esa-sceva/satcom-chunk-collection huggingface dataset:
# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 2 PhotoMatch
### Feature Extraction  
The feature extraction includes the detection and description of keypoints. The tool includes several alternatives that can be classified as hand-crafted or learning-based feature-extraction methods (Figure 1).  
A total of 20 different hand-crafted methods were implemented. These include: SURF [28], SIFT [26], AKAZE [53] or MSD [29]. Most of the hand-crafted algorithms include a detector and a descriptor, which can be combined (e.g., SURF detector and SIFT descriptor). Different advanced parameters can be tuned, providing educational support for each available algorithm. An example of the MSD and SIFT options is provided in Figure 2b.  
In addition, the Affine SIFT (ASIFT) [54] algorithm is also available. This algorithm computes a fully affine invariant matching. It is specifically designed to deal with images that present considerable geometric variations in terms of scale and perspective. The algorithm simulates all possible views by modifying the longitude and latitude of the camera orientation parameters. The ASIFT algorithm can also be used, in combination with other similarity invariant-matching methods such as SURF, BRISK [55] or AKAZE.  
Figure 1: PhotoMatch pipeline, including a list of available algorithms/options for each step.  
Regarding the learning-based methods, two deep learning detectors/descriptors were incorporated in PhotoMatch: D2-Net [43] and R2D2 [44]. This selection was made based on their outstanding performance, and considering that these algorithms are freely available and use pretrained models, so they are not designed for a specific type of data.  
D2-Net uses a single convolutional neural network for simultaneous feature description and detection. Instead of carrying out the detection of low-level image structures, the process is carried out after the computation of the feature maps, when more reliable information is available. This has been assessed on multi-modal datasets, where it has proven to perform well for the matching of features under challenging illumination or weather conditions.  
R2D2 also simultaneously acts as a keypoint detector and descriptor. This includes a local predictor of discriminativeness during learning, to avoid areas with salient features but where accurate matching is not possible due to repetitiveness (e.g., sea waves or canopy). It has been proven to perform especially well for the matching of day and night images.  
The included deep learning algorithms were incorporated within PhotoMatch with pretrained models, while the selection of different pretrained models is also an option.  
Figure 2: Selection of parameters for preprocessing (**a**), feature extraction (**b**) and matching (**c**) in PhotoMatch. The help menu provides educational support for each advanced parameter.
## 6 Discussion
### Main Findings  
Our research demonstrated that integrating additional emotion dictionaries into the ML-Ask system improved its performance, albeit modestly, by approximately 2 percentage points. The manual expansion process highlighted significant differences in the interpretation of emotive expressions across dictionaries, underscoring the complexity of emotion lexicon development. Additionally, our automatic extraction method, while promising, introduced non-emotive expressions, necessitating manual curation to maintain accuracy. Specifically, chunk processing and the OkapiBM25 weighting coefficient proved most effective for segmentation and weighting, respectively.
## 3 Results
### Spatial Distribution of GPP  
We acquired the multi-year mean AG in spring, summer, autumn, and the growing season to analyze the spatial distribution of GPP in the GKM over the past 34 years (1982-2015), and the results are shown in Figure 2. The spatial distribution of AG in the three seasons and the growing season differed considerably; in spring, the AG decreased from the northeast (134 g C/m\({}^{2}\)) to southwest (73 g C/m\({}^{2}\)). In summer, the northern extreme and southern extreme had large GPP values (>755 g C/m\({}^{2}\)), while the west central GKM had the smallest GPP values (<665 g C/m\({}^{2}\)). Different from spring and summer, high values began to concentrate in the southern GKM in autumn, whereas the middle part still had the lowest values. The spatial pattern of the AG during the growing season (AGG) showed great similarities to the AG in summer, but had larger spatial heterogeneity. The highest AGG value over the whole region reached 1070 g C/m\({}^{2}\), and the lowest AGG value was about 680 g C/m\({}^{2}\).  
Figure 2: Spatial distribution of (**a**) spring-average accumulated gross primary productivity (AG); (**b**) summer-average AG; (**c**) autumn-average AG; and, (**d**) growing season (GS)-average AG during 1982-2015.
# Selective Extraction of REEs Thanks to One-Pot Silica Hybrid Materials
## 3 Results and Discussion
### Extraction of Rare-Earth Elements (Nd, Pr, Dy) towards Competitive Elements  
The obtained results suggest the application of SHM-4 for the recycling of NdFeB permanent magnets, while SHM-3 was not investigated due to its unspecificity toward REEs, as mentioned earlier. To investigate this, extraction experiments were performed using a simulated leachate, HNO\({}_{3}\) at pH 1, which contained Nd next to other typical REEs like Pr and Dy as well as typical competitive ions like B, Co, Ni, and Fe. The exact composition of the solution is given in Table 2.  
In this experiment, the solution/solid ratio was between 125 and 3500 (V = 1-25 mL; m = 7.5 mg), which corresponds to a material concentration between 8 and 0.3 g/L. The results are illustrated in Figure 6.  
Whatever the concentration, only REEs were extracted; this result in is good agreement with the specificity of the diglycolamide functional group for lanthanides [41; 42]. Negative values for the non-REEs indicate that the extraction quantities are below the detection limit. Even with a large excess of ligands (V/\(m\) = 1 mL/7.5 mg), no extraction of competitive ions was observed. At increasing solution volume, the percentage of extracted ions decreases because the metal uptake capacity of the material reaches saturation (Figure S5). The specificity for the REEs was also highlighted when the experiments were performed at constant metal concentration and by increasing the concentration of the material engaged in the extraction (Table S2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Elements** & **B** & **Co** & **Dy** & **Fe** & **Nd** & **Ni** & **Pr** \\ \hline
**w\%** & 1.1 & 1.6 & 1.3 & 67.2 & 24.5 & 0.6 & 3.7 \\
**Concentration (ppm)** & 11.32 & 15.45 & 12.35 & 641.6 & 218.7 & 3.05 & 34.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of the simulated leachate of a NdFeB magnet.  
Figure 6: Extraction efficiencies (\(Q_{c}\)) of the different metals present in the described permanent magnet leachate as a function of the quantity of functionalized SHM-4. Initial conditions: pH = 1; composition, see Table 2; volume (Solution) = 1–25 mL; m (SHM-4) = 7.5 mg. Contact for 24 h, T = 25 \({}^{\circ}\)C.  
The obtained results show the potential of SHM-4 for the highly selective extraction of NdFeB magnet leachates that contain large quantities of Fe (>65 w%). Even under these conditions, separation factors \(SF_{\text{(REE/Fe)}}>100\) were obtained even when the concentration of the material was about 7.5 g/L.
## 8 Case Study Application
First, the effects of environmental conditions may challenge feature extraction compared to synthetic signals. Second, the measured data will be affected by measurement errors due to the accuracy of the sensor. This could affect the goodness of the proposed features and, if this goodness is compromised, new features must be defined, or the existing ones must be improved. In fact, this is not a major problem if high-performance sensors, measurement systems and data transmission infrastructures that are used. Assuming that this framework is widely used, one would need to address the computational power required for AI training, especially with respect to the creation of the various synthetic signals through dynamic FE analysis, which is the real bottleneck of the whole process. Assuming that the random and parametric training in Figure 12, which was performed for research purposes and to understand the effectiveness of Bayesian optimization, should be avoided and, instead, once the signals are generated, we should proceed with the training through hyperparameter optimization, the question arises as to how much computational power or how much time is required to generate signals of a structure with multiple types of damage and different severity levels. As a simplified estimate, we can easily determine the number of analyses and thus the computational time required. Indeed, it is possible to calculate the total computational time with the following formula:  
\[TT\ =\ SAT\cdot NA_{ID}\cdot D^{\prime}{}_{n,k} \tag{3}\]  
where:  
_TT_ represents the total time to create the synthetic signals  
_SAT_ represents the time to perform a single analysis with the FE model  
_NAID_ represents the number of analyses to be performed for the same damage configuration to account for the variability of stochastic parameters  
_D_n,k is the number of the arrangements with repetition where: \(n\) is the number of damage severities and \(k\) is the number of the different types of damage to be investigated  
When calculating analysis time for real-world use cases, it is possible to see how much time is required for complex civil structures that easily reaches the order of hundreds, if not thousands, of years. However, this is where methods to increase computational power, such as parallel computing and cloud computing, come to the rescue. With these techniques, it is possible to divide a large problem into smaller problems that can be developed and solved in parallel. In fact, these techniques are easy to implement in the proposed framework, since  
Figure 18: Development prospective for the calculation of RUL over the entire life cycle of the structure. each analysis is independent of the others. Even if, at first sight, the results just reported give the impression that the application of this methodology is difficult, it is necessary to consider the various advantages that can be obtained.
## 3 Results
### Genomic Template Stability (% GTS)  
Exposure to benzene significantly reduced genome stability starting at 30 min (\(p<0.05\)). When zebrafish blood cells were exposed to EA or Cur, the rate of genomic DNA stability was identical as per the negative control at all exposure timepoints. GTS% did not change when blood cells were co-exposed to EA and benzene in comparison with the negative control, while co-exposure to Cur and benzene determined a significant reduction of genome stability only at 30 min compared to the negative control (Figure 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Treatment** & **Exposure Minutes** & **Gained Bands** & **Lost Bands \({}^{*}\)** \\ \hline \multirow{3}{*}{Benzene [10 \(\mu\)L/mL]} & 30 & 220, 280 & 950 \\ \cline{2-4}  & 60 & 290, 750, 900 & 600 \\ \cline{2-4}  & 90 & 100, 220, 850 & 300, 600 \\ \hline \multirow{3}{*}{EA [100 \(\mu\)M]} & 30 & - & - \\ \cline{2-4}  & 60 & - & - \\ \cline{2-4}  & 90 & - & - \\ \hline \multirow{3}{*}{Cur [40 \(\mu\)M]} & 30 & - & - \\ \cline{2-4}  & 60 & - & - \\ \cline{2-4}  & 90 & 450 & - \\ \hline \multirow{3}{*}{EA [100 \(\mu\)M] + benzene [10 \(\mu\)L/mL]} & 30 & - & - \\ \cline{2-4}  & 60 & - & - \\ \cline{1-1} \cline{2-4}  & 90 & 750 & - \\ \cline{1-1} \cline{2-4}  & 30 & 220, 390 & - \\ \cline{1-1} \cline{2-4}  & 60 & 220 & - \\ \cline{1-1} \cline{2-4}  & 90 & - & - \\ \hline \end{tabular} \({}^{*}\)Control bands are at: 200, 300, 350, 400, 500, 520, 600, 700, 800, 950, 980 bp.  
\end{table}
Table 1: Molecular sizes (bp) of appeared and disappeared bands after amplification with primer 6 in zebrafish blood cells DNA exposed to different substances. EA: ellagic acid; Cur: curcumin.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 2 Antenna Design
### Parametric Analysis  
Before moving on to the final results discussion, we are going to thoroughly discuss the final results achieved in six different steps. Following is the scenario in Figure 4a from which we obtained our desired results in Figure 4b:  
**Step 01:**: Designing a Y-shaped antenna element with the full ground.
**Step 02:**: Designing an X-shaped antenna element with the full ground.
**Step 03:**: The slight shift of an X-shaped antenna to an open scissor-shaped antenna.
**Step 04:**: An open scissor lower section is filled while the ground is kept full.  
Figure 3: Fabricated prototype (**a**) Front view. (**b**) Rear view.  
Figure 2: Detailed dimensions of the proposed ten-element MIMO array.
# Initial Characterization of PDMAEMA: Styrene Porous Polymer Monolithic Morphologies
###### Abstract  
This research aimed to investigate a new fabrication of poly-(2-dimethyl)(aminoethyl) methacrylate)-co-styrene in a porous polymer monolithic morphology. Poly-(2-dimethyl(aminoethyl) methacrylate) (PDMAEMA) is responsive to changes in pH while styrene remains unresponsive to external stimuli. IR, UV/Vis, and SEM were employed to determine that the proposed porous polymer (PPM) monolith can present pH-initiated stimuli response while remaining intact. The copolymerization of PDMAEMA with styrene has not been previously documented in a PPM morphology. It is important to demonstrate that the PPM retains tunable capabilities without destruction to the material. The utility of PDMAEMA copolymers is wide-reaching and this new adaptation of its tunability in a resilient PPM can serve as a distinct preface to original applications in fields such as surface modification, membrane technologies and stationary phases. To further the groundwork of this communication, dynamic studies on the interactions of small molecules with the pores of the monolith will be essential and accomplished via capillary electrochromatography.  
mechraylate monolith; porous polymer monoliths; external stimuli response; pH response +
Footnote †: journal: Applied Sciences  
dynamics at the solid-liquid interface, an attempt to form a monolithic co-block polymer of PDMAEMA-co-styrene and assess the sturdiness of the monolith under changing environments was made. While polystyrene is not a stimuli-responsive material, it is a strong vinyl polymer ubiquitous in everyday materials. The goal of the present experiment was to create a monolith that retained the structural tunability of the PDMAEMA that also possessed the stability and strength of the polystyrene and proved resistant to degradation. A copolymer with a pH-dependent structural response and a resistance to degradation over time could contribute greatly to work on analyte capture and release, drug delivery, separation science, sensors, and more. The present study attempts to confirm the presence of the tunable features of the co-block polymer in PPM form while remaining intact.
## 3 Results and Discussion  
Figure 3 shows the shape of the MMW pulse around 0.11 GHz, which was detected by three alternative methods. The blue graph represents the Schottky diode detector, which had a long decrease time due to the characteristic of the discharging capacitor. The red graph represents the electron method for the GDD detector. The GDD was connected to an electronic circuit that causes a limited response time of about 1 \(\upmu\)s [17]. However, the response time of the plasma inside the GDD was in the order of picoseconds, which explains why the yellow graph, which represents the optical method for the GDD, had the shortest response time.  
The optical GDD detection speed is derived largely from the speed of the photodetector. The electric GDD detection is limited in the response time, this driven by the parasitic capacitance of the GDD. Figure 3 shows that the Schottky diode detector has a longer decay time than the GDD. This means that the discharge of the parasitic capacitance of the GDD and the photodetector is shorter than the capacitance of a Schottky diode. In order to improve the recording of the falling signal of the Schottky diode detector, a 50\(\backslash\)varOmega load was added to an oscilloscope (oscilloscope input channel had a 1M\(\backslash\)varOmega impedance). Figure 4 shows the mixed-signal between MMW/THz pulse and local oscillator (frequency chirp of the MMW/THz pulse). This pulse contained more than one frequency, i.e., there was a change in frequency in the same pulse.  
Figure 5 shows the power detection of the Schottky diode and the GDD detectors for the pulse, of which the frequency appears in Figure 4, i.e., the power detection of a pulse of which the variable frequency over time was detected by three alternative methods. This result shows that the addition of impedance to the oscilloscope significantly improved the decrease time of the Schottky diode. The frequency change response in the MMW/THz pulse for the Schottky diode and the GDD detectors shows that, due to the high bandwidth of the GDD, it was much less sensitive to frequency change in the MMW/THz pulse.  
Figure 3: A 0.1 THz pulse detected by three different methods.  
Figure 5 shows a power detection for a THz pulse that has a frequency chirp. Due to the limited bandwidth of a Schottky detector, it is sensitive to frequency chirp, i.e., although there is an intensity difference between the low and the high frequencies, the Schottky diode detects the power of the low frequency in the pulse as much as the high frequency. Figure 5 shows that the GDD detectors are less sensitive to the frequency chirp, due to the large bandwidth of the GDD detectors.  
Figure 4: Mixed signal for variable frequency pulse.  
Figure 5: Comparison between Schottky diode and glow-discharge detector (GDD) respected the frequency chirp.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 3 Smartglass-Assisted Interactive Telementoring  
In our review of recent remote collaboration and telementoring studies in the previous section, we identified studies that use smartglasses and satisfy elementoring requirements as shown in Table 2. Using these selected studies, we define a specialized use case of elementoring, which we call \"smartglass-assisted interactive telementoring (SAIT)\". As the terminology suggests, smartglasses are the required hardware display unlike traditional medical and surgical telementoring that allow other alternative displays [31]. More specifically, the SAIT has the following characteristics over typical remote collaboration or elementoring in general.  
* _Mentors and Mentees of Different Levels._ The SAIT involves two groups of users categorized as mentors and mentees. Mentors are the sufficiently more knowledgeable and experienced groups with professional or technical expertise. Mentees are the less knowledgeable and experienced groups who would benefit or improve their performance with the help received from their mentors.
* _Sharing of First-person and Eye-level View via Smartglass._ Either or both mentors and mentees share their eye-level view captured by their HMDs with sufficient FoV to provide enough details and video quality for the intended telementoring tasks.
* _Extended Use Cases with Bi-directional Interaction._ The SAIT aims to cover flexible and casual use cases beyond the medical and surgical telementoring scenarios. In the SAIT, mentors and mentees are preferably on the go, situated, and hands-free for exploiting multiple interaction channels for various occasions.  
To elaborate on these characteristics of the SAIT applications, five illustrative and descriptive scenarios are presented in the next section.
## 2 Results and Discussion
### TFME Procedure Optimization
#### 2.2.2 Extraction and Desorption Time  
Since the extraction of the analyte from the sample to the TFME blade, as well as its later desorption to the solvent, only takes place until the equilibrium is reached between analyte concentrations in both phases (liquid-solid, solid-liquid), further extending the duration of both processes does not increase their efficiency. According to the results, the highest recovery and the best precision was achieved for a 30-min extraction time and 45-min desorption time (Figure 3).
## 3 Methods
### Digital Technologies Enabling the ReSOLVE Framework
#### 5.1.6 DTs Enabling the Exchange Area  
The area has been largely addressed mainly from a general, broad and theoretical perspective (Figure 10), understanding how the presence and adoption of new technologies could foster and support the transition towards new update CE practices. DTs could indeed offer a solution for core data records concerning a sustainable product and material database [79]. Opportunities can be found at different levels, as the optimization of the resources use, the engagement in business models enabled by software development, the share of information on a network level, the creation of infrastructures supporting the tracking and monitoring [104], while also fostering cleaner production [84]. Although so far, the discourse has been mainly theoretical, some first empirical applications can be found for specific CE aspects, as redistributed manufacturing [111].  
Concerning specific DTs, the discourse mainly developed around the adoption of IoT and BDA [16,67,77,112], with the latter playing a fundamental role also in terms of predictive analytics [64] with the support of CLOUD [67]. Positive impacts were also observed concerning AM, as it could easily support CE strategies focused on materials [76].  
At this stage, contributions also focused on the identification of barriers and challenges to and for DIGIT [67,100,104]. Some of the barriers refer to organizational aspects (lack of competencies, need for coordination, need for technical development), to economic aspects (financial and operational risk), as well as the digitalization process itself. Particularly, the adoption of DTs can be strongly hindered by organizational resistance deriving from both the employees and the management, who can oppose the change within their organization [125] and might lack specific competence and skills [126]; particularly, the role of managers has been considered of fundamental importance to support the integration of DTs in the light of CE [12]. Additional challenges seem to emerge concerning the context of the investigation, with developed countries facing mainly issues related to the low maturity level of the desired technology [127] after national strategies and policy have been formulated [128], and the developing countries still struggling with the setting of proper standards and legislation [127]. Specific barriers in the context of emerging economies were indeed pointed out in terms of the macro environment [90]. Additionally, an important role is played by the availability of the technologies, also from an economic perspective [17]. In this scenario, it is pivotal to identify the best drivers to overcome the barriers [129].