esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Fleet Management System
### Global Communication with the Robots  
Global communication refers to the interconnection of all robots with the fleet management system, allowing for the efficient coordination of operations. In this model, the different AGVs and AMRs continuously transmit information to a central server, which processes the data and distributes commands to optimize the fleet operation. Figure 9 thus illustrates the global communication with all robots.  
Figure 10 presents the architecture of the fleet management system runtime environment, highlighting its modular structure and its capacity for integration with the Industrial Internet of Things (IIoT). Although the communication with IIoT systems is not yet active in the current implementation, the architecture was designed to allow for this expansion in a seamless manner. The lower layer of the diagram represents this future capability, including two main mechanisms: a REST API, which exposes structured data (such as robot positions, statuses, and events) to systems like MES and ERP, and an MQTT broker, intended to enable real-time event publishing from the AMRs. This approach ensures compatibility with IIoT platforms such as Node-RED, Grafana, and other industrial solutions. Additionally, the architecture has been designed to support the future integration with the VDA 5050 protocol, promoting interoperability with fleet control systems from multiple manufacturers in a standardized production environment, in line with the principles of Industry 4.0.  
Figure 9: Global communication method.
### [PERSON] and [PERSON] *  
Digital Communications Laboratory, Electronic Engineering, Gangneung-Wonju National University, Gangneung, Gangwon 201-702, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 20 December 2011; in revised form: 2 February 2012 / Accepted: 2 February 2012 / Published: 20 February 2012_  
**Abstract:** For decades, underwater acoustic communication has been restricted to the point-to-point long distance applications such as deep sea probes and offshore oil fields. For this reason, previous acoustic modems were typically characterized by high data rates and long working ranges at the expense of large size and high power consumption. Recently, as the need for underwater wireless sensor networks (UWSNs) has increased, the research and development of compact and low-power consuming communication devices has become the focus. From the consideration that the requisites of acoustic modems for UWSNs are low power consumption, omni-directional beam pattern, low cost and so on, in this paper, we design and implement an omni-directional underwater acoustic micro-modem satisfying these requirements. In order to execute fast digital domain signal processing and support flexible interfaces with other peripherals, an ARM Cortex-M3 is embedded in the micro-modem. Also, for the realization of small and omni-directional properties, a spherical transducer having a resonant frequency of 70 kHz and a diameter of 34 mm is utilized for the implementation. Physical layer frame format and symbol structure for efficient packet-based underwater communication systems are also investigated. The developed acoustic micro-modem is verified analytically and experimentally in indoor and outdoor environments in terms of functionality and performance. Since the modem satisfies the requirements for use in UWSNs, it could be deployed in a wide range of applications requiring underwater acoustic communication.  
**Keywords:** underwater communication; underwater wireless sensor network; underwater acoustic modem; micro-modem; acoustic wave; Cortex-M3; omni-directional transducer
## 4 Experimental Setup
### Model Evaluation Indicators  
We use PPL (perplexity), ACC (accuracy), and BPC (bits-per-character) as performance metrics for our experiments. PPL measures the average number of choices available to the model when predicting the next word in a sentence and is calculated using the following formula:  
\[\mathrm{PPL}(S)=\sqrt[n]{\frac{1}{p(w_{1},w_{2},\dots,w_{m})}}=\sqrt[n]{\prod _{i=1}^{m}\frac{1}{p(w_{i}\mid w_{1},\dots,w_{i-1})}} \tag{6}\]  
where \(S\) is the sentence being evaluated, \(m\) is the length of the sentence, and \(p(w_{i}\mid w_{1},\dots,w_{i-1})\) is the probability of the \(i\)-th word given the preceding words in the sentence. A lower PPL value indicates better model performance.  
ACC measures the percentage of correct judgments out of all judgment cases and is calculated using the following formula:  
\[\mathrm{ACC}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{ FP}+\mathrm{FN}} \tag{7}\]  
where TP (true positive) is the number of cases correctly judged as positive, TN (true negative) is the number of cases correctly judged as negative, FP (false positive) is the number of cases incorrectly judged as positive, and FN (false negative) is the number of cases incorrectly judged as negative.  
In the this work, accuracy refers to the percentage of correctly predicted tokens in the test dataset. In other words, it measures how often the model predicted the correct next word given the previous words in the sentence. This metric is commonly used to evaluate the performance of language models.  
BPC measures the number of bits required on average to encode each character in the text and is calculated using the following formula:  
\[\mathrm{BPC}=-\frac{1}{m}\sum_{i=1}^{m}\log_{2}p(w_{i}\mid w_{1},\dots,w_{i-1}) \tag{8}\]  
where \(m\) is the length of the text, and \(\log_{2}p(w_{i}\mid w_{1},\dots,w_{i-1})\) is the number of bits required to encode the \(i\)-th character given the preceding characters in the text. A lower BPC value indicates better model performance.  
Specifically, BPC measures the number of bits needed to encode each character in the text. Lower BPC values indicate better compression, which in turn indicates that the model has learned to better capture the patterns and structure of the text.
## 3 Application to Immersed Tunnel
### Boundary Conditions and Input Load
#### 3.3.2 Input Seismic Load  
Large numbers of underground structures were seriously damaged by the 1995 Kobe earthquake in Japan. Since then, people have begun to pay attention to the seismic problem of underground structures. The representative Kobe wave was selected from the Cosmos Virtual Data Center as the input seismic wave, which was recorded by the Japan Meteorological Agency, and the closest distance to the fault was only 1 km. According to the seismic safety evaluation of the engineering site in the area of the HZM Bridge, the peak acceleration of the wave was modulated to \(-2.8\) m/s\({}^{2}\) at 8.52 s, with the predominant frequency of 1.46 Hz. To save computing time, the first 20 s of time history reflecting the Kobe wave characteristics was taken as input (Figure 9).  
The incident direction of the seismic wave is parallel to the \(x\)-\(z\) plane and forms an angle \(\alpha\) of 0\({}^{\circ}\) (vertical incidence), 15\({}^{\circ}\), and 30\({}^{\circ}\) with the \(y\)-\(z\) plane. According to the direction of the axes in Figure 8, the \(x\)-, \(y\)-, and \(z\)-axes refer to the transverse, longitudinal, and vertical directions of the tunnel, respectively.  
The displacement and velocity time history of the seismic wave can be obtained by integral calculation of the acceleration time history shown in Figure 9, and the corresponding free-field stress can be calculated using Equations (5)-(7). Then, the equivalent nodal force based on VSAB can be obtained by substituting the calculated free-field stress into  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** & **Poisson’s Ratio** & \
\begin{tabular}{c} **Modulus of** \\ **Elasticity (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Shear Wave** \\ **Velocity (m/s)** \\ \end{tabular} \\ \hline Concrete & 2500 & 0.16 & 32,500 & - \\ Soil layer & 2000 & 0.27 & 101.6 & 141 \\ Overlying gravel soil & 1500 & 0.15 & 150 & 209 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material properties of the model.  
Figure 9: The Kobe wave: (**a**) time history; (**b**) Fourier spectrum.  
Equation (10), and the input of seismic load based on VSAB can be realized by applying the amplitude function in ABAQUS. Since there are many nodes in the model boundary, the setting of VSAB and the calculation and application of the equivalent node force can be realized by programming with FORTRAN language.
# Security Architecture for Swarms of Autonomous Vehicles in Smart Farming
## 2 Materials and Methods
### State of the Art
#### 2.2.4 IoT Reference Architectures for Distributed Systems  
The authors of this article analysed several IoT reference architectures in order to decide the best options to provide a cyber-security design for a distributed architecture.  
Microsoft's Azure architecture summarises a set of generic security considerations [21] when dealing with data exchanges between IoT devices an Azure's PaaS (Platform as a Service) components: TLS 1.2 (Transport layer Security) compatibility provision; data encryption through a secure symmetric key encryption algorithm; and device authentication through a secure method, such as Digital Signature based on a secure symmetric key encryption algorithm, pre-shared keys, X.509 certificates or unique tokens per device stored in a KeyStore. Azure also recommends implementing mechanisms for device firmware updates that enable remediation of discovered security vulnerabilities.  
Similarly, Amazon's AWS (Amazon Web Services) IoT security architecture [22] is focused on securing communications between IoT devices and AWS Cloud services. Its Cloud security model proposes TLS encrypted communications between any IoT device and the Cloud, and unique credentials for every IoT device that could be established through X.509 certificates, AWS credentials, Amazon Cognito identities or custom authentication tokens. Every credential must be granted specific permissions to access Cloud services.  
The Intel IoT Platform [23] implements its own tools for a layered security approach. The Endpoint Device Level focuses on device authentication and data confidentiality based on EPID (Intel Enhanced Privacy ID), which provides immutable identity and end-to-end data protection for every device. The Network Level ensures data security in transit through the use of intelligent gateway solutions developed in cooperation with McAfee. The Cloud Level focuses on protecting data centres.  
Google Cloud provides specific security services, such as Chronicle [24], a service that extracts signals from the IoT telemetry of a device to find threats, or the Asset Inventory [25], an inventory tool to monitor the identity of any access to the Google Cloud Platform resources of a project.  
As can be seen, all of these Cloud service providers agree on the security needs of IoT architectures (data encryption, device authentication, TLS compatibility, databases security, etc.) but there is no one-fits-all solution to follow and each of them implements its own particular solution.
## 4 Experiment and Analysis
### Product Accuracy Verification  
In order to further evaluate the performance of GA-BP inversion of soil moisture map. We calculate the R-value between the NASA-USDA product and the GA-BP Inversion soil moisture map at 392 fixed points extracted from the study area. Since the NASA-USDA product is only available for three days, for this reason, the GA-BP Inversion soil moisture values were averaged with equal weights. Figure 9 shows the distribution of R-values between NASA-USDA product and the GA-BP Inversion soil moisture at 392 fixed points, and Table 6 shows the statistics of Pearson's coefficient R at 392 sites.  
As seen in Table 6, 25 of the 392 fixed sites did not correlate due to the absence of soil moisture fluctuations in NASA-USDA products. A total of 299 of the remaining 367 sites had Pearson correlation coefficients R more significant than 0.6, accounting for 81.5% of the total sites. There were 27 between [0.4-0.6] and 42 less than 0.4. Overall, the multi-data fusion model in this study has strong reliability. As can be seen from Figure 9, the regions with low Pearson coefficients are mainly concentrated in the southeastern part of the experimental area, which is mostly desert and barren areas where the soil moisture inversion is not so ideal. The soil moisture located in the eastern coastal region has a Pearson coefficient greater than 0.8 in most places except for a few areas lacking fluctuations. It can be seen that the GA-BP Inversion soil moisture in this study is consistent with the soil moisture values of NASA products. In conclusion, the multi-data fusion soil moisture inversion model based on GA-BP neural network in this experiment has a particular facilitating effect for predicting soil moisture.
## 3 An Improved Effectiveness Evaluation Method Based on Factor Analysis
### Analysis on the Importance of Evaluation Index  
The square sum of the loads of the \(i\)th index on all m factors is called the commonness of the index [55], which reflects the effect of the index on all factors, that is, the importance of each original index. The calculation expression is shown as follows.  
\[H_{i}=\sum\limits_{j=1}^{m}a_{\bar{i}j}^{2}\ \ i=1,2,\cdots,p \tag{13}\]  
\(a_{\bar{i}j}\) is the load of the \(i\)th index on the \(j\)th factor, \(m\) is the number of factors and \(p\) is the number of original indexes.  
By comparing the commonness of the indexes, we can observe which variable plays a greater role. Therefore, the commonality of all variables can be normalized to the weight of the variable.  
\[W_{i}=\frac{H_{i}}{\sum\limits_{j=1}^{p}H_{j}} \tag{14}\]  
\(H_{i}\) is commonness of the index \(i\) and \(p\) is the number of original indexes.  
According to the factor loading matrix of Table 3, the weight of each index can be calculated as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Factor 1** & **Factor 2** & **Factor 3** \\ \hline X1 & 0.044136 & \(-\)0.155257 & 0.789911 \\ X2 & 0.243241 & \(-\)0.063296 & 0.219128 \\ X3 & 0.360151 & \(-\)0.171547 & 0.122934 \\ X4 & \(-\)0.347005 & 0.631644 & \(-\)0.022096 \\ X5 & \(-\)0.219985 & 0.550707 & \(-\)0.218813 \\ X6 & \(-\)0.021879 & 0.313877 & \(-\)0.050131 \\ X7 & 0.382698 & \(-\)0.146096 & \(-\)0.376688 \\ X8 & 0.437015 & \(-\)0.252909 & 0.101338 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Factor score coefficient table.  
According to the data in Table 5, the top four indexes in terms of weight are as follows: Voice link building time, information encryption intensity, end to end average delay and network state perception. By observing the factor load after rotation in Table 5, it can be observed that the load of the 'voice link building time' index is 0.9032, 0.3425 and 0.1302, respectively. This means that this index has a higher influence on the three factors. Similarly, the reason why \(X3\) and \(X4\) have higher weights is that they have higher influence factors among the three factors. The load of \(X1\) index in three factors is 0.0833, 0.1625 and 0.9299, which means that this index has a higher influence on the second and third factors.
## 1 Collagen Type I Characteristics  
Collagen type I is a major connective tissue protein. It increases the strength and stability of the cytoskeleton. The exceptional strength of skin, ligaments, tendons, and vessels requires a long protein chain characterized by repeated amino acid residues and a regular secondary structure.  
Type I collagen is fibrillar collagen and a major part of the interstitial membrane's structure. It is the most prevalent type of collagen and a key structural composition of many tissues. It is found practically in all structures involving connective tissue. Type I collagen is the main structural protein of bone, skin, tendon, ligaments, sclera, cornea, and blood vessels, as well as an important component of other tissues. It is gathered in fibers forming a structural-mechanical scaffold (matrix) of bones, skin, tendons, cornea, blood vessel walls, and other connective tissues [1].  
The COL1A1 gene produces the pro-alpha1 (I) chain. This chain combines with another pro-alpha1 (I) chain and also with a pro-alpha2 (I) chain (produced by the COL1A2 gene) to make a molecule of type I procollagen. These triple-stranded, rope-like procollagen molecules must be processed by enzymes outside the cell. Once these molecules are processed, they arrange themselves into long,thin fibrils that cross-link to one another in the spaces around cells. The cross-links result in the formation of very strong mature type I collagen fibers.  
\"Heterotrimers of two \(\alpha\)1 (I) and one \(\alpha\)2 (I) chains are the dominant isoform of type I collagen. Homotrimers of three \(\alpha\)1 (I) chains are found in fetal tissues and some fibrous lesions. The homotrimic isoform is more resistant to cleavage than collagenases. This may explain its abnormal accumulation and important role in the pathogenesis of tumors and fibrosis\" [1].  
In vivo, the triple helical fibers are mostly incorporated into a composite containing either type III collagen (in skin and reticular fibers) or type V collagen (in bone, tendon, cornea) [2; 3]. Type I collagen provides tensile stiffness in tendons and fascia, while in bone, it defines considerable biomechanical properties concerning load bearing and tensile [4]
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 2 Methods
### Time Transfer Model  
Inter-station time transfer is the term used to describe time transfer. First, a receiver clock offset between the atomic clock's time and the reference time is obtained by the PPP engine. Second, the difference between the reference station clock offset and the other station clock offsets is calculated [30], which can be expressed as  
\[\begin{array}{rl}dt&=\delta t_{1}-\delta t_{2}\\ &=(BDT_{1}-t_{ref})-(BDT_{2}-t_{ref})\\ &=BDT_{1}-BDT_{2}\end{array} \tag{17}\]  
where \(\delta t\) indicates receiver clock of the local time of atomic clock (\(BDT\)) with respect to reference time (\(t_{ref}\)). Meanwhile, the hardware delay at the receiver end should be calibrated during GNSS data processing. Figure 1 shows the principle of the B2b-RTPPP.
## 8 Simulation
### Experimental Results  
#### 8.2.1 Cluster Head Cardinality  
It represents the average number of generated clusters. This metric allows the evaluation of clustering efficiency. Low CHs cardinality reveals good characteristics of the clustering scheme as it represents the number of communication channels established with the BS. Moreover, reducing the CHs cardinality limits the usage of long-range communication channels which reduces the risk of congestion and improve the energy consumption. Figure 12 shows the average number of cluster heads generated by DC2 HC, Mezghani [34]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network size (\(\Delta^{2}\)) & 1000 \(\times\) 1000 m\({}^{2}\) \\ Node density & \(\delta\in[40,1300]\) \\ Distribution of nodes & Random \\ connectivity model & Unit Disk Graph (UDG [59]) \\ Transmitting range (\(Tr\)) & 70 m \\ Maximum hop constraint k & \(\{1,2,3\}\) \\ \(\alpha,\beta,\gamma\) & \(1/3,1/3,1/3\) \\ \(E_{dec}\) & 50 nJ/bit \\ \(\varepsilon_{FS}\) & 10 pJ/bit/ M\({}^{2}\) \\ \(\varepsilon_{Mfs}\) & 0.0013 pJ/bit/ M\({}^{4}\) \\ Data packet size & 100 bytes \\ Initial energy & 1 Joule \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters used in the experiment setting.  
and MH-LEACH [46] protocols under different nodes density \(\delta\in[40,1300]\). We observe that MH-LEACH generates the highest cardinality. Indeed, MH-LEACH uses a probabilistic technique for load balancing the CH task among nodes. This technique does not consider the residual energy or the surrounding environment of nodes. When the density converges from 200 to 800 (\(\delta\in[200,800]\)) with \(k\in\{2,3\}\), the environment tends to be more connected which enlarges the set of neighbors within the transmitting range \(Tr\) of each node. Therefore, in Figure 12b,c, we observe that clusters cardinality of MH-LEACH start to decrease in the range between \(\delta\in[400,800]\) because the coverage of CHs regroups more nodes. Mezghani protocol considers the residual energy of nodes which improves the cardinality when \(k=1\) (improved by an average of 43.7% compared to MH-LEACH). However, the clustering process only considers the average one-hop degree of nodes, accordingly, when \(k\in\{2,3\}\), the protocol slightly degrades in performance. The proposed scheme uses the k-hop clustering which minimizes the CHs cardinality and avoids having remote nodes within a forced singleton clusters. According to Figure 12, the proposed scheme shows better performance, it reduces the cardinality by an average of 18.3% and 62% when compared to Mezghani and MH-LEACH respectively. Table 5 shows the average gain of DC2 HC compared to Mezghani and MH-LEACH in terms of CHs cardinality.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 18 Dispatching of Mobile Sensor Nodes in a WSN to Sense a Region of Interest
### Problem Formulation  
The problem was formulated as a maximum-weight maximum-matching problem. Two objective functions were considered, one minimizes the energy expended due to the motion of the sensors and one maximizes the average remaining energy of sensors after their movement. We find that both are equivalent to each other. The more distance traveled by a node, the more it expends energy for that motion.  
Given a set of placement positions \(L=\left\{\left(x_{1},y_{1}\right),\left(x_{2},y_{2}\right),...,\left(x_{m},y_ {m}\right)\right\}\), the cost to move every sensor \(s_{i}\) to a location \(\left(x_{i},y_{i}\right)\) was calculated as \(c\left(s_{i},\left(x_{j},y_{j}\right)\right)=\Delta_{m}d\left(s_{i},\left(x_{ j},y_{j}\right)\right)\), where \(\Delta_{m}\) is the energy cost per unit distance and \(d\left(s_{i},\left(x_{j},y_{j}\right)\right)\) is the minimum distance between \(s_{i}\) and \(\left(x_{j},y_{j}\right)\). The weight of each edge is the negative of its cost.  
The bipartite graph \(G\) that was used to model the assignment problem was balanced and transformed to \(\widehat{G}\) by adding the set of virtual location points \(\widehat{L}\) that act as dummy nodes of \(\widehat{G}\). \(\widehat{G}\) is hence a maximum-weight perfect-matching problem. The weights of the edges incident on virtual points are assigned a value lower than the weight of edges incident on non-virtual locations so that they have no impact on the solution.  
The shortest distance \(d_{i}\left(s_{i},\left(x_{j},y_{j}\right)\right)\) is supposed to be collision free on a path that can have obstacles as shown in Figure 19. To make sure that sensor \(s_{i}\) does not collide with the areas' boundaries or any obstacles, the sensor was modeled as a circle in a 2D plane with a radius \(r\). The perimeters of the 2D obstacles were expanded by \(r\) and those of the boundaries are contracted by \(r\). The positions of the corners of the expanded obstacles and contracted boundaries were made elements of the set of vertices \(V\). The problem was then modeled as a graph \(H=\left(s_{i}\cup\left(x_{j},y_{j}\right)\cup V,E\right)\) where \(E\) is the set of edges \(\left(u,v\right)\) where \(u,v\in\left\{s_{i}\cup L\cup V\right\}\) and the edge does not pass through any expanded perimeters.  
Figure 19: Finding a collision free path in [13].