esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 5 Simulation Results
### Simulation Results
#### 5.2.2 OA-CMAC Protocol Node Energy Consumption Simulation  
In this paper, the initial energy of the OA-UWSN node was set to 3600 kJ, and the energy threshold of the node was 1080 kJ. When the node energy was lower than 360 kJ, the node was considered dead, and the control center replaced the new node. When the node energy was 360 kJ-1080 kJ, the node stopped acoustic communication and preferentially used optical communication to transmit small-capacity data with high priority, such as temperature, salinity, and depth. If the optical handshake was not successful, the data was sent via acoustic communication.  
Since the OA-CMAC protocol proposed in this paper increases the limitation of the energy threshold, the lifetime of the UWSN node is further extended compared to the OA-CMAC that does not set that energy threshold. Figure 9 shows node energy consumption simulations in the OA-CMAC and OAs-CMAC protocols. The simulation curve is the node's two lifetimes. In each curve, the gentler part of the decrease is the energy consumption during the acoustic handshake and optical handshake. The steep part is the energy consumption of optical communications, and the steepest is the loss of acoustic communication energy. As can be seen from the figure, the lifetime of the OA-UWSN node was 7800 h, the lifetime of the OA-UWSN node without the energy threshold was 6400 h, and the lifetime of a single acoustic communication node was 6000 h. Therefore, the lifetime of the OA-UWSN node increased by 14% compared to the OA-UWSN node without an energy threshold, which was 30% longer than the lifetime of the OAs-CMAC.
## 3 TinyML Implemenatation
### Results Deploying the DNN in Embedded Systems
The same interpretation is used for the rest of the table shown in Figure 24.  
It is necessary to mention that the processing speeds of each system are different because they have different hardware characteristics; see Table 14. Table 15 shows the time, in milliseconds, that each embedded system took to deploy the DNN using the same trained model and the same input data. The clock frequency processor and the memory size are very important features to be considered. In the case of 32- and 64-bit architectures, the features available on the device do not represent a problem since the implementation results have shown that they have sufficient resources for TinyML applications. For hardware-constrained embedded systems with an 8-bit architecture, the clock frequency processor does not represent a limitation to deploying TinyML algorithms. However, the RAM size still represents a big problem when deploying TinyML algorithms. To face the problem of RAM size limitation, the community dedicated to TinyML turns its attention to the optimization and compression of files used in TinyML, as is the case of trained models, to achieve optimal implementations on 8-bit architectures. Table 15 shows, more specifically, the latency time that the algorithm has since it invokes the trained model, and a prediction is made with a vector of input characteristics. Likewise, the measurement of energy consumed by each embedded system is presented when it is running the algorithm continuously. The RAM allocated for invoking the trained model and the results when predicting the flower type are presented. As described in Table 16, the Raspberry Pi Pico has lower latency than the ESP32 and Arduino Nano BLE, as well as lower power consumption, when the embedded systems deploy the DNN in real time. However, it is possible that for DNNs with more complicated and larger models, the performance of the Raspberry Pi Pico begins to reduce compared to the ESP32 and Arduino Nano BLE due to the hardware resources that each system has. The analysis of the performance of each embedded system when executing DNNs with more complex models for classification and regression can be addressed in depth in future work. It should be mentioned that the implementation only shows the execution of the DNN in embedded systems without analyzing its accuracy when performing classifications with inputs different from those contained in the Iris dataset. The optimization of the model is not analyzed from training, as improvements can be achieved throughout the process by choosing another dataset for training, changing the architecture of the DNN, or modifying training parameters and hyperparameters. The performance of the real-time DNN executed on hardware-constrained embedded systems by modifying the training and the model's characteristics can be analyzed in future works. Figure 19:0 Results when the flower classification process is deployed within ESP32.
## Scientific Concept  
The presented approach builds on the development and evaluation of risk treatment paths following [PERSON] et al. [6]. It is based on the determination of interactions through interpretive structural modeling and the calculation of conditional probabilities using Bayesian networks. The result is a multitude of risk treatment paths that contain risks and measures that can occur in a production system under consideration.  
The generation of the risk-treatment paths in [PERSON] et al. [6] is subject to several assumptions, the most important of which are:  
1. The regarded area of application is limited to the use of electrical energy in production systems,
2. Processes are formalized in discrete production timetables,
3. Experts are available to supply the necessary information to generate the Bayesian networks.  
Any further limitations named in [PERSON] et al. [6] equally apply to the approach in this paper. The following assumptions apply specifically to the approach presented in this paper:  
1. A discretized, energy-optimal production schedule is available,
2. For the risk-treated energy-oriented PPC, the planned energy schedules including the maximum allowances for peak loads are available,
3. A detailed risk inventory including measures is available, wherein process-specific parameters in the dimensions of time and energy consumption are known for every risk and every measure.  
Figure 2 depicts a graphical overview of the approach proposed in this paper. The boxed numbers in Figure 2 additionally show in which section one can find details on the respective process step. The main process steps of the approach are:  
1. Calculation of the final criteria weights by combining AHP and Shannon entropy,
2. Calculation of the marginal utility scores according to the problem-specific utility functions,
3. Calculation of the final utility score and subsequent prioritizing the risk-measure path profiles in descending order,  
Figure 2: Overview of the approach.  
Classification of the relevant measures into preventive, reactive, and non-distinguishable in accordance with [37],
* Integration of the measures into the process schedule depending on the prior classification, resulting in a risk-treated process schedule.  
The application of the approach produces three overarching results: the prioritized risk treatment paths, a catalog of categorized measures, and as the final result, the risk-treated production schedule.
## 2 Theory, Requirements and Synthesis
### Closed-Loop Control (CLC)
#### 2.3.3 Closed-Loop Control Synthesis
Then, the derivative is taken \(\frac{\mathrm{d}u_{\text{out}}}{\mathrm{d}t}\) and multiplied by the derivative of the original triangle wave \(\frac{\mathrm{d}u_{\triangle}}{\mathrm{d}t}\). If these two are in-phase, the product is positively biased, while when out-of-phase, the product is negatively biased. This causes the integrator to chirp either up or down. In practice, there can be an additional phase shift at \(u_{\text{out}}\), which might need to be compensated by adding a relative phase shift to the reference signal \(\frac{\mathrm{d}u_{\triangle}}{\mathrm{d}t}\). When the maximum point is reached, the product loses the DC component; therefore, the output of the integrator remains constant, as required. To determine the time constants of these building blocks we must consider the following. First, the exciting triangle wave signal should be slow enough not to be filtered out by the low-pass response of a TEC element. Therefore, we propose a time constant in a similar range, about \(\tau_{\triangle}\approx 1\) s. The integrator, on the other hand, should be slow enough to be filtered out by the high-pass filter at the input, i.e., \(\tau_{\int}\ \gg\tau_{\triangle}\).
## 1 Introduction
### Proposed Approach  
In the proposed approach for this study, it is assumed that there is a dataset of \(n\) customers, one truck, and \(k\) drones traveling with the truck to deliver to these customers. The main goal in this scenario is to minimize the delivery completion time. The completion time of the delivery is the sum of the time taken by the entire process, starting with the truck leaving the warehouse and ending with the truck returning to the warehouse. If the stages of the process for the proposed approach are listed:  
* There is only one warehouse, and its location is fixed;
* Initially, vehicles leave the warehouse and return to the warehouse after completing the delivery;
* Delivery vehicles include one truck and one or more drones;
* All drones are identical and can operate within a certain range;
* Drones start their movement from the truck and can only deliver to one point at a time, and after completing the delivery, they go to the appointment point and complete their movement when they reach the truck;
* The speeds of the vehicles are constant;
* All packages expected to be delivered can be delivered by both truck and drone (in terms of size, weight, etc.);
* The locations to be delivered to and the distances between these locations are known in advance and are fixed; they do not change after the delivery has started;
* Only one delivery should be made to all customers on the route.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 4 Experimental Flow
### Decryption Algorithm  
Figure 32 shows an encrypted text that has been decrypted.  
_Step 1:_ Each ciphertext character is converted to ASCII, as shown in Figure 33.  
_Step 2:_ Each value is converted to binary after conversion to ASCII, as shown in Figure 34.  
_Step 3:_ After conversion to binary, the binary values of the ciphertext and key are XORed, as shown in Figure 35.  
_Step 4:_ The binary values obtained by the XOR operation are converted to decimal, as shown in Figure 36.  
_Step 5:_ Different ASCII values are obtained after obtaining the decimal values, as shown in Figure 37.  
_Step 6:_ The rules of the cipher matrix are applied to the ASCII values, for which the cipher matrix text is divided into pairs first, as shown in Figure 38.  
Figure 41: [PERSON] cipher decryption formula.  
Figure 42: Decimal to Binary conversion.  
Figure 43: Remove Salting from bits.  
Figure 44: Reversing mechanism.  
Figure 38: Pairs of encrypted text.  
Figure 40: Generating ASCII values.  
Figure 41: [PERSON] cipher decryption formula.  
Figure 39: Text after implementing Cipher Matrix.  
When the rules of the cipher matrix technique are properly applied, and various techniques such as salting, bit reversing, and Caesar ciphers are used with this technique as used in this paper, it is difficult for the attacker to break the ciphertext security, enabling cloud transmission in a reliable environment.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 3 Experimental Verification
### Comparative Analysis for the Feeding Performance  
#### 3.5.1 Feeding Time Analysis  
Table 5 provides the time required to complete feeding at all feeding points in a pond area of 10,000 square meters. The table showcases the mean distribution time and the standard deviation (S.D.). Feeding is mainly carried out in two ways: manual feeding and automatic feeding by unmanned boats. All the boats operate at a consistent speed of 0.5 m/s. In the manual feeding method, the feeding time ranges from 45 to 51 min. By contrast, the unmanned boat feeding method has a feeding time range of 19 to 20 min. For the manual feeding method, the average feeding time is 47 min, with a standard deviation of 4.3 min. On the other hand, the unmanned boat feeding method has an average duration of 20 min, with a significantly smaller standard deviation of just 0.2 min. This highlights the efficiency and consistency of the boat feeding method. This indicates that using unmanned boats for feeding can greatly save time and ensure more stable feeding times. The advantages of the unmanned boat feeding method in terms of average distribution time and lower variability underscore its potential in feeding operations, thereby enhancing resource utilization and productivity.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 2 Software-Defined Radios  
During the last few decades, telecommunications have been in constant evolution. Among the most relevant technological contributions is the digital processor. This item offers to radio equipment the flexibility of a programmable system. In other words, the behavior of a communication system can be modified simply by changing its software. This benefit introduced a new radio paradigm called software-defined radio. Under this paradigm, the task of configuring the behavior of the radio is transferred to the software, leaving the hardware only to implement the radiofrequency front end. With this, the radio is no longer static, it is defined by its circuits and becomes a dynamic element, which can change its operational characteristics, such as bandwidth, modulation, and coding rate, even modified during the execution time according to the software configuration [32].  
SDR is defined as \"radio in which some or all physical layer functions are defined by software\" [32]. In SDR devices, the software modules are executed in real time on microprocessor platforms or digital signal processors. Moreover, most of the devices consider FPGA for transmitting or receiving radio signals. The main operational characteristics of SDRs can be modified at runtime; the system can be easily reconfigured to perform different functions as needed. Due to this, transmitters (Tx) or receivers (Rx) can be created for any type of signal through software or firmware functions [26; 27].  
SDRs can be used in various radiofrequency technologies, such as Bluetooth, WLAN, GPS, Radar, WiMAX, or LTE. The rapid evolution of wireless communication systems and their standards has made base station software updates a more attractive solution than costly base station replacement, both for the military sector and for the commercial sector. For example, updating the software of the satellite communication equipment deployed will allow changes in communication functionality and multiple uses during the life of the satellite [33]. SDR opens a range of possibilities by making existing types of radio applications easier to implement and by enabling new types of applications. In particular, the computing power and flexibility of the SDR can be leveraged to develop CR [34].  
The emergence of relatively affordable devices that receive and digitize radiofrequency signals has brought SDR to the desks of many communications engineers. Nevertheless, the latest availability of very-low-cost SDR devices, such as RTL-SDR, has brought SDR to the home desks of undergraduate and graduate students, as well as professional engineers and creator communities [35]. In this work, the implementation of an MBSS technique is proposed with the use of general-purpose communication devices such as SDR-RTL [36], HackRF One [37], and LimeSDR Mini [38]. Table 1 shows the main characteristics of these devices.
## 3 Results
### Post-Simulation Analysis
#### 3.10.2 Binding Affinity Calculations Using MM-GBSA  
Table 5 presents the \(\Delta\)G\({}_{\text{bind}}\) calculations and its components for two systems, VLP-PGN and VLP-\(\beta\)-glucan, across different simulation time points (0 ns, 25 ns, 50 ns, 75 ns, and 100 ns). The \(\Delta\)G\({}_{\text{bind}}\) for VLP-PGN and VLP-\(\beta\)-glucan exhibited fluctuations over the simulation time, with VLP-PGN generally showing stronger binding than VLP-\(\beta\)-glucan. The \(\Delta\)G\({}_{\text{bind}}\) analysis revealed that VLP-PGN exhibits more substantial binding stability than VLP-\(\beta\)-glucan across all simulation time points. At 0 ns, \(\Delta\)Gbind for VLP-PGN is \(-85.41\) kcal/mol, which decreases to \(-64.65\) kcal/mol at 100 ns. In contrast, VLP-\(\beta\)-glucan exhibits weaker binding, starting at \(-66.09\) kcal/mol and decreasing to \(-43.87\) kcal/mol. On average, VLP-PGN maintains better stability than VLP-\(\beta\)-glucan. The van der Waals (\(\Delta\)G\({}_{\text{bind}}\)vdW) interactions are a major contributor to the binding energy for both systems, with VLP-PGN showing consistently stronger interactions. Hydrogen bonding (\(\Delta\)G\({}_{\text{bind}}\)H\({}_{\text{bond}}\)) is slightly more pronounced in VLP-\(\beta\)-glucan, while \(\Delta\)G\({}_{\text{bind}}\)Solv GB imposes a higher penalty for VLP-PGN.
# HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications
## 2 Relevant Knowledge  
### Advanced Encryption Standard--AES  
The need for data security and privacy protection has become increasingly pressing in the digital age. Consequently, the research and development of encryption algorithms have taken on paramount importance. The advanced encryption standard (AES), also known as the Rijndael encryption algorithm, is a block cipher standard established by the National Institute of Standards and Technology (NIST) in 2001 [12]. AES operates on data blocks of 128 bits, and its variants are categorized based on key length: AES-128, AES-192, and AES-256. Each variant has a different number of encryption rounds, as detailed in Table 1.  
The AES algorithm operates on the principle of a block cipher, wherein plaintext data are divided into fixed-length blocks, and encryption and decryption are achieved through iterative operations and key expansion. The AES encryption process is depicted in Figure 1. This process ensures data protection through iterative rounds of encryption, with each round comprising specific steps such as byte exchange, row shift, column mix, and round key plus. A detailed breakdown of Figure 1 is as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**AES Type** & **Key Length/Bit** & **Packet Length/Bit** & **Encrypted Rounds** \\ \hline AES-128 & 128 & 128 & 10 \\ AES-192 & 192 & 128 & 12 \\ AES-256 & 256 & 128 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: AES classification.  
This procedure is iteratively executed across multiple rounds, with each round utilizing a distinct round key, culminating in the generation of the final ciphertext.  
The AES algorithm has achieved international standardization and has been extensively adopted across diverse application domains. Given the existing computational capabilities, it offers rapid encryption and decryption through a highly optimized algorithmic design and is currently resistant to cracking. However, the AES algorithm necessitates rigorous key management, and the complexity associated with key management could potentially compromise security. As technology continues to evolve, the long-term security of the AES algorithm warrants ongoing research and validation.
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 5 Discussions  
This study set out to gain further understanding of UAV-based 3D map usage in the construction industry by seamlessly integrating the earthwork design review and earthwork planning. This study incorporated the real data from a housing construction project in Korea to prove the feasibility of the integration process of UAV-based 3D map acquisition to earthwork planning. In terms of the earthwork design review, altitude errors occurred at most points. It is suspected that it is probably caused by weathering. In fact, the site is located at the edge of the mountain and has a high slope in the north, so it is believed that many soils have been swept away through the slope due to rain or snow melting. Therefore, the earthwork calculated from the actual design was over-estimated, in turn, the fill volume was under-estimated. The National Geographic Information Institute (NGII) of Korea provides DEM for public use, and the DEM must be regularly updated by local governments. The update frequency, however, depends on the local government's budget situation, so, in some rural areas where a sufficient budget is not available to have outdated DEM, it could be up to 10 years. To mitigate such a discrepancy, UAV-based mapping should be conducted prior to the initial design. Although it is always common for field engineers to make immediate and on-the-fly decisions on earthwork equipment plans and specific work sequences, there have never been any studies reviewed from the design stage to the work sequences of the earthwork equipment. Based on the actual construction site case, it is confirmed that the UAV, BIM, and earthwork equipment task planning process could be integrated into a single pipeline, and it could contribute to reducing construction costs and duration in that it could solve problems such as redesign and reconstruction due to design errors. Additionally, these findings might help us to develop an automated earthwork planning system.
## 5 Application in Real-World HDS System  
We conduct a case study to apply our proposed method to Huade Co., Ltd. to check the feasibility of our algorithm in a real-world HDS system. The conducted HDS system has a similar shape and size (22 \(\times\) 20) as the fulfillment center, and it can be topologically represented in a medium-scale mesh-tree grid map. The topological representation of the conducted HDS system is displayed in Figure 11. Four four-way shuttles are allowed to operate in this HDS system. To simplify our case study and match the design of our algorithm, only one four-way shuttle and one conveyor are considered. The start position of shuttle D (bottom left corner) is the actual starting shipping position in our case study.
## 2 Results
### Feasibility  
The experimental apparatus shown in Figure 1 is much the same as those of the quantum key distribution (QKD) and quantum private query protocols in refs. [57; 58; 59]. The only difference is that our protocol requires two more storage rings \(SR_{x}\) and \(SR_{y}\). The QKD protocol in ref. [57] was already realized experimentally in ref. [60]. By comparing our apparatus with Figure 1 of ref. [60], we can see that the technology in ref. [60] is sufficient for implementing our protocol too. Detailed description of the actual experimental devices can be found in Section III of ref. [60].  
An important part of the implementation is to find storage rings \(SR_{x}\) and \(SR_{y}\) with a sufficiently long delay time because they determine the holding time (the time interval between the commit phase and the unveil phase) of the protocol. Using 150 km of optical fiber (which was proven to be able to guarantee sufficiently high key rate for QKD in practice) to make the storage ring can generate about 500 us delay time. While such a holding time seems short, it is already sufficient for practical applications such as quantum coin tossing, as shown in Appendix B.