esa-sceva/satcom-chunk-collection huggingface dataset:
# PSON: A Serialization Format for IoT Sensor Networks
## 2 Data Serialization Formats
Developed by Facebook, it was open sourced in 2007 and entered the Apache Incubator in 2008, becoming an Apache Top-Level Project (TLP) in 2010. It is rigorously maintained, and its latest release was published in March 2021. This and all previous releases since 2009 can be found in [39]. Apache Thrift allows reliable performance communication and data serialization across a variety of programming languages and use cases. The project team aimed for Thrift to embody several characteristics: Simplicity, with a simple and approachable code, free of unnecessary dependencies; Transparency, conforming to the most common idioms in all languages; Consistency, with niche, language-specific features in extensions, not the core library; and Performance, striving for performance first, elegance second. * Apache Avro. Avro joined the Apache Software Foundation as a Hadoop subproject in 2009. Since then, it has been very intensively maintained, and more than thirty releases have been published, with the latest one being 1.10.2 in 2021. All versions can be found in [40]. This is a data serialization system that relies on schemas. When Avro data is read, the schema used when writing it is always present. This permits each datum to be written with no per-value overheads, which also allows its use with dynamic scripting languages since the data, together with their schema, are fully self-describing. The developer team indicates that Avro is intended to provide rich data structures; a compact, fast, binary data format; a container file to store persistent data; a remote procedure call (RPC); and simple integration with dynamic languages based on the fact that code generation is not required to read or write data files, nor do RPC protocols need to be implemented. For this reason, code generation is an optional optimization step and is only worth implementing for statically typed languages.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 2 \(\Phi\)-OTDR System Principle
### Experiment Setup  
The experiment setup is illustrated in Figure 2 based on the principle of the \(\Phi\)-OTDR sensing system. The laser source has an ultra-narrow linewidth of 100 Hz and a center wavelength of 1550 nm. The polarization state of the output laser will be affected by the birefringence property of the optical fiber. Therefore, the polarization controller is used to adjust the polarization state of the laser [36]. The FPGA module drives the acousto-optical modulator (AOM) to modulate the continuous light into pulsed light, while the repetition frequency is 8 kHz and the pulse width is 200 ns, which corresponds to 20 m spatial resolution. More concretely, the FPGA board generates a series of clock sequences with an adjustable repetition frequency and pulse width in the form of a transistor-transistor logic (TTL) signal, which is amplified by the boost circuit to adapt the input voltage of AOM. This electrical modulation signal acts on the electroacoustic transducer of AOM, then transforms into the ultrasonic field in the form of electrical signal. When the light wave passes through the acousto-optic medium, due to the acousto-optic effect, the continuous light is modulated and becomes the intensity modulation wave in the form of an optical pulse. Then, the pulse is injected into an erbium-doped fiber amplifier (EDFA), which could amplify the power of the pulsed light. Following this, the pulsed light goes through a dense wavelength division multiplexer (DWDM), which can combine and transmit different wavelengths in the same fiber. Therefore, the light, including other wavelengths (except 1550 nm), could be filtered out by the DWDM, and a purer optical wave at 1550 nm could be obtained at the output of the DWDM module. Then, the pulsed light is launched into the sensing optical fiber via an optical circulator (OC). A piezoelectric transducer (PZT) is placed on a certain location of the sensing optical fiber in order to produce a vibration signal. Eventually, the coherent RBS light is monitored by the photodetector (PD) and is then transformed into an electric signal. The FPGA board could realize the data acquisition with the hardware language of VerilogHDL and the parallel programming tool Quartus II. In the host computer, the type of CPU is a CORE i7 8700K, using C programming language and the Visual Studio programming tool.
## 3 Results and Discussion
### Simple Sugar Content  
Glucose, fructose, and sucrose are major sugars found in carob pulp [62; 63], and they were identified and quantified in this study. The results revealed significant differences in these parameters depending on the geographical origin. The glucose concentration varied among the samples being the Moroccan carob pods those with the higher value, 3.16 \(\pm\) 1.75 g/100 g DW followed by the Spanish and Lebanese ones (2.85 \(\pm\) 1.16 and 2.03 \(\pm\) 1.47 g/100 g DW, respectively) (Table 1). Moreover, regarding the sucrose content, Spanish carob pulp samples scored the highest content (28.10 \(\pm\) 7.67 g/100 g DW), followed by Lebanese ones (25.60 \(\pm\) 9.63 g/100 g DW), while Moroccan samples scored the lowest value (13.70 \(\pm\) 6.41 g/100 g DW) (Table 1). With respect to the fructose content, we observed that the values ranged between 4.95 \(\pm\) 1.99 g/100 g DW for Lebanese samples and 7.73 \(\pm\) 1.72 g/100 g DW (Table 1) for the Moroccan ones. Sugar content in carob pulp is affected by several factors, such as genetic variation, environmental factors, and traditional agricultural practices [64]. In fact, distinct carob genomes adapted through different years to certain climate conditions across different regions of the Mediterranean area could contribute to sugar metabolism and content, including biosynthesis and decomposition of sugars [62; 65]. Glucose plays a role as a vital cell nutrient, acting as a regulatory metabolite, impacting gene expression, and maintaining cell stability under environmental disturbance [66; 67; 68; 69]. Moreover, it plays a crucial role in several physiological processes, including growth, stress responses, and energy signaling [70; 71; 72]. In addition, glucose is crucial for carob tree compensatory mechanisms to ensure a constant supply of energy for plant growth and development. Thus, this fact could explain glucose stability in this study across Lebanese, Spanish, and Moroccan carob pulp samples.  
On the other hand, scarce studies have addressed the carob sugar content including the three main sugars found in carob pulp (sucrose, glucose, and fructose). The cases found in the literature agreed that sucrose was the predominant sugar in the carob pulp and powder from Bulgarian, Turkish, and Tunisian carob samples, with a concentration of 34.2, 16.5, and 33.70 g/100 g DW, respectively [63; 73]. This finding aligns with the results of this study and with previous studies on Sicilian and Turkish caro samples [27; 44]. In addition, [PERSON] et al. [74] revealed that carob pulp samples from Turkey contain significant levels of sucrose, glucose, and fructose (34.2 g/100 g DW, 11.1 g/100 g DW, and 6.5 g/100 g DW; respectively). As mentioned before, carob chemical compounds can vary based on different factors, especially the sugar content, which is taken into consideration in the selection of carob commercial varieties.
## 1 Introduction
### Related Work
This interference suppression is achieved by incorporating a randomness pattern into each transmitted waveform known only to the intended receiver. The random pattern serves as the key for establishing communication between the receiver and its corresponding transmitter. (3) Another advantage of spread spectrum signals is their ability to prevent signal eavesdropping. By hiding the signal within the system's background noise through pattern coding, DSSS signals can be transmitted at a low average power across a wide bandwidth. This property results in signals with a low probability of interception (LPI), making them highly recommended for modern radar applications. (4) Additionally, DSSS signals ensure signal privacy by assigning a different pseudorandom pattern to each transmitted signal. Only the intended receiver, with prior knowledge of the specific pattern key used by the transmitter, can successfully demodulate the coded signal. In summary, DSSS signals have robust autocorrelation characteristics, can overcome transmission interference, enhance information security, and provide advantages such as interference suppression, signal hiding within background noise, and signal privacy through the use of pseudorandom patterns. Thesecharacteristics make spread spectrum signals particularly valuable in various applications, including radar systems. This paper presents an ISAC implementation based on single-carrier signals, which includes coding the transmit signal with pseudorandom sequences, much like spread spectrum communications and m-sequence radar. ISAC implementations based on spread spectrum signals have been reported earlier in the literature [13; 14; 15; 16; 17; 18; 19]. These approaches fall under the category of communication-centric designs. Implementing such techniques involves using high spreading factors to impart favorable autocorrelation properties to the signal due to the good autocorrelation properties of the spreading code. On the other hand, microwave photonics technology, known for its high operating frequency, broad instantaneous bandwidth, and strong immunity to electromagnetic interference, has been widely applied in radar imaging and wireless communication. Recently, there has been growing interest in using microwave photonics to implement ISAC systems, offering solutions for high-range resolution radar detection and relatively high-capacity communication [20; 21; 22; 23]. Photonics and optical fibers enable long-distance transmission, making them ideal for applications such as border protection, securing restricted areas, or linking a central unit with a remote unit over vast distances. Radio-over-fiber (RoF) technology also simplifies the overall system, enhancing efficiency and cost-effectiveness.
## 5 Performance Evaluation
### Basic Performance Evaluation
* DT + RED-Based Probabilistic Forwarding: This experiment combines DT with RED-based Active Queue Management (AQM). DT computes a global threshold, while RED enables flow-level management. The forwarding probability is based on the size of the flow, and the packet with the smallest sequence number in its Red-Black Tree is forwarded. This experiment demonstrates the effectiveness of our buffer replacement \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline Bandwidth (Mbps) & \{15,18,20,22,24\} & \{13,17,19,24,27\} & \{11,16,16,24,31\} & \{8,12,12,22,36\} \\ Latency (ms) & \{5,10,20,24,30\} & \{6,10,10,16,16\} & \{2,4,6,8,10\} & \{2,4,4,6,6\} \\ Bandwidth Std. Dev. & 3.12 & 4.97 & 7.06 & 10.12 \\ Latency Std. Dev. & 8.85 & 3.88 & 2.83 & 1.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental scenario setups. strategy. The RED parameters used in the experiment are as follows: \(Th_{\text{min}}=20\%\), \(Th_{\text{max}}=60\%\), and \(\omega=0.2\). * Receiver-centric byte reorderer and buffering (RCBRB) algorithm [36]: The RCBRB algorithm maintains independent per-flow buffers at the receiver and employs queues to store and manage out-of-order packets. This experiment compares the performance of RBMM against existing Reordering Buffer Management Methods in multipath transmission scenarios.
# Optimization of Bituminous Road Surfacing Rehabilitations Based on Optimization of Road Asset Value
## 3 Rehabilitation Planning Optimization Model
For the calculation of road user costs, different techniques can be used applied such as PIARC's Highway Development and Management tool [44] or different national calculation tools such as RoSy or ISEH [45]. \[RLCE=T_{2}-T_{1} \tag{8}\]  
where:  
_RLCE_: real life-cycle extension [year],  
\(T_{0}\): year of rehabilitation [year],  
\(T_{1}\): expected end of the life-cycle [year],  
\(T_{2}\): extended end of life-cycle after rehabilitation [year]. Optimization is performed following Equation (9). The optimization index is calculated in each year of the life-cycle. Every year, rehabilitation technology is chosen according to pavement serviceability predictions (see Section 4.2), the technology must be designed to completely restore the pavement serviceability. This rehabilitation extends the life-cycle of the pavement by the time span equal to the period at which the rehabilitation is performed, i.e., rehabilitation in year 10 extends the life-cycle by 10 years. Rehabilitation restarts the degradation process. The optimization index is calculated for each year of the life cycle following formula 9; the index is a unitless number. When plotted into a chart, the optimization indexes in each year form a U-curve, and the optimal rehabilitation timing is the lowest point on the U-curve; see Figure 2.  
\[OI=\frac{RC+NPAV_{BR}+NPAV_{AR}}{T_{t}} \tag{9}\]  
where:  
_OI_: optimization index,  
_RC_: rehabilitation costs [**c**],  
_NPAV\({}_{BR}\)_: net present asset value before rehabilitation [**c**],  
_NPAV\({}_{AR}\)_: net present asset value after rehabilitation [**c**]  
_T\({}_{t}\)_: extended life-cycle [**c**].
## 5 Conclusions  
This paper shows that DNN-IoT-BA offers an efficient routing of vehicles on high-speed roads in VANETs. This DNN-IOT-BA is the best way of increasing energy efficiency. The IoT-BA examines the entire network to find the moving states of every vehicle in VANETs. The DNN-IoT-BA then optimizes routing decisions based on the MA inputs, with higher traffic congestions in the network. The DNN effectively processes routing decisions at a faster rate and provides a network solution to set optimum routes, thus reducing network congestion more quickly. The use of the routing table for regular vehicle updates at RSUs ensures optimal selection of vehicles and stable routing decisions. Data and transmission rates were demonstrated through variable speed, distance, and vehicle density, confirming that DNN-IoT-BA offers quicker routing decisions than existing DNN and ANN models. Simulation results show the higher connectivity, throughput, packet delivery, and end-to-end latency of the DNN-IoT-BA model. The results also show that the probability of connection termination in DNN-IoT-BA is lower, which supports increased data transmission. This means that simplified routing choices based on the continuous IoT-BA monitoring of DNN have optimally retained the traffic density, which ensures an efficient packet delivery to the destination nodes. Finally, the application of low-mobility vehicle routing decisions is efficient compared with those with a faster mobility of vehicles. In future, the application of other deep learning algorithms considering vehicles with mixed traffic data can be utilized for vehicular traffic management.  
Figure 8: Impacts on throughput with variable velocity.  
**Author Contributions:** Authors contributions are as the following: Conceptualization, S.K., G.D. and Y.N.; methodology, S.N.M.; software, A.S. and M.S.; validation, A.A. and M.G.; investigation, U.E.; resources, H.G.; writing--review and editing, M.G.; funding acquisition, H.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Project 61950410603.  
**Acknowledgments:** Special thanks to Islamic Azad University, Iran.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Literature Review
### Discussion
#### Advantages  
Among the four technologies considered in this paper, sensor nodes are the simplest and least expensive to design and implement. The resources required to build such a node are widely available and supported by an extensive collection of open-source codes, drivers, libraries, etc. These factors allow for a high degree of personalization and customization, hence attracting relatively more users from diverse backgrounds and promoting discoveries. Sensor nodes also consume significantly lesser amount of energy, compared to their counterparts, for the same surveillance duration, therefore decreasing the demands on design complexity for power supply and storage solutions. A well-planned sensor node network can easily achieve the finest spatial coverage and temporal frequency for monitoring an area for wildfires. In the last few years, this category has received the most attention, with many large-scale projects funded by governments. For example, in 2021 USA's Department of Homeland Security (DHS) Science and Technology directorate collaborated with four industry partners--Ai4 Technologies (San Francisco, CA, USA), Breeze Technologies (Hamburg, Germany), N5 Technologies (Rockville, MD, USA) and Valor Fire Safety of Londonderry (Londonderry, NH, USA)--to find innovative solutions for early wildfire detection [38].
## 4 Path Renewal Method  
In our network model, routing paths are established by the flooding of a control message. This fashion is commonly used in most routing protocols at the initial establishment of routing path. Similar to PSM, a control message includes information on the partition of the keys (PIK) and on hop counts from the base station (HC). Also, each node can know its own in-degree.  
Figure 1: In-degree and out-degree.  
Figure 2 shows a propagation of a flooding message. In the figure, the node that received the message inserts its PIK to the message and forwards it to the next hop (toward terminals). Suppose \(N_{2}\) receives the control message including PIK of 1 and 7 from \(N_{1}\). When \(N_{2}\) sends the message to the next node, it inserts its PIK to the message. Here, given \(N_{1}\), \(N_{1.\text{PIK}(5)}\) implies that \(N_{1}\) stores PIK that is 5. \(N_{3}\) does not need to insert the PIK to the message since PIK in the message already has PIK(7).  
After the paths are established, all nodes store their in-degree and the list of the sub-nodes by elapsed time. Each node manages the list. The list is comprised of IDs of sub-nodes and the number of data transmissions of each sub-node. For an arbitrary super-node \(N_{\text{sup}}\) and three sub-nodes \(N_{\text{sub}.1}\), \(N_{\text{sub}.2}\) and \(N_{\text{sub}.3}\), the process of path renewal is as follows:  
In the table, TE, EM, and FM are threshold energy, eviction message, and fare message, respectively. Let \(N_{\text{sub}.3}\) have the highest number of the transmissions in the list. If the remaining energy of \(N_{\text{sup}}\) is less than TE, \(N_{\text{sup}}\) sends EM to \(N_{\text{sub}.3}\). EM includes the fitness value of \(N_{\text{sup}}\). \(N_{\text{sub}.3}\) finds a new super-node in the neighboring nodes. Each of the neighboring nodes sends its own fitness value to \(N_{\text{sub}.3}\). If \(N_{\text{sub}.3}\) finds a new super-node that has the highest fitness value, \(N_{\text{sub}.3}\) sends FM to \(N_{\text{sup}}\) and migrates to other super-node. After \(N_{\text{sup}}\) receives the RM from \(N_{\text{sub}.3}\), \(N_{\text{sup}}\) removes \(N_{\text{sub}.3}\) in the list.  
\begin{table}
\begin{tabular}{|c|} \hline SuperNode N\({}_{\text{sup}}\); \\ SubNode N\({}_{\text{sub}.1}\), N\({}_{\text{sub}.2}\), N\({}_{\text{sub}.3}\); \\ IF N\({}_{\text{sup}.\text{energy}}<\) TE THEN \\ N\({}_{\text{sup}}\) sends EM to N\({}_{\text{sub}.3}\); \\ N\({}_{\text{sub}.3}\) finds neighbor nodes; \\ IF neighbor nodes is NOT NULL AND \\ fitness(neighbor nodes) \(>\) fitness(N\({}_{\text{sup}}\)) THEN \\ Send FM to N\({}_{\text{sup}}\); \\ N\({}_{\text{sub}.3}\) migrates to new SuperNode; \\ \hline \end{tabular}
\end{table}
Table 1: Migration of super-node.  
Figure 2: Flooding control message.
## 3 Results and Discussion
### Flow Analysis Results
For the test vehicle, the maximum values and reaching times were 0.00695 m and 0.638 s, respectively. When the added current was 0 A, 0.6 A, and 1 A, the maximum values were 0.118 m/s, 0.123 m/s, and 0.127 m/s, and the time to reach them was 0.5635 s, 0.536 s, and 0.527 s, respectively. For the test vehicle, the maximum value and reaching time were 0.125 m/s and 0.535 s, respectively. Concerning acceleration, when the added current was 0 A, 0.6 A, and 1 A, the maximum values were 1.79 m/s2, 1.88 m/s2, and 1.94 m/s2, and the time to reach them was 0.4535 s, 0.43 s, and 0.422 s, respectively. For the test vehicle, the maximum value and reaching time were 1.92 m/s2 and 0.425 s, respectively. When the vehicle speed was 90 km/h, it was confirmed that the higher the added current, the larger the extreme value and the longer the time to reach it. Based on the vibration analysis, the following conclusions were reached. In the case of sinusoidal excitation, at speeds of 30 km/h and 60 km/h, relatively small maximum values of displacement, speed, and acceleration and a small response time were observed at 1.0 A, which has a damping force similar to that of the suspension of a general vehicle. As the applied current decreases, the maximum values and response times increased, as shown in Tables 11 and 12. However, at a speed of 90 km/h, large displacement, speed, and acceleration maximum values and a slow response time were seen at 1.0 A, which has a damping force similar to that of a general vehicle suspension. The maximum value and response time decrease as the applied current decreases. To verify the AMSEim program analysis results, the Laplace transform of sinusoidal function Equation (29) is as follows:  
Figure 26.3  
Substituting the vehicle parameters from Table 2 into Equation (33), the transfer function is as follows:  
Figure 26.4  
To find the function \(Z_{s}(t)\), rearrange Equation (34) for \(Z_{s}(s)\). \[ \begin{split} Z_{s}(s)=\frac{4698.83\times(s+15.41)}{s^{4}+58.24 s^{3}+1740.59s^{2}}\times\frac{0.01\times 5.24}{s^{2}+5.24^{2}}\\ +44583.3s+2545997.18\end{split}  \tag{35}\]  
A comparison between \(Z_{s}(t)\) calculated by the inverse Laplace transform using Python and the displacement graph obtained from AMSEim for periods of 6 to 8 is shown in Figure 37. The error rates of the maximum amplitude and period were 5.23% and 0.01%, respectively, as shown in Table 14. This indicates that the AMSEim model used in the numerical analysis is reasonable.