esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and discussion  
### Morphological and Textural Properties of the Photocatalysts  
Figure 1 and Figure S2 display the results of the SEM/EDX analysis on the as-calcined photocatalysts. Although all the samples consist of NFs, the variation of the Ti:Zn molar ratio from 1:0 to 0:1 reflects on evident changes in the NF texture, morphology (Figure 1a-e), and diameters (Figure 1f). In sample TZ10, the fibers look relatively smooth and uniform in diameter along their axis; only a few beads are observed. On the contrary, in samples TZ21, TZ11, and TZ12, the NFs are beaded to larger extents. Sample TZ01 exhibits rough NFs, already described in detail in previous work [21]. The comparison with other electrospun Ti- and/or Zn-oxide NFs [26; 27; 28] evidences some morphological differences; they arise from the different preparation conditions selected. The NF diameter distributions, as measured by the image analysis software of the microscope, are shown in the insets of Figure 1a-e; the corresponding ranges of NF diameters and center values of the distributions are reported in Figure 1f. The center value of the distributions increases in the order TZ01 \(<\) TZ10 \(<\) TZ11 \(<\) TZ21 \(<\) TZ12.  
The EDX analysis (Figure S2) confirms the inorganic nature of the NFs and the complete degradation of the organic constituents present before calcination; the elemental mapping proves the spatially uniform distribution of oxygen, titanium, and/or zinc throughout the NFs.
## 1 Introduction
This can be made possible in DASH, where the video content management is based on an information collecting procedure, and the video content is organized through a proper media presentation description (MPD) format [2]. MPD includes segment information such as video resolution and bit rates, used as metrics for the quality of experience (QoE) evaluation. Based on the target quality level, a decision is made with possible transcoding or scalable video coding. Despite some recent research in this field, the possibility of exploiting fog and edge nodes for assisting DASH-based video delivery is still unexplored in depth. Our goal is to focus on two algorithms able to optimize the segment placement in a distributed fog network. To this aim, both communication and storage constraints have been considered, aiming at optimally placing the video segments while respecting both target quality and latency requirements. While the first proposed algorithm considers the target quality as a primary constraint, in the second algorithm, the quality could be degraded in case the latency requirement cannot be respected. In particular, both SVC (based on a cloud repository) and transcoding (based on an edge server) techniques are considered when evaluating the latency and the quality requirements. The paper is structured in this way. In Section 2, the literature background in the area of VoD and its implementation through edge and fog computing infrastructure is reviewed. In Section 3, the system model is described with a particular focus on the latency model, while in Section 4, the problem is formulated by introducing a cost function aiming to map the considered scenario. The proposed algorithms are explained in Section 5, while in Section 6, the numerical results obtained through computer simulations are shown. Finally, conclusions are drawn in Section 7.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 3 Proposed Underwater Acoustic Modem  
### Spread Spectrum Principle  
The spread spectrum is a very well-known method used in communication. It refers to a system originally developed for military applications, to provide secure communications by spreading the signal over a large frequency band [10; 11]. This system is built upon the principle of transmitting information signals over a much wider bandwidth than is strictly necessary for transferring the information. We use more bandwidth than the original message while maintaining the same signal power. By transmitting over a larger bandwidth, robustness against external narrowband interference is increased, since the wider the bandwidth of any transmitted signal the lower will be the relative influence of interference over a small part of the bandwidth.
## 1 Introduction
Once the model is created, it requires discretization, which involves dividing it into regular geometric elements of selectable sizes. These elements, although they behave similarly to the overall structure, become easier to analyze due to their standardized geometry, imposed by the software. The choice of element type (tetrahedral, hexahedral, etc.) is often arbitrary, as the polymorphic structures of anatomical models necessitate a combination of these elements, sometimes constrained by software limitations [9]. The greater the number of elements in a discretized model (mesh), the higher the accuracy of the final analysis. However, an increased number of elements will lead to a larger system of equations, resulting in longer processing times, sometimes excessively so. Simplifying the model reduces the computational power requirements but also decreases the accuracy of the analysis. Subsequently, material properties and boundary conditions are defined for each element. This results in a system of \(n\) equations with \(n\) unknowns, where \(n\) represents the number of nodes. Consequently, the computational capacity required is significant, but modern processing speeds and advanced algorithms enable the efficient management of this complexity [7]. The software includes a material database characterized by key parameters. If the materials of interest are not included in the database, the user can define them by inputting the relevant constants for this study. These constants typically include Poisson'sratio, the modulus of elasticity, density, compressive strength, tensile strength, or melting temperature. Depending on the type of experiment--whether mechanical stress (tension, compression, impact) or thermal studies--some analyses may require only two or three material properties.
## Materials and Methods  
### Materials  
The skin of tuna (bigeye tuna, _Thunnus obesus_) was obtained from PT Maluku Prima Makmur, Ambon, Indonesia. Other materials were 1-butanol, sodium hydroxide, acetic acid (glacial) 100%, pepsin (700 FIP-U/g, 0.7 Ph Eur-E/mg; EC 3.4.23.1), sodium chloride GR for analysis, ethanol absolute for analysis, methanol gradient grade for liquid chromatography, L(+)-ascorbic acid, buffer solution (di-sodium hydrogen phosphate) pH 7.00 (20 \({}^{\circ}\)C), purchased from Merck (Darmstadt, Germany); bromelin from pineapple stem (\(\geq\)3 units/mg protein; EC 3.4.22.32), alcalase or protease from _Bacillus licheniformis_ (\(\geq\)2.4 U/g), 2,2-diphenyl-1-picrylhydrazil, bovine serum albumin, purchased from Sigma-Aldrich (St. Louis, MO, USA); neutrale 0.8 L (0.8 AU-N/g), savinase 16 L (16 KNPU-S/g), purchased from novozymes A/S (Denmark, Germany); distilled water, purchased from IKA Pharmindo Putramas Co. (Jakarta, Indonesia).  
The explanation of the activity unit of the protease enzyme is as follows: 1 unit (U) is the amount of enzyme that catalyzes the reaction of 1 \(\upmu\)mol of substrate per minute. One Novo Protease Units (NPU) is the amount of enzyme that hydrolyzes casein at such a rate that the initial rate of formation of peptides/minute corresponds to 1 micromole of glycine/minute, meanwhile 1 KNPU (Kilo NPU) equals 1000 NPU. One Anson unit (AU) is approximately equal to 3 KNPU. Baseline lipase, amylase, and free and total protease activity levels were determined for each sample in accordance with procedures specified by the Federation Internationale Pharmaceutique (FIP) and the European Pharmacopoeia (PhEur). Results were converted from FIP/PhEur units to United States Pharmacopoeia (USP) units. For protease, 1 FIP/PhEur unit = 62.5 USP units.
## 1 Introduction
### Critical Drilling Operations
#### 1.2.4 Crisis Prevention  
Crisis prevention is a type of system interaction where, even without any signs of an upcoming crisis occurring, recommendations for \"crisis-prevention operations\" (such as ream and wash operations) are provided to a system operator. First, an important aspect of this is the provision of feasible parameters for regular operations. For instance, the parameters to guarantee an appropriate pump startup should be proposed by the system  
Figure 1: Timeline for drilling interaction leading to a disaster. The vertical axis represents both curves of Event Improbability (the declining curve) and Hazard Potential (the growing curve) ranging between two generally defined bands of low and high.  
Figure 2: Four Critical Operations in Decision Making for Drilling Disaster Management.  
so that the operation does not cause an evolution towards a potential critical situation. Second, a justification for the recommended system actions should also be generated. Third, the system should be aware of whether or not an operator has followed the system recommendations. Thus, in the case that the proposed counter-actions are ignored by operators, the system can adapt the recommendations to the new situation or even issue an alarm.
## 6 Results
### Visualization of Optimized Routes
#### 6.4.2 Optimized Routes for the 25-Location Problem  
Figure 7a presents the best route obtained using the Standard Genetic Algorithm (GA). The route contains multiple crossings and inefficient detours, suggesting that the optimization process struggles to escape local optima. The presence of redundant loops increases the total travel distance and time. Figure 7b illustrates the optimized route obtained with GA-AM, which incorporates Adaptive Mutation. Compared to the Standard GA, this route reduces unnecessary deviations and follows a more structured path, leading  
Figure 6: Best routes obtained for the 15-location problem using different genetic algorithm variants. (**a**) The Standard GA route shows multiple crossings and inefficiencies. (**b**) GA-AM achieves a more structured and optimized path. (**c**) GAAM-TS minimizes redundant loops and provides the most efficient route.  
to better travel efficiency. However, some minor inefficiencies remain. Figure 7c displays the most optimized route, generated using GAAM-TS, which integrates Adaptive Mutation and Tabu Search. This route eliminates redundant loops and achieves the best balance in the order of client visits, confirming the effectiveness of Tabu Search in refining the optimization process. The final travel distance and time are significantly reduced compared to the other approaches.
## 3 Flight Coordination Control Methods  
The flight coordination control methods we propose in this study are designed for the two topics on UAVs that are implemented in the system described in Section 2. One topic is collision detection and avoidance among multiple UAVs, where each UAV works in passive  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Range** & **Size (bit)** \\ \hline UAV ID & 0\(\sim\)FFFFFFFF (HEX) & 32 \\ \hline Time (UTC) & 0\(\sim\)86,400 (s) & 17 \\ \hline Latitude & \(\pm\)ddmm.mmmm & 28 \\ \hline Longitude & \(\pm\)ddmm.mmmm & 28 \\ \hline Altitude & \(-\)32,767\(\sim\)32,767 (m) & 16 \\ \hline Speed & 0\(\sim\)1023 (km/h) & 10 \\ \hline Direction & 0\(\sim\)720 (Decimal/2) degree & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shared information format of the Drone Mapper device.  
Figure 6: Functional block diagram of flight coordination control unit.  
Figure 7: Hardware photograph of the developed flight coordination control unit.  
mode. The other one is the formation flight, where UAVs work in active mode. For passive mode, also known as distributed mode, the UAVs are autonomously accomplishing their missions without requiring external control commands throughout the entire flight process, such as negative method-based collision avoidance [27]. In contrast, for active mode, the UAV follows externally specified commands to accomplish its assigned missions, such as formation flights along designated routes [12]. The objective of the first topic is to ensure that multiple UAVs can fly safely and autonomously in the same open airspace, without the risk of collisions. On the other hand, the second topic, i.e., formation flight, means the coordinated flight of multiple UAVs in a pre-defined pattern or formation with a single communication link between a representative (or leader) UAV and a ground station, instead of separate communication links between all of the UAVs and a ground station. The goal of formation flight is to achieve a specific mission that is difficult or impossible for a single UAV to accomplish alone. In this section, we investigate these two topics in detail separately and propose effective methods for these topics.
## 3 Demonstration of Tunable Shielding Effectiveness in GHz Bands
### Design and Experiments of Different Shielding Film Structures
#### 3.1.4 Experiments on Shielding Effectiveness of Multilayer MIM Structure  
As shown in Figure 7, four MIM samples fabricated by the same process were separated by PET films with a thickness of 0.35 mm, and the electrodes were connected in parallel, forming a cascade structure with a total thickness of about 2.25 mm. The results of the SE were tested and the curves were drawn as follows:  
Figure 5: (**a**) The Fermi level of graphene under bias voltage; (**b**) SE curves of MIM structure when voltage increases from initial.  
Figure 6: Shielding effectiveness of the (**a**) MIM and (**b**) BIB structure with different voltages.  
On the one hand, the minimum SE value of the cascade structure was much greater than that of a single GIG structure. On the other hand, the SE regulation range of the cascade structure was only 5 dB, which was significantly lower than that of the single structure. The main reason was that the uniformity of the ion gel in the four structures was different, resulting in different doping levels of the monolayer graphene under the same bias voltage.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 5 New Localization Module: GNSS/SLAM Fusion
### Kalman Filter  
As mentioned above, we decided to use a Kalman filter for the fusion of the two high-level localization subsystems. This choice is motivated by the fact that we have found a simple linear model with Gaussian noise that is sufficient to represent our fusion problem, and the Kalman filter is the optimal solution under these assumptions. It should be noted that the aim of this filter is not the localization itself, but to fuse sources from complex localization algorithms that provide filtered information. For this reason, we can simplify the model and avoid the Extended Kalman filter and its problems derived from linearization. The state vector in this model is a pose vector: \(\mathbf{x}=(x\ y\ \theta)^{T}\). The state transition equations \(\mathbf{x}\gets f(\mathbf{x}_{k-1},\mathbf{u},\mathbf{w})\) are defined as:  
\[x_{k}=x_{k-1}+\Delta u_{x}+w_{x} \tag{3}\]  
\[y_{k}=y_{k-1}+\Delta u_{y}+w_{y} \tag{4}\]  
\[\theta_{k}=\theta_{k-1}+\Delta u_{\theta}+w_{\theta}, \tag{5}\]  
where \(\mathbf{u}=(u_{x}\ u_{y}\ u_{\theta})^{T}\) is the control signal, which in this case is the odometry generated by our low-level system, being \(\Delta\mathbf{u}=\mathbf{u}_{k}-\mathbf{u}_{k-1}=(\Delta u_{x}\ \Delta u_{y}\ \Delta u_{\theta})^{T}\), where \(\mathbf{w}=(w_{x}\ w_{y}\ w_{\theta})^{T}\) is the system perturbation noise. As the state is fully observable by the two subsystems described, the observation equation is defined as:  
\[\mathbf{y}=H\mathbf{x}_{k}+v, \tag{6}\]  
where \(\mathbf{y}=(y_{x}\ y_{y}\ y_{\theta})^{T}\) is the observation vector from the localization subsystems, and where \(\mathbf{v}=(v_{x}\ v_{y}\ v_{\theta})^{T}\) is the observations noise. As the observations are of the same nature as the state vector, the matrix that relates them is an identity matrix \(H=I_{3x3}\). Using this Kalman filter implementation, we can obtain a high-frequency prediction step which rate is given by the odometry (see (3)-(5)), while applying the filter correction step only when a new high-level observation becomes available. In this way, the positioning rate is sufficient for the control loop. We follow the notation used in [40] where a thorough explanation of Kalman filter equations can be found.
## 4 Experimental Results
### Application
#### 4.7.4 Results Analysis  
In this section, we analyze accuracy and time consumption in the data encryption application. In the aspect of accuracy, there are \(200\times 199=39\),800 comparisons at different key lengths in data encryption and decryption processes. Among them, genuine comparisons have 3800 trials and imposter comparisons have 36,000 trials. The verification results are listed in Table 6. Our GAR@1%FAR is close to 100% under different key lengths. However, the several obtained face images include large pose changes and partial occlusion, which slightly reduce the accuracy. In practical application, face images are generally obtained when a user maintains a normal posture. Therefore, this accuracy is acceptable.  
In the aspect of time consumption, the cost time of data encryption and decryption is shown in Figure 10. The face detection and key generation modules account for nearly half of the total time consumption for the key lengths of 128, 192, and 256 bits. This is because these two modules both adopt DNN architecture, thus they are more time-consuming than the AES encryption module. In general, the time for once encryption and decryption is within 250 ms on this application. Specially, the time of key generation is less than 130 ms of 128, 192, and 256 key lengths. For the data encryption application, this cost consumption is worthwhile, and our scheme is effective and practical for real-world bio-key applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Length** & **GAR@1\%FAR** & **EER** \\ \hline
128 & 99.92\% & 0.05\% \\
192 & 99.96\% & 0.03\% \\
256 & 99.95\% & 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Verification results from different users in data encryption and decryption.  
Figure 9: Illustration of encryption and decryption process on the personal computer platform.
## 1 Introduction
Our research focuses on optimizing real-time updates through a distributed architecture based on an IoT sensor network, ensuring continuous and high-frequency data acquisition. The adopted multi-scale modeling allows the integration of heterogeneous data from different sources, providing a high-fidelity representation of the physical system. An additional challenge in DTs is the management of large volumes of data generated in real time. In our approach, this problem is mitigated using edge computing techniques, which allow local data processing directly at the acquisition sources, reducing latency and optimizing the transfer of information to the cloud or other computational nodes. Furthermore, the proposed framework addresses scalability issues thanks to an optimized architecture for the management of fluid visualizations even on high-resolution geospatial datasets, preserving the integrity and reactivity of the system. The application of the developed methodology is particularly relevant for the analysis and management of urbanized environments, an emerging sector that requires advanced monitoring and forecasting tools. Our work has been implemented in a geographical context of particular interest, the Calabria Region (Italy), characterized by critical issues related to the management of hydrogeological risk and the optimization of urban lighting. The integration of the digital twin in this scenario allows for the support of risk mitigation and energy efficiency strategies, providing an innovative operational model for territorial governance based on dynamic and real-time updated data.
# CFD Analysis on the Heat Dissipation of a Dry-Lubricated Gear Stage
## 2 Methods and Materials
### Numerical Modeling and Calculation
#### 2.3.6 Heat Transfer  
There are three physical transport mechanisms of heat transfer, namely conduction, convection, and radiation. Within this work, only convection is considered as it is the dominating mechanism of heat transfer within turbulent flows [37]. The energy equation is solved and the heat dissipation of the gears is calculated. The heat dissipation is evaluated by the heat transfer coefficient (HTC) between the gears and the air. Ansys Fluent provides three different methods for evaluating the HTC, namely the wall-adjacent HTC, the wall function HTC, and the surface HTC. The calculation of the surface HTC \(h\) is based on the Newtonian approach by the quotient of the heat flux \(q\) and the temperature difference between the wall temperature \(T_{wall}\) and reference temperature \(T_{ref}\), see Equation (5).  
Because the wall and reference temperatures have fixed values, the calculation of the surface HTC results in a mesh-independent solution in contrast to the wall-adjacent HTC and the wall function HTC. Note that the calculation of the flow field requires a suitable mesh addressing the flow characteristics. Hence, the surface HTC is evaluated in this work.  
Ansys Fluent calculates the heat flux \(q\) referenced in Equation (5) based on the temperatures \(T_{wall}\) and \(T_{ref}\), the fluid properties \(\rho\) and \(c_{p}\), the dimensionless temperature \(T_{c}^{*}\), and dimensionless velocity \(u^{*}\).  
\[h= \frac{q}{T_{wall}-T_{ref}} \tag{6}\] \[q = \frac{\rho c_{p}u^{*}}{T_{c}^{*}}-\frac{D}{(T_{wall}-T_{ref}) \cdot T_{c}^{*}} \tag{7}\]
## 3 Case Study Example  
We illustrated our proposal with an example. Figure 3 shows a multidimensional logical model presenting the facts and the dimensions of a data warehouse. The fact contained a measure attribute that could be aggregated through dimension levels. The fact class presented the measure \"sale goals\" linked to salespersons and products (e.g., products such as cars and trucks). Each salesperson had a sales goal that she/he had to reach for each product she/he could sell. In the fact relation, the \"salespersonID\" and \"productID\" foreign keys came from the salesperson and product relation. A salesperson could have a line manager (\"salespersonManagerID\" attribute) who was another salesperson. The product could be aggregated in the product types (e.g., the product categories). The attributes \"productTypeRate\" and \"productDuration\" are explained and used in Section 7.  
Below is an example of an instance of this multidimensional model in Prolog. The attribute ordering was the same in Prolog and in the logical model of Figure 3. In a traditional relational approach, this database would be in a fourth normal form.  
salesperson('idsp01', '[PERSON]', 'idsp02').  
salesperson('idsp02','[PERSON]', 'idsp03').  
salesperson('idsp03','[PERSON]', 'none').  
salesperson('idsp04','[PERSON]', 'idsp03').  
product('idprdtP1', 'GTFC', 1).  
product('idprdtP2', 'DDAR', 1).  
product('idprdtP3', 'X11', 2).  
product('idprdtP4', 'X12', 3).  
product('idprdtP5', 'WFG', 3).  
Figure 3: Sale multidimensional logical model.  
product('idprdtP6', 'FGHY', 4). product('idprdtP7', 'CFVG', 5). product('idprdtP8', 'X13', 6). productType(1, 0.05, 5). productType(2, 0.045, 5). productType(3, 0.055, 6). productType(4, 0.05, 4). productType(5, 0.07, 5). productType(6, 0.035, 6). fact('idsp01', 'idprdtP1', 100000). fact('idsp01', 'idprdtP3', 550000). fact('idsp01', 'idprdtP5', 500000). fact('idsp02', 'idprdtP2', 300000). fact('idsp03', 'idprdtP2', 500000). fact('idsp04', 'idprdtP4', 100000). fact('idsp04', 'idprdtP5', 350000).  
Traditional data warehouse queries consist of aggregating a measure (e.g., saleGoal in the example) according to the dimension levels (e.g., by productID, salespersonID or productTypeID). Examples of numeric aggregation functions are sum, average and count.