esa-sceva/satcom-chunk-collection huggingface dataset:
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### System Components  
RiverCore--a static low-cost gauging station for areas with cellular radio coverage.  
RiverCore fixed node hardware is composed of several components that allow it to retrieve data from sensors and send it to the data acquisition platform through the cellular network. It uses a 32-bit AT915 AM3 CU microcontroller, based on open-source hardware design. Months of testing prove its capabilities and perfect fit in the fixed node. Subsequently, the ultrasonic sensor originally used was replaced by a Toughsonic Water Level Sensor, and a soil moisture sensor was added to achieve more robust and precise readings. The fixed nodes communicate with mobile nodes (drifter) through XBee or LoRa radio interchangeably (\"LoRa is a proprietary spread spectrum modulation scheme that is derivative of Chirp Spread Spectrum modulation (CSS) and which trades data rate for sensitivity within a fixed channel bandwidth.\" [18]). To accommodate the new hardware, a daughterboard was  
Figure 1: Elements of the early warning system (RiverCore, weather station, drifter, and sniffer).  
designed to serve as a physical connection interface between the microcontroller board, sensors, RS-485 transceiver, and wireless radios. The hardware architecture can be seen in Figure 2.  
The RiverCore fixed node is physically composed of a 32-bit microcontroller unit, a 3G cellular modem electronic board, an XBee (802.15.4) (\"modules seamlessly interface with compatible gateways, device adapters and range extenders, providing developers with true beyond-the-horizon connectivity.\" [19]), or LoRa radio, shield/daughterboard, an RS-485 transceiver, a regulated power supply, a solar charge controller, and a 12 v 80 A h battery.
# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
## 2 Material
### NPP  
Forest NPP was obtained based on the combination of the GPP calculated through equation (1), and the growth (R\({}_{\rm G}\)) and maintenance (R\({}_{\rm M}\)) respirations simulated by version 4.2 of the ecosystem process model Biome-BGC [38; 39]. Daily precipitation, maximum and minimum air temperature images, and the DIDSSF product described above were used. Daylength, daylight average partial pressure of water vapor, and daily average air temperature were simulated by the microclimate simulation model MT-CLIM [40] version 4.3. The needed ecophysiological parameters were obtained in a previous study over an area of similar eco-climatic features [41]. Elevation and forest type described in Section 2.2 were also used. The rooting depth calibration, soil texture maps description, and more details on how to use Biome-BGC over the study area can be found in [28].  
In this study, forest NPP is calculated as  
\[\rm{NPP}=f\ (GPP-R_{\rm G})-v\ R_{\rm M} \tag{4}\]  
where \(f\) is the actual to potential tree cover ratio and \(v\) is the actual to potential growing stock volume (GSV) ratio. GPP calculated from Equation (1) needs to be corrected to account only for the fraction of forest present in the pixel. This expression was proposed in [42], based on the ecosystems distance to climax concept. These ratios are calculated as  
\[f=[1-\exp\ (\mbox{-}v\ \rm{LA})]/[1-\exp\ (\mbox{-}LA)] \tag{5}\]  
and  
\[v=50\ \rm{GSV}\ \rho\ \rm{C}/\sigma \tag{6}\]  
where LAI is the annual maximum leaf area index simulated by Biome-BGC, \(\rho\) is the basic wood density, \(C\) is a biomass expansion factor, \(\sigma\) is the annual maximum dead stem carbon simulated by Biome-BGC, and the factor 50 accounts for the unit conversion to m\({}^{3}\)/ha and the transformation from carbon mass to dry mass (2 kg/kg). The growing stock volume GSV was obtained in a previous study combining remotely sensed and forest inventory data [43]. NPP was only calculated in forest pixels because GSV is only available for trees in the Spanish Forest Inventory described in [44]. For the main Mediterranean forests, \(\rho\) and \(C\) can be found in [45].  
This methodology was previously assessed in the study area using forest inventory observations. Coefficients of determination between 0.5 and 0.7 in [46] were obtained. A detailed description on modeling of forest carbon storage can be found in [46].
# Study on Optimization of Downward Mining Schemes of Sanshandao Gold Mine
## 6 Engineering Application and Limitations  
The preferred Scheme 2 was implemented in an industrial trial at the mine site, with the test area selected between the 1600 and 1800 lines at the \(-945\) m to \(-975\) m level. Key parameters of the test area included cut-off grade: 1.00 g/t, total ore volume: 181,642 t, average grade: 2.5 g/t, and metal content: 454.105 kg. For the medium-deep hole high-section slopes in the lower wall between \(-945\) m and \(-975\) m, the downward drilling method was employed to extract Stopes 1#, 2#, and 3#, with subsequent analysis of economic performance metrics. The average daily production capacity in the test panel (medium-deep hole mining): 364.3 t. The total ore extracted from the three test slopes was 33,452 t, the ore recovery rate was 92.38%, and the mining dilution rate was 6.18%. Figure 23 shows the blasting effect of the 2# stope cutting patio, and Figure 24 shows the stope blasting effect. The trial achieved outstanding technical and economic performance, confirming the efficacy of the methodology. Notably, the results validated Scheme 2 as the optimal mining strategy for the given geological and operational conditions.  
While the study provides valuable insights, certain limitations should be acknowledged. The optimization of mining schemes was based on numerical simulations using FLAC3D. However, numerical models inherently involve simplifications and assumptions that may not fully capture actual complexities. Future research should incorporate high-performance numerical software with enhanced computational capabilities to improve simulation accuracy and account for multi-physics interactions. Simultaneously, the fuzzy comprehensive evaluation method, while effective, introduces inherent randomness in decision-making. To strengthen the robustness of scheme selection, multi-method validation is recommended, such as theoretical analysis, physical similarity experiments, integration of machine learning algorithms, etc. Furthermore, a combination of holistic  
\begin{table} \end{table}
Table 3: Average random consistency indicators.  
approach would better address uncertainties in geological variability, rock mass behavior, and operational constraints.
## 3 Advanced Techniques in Remote Sensing of EBC
### Image Classification
A variety of studies applied object-oriented classification into the science of EBC. For example, [PERSON] _et al._ classified agricultural areas in Alberta grizzly bear habitats based on one of the object-oriented classification techniques - sequential supervised masking (SSM), in order to help ecologists understand the relationship between crop types and grizzly bear presence [5]. [PERSON] _et al._ proposed that object-oriented classification may traverse the possible Landsat-gap on applications such as landscape pattern analysis or ecological models [44]. Traditionally, land cover information is assigned into a finite number of non-overlapping classes, and the classes are mutually exclusive [70], which is described as the one-entity-one-class method [71]. However, pixels may contain more than one class because of the heterogeneity and the limitation in spatial resolution of remotely sensed data, especially in medium and coarse spatial resolution imagery [70]. And the presence of mixed pixels could not be removed totally no matter how accurately map classes are defined [71]. Therefore, fuzzy classification, also called subpixel classification, arose in the context of the uncertainty associated to class mixtures. In fuzzy systems, every pixel is supposed to consist of multiple and partial memberships of all candidate classes [70]. Spectral mixture analysis (SMA) is one of the most popular and most effective approaches for dealing with mixed pixel problem [60, 70]. For example, [PERSON] and [PERSON] used linear SMA to explore the relationship between urban thermal features and biophysical descriptors based on ASTER images [72]. [PERSON] _et al._ estimated species abundance in a northern temperate forest using SMA for better understanding changes in biodiversity, habitat quality, climate, and nutrient cycling [73].
# Class Activation Map Guided Backpropagation for Discriminative Explanations
## 4 Results
### Quantitative Analysis  
In this section, we evaluate the CGBP method and comparablemethods using the aforementioned metrics. For SIC, AIC, and IS, higher scores indicate better performance, while for DS, lower scores are preferred. The evaluation results are summarized in Table 1, with the best results highlighted in bold.  
Grad's saliency maps were noisy, resulting in the lowest SIC and AIC scores on VGG and ResNet models. In contrast, path integral-based methods like IG, IDG, and GIG performed better. GBP, RectGrad, and CGBP, employing selective gradient backpropagation, outperformed the previous methods. Comparing GBP, RectGrad, and CGBP, GBP showed almost no category differentiation. RectGrad's stricter backpropagation improved category discrimination by allowing only gradients above a threshold to propagate, resulting in  
Figure 4: Visualization of multiple samples using different methods to generate saliency maps, with each category indicated on the left side of the corresponding sample. The top four rows display results from VGG16, while the bottom four rows present results from ResNet50, separated by a red line.  
higher scores than GBP. However, it struggled to distinguish between target and hostile gradients, producing similar maps across categories. CGBP addressed this by using class activation maps to better separate target and hostile gradients, achieving the highest scores. Notably, on VGG16, CGBP improved SIC and AIC scores by 10.3% and 11.5%, respectively, compared to RectGrad, demonstrating its superior performance.  
The IS results followed a similar pattern to SIC and AIC, with CGBP consistently achieving the highest scores across all models. In contrast, the DS metric presented a different trend. GIG performed best, while IG, IDG, and GBP exhibited similar performance. Grad, RectGrad, and CGBP showed poorer results; however, CGBP performed slightly better than RectGrad. On VGG16, CGBP improved the DS score by 4.5% compared to RectGrad. This improvement can be attributed to CGBP's more precise separation of target and hostile gradients. Both RectGrad and CGBP preserve only the most important gradients during backpropagation, leading to saliency maps where non-essential regions have near-zero importance scores. As a result, the softmax value remains relatively high, even after key pixels are removed, mimicking the effect of random pixel removal. Consequently, the softmax value decreases more slowly, leading to poor DS performance.
## 4 Discussion
The grain size (<100 nm) can be calculated by the Scherrer formula: D = \(\frac{K\lambda}{\beta\cos\theta}\), where D is the average grain size, \(\theta\) is the scattering angle, \(\lambda\) is the wavelength of X-ray (0.15406 nm), \(\beta\) is the half height and width of the diffraction peak, and K is the Scherrer constant (0.89). The grain sizes of the Zr, ZrvHf and TiZrv films were measured and calculated to be 17 A, 23 A, 133 A, respectively. In addition to the interstitial diffusion, grain boundary diffusion (including free surface diffusion) plays an important role in the reduction of the alloy NEG film. Since there are many defects (holes, impurity atoms and dislocations) at the grain boundary, and the atoms at the grain boundary deviate from the equilibrium, and hence, have higher energy, the diffusion coefficient at the grain boundary is generally greater than that in the bulk. At room temperature, the irregular arrangement of the atoms at the grain boundaries will hinder the movement of dislocations, resulting in high strength andhardness [36] of the NEG films at macroscopic scale. The grain boundary is like a short-circuit for the residual gas atoms to diffuse much easier at a lower activation temperature. Since Ti, Zr and Hf are hexagonally close-packed (hcp) and V is body-centered-cubic (bcc), V cannot form an infinite solution with Zr, V and Hf. This difference is aggravated during the film growth process and heating treatment, resulting in the precipitation of V at the grain boundaries. The content of V and Ti is relatively higher at grain boundaries, while the content of Zr and Hf is relatively higher in the bulk [37]. The V and Ti precipitates protect Zr and Hf from oxidation to a certain extent. Consequently, the break of the Zr-O bond is easier at a relatively lower temperature in the TiZrV films.
## 3 The Results of the Statistical Analysis of the Work of the Optical and Electrochemical Methods
### Analysis of the Optical Method for Measuring the Concentration of Exhaust Gas
NO\({}_{2}\), NO\({}_{x}\), CO; (**b**)—for O\({}_{2}\). Analysis of the values gained through the Fisher's, \(t\)-, and ANOVA tests. The bar graph demonstrates the difference between them in comparison (Figure 9). Further analysis can be performed only in the case of the prepared graphs (Figure 10). During the analysis, it was determined that the relative error for SO\({}_{2}\) was 7.19%, for NO it was 44.0985%, for NO\({}_{2}\) it was 733.26%, for NO\({}_{x}\) it was 7.39%, for O\({}_{2}\) it was 2.75%, and for CO it was 60.81% (Figure 11). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Component** & **f (Fisher’s Test)** & **t-Value (t-Test)** & **F-Value (ANOVA)** & **_p-Value_** (t-Test)** & **_p-Value_** (ANOVA)** \\ \hline SO\({}_{2}\) & 1.54 & 0.5438 & 0.2957 & 0.5923 & 0.5921 \\ NO & 3.09 & 3.8056 & 14.4828 & 0.0014 & 0.001 \\ NO\({}_{2}\) & 3958.2 & 9.6284 & 92.7067 & 0 & 0 \\ NO\({}_{x}\) & 6.46 & 0.7255 & 0.5263 & 0.4799 & 0.4758 \\ O\({}_{2}\) & 6.21 & 1.0471 & 1.0964 & 0.3122 & 0.3064 \\ CO & 4.69 & 15.2198 & 231.6424 & 0 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: The results of statistical analysis in comparison with three methods: [PERSON]’s test, \(t\)-test, ANOVA test. Figure 9: Comparison of statistical values for components: (**a**)—for SO\({}_{2}\), NO. NO\({}_{x}\), O\({}_{2}\); (**b**)—for NO\({}_{2}\), CO. Figure 11 demonstrates that most of the components had high errors of measurement.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
### Main Air Pollutants
#### 1.1.2 Sulphur Dioxide  
SO\({}_{2}\) is emitted through the burning of fossil fuels (for vehicles, heating, and power generation) and processing of ores containing sulphur. Exposure to SO\({}_{2}\) causes irritation of the eyes and lungs, causing coughing and aggravation of chronic bronchitis and asthma. Higher SO\({}_{2}\) levels are correlated with mortality from cardiac diseases [17].  
Kampala city has a high number of vehicle growth and most of them are old fleets driven with dirty fuel on a poorly planned public transport system and road network [18].
## 5 Building Smart Home by Using IoT AP
### ZigBee/Wi-Fi Wireless Channel Configuration  
The proposed IoT AP performs two wireless protocols at the same time, including ZigBee (IEEE 802.15.4) and Wi-Fi (IEEE 802.11n). The ZigBee home network environment can be established by a ZigBee wireless protocol. The ZigBee devices are connected with each other within the established smart home environment. On the other hand, the Wi-Fi wireless protocol can provide access to the ZigBee devices for handheld devices through the Internet. However, ZigBee shares a 2.4 GHz industrial, scientific, and medical (ISM) band with other existing radio transceivers such as Wi-Fi. Therefore, coexistence of ZigBee with Wi-Fi becomes an increasingly important issue because of their wideband and popular uses.  
The ZigBee communication protocol divides each 5 MHz frequency band into 16 channels with channel numbers 11-26. The frequency of each channel is 2 MHz and there is no spectral overlap between channels. The Wi-Fi communication protocol divides the frequency band into 13 channels with channel numbers 1-13. The coverage area of each channel is 22 MHz. Therefore, the spectral overlaps do not overlapped among channel 1, channel 6, and channel 11. Because the spectral overlap causes reduction of transmission speed, channels 1, 6, or 11 are used for channel configuration of Wi-Fi network in the established environment. As shown in Figure 9, ZigBee shares the congested 2.4 GHz ISM band with an IEEE 802.11n WLAN with wide RF bandwidth. To reduce the opportunity of the interference of wireless signals, this paper makes Wi-Fi with higher opportunity to occupy one of the three channels 1, 6, and 11, whereas ZigBee higher opportunity to occupy one of the four channels 15, 20, 25, and 26, which fall between the non-overlapping IEEE 802.11n channels. Consequently, the proposed IoT AP can normally operate ZigBee and Wi-Fi without any signal interference and the smart life application can be built by using the proposed IoT AP.
## 4 Testbed Design
### Experimental Testbed  
For the experimentation, we consider a bare-metal Kubernetes cluster with four server-grade physical nodes, each equipped with 24 cores over 2 Intel Xeon E5-2630v3 processors, 128 GB of DDR3 ECC main memory, and 250 GB of locally attached NVMe storage. The four nodes are connected via 1 Gbps links to the top-of-rack switch. On top of the physical infrastructure, we deploy the rest of our end-to-end evaluation software stack, which operates as our main evaluation tool and automates the submission of FL systems, algorithms and ML models.
# Functionalization and Modification of Bitumen by Silica Nanoparticles
## 4 Conclusions  
Bared and functionalized silica nanoparticles were successfully dispersed in bitumen and their effects on the mechanical performance of the bitumen were investigated in depth. The size of the bared nanoparticles was controlled by modifying the experimental conditions during their synthesis (temperature and ethanol/water ratio), obtaining spherical nanoparticles of diameters ranging from ca. 100 nm to almost 1 \(\upmu\)m. When mixed with bitumen (1 wt% ratio), the rheological properties of bitumen are almost unaltered, independent of the size of the nanoparticles. Instead, chemical modification of the surface of the nanoparticles by grafting long alkyl chains proved to be an efficient methodology to improve the mechanical performance of the bitumen. NH2 free end groups were introduced first by reacting the nanoparticles with APTES, and in a second step, long alkyl chains were introduced by reaction of the free amine groups with tetradecyl aldehyde. When still using such a low amount of nanoparticles (1% weight) in bitumen, the introduction of NH2 groups improves the mechanical performance of the asphalt, but better results were observed for the alkylated nanoparticles. These results confirm the effectiveness of functionalized silica nanoparticles as modifiers of bitumen. The shift of the transition from viscoelastic to liquid phase is significant. The comparison of the viscoelastic trends with the ineffective CaCO\({}_{3}\) filler indicates clearly that 1 wt% can be considered a sufficient dosage for bitumen modification. The present study thus paves the way for the use of functionalised silica nanoparticles as a new additive for road pavements.  
Conceptualization, N.G.; Writing-Original draft preparation I.A. and N.G.; Writing-review and Editing Y.T. and Y.O.; Formal analysis Y.O.; Investigation A.Z. and A.L.V.; Supervision P.C. and C.O.R. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by Loprete Costruzioni Stradali (Terranova Sappo Minulio (RC), Italy).  
The authors greatly acknowledge [PERSON] (IMIP-CNR laboratory) for the acquisition of SEM images.  
The authors declare no conflict of interest.
## 2 Overview of Multi-Link Operation in IEEE 802.11 be WLANs  
Multi-link operation (MLO) is the concurrent utilization of multiple radio links of different frequency channels/bands by an AP, a client, or both. It is a MAC-layer solution for concurrently using multiple links and thus exposes a single MAC service access point (SAP) to the upper sublayer of the data link layer, i.e., the logical link control (LLC) layer. Therefore, existing user services and higher-layer protocols need not expend any effort to enjoy its benefits.  
The motivation for MLO is to increase throughput by aggregating multiple bandwidths across bands at a low hardware cost. Ideally, the maximum achievable throughput of MLO is the sum of the achievable throughput for each link. For example, if each of \(n\) links can achieve identical throughput, their MLO should ideally achieve \(n\times\) throughput.  
This throughput increase enabled by MLO comes from the use of a wider bandwidth through the aggregation of multiple links. MLO has several advantages over conventional single-link operation (SLO) using a wide bandwidth. First, it allows the aggregation of available bandwidths across bands, such as 2.4, 5, 6 and even 60 GHz, at an affordable hardware cost by reusing most of the existing physical (PHY)-layer and lower-MAC (LMAC) functionalities while reinventing the upper-MAC (UMAC) component only. Second, it has the opportunity to run multiple channel access processes in individual links thanks to multiple RF chains. This is beneficial in exploiting each link's bandwidth in a manner that is more adaptive to its conditions, such as contention intensity. In contrast, the conventional wideband operation of IEEE 802.11 runs a single channel access process in a primary channel only, and thus significantly depends on the conditions of this single channel; for example, if the primary channel is crowded, fewer transmission opportunities are available for wideband operation regardless of the conditions of the other channels.
# Low-Rank Approximation of Difference between Correlation Matrices Using Inner Product
## 2 Methods
### Formulation of Proposed Method  
In this subsection, we show the proposed dimensional reduction clustering approach based on Equation (6). The proposed method consists of two steps. First, a low-rank correlation matrix, which indicates the difference between two correlation matrices, is estimated. Second, a traditional clustering algorithm such as \(k\)-means [14] is applied to \(\mathbf{X}\) to obtain the clustering structure of variables. Although such two-step approaches have already been proposed in [26; 27], those methods were proposed for multivariate data, not square matrices.  
Afterward, the optimization problem of estimating a low-rank correlation matrix is described. Given \(\mathbf{\Delta}\) and \(d\), the optimization problem is formulated as follows:  
\[L(\mathbf{X}|\ \mathbf{\Delta})=\left\|\mathbf{\Delta}-\mathbf{d}\mathbf{d}^{T}\odot\mathbf{X} \mathbf{X}^{T}\right\|_{F}^{2}\rightarrow\text{Min}\] (8) subject to \[\left\|\mathbf{x}_{i}\right\|=1\quad(i=1,2,\cdots,p), \tag{9}\]  
where \(\|\cdot\|_{F}\) indicates the Frobenius norm. In this study, to estimate \(\mathbf{X}\) such that Equation (8) is minimized with the constraint of Equation (9), the majorize-minimization (MM) algorithm is used.