esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary  
The study identified 350 MPa compaction pressure, 600 \({}^{\circ}\)C sintering temperature, and 60 min sintering time as the optimal processing parameters for AZ31/1.75 wt.% GNP composites. These conditions led to significant mechanical improvements, with microhardness reaching 108.5 Hv (compared to 49 Hv for the base AZ31 alloy) and compressive strength increasing to 452.2 MPa (from 358 MPa). Among the parameters, compaction pressure had the highest impact, contributing 72.99% to microhardness variation and 68.38% to compressive strength variation, followed by sintering temperature and sintering time. The microstructural analysis confirmed enhanced densification, reduced porosity, and uniform graphene nanoplatelet dispersion, which contributed to improved mechanical properties. XRD analysis further validated the presence of \(\alpha\)-Mg phases and MgO, indicating strong interfacial bonding without adverse reactions. The reliability of the findings was reinforced through statistical validation, with regression models exhibiting R\({}^{2}\) values greater than 85%. Additionally, experimental validation showed minimal deviations, with a maximum relative error of \(\leq\)2.51% for microhardness and \(\leq\)6.01% for compressive strength, confirming the high predictive accuracy of the optimization approach. These findings highlight the effectiveness of parameter optimization in enhancing material performance, making AZ31/GNP composites suitable for lightweight, high-strength applications in the aerospace and automotive industries.  
Conceptualization, S.K.S., L.K.S. and Y.S.; Methodology, S.K.S., L.I., S.G., L.K.S. and Y.S.; Validation, S.M. and B.S.; Investigation, S.K.S.; Writing--original draft, S.K.S.; Writing--review and editing, S.K.S., L.I., S.G., S.M. and B.S.; Funding acquisition, L.I., S.G., S.M. and B.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article. The authors declare no conflict of interest.
## 5 Experiment Results and Discussion
### Analysis of the Calculated Settings for Individual Users
#### 5.3.1 Translation Interaction Mechanisms  
One visually impaired participant was assigned indirect controls for all virtual object transformation tasks (it is not shown in the table). All participants with auditory impairments were assigned touch gestures for the _Translation_ task. Participants from the remaining disability groups have an approximately equal division for the _Translation_ task, where a slightly larger share of participants was assigned indirect control (60% of the participants with physical disabilities, 62.5% with cognitive and neurological disorders and/or specific learning disabilities, and 58.3% with multiple disabilities).  
Participants from most age groups were assigned the touch gesture, i.e., for the groups 25-34 years old (58.3%), 35-44 years old (83.3%) and 65 or more years old (66.7%), while the participants from the remaining groups were assigned indirect controls for _Translation_ task, namely the groups Under 18 years old (66.7%), 18-24 years old (55.3%), 45-54 years old (71.4%) and 55-64 years old (60%).
Our proposed method was tested against challenges in face recognition such as pose and lighting conditions, facial expressions, dark images, image acquisition variations like time gap and angle of the face profile, and attributes on faces like glasses and makeup. We showed that our proposedmethod produced high recognition accuracy and was robust against challenges such as lighting conditions, facial expressions, head pose, 180-degree rotation of the face profile, dark images, acquisition with time gap, and conditions where the person uses attributes such as glasses. To be able to explain our research more clearly, we list several contributions from this paper:  
* We proposed a new combination of image fusion using opposite-frequency components from discrete wavelet transform sub-bands and Gaussian-filtered images or Difference of Gaussian. The proposed method needed only the same images to produce the opposite frequency and solve the problems of inputs from multiple sources, and the requirements for features needed to be as complementary as possible. * We examined the effects of Laplacian pyramid image fusion and discrete wavelet transform image fusion to improve recognition performance. * We investigated the variation in parameters inside the methods, such as the wavelet family employed in multi-resolution analysis of discrete wavelet transform and the wavelet family and level of decomposition employed in discrete wavelet transform image fusion. * Due to the high potential of other combinations inside our proposed method, including pixel-level or feature-level fusion of features, we showed that our chosen combination outperformed other combinations' recognition performance. * The classification methods were compared using a support vector machine with linear, quadratic, and cubic kernels; nearest neighbor; and neural network. * Our proposed method was tested against challenges from six face datasets (four datasets and two sub-datasets) and compared with other image fusion and non-fusion methods. The rest of this paper is organized as follows: Section 2 displays the materials and methods in our research; Section 3 discusses the findings and compares our results against other methods; Section 4 concludes our research.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 3 Test Setup
### Hardware  
A low-cost robotic manipulator was developed to validate the study of over-sensored systems within an academic context. With this purpose in mind, the manipulator was designed based on three criteria:  
* Its structural components musts be 3D printable;
* The design and sensors have to be low-cost;
* It should be as modular as possible.  
Following these objectives, the manipulator presented in Figure 2a was designed and then physically built, as shown in Figure 2b.  
It is equipped with 3 JGY-371 motors with a worm gearbox and incremental encoder. The worm gearbox gives the motor the unique capability of self-locking, being able to save energy while the motor is stopped. There are 2 TAL220 load cells, mounted on both connection links, with the corresponding signal amplifiers (HX711). These make the system capable of measuring the torque applied in each link. Links 2 and 3 are also equipped with inertial measurement units. There is one MPU-9250 IMU in Link 2 and one Adafruit 3463 Precision 9-DOF IMU in Link 3. An Arduino Uno is used to process the data from the sensors and control the motors through an Adafruit Motor Shield v2. Table 1 contains a bill of materials with the price range of each component.
## 3 Results
### Uncertainty of Satellite Data in MODIS GPP Simulation over Ecosystems in the Arid Region
#### 3.2.2 Impacts of Uncertainty of FPAR Data on MODIS GPP Simulation  
Figure 8 showed the comparisons of AccuPAR observed FPAR data with the MOD15A2H FPAR data in the corresponding sites in HRB. We found the MODIS FPAR data was overestimated compared to the observations in the growing season of desert grassland sites, as well as the low values of FPAR  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & \multicolumn{2}{c}{**GPP\_MODIS**} & \multicolumn{2}{c}{**GPP\_Insitu**} & \multicolumn{2}{c}{**GPP\_LUE**} & \multicolumn{2}{c}{**GPP\_Fiveopt**} \\ \hline  & R\({}^{2}\) & RMSE & R\({}^{2}\) & RMSE & R\({}^{2}\) & RMSE & R\({}^{2}\) & RMSE \\ \hline ARZ & 0.85 & 1.13 & 0.92 & 1.58 & 0.92 & 1.58 & 0.92 & 0.82 \\ DSL & 0.79 & 1.01 & 0.86 & 1.18 & 0.85 & 0.74 & 0.82 & 0.59 \\ YKZ & 0.72 & 0.41 & 0.76 & 0.30 & 0.76 & 0.58 & 0.78 & 0.65 \\ HZZ & 0.38 & 0.42 & 0.40 & 0.32 & 0.40 & 0.53 & 0.40 & 0.54 \\ GBZ & 0.37 & 0.28 & 0.41 & 0.27 & 0.41 & 0.60 & 0.42 & 0.68 \\ LDZ & 0.50 & 0.44 & 0.73 & 0.41 & 0.73 & 0.59 & 0.72 & 0.61 \\ DMZ & 0.86 & 3.43 & 0.94 & 3.69 & 0.94 & 1.27 & 0.96 & 0.96 \\ NTZ & - & - & 0.63 & 2.30 & 0.55 & 1.82 & 0.63 & 1.42 \\ SDZ & 0.85 & 1.55 & 0.88 & 1.76 & 0.88 & 1.16 & 0.91 & 0.98 \\ HHL & 0.57 & 2.48 & 0.77 & 2.30 & 0.68 & 1.21 & 0.87 & 0.75 \\ SDQ & 0.56 & 1.68 & 0.81 & 1.46 & 0.74 & 0.85 & 0.85 & 0.66 \\ HYL & 0.54 & 2.14 & 0.76 & 1.86 & 0.67 & 1.10 & 0.89 & 0.65 \\ \hline \end{tabular}
\end{table}
Table 2: A summary of the performances of the MOD17 algorithm (GPP_MODIS) and the in situ metrological data forced GPP, LUEmax parameter optimized GPP (GPP_LUE), and five parameters optimized GPP (GPP_Fiveopt). GPP_LUE and GPP_Fiveopt were estimated from the in situ climate data. In the GPP_Insitu and GPP_LUE algorithms, the default values for model parameters were used in MOD17 for the original land cover types and optimal parameter values for the optimization approach.  
Figure 7: Misclassification of land cover in the MOD12Q1 products at the downstream HRB, which classified the land cover of forest (i.e., SDQ, HHL, HYL) and cropland (NTZ) as the grassland type in MOD12Q1 data. (**a**) SDQ, (**b**) HHL, (**c**) HYL, and (**d**) NTZ.  
in the cropland sites, and underestimated in some stages of the high values of FPAR in the cropland sites. The overestimated FPAR impacted the APAR, thus leading to an overestimated GPP. In contrast, the underestimated FPAR would underestimate the GPP. Meanwhile, a good correlation between MODIS FPAR and observed FPAR occurred in cropland sites, while in the desert grassland sites, no significant relationship was found. This revealed that the accuracy of the current MOD15A2H FPAR data in the arid region needs to be further improved, and could also be an important source of uncertainty for the estimation of GPP in desert ecosystems.
# Design of Supply Chain System Based on Blockchain Technology
## 5 Experiment and Analysis
### Basic Functions of the System  
The system function test was divided into tests of the trade chain and information chain platforms. We tested the trading function of the trade chain platform and the information upload and acquisition functions of the information chain platform.  
A test was performed on the trading function of the trade chain platform. During the transaction process, the trade chain platform must complete legality verification, result confirmation, and fund transfer. The test simulated the transaction process through virtual transactions between any two nodes in the system. The details of the transaction process were controlled by the trade smart contract. The system functions were analyzed by the transaction information stored in the corresponding block after the transaction was completed. The block information is shown in Figure 10. According to the test results, the trade chain platform could realize the transaction legality verification, transaction result identification, and transaction fund transfer functions, and could store transaction information, which meets the basic requirements of supply chain transactions.  
The function test of the information chain platform was then carried out. When a node uploads or obtains information, the information chain platform must verify the node's identity, permissions, and operation objects. After the operation is completed, the information chain platform stores relevant records in the information alliance chain for later verification. The test simulated the workflow of an information chain platform by uploading virtual information through a node and obtaining the information from another node. Figure 11 shows the product information uploaded by an enterprise to the information alliance chain, and Figure 12 shows the operation record of the information smart contract after a user obtained product information. It can be seen that the information chain platform could realize basic information upload and information acquisition functions.  
Figure 9: Info smart contract deployment results.  
Figure 11: Product information block.  
Figure 12: Records fetched.  
Figure 10: Trading block information.  
traceability, search, third-party access and access control. The results are shown in Table 1. Through comparison, it can be noticed that this program had certain advantages as a whole, but compared to other research results, each of its functions has not been fully perfected. For example, the search function does not support keyword search, and the access control mechanism is relatively simple. It is necessary to continue improving the system functions in future research.
###### Abstract  
Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite-UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite-UAV collaborative IoT systems.  
satellite IoT; UAV; topology optimization; graph partitioning; simulated annealing
## 4 Results  
### Simulation  
Figure 2 displays simulated images obtained by different methods. Regions indicated by the circle and box in Figure 2a were selected to calculate image quality metrics, the regions indicated by the cyan dashed lines were used to plot the lateral variations, and the point targets indicated by two cyan boxes were used to calculate FWHM values. The DMAS image shows an improved contrast over the DAS image; however, the background speckle brightness decreases. It can be seen from the SLSC image that the background speckle texture and the lesion detectability are enhanced; however, the point resolution is poor, and dark-region artifacts appears beside point targets. CMSB-based images show narrowed point targets compared with DMAS images. The CMSB (\(\delta=\delta_{ac}\)) image shows a brighter speckle compared with the CMSB image. LOCw-CMSB reduces noise in the cyst target significantly in comparison with CMSB. In comparison with CMSB images, the LOCw-CMSB (\(\delta=\delta_{ac}\)) image shows a smoothed speckle texture and higher brightness simultaneously. Note that the two axially closely spaced point targets cannot be distinguished by CMSB-based methods.  
Figure 3 plots the lateral variations through point targets indicated by cyan boxes and the cyst target indicted by the cyan dashed line. As can be seen, CMSB-based methods all obtain a narrowed mainlobe compared with DAS and DMAS as seen from Figure 3a. In addition, CMSB methods can distinguish the two closely spaced point targets as seen from Figure 3b. From Figure 3c, we can see that CMSB-based methods, as well as DMAS, obtain the lowest noise level in the cyst target. Additionally, LOCw-CMSB and LOCw-CMSB (\(\delta=\delta_{ac}\)) obtain the best cyst edge resolution.  
Table 2 lists image quality metrics of simulated tissue-mimicking images. Note that the resolution of the SLSC is not calculated because the image is not log-compressed. Compared with DAS, SLSC obtains a degradation of 9.1 dB in contrast, but it obtains the highest gCNR and sSNR. CMSB (\(\delta=1.3\)) improves the resolution by 52.7% and 40.9% over DAS and DMAS, respectively. The proposed methods maintain the resolution performance of CMSB (\(\delta=1.3\)). In addition, LOCw-CMSB improves the CR by 5.8% and 14.7% compared to CMSB (\(\delta=0.7\)) and CMSB (\(\delta=1.3\)), respectively, whereas the gCNR and sSNR are slightly higher than that of CMSB (\(\delta=0.7\)). The gCNR and sSNR obtained by CMSB (\(\delta=\delta_{ac}\)) are 4.3% and 40.6% larger than that of CMSB (\(\delta=1.3\)), whereas CR decreases by 2.0%. Furthermore, LOCw-CMSB (\(\delta=\delta_{ac}\)) leads to improvements of 12.4%, 4.3%, and 36.6% in CR, gCNR, and sSNR over CMSB (\(\delta=1.3\)).  
Figure 3: Lateral variations through point targets at the depth of (**a**) 23.1 mm and (**b**) 27 mm, and (**c**) the cyst target in simulated tissue-mimicking images.  
Figure 2: Simulated images reconstructed by (**a**) DAS, (**b**) DMAS, (**c**) SLSC, (**d**) CMSB (\(\delta=0.7\)), (**e**) CMSB (\(\delta=1.3\)), (**f**) LOCw-CMSB, (**g**) CMSB (\(\delta=\delta_{ac}\)), and (**h**) LOCw-CMSB (\(\delta=\delta_{ac}\)).
## 2 Materials and Methods
### Measurements
#### 2.4.2 Patient-Reported Outcome Measures (PROMs)  
The number of falls suffered in the last 3 months was recorded by asking participants to answer the question, \"How many falls have you suffered in the last 3 months?\", with a fall described as an \"unexpected event that causes the person to fall to the ground against his or her will\" [27].  
To record the severity of the disease, the Fibromyalgiaiga Impact Questionnaire (FIQ) [28] was used, with the total scores of which ranging from 0 to 100 points. The FIQ consists of 3 categories according to the level of affectation: mild vital affectation (<50 points); moderate vital affectation (50-75 points); severe vital affectation with impossibility to carry out a job or function (>75 points).  
The Numerical Pain Rating Scale (NPRS) [29] was used to measure participants' pain intensity, the scores of which range from 0 (absence of pain) to 10 (the most intense pain perceived.  
The level of central sensitization was determined through total scores (0-100 points) of the Central Sensitization Inventory [30].  
The level of catastrophism was assessed with the Pain Catastrophizing Scale (PCS) [31]. It consists of 13 items, with the total score ranging from 0 to 52 points, and three subscales: Pain Catastrophizing Scale Rumination, Magnification, and Hopelessness.  
Fear of movement was analyzed through the Tampa Scale of Kinesiophobia (TSK) [32]. It consists of 11 items, with the total scores ranging from 11 to 44 points.  
The disability perceived by vertigo or dizziness was analyzed though the Dizziness Handicrap Inventory (DHI) [33]. It consists of 25 items divided into three separate subscales: the emotional (DHI-E), the functional (DHI-F), and the Physical (DHI-P) subscales. Total DHI scores range from 0 to 100 points.  
Figure 3: SVV and RFT experiment.  
The level of confidence in balance was assessed with the Activities-specific Balance Confidence Scale (ABC-16) [34]. It consists of 16 items with a scale of ascending answers from not at all confident (0%) to completely safe (100%) and a total score ranging between 0 and 100%.  
Fear of falling was analyzed with the Falls Efficacy Scale International (FES-I) [35]. It consists of 16 items, with a total score ranging from 16 to 64 points. Higher scores indicate a greater fear of falling.  
The level of fatigue was determined through the Fatigue Severity Scale (FSS) [36]. It consists of 9 items that analyze aspects of physical and mental fatigue, with scores ranging from 9 to 63 points, where scores \(\geq\) 36 indicate severe fatigue.  
In all cases, higher scores indicate a higher level of the corresponding variable.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 4 Experimental Tests of the Monitoring System
### Latency and Jitter for Broadcast Measurement Command Frames  
This test is aimed at measuring the minimum latency and jitter of a broadcast measurement command frame. Frames were sent periodically by the Coordinator Node, executed over the NI myRIO platform, to the Smart Measuring Nodes. To better synchronize the distributed measures, messages were sent according to the transaction clock, centralized at the Coordinator Node. Hence, latency and jitter for this operation were key values in order to achieve an appropriate behavior of the monitoring system.  
Latency was measured with an oscilloscope (see Figure 11). Specifically, it was measured the instant in which the Coordinator Node sends the broadcast measurement message (in yellow) and the reception at the Smart Measuring Node (in green). The duration of the test was of 10 min, and frames were sent periodically at a rate of 60 ms. Oscilloscope data were captured using the persistence property to obtain the maximum and minimum latency values of the test. Figure 11 shows the results when both devices were located at a very short distance. In such cases, the measured values were 9.62 and 10.60 ms, respectively. Consequently, the transmission latency of a broadcast measurement command is around 10 ms, with a jitter of 0.98 ms. Figure 12 shows the repeatability of the measure at a different time scale. Yellow lines indicate when the broadcast measurement command is issued by the Coordinator Node, whereas green lines show when these messages are received at the Smart Measuring Nodes.  
Figure 10: Scenario 2: combination of several SMNs using different sampling configurations.  
Figure 11: Minimum and maximum latency for a broadcast measurement command frame.  
Figure 12: Periodical sending of a broadcast measurement frame at 60 ms.  
around 12 m did the value of the jitter increase. However, this value may be acceptable for some applications depending on the dynamics of the process.
# Bio-Sourced and Biodegradable Membranes
## 2 Green Membrane Materials
### Synthetic Biodegradable Polymers  
#### 2.2.1 Polymers Synthesized from Renewable Sources  
Polylactic acid (PLA) is a thermoplastic polyester that is produced by polycondensation of lactic acid or ring-opening polymerization of cyclic diester-lactide (see Figure 4). Lactic acid, in turn, is produced from a renewable bio-source, such as sugarcane or starch, [62] by aerobic fermentation [63]. PLA is one of the most researched and mass-produced biodegradable polymers.  
PLA belongs to semicrystalline polymers. PLA-application temperatures are limited by its low glass-transition temperature (T\({}_{\rm g}\) = 55-60 \({}^{\circ}\)C) and melting temperature (T\({}_{\rm m}\) = 170-180 \({}^{\circ}\)C) [64]. PLA is suitable to prepare membranes based on electro-spun fibers for oil-water separation [65] and tissue regeneration [66]. PLA biodegradation proceeds quite fast to CO\({}_{2}\) and water [67], which meets all the requirements of all standards for biodegradable plastics [68]. PLA is also suitable for decomposition as a part of compost [69].  
Polybutylene succinate (PBS) is another (like PLA) example of a biodegradable polyester that is produced by polycondensation of succinic acid and 1,4-butanediol. succinic acid can be produced through biomass fermentation [70] by further hydrogenation to 1,4-butanediol [71]. The further polycondensation process of succinic acid and 1,4-butanediol proceeds in two steps and requires catalysts to produce a polymeric PBS from its oligomers, obtained in the first step (see Figure 5). The first mass production of this polymer began in 1990s in Japan by the Showa Highpolymer company [72]. PBS are reported to be used for the fabrication of membranes for pervaporation and other applications [73; 74; 75]. PBS biodegradation in soil was reported to proceed with 65% carbon mineralization (conversion to CO\({}_{2}\)) for 425 days [76].