esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
EHN has already been used as a cetane improver in biodiesel [34] and in blends with various organic compounds such as 2,5-dimethylfuran [35], dimethylcarbamate [33], and alcohols such as diesel-ethanol blends [36], n-pentanol-diesel blends [37], and even gasoline-ethanol mixtures for I.C. engines [38]. EHN has also been used to improve the cetane number in methanol-biodiesel blends [39], Karanja biodiesel [40], Yellow Oleander biodiesel [41], and SVO-based blends like hazelnut oil mixed with alcohol [42]. Globally, EHN is produced at an industrial scale, with an estimated annual production of approximately 100,000 tons [43]. The use of EHN as a cetane improver could be further used if we used EHN directly as a solvent. Leveraging EHN's existing use as a cetane improver, this research explores its novel application as an LVHC solvent. Its potential benefits include enhanced fuel ignitability, reduced ignition time delay, lower combustion temperatures, and diminished combustion noise (cita 65). Furthermore, EHN could achieve renewable status if it is synthesized using green ammonia produced by sustainable processes such as water electrolysis powered by renewable energy sources. This would eliminate dependence on fossil fuel-derived ammonia, significantly reducing the environmental impact of EHN. As EHN is produced through the nitration of 2-ethylhexanol, the use of green ammonia would enable a fully renewable production pathway. This is not only in line with the principles of sustainable chemistry but also reinforces EHN's potential as a green, high-performance fuel additive, paving the way for its integration into a circular bio-economy model [44]. Therefore, the primary goal of this research is to evaluate the use of 2-ethylhexyl nitrate (EHN) as a low-viscosity, high-etane (LVHC) solvent to enable castor oil and sunflower oil to be directly utilized as biofuels in diesel combustion engines. This study explores the potential of EHN in triple blends of Diesel/EHN/SVO, given its established role as a cetane enhancer in diesel fuels and its ability to reduce viscosity. Moreover, EHN could be classified as renewable if synthesized using green ammonia, as it is derived from the nitration of 2-ethylhexanol.
## 3 Improved Parallel Architecture
### Parallel Preprocess
#### 3.1.2 Resource-Saving Data Rearrangement  
Timing error is caused by timing frequency and phase offset. The phase offset caused error is a constant value, but timing error will continuously increase or decrease to infinite if there exists timing frequency offset. To restrict the error, source signals need to be deleted or kept when the error has  
Figure 2: Field Programmable Gate Array (FPGA) block diagram of improved parallel timing architecture.  
accumulated to a certain amount. However, in a parallel structure, the parallel source signal sequence will be disordered once the delete or keep operation occurs.  
Data rearrangement is adopted to delete/keep the source signal and then adjust the disordered signals into a correct order. In [10], the authors have taken a parallel FIFO based delete-keep method to make this adjustment. However, as shown in the first column of Table 1, the subscripts of indexes are variable. This leads to Look Up Table (LUT) being consumed in every parallel channel. More seriously, the LUT consumption increases exponentially with parallel number.  
To solve this problem, an index-associated method is proposed. It is easy to find out that the increment of subscript shares the same value with the increment of index value from 1. Thus, the variables can be translated into index(i) plus the corresponding subscripts, as exhibited in the second column of Table 1. With the proposed associated index method, only one LUT is consumed because the other LUTs in [10] can be replaced by add operations. Taking an \(m\) parallel system for instance, the LUT are carried out only in index(i), and the other \(m-1\) LUTs are replaced with \(m-1\) adders.  
Our work aims at achieving a 20 Gbps rate communication system, a 64 parallel architecture will be a wise choice. Because the FPGA clock frequency will be running around 156.25 MHz, which will not be limited by the current hardware. So the comparison of synthesised FPGA resource utilization with 64-parallel FIFO is exhibited in Table 2. It shows that the proposed method saves about 67% LUTs compared to the method in [10].
# Automatic Blob Detection for Dental Caries
## 2 Methods and Techniques
### Feature Extraction
#### 2.3.2 Caries Candidate Detection  
Geometric features are the most important extraction features to look for when detecting caries on dental radiographs. Geometric features on images are enhanced through the adoption of Hessian analysis [18]. Hessian analysis technique computes the second derivatives along 3D directions by convoluting the given image with derivatives of the Gaussian kernel [19]. The Hessian analysis matrix is defined as:  
\[H_{\sigma}(x,y,z)=\begin{pmatrix}I_{xx}&I_{xy}&I_{xz}\\ I_{yx}&I_{yy}&I_{yz}\\ I_{zx}&I_{zy}&I_{zz}\end{pmatrix} \tag{2}\]  
where \(\sigma\) is the standard deviation of the Gaussian distribution and \(I_{ij}\) is the second deviation along \(i\)th and \(j\)th direction. The standard deviation \(\sigma\) could be used to control the radius  
Figure 3: **First Row**: Teeth boundary detection via active contours method. **Second Row**: All blobs detected on masked images. **Third Row**: Caries detected blobs on masked images.  
of blob-like structures. Eigen values that indicate three orthogonal directions, and Eigen vectors that indicate degrees of curvature along the given directions can be used to solve the hessian matrix at each pixel. Therefore, the likelihood of blobs together with the magnitude of pixel's Eigen values can be formulated as:  
\[R_{B}=\frac{|\lambda_{1}|}{\sqrt{|\lambda_{2}||\lambda_{3}|}} \tag{3}\]  
\[M=\sqrt{\sum_{n=1}^{3}\lambda^{2}{}_{n}} \tag{4}\]  
where \(R_{B}\) and \(M\) represent the likelihood of blobs and the magnitude of pixel's Eigen values, respectively. Generally, the magnitude of Eigen values of objects are larger than that of the background. Blobs can be enhanced further using the formula:  
\[B_{\sigma}(\lambda_{p})=\{\begin{subarray}{c}(1-e-\frac{R_{B}}{24}),if\; \lambda_{1}>0,\lambda_{2}>0,and\;\lambda_{3}>0\\ 0,otherwise\end{subarray}\} \tag{5}\]  
where \(\lambda_{p}\) indicates Eigen values at position \(p=(x,y,z)\) and \(\alpha\) and \(\beta\) refer to sensitivity parameters of \(R_{B}\) and, \(M\), respectively. In our approach, the sensitivity parameter was set at 0.3 to control the number of blobs detected in the darker regions. The condition \(\lambda_{1}>0,\lambda_{2}>0,and\;\lambda_{3}>0\) is used to enhance only dark objects. Furthermore, carious lesions of different sizes can be detected, by adjusting the formula above as:  
\[Blobness(\lambda_{p})=maxB\sigma(\lambda_{p})_{\sigma_{min}\leq(\sigma)\leq \sigma_{max}} \tag{6}\]  
where \(\sigma_{min}\) is the minimum scale and \(\sigma_{max}\) the maximum scale of carious lesions.
## 6 VLC-IoT: Existing and Proposed Approaches
### Handover Mechanisms in VLC-IoT  
A quick search of the latest research contributions in VLC shows that more contributions are aimed at supporting multi-user access, enabling bi-directional transmission,and achieving high speed in VLC systems. Nonetheless, an essential albeit not widely researched topic for the VLC is the handover process. The handover process guarantees that VLC-IoT devices can maintain seamless connection when they move from one access point (AP) to another AP without dropping connection.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 6 Conclusions  
Optimization of the congestion control algorithm is an area that continues to attract the attention of researchers. The motivation of this study is to reduce the time required to consume the available bandwidth after the fast-recovery phase for Fast TCP/MFast TCP. Therefore, the CCAFR algorithm is found suitable for the transport protocols that operate in high-bandwidth and long-distance networks. The obtained results and their analysis illustrated that the CCAFR algorithm positively outperformed Fast TCP/MFast TCP (delay-based congestion control schemes) and TCP New Reno and SCTP (loss-based congestion control schemes). This indicates that CCAFR is more suitable for the protocol that adjusts their congestion window on each RTT. The proposed algorithm can be used for the protocols that use delay as congestion indicator and adjust their congestion window on each RTT.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/21/4698/s1](http://www.mdpi.com/2076-3417/9/21/4698/s1)]([http://www.mdpi.com/2076-3417/9/21/4698/s1](http://www.mdpi.com/2076-3417/9/21/4698/s1)). Table S1: Convergence time of the protocols after fast recovery phase. (Test # 1 to 5), Table S3: End-to-end goodput of the protocols (Test # 1 to 5).  
The authors declare no conflict of interest.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction  
In recent years, the field of building energy consumption prediction has witnessed the emergence of various advanced models, with polynomial regression, SVR, MLP, LSTM, Transformers, among others, being particularly prevalent.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 6 Experimental and Result Evaluations
### Complexity Analysis  
In Figure 6, we present the experimental results of the complexity of 100 independent implementations of classical RA and enhanced RA. The complexity is in terms of search steps. The experimental results show that the proposed scheme brings certain complexity increase compared with the traditional scheme, because the proposed scheme evaluates the objective function at the solution set in the neighborhood.  
Figure 4: Performance of the boundary definition method. (a) Classical technique; (b) Wrapping around of the boundary.  
Figure 5: The cumulative distribution function (CDF) diagram of search results at different neighborhood radius.  
However, it is emphasized that, such increase in complexity can make up for the increased complexity of the repeated search brought by the traditional scheme. The proposed algorithm greatly increases the probability of successful searches at the cost of moderate complexity increase. With the further increase in the number of elements, the proposed algorithm can still be implemented efficiently, which is of great significance for practical applications.
## 5 Proposed Methods Based on FTSP
### Adjusted Temperature FTSP (AT-FTSP)  
The first approach proposed in this work is called _Adjusted Temperature FTSP_ (AT-FTSP). Based on Equation (1), it is possible to obtain a correction factor of the clock skew using the temperature of the local mote. For the sake of helping the reader to better understand the mathematical model, Table 1 collects all the parameters used.  
\[f_{n}=f_{0}^{n}(1+\beta_{n}(T_{n}-T0)^{2}) \tag{2}\]  
Working on Equation (2), Equation (3) has been obtained:  
\[Skew_{\mathrm{AT}}=f_{0}^{n}(1+\beta_{n}(\Delta T_{n})^{2}) \tag{3}\]  
By using the correction factor proposed in Equation (3) and the _clock skew_ value of FTSP, a new _clock skew_ value is obtained:  
\[Skew=Skew_{\mathrm{FTSP}}\cdot Skew_{\mathrm{AT}} \tag{4}\]  
AT-FTSP is to be used in those environments where the clock of the reference node cannot be accessed. For example, AT-FTSP is the perfect option for a scenario where the temperature sensor of the reference node is broken, miscalibrated or is known to provide Byzantine errors. On the contrary, when the temperature of the reference node is available, A2T-FTSP, which is described below, can be used to incorporate that information in the clock skew computation process.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Variable** & **Description** \\ \hline \(\mathrm{Skew_{n}^{T}}\) & Correction factor based on temperature. \\ Skew & Clock skew obtained empirically. \\ \(\mathrm{f_{n}}\) & Real frequency in node N. \\ \(\mathrm{f_{r}}\) & Real frequency in root node. \\ \(\mathrm{f_{0}^{n}}\) & Nominal frequency in node N. \\ \(\mathrm{f_{0}^{r}}\) & Nominal frequency in root node. \\ \(\mathrm{\beta_{n}}\) & Temperature coefficient in node N. \\ \(\mathrm{\beta_{r}}\) & Temperature coefficient in root node. \\ \(\mathrm{T_{n}}\) & Temperature in node N. \\ \(\mathrm{T_{r}}\) & Temperature in root node. \\ \(\mathrm{T_{0}}\) & Nominal temperature (25 \({}^{\circ}\)C). \\ \hline \end{tabular}
\end{table}
Table 1: Variables in the mathematical model.
## 5 Simulation Analysis
### Simulation under Different Level Disturbances  
Disturbances include position disturbances and angular disturbances. In this paper, the covariance matrix is used to represent the disturbances. The covariance matrix is set to \(N_{m,k}^{noise}=diag(0.1\text{ m},\ 0.1\text{ m},\ 0.5\text{ deg})\). The last one represents the angular disturbance and the other two terms are the position disturbances. The starts and goals of the robot are shown in Table 1. The operation of the multi-robot system is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c|c c c} \hline
**ID** & **Start** & **Goal** & **ID** & **Start** & **Goal** \\ \hline
1 & (4,0) & (\(-\)4,0) & 9 & (\(-\)4,\(-\)2) & (4,2) \\
2 & (4,2) & (\(-\)4,\(-\)2) & 10 & (\(-\)4,\(-\)4) & (4,4) \\
3 & (4,4) & (\(-\)4,\(-\)4) & 11 & (\(-\)2,\(-\)4) & (2,4) \\
4 & (0,4) & (\(0,-\)4) & 12 & (\(0,-\)4) & (0,4) \\
5 & (\(-\)2,4) & (\(2,-\)4) & 13 & (\(2,-\)4) & (\(-\)2,4) \\
6 & (\(-\)4,4) & (\(4,-\)4) & 14 & (\(4,-\)4) & (\(-\)4,4) \\
7 & (\(-\)4,2) & (\(4,-\)2) & 15 & (2,4) & (\(-\)2,\(-\)4) \\
8 & (\(-\)4,0) & (4,0) & 16 & (\(4,-\)2) & (\(-\)4,2) \\ \hline \end{tabular}
\end{table}
Table 1: Start and Goal of Robots.  
The minimum distance between robot and obstacle and the minimum distance between robots during the operation of the multi-robot system are recorded. The solid line in Figure 9 indicates the minimum distance between obstacle and robot. The solid line in Figure 9 presents the minimum distance between robots. It can be seen that each robot can arrive at the goals safely at the end.  
Figure 8: Simulation of multi-robot motion planning, (**a**) T = 0, (**b**) T = 4 s, (**c**) T = 8 s, (**d**) T = 12 s, (**e**) T = 16 s, (**f**) T = 20 s.  
To verify the effectiveness of the proposed method. The simulation is done under different level disturbances. In this paper, the proposed method is evaluated in terms of safety, task success rate, operational efficiency, and computational efficiency. The simulation is done fifteen times under each condition, and the results are shown in Table 2.  
In terms of safety, it can be seen from Table 2 that as the level of disturbance increases, the minimum distance between robot and obstacle increases, and the minimum distance between robots increases. Moreover, it can be seen from Figure 9 that there is no collision between robots and no collision between robot and obstacles during the entire operation of the robots. In terms of task success rate, our method achieves a 100% task success rate at various levels of disturbances. In terms of operational efficiency, the average velocity of the multi-robot system decreases, and task time increases as the level of disturbance increases. However, the difference is not very large. In terms of computational efficiency, it can be seen from Table 2 that the difference in computational time consumption is not significant.
## 4 Reconstructing Global Stress for a Marine Connector
### Optimization of the Number of Stress Sensors  
The 50 feature elements were sorted in descending order according to their stress amplitudes, and the number of correlated elements for each feature element was counted, as shown in Figure 8. The results showed that the number of finite elements that had a correlation with the 50 feature elements was 220,905, exceeding the total number of entire elements, which was 30,858. This indicates that there are multiple intersections among the 50 subsets, and fewer feature units can be used to obtain the full-field stress distribution.  
Figure 6: Selection process of the feature finite elements.  
Figure 7: Identifiers of the selected 50 feature finite elements.  
Subsequently, a neural network was trained for global stress by selecting 5, 10, 15, 20, 25, 30, 40, and 50 feature elements in descending order of their stress amplitude as input variables. The influence of different numbers of feature elements on the accuracy of the global stress solutions was analyzed, and the results are shown in Figure 9. It was observed that as the number of feature elements gradually increased, the calculation accuracy also improved, and the algorithm's accuracy was found to be stable when the number of selected feature elements was 20. This indicates that most of the finite elements in the connector structure are strongly correlated with the first 20 feature elements, and the global stress of the connector can be accurately obtained using these 20 feature elements.
## 2 Adaptive Optics Enabled Free-Space Optical Communication
### Experimental Setup
Such ASE noise is added to the optical signal using an optical coupler, after which an OTF with the central wavelength of 1550.12 nm is applied to single out the corresponding optical wavelength channel. In the coherent receiver, the local oscillator (LO) light and the optical signal are mixed in an optical 90\({}^{\circ}\) hybrid, detected by two PDs, and digitized by a real-time oscilloscope. After the off-line digital signal processing (DSP) signal recovery, the sum-product algorithm is used in the LDPC decoding procedure with a maximum of 50 iterations [34]. Figure 1: Experimental setup for adaptive optics (AO) enabled free-space optical (FSO) communication system.
## 3 Probabilistic Topology Control Algorithm
### Communication Network Architecture of SWARMs Project
#### 3.1.4 Propagation Model  
According to the conclusion in [32], the path loss model of underwater acoustic channel over a distance \(l\) with signal frequency \(f\) is given as:  
\[A(l,f)=l^{k}a(f)^{l}, \tag{9}\]  
where \(k\) is the spreading factor, \(a(f)\) is the absorption coefficient. The pass loss model shown in Equation (9) can be expressed in dB, which is given by:  
\[10\log A(l,f)=k\cdot 10\log l+l\cdot 10\log a(f), \tag{10}\]  
where \(k\cdot 10\ \log l\) is the spreading loss; \(l\cdot 10\ \log a(f)\) means the absorption loss. The \(k\) is the spreading factor which describes the geometry of propagation and the values are: (1) \(k=2\) for spherical spreading; (2) \(k=1\) for cylindrical spreading; (3) \(k=1.5\) for practical spreading.  
The absorption coefficient \(a(f)\) can be expressed by using [PERSON]'s formula, which is an empirical formula; the \(a(f)\) can be expressed as:  
\[10\ \log a(f)=0.11\times\frac{f^{2}}{1+f^{2}}+44\times\frac{f^{2}}{4100+f^{2}}+ 2.75\times 10^{-4}f^{2}+0.003, \tag{11}\]  
Equation (11) is used for frequencies above a few hundred Hz. If the frequencies are low, then Equation (11) can be rewritten as:  
\[10\ \log a(f)=0.11\times\frac{f^{2}}{1+f^{2}}+0.011f^{2}+0.002, \tag{12}\]  
Therefore, when the transmission power is \(P\), the received signal power will be:  
\[P_{r}=\frac{P}{A(l,f)}=\frac{P}{l^{k}a(f)^{l}}, \tag{13}\]  
According to Equation (13), when the received signal power is equal to the receive threshold \(P_{rth}\), the transmission range \(r\) of this AUV can be calculated based on Equation (13).
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 6 Performance Evaluation and Simulation Environment
### Simulation Methodology  
The simulation is built using real site estimation where LoRa networks are deployed. The network is assumed to cover an urban area of a 2000 m radius using 4 GWs and having 500 EDs. The GWs and EDs are uniformly distributed in the area with one GW located in the middle as represented in Figure 8.  
Since LoRaWAN allows multiple channels depending on regional parameters, ED's number was chosen to be 500 to represent a realistic quantity that can be found communicating on one single channel (same for other channels). The simulation assumed that each ED would send one packet in the air, and the GWs, depending on the RSSI level compared to their sensitivity threshold, would reject or decode the message. Unless otherwise specified, the configuration and parameters used in the simulation are described in Table 1. These parameters are used to calculate path-loss and rejected packets based on LoRaWAN regional parameters Okumura and Hata (1980); Okumura and Hata (1980) and defined in Matlab as a matrix of packet duration, GW sensitivity, and SF (signal to interference and noise ratio). This is in addition to other parameters for radio from Okumura-Hata such as the antenna's height and gain, power, and the ED's antenna height. The NS will identify most free bands and allocate them to EDs accordingly. For simplicity, we have used two frequencies in LoRaCog, the original EU 868 MHz from LoRaWAN and an additional licensed frequency 438 MHz Okumura and Hata (1980). The simulation took into consideration different split ratios between 868 and 438 MHz, as illustrated later. For example, LoRaCog 80 by 20 would mean 80% of EDs are operating on 868 MHz and 20% are operating on 438 MHz.
## 5 Challenges and Open Research Directions
### Interoperability  
Integrating Holochain with existing IoT systems and protocols presents a significant challenge. IoT networks often rely on standardized protocols such as MQTT, CoAP, and HTTP for interoperability. These protocols may not be directly compatible with Holochain's unique decentralized architecture, which uses DHTs for data storage and retrieval. Holochain's DHT-based P2P model requires translation layers for seamless integration. For example, middleware solutions could translate between Holochain's agent-centric model and the request-response or publish-subscribe models of conventional IoT protocols. Such an approach would allow Holochain-based IoT systems to interact with legacy devices while preserving decentralization and data integrity. Future research in this area could focus on the following:  
* Protocol adapters that translate MQTT/CoAP messages into a format that Holochain can process.
* Edge computing integration, where edge devices serve as intermediaries, preprocessing data before DHT storage.
* Decentralized identity and access management, ensuring secure device authentication within an agent-centric network.  
Figure 5: Simulation results for latency and throughput of Holochain vs. blockchain.