esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ecological balance and biodiversity of the alpine forest is endangered by global and local climatic extremes. It spurs a need for comprehensive forest monitoring, including in depth analyses of drought impact on the alpine woodland ecosystems. Addressing an arising knowledge gap, we identified and analyzed 2002-2012 aridity related responses within the alpine mountain forest of South Tyrol. The study exploited a 5-mode PCA (Principal Component Analysis) based synergy between meteorological conditions rendered by the scPDSI (self-calibrated Palmer Drought Severity Index) and forest status approximated through MODIS (Moderate Resolution Imaging Spectroradiometer) derived NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index based on MODIS band 7) time series. Besides characterizing predominant forest temporal response to drought, we identified corresponding spatial footprints of drought impact, as well as examined aridity-related changes in forest phenology and biomass production. The latter was further evaluated in relation to forest type, elevation, aspect and slope. Recognized meteorological conditions highlighted: prolonged 2003-2007 mild to extreme drought, and overall regional drying tendencies. Arising remotely sensed forest responses accounted on localized decline in foliage water content and/or photosynthetic activity, but also indicated regions where forest condition improved despite the meteorological stress. Perceived variability in the forest response to drought conditions was governed by geographic location, species structure, elevation and exposition, and featured complexity of the alpine forest ecosystem. Among the inspected biophysical factors elevation had the strongest influence on forest phenology and green biomass production under meteorological stress conditions. Stands growing above 1400 m a.s.l. demonstrated initial increase in annual biomass growth at the beginning of the dry spell in 2003. Conversely, woodlands at lower altitudes comprising considerable share of hardwood species were more prone to biomass decline in 2003, but experienced an overall upturn in biomass production during the following years of the dry spell. Aspect showed moderate effect on drought-related phenology and green biomass production responses. Diverse forest ecosystem responses identified in this study were in line with known local and regional analyses, but also shed some new light on drought induced alternation of forest status.  
the Alps; drought; forest green biomass production; forest phenology; MODIS; Principal Component Analysis (PCA) 2018
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 5 Numerical and Simulation Results  
In this section, we will verify the tightness of our closed-form expressions. The path-loss exponent is set as \(v=3.8\). Unless stated otherwise, we set \(\xi=1\), \(r=200\) m, \(K=10\), \(P_{c}=30\) mW, \(\sigma^{2}=-90\) dBm, and \(P_{0}=40\) dBm. Since the EE depends on the path-loss, we only adopt one realization of the spatial distribution of UEs to evaluate our performance. The distances from UEs to the the centre of the distributed circle are set as \([60\) 120 180 240 300 360 420 480 540 600] m.  
Figure 2 shows the EE versus the number of antennas. The graph indicates that EE is concave with the number of antennas. In addition, the derived closed-form expressions are in agreement with the simulation results.  
Figure 3 compares our closed-form power allocation and the optimal power allocation using exhaustive search in terms of the EE. It shows that our closed-form power allocation agrees reasonably well with the optimal power allocation when the number of antennas is large enough.  
Figure 2: The energy efficiency (EE) versus the number of antennas.
# CenterInst: Center-Based Real-Time Instance Segmentation
## 3 Methods  
### Overall Architecture  
As illustrated in Figure 1, CenterInst consists of three modules: the backbone, neck, and dual-path decoder. We began by inputting the image \(Img\in\mathbb{R}^{H\times W\times 3}\) into the backbone network, which yielded features at three distinct scales: \(B_{3}\), \(B_{4}\), and \(B_{5}\). These multiscale features were further processed using the PPM-FPN [3] module for extraction and enhancement, resulting in feature maps \(E_{3}\), \(E_{4}\), and \(E_{5}\). These feature maps had resolutions of 1/8, 1/16, and 1/32 relative to the input image, respectively.  
Next, the center-guided query selector estimated the likelihood of each pixel in feature map \(E_{4}\) being an instance center and selected the top \(N\) probabilities to propose instance center positions as follows: \(C_{0}\in\mathbb{R}^{N\times 4}\). Using these central position proposals, we performed bilinear interpolation sampling on the high-resolution feature map \(E_{3}\) to generate \(N\) instance query proposals, represented as \(Q_{0}\in\mathbb{R}^{N\times 256}\).  
Following this, we fed these query proposals \(Q_{0}\), central position proposals \(C_{0}\), and multiscale feature maps into the dual-path decoder, which consists of two branches: the query decoder and pixel feature decoder. The center-guided sampling module of the query decoder branch calculates multiscale offsets for each instance query centered on its respective center point, enabling the determination of sampling point positions. Bilinear interpolation was used to sample the multiscale features, followed by a query update through adaptive mixing and self-attention. Subsequently, cross-attention was applied between the updated queries and the high-resolution pixel features \(E3\) from the neck, thereby updating the pixel features used for assembling the segmentation results. The updated pixel features are denoted as \(F_{mask}\in\mathbb{R}^{(\frac{H}{8}\times\frac{W}{8})\times 256}\). The dual-path decoder iteratively updated \(I\) times, recursively utilizing the updated queries and center point positions from the previous stage. After each iteration, the updated queries \(Q_{I}\) and updated pixel features \(F_{mask}\) were employed for tasks related to detection, classification, and segmentation.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 5 Conclusions  
This research proposed a fusion method to extract winter wheat in Shandong Province using the GEE as a computational platform and time-series Sentinel-2 images. The fusion method made full use of the advantages of the pixel-based and object-oriented classification methods to develop a high-quality spatial distribution map of winter wheat. The overall accuracy of the map is 92.2%, with a kappa coefficient of 0.84, which is strongly consistent with the actual situation. It was found that the fusion method was superior to the pure pixel-based or object-oriented methods for the identification of winter wheat, especially in hilly areas and at the intersection of different land cover types. In addition, the spatial distribution of winter wheat in Shandong Province has obvious geographical characteristics, which coincides with the general trend of topographic relief. The overall distribution characteristics are \"more in the west and less in the east, dense and uniform in the west, and sparse and scattered in the east\". This is the result of multiple influencing factors, with topographic conditions being the main influence. This research not only demonstrated the advantages of combining pixel-based and object-oriented methods for other related studies, but also produced a spatial distribution map of winter wheat, which is very useful and contributes to the planning of cultivated land resources in Shandong Province.  
**Author Contributions:** Conceptualization, Y.F., B.C. and L.Z.; Methodology, B.C., W.L. and L.Z.; Software, B.C. and W.L.; Validation, Y.F., W.L., X.X., T.L. and L.Z.; Formal analysis, Y.F., L.Z. and H.X.; Investigation, Y.F., X.X. and T.L.; Resources, W.L., X.X. and T.L.; Data curation, Y.F., W.L., T.L. and L.Z.; Writing--original draft, Y.F. and B.C.; Writing--review & editing, B.C. and H.X.; Visualization, B.C. and H.X.; Supervision, B.C., X.X., T.L. and H.X.; Project administration, T.L.; Funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shandong Provincial Natural Science Foundation (No. ZR2022 YQ36); Youth Innovation Team Project of Higher School in Shandong Province (No. 2022 KJ201).  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
Author [PERSON] was employed by the company Shandong Yuanhong Survey Planning and Design Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Chemical Analysis of Gunpowder and Gunshot Residues
## 3 Analysis of Gunpowder and Gunshot Residue
### Chemical Analysis
#### 3.2.1 Sample Preparation
Finally, enables the preservation of the original sample, allowing for subsequent analysis using complementary techniques or the re-evaluation of findings as new knowledge emerges. This aspect contributes to the accumulation of a comprehensive database of OGSR profiles, aiding in the establishment of robust forensic protocols and supporting the advancement of forensic ballistics research. Headspace Sportive Extraction (HSSE)  
Headspace sorptive extraction (HSSE) is an extraction technique introduced recently for trace and ultra-trace analysis of volatiles and semivolatile compounds [61; 108; 109; 110]. HSSE has the same theoretical principles as SPME but does not use a thin fibre. Instead, it uses a magnetic stir bar coated with a much larger volume of PDMS or other phases (up to 110 \(\mu\)L) in comparison to SPME (up to 0.5 \(\mu\)L) [61]. Compared to SPME, HSSE has the advantage of better extraction efficiency, higher recovery yields, and the ability to detect more compounds, which directly impact sensitivity and repeatability [9; 29; 61]. However, to thermally desorb the extracted compounds for GC analysis, a dedicated unit must be installed in the apparatus, seriously limiting the applicability of the methodology. Using HSSE followed by GC-MS, [PERSON] et al. [61] evaluated the composition and variability of volatile compounds in OGSR from nine handgun ammunition types of two calibres (.357 Magnum and.45 Auto). The authors identified 166 compounds, mainly particles from the propellant, that did not undergo combustion (e.g., additives) [61]. In another study, [PERSON] et al. [29] evaluated the ageing of several OGSR volatiles compounds in two types of.45 Auto ammunition using HSSE, followed by GC-MS analysis. The procedure allowed the detection of 51 GSR compounds from spent cartridges, noting significant differences in their chemical profiles, which the authors believed to correlate with ageing [29]. The authors also claimed that HSSE is more reproducible and effective than SPME and allows the simultaneous analysis of more analytes [29]. Compound-to-compound signal ratios were beneficial in reducing the variability of the ageing curve. This allowed to enhance the time window and were useful for producing data with forensic value [29]. Finally, [PERSON] et al. [108] also developed, optimised, and validated an HSSE/GC-MS method to estimate the time since discharge of 9 mm cartridges, producing data which later allowed the estimation of the time since discharge through multivariate regression analysis [109]. HSSE has shown to be a suitable technique for enriching OGSR from spent cartridges, especially when compared to SPME. However, configurations with different sorbent materials are not easily found on the market, hindering the tailoring effect when compared to SPME. Likewise, the HSSE is less suitable for online thermal desorption since it requires a dedicated unit for this purpose.
## 4 Analysis
### Probabilistic Model (PM) for the Distributed Approach
We may interpret \(X_{i}\) as an indicator that sensor \(i\) is chosen to be a round member with probability \(p_{i}\) and \(S_{n}\) is the number of members in a round. Denote \(Y_{i}=X_{i}-p_{i}\). Hence:  
\[S_{n}^{Y}\equiv\sum_{i=1}^{n}Y_{i}=\sum_{i=1}^{n}X_{i}-\sum_{i=1}^{n}p_{i}=S_{ n}-\sum_{i=1}^{n}p_{i}, \tag{19}\]  
\[E[Y_{i}]=E[X_{i}]-p_{i}=0, \tag{20}\]  
\[\sigma_{Y_{i}}^{2}=\sigma_{X_{i}}^{2}=p_{i}(1-p_{i}), \tag{21}\]  
\[\sigma_{s_{n}}^{2}=\sum_{i=1}^{n}\sigma_{Y_{i}}^{2}=\sum_{i=1}^{n}\sigma_{X_{i }}^{2}=\sum_{i=1}^{n}p_{i}(1-p_{i}). \tag{22}\]  
For our case, the Lindeberg condition [29] reduces to:  
\[\lim_{n\rightarrow\infty}\sum_{i=1}^{n}\frac{1}{s_{n}^{2}}\int_{|Y_{i}|\geq \epsilon s_{n}}Y_{i}^{2}dP\leq\lim_{n\rightarrow\infty}\sum_{i=1}^{n}\frac{1}{ s_{n}^{2}}\int_{|Y_{i}|\geq\epsilon s_{n}}dP=0, \tag{23}\]  
which holds because all the random variables are bounded by 1 and \([|Y_{i}|\geq\epsilon s_{n}]\to 0\) as \(n\rightarrow\infty\). **Theorem 2**: _Suppose that \(Y_{i}\) is an independent sequence of random variables and satisfies \(E[Y_{i}]=0\), \(\sigma_{Y_{i}}^{2}=E[Y_{i}^{2}]\), \(S_{n}^{Y}=\sum_{i=1}^{n}Y_{i}\), and \(s_{n}^{2}=\sum_{i=1}^{n}\sigma_{Y_{i}}^{2}\). If the Lindeberg condition (23) holds, then \(S_{n}^{Y}/s_{n}\rightarrow\mathcal{N}(0,1).\)_  
Observe that \(p_{i}\) may be described by the overlap fraction of sensor \(i\) since the sensors with less overlap between its neighboring sensors has a larger chance to be selected as a round group member in the round competition, which coincides with the operation of the DASA and the setting of the waiting timer.
# Role of 4-Hexylresorcinol in the Field of Tissue Engineering
## 3 4-HR Application in the Tissue Engineering
### Application of 4-HR in Bone Regeneration
#### 3.1.2 4-HR with Guided Bone Regeneration Membrane  
Guided bone regeneration (GBR) is a technique to promote new bone formation in bony defects, using barrier membranes [38]. GBR membranes prevent soft tissue cell infiltration, allow osteogenic cell migration, and promote new bone formation [39]. As a component of the GBR membrane, 4-HR was successfully incorporated into a silk-based membrane and used as an ingredient in the GBR membrane [8]. The antimicrobial property of 4-HR incorporated into the silk membrane does not change after sterilization, including by autoclaving and with ethylene oxide gas [40]. 4-HR incorporated into the silk membrane shows more bone formation than the silk-only membrane in rabbit calvarial defects (Figure 3). In histomorphometric analysis, 4-HR-incorporating silk and only-silk groups have  
Figure 2: Increase in the vascular endothelial growth factor (VEGF)-A and C, and angiogenin expression after treatment of 4-HR. Reproduced with permission from [PERSON] [34].  
significantly more new bone formation than the control (Table 1) [41]. 4-HR has an antimicrobial and osteoclast inactivation property, and it can contribute to new bone formation. 4-HR incorporated into the silk membrane yields more new bone formation in the rabbit calvarial defect than the commercial collagen membrane [40; 42]. In the peri-implant defect model in rabbit tibia, a 4-HR-incorporated silk membrane was applied to cover the defect and to induce bone regeneration. The implant group using the membrane has more favorable osteointegration and bone-to-implant contact compared with the untreated implant group [8]. As an ingredient in the GBR membrane, 4-HR has been effectively used to promote new bone formation in bony defects.  
Figure 3: (**a**) For bilateral parietal bone defects of rabbits, either an 4-HR-incorporating silk fabric membrane (SFM) or a conventional SFM is placed on calvarial defect. Some defects remain uncovered as controls. Histological images 8 weeks after operation: (**b**) 4-HR-incorporating SFM; (**c**) conventional SFM (hematoxylin and eosin stain, scale bar = 1 mm; SFM: silk fabric membrane) Reproduced with permission from [PERSON] [41].
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 4 Discussion and Future Work
### Future Work  
* The field validation of 5G interference exclusion zones using real-time altimeter feedback.
* The certification and deployment of airworthy UAV-based RFI detection platforms.
* Standardized VHF anomaly detection using AI-based classifiers.
* The development of adaptive back-door coupling suppression systems.
* Broader testing of GNSSs in multipath-heavy conditions.
* Lightweight RF immunity designs for UAV and urban inspection tasks.
# Study on the Macro-Fine Mechanical Behavior of Ore Flow Based on the Discrete Element Method
## 1 Introduction
[16] analyzed the mechanism of the influence of the friction coefficient of the silo wall on the lateral pressure of the silo wall during the discharge of the rectangular hopper. [PERSON] et al. [17] analyzed the variation characteristics of the ore-rock block flow field and the mesoscopic-stress field in the discharge process of a shallow circular silo and explained the mechanical behavior of the ore-rock fragment on the silo wall from the perspective of the ore-rock flow. [PERSON] et al. [18] studied the influence of ore-rock flow on the dynamic lateral pressure of the silo wall and analyzed the stress characteristics of the silo wall under the arch effect during ore drawing. [PERSON] et al. [19] studied the flow characteristics of the ore-rock particles inside the silo under different vibration frequencies and believed that the tangential motion of the silo wall effectively contained the formation of the Janssen effect. Using physical experiments and DEM numerical simulations, [PERSON] et al. [20] experimentally validated the mathematical derivation of [PERSON]'s theory, providing important information and insights for the design of storage facilities. Considering the problem of the static packing assumption of the Janssen model, [PERSON] et al. [21] explored the stress redistribution of particles in a container with a laterally moving sidewall, and extended and improved the model for characterizing the constrained dynamic system. [PERSON] et al. [22], using the DEM numerical simulation method, studied the static stress distribution in confined granular columns, and found that the lateral pressure coefficient is not a constant value that decreases gradually to a constant with the packing depth. Presently, research on granular flow and other related aspects mainly focuses on grain silos, storage bins, and the hanging of the ore pass, and few involve the mechanical damage of the ore-pass wall. Moreover, the existing research still lacks an understanding of the macro-flow and micromechanical behavior of ore-rock bulk particles. Therefore, this paper analyzes the flow characteristics of ore-rock bulk via the discrete element method. On this basis, the contact density, internal stress distribution, contact force distribution, and so on are discussed, and the mechanical evolution mechanism of the bulk system and the stress characteristics of the ore pass of the storage section are revealed in the process of ore-rock flow, which lays a foundation for future study on the stability of the ore-pass wall.
## 4 Model of Preferences and Judgments of a DM with a Non-Compensatory Aggregation of Preferences
### Finding the Best Compromise solution to Problem 11  
According to Assumption 2, each DM is capable of finding their best solution to Problem 11. Here we discuss a method for this purpose.  
**Definition 10** ((\(\beta,\lambda\)) non-strictly outranked solution).: _A solution \(x\in O\) such that there is no \(y\in O\) fulfilling \(yP_{r}(\beta,\lambda)x\) is called a (\(\beta,\lambda\)) non-strictly outranked solution. The (\(\beta,\lambda\)) non-strictly outranked frontier of Problem 11 is the set of these solutions._  
**Remark 3**.:
1. _With appropriate values of_ \(\beta\) _and_ \(\lambda\)_, a non-strictly outranked solution fulfills Condition A of Definition 7, that is, the first necessary condition to be the best compromise._
2. _Condition B of Definition_ 7 _is proved on the non-strictly outranked frontier, using the outranking strength measure. This measure is described as_ \(OS(x)=\text{card}\ \{a_{i}\in NSF\ \text{such that}\ \ xS(\beta,\lambda)a_{i}\}\)_, where_ \(NSF\) _denotes the (_\(\beta,\lambda\)_) non-strictly outranked frontier._
3. _More than one solution can fulfill the necessary conditions of Definition_ 7_. The solution selected as the final best compromise should be one with the highest value of the outranking strength._Problem 11 can be solved through the following steps:  
1. The individual DM (perhaps helped by a decision analyst) sets their model parameters according to Assumption 3.
2. The (\(\beta\),\(\lambda\)) non-strictly outranked frontier is identified by an optimization algorithm; the set of solutions that fulfill Definition 10 is determined;
3. The DM selects the best compromise solution according to Remark 3.iii.  
The optimization algorithm for the step 2 depends on the problem characteristics. [PERSON] et al. (2019) proposed an evolutionary algorithm called I-NOSGA for solving project portfolio optimization problems with several or many interval-valued objective functions and with an outranking relation defined on the objective space. I-NOSGA separates each population into non-strictly outranked fronts. An extension of I-NOSGA to address the optimization problems in this paper is provided in Appendix A.
# A Scalable Spatial-Temporal Correlated Non-Stationary Channel Fading Generation Method
###### Abstract  
To address the challenges of complex implementation structures and high hardware resource consumption in multiple-input multiple-output (MIMO) channel emulators, this paper proposes a hardware generation method for spatial-temporal correlated non-stationary channel fading. Firstly, a hardware generation architecture is developed for field-programmable gate array (FPGA) platforms, which can also reduce the complexity of the channel emulator. Secondly, an improved CORDIC method is introduced to reduce algorithm latency and hardware consumption while expanding the function convergence domain, with a relative error maintained at the level of 10-4. Furthermore, based on the idea of time-division multiplexing, an efficient hardware operation of a lower triangular matrix is adopted to minimize the consumption of hardware resources. Finally, the measured results demonstrate that the statistical characteristics of the channel fading generated by the proposed method are in good agreement with the theoretical ones, with an average error of less than 2%. Additionally, under identical simulation conditions, hardware resource consumption is reduced by 6.87%. These findings provide compelling evidence of the enhanced efficiency and accuracy in simulating MIMO channels achieved through the proposed method.  
channel fading generation; MIMO; FPGA; CORDIC; matrix operation +
Footnote †: journal: Electronics and Applications