esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Ablation Study
#### 5.4.1 Ablation Tests About GAS and CGP
In general, the change of \(\lambda_{I}\) does affect the final _WR_, and the decrease becomes significant as the complexity and difficulty of the task increase. G-QMIX is generally less affected by the change of \(\lambda_{I}\) than G-MAPPO. The purpose of \(\lambda_{I}\) is to adjust the magnitude of CGP loss. Therefore, an optimal upper bound exists. Increasing \(\lambda_{I}\) may draw a large impact in complex scenarios. Decreasing \(\lambda_{I}\) reduces the limitation to the extra GAS parameters and the cooperation ability of the MARL method, and thus, the final results are affected. For better performance of applying our method in other environments, additional tests for searching optimal \(\lambda_{I}\) are required. \(\epsilon_{p}\) and \(\epsilon_{v}\) are default MAPPO hyper-parameters for determining the clipping range of the ratio of the new policy to the old policy and the value function updating gradient. The tables show that the default value 0.2 is generally a good enough value. Increasing or decreasing the value results in a decrease in the mean _WR_ and an increase in the variance. The impact of changing \(\epsilon_{v}\) is greater than changing \(\epsilon_{p}\), because the value function is optimized with the ground-truth _Return_ value. Therefore, the change of \(\epsilon_{v}\) results in the unreliability of the value function and further results in the unreliability of the advantage function, which further affects the optimization of the policy function.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Characteristics of Metro Station Interiors  
Metro stations have a unique construction that sets them apart from other buildings. Understanding the characteristic structure of the Warsaw Metro station is important for modelling the data.  
Stations typically feature multiple entrances located on various sides of a street, without the possibility of assigning a single address representing the station in a search engine. The shape of stations is generally linear, consisting primarily of corridors. Some stations feature an intermediate level called a mezzanine floor, situated between the platform and the ground surface. The mezzanine can be simple and located on one side of the station, or it can be extensive, connecting to major transfer stations and incorporating small galleries and service points. Some metro stations have connected platforms with a single access, while others have two separate platforms for trains travelling in opposite directions. Metro stations typically feature numerous points of interest, including turnstiles, platforms, and ticket machines. A 3D view based on the LIDAR point cloud for one of the Warsaw Metro stations is presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Data Structure** & **Objects** & **Use of the Algorithms** \\ \hline Transport network & Graph & Edges represent segments of pedestrian paths; nodes represent connections between path segments. & This graph is primarily used to calculate the route and create the route geometry. In the user tracking process during navigation, the geometries are used in map-matching. \\ \hline Room topology & Graph & Nodes represent rooms; edges represent connectivity between rooms. & Once the route is calculated, these data allow the enrichment of directions by adding the overall information about the building, i.e., room changes. \\ \hline Topography & Spatial objects & Floors, rooms, stairs, lifts, etc., represent parts of the building; landmarks represent any objects intentionally selected. & Provide additional information that can be used in creating comprehensive directions, such as room numbers, names, accessibility, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data layers used in the algorithm for the automatic generation of indoor guidance.
# Single-Carrier Rotation-Interleaved Space-Time Code for Frequency-Selective Fading Channels +
## 2 System Model
Our goal is the minimize the average error-rate probability by choosing a good space-time code for the above MIMO channel. In the pursuit of good code, the pairwise error probability (PEP) is a key criterion as the average error-rate can be upper-bounded by, via the union bound, the weighted summation of PEPs among all pairs of codewords. Thus, as in [18], we proceed by examining the PEP, which is defined to be the probability that the decision at the receiver is made erroneously in favor of a codeword \(\mathbf{\hat{C}}\) over the correct \(\mathbf{C}\) that is transmitted. Let the codeword difference matrix be \(\mathbf{B}=\mathbf{\hat{C}}-\mathbf{C}\) and \(\mathbf{A}=\mathbf{B}\mathbf{B}^{H}\). Using the assumption that the power delay profile is uniform and \(\mathbf{H}\) contains i.i.d. random variables, the pairwise error probability is upper-bounded by (at high SNR) [18; 23]  
\[P(\mathbf{C}\rightarrow\mathbf{\hat{C}})\leq\left(\frac{E_{s}}{4N_{0}}\right) ^{qM_{R}}\left(\prod_{i=1}^{q}(\lambda_{i})^{-M_{R}}\right) \tag{8}\]  
where \(q\) is the rank of matrix \(\mathbf{A}\) (or \(\mathbf{B}\)), and \(\lambda_{i},i=1,\cdots,q\) are the eigenvalues of \(\mathbf{A}\). Since the maximum rank of \(\mathbf{A}\) is \(M_{T}L\), the maximum _space-time-multipath_ diversity order one can achieve in this MIMO system is \(M_{T}M_{R}L\). Therefore, in order to minimize the PEP, the general STC design criteria are to maximize the rank, i.e., the diversity order, and the determinant (given by the product of the eigenvalues) of matrix \(\mathbf{A}\) over all pairs of codewords (\(\mathbf{C}\), \(\mathbf{\hat{C}}\)) [23]. One notable conclusion from their analysis in [23] is that an STC designed for maximum diversity diversity order (\(M_{T}M_{R}\)) in a flat fading channel does not necessarily lead to maximum diversity order (\(M_{T}M_{R}L\)) when used in a frequency-selective channel. Furthermore, unlike the flat fading case, not all the rows in \(\mathbf{C}\) can be designed independently as some of them are delayed versions of the others. As such, designing new STCs to achieve the maximum diversity order for frequency-selective channels becomes a challenging task. The goal of this paper is to obtain the maximum diversity order (\(M_{T}M_{R}L\)) with proper space-time code design.
## 3 Results
### Real-Time Improvement for Three Speckle Patterns Projection  
Following the previous optimal parameter analysis, comprehensively considering the measurement accuracy, image acquisition time, cost, volume, power consumption, use environment and other factors, the design scheme of a compact 3D face acquisition device was adopted with three fixed speckle projectors. Based on the optimum parameter combination of the three-speckle pattern projections obtained from the previous analyses, the coarse matching window with a size of 11 \(\times\) 11, grid interval of 11 and fine matching size of 7 \(\times\) 7 was selected. The parameters were applied to the GPU parallel acceleration process of the spatiotemporal correlation computations. The multi-core parallel function library, openMP, was employed for dense point cloud generation and triangulation of 3D surfaces. The point-cloud dynamic real-time display was achieved using the OpenGL pipeline strategy. The image acquisition, epipolar rectification and disparity estimation, point-cloud generation and 3D display were implemented in parallel through a multi-threaded pipeline. Based on the three-speckle pattern projection equipment, high-speed image acquisition and real-time 3D face reconstruction were achieved with an average  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pattern Number** & \begin{tabular}{c} **Optimal** \\ **Window** (1) \\ **(pixel)** \\ \end{tabular} & **Min. Avg. Err (2)** & **Min. Std. (3)** & \begin{tabular}{c} **Computation** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Computation** \\ **Time on CPU** (4) \\ **(ms)** \\ \end{tabular} &
\begin{tabular}{c} **Time on GPU** (5) \\ **(ms)** \\ \end{tabular} \\ \hline N = 1 & 9 \(\times\) 9 & 0.149 & 0.144 & 218 & 30 \\ N = 3 & 7 \(\times\) 7 & 0.097 & 0.133 & 280 & 35 \\ N = 6 & 5 \(\times\) 5 & 0.079 & 0.109 & 338 & 65 \\ N = 9 & 3 \(\times\) 3 & 0.076 & 0.102 & 390 & 97 \\ N = 12 & 3 \(\times\) 3 & 0.071 & 0.091 & 554 & 121 \\ \hline \hline \end{tabular}  
* Optimal matching window corresponding to the pattern number. (2) Minimum average error of fitting the face mask between ours and Core 300. (3) Minimum standard derivation of fitting the face mask between the proposed method and Core 300. (4): The computation time on the CPU when the optimal matching window corresponds to N. (5): Computation time on the GPU when the optimal matching window corresponding to N.  
\end{table}
Table 1: Minimal error statistics and computation time of measuring a face mask (Unit: mm).  
Figure 16: Three-dimensional reconstruction results of the author’s various expressions when N = 12 the speckle stereo image pairs.  
error of 0.098 mm, a standard deviation of 0.133 mm, a 15-ms image acquisition time and a 35-ms disparity computational time.
# Exploring New Fruit- and Vegetable-Derived Rennet for Cheese Making
## 3 Results
### Milk-Clotting Activity (MCA)  
Milk coagulation is a fundamental step in cheese making. The clotting enzymes influence the cheese yield, texture, and flavor. For this reason, the MCA was measured for the vegetable and fruit extracts. The activity was measured according to [PERSON] et al. [13], by recording the time for the formation of the first visible clot. The milk-clotting activity (MCA) Standard Units (SU), which refer to the specificity of hydrolysis of the clotting enzyme towards \(\kappa\)-casein, are listed in Table 1, while Figure 2 depicts the samples experimentally investigated.  
The most efficient clotting enzymes were from calf rennet and fig milky sap, showing values of 8000 SU. Lower efficiencies were found for cardoon, papaya, and oyster mushroom, having MCAs of about 600 SU, whereas the extract from artichoke internal bracts was 10-fold less efficient than calf rennet (800 SU). Finally, the pineapple extract was about 8-fold less efficient than calf rennet, showing an MCA value equal to 1067 SU.
## 5 Results and Discussion  
### Performance Metric Results on Selected Kinematic Parameters for Healthy and HOA Gaits  
This subsection presents experimental results. The proposed HLSTM-CNN model is evaluated on the selected kinematic parameters' test dataset, specifically, to distinguish between healthy and HOA gaits, as described in Table 5. The test dataset is randomly chosen and was never used during the training procedure. The sagittal angles of the knee 14 and the hip provide the best performance results with G-Mean scores of 97.6% and 97.8%, respectively. Apart from its concurrence with other published reports [23,24] as the most discriminating kinematic parameters, the outstanding performance results highlight DL superiority over traditional ML algorithms. Notably, healthy gaits of these kinematic parameters are not misclassified as HOA gaits, which is reflected in its sensitivity result of 100%. The next two best performing kinematic parameters are the font angles of the knee and FPA with G-Mean scores of 96.5% and 94.9%, respectively. This result well-aligned with those published in the literature.  
On the other hand, the worst performing kinematic parameters are the sagittal angles of ankle and FPA and the transverse angle of the pelvis with G-Mean scores below 90%. Particularly, the sagittal angle of the FPA is the only kinematic parameter that registers below 80% accuracy and G-Mean score. Put in context, its accuracy of 75% is already comparable with traditional ML algorithms and agrees with prior studies as the least discriminating kinematic parameter.
## 3 Results and Discussion
### Evaluation of Self-Healing Performance as a Function of Temperature and pH
#### 3.2.3 Crystallographic Change in Calcium Carbonate Compound (CaCO\({}_{3}\))  
To check for the possibility of generating vaterite as a denser calcium carbonate crystal and controlling its formation by regulating the temperature or pH, SEM analysis was conducted on the CN series of test bodies under each self-healing condition. The SEM with a probe diameter of 0.5-5 \(\upmu\)m was operated at an accelerating voltage of 15 kV. Figures 17 and 18 show SEM images of self-healing precipitates generated at different temperatures (20, 40, and 60 \({}^{\circ}\)C) and pH values (9.0, 10.5, and 12.0). The images suggest that calcite was formed at 20 \({}^{\circ}\)C, vaterite was formed at 40 \({}^{\circ}\)C, and aragonite was formed at 60 \({}^{\circ}\)C when the pH was set at 9.0 (Figure 17) [24,25,29]. These values represent the optimum temperature for generating a large amount of vaterite; they also suggest that the type of CaCO\({}_{3}\) crystals can be controlled by changing the self-healing temperature. Although the generation of vaterite was affirmed in all cases, the pH value is believed to have some effect on the CaCO\({}_{3}\) crystals when the self-healing temperature was kept constant at 40 \({}^{\circ}\)C (Figure 18) [24,29]. The current experimental results suggest that temperature had a greater effect than pH on the type of CaCO\({}_{3}\) crystals generated during self-healing. In particular, for the sample CN-40-9.0 (immersed in Ca(OH)\({}_{2}\) solution with a temperature of 40 \({}^{\circ}\)C and pH of 9.0), Ca(OH)\({}_{2}\) was not observed among the hydrated products, and the amount of generated vaterite attached to the surface was the largest, along with a large amount of C-S-H gel. Hence, based on the findings of reported studies, the controlled generation of vaterite as a denser crystal was possible by setting the temperature to 40 \({}^{\circ}\)C and the pH to 9.0 [24,25].  
Figure 17: SEM images of self-healing precipitates generated at different temperatures (20, 40, and 60 \({}^{\circ}\)C) and a constant pH of 9.0.  
Figure 18: _Cont._
## 3 Link Quality Estimation Routing Protocol Design  
LQER protocol makes path selecting based on historical states of link quality after minimum hop field is established. Firstly, dynamic window concept (\(m,k\)) is presented to evaluate link reliability in subsection 3.1, and then subsection 3.2 describes how to establish minimum hop field. The path selecting of proposed LQER is described in detail in subsection 3.3.
# How Does _Bacillus thuringiensis_ Crystallize Such a Large Diversity of Toxins?
## 3 Toxins Evolved to Be Crystallization-Prone
These toxins possess a C-terminal domain--generally referred to as 'crystallization domain'--which represents approximately half of the protoxin size, is highly conserved among 130-140 kDa toxins and is mandatory for their crystallization [18]. While the N-terminal core toxin is devoid of cysteines, the C-terminal domain contains 14-19 cysteines located on flexible loops that form intra- and intermolecular bonds contributing to the formation and/or stabilization of the crystal [10; 18; 42]. However, not all cysteines are of equal importance. Full-length Cry1 Ac contains a total of 16 cysteines, 14 of which are located in the C-terminal domain while the two others are on the short N-terminal propeptide. When produced in _E. coli_, purified Cry1 Ac protokins tend to rapidly form hardly soluble aggregates through intermolecular disulfide cross-linking [10; 19]. Mutation of the 14 cysteines from the C- terminal domain led to a more stable protein in solution, which could form crystals in vitro with similar crystal packing as in vivo-grown crystals [10; 43], suggesting that these cysteines have some role in protein-protein interactions but do not drive the crystallization. However, when all 16 cysteines were mutated, crystals formed in vivo promptly solubilized after sporulation, indicating that the two cysteines from the N- terminal propeptide play a major role in the protoxin crystal stability outside the cell, although the exact mechanism is yet to be determined [10; 18]. Structures directly determined from in vivo-grown crystals are still lacking for investigating further the role of these cysteines in Cry1 crystal formation and dissolution pathways.
## Materials and Methods  
### Fabrication and Characterization  
The models of the aquatic UAV were fabricated via 3D-printing technology [33], using white photosensitive resin (Xiamen Jiecheng 3D Technology Co., Ltd., Xiamen, China). The dimensions of the aquatic-UAV model in the axial and spanwise directions are 103 mm and 99 mm, respectively. A textured microstructure was constructed on both models' surfaces; its direction is perpendicular to the longitudinal axis of the model, and the grooves and ribs are equidistant. A 3D super-depth-of-field microscope (KEYENCE Co., Ltd., Osaka, Japan, VHX-6000) was used to characterize the surface microstructure, with a magnification of 100 (Figure 1c). The rib length, groove width, and groove depth on the surface of the aircraft are 100 \(\upmu\)m, 70 \(\upmu\)m, and 50 \(\upmu\)m, respectively. Where the model has significant curvature, such as the head and fuselage, the textured structure is annular.  
A contact-angle meter (DataPhysics Instruments OCA20, Filderstadt, Germany) characterized the static hydrophobicity using a single-droplet volume of 4 \(\upmu\)L (diameter of about 3 mm) at an observation point on the airfoil. The surface with a textured microstructure  
Figure 1: Surface texture and hydrophobicity characterizations of the models: (**a**) the 3D-printed models of aquatic UAV for comparative experiments; the insets indicate static wettability characterization of each model, indicating hydrophilic (\(\theta=88^{\circ}\)) on the left, and superhydrophobic (\(\theta=155^{\circ}\), \(\theta_{\rm S}=3^{\circ}\)) on the right; (**b**,c**) show the anisotropic structure of the surface, which promotes the directional flow of liquids, observed by ultra-depth-of-field microscopy; the yellow scale bar is 400 \(\upmu\)m, and the magnification is 100.  
has a contact angle of about 88\({}^{\circ}\) without modification, which is hydrophilic. To create the hydrophobic model, commercial coatings [34] were applied to one model (to the right in Figure 1a) to reduce the surface energy. The modified surface with a low-surface-energy coating has a contact angle of about 155\({}^{\circ}\) and a roll-off angle of 3\({}^{\circ}\), which is an anisotropic superhydrophobic surface (see \"S6. Superhydrophobicization and Characterization of Surfaces\" in the Supplementary Material).
# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Sensor Node-to-Sensor Node Metadata Exchange  
In both systems, the stored photo is merged with metadata. To avoid sharing redundant photos between nodes, the metadata of the photos were first exchanged to calculate the semantic redundancy between the photos. Figure 11 shows the metadata traffic exchanged during the simulation for both systems. For example, for case 1, the application of the proposed scheme requires 14 Mbytes at time 15, while the application of the PoIs scheme for the same case requires 4 Mbytes at time 15. For case 2, the use of the proposed scheme requires 15 Mbytes, while the use of the PoIs scheme for the same case requires 5 Mbytes at time 15. The PoIs scheme involves the metadata sharing among nodes for each photo involving four parameters of location, direction, FoV, and sensing radius. The aforementioned photo parameters are for assisting the nodes in identifying the semantic redundancy occurring between photos in the PoIs scheme. For each photo, 5 bytes are allocated for the location, 2 bytes for the direction, 2 bytes for the FoV, and 1 byte for the sensing radius. As such, the size of the control message (metadata) becomes the number of photos sent to each node multiplied by 10 bytes for each photo. The Photo ID involves FoV, direction, sensing radius, and location. Each photo ID is 10 bytes in size. In the proposed system, the contact between two nodes involves sharing four features about each photo inside the clustering tree (sub-region ID, BoIs ID, sector ID, and Photo ID). Two Bytes are allocated to the sub-region attribute, one byte for the BoIs attribute, and one byte for the sector attribute. The Photo ID involves FoV, direction, sensing radius, and location. Each photo ID is 10 bytes in size. For example, assume that photo \(a\) has the features {(1, 1, 1) (1, 2, 1) (1, 3, 1)}. Accordingly, the following metadata traffic will be shared {(1, 1, 1, _a_), (1, 2, 1, _a_)}. Thus, the size of the metadata is (\(2+1+1+10\)) \(\times\) 3. Assume the number of photos is \(N\), and the number of features set of each photo is \(F\). The total size of metadata traffic (in bytes) is:  
\[S_{metadata}=\sum_{p=1}^{N}(14\times F_{p}) \tag{4}\]  
where \(p\) represents the photo number.  
Hence, the proposed system has higher SN-to-SN metadata traffic compared with the PoIs scheme as the proposed system involves sharing more features about each photo compared to the PoIs scheme.  
Figure 10: Sensor node-to-sensor node photos exchange.
## 2 The TASIPPS Conceptual Framework
### Case Study of the TASIPPS Framework
* Audit logging for compliance: the central platform logs the entire transaction, including the identities of the requesting doctor, the patient's approval or denial, the time of access, and the specific records accessed. This audit trail helps ensure accountability and compliance with healthcare privacy/data protection regulations. In the case study, the privacy and security mechanisms, including AES-256 encryption and role-based access controls, were fully functional, ensuring that only authorized personnel could access sensitive patient information. The consent management system was also successfully tested, allowing for patients to approve or deny access to their medical records before the data exchange took place. This case study, supported by the test results, showcases the framework's ability to facilitate interoperability, enhance data security, and comply with privacy regulations. It proves the system's effectiveness in real-world settings, making it a valuable solution to the challenges regarding the interoperability of healthcare.