esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion and Classification of IoT Attacks and Countermeasures
### Defenses and Countermeasures
This set of properties makes the blockchain a compelling solution for some of the security challenges present in the IoT domain [19]. For instance, [PERSON] et al. [28] proposed a blockchain-based security solution for smart homes. The utilization of the blockchain for IoT security is challenging due to the low computational resources, high-latency transaction execution, and lower scalability [28; 29]. Therefore, the authors introduced a blockchain-based solution that addresses these challenges. Additionally, the presented approach was evaluated with regards to multiple important security requirements: confidentiality, integrity, availability (also known as the CIA security triad), user control, and authorization. These requirements are fulfilled with the help of multiple techniques such as transaction logging into the blockchain, hashing, and symmetric encryption [28]. Furthermore, the proposed approach also serves as a defense against two common IoT attacks: DDoS and linking attacks (Linking attacks try to identify users within an anonymous environment by combining partial identifiers (e.g., zip code, gender, etc.) in an attempt to infer the complete user identity). In addition to this example, [PERSON] et al. [1] summarized some of the main benefits of blockchain security for IoT as follows:  
* The blockchain can serve as a secure distributed data storage medium. The data stored in the blockchain are secured against tampering with the help of cryptographic hashing algorithms, and there is guaranteed data redundancy due to the absence of a single point of failure in the blockchain network. * Nodes in the network are registered on the blockchain and therefore can be authenticated and identified, which prevents spoofing attacks. * The blockchain serves as a decentralized alternative to traditional cloud servers. Centralized storage of information is a major target for perpetrators that want to steal sensitive data. Given that the cloud services provide shared infrastructure to many users at the same time, cloud storage can be compromised more easily compared with alternative blockchain-based approaches. Additionally, the data stored in the blockchain are distributed across all nodes in the network and signed (often also encrypted), which makes data theft attacks more difficult.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 4 Performance of fs-LIMS Instruments in the Element and Isotope Analysis
### Measurements of Elements and Isotopes
#### 4.4.2 Measurements of Isotopes
When correlating the intensities for multiple isotopes of the same element, one should obtain well-defined linear correlations. Deviation from the linearity can be due to various effects, e.g., ion source instability, surface and space charge, detector saturation, and presence of isobaric interferences or local inhomogeneity of sampling area. A recently proposed data protocols allow the removal of spectra showing such effects and consequently improve quantification [112; 152]. The studies conducted on micrometre-sized samples indicate that spectral filtering can be an important factor to achieve high accuracy isotope measurements [152]. Furthermore, determination of the isotope ratio from the slope using the linear regression method offers more accurate determination as from the mass peak integration alone [153]. A similar analysis method was applied for isotope ratio determination by LA-ICP-MS and improved accuracies were demonstrated in several studies [153; 166]. The UV DP fs-ablation studies demonstrated (\(\delta\)-notation) accuracies at the per mill level for measurements on an Ni isotope standard and several geological samples [152]. Similar accuracies were obtained for determining the Mg and B isotopes in the same samples. Figure 9B shows the method of analysis that allows for an improvement of the accuracy and precision of \(\delta\)58 Ni-values by about a factor of 10, the values from the raw data with those obtained after the spectral filtering. The accuracy and precision of the measurements can be affected additionally by the presence of isobaric interferences due to the presence of polyatomic ions. The data show that they were significantly reduced while applying DP fs-laser ablation [112; 152]. These initial results obtained by fs-UV DP LMS are promising for the application in the in situ geochemical research. LIMS can also be beneficial in the isotope analysis of micrometre-sized objects such as minerals or microorganisms, including microscopic fossils [152].
# Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem
## 2 Materials and Methods
### Flock Controller Algorithm  
The proposed flock controller algorithm (Algorithm 1) shows the flock controller's basic steps, and illustrates when the sensing power is used.  
Figure 2: Sensing power and its field of attraction.  
```
Input: Arena specification, number of agents \(n\) Output: Time to reach the goal zone, the number of agents within the goal area, time to match the velocity, the number of applied flocking rules (separation, alignment, and cohesion), the number of collisions accrued, goal localization time, center of mass, time to complete the flocking task
1 begin
2 Determine goal position \((g_{x},g_{y})\) and goal zone with radius \(r_{g}\);
3 Determine initiate zone position with radius \(r_{z}\);
4 Generate \(n\) agents with random position, direction and velocity within \((v_{min},v_{max})\);
5 while not all agents \(i\) within goal zone do foreach agent \(i\) in the flock do if(sensing power criteria applied)) then // apply sensing power Update agent direction using \(sp_{i}\) (Equation (6));
6 end if Update vectors \(vg_{i},ve_{i},vb_{i},vr_{i},va_{i},vo_{i}\);
7 Calculate unit vectors \(d\hat{o_{i}}\), \(\hat{d\hat{a_{i}}},\hat{d\hat{b_{i}}},\hat{d\hat{e_{i}}},d\hat{g_{i}},\hat{d \hat{r_{i}}}\);
8 Compute \(d_{i}\) based on Equations (1) and (2) ;
9 end if
10 move;
11 end if
```  
**Algorithm 1**Flock Controller.  
The flock controller starts by determining the goal position and its radius, as well as the initial zone and its radius. After that, \(n\) agents are generated, with random position, direction, and velocity, within the initial zone. Velocities are bounded with minimum and maximum values \((v_{min},v_{max})\), thus avoiding high differences between the velocities of agents, which may cause flock fragmentation. As these agents are generated, each agent checks whether its randomly selected position is not already taken by another agent, such that no two agents have the same position. Then, the agents start exploring. If the flock task has just started, then each agent finds the sensing power angle and determines the final desired direction. Then, all agents begin to move. Otherwise, they must find the final desired direction for each agent and then move. Meanwhile, each agent checks whether it is within the goal zone area or explores the arena. If all agents are within the goal zone area, the flock controller is terminated.  
Moreover, while the flock controller is processing, some outputs are generated, such as the number of agents within the goal area, the time to match the velocity, the number of applied flocking rules, the number of occurred collisions, the localization time, and the distance to the center of mass. When the flock's task is completed, the flock controller is terminated and the time to reach the goal zone is determined, as well as the localization window. These outputs are used to test the model's performance.
## 2 The Test Case
This event has been already investigated by Reference [31], which, by integrating multi-temporal COSMO Sky MED SAR intensity images and interferometric-SAR coherence data with geomorphic and other ground information within a Bayesian network, detected flooded area, reaching accuracies of up to 89%, in the 2-3 December 2013 period focusing on a small subset of Figure 1 centred on the Bradano River mouth. [37] extended the work of Reference [31] adding to the two above-mentioned COSMO Sky Med SAR imagery a Pleiades-1B High-Resolution data of 5 December 2013 to investigate a limited portion of Figure 1 centred on the Basento River mouth. SAR data were processed and used as in Reference [31] to identify flooded area on the above-mentioned days, while the Normalized Difference Vegetation Index (NDVI) was implemented on the Pleiades image to highlight water presence at the end of the event. A visual interpretation of the optical data combined with a field campaign allowed inferring information about results uncertainties [37]. Concerning the selected test case, the integration of outcomes of these two previous works gives an overview of the flood effects in a very small area (e.g., about 20 km\({}^{2}\)) and for a short temporal range (up to four days). The approach proposed in this work should allow for both the extension of the spatiotemporal domain of the investigation of the event and the deep analysis of the phenomenon, thanks to the large swath as well as the continuity of observation ensured by sensors on-board polar satellites, like VIIRS.
## 1 Introduction  
During the last decade, the development of nanofluidic devices has grown considerably owing to their ability to control small amounts of fluid. Their unique characteristics have attracted researchers' interest in several fields such as chemical, biological, and biomedical applications [1; 2; 3; 4]. In the biomedical field, many biochemical processes such as mixing [5] and sensing [6; 7] can be manufactured and integrated in a single chip. Microfluidic biosensors offer several advantages such as reduced consumption of the reagents, short analysis time, and simultaneous detection of multiple analytes [8]. Immunosensors represent a class of biosensors using the sensitivity of antibody-antigen interactions for the detection of appropriate analytes. This technique is used to quantify the proteins and biological small molecules. Two types of immunoassay have been found: homogeneous immunoassay, where the interaction between antibodies and antigens is made in solution, and heterogeneous immunosassays where the interaction is made between antibodies immobilized on a solid membrane and the antigen present at the boundary layer [9]. In heterogeneous immunosassays, the concentration of the binding complex of antigen-antibody on the reaction surface has a crucial role. The chemical reaction time scale and the transport phenomena rate by diffusion and convection occurring in a heterogeneous immunosensors are related by the Damkohler number (Da). When Da is higher, mass transport is limited, while association kinetic is lower for low Da values [10]. For the microfluidic biosensors, the ratio of the diffusion velocity of molecules to the reaction surface is comparatively small. A diffusion boundary layer is developed and it leads to limiting the efficiency of biosensor [11; 12]. Several methods have been tested to improve the phenomenon of transport of the analytes to the reaction surface such as microfluidic confinement [13], and electrothermal effect [14; 15; 16; 17].  
In our previous studies, we proposed two methods to improve the reaction rate: one based on inserting a cylindrical obstacle within the microchannel near to the reaction surface and the other based on flow confinement [18; 19; 20]. These studies are compared to the electrothermal flow and show its efficiencies. However, these numerical simulations have been done for two-dimensional configurations. This is somewhat constraining for spatial factors and practical design, especially the sensing area.  
In the present work, three-dimensional simulations are performed. The effect of the sensing area shape and the geometrical location of the electrodes on the microfluidic biosensor performance are investigated.
# Research on Two-Dimensional Digital Map Modeling Method Based on UAV Aerial Images
## 3 Image Stitching Based on the Novel Energy Function
The minimum cut and maximum flow algorithm is then applied to assign labels to pixels within the overlapping region, thereby identifying the optimal seam. Finally, Poisson fusion is used for color correction of the stitched image, resulting in an ideal panorama. When seeking the optimal seam, the search begins with pixels at the first row of each column within the overlapping region's boundary. The energy function is calculated for each starting point, and then the search is expanded downward according to the energy formula, computing the energy values of the three adjacent pixels in the next row within the same column and summing them with the energy values of the starting expansion point from the previous row. This process continues until the last row is reached. Ultimately, the line formed by connecting the pixels with the minimum energy values is selected as the optimal stitching line. ```
Input: Source Image \(\mathbf{I}_{1}\) and Source Image \(\mathbf{I}_{2}\). Output: Panoramic Image \(\mathbf{I}\). 1: Perform feature extraction on the source images using the C-SURF algorithm. 2: Conduct a coarse matching of the extracted features using a brute-force approach and refine the matches using the RANSAC algorithm. 3: Perform bundle adjustment on the images and calculate the global perspective transformation matrix \(\mathbf{H}\). 4: Determine the overlapping region \(\mathbf{R}\) based on matrix \(\mathbf{H}\) and identify the boundaries of region \(\mathbf{R}\). 5: Compute the data term \(E_{data}\) according to the Equation (8). 6: Compute the smoothness term \(E_{smoothness}\) according to the Equation (11). 7: Compute the texture term \(E_{texture}\) according to the Equation (7). 8: Merge the data term \(E_{data}\) smoothness term \(E_{smoothness}\), and texture term \(E_{texture}\) into the novel energy function for computation, as shown in the Equation (6). 9: Solve the energy function equation using the minimum cut and maximum flow algorithm, and assign labels to pixels within the overlapping region. 10: Obtain the stitched panoramic image using Poisson fusion. ```  
**Algorithm 1**Image Stitching Algorithm Based on the Novel Energy Function  
In summary, the specific process of the proposed two-dimensional digital map modeling method based on UAV aerial images is illustrated in Figure 3.
# Determination of pH in Powdered Concrete Samples or in Suspension
## 2 Materials and Methods  
### Microfluidics-Based Platform  
Figure 1a shows the design of the first sensor platform, whereas Figure 1b depicts the complete fabricated structure. The structure was designed to have two parallel microfluidic channels, one containing pH-Low concrete solution injected via the relevant inlet hole and the other containing pH-High solution similarly injected. A comparison of these solutions is allowed by measuring output capacitance at the contact pads (terminals). The first layer, similarly to subsequent layers of this multi-layered device, used 80 \(\upmu\)m-thick PVC foil as a substrate. One side (electrode) of the parallel plate capacitors was created from gold leaf with dimensions of 3 cm \(\times\) 0.7 cm, each. In the middle, discrete, disconnected channels were created. Each channel was created from 3 PVC foil layers giving the thickness of the channel (distance between gold electrodes) equal to 240 \(\upmu\)m. The next layer represents the second electrodes of the parallel plate capacitor. The thickness of the gold conductive material was 10 \(\upmu\)m. In the last layer, holes for inlets and outlets for fluid injection were created. From the fabricated structure two short wires were connected, using silver paste, to create through-hole components and to enable measurement. The overall dimensions of this platform, shown in Figure 1b, are 5 cm \(\times\) 3.5 cm.  
The main fabrication steps of this platform were as follows: (1) putting the foil in a cutting ploter machine; (2) cutting the foil in the overall dimension of the chip; (3) engraving holes for contacts; (4) gluing gold conductive leaves for bottom and top electrodes; (5) engraving microfluidic channels; (6) cutting holes for inlets and outlets; and (7) laminating separate layers of PVC foils to create a multi-layered compact microfluidic sensor platform.  
Figure 1: (**a**) Design of separate layers, and (**b**) the fabricated microfluidic-based structure.  
The working principle of this platform is based on a parallel electrodes capacitive sensor. More specifically, when the suspension of the concrete sample is injected into inlets through the microfluidic channels and goes out of the outlet, the channel is full with the suspension, increasing the dielectric constant of the medium between the gold electrodes. If the dielectric constant is changed then the total capacitance is changed and this variation is correlated with the pH value of the concrete in suspension form, inside the microfluidic channels.
## 6 Discussion
### Transfers of Knowledge  
We have tested our algorithm in the classical setups of transfer of knowledge : **cross-task transfer, cross-learner transfer and by imitation learning**. We have shown that **SGIM-PB can autonomously determine** the main questions of Transfer Learning as theorised in [5]:  
* What information to transfer? For compositional tasks, a demonstration of task decomposition is more useful than a demonstration of an action, as it helps bootstrap cross-task transfer of knowledge. Our case study shows a clear advantage of procedure demonstrations and procedure exploration. On the contrary, for simple tasks, action demonstrations and action space exploration show more advantages. Furthermore, for cross-learner transfer, especially when the learners have different embodiments, this case study indicates that demonstrations of procedures are still helpful, whereas demonstrations of actions are no longer relevant.
* How to transfer? We showed that decomposition of a hierarchical task, through procedure exploration and imitation, is more efficient than learning directly action parameters, i.e., interpolation of action parameters. This confirms the results found in a more simple setup in [35].
* When to transfer? Our last setup shows that an active imitation learner asking adapted demonstrations, as its competence increases, performs almost better than when it is given a significantly larger batch of data at initialisation time. More generally for a less data-hungry transfer learning algorithm, the transferred dataset should be given to the learner in a timely manner so that the information is adapted to the current level of the learner, i.e., its zone of proximal development [52]. This advantage has already been shown by an active and strategic learner--SGIM-IM [53], a simpler versions of SGIM-PB--which had better performance than passive learners for multi-task learning using action primitives.
* Which source of information? Our strategical learner chooses for each task the most efficient strategy between self-exploration and imitation. Most of all, it could understand the domain of expertise of the different teachers and choose the most appropriate expert to imitate. The results of this case study confirms the results found in [35] in a simpler setup and in [38] for learning action primitives.
## 2 Cutting Test
### Analysis of the Effect of Cutting Parameters on Surface Roughness  
#### 3.2.1 Effect of Cutting Speed on Surface Roughness  
A bar graph of the variation of machined surface roughness of the workpiece with cutting speed for the parameters of Table 4 is given in Figure 7. The parameters of feed rate of 16 \(\upmu\)m/rev, depth of cut of 3 \(\upmu\)m, ultrasonic amplitude of \(A_{y}\) = 4.5 \(\upmu\)m and \(A_{z}\) = 1.2 \(\upmu\)m, and a tool nose radius of 0.5 mm were selected. Surface roughness tends to decrease and then increase with increasing cutting speed. Specifically, the roughness of the machined surface shows a continuous decreasing trend when the cutting speed is increased from 1 m/min to 3 m/min, indicating that the cutting effect is gradually optimized in this speed range. However, when the cutting speed is increased to 4 m/min, the roughness of the machined surface increases sharply, and deterioration occurs. Therefore, selecting the appropriate cutting speed during ultrasonic elliptical vibratory cutting of GH4169 is crucial. Maintaining the cutting speed below 3 m/min can effectively control the surface roughness at a low level, thus obtaining an ideal machined surface.  
Figure 8 shows the white-light interferograms for cutting speeds of 1 m/min, 2 m/min, 3 m/min, and 4 m/min. From the figure, it can be seen that with the change of cutting speed, the machined surface quality appears to be different, especially when the cutting speed is 4 m/min: The height difference along the feed direction changes very significantly, and the surface quality decreases significantly at this speed. The surface roughness value of ultrasonic elliptical vibratory cutting is more significant when the cutting speed is lower (e.g., 1 m/min and 2 m/min), mainly because at lower cutting speeds, the tool is in contact with the machined surface for a more extended period, which leads to an increase in frictional heat, which promotes the formation of the built-up edge and burrs and may reduce the rigidity of the machine tool due to the frequent action of the tool on the machined surface, thus increasing the roughness of the workpiece surface. On the contrary, when the cutting speed is increased to 4 m/min, although the contact time between the tool and the machined surface is reduced, at the same time, the cutting force increases, and the cutting temperature rises [23], which is also detrimental to the improvement of surface quality. Especially in high-speed cutting conditions, the contact time between the chip and the front face of the tool is shortened, which may lead to unstable cutting force and accelerated tool wear, affecting the machined surface quality. Therefore, choosing a reasonable cutting speed ensures excellent surface morphology and low surface roughness. Machining quality and productivity can be significantly improved by adjusting the cutting speed.  
Figure 7: Surface roughness change with cutting speed in UEVC.
# Convective-Microwave-IR Hybrid Drying of Kaolin Clay--Kinetics of Process
###### Abstract  
Kaolin clay is one of the essential components of utility and technical ceramic products. Drying is one of the stages of its production and is crucial for the quality of the obtained products. Due to the low energy efficiency of the dryers, it is also one of the most energy-intensive operations performed not only in the production of ceramics but in the industry as a whole. For this reason, modern drying techniques are sought. They are required to be energy efficient, sustainable and produce high-quality products. An example is the so-called hybrid drying, which combines several drying techniques (energy sources) into one process. The aim of this work was to determine the impact of microwave and infrared radiation on the kinetics and energy consumption of convective drying of kaolin clay and the quality of the products. The interaction of convective, microwave and infrared drying was investigated. Drying times, energy consumption and visual quality were compared for the tested processes. The fastest process was convection-microwave drying, where a reduction in drying time of 70% and energy consumption of 50% was observed. Unfortunately, intensive drying had a negative impact on the quality of the products (numerous cracks on the side surface). The best drying methods are those that use all energy sources simultaneously and periodically. Hybrid processes that use all energy sources in a periodic manner had the greatest efficiency. The drying time in these programs was shortened in relation solely to the convection process by 45 to 50% while reducing energy consumption by 3-18%. The product had the best quality.  
hybrid drying; convective drying; microwave drying; infrared drying; kaolin; experiments +
Footnote †: journal: Applied  
doi: 10.3090/acpp131.37451