esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
### Sulfur Isotope Constraints on Metal Sources  
The sulfide mineral assemblage of the Hongtoushan Cu-Zn VMS deposit mainly includes pyrite (FeS\({}_{2}\)), pyrrhotite (Fe\({}_{1-X}\)S), chalcopyrite (CuFeS\({}_{2}\)), and sphalerite (ZnS). The degree of sulfur isotope fractionation is influenced by the temperature, with smaller fractionation occurring at higher temperatures and more pronounced fractionation between light and heavy sulfur isotopes at lower temperatures [58]. And, under reducing conditions, pyrite tends to be enriched in light sulfur isotopes, while under oxidizing conditions, it is more likely to be enriched in heavy sulfur isotopes [59].  
The presence of pyrite and pyrrhotite porphyroblasts with widespread triple-junction recrystallization annealing textures in the Type-2 massive ores indicates high-temperature conditions. [PERSON] and [PERSON] (1964) demonstrated that the pyrite-pyrrhotite assemblage can be used as a geothermometer [60]. Applying this geothermometer to the triple junction textures in the Type-2 massive ores suggests formation temperatures of around 600-800 \({}^{\circ}\)C, indicative of peak metamorphic conditions.  
At high temperatures, pyrite is unstable, and transforms to pyrrhotite through desulfurization. In this process, the heavy \({}^{34}\)S is enriched in the production of pyrrhotite, and the sulfur with a lighter \(\delta^{34}\)S composition is released into the fluid [61]. The wider and positive  
Figure 7: (**A**) Raman spectrum of the fluid inclusions in Type-1 massive ores; (**B**) Type-2 massive ores; (**C**,**D**) Type-3 massive ores from the Hongtoushan deposit.  
\(\delta^{34}\)S values of pyrrhotite in the studied Type-1 and Type-2 massive ores might indicate that the pyrrhotite is formed by the desulfurization of pyrite under relatively reducing environments and higher temperature conditions (Figure 8A). At the same time, the pyrite desulfurization would release sulfur with negative S isotope values into the fluid. The pyrite and pyrrhotite in the Type-3 massive ores have \(\delta^{34}\)S values of \(-\)0.7 to 0.2% and \(-\)0.2 to 2.0% (Figure 8A), which are significantly lower than the other sulfides in the deposit. This suggests that the Type-3 massive ores may have formed under relatively oxidizing environments and lower temperature conditions.  
The observed \(\delta^{34}\)S values of most sulfides from the ores in the Hongtoushan VMS deposit are mostly distributed in a narrow range close to 0% (\(-\)0.7 to 4.2%), which indicated a magmatic (mantle-derived) source [62, 63]. The slightly higher \(\delta^{34}\)S values of the sulfides suggest that the sulfur may have undergone a hydrothermal fractionation processes. Based on the evidence presented, we propose that the sulfur was more likely derived from igneous rocks through a mobilization processes. However, given the submarine hydrothermal setting of VMS deposits, the involvement of seawater and its potential influence on the sulfur isotope composition cannot be overlooked.
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ API & Application Programming Interface \\ ASIC & Application Specific Integrated Circuit \\ CNN & Convolutional Neural Network \\ COTS & Commercial Off-The-Shelf \\ CPU & Central Processing Unit \\ CU & Compute Unit \\ CV & Computer Vision \\ DDR4 & Double Data Rate 4 \\ DL & Deep Learning \\ DMA & Direct Memory Access \\ DNN & Deep Neural Network \\ DSP & Digital Signal Processing \\ EO & Earth Observation \\ ESA & European Space Agency \\ FPGA & Field Programmable Gate Array \\ FPS & Frames Per Second \\ GOPS & Giga Operations per Second \\ GPGPU & General Purpose computing on GPU \\ GPU & Graphic Processing Unit \\ IoT & Internet of Things \\ IP & Intellectual Property \\ ISL & Inter-Satellite Link \\ KPI & Key Performance Indicator \\ LEO & Low Earth Orbit \\ M2M & Machine-to-Machine \\ ML & Machine Learning \\ MPSoC & Multi-Processor System-on-a-Chip \\ NN & Neural Network \\ PC & Program Counter \\ PE & Processing Element \\ PL & Programmable Logic \\ PS & Processing System \\ QoS & Quality of Service \\ SEE & Single Event Effect \\ SEL & Single Event Latchup \\ SEU & Single Event Upset \\ \end{tabular}
## 4. Discussion
### Numerical Simulations
The combined effect of SLR and future precipitation changes raised the flood depth by 34.9%, indicating that future rainfall changes have a greater impact on floodwaters in the study area than SLR. Several researchers have used the SWMM model to analyze the risk of compound floods under extreme rainfall and urbanization scenarios [65; 90]. The SWMM model is primarily used for 1D flow simulations, utilizing a structured grid system that keeps computational costs low, making it particularly suitable for simulating urban drainage systems. VIC uses a regular grid system, limiting its flexibility in handling complex terrains, but it is widely used in large-scale watershed simulations. MIKE SHE, which employs an unstructured grid for 3D simulations, provides high computational accuracy. However, its longer computation time and significant resource demands result in higher computational costs. The compound flood model dominated by climate change hydrodynamics uses observed or simulated seawater levels and runoff levels as input data to directly calculate the inundation range. Common models include LISFLOOD-FP, HEC-RAS, and MIKE21. In this approach, the output from one model is used as input for the next, with the models operating independently without interaction, which may result in lower accuracy. [PERSON] et al. (2020) [106] analyzed the impact of rainfall floods, river floods, and compound floods on different land use types in cities by establishing a SWMM-LISFLOOD-FP coupled model. The risk of compound floods under varying river flows and seawater levels in Klaipeda City, Lithuania, was studied using HEC-RAS, with visualization in ArcGIS Pro. The results showed that a 1 m rise in sea level could lead to a threefold increase in the flood impact area [47]. [PERSON] et al. (2022) [86] comprehensively considered the impact of climate change scenarios, such as SLR and storm surges, alongside urbanization factors like land subsidence and land-type changes, on compound floods. They applied the FLUS and LISFLOOD-FP models to explore potential flood areas in Shanghai, China, under the RCP2.6 and RCP8.5 scenarios in 2030 and 2050. Hydrodynamic-dominated compound flood models primarily use 2D simulations. Computation time is constrained by the grid resolution of the simulation area, with higher-resolution grids requiring more time to run. LISFLOOD-FP uses a structured grid system and achieves high accuracy in large-scale, low-complexity coastal regions, but its accuracy is limited in areas with complex terrain. HEC-RAS integrates both 1D and 2D models, making it suitable for simulating river flow and flood propagation. MIKE21, which employs an unstructured triangular grid, provides high accuracy in complex hydrodynamic environments and irregular coastal terrains, though it comes with relatively high computational costs.
## 3 Use Case: Watson Analytics as AaaS to Identify HF Patients Analyzing Only the ECG Signal
### Medical and ECG-Analysis Background  
#### 3.2.1 Heart Failure  
The Heart Failure is an alteration of the structure and function of the heart that involves the body's inability to provide the proper amount of blood to the organism. The body's reaction to HF causes sodium and water accumulation in the lungs and tissues causing fatigue, difficulty to perform physical efforts, shortness of breath and pulmonary edema. As time goes, patient's condition can worsen increasing the severity of lung edema and causing death. Clinical course of the disease leads the patient through a chronic stage that is quite stable but often alternated with worsening requiring hospitalization. In some cases, these severe episodes could be avoided with a preventive therapy. Obtaining additional information on this disease and its evolution would be greatly important for the health of patients. The overall prevalence of HF is slowly increasing due to the aging of the population and the success in the survival of patients suffering from a heart attack [25].  
The literature shows improved outcomes for HF patients supported by tele-monitoring systems [26; 27]; hence, a non-invasive system of investigation on HF presence based on analysis of ECG recordings (possibly Short-Term), may be useful to timely administer a specific therapy and to prevent worsening.
## 3 Results and Discussion
### Carboxymethylcellulose Characterization
#### 3.1.1 Structural Analysis  
The results of the samples structural analysis can be seen in Figure 2.  
The normalized FT-IR spectra of studied CMCs highlighted in Figure 2b exhibited three strong absorption bands at 1318, 1417, and 1591 cm\({}^{-1}\) assigned to the bending vibration of O-H group, CH\({}_{2}\) scissoring, and COO-asymmetrical stretching, respectively, characteristic of the carboxymethyl functional group [24]. The broad peak at 2980-3680 cm\({}^{-1}\) is attributed to the -OH from the carboxymethyl functional group and to the O-H stretching from the glucose ring, while the weak absorption band at 2882-2984 cm\({}^{-1}\) indicates the C-H stretching vibration within the glucose unit [24; 25]. The absorption peak at 1023 cm\({}^{-1}\) is associated with the C-O stretching vibration and the absorption peak at 899 cm\({}^{-1}\) is assigned to the glycosidic -C1-H stretching [24].  
The analysis of the powder X-ray diffraction patterns of the four CMC samples revealed (Figure 2c) a single reflection located at 20 = 20.3\({}^{\circ}\) with an amorphous phase over to a highly crystalline region, which is in agreement with the literature [26; 27]. The predominant amorphous character can be related to the breakage of hydrogen bonds in the crystalline region of cellulose starting material, along with the electrostatic interactions between the CMC compounds due to the anionic--CH2 COO--groups [26]. Besides the clear amorphousness of the four CMC samples, the %CI was determined to attain the crystallinity extent of the different samples. The %CI calculation was performed on the PDXL2 da Rigaku(r) (version 2.8.1.1) software, after the adjustment of the diffractogram profile and the baseline, considering the area measurement under the single reflection,followed by the modelling of the amorphous background. Then, the area corresponding to the crystalline component was obtained by the subtraction of the area of the amorphous background from the total area, with the %CI being obtained by the ratio between the area of crystalline and the total area. According to this analysis, CMC4 exhibited the higher crystallinity (42.1%), followed by CMC2 (40.6%), CMC3 (35.9%), and CMC1 (32.3%).  
The microstructure analysis of all CMC samples by SEM (Figure 2a) showed the presence of smooth particles aggregates. CMC2 exhibited the largest aggregates, followed by CMC 1, while the samples CMC3 and CMC4 seem to be very similar.
## 4 Experiments
### Ablation Experiments
#### 4.5.2 The Selection of the Feature Fusion Method  
As described above, different feature fusion methods were used between the branches within the encoder and decoder, where the encoder used the CAC to fuse the dual-branch features, and the decoder used the TA to aggregate the triple-branch features. To confirm the adaptability of the two fusion methods in the encoder and decoder, this paper tried to use the TA in the encoder and the CAC in the decoder, based on which ablation experiments were implemented on the BCDD dataset. Table 8 includes the results, which shows that the effect of using the TA in the encoder was somewhat lower than that of the original fusion mode in the F1 score, IoU, and the OA. In addition, we changed the TA in the decoder to the CAC, and Table 9 lists the results, whose performance was also reduced as opposed to the original TA fusion
## 2 Materials and Methods
### Chamber Measurements of CO\({}_{2}\) Fluxes  
Carbon dioxide fluxes were measured with the closed dynamic (non-steady-state flow-through) portable chamber system as described in [PERSON] et al. [34; 35; 36] and [PERSON] et al. [37]. Two types of chambers were applied--a transparent chamber made from 3 mm thick Plexiglas (Evonik Industries, Darmstadt, Germany) and a non-transparent chamber made from white 3 mm thick PVC, in order to facilitate measurements of net ecosystem exchange (NEE) and ecosystem respiration (Reco), respectively. The chamber dimensions were 0.78 \(\times\) 0.78 \(\times\) 0.50 m, and their volume was 0.296 m\({}^{3}\). The gas concentration changes in the chamber headspace were measured by an LI-840 gas analyzer (LI-COR Biosciences, Lincoln, Nebraska, USA) installed in the portable box (equipped with a pump, air-flow controller, filter, batteries, and CR-1000 datalogger (Campbell Sci., Logan, UT, USA). The air was circulated in the closed system between chambers and the gas analyzer through 3 m long Teflon tubes with the constant rate of 0.7 L\(\cdot\)m\({}^{-1}\). Each chamber was equipped with a shielded HygroVUE\({}^{\text{TM5}}\) temperature and relative humidity sensor (Campbell Sci., Logan, UT, USA), 2 computer fans (1.4 W each) to mix the air in the chamber headspace, and a vent to equilibrate air pressure during measurements [34; 35]. The transparent chamber was also equipped with an SKP215 sensor to monitor PAR radiation during measurements. Chambers were placed on preinstalled 20 cm high PVC soil frames (0.75 \(\times\) 0.75 m) and inserted 15 cm deep into the soil to ensure an adequate seal between the soil and the atmosphere and reduce horizontal gas flow. The tightness of the chamber system and sealing were assured through the rubber installed at the bottom edge of each chamber's walls. Three soil frames were installed per each plot as replicates, resulting in 12 frames installed for the purpose of the experiment.  
Chamber measurements were taken every 3 to 5 weeks, only during sunny and cloudless conditions, resulting in 15 and 13 campaigns in 2021 and 2022, respectively. Measurements started early in the morning and were taken until the late afternoon. At each of the plots, NEE measurements preceded the Reco measurements. The chamber closure time was 90 and 150 s for NEE and Reco measurements, respectively.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 2 Related Works
Then, routing protocols were investigated based on the suggested classification in this paper. In [25], the authors reviewed various routing protocols in vehicular ad hoc networks. In this paper, routing methods were divided into four categories: unicast-based routing, multicast-based routing, geocast-based routing, and broadcast-based routing. It is a very comprehensive and applicable review paper in VANETs. However, this paper does not consider the reinforcement learning-based routing methods. In [26], the authors studied various issues, including architecture, application, and different routing protocols in flying ad hoc networks. This paper discusses routing methods for highly dynamic networks. However, the authors have not mentioned an important category of routing methods: namely, reinforcement learning-based methods. This paper focuses specifically on security challenges in FANETs. In [13], the authors provided a comprehensive review paper on the routing protocols in FANETs. They described issues such as mobility models and UAV applications. Furthermore, different routing protocols were compared in terms of performance scales. The most important weakness of this paper is the focus on a special type of routing protocol; namely, position-based routing protocols. In [27], the authors analyzed the various types of routing protocols in flying ad hoc networks. They evaluated and compared these methods from different aspects. The authors studied these protocols in terms of network conditions and application needs. However, this paper is not comprehensive. It does not consider other routing methods; namely, hierarchical routing, probabilistic routing, and reinforcement learning-based routing. In [28], the authors evaluated various issues of FANETs such as architecture, characteristics, routing protocols, and challenges in this area. They divided the routing methods into three groups: deterministic routing, stochastic routing, and social network-based routing. Finally, these routing methods were compared in terms of features and performance. However, this paper does not address reinforcement learning-based routing approaches in FANETs.
## 4 Applications to Real Measured Responses
### Application to a Five-Story Steel Frame  
The investigated frame, illustrated in Figure 14, has dimensions of 3 m in length, 2 m in width, and a height of 13 m. Each floor was loaded with lead blocks to achieve an approximate mass of 3664 kg per floor. In subjecting the frame to a base excitation equivalent to 60% of the intensity of the Kobe earthquake, as shown in Figure 15, the acceleration responses of the first and fifth floors in the long-span direction (denoted as the \(x\)-direction in Figure 14) were recorded at a sampling rate of 250 Hz. These data served as input for identifying the modal parameters of the frame.  
[PERSON] et al. (2019) observed clear permanent strains in the measured strains at a column of the first story, indicating the yielding of the steel columns under the considered base excitation. Although not presented here, the first mode scalogram from the measurement of the fifth floor illustrates the variation in the fundamental frequency over time, suggesting nonlinear behavior in the frame under the considered base excitations. It is worth noting that in a nonlinear system, the stiffness and damping matrices can be functions of displacement or velocity responses, which in turn depend on time. Consequently, the instantaneous modal parameters of the system exhibit time variation, and our proposed approach is capable of identifying these time-varying modal parameters, as demonstrated below.  
Assuming rigid floors, which are commonly employed in building designs, the frame can be simplified into a five-degrees-of-freedom system in each of its long-span and short-span directions. Modal parameter identification involved windowed responses with a window length of 2 \(L\), equal to 2 s. In Equation (25), the parameter '\(d\)' in the weighting function was set to 0.06 s, and adjacent windows had an overlay of 1.9 s. The proposed approach yielded time-varying modal frequencies and modal damping ratios for  
Figure 13: Mode shapes of the bridge in the transverse direction.  
five modes, as depicted in Figure 16. Figure 16 also includes results from [PERSON] et al. (2019), who utilized a time-varying autoregressive model with exogenous input (TVARX) with the continuous Cauchy wavelet transform to process responses from all five floors. The identified results from the present study exhibit favorable agreement with the published findings.  
Figure 16 reveals significant variations in natural frequencies and modal damping ratios over time when 4.5 s < t < 7 s. Such variations indicate that the frame exhibited nonlinear behaviors under the considered base excitations within this time range. These nonlinear behaviors are indicative of earthquake-induced damage to the frame. Beyond this specific interval, the frame behaved linearly, and the variations in identified modal parameters with time are not as significant as the published ones.  
Figure 14: Schematic of the five-story steel frame.  
Figure 15: The relative acceleration responses of the first and fifth floors of the steel frame under shaking table tests.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Invertebrates
#### 2.1.1 Jumping Spider Inspired Jumping Robots  
The jumping spider (Araneae Salticidae) is a special case among jumping invertebrates, due to the lack of skeleton structures in its legs, and its jumping mechanism is particularly complex and differs between species. In some of them, the hydrostatic pressure could be the main energy source [68]. In others, like _Phidippus regius_, the muscle mass is sufficient to provide the power required for jumps [43] and, when necessary, they jump with hydraulic augmentation. An interesting feature in these spiders is that the silk is used to right the body pitch angle, in order to optimize the landing process. This aerial righting mechanism is crucial for dynamic stability and prey capture [66].  
[PERSON] et al. proposed a spider-inspired jumping robot [73; 74], which mimics the anatomy of jumping spiders [75] and whose hind legs are specifically dedicated to take-off powering, while their front legs adjust the take-off angle in the same way as locusts.  
A team at the Cornell University, instead, is focusing on the design of a miniature jumping robot called \"AracnnaBot\" [76]. It takes inspiration from the muscles and the hydraulically augmented joints of jumping spiders: the tendon system is actuated by a motor located on the top in order to lock the joint, and the pneumatic system is used to unlock the joint suddenly when the air pressure overcomes the threshold that is set.  
Another robot that takes inspiration from jumping spiders was proposed by [PERSON] et al. [77]. It is a six-leg jumping robot which can realize vertical jumps, sideway jumps, and forward jumps with preadjustment of all its six 4-DOF (Degree of Freedom) legs. Simulated results indicate that it can maintain stability and achieve multi-directional jumping. This is crucial for real applications of jumping robots.  
Finally, [PERSON] et al. focused on the aerial righting capability of jumping spiders by means of their draglines. They manufactured a robot capable to control and adjust the aerial pitch angle [78]. This robot has the advantage of being small and lightweight but the control of kinematics is not so effective because of too uncertain feedbacks.
## 2 Materials and Methods
### PCR Reactions  
Amplification reactions were performed according to previously reported methods using the PCR 2x Master Mix (Promega Italia) in a GeneAmp PCR System 9700 (Thermo Fisher Scientific). PCR products were analyzed by capillary electrophoresis using the QIAxcel(r)Advanced System (Qiagen, London, UK). For the identification of bacteria, the MicroSEQ(r)500 16S rDNA Bacterial Identification System (Thermo Fisher Scientific), amplifying a 500 bp fragment from the 5\({}^{\prime}\) end of the 16S ribosomal RNA gene, was used according to the manufacturer's instructions.  
_L. casei_ and _L. paracasei_ were differentiated by PCR, according to [PERSON] and Timmins [20], after 16S ribosomal RNA gene sequencing.  
The identification of genetic loci involved in the production of lantibiotics (genes _lan_B, _lan_C and _lan_M); BAs (genes _hdc1_, _hdc2_, _tdc_ and _odc_); and enterococcal virulence factors gelatinase (_gel_E), enterococcal surface protein (_esp_), collagen adhesin (_ace_), endocarditis antigen (_eftA_), cytolisin (_cylA_), hyaluronidase (_hyl_) and aggregation substance (_as_a_1) was carried out as reported by [PERSON] et al. [12]. The _nis_A gene was amplified according to [PERSON] and [PERSON] [21]. The detection of vancomycin resistance genes was carried out according to [PERSON] et al. [22].  
Repetitive Extracgenic Palydrome (Rep) PCR was carried out with the GTG\({}_{5}\) primer as described by [PERSON] et al. [23]. Image analysis and clustering were performed with the BioNumerics V7.5 software (Applied Maths N.V., Sint Martens Latem, Belgium), using the Dice coefficient for pairwise comparison and the Unweighted Pair Group Method using Arithmetic Averages (UPGMA) for clustering. Some isolates were analyzed in duplicate in the same experiment in order to establish a cut off for considering isolates distinct. Consequently, the cut off was set at 90% similarity.