esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Topology Optimization  
In the general formulation for topology optimization, the design variables are assigned to each element in the mesh and are denoted as \(\eta_{i}\), a pseudo-density ranging from 0 to 1. A value of 0 indicates that the material should be removed, while a value of 1 indicates that it should be retained. The goal of the optimization process is to find the optimal values of these design variables to achieve the desired objective [21].  
The mathematical formulation for topology optimization can be represented as:  
\[\text{Maximize or minimize }f\left(\eta_{i}\right) \tag{1}\]  
\[\text{subject to }0\ <\eta_{i}\leq 1\qquad\left(i=1,2,3\ldots,m\right) \tag{2}\]  
\[g_{j}\leq g_{j}^{\max}\qquad\left(j=1,2,3\ldots,n\right) \tag{3}\]  
Here, \(m\) and \(n\) are the number of finite elements and the number of constraints, respectively, and \(g_{j}^{\max}\) is the upper limit for the constraint \(g_{j}\).
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 3 Results
### Agile Stage-Gate Results  
The Agile Stage-Gate methodology for the AztechSat-1 project worked for not only the technical development process but also for the conceptualization and design stages. The hybrid model was able to integrate the best of both the Agile and Stage-Gate systems and allowed us to handle changing requirements during the different stages of the project. Themes from an analysis of interviews allowed us to identify and support our theory regarding application of the Agile Stage-Gate methodology in physical product development. Figure 17 links the concepts to themes explaining the contributing mechanisms.  
All team members participated from the beginning of the project, which yielded good results. Through training, it was possible to gather an excellent team. Dealing with innovative projects always entails uncertainty. However, assessing and validating stages through gates--as in the traditional model--supported by an Agile methodology was found to be an efficient technique for managing product development.  
The applied methodology allowed us to adapt to, respond to, and learn from changing situations within certain restrictions. Gates played an important role in the project because experts could review the project from strategic points, which eliminated project weaknesses and optimized human resources and materials. The sprint iteration during each stage of the project inspired early testing for self-developed modules. Through prototyping, the Agile Stage-Gate methodology became more adaptive.  
Figure 16: Thermal vacuum test setup.
# New Optical Design Method of Floating Type Collimator for Microscopic Camera Inspection
## 3 Results and Discussion
In this case, a spline interpolation is conducted on the locus of all lens groups, and the amount of movement of a specific lens group that satisfies the desired value of the image distance of the optical system is obtained [54]. In addition, the movement amount of the lens group can be calculated using an analytical formula with precision within the depth of focus [55]. Table 3 shows each lens group locus when using this method. In Table 3, Group 1 to Group 4 means \(z_{1}\) to \(z_{4}\) of Equation (1), respectively, and is the interval between each lens group. We observe that the distance from Group 4 to the aperture does not change. In addition, 'Image' in Table 3 means the distance from the test lens to the image surface. The locus for the lens groups and the magnification of the test lens are shown in Figure 6. Table 3 lists the image distances of the optical system. Here, the value minus the focal length of the test lens is \(\Delta z\). The magnification of the test lens from the virtual image distance by the collimator and \(\Delta z\) is shown in Equation (7). The magnification in Figure 6 is the value calculated using Equation (6) [4; 56; 57]. As shown in Table 3 and Figure 6, Group 1 (the first lens group) and Group 4 (the last lens group) can be fixed. Therefore, the distance from Group 4 to the aperture is designed to have a constant value, so the test lens can be measured by placing it at a constant distance from the last surface of the collimator. \[m_{T}=-\frac{f_{T}}{l-f_{T}}=-\frac{f_{T}}{\frac{f_{T}^{2}}{\Delta z}}=-\frac {\Delta z}{f_{T}} \tag{7}\]  
The optical system used in this study did not require a tolerance analysis. This is because the number of optical systems is not large, a barrel made of metal and lens is accurately manufactured and assembled. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Cam Angle** & **Object** & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **Image** & \(\Delta z\) & **Mag.** \\ \hline
0 (infinity) & 10 & 3.592 & 6.821 & 14.331 & 10.666 & 4 & 0 & 0 \\
0.1 & 10 & 3.614 & 7.912 & 13.218 & 10.666 & 4.008 & 0.008 & \(-\)0.002 \\
0.3 & 10 & 3.657 & 9.994 & 11.093 & 10.666 & 4.024 & 0.024 & \(-\)0.006 \\
0.5 & 10 & 3.508 & 12.160 & 9.076 & 10.666 & 4.040 & 0.040 & \(-\)0.010 \\
0.7 & 10 & 3.089 & 14.509 & 7.146 & 10.666 & 4.057 & 0.057 & \(-\)0.014 \\
1.0 (near) & 10 & 1.914 & 18.435 & 4.395 & 10.666 & 4.081 & 0.081 & \(-\)0.020 \\ \hline \end{tabular}
\end{table} 
Table 3: Locus table for our designed final collimator system.
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
###### Abstract  
The blackcurrant (_Ribes nigrum_ L.) is a small fruit known for its health benefits, but treatment effects on postharvest storage for fresh markets remain understudied compared with other berries, such as blueberries (_Vacinium_ spp.). This work aimed to identify the effects of postharvest storage conditions including chitosan coating, ultraviolet a (UVA) light, and combined UVA-chitosan treatments on the physicochemical and microbial properties of blackcurrant. Blackcurrants were harvested, stored under the three conditions, and analyzed at every three days of storage throughout this experiment for a total of 15 days. The results indicated that chitosan treatments had positive effects on reducing berry weight loss, maintaining berry firmness, and reducing mold populations. UVA influenced certain bioactive compounds, such as cyanidin-3-galactoside and ruin. The interaction effects from these two treatments were minimal. This study provides important information for blackcurrant postharvest storage and further small fruit storage work, considering both UVA and chitosan had differential beneficial effects on blackcurrant berries' physical and chemical attributes.  
blackcurrent; 'Tofino'; UVA; chitosan; postharvest storage Article
# Validating a 5G-Enabled Neutral Host Framework in City-Wide Deployments
## 4 City-Wide Deployments
### Infrastructure Deployment in the City of Barcelona
#### 4.1.3 RAN Tier  
The RAN equipment in the _22@ area_ is mounted on six lampposts with their own energy supply and 1 Gbps fiber connection. Three of them are equipped with LTE small cells (Accelleran E1010), whereas the other three lampposts are equipped with Wi-Fi nodes  
Figure 3: Infrastructure deployed in (**a**) Barcelona, (**b**) Bristol and (**c**) Lucca.  
(i2 CAT custom hardware). The RAN equipment in the city hall consists of two LTE small cells installed next to the _Salo de Cent_ room covering city council meetings and public events. The small cells deployed in Barcelona use Band 42 and follow the TDD config mode 2 on 20 MHz, providing a maximum of 90 Mbps Down-Link (DL) and 10 Mbps Up-Link (UL).
## 3 Survey of Techniques for Updating
### Update Fault Detection and Recovery
#### 3.4.4 Autonomous and Distributed Node Recovery (ADNR)  
Detection of, and recovery from, of faults deliberately inserted into nodes in a WSN is considered in this work [89]. A network is divided into clusters, and for intrusion detection (IDS) each node observes the behavior of its cluster neighbours. The IDS may be of any form--for example: _malignancy counters_, active probing, rule-based, reputation-based, or locally verified correctness properties. Two key protection features are identified: _logical protection_ to ensure that the recovery system remains available and uncompromised, no matter what malignant software does (for example, using a dedicated hardware interrupt vectoring to write-protected memory); and _physical protection_ to make the hardware (reasonably) tamper-proof. As long as the recovery component of a faulty node is intact, recovery is through a restart, or a code update. If the node does not respond to these measures, then it is expelled through a majority decision in order to avoid inconsistencies. Recovery takes places in two phases: an _accusation phase_ in which nodes accuse all neighbours which have been identified as faulty, and a _recovery phase_ whereby if a node is accused by at least two or three of its neighbours, countermeasures are executed. This may consist of a node reloading the correct software update from its neighbours, using hash codes to ensure the integrity of each fragment, and using rounds to distribute the load. Or a node may reboot, or shut down permanently.  
Autonomous and Distributed Node Recovery defines a complementary mechanism, with many similarities, targeted at identifying and recovering from malicious code deliberately inserted into a node as an update.
## 3 Biological Mechanisms in Bacterial Adhesion as Potential Subjects of Label-Free Monitoring
### Adhesive Molecules of Bacteria and Bacterial Entry into Host Cells
#### 3.1.1 Type III Secretion-Associated Invasion Mechanisms  
_Salmonella_ and _Shigella_ use a trigger mechanism to induce their penetration into host cells (see Figure 2) [271]. The constituents that mediate invasion in both microbes are homologous and involve a type III secretion system which uses effector proteins into host organisms [272]. This induces host-signaling pathways required for cytoskeletal rearrangements resulting in bacterial penetration [17]. Type III secretion systems are found in plant, animal, and human pathogenic bacteria and contain approximately 20 proteins that are very homologous between the various species [273]. In general, type III secretion proteins are associated with the bacterial membrane, forming a secretion pore complex with a short hair-like structure that resembles an injection needle, like the flagella motor and the hook [17; 274]. The induction of the type III secretion system involves the mobilization and association of proteins which already exist in the bacterial cytosol [17]. A small number of invasion factors can be competent to bind to the receptors of the host organism, which then induce the signal for the uptake of the pathogens [275]. Secreted Ipa proteins in _Shigella_[276] and Sip proteins in Salmonella are required for invasion [277]. _Shigella_ Ipa proteins can interact with \(\alpha\$61\) integrins, which lead to the activation/ phosphorylation of the pFAK125  
Figure 3: Schematic illustration of bacterial cell structure. The main types of adhesive molecules are also illustrated. Adhesins mediate attachment of pathogenic bacteria to host cells. Adhesive molecules that also promote the internalization are called invasins. However, not all the bacterial species have all the illustrated adhesive molecules. For example, enteropathogenic _Yersinia_ has YadA, that helps to adhere to epithelial cells. _N. meningitidisidis_ and _N. gonorrhoeae_ express a number of Opas and also Opcs, highly variable opacity-associated outer membrane proteins. _E. coli_ strains (enteropathogenic and enterohemorrhagic) produce the outer membrane protein intimin that recognizes minimum two types of receptors on the surface eukaryotic cell. _L. monocytogenes_ syntheries varies various homologous surface proteins, termed internalins, which are significant for bacterial entry into eukaryotic cells. Furthermore, bacteria can adhere to a plant, animal, or another bacterial cell by juxtaposing its own glycocalyx to the surface of the desired cell. Bacterial adhesion to an inert, solid surface must overcome the evolving electrostatic repulsive forces.  
(focal adhesion kinase) and paxillin (cytoplasmic adapter protein) [17; 278; 279]. IpaB binds to the extracellular domain of the receptor CD44 [280]. CD44 cell-surface glycoprotein is recruited at bacterial entry sites and appears to be necessary for Shigella invasion by starting the early steps of penetration [280].
# Structure and Sound Absorption Properties of Spiral Vane Electrospun PVA/PEO Nanofiber Membranes
## 3 Results and Discussion
### Morphological Analysis  
The surface morphology and the cross section morphology of the nanofiber membranes with different contents of PEO were presented in Figure 5. We can find that the morphology of fibers changed gradually. In Figure 5A, the fibers were straight, smooth, and even, and they were criss-crossed and intertwined closely as well as the fibers in Figure 5a. As a result, in Figure 6 the surface of the sample was flat and uniform. When added PEO to PVA, the morphology of the fibers, the surface and the internal structure of the films have undergone some changes. Adding a small amount of PEO, in Figure 5B the fibers became uneven, curved with wrinkles appeared. In Figure 5C,D, the adhesion and interlacing between the fibers was more obvious when the content of PEO was increased. This uneven arrangement resulted in larger holes and narrow gaps existing between the fibers. As showed in Figure 5a-d, the internal construct of films were from dense to fluffy. The fibers changed from reticular to lamellar distribution correspondingly, there were gaps between the layers. When the blending ratios were 80/20 (Figure 5c) and 70/30 (Figure 5d), a large amount of solid block structure appeared in the films, it was probably because a large number of fibers adhered together.  
Figure 4: X-ray diffraction of PVA/PEO membranes. (**a**) 8 wt % PVA; (**b**) 8 wt % PVA/3 wt % PEO (90/10); (**c**) 8 wt % PVA/3 wt % PEO (80/20); (**d**) 8 wt % PVA/3 wt % PEO (70/30); (**e**) 3 wt % PEO.  
Figure 5: SEM of surface and cross-section of PVA/PEO membranes. (**A**)(**a**) 8 wt % PVA; (**B**)(**b**) 8 wt % PVA/3 wt % PEO (90/10); (**C**)(**c**) 8 wt % PVA/3 wt % PEO (80/20); (**D**)(**d**) 8 wt % PVA/3 wt % PEO (70/30).  
Figure 6: _Cont._  
Electrospun films were composed of numerous intertwined fibers, so the structure of membranes are related to fibers, morphology closely. It can be found from Figure 6, that the surfaces of films were changed from flat to rugged. The height difference of sample a was only 241 \(\upmu\)m, while sample b was up to 1227 \(\upmu\)m, which nearly five times of sample a. However, the convex parts of sample b were distributed unevenly. The convex parts of samples c and d were denser and the height differences were 454 \(\upmu\)m and 609 \(\upmu\)m, respectively. As showed in Figure 5, there were many pores and cavity structures inside the films, it is probably because the addition of PEO changed the morphology and arrangement of fibers. The electrospinning films with uneven surface and cavity structures may be considered a defect, but as a kind of sound-absorbing material, they play an important role.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 3 Analysis of Numerical Simulation Results
### Probability Density Distribution of Light Intensity
The skewness and kurtosis of the log-intensity distribution are negatively correlated and consistently satisfy S < 0 and K > 0.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 2 Figures of Merit for Photodetectors
### Detectivity and Specific Detectivity  
A photodetector with a low NEP is preferred in order to achieve optimal performance. In the field of device characterization, it is common to use a parameter that quantifies the performance of a device, where a larger value indicates better performance. However, it is not desirable to state that a smaller parameter value corresponds to better device performance. To address this, another parameter called detectivity (\(D\)) is defined as the reciprocal of NEP, as shown in Equation (8):  
\[D=\frac{1}{P_{NEP}} \tag{8}\]  
It is important to note that the detectivity value depends on the photosensitive area of the device. To facilitate performance comparisons between photodetectors with different photosensitive areas, a parameter called specific detectivity (\(D^{\star}\)) is introduced, as shown in Equation (9):  
\[D^{\star}=\frac{\sqrt{A}}{P_{NEP}} \tag{9}\]  
where \(A\) is the photosensitive area of the device. The unit of specific detectivity is cm\(\cdot\)Hz\({}^{1/2}\)/W, which is also written as Jones. For devices where shot noise dominates the noise characteristics, the \(D^{\star}\) is expressed as:  
\[D^{\star}=\frac{R_{I}\sqrt{A}}{\sqrt{2 eI_{d}}} \tag{10}\]  
where \(I_{\text{d}}\) is the dark current of the device. In the case of diode-like devices operating at zero bias, \(D^{\star}\) is expressed as [67]:  
\[D^{\star}=\frac{R_{I}\sqrt{A}}{2\sqrt{eI_{sat}}} \tag{11}\]  
where \(I_{\text{sat}}\) is the absolute value of the reverse saturation current. The specific detectivity provides a normalized value that takes into account both photosensitive area and bandwidth, enabling effective performance comparisons between different devices.
## 5 Conclusions  
In this paper, we propose an innovative approach to expedite vision transformers by harnessing the sparsity of informative patches present in the input video frames. Our strategy, encapsulated within our TSNet framework, dynamically prunes less significant tokens for each input instance, guided by a personalized binary decision mask generated from a lightweight selection module. This selection module is introduced at multiple layers, allowing token pruning to occur hierarchically. Gumbel-softmax is employed in the selection module to achieve comprehensive end-to-end training. Attention masking techniques can mask redundant tokens during the training process to prevent interference with other tokens. A linear projection is utilized for pruned tokens in order to preserve the unchanged token quantity. During the inference process, our methodology effectively enhances efficiency by pruning the input tokens. Our framework proves to be versatile and applicable to a broad array of network structures, including, but not limited to, transformers equipped with convolutions or shifted windows. Although the key focus of this paper is on the task of action recognition, extending our methodology to other applications such as image classification and dense prediction tasks presents interesting prospects. In summary, our TSNet offers a significant contribution to transformer models' optimization, highlighting the importance of dynamic selection and pruning within this context.  
**Author Contributions:** Conceptualization, H.W.; methodology, H.W.; software, H.W.; validation, H.W. and W.Z.; formal analysis, H.W.; data curation, H.W. and W.Z.; writing--original draft preparation, H.W.; writing--review and editing, G.L.; visualization, H.W. and W.Z.; supervision, G.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **GFLOPs** & **Top-1 (\%)** & **Top-5 (\%)** \\ \hline \(\mathrm{L}_{0.6}^{1}\)\(\mathrm{H}_{1}^{1}\) & 134.8 (\(-\)19.3\%) & 79.0 & 92.7 \\ \(\mathrm{L}_{0.2}^{1}\)\(\mathrm{H}_{1}^{1}\) & 134.2 (\(-\)19.6\%) & 79.5 & 93.1 \\ \(\mathrm{L}_{0.8}^{0.6}\)\(\mathrm{H}_{1}^{1}\) & 132.4 (\(-\)20.7\%) & 79.3 & 93.5 \\ \(\mathrm{L}_{0.8}^{0.8}\)\(\mathrm{H}_{0.5}^{0.5}\) & 134.1 (\(-\)19.7\%) & 79.6 & 93.5 \\ \(\mathrm{L}_{0.9}^{0.6}\)\(\mathrm{H}_{0.5}^{0.5}\) & 134.9 (\(-\)19.2\%) & 80.0 & 93.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies to assess different location for token selection.  
Data Availability Statement: In this manuscript, the employed datasets have been taken with license agreements from the corresponding institutions with proper channels.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**OGRU-CSO Model with 50:50% Training and Testing of Data**} \\ \hline
**Features** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MCC (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline HoG & 78.62 & 88.82 & 83.40 & 90.99 & 90.22 & 90.00 \\ LBP & 88.50 & 90.13 & 90.14 & 89.77 & 89.65 & 90.02 \\ LTP & 90.70 & 82.89 & 90.78 & 90.24 & 88.66 & 82.93 \\ Hybrid & 92.80 & 90.28 & 91.90 & 92.91 & 90.48 & 90.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation results of the proposed model by varying the features with 50:50% training and testing of data. Figure 10: Graphical comparison of dissimilar optimizers with 80% training and 20% testing of data. Figure 9: Graphical representation of dissimilar optimizers with 50% training and 50% testing of data.
## 4 Results and Discussion
### WM-OA-ICOS Combined with White Noise Perturbation
#### 4.2.2 Measurement Stability and MDL for WM-OA-ICOS  
In order to fully evaluate the performance of the sensor, measurements were conducted on standard CH\({}_{4}\) gas with the concentration of 1000 ppm for 10 min with 1 s sampling intervals. The MDL was analyzed in detail by the Allan deviation. The concentration data were collected by the sensor under the original signal and the white noise perturbation of 30% modulation depth, as shown in Figure 8a. The results showed that the concentration fluctuation range of the original signal (black) was less than 40 ppm, while the fluctuation range of the white noise perturbation (red) was reduced to 25 ppm. The average concentration, standard deviation, and maximum uncertainty obtained by the original system were 1008.2 ppm, 6.2 ppm, and 3.1%, respectively. The average concentration, standard deviation, and maximum uncertainty obtained after adding the white noise perturbation were 1004.1 ppm, 2.6 ppm, and 1.2%, respectively. The Allan deviation was further calculated for the original signal and the white noise perturbation, as shown in Figure 8b. The detection limit of the original signal (black) was 6.3 ppm at 2 s integration time, and the MDL was 2.8 ppm at 124 s integration time. The detection limit of the white noise perturbation of 30% modulation depth (red) was 2.6 ppm at 2 s integration time, and the MDL was 1.2 ppm at 106 s integration time. Compared to the original system under the same conditions, the sensing system with the white noise perturbation of 30% modulation depth improved the MDL by 2.3 times. It was shown that the addition of white noise perturbation not only enhanced the stability of the sensor, but also substantially improved the measurement sensitivity.  
Figure 7: Fitting curves between CH\({}_{4}\) concentration and the 2f signal absorption peak under the original signal (black) and the white noise perturbation of 30% modulation depth (red).