esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multipath Modelling and Mathematic Tools for Analysis
### Interpolation with Moving-Neighborhood LSC  
LSC, widely used in physical geodesy, is a statistical estimation technique employed to separate systematic trends, spatially correlated signals, and random noise in observation data. Originally developed for geoid determination, LSC is based on the spatial autocorrelations of observables, such as gravity, which are described by a covariance function [21; 22; 23]. This function can be homogeneous and isotropic [30], meaning its value depends solely on distance and exhibits rotational symmetry, or it can be anisotropic. The primary goal of LSC is to predict unknown values at unmeasured locations (or times) using known observations, accounting for both spatial correlation and random noise. By leveraging the covariance characteristics of the data, LSC provides an optimal estimation of the underlying signal, making it a powerful tool for improving accuracy in geodetic applications.  
It decomposes the observed values into three components: the trend, signal, and noise [31]. The observation equation, represented by a vector matrix, can be expressed as  
\[l=AX+t+e \tag{7}\]  
where \(l\) is the measurement vector of the samples, \(A\) denotes the design matrix of the trend model, \(X\) represents the non-random parameters, \(t\) stands for the autocorrelated random parameters at the observation points, and \(e\) is the random noise.  
Assuming that the other signals at the prediction points are vectors \(s\) correlated with the signal \(t\), despite their potential spatial differences, the solution for the unknown signal vector \(s\) is derived based on the minimum principle of LSC given as [23]  
\[\text{Min}\left(e^{\tau}C_{\alpha c}^{-1}e+s^{\tau}C_{ss}^{-1}s\right) \tag{8}\]  
where \(C_{\alpha c}\) is the variance-covariance (VC) matrix of the noise \(e\), and \(C_{ss}\) is the VC matrix of the signal \(s\).  
If the trend is neglected or has been calibrated, the estimate of the unknown signal s is expressed by [23]  
\[\hat{s}=C_{st}C_{ll}^{-1}l \tag{9}\]  
where \(C_{ll}\) is the VC matrix of the measurements \(l\) and has \(C_{ll}=C_{tt}+C_{ee}\), and \(C_{st}\) refers to the covariance matrix between the signal \(s\) at the prediction point and the signal \(t\) at the measurement point. Basically, the estimated \(\hat{s}\) is predicted based on the spatial correlation between the observed and predicted points. The elements in the matrices \(C_{ll}\) and \(C_{st}\) are computed using the covariance function, which is determined based on the distances between the sample points and the current satellite. Specifically, the covariance function describes how the spatial correlation between measurements decreases with increasing distance.  
LSC considers both random and non-random components in observations. It can be applied when observations are spatially correlated, which is often the case in geodetic and GNSS applications. LSC allows for accurate interpolation of values at unmeasured points by leveraging spatial autocorrelation.
## 5 Simulation Results and Discussion
### Energy Issue  
Total energy consumption and energy efficiency are the key terms to evaluate the energy efficient performance. For simulation, it is considered that all the sensors in a cluster are transmitting the same data size of \(L_{i}\) = 10 \(kb\). The simulation is performed based on the cluster size of \(N_{t}\) = 4. In Figure 6, the total energy consumption over distance is shown for cluster to cluster data transmission. From Figure 6 it is clear that the cooperative MIMO is more energy efficient than SISO transmission. The simulation is taken for two different code rates \(r=1/2\) and \(r=3/4\). When the code rate increases, parity bits compared to message bits are reduced. Therefore, total energy consumption reduces. This is verified in Figure 6.
# Vertex-Weighted Consensus-Based Formation Control with Area Constraints and Collision Avoidance
## 4 Discussion  
Based on the obtained results, we have verified that the proposed control law (13) preserves the advantages of the formation control law outlined in [6]. These advantages include rotational and translation independence, as well as collision avoidance among connected agents. Notably, the proposed approach does not rely on the virtual relabeling algorithm or broadcasting process.  
The proposed approach consistently achieves the desired formation under various conditions, regardless of the initial state, as evidenced by Figures 5, 6, 8 and 9. This achievement sets it apart from the conventional approach. In Experiment 1, we can see that, in real-world applications, the conventional approach drives the robots out of the line formation after a few seconds. This is mainly due to a range of factors, including errors in odometry, signal noise, and surface conditions. In such scenarios, our dependence on these factors becomes essential to attaining the desired formation. Consequently, there is a continuous need for the application of the virtual relabeling algorithm because, in the initial moments, it is not possible to ascertain correct virtual labels until the line formation is disrupted. Regrettably, this ongoing process can lead to increased latency in network communication.  
Moreover, the proposed approach effectively handles ambiguities in the formation control problem without necessitating additional connections between agents, as illustrated in Figure 7. This is in contrast to the conventional approach, which would require the creation of a connection between agent 1 and agent 4 to resolve the ambiguity, similar to Experiment 2.  
By utilizing (8), our proposed approach operates in a fully decentralized manner, relying only on the relative positions of neighboring agents. It also accurately recognizes the role of each agent within the formation. For example, if the goal is to arrange all robots in a counter-clockwise direction, the proposed approach can achieve it, a feat not possible with the conventional approach for certain initial conditions, as demonstrated in Figures 3 and 4.  
The simulations and real-world experiments showed that the robots successfully achieved the desired formation with smooth trajectories. However, the input control signals in real-world implementations exhibited some noise, primarily due to non-modeled dynamics, packet loss, actuator limitations, and other real-world factors.  
This work predominantly centers on the realm of 2D planes featuring differential mobile robots. For future endeavors, analogous concepts can be explored in the domain of 3D space, specifically with aerial robots or similar platforms. In this scenario, the study would incorporate the notion of the signed volume of a tetrahedron comprising four vertices. Consequently, a minimum of four connected agents would be essential to establish volumetric formations.
## 4 Analysis of Old Hanger Test Detection
### Hanger Overall Static Load Test  
The demolished old hanger is 13,960 mm long and 65 mm in diameter, and the nominal breaking force \(\mathrm{P_{b}}\) is 2787 kN. To accurately reflect the operating performance of the old hanger, the overall static load test is carried out. The 2 YBZ2-80 oil pump and YDC6500-1000 jack were used to load this. Each stage's hanger length change was measured after 5 min loading, from 0.1\(\mathrm{P_{b}}\) to 0.1\(\mathrm{P_{b}}\) per stage, and the loading rate was no more than 100 MPa/min. When loaded to 0.8\(\mathrm{P_{b}}\) after 30 min loading, loading continued, with each level at 0.05 \(\mathrm{P_{b}}\). After 5 min, each stage's hanger length changes, record tensile load, and sample elongation were measured.  
The results show that when the test force is loaded to 2647.6 kN, the static load efficiency coefficient of the hanger is \(\geq\)0.95, which meets the requirements of the specification [18]. The total strain of the hanger in this static load test is 2.8%, which meets the specification requirements of ultimate elongation with a strain greater than 2% [18]. The overall bearing capacity of the hanger is not significantly weakened. The overall static load test of the hanger is shown in Figure 13.  
Figure 12: Old hanger appearance detection: (**a**) corrosion of anchor cup and sealing cylinder, (**b**) corrosion of anchor ring, (**c**) damage to heat-shrinkable casing, and (**d–f**) PE sheath damage.
## 3 Results
### NMR T\({}_{2}\) Spectrum with Different Pressures and Temperatures  
The NMR T\({}_{2}\) spectra of saturated methane in cores of shale with different burial depths in the Zhaotong, Weiyuan, and Luzhou areas under different temperature and pressure conditions were taken. As shown in Figure 6, the peak area of the saturated methane NMR T\({}_{2}\) spectrum increases with increasing pressure and decreases with increasing temperature. Each sample exhibits a bimodal feature in the saturated methane NMR T\({}_{2}\) spectrum, which is different from the multi-peak feature of the crushed sample and plug sample of saturated methane with low pressure and low temperature in the NMR T\({}_{2}\) spectrum [31, 43]. The T\({}_{2}\) times corresponding to the P\({}_{1}\) and P\({}_{2}\) peaks are 0.1-20 ms and 20-10,000 ms, respectively. Overall, the peak area of the NMR T\({}_{2}\) spectrum between 0.1 and 1 ms is relatively small, being less than 5% of the total peak area. Therefore, we mainly analyze the NMR response characteristics of free gases that are greater than 1 ms. The smaller pores relax faster than the larger pores. The 1-20 ms NMR T\({}_{2}\) peak (P\({}_{1}\)) comes from the surface relaxation of free gas in shale pores, while the 20-10,000 ms NMR T\({}_{2}\) peak (P\({}_{2}\)) comes from the bulk relaxation of free gas micro-fractures and larger inorganic pores [17, 31].  
For sample YS137, the maximum pressure NMR peak areas at 60 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C were 18,616, 13,707, and 7354 a.u. (absolute unit), respectively, and the peak area of P\({}_{1}\) is much larger than that of P\({}_{2}\) (Figure 6a-c). As for sample W208, the maximum pressure NMR peak areas at 60 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C are 9149, 6856, and 5320 a.u., respectively, but the peak area of P\({}_{1}\) is basically equal to that of P\({}_{2}\) (Figure 6d-f). The NMR T\({}_{2}\) spectrum peak areas of the L214 sample at 60 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, and 100 \({}^{\circ}\)C are 8979, 7363, and 5570 a.u., respectively, and the peak area of P\({}_{1}\) is slightly larger than that of P\({}_{2}\) (Figure 6g-i). Compared to others, the NMR T\({}_{2}\) spectrum of sample YS137 in the Zhaotong area, with a burial depth of less than 2000 m, has the largest peak area, probably because of its highest porosity (Table 1).  
Figure 6: NMR T\({}_{2}\) spectra of typical wells from the Zhaotong, Weiyuan, and Luzhou areas under different temperature (60, 80, 100 \({}^{\circ}\)C) and pressure (14-38 MPa) values. P1 represents the left peak of NMR T\({}_{2}\) spectrum, and P2 represents the right peak of NMR T\({}_{2}\) spectrum.
# Comparative Study of Energy Savings for Various Control Strategies in the Tunnel Lighting System
## 3 Materials and Methods
### Luminance Demand in Tunnel Lighting System  
Road tunnels require safe and reliable lighting, maximizing drivers' visibility, which helps drivers identify the obstacles and other vehicles in the tunnel and plays an essential role in accident prevention. Tunnel lighting must provide very high amounts of luminance intensity during the daytime (when drivers are going from very bright to darker environments), especially during the first part of the tunnel, to eliminate \"black hole\" phenomena and ensure correct visual adaptation. There is no tunnel luminance change during the nighttime, and the average road surface luminance inside the tunnel must not be less than 1 cd/m\({}^{2}\) based on the CIE Publ. 88 [42] and China-JTG-201 [43] standards.  
Figure 4: Relationship between control strategy and energy consumption/system cost.  
\begin{table}
\begin{tabular}{c c l l} \hline \hline
**Strategy** & \multicolumn{2}{c}{**Main Advantage**} & \multicolumn{2}{c}{**Main Disadvantage**} \\ \hline Manual & - & Easy to implement and more stable & - & Considerable energy consumption \\  & - & Flexible lamp selection & - & Poor luminance continuity and \\  & - & Low system cost & & uniformity \\ \hline Time scheduling & - & Simple programming and circuit design & - & Poor luminance continuity \\  & - & Intermediate potential of energy saving & - & Relatively high system cost \\  & - & Intermediate potential of energy saving & - & Hardly have any energy-saving \\  & - & Intermediate complex of control algorithm & - & Relatively high system cost \\ Daylight adaptation & - & Achieved continuous tuning of lighting level & - & Consumes more energy at low \\  & - & Intermediate potential of energy saving & - & traffic volume condition \\ \hline Intelligent & - & Achieved continuous tuning of lighting level & - & Highly complex control algorithm \\  & - & High potential of energy saving & - & High system cost \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lighting control strategies’ analysis.  
To allow the driver's eyes to adapt safely to the changing lighting levels during the daytime, the CIE Publ. 88 and China-JTG-2014 standards divide the tunnel's longitudinal section into five reference zones, as shown in Figure 5. These reference zones are defined based on the daylight lighting requirements: access zone, threshold zone, transition zone, interior zone, and exit zone. The luminance of the threshold zone is directly related to the luminance level of the exterior environment. The interior zone is used to provide basic lighting. JTG-2014 is slightly different from CIE Publ. 88, but many provisions in JTG-2014 referenced CIE Publ. 88. As defined in ref. [43], threshold zone 1 is divided into two subzones, and transition zone 2 into three subzones, while the exit zone is divided into two subzones. In each zone, the luminance value decreases progressively, and the luminance of each subzone can be calculated based on Table 5.
## 3 Methods
### Improved DWA Algorithm
#### 3.2.2 DWA Fuzzy Controller Design  
Based on the principle of fuzzy control [31], a DWA fuzzy controller is designed which adjusts the weight coefficients in real-time according to environmental information variables to enhance the environmental adaptability of the algorithm.  
* Fuzzification Inputs: The distance of the vehicle from the target point G-dist (G), the distance of the vehicle from the nearest obstacle O-dist (O). The fuzzy definition of input quantities: The fuzzy set of G is defined as [S,M,L], that is, the distance between the corresponding vehicle and the target point is near (the red line), medium (the green line), and far (the purple line), and the domain is [0, 2]. The definition of the O fuzzy set is also [S,M,L], where the distance of the vehicle from the nearest obstacle is near (the red line), medium (the green line), and far (the purple line), and the domain is [0, 3]. The membership functions of the fuzzy sets G and O are shown in Figure 6.  
Figure 5: Three algorithm simulation comparison experiments. (**a**) Comparison of experimental results in Scenario 1; (**b**) comparison of experimental results in Scenario 2.  
The output quantities are \(\alpha\), \(\beta\), and \(\gamma\), and the fuzzy sets of \(\alpha\), \(\beta\), and \(\gamma\) are defined as [S,M,L], that is, the corresponding weights are small (the red line), medium (the green line), and large (the purple line), and the domains are all [0, 1]. The membership function is shown in Figure 7.
## 3 The Architecture of the Proposed E-BIOSARP
The status is marked as normal by the authentication state if the output is between 0 and \(R\), and as a result, the mathematical decryption function is returned to the BIOSARP routing mechanism to process the packet. Furthermore, in the case that a new wireless node wishes to join the network, a control packet is transmitted that contains the newly encrypted \(R\) with the master key _k1_. In BIOSARP, the AIS-based autonomous security mechanism requires a certain amount of time after deployment to accrue the necessary knowledge and to begin classification [23; 24; 25]. A WSN in this  
Figure 2: State machine diagram for packet encryption and decryption [27]. initialization phase is insecure and, as a result, can be hacked, taken over or damaged by adversaries. Therefore, E-BIOSARP is enhanced with packet encryption to secure the WSN during the initialization stage. During communication over a WSN, the proposed mechanism encrypts and decrypts the control and data packets. The algorithm pseudo codes for encryption with authentication and decryption with authentication are presented in Figures 3 and 4 respectively. Both processes begin with the reading of the packet header fields _DID_ and _SID_. The node will further process the packet if it is authenticated; otherwise, the node drops/discards the packet. During the encryption process, the _Pkt_ID_, _D_ID_, and _S_ID_ values are obtained from the header of the packet that has just been received. Of the extracted values, _Pkt_ID_ and \(k\) are utilized to generate a pseudo-random number. The Mathematical Encryption Function is then used to encrypt the packet header fields before the secure packet is sent. The maximum field size is 4 bytes (\(2^{31}-1\)), and if the encryption function generates a number greater than this maximum size, the modulo function will be used, as shown in Figure 3.  
At the other end of the transmission process, when a node receives a packet with encrypted header fields, it calls the Mathematical Decryption Function to perform the decryption based on the _Pkt_ID_, _D_ID_, and _S_ID_ values extracted from the received packet header and the pseudo-random number calculated using _Pkt_ID_ and \(k\), as shown in Figure 4. The _D_ID_ and _S_ID_ values are verified using the modulo function (\(2^{31}-1\)) in reverse to ensure the authenticity of the packet. If the value  
Figure 4: Decryption with authentication. Figure 3: Encryption with authentication. obtained in this reverse process is between \(0\) and \(R\), then the packet is categorized as legitimate and is further processed; otherwise, it is discarded.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 3 Proposed EPBTL-RDHEI
### Pixel Labeling and Data Hiding
#### 3.2.1 Recording Parameters  
The parameter values of \(\alpha\) and \(\beta\) are converted into 4-bit and 3-bit, respectively. Then, the 7 bits is recorded to the special pixel \(P_{s}\). The remaining 1 bit is reserved to record the version code, which will be explained later. The replaced eight bits of encrypted pixel value are treated as auxiliary information and appended to the front of secret data.
# Third-Generation Sequencing in Clinical Practice: The New Era of Precision Medicine?
## 2 Infectious Diseases  
Undoubtedly, the study of infectious diseases represents the main ONT application in clinical practice. Despite its short life, the technology has yet to be applied worldwide to support the rapid identification and control of Ebola, Zika, Yellow Fever, Swine flu, and COVID-19 disease outbreaks, making genomic epidemiology fast, easy, and cheap.  
In April 2015, three ONT MinION platforms, four laptops, a thermocycler, a heat block, pipettes, and reagents were packed into less than 50 kg of standard airline travel luggage, transported to Guinea, and used for real-time genomic surveillance (less than 24 h from sample receiving) of the ongoing Ebola epidemy [2]. In June 2016, the ZiBRA project used MinION platforms to undertake fieldwork in a mobile laboratory travelling acrossfive federal states in the Northeast region of Brazil, with the highest numbers of notified Zika cases [3].  
But the real worldwide use of nanopore technology is still ongoing. From the initial characterization of the SARS-CoV-2 virus genome to rapid variants detection, NS has been widely used to combat the spread of COVID-19 disease [4]. ONT allows the fastest SARS-CoV-2 sequencing (<8 h from RNA extraction to answer) with the best scalability on three different platforms (MinION, GridION and PromethION): from 12 (MinION) to >1000 (PromethION) samples in a single run, with cost as low as $9.55 per sample [5]. Data produced on NS platforms become readily available to the research community on public databases such as GISAID [4,6] speeding up the diffusion of SARS-CoV-2 genotype and the worldwide knowledge of its variants.  
Genomic epidemiology, powered by ONT rapidity and accessibility, provides vital information and time savings essential for rapid disease identification and control; furthermore, it supports precise tracking of pathogen evolution, allowing the potential association of novel variants with disease severity, transmission, and therapeutic efficacy. Last but not least, ONT platforms enable real-time metagenomic sequencing approaches, providing in COVID-19 patients in intensive care units the rapid identification of secondary infections, antimicrobial resistance, and nosocomial transmission [7].  
ONT's success in infectious diseases management arises because of its affordability and the ability to generate long-reads. In fact, the capability to produce reads of >4 Mb greatly simplifies the assembly of microbial genomes, overcoming all known difficulties of short-reads (typically 150-300 bps) sequencing and allowing the sequencing of smaller genomes in single reads, negating the need for assembly [8]. A true revolution in the study and management of infectious diseases!
_Algorithms_**2015**, \(8\), 435-458; doi:10.3390/a8030435  
_[[https://github.com/journals/articles/article/_](https://github.com/journals/articles/article/_)]([https://github.com/journals/articles/article/_](https://github.com/journals/articles/article/_))  
_Article_  
A Benchmarking Algorithm to Determine Minimum Aggregation Delay for Data Gathering Trees and an Analysis of the Diameter-Aggregation Delay Tradeoff  
**[PERSON]**  
Computer Science, Jackson State University, Jackson, MS 39217, USA;  
E-Mail: [EMAIL_ADDRESS]; Tel.: +1-601-979-3661  
Academic Editor: [PERSON]  
_Received: 5 May 2015 / Accepted: 23 June 2015 / Published: 10 July 2015_  
**Abstract:** Aggregation delay is the minimum number of time slots required to aggregate data along the edges of a data gathering tree (DG tree) spanning all the nodes in a wireless sensor network (WSN). We propose a benchmarking algorithm to determine the minimum possible aggregation delay for DG trees in a WSN. We assume the availability of a sufficient number of unique CDMA (Code Division Multiple Access) codes for the intermediate nodes to simultaneously aggregate data from their child nodes if the latter are ready with the data. An intermediate node has to still schedule non-overlapping time slots to sequentially aggregate data from its own child nodes (one time slot per child node). We show that the minimum aggregation delay for a DG tree depends on the underlying design choices (bottleneck node-weight based or bottleneck link-weight based) behind its construction. We observe the bottleneck node-weight based DG trees incur a smaller diameter and a larger number of child nodes per intermediate node; whereas, the bottleneck link-weight based DG trees incur a larger diameter and a much lower number of child nodes per intermediate node. As a result, we observe a complex diameter-aggregation delay tradeoff for data gathering trees in WSNs.  
**Keywords:** aggregation delay; data gathering tree; diameter; wireless sensor networks; tradeoff