esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Energy waste may occur if the system switches from Formation A to Formation B without position reassignment. For multi-agent systems with a limited load capacity, saving energy in the process of collaboration is particularly important. As a consequence, this paper applies the switching communication topology to the process of formation reconfiguration and adds the improved Hungarian algorithm for the distribution of reconfiguration target points. The method takes the total distance of the system as the shortest to obtain the optimal performance and improve the efficiency of cluster reconstruction and collaborative task execution. On this basis, a multi-agent distributed formation control strategy based on the double integrator model is proposed, and the convergence and stability of the system are discussed. Finally, the formation reconstruction of the time-varying topology is realized through simulation, which verifies the feasibility of the reconstruction strategy designed in this paper. The contribution of this paper can be summarized as follows:  
1. A graphic library is innovatively established, and four possible multi-agent formation configurations are designed. After the failure of a single agent, topology images can be searched and dispatched in the graphic library according to the number of remaining agents, which can help the multi-agent system quickly establish a new configuration. 2. The improved Hungarian algorithm is adopted to plan the target points so that each agent in the system can obtain specific target points in the formation using the ideal of ranking and a gradient weighting factor in the auction algorithm to further save time in formation reconfiguration. 3. In each topological cycle, a time-varying topological formation control law based on consistency theory is established. The control law can quickly reduce the tracking error to form a nominal configuration, and the stability of the formation reconfiguration system in a single time interval is proved by using the Lyapunov function. The framework for the rest of this article is as follows. The second section first provides the concept of graph theory and the definition of time-varying target formations. The third section designs different numbers of time-varying formation topology configurations and builds corresponding graphics libraries. For multi-agent systems, the improved Hungarian algorithm is proposed to solve the problem of target position assignment after topology switching. The multi-agent formation reconstruction control strategy and stability analysis of the multi-agent systems are also provided. The fourth section obtains simulation images of multi-agent systems collaborative formation and reconstruction. The fifth section summarizes this article.
## 5 Results and Discussion
### Disposal Costs or Resale/Recovery Value  
In this section, traditional masonry methods are compared with the adobe block system in various aspects.  
Adobe bricks are considered more environmentally friendly because they are made from natural and locally sourced materials. The production of adobe bricks consumes less energy than that of concrete blocks, which require cement production. Concrete production is a significant source of carbon emissions [51].  
[PERSON] et al. [49] confirmed that adobe bricks exhibited excellent thermal insulation properties owing to their composition. They can regulate temperature effectively, keeping the interior cooler in hot climates and warmer in cold climates. On the other hand, concrete blocks have lower insulation properties and may require additional insulation materials for thermal effectiveness.  
Concrete blocks are stronger and more durable than adobe bricks [29]. Concrete has high compressive strength, making it suitable for load-bearing structures. However, adobe bricks may be more prone to weathering over time, particularly in wet climates, and proper maintenance can extend their lifespan [26].  
Adobe bricks are generally weaker than concrete blocks. Although they can withstand normal loads and provide sufficient stability, they are more susceptible to water damage over time and may require maintenance. Concrete blocks are known to have superior strength and durability. They can withstand higher loads and fire, and are more resistant to water damage than adobe bricks [52].
## 2 Materials and Methods
### Sigma 0 (\(\sigma_{0}\)) Criterion  
As discussed in Section 2.1.2, finding a suitable location for a CR to be visible above the clutter is crucial. Clutter's magnitude depends on the land cover type, the SAR resolution, the signal incidence angle, wavelength, and polarization. According to [PERSON] et al. [22], a site with a clutter level at C-band within the range \(-10\) dB to \(-14\) dB is suitable for CR installation.  
Sigma 0 was calculated from Sentinel 1 ascending (160A) and descending (167D) relative orbit directions over Cyprus. Level-1 Single Look Complex (SLC) products with Interferometric Wide swath (IW) mode and VV single-polarization were used. The images were imported and analyzed using SNAP software, and the generated images are illustrated in Figures 10 and 11.  
The reason for the above-mentioned SAR image product selection is because according to ESA [35], (a) the principal acquisition mode over land is IW swath, (b) SLC products preserved phase information and are processed at the natural pixel spacing, and (c) as regards the single polarization choice, trihedral CR returns linearly polarized incident waves unchanged [36]; thus, it will only have a response in a single polarization.  
After making the sigma 0 images in SNAP, they were imported into ArcGIS Pro. Subsequently, a subset of the images was overlayed on each DSM's area of interest to see  
Figure 10: The generated sigma 0 images. (**a**) The values acquired from the ascending pass; (**b**) The values acquired from the descending pass. The purple line designates the suitable sigma 0 value of a Sentinel 1-SLC product. Lower values are more favourable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class Name** & **Colour** \\ \hline Tree & \\ Bush & \\ Soil & \\ Exposed rock & \\ Road hard surface & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The classified values of the land cover map.  
how it appears at first glance related to the 3D visualized terrain. Figure 11 illustrates the sigma 0 raster image overlayed on the ALEV's DSM.  
The sigma 0 values from the ascending and descending raster images will be used as variables of the criterion \"Sigma 0\" to determine the suitability of CR's permanent installation for the ascending and descending passes, respectively. The most favourable variables were the values less than \(-10\) dB, whereas the values equal to or greater than 0 dB were excluded from the analysis.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
### Classification of Cryptography  
Cryptography has been classified into three categories: symmetric key cryptography, asymmetric key cryptography, and hashed function [20]. In symmetric key cryptography, data encryption between sender and receiver is done by a single key called a secret key [21]. The data are encrypted from the sender side using the secret key, and the data are transmitted to the receiver side [22]. It is necessary to use the same key that is used on the sender side to convert encrypted data to a readable format [23]. Asymmetric key encryption involves encrypting data between the sender and receiver using public and  
Figure 1: Fake message transmission.  
private keys [24]. The first key belongs to the sender, which is used to encrypt the data [25], while the other is the receiver key that allows it to decrypt the data. The sender and receiver create public and private keys in the asymmetric mechanism. When data are encrypted, the receiver's public key is used, and the data from that key are encrypted and transmitted to the receiver [26]. If the data have to be decrypted, it is done from the receiver's private key [27]. Hashing is a one-way function in which, once the data are encrypted, the data can never be decrypted again [28]. Whenever data or a file pass through a hashing algorithm, that algorithm generates a hashed code [29]. Hashing is divided into two categories [30]. The first one is cryptographic hashed, and the other is non-cryptographic hashed. A non-cryptographic hashed is used much less than a cryptographic [31; 32]. Different symmetric and asymmetric cryptography and hashed mechanisms are discussed in Figure 2.
## 3 Case Study: Design and Implementation of the Photovoltaic Control System
### Modeling of the Photovoltaic Control System  
The Petri net-based model of the proposed photovoltaic control system is shown in Figure 9. The discussed Petri net consists of 109 places and 108 transitions.  
Looking from the general point of view, the presented model consists of eight block modules that strictly refer to the particular photovoltaic modules (solar cells). Since the whole specification is large, a single module (a part of the system) is shown in Figure 10.  
The specification has been initially prepared in the _IOPT-Tools_ v 1.2 software and further exported to the _Hippo_ system [19]. Note that the presented figures are directly taken from _Hippo_, and they show the general overview of the system (without input/output signals associated with places/transitions). Table 1 shows the associations between the input signals and transitions. Note that there are values for the first module presented, while the associations for the remaining modules are similar. For example, signal \(e\)1 (1 st module is enabled) is assigned to the transition \(t\)5, etc.  
Similarly, output signals are associated with places of the interpreted Petri net. There are 12 outputs in total grouped into three 4-bit vectors, as shown in Table 2. The outputs contain the values determined by the system (status of the system): the number of enabled (working) modules, the number of panels with correct power (e.g., within the assumed ranges), and the number of panels with the correct temperature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Signal** & **Transition** & **Description** \\ \hline \(e1\) & \(t_{5}\) & 1 st module is enabled \\ \(le1\) & \(t_{6}\) & 1 st module is disabled \\ \(pv1\) & \(t_{108}\) & Power from 1 st module is correct \\ \(!pv1\) & \(t_{10}\) & Power from 1 st module is not correct \\ \(tp1\) & \(t_{14}\) & Temperature of 1 st module is correct \\ \(!tp1\) & \(t_{13}\) & Temperature of 1 st module is not correct \\ \hline \hline \end{tabular}
\end{table}
Table 1: Association between transitions and input signals of the interpreted Petri net.  
Figure 9: An interpreted Petri net model of the discussed photovoltaic system.  
Once the system was modeled within IOPT-Tools, it was verified and validated. The validation was conducted within IOPT-Tools by simulating the behavior of the system. However, IOPT-Tools was not able to perform the verification of the system due to the state explosion problem. Moreover, the application of another popular tool PIPE [84] was also not successful. Therefore, the modeled system was converted to the Hippo system and analyzed with the use of the authors' methods and techniques (see [20; 23; 88] for more details). The verification was successful, and the system was ready for further steps of the proposed prototyping flow.
## 4 System Integration and Performance
In order to evaluate autonomous operation of the sensor, end-to-end efficiency from the PV module to the battery has been simulated in different illumination levels. As can be seen in Table 5, even in 1% light intensity, the PV module delivers average power of 18.4 \(\mu\)W to the battery with 89.8% average efficiency. The power that can be delivered to the battery in 1% can be seen in Figure 9. When battery voltage is close to V\({}_{\mathrm{EOD}}\), 15.9 \(\mu\)W power is delivered to the battery with 76.8% efficiency. As in this situation, the system is operating in S\({}_{\mathrm{L0}}\) mode, its average power consumption is less than 8 \(\mu\)W and the battery gets charged. By increasing the battery voltage, efficiency is improved and more power is delivered to the battery. As a result, even in 1% light intensity, the harvested energy is enough for autonomous operation of the whole system. Meanwhile, as the whole sensor node \begin{table}
\begin{tabular}{l l l l l} \hline
**Block** & \(\boldsymbol{S_{L3}}\) & \(\boldsymbol{S_{L2}}\) & \(\boldsymbol{S_{L1}}\) & \(\boldsymbol{S_{L0}}\) \\ \hline Detected V\({}_{\mathrm{buf}}\) during 5 CA discharge (mV) & 1,104 & 1,051 & 1,002 & 947 \\ \hline Battery discharge capacity (\%) & \(<\)20\% & \(<\)50\% & \(<\)65\% & \(<\)75\% \\ \hline \multirow{2}{*}{Battery threshold voltage (V)} & Measured & 1.114 & 1.055 & 1.003 & 0.955 \\ \cline{2-5}  & Simulated & 1.122 & 1.063 & 1.010 & 0.962 \\ \hline \multirow{2}{*}{Clock frequency (kHz)} & Measured & 950 & 488 & 249 & 127 \\ \cline{2-5}  & Simulated & 980 & 502 & 257 & 132 \\ \hline Time interval of sensing and data transmission (seconds) & 15 & 30 & 60 & 120 \\ \hline Power consumption of clock generator (nW) & 165 & 85 & 44 & 23 \\ \hline Power consumption of digital control unit (nW) & 90 & 46 & 24 & 13 \\ \hline Average power consumption of & Measured & 346 & 210 & 142 & 103 \\ energy harvester (nW) & Simulated & 293 & 169 & 106 & 74 \\ \hline Average power consumption of & Measured & 1730 & 1120 & 790 & 640 \\ sensor interface circuit (nW) & Simulated & 1360 & 870 & 625 & 500 \\ \hline Average power consumption of the integrated circuit (nW) & 2076 & 1330 & 932 & 707 \\ \hline Average current consumption of gas sensor bias circuit (\(\mu\)A) & 4.67 & 2.33 & 1.16 & 0.58 \\ \hline Average current consumption of the wireless transceiver (\(\mu\)A) & 9.4 & 7.7 & 6.6 & 5.55 \\ \hline Average current consumption of the complete system (\(\mu\)A) & 16 & 11 & 8.5 & 6.7 \\ \hline \end{tabular}
\end{table} 
Table 4: System performance and power consumption in different system operation modes. consumes less than 16 \(\mu\)A for sensing and data transmission, the target 6 mAh battery can provide enough power for approximately 375 hours operation, even without energy harvesting.
## 4 Discussion
The synthesized nanoplates exhibit distinct optical properties evidenced by UV-VIS spectroscopy, showcasing multiple absorption peaks indicative of their anisotropic structure inferred from XRD analysis. Characterization through electron microscopy and FTIR spectroscopy further confirms their nanoscale dimensions and chemical composition. The hierarchical organization observed in SEM images suggests self-assembly processes, highlighting the innovative potential of this green synthesis approach. This novel pathway not only expands our understanding of aluminum phosphate nanoplate formation but also opens doors for future applications in fields requiring directional optical properties and advanced nanomaterials. Aluminum phosphates have a broad range of applications, as highlighted by [PERSON] et al. [57]. The unique properties of aluminum phosphate nanoplates are crucial in determining their potential across various fields. For example, studies have shown that hexagonal and square planar morphologies of zinc aluminum phosphate nanoplates enhance catalytic activity, particularly in biofuel production [58]. Additionally, the nanoporous structure of aluminum phosphates has shown promise as electrode materials for energy storage, offering superior capacitance and retention [59]. The self-assembly of these nanoplates into chain-like architectures has also been found to improve charge transport and active site availability in oxygen evolution reaction (OER) catalysis [60]. Their potential as a vaccine adjuvant has been explored as well, with aluminum phosphate nanoparticles stabilized by amino acids demonstrating biocompatibility without affecting antigen-specific antibody production [61]. Furthermore, similar nanoplates have been employed in drug-delivery systems, offering enhanced protein loading and controlled release [62]. Aluminum phosphate nanostructures also show promise as flame-retardant materials, owing to their stable 3D core-shell structures [63]. Recent research highlights the electrorheological behavior of anhydrous aluminum orthophosphate particles with tridymite-like structures, showcasing their potential in advanced rheological applications [64]. Additionally, the strong interaction of aluminum (III) with biomolecular phosphates such as ATP suggests significant roles in biological processes like enzymatic reactions and signal transduction [65]. These findings underscore the diverse potential applications of aluminum phosphate nanoplates, demonstrating that their unique properties not only enhance performance in established areas but may also lead to novel applications in advanced materials, environmental remediation, and biomedicine. Future research will continue to explore these possibilities, building on the foundational synthesis and characterization presented in this study.
## 2 Background Knowledge
### Case-Based Reasoning  
The main idea of case-based reasoning (CBR) is to store a large set of previous (solved) cases with their solutions in a case base (or case library) and use them to deal with (solve) new (similar) cases [6; 17]. There is no specific way to represent stored cases. Various schemes can be used for that, like semantic nets, frames, objects, patterns, and even rules.  
CBR works in a way that can be represented by the so-called CBR cycle [16]: (a) _retrieve_ (the most similar case), (b) _reuse_ (that case to create a solution), (c) _revise_ (the solution to adapt to the case at hand), and (d) _retain_ (the produced case as a new case).  
Whenever a new input case has to be dealt with, the case-based system performs an inference following the above four phases. In the retrieval phase, the system retrieves from the case base the most relevant stored case(s) to the new case. In the reuse phase, a solution for the new case is created based on the most relevant case(s) retrieved. The revise phase validates the correctness of the proposed solution, perhaps with the intervention of the user. Finally, the retain phase decides whether the knowledge learned from the solution of the new case is important enough to be incorporated into the system's case base.  
Similarity metrics are used for assessing the relevance of the existing cases to the new cases, such as 'Jaccard', 'Sorensen-Dice', 'Otsuka-Ochiai', 'Simpson', 'Kulczynski2', etc. There are various methods that use the above metrics, like the nearest neighbor approach, which is most commonly used for small cases.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 3 Two-Wheel Differential Drive Platform Kinematics  
This research is based on a simple 2-wheel differential drive mobile platform that has two independently controlled wheels and one omnidirectional wheel for balancing. The robot moves on the flat lane, localization by LiDAR and can be express by relation between reference frame {W} and the mobile robot's own frame {R}. Robot frame {R} = (P\({}_{R}\)(\(t\)),\(\psi\)\({}_{R}\)(t)) is defined by position vector of robot respect to reference frame and robot heading, respectively. Let (V\({}_{R}\)(t), \(\omega\)\({}_{R}\)(t)) be the instantaneous linear speed and angular speed of robot body based on {W}, (\(\theta\)i(t),\(\theta\)\({}_{r}\)(t)) be the left and right wheel angular speed, \(r\) be the radius of the driving wheel, \(L\) be a distance between two wheels. Because each command of a CPU just can accelerate a circular motion only one wheel independently, the control equation based on robot motion has form like as.  
\[\left[\begin{array}{c}\theta_{r}(t)\\ \theta_{l}(t)\end{array}\right]=\left[\begin{array}{cc}\frac{1}{r_{w}}&- \frac{l_{w}}{2r_{w}}\\ \frac{1}{r_{w}}&\frac{l_{w}}{2r_{w}}\end{array}\right]\left[\begin{array}{c }V_{R}(t)\\ \omega_{R}(t)\end{array}\right] \tag{1}\]  
Figure 1: Flowchart of our proposed hybrid navigation system.  
On the other hand, in this work, robot will continuously move along the smoothed path. The necessary condition in the path design procedure is a twice differentiable path. The given smoothed path featured by its curvature at each point on the curve. Let the curve **C**(t) is the desired path of the robot, we can archive the curvature by below equation.  
\[\kappa(t)=\frac{x_{\textbf{C}}(t)\overset{\text{\tiny{$\vec{\
u}_{\textbf{C}}$} }}{y_{\textbf{C}}}(t)-\overset{\text{\tiny{$\vec{\
u}_{\textbf{C}}$}}}{x_{ \textbf{C}}}(t)y_{\textbf{C}}(t)}{\left(\sqrt{x_{\textbf{C}}^{2}(t)+y_{\textbf {C}}^{2}(t)}\right)^{3}} \tag{2}\]  
Because the unit of curvature does not involve time and is supposed to be intrinsic to a curve **C**(t), it is independent of the speed of the robot. Let \(V_{\textbf{S}}\)(t) and \(\omega_{\textbf{S}}\)(t) be the desired linear velocity and angular velocity of robot when moving along the desired path. According to Equation (2), we have:  
\[\kappa(t)=\frac{\omega_{\textbf{S}}(t)}{V_{\textbf{S}}(t)} \tag{3}\]
# Factory Logistics Improvement: A Case Study Analysis of Companies in Northern Thailand, 2022-2024
## 2 Background
### Industries in Northern Thailand  
Northern Thailand represents a significant portion of the country's demographic and economic landscape. The region is home to 16.1% of Thailand's total population and contributes 7.6% to the nation's GPP. According to the Ministry of Industry's database, Northern Thailand hosts 100,647 industrial establishments, constituting 15.7% of the country's factories. The National Statistical Office census reveals that the region's industrial sector is dominated by food product manufacturing (27.1%), followed by wood and wood products (18.1%), wearing apparel (14.4%), and textiles (8.1%). Furthermore, the region employs 9.1% of Thailand's total labor force.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 2 Optical Camera Communication
### Binarization  
Finally, the demodulated signal is converted into a binary signal using the \(P_{b}\) factor, representing the number of pixels contained per bit, given by [19]  
\[P_{b}=\frac{\Sigma_{width}\times fps}{\Delta_{col}\times bps} \tag{1}\]  
where \(fps\) is the camera frame rate expressed in frames per second, \(bps\) is the signal bit rate in bits per second, \(\Delta_{col}\) is the column decimation factor, and \(\Sigma_{width}\) is the total pixel width of a frame, taking into account the Inter-Frame Gap (IFG) [22], representing the gap time between consecutive frames when the camera cannot receive the incident light, as illustrated in Figure 3:  
With the decimation factor \(\Delta_{col}\) reducing the amount of pixels in the extracted signal to process, the latter must be carefully selected according to the oversampling factor of the camera in order to ensure the correct recovery of the information. There is therefore a trade-off to be made between the required processing time of the signal and the communication performance, especially with a poor contrast between the bright and dark strips due to the presence of noise.