived using the MWIR method) and reference data for a fire event in California/USA, November 2018. (**a**) shows active fires derived from four FireBIRD/HSRS overpasses (10–21 November 2018), pigmented by FRP. (**b**) shows MODIS active fire data of the Aqua/Terra overpasses in closest temporal vicinity to the respective FireBIRD overpasses. (**c**) shows VIIRS active fire data of the Suomi-NPP overpasses in closest temporal vicinity to the respective FireBIRD overpasses. Background: Stamen/OpenStreetMap shaded terrain map.
## 3 Methodology
### Non-Stationary Transformer with Deep Deterministic Policy Gradient
#### 3.3.6 Action and Reward Formulation  
At any discrete time step \(t\), the action \(a_{t}\) is identified with the portfolio's asset allocation vector \(w_{t}\). The policy endeavours to optimise the allocation for the forthcoming period \(w_{t+1}\), maintaining the constraint of unitary sum across all asset weights. The updated allocation, which adapts to price fluctuations \(y_{t}\), is formalised as:  
\[w_{t+1}=\frac{y_{t}\odot w_{t}}{y_{t}\cdot w_{t}} \tag{14}\]  
Here, \(\odot\) denotes the Hadamard product and \(\cdot\) represents the conventional dot product.  
The reward function, a pivotal determinant of the portfolio's efficacy, encompasses transaction costs denoted by \(\beta\), and is thus defined to capture the realised profit at time \(t\):  
\[r_{t}=w_{t}\cdot y_{t}-\beta\sum_{i=1}^{n}|w_{i,t+1}-w_{i,t}| \tag{15}\]  
Enhancing the practical applicability and differentiability of the reward function, we integrate fixed commission rates \(c_{b}\) for buying and \(c_{s}\) for selling. The reward function is consequently defined as:  
\[r_{t}=\ln(\beta_{t}\cdot(w_{t}\cdot y_{t})) \tag{16}\]  
The ultimate objective is to amplify the cumulative return \(R\) across a designated interval \(T\), expressed as:  
\[R(s_{1},w_{1},\dots,s_{T},w_{T})=\frac{1}{T}\sum_{t=1}^{T}\ln(\beta_{t}\cdot( w_{t}\cdot y_{t})) \tag{17}\]The optimisation conundrum thus revolves around ascertaining the optimal policy \(\mu_{\theta}\) that maximises this return:  
\[\mu_{\theta}^{*}=\arg\max_{\mu_{\theta}}\frac{1}{T}\sum_{t=1}^{T}\ln(\beta_{t} \cdot(w_{t}\cdot y_{t})) \tag{18}\]  
The policy gradient with respect to the parameters \(\theta\) is meticulously derived:  
\[\
abla_{\theta}\mu_{\theta}(\tau)=\frac{1}{T}\sum_{t=1}^{T}\
abla_{\theta}\ln (\mu_{\theta}(w_{t},s_{t})) \tag{19}\]  
and the parameters are then conscientiously updated with a learning rate \(\lambda\):  
\[\theta\leftarrow\theta+\lambda\
abla_{\theta}J_{T}(\mu_{\theta}) \tag{20}\]  
The normalisation by \(T\) is crucial, as it appropriately scales the gradient irrespective of batch sizes, an essential factor for effective mini-batch training in a stochastic environment.  
Overall, this structured approach in the methodology section ensures a comprehensive understanding of the sophisticated non-stationary transformer model and its integration with the DDPG framework, which is crucial for advanced portfolio management techniques.
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 4 Direct Method for the Estimation of the Rain Rate
### Direct Model Concept  
By the \"direct method\" we mean a direct mapping rule \(\Psi(\cdot)\) from a signal attenuation measure \(\hat{A}\) to an estimate \(\hat{R}\) of actual rainfall rate, without the need to resort to intermediate or auxiliary (location dependent) statistical descriptions, such as the probabilities \(F_{A}(\overline{A})\) and \(F_{R}(\overline{R})\) involved in statistical models (see Section 3.2). In comparison with the above-described (inverse) ITU-R and Global Crane approaches, a direct method is expected to provide better accuracy in estimating the rainfall rate, since it utilizes the actual knowledge of a larger number of parameters affecting path attenuation; notably, the height of the liquid precipitation layer.
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 5 Diabetes Mellitus Type 2  
Current research indicates that irisin stimulates the transformation of white adipocytes to brown adipocytes and increases thermogenesis, thus improving glucose tolerance, insulin sensitivity, body weight, and body composition in mice. Studies have shown that irisin promotes glucose uptake in skeletal muscle while improving hepatic glucose and lipid metabolism [11]. Irisin may play an important role in a variety of disorders, including obesity, insulin sensitivity, and diabetes mellitus type 2 (T2 DM) [9].  
The role of irisin in T2 DM remains unclear. Some studies have reported decreased circulating irisin levels in T2 DM patients, compared to healthy individuals, whereas studies performed in obese individuals (some of whom had T2 DM) have reported elevated levels [8; 10]. Significantly lower circulating irisin levels have been found in gestational diabetes [12].  
In obese T2 DM patients, the elevation of circulating irisin may represent a compensatory response to decreased energy expenditure. It is also plausible that excessive adipose tissue in these patients could provide an extra source of irisin. Recent studies in obese subjects have shown that plasma irisin decreases markedly in response to a hypocaloric diet and weight loss, together with significant improvements in hyperglycemia and hyperinsulinemia [10].  
It is possible that irisin plays an important role in T2 DM-relevant organs, such as the liver and pancreas, but the proposed mechanism(s) of action by which irisin regulates hepatic metabolism and islet function are yet to be solved [11].
## 3 Study on the Identification Method of Damping System Category Based on Interfacial Motion States
### The Analysis Model for SSI Damping System  
There are three parts of soil, foundation (including piles) and superstructure for the soil-structure interaction (SSI) system. The foundation and superstructure are treated as a whole of structural part because they two are connected rigidly with little difference of material characteristics on the process of dynamic effect. Soil is a semi-infinite space medium, and the mainly affected part of it is selected reasonably by artificial boundary conditions in theoretical analysis. In addition, the soil is usually divided into two parts: one under the foundation and the other on the sides. The soil under the foundation is the dynamic force, and the soil on the sides impedes the motion. The schematic diagram of the simplified physical model for the structural system considering the SSI effect is shown in Figure 5. The soil on the sides of the structure is the central part undertaking active and passive earth pressure, and some parts of soil under the foundation move with the structure and bear active and passive earth pressure a little.  
The characteristics of the damping system are related to the continuity of motion states at the material interface of the system, as shown in Section 2. The stick-slip motion states exist easily at the interaction interface between soil and structure because of their significant differences of physical and mechanical properties, which means the discontinuous motion states appear at the soil-structure interaction interface most likely. Therefore, the analyses of motion states' continuity at the material interface between soil and structure become the focus of this paper. The motion states at the interface are the necessary and sufficient conditions for a classical damping system; namely, the displacement, velocity, and acceleration on both sides of the interface are all equal. Only motion states along the direction of motion are analyzed in order to highlight the problem of proof. In addition, the interaction between soil under the foundation and the structure is weak enough for ignoring. The interface of soil-structure interaction is the sidewalls of piles and the cap, shown as the red part in Figure 6.  
Figure 5: The schematic diagram of simplified physical model for SSI system.
## 3 The Selection Parameters of the MPPT Algorithms
### Accuracy  
Accuracy in MPPT refers to how precisely the MPPT algorithm tracks the true MPP. High accuracy means the system operates close to the actual MPP, maximizing energy harvest. Some algorithms achieve high accuracy at the cost of increased complexity or slower tracking, while others may be less accurate but simpler or faster.
## 4 Numerical Results
### MLR Model Developed under Op2  
The constructed MLR model is:  
\[\begin{split} y=-0.8947+0.7525x_{1}+33.66x_{4}-0.2008x_{1}^{2}- 274.1x_{4}^{2}-0.7306x_{1}x_{3}+e\\ (0.303)\ (0.116)\ (8.82)\ (0.0211)\ (74.5)\ \ (0.348)\ (0.116) \end{split} \tag{52}\]  
The performance measures are shown in the second column of Table 3. The diagnostics tests produce the following:  
(a) CODPA (see Section 3.4) was applied at the end in Op2. Two outliers were identified (both in the first cycle): observations 40 and 84. That is why the final model was constructed with a training set containing \(n=124\) observations.  
(b) The stepwise regression algorithm MSRA converged in 15 steps all in phase one. After the outlier rejections, the algorithm MSRA was applied over the purged data and converged in 2 steps all in phase one.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Op1** & **Op2** & **Op3** & **Op4** \\ \hline _RMSE_ & 0.113 & 0.108 & 0.102 & 0.0958 \\ _MAE_ & 0.0903 & 0.0865 & 0.0792 & 0.0760 \\ \(R^{2}\) & 0.771 & 0.783 & 0.792 & 0.822 \\ \(R_{adj}^{2}\) & 0.759 & 0.774 & 0.780 & 0.911 \\ _RMSE\({}_{HO}\)_ & 0.123 & 0.116 & 0.111 & 0.105 \\ _MAE\({}_{HO}\)_ & 0.0961 & 0.0918 & 0.0856 & 0.0823 \\ \(R_{HO}^{2}\) & 0.730 & 0.752 & 0.753 & 0.787 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance measures of the models developed under the four outlier detection options.  
(c) The ANOVA test for validity of the regression produced test statistics \(F_{stats}\) = 85.1 which resulted in \(p\)-\(value_{F}\) < 2.2(10\({}^{-16}\)). The conclusion of the test is that the model is valid. The same results from the ANOVA test were acquired in the previous 16 steps of MSRA.  
(d) The expected error nullity test produced test statistics \(t_{stats,null}\) = 0.0141 resulting in a \(p\)-\(value_{null}\) = 0.9888. The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the expected error in the regression model is zero. The same results for the expected error nullity test were acquired in the previous 16 steps of MSRA.  
(e) The heteroskedasticity testing algorithm MHTRA produced valid auxiliary regression with ANOVA in \(p\)-\(value_{F}\) = 6.88(10\({}^{-3}\)). However, its adjusted coefficient of determination is \(R^{2}_{adj}\)=0.091. The conclusion of MHTRA is that the constructed regression is statistically significantly heteroskedastic, but the latter is practically insignificant. Similar conclusions for the heteroskedasticity were acquired in the previous 16 steps of MSRA.  
(f) The modified Jarque-Bera Monte-Carlo test for error normality produced test statistics \(|B_{stats,null}\) = 6.05, which resulted in \(p\)-\(value_{B}\) = 0.051. The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the error in the regression model is normal. The same results for the modified Jarque-Bera Monte Carlo test were acquired in the previous 16 steps of MSRA.