esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerospace
## 4 Single Small Satellite to Mars Orbit as Secondary Payload
### Tradespace Exploration and Baseline Mission Concept  
The study uses the framework and the Aerocapture Mission Analysis Tool (AMAT) developed at Purdue University for a rapid conceptual design of aerocapture missions [27; 36]. The aerocapture feasibility chart is a graphical method for initial tradespace exploration and selecting a baseline interplanetary arrival \(V_{\infty}\) and ballistic coefficient ratio \(\beta_{2}/\beta_{1}\) for further analysis. The aerocapture feasibility chart has contours of theoretical corridor width (TCW), g-load, peak heat rate \(\dot{q}\), and total heat load \(Q\) as a function of arrival \(V_{\infty}\) and \(\beta_{2}/\beta_{1}\)[37]. Figure 5 shows the drag-modulation aerocapture feasibility chart for Mars using a vehicle with \(\beta_{1}\) = 20 kg/m\({}^{2}\), \(R_{N}\) = 0.235 m, and target apoapsis = 2000 km. The drag modulation vehicle design presented in Section 3 with \(\beta_{2}/\beta_{1}\) = 7.5 is seen to provide the required TCW of 1 deg. across the typical range of arrival \(V_{\infty}\) at Mars (2 to 4 km/s), and thus offers sufficient control authority for aerocapture and keeps the peak heat rate under 40 W/cm\({}^{2}\).  
For the purpose of this study, the transfer trajectory used by the Mars 2020 mission with a launch date of 30 July 2020 and Mars arrival date of 18 February 2021 is used as a representative trajectory [38]. The arrival \(V_{\infty}\) vector in the International Celestial Reference Frame (ICRF) is [2.239, 1.200, \(-\)0.7368] km/s, and the \(V_{\infty}\) magnitude is 2.65 km/s. The arrival declination is 1.65 deg. The selected baseline \(\beta_{2}/\beta_{1}\) and \(V_{\infty}\) is indicated by the black star in Figure 5, which offers TCW \(>\) 1 deg., keeps the g-load below 3\(g\), and keeps the heat rate below 40 W/cm\({}^{2}\).
# Matheuristics for the Design of a Multi-Step, Multi-Product Supply Chain with Multimodal Transport
## 4 MILP Model and Matheuristics
### Mixed-Integer Linear Programming Model (MILP)
#### 4.1.1 Decision Variables  
_Binary Variables_  
\[z_{fq}=\left\{\begin{array}{ll}1&\text{if it is decided to open a factory $w$ with a capacity level $q$, ($f\in F;q\in Q$)}\\ 0&\text{other case}\end{array}\right.\]  
Figure 2: Representation of the BOM of the products.  
\[z_{wq}=\left\{\begin{array}{ll}1&\text{if it is decided to open a warehouse $w$ with a capacity level $q$, $(w\in W;q\in Q)$}\\ 0&\text{other case}\end{array}\right.\]  
\[v^{r}_{sf}=\left\{\begin{array}{ll}1&\text{if material $r$ is supplied to factory $f$ by a supplier $s$, $(s\in S;f\in F;r\in R)$}\\ 0&\text{other case}\end{array}\right.\]  
\[u^{t}_{od}=\left\{\begin{array}{ll}1&\text{if origin $o$ supplies destination $d$ by mode of transport $t$,}\\ &((o,d)\in\{(S,F)\cup(F,W)\cup(W,C)\};t\in T)\\ 0&\text{other case}\end{array}\right.\]  
_Real Variables_  
\(x^{rt}_{sf}\): Amount of material \(r\) that is supplied by supplier \(s\) to factory \(f\) by mode of transport \(t\),  
\((s\in S;f\in F;r\in R;t\in T)\)  
\(x^{rt}_{fw}\): Amount of product \(p\) that is sent from factory \(f\) to warehouse \(w\) by mode of transport \(t\),  
\((f\in F;w\in W;p\in P;t\in T)\)
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 2 Related Work
### Prediction of Network Traffic  
The time series forecasting problem has always been of great importance, with influential applications in various fields such as finance [23], weather [24], transportation [25], and networking [26]. Before the era of big data, people mainly used linear models to solve time series forecasting problems, such as autoregressive (AR) [27], exponential smoothing [28], or structural time series models [29]. However, these traditional methods are only applicable when data have an explainable structure [30]. With the increasing amount of data and the improvement of computing capability, machine learning [31; 32; 33] has become one of the most critical methods for time series forecasting. In addition to traditional machine learning, such as SVM [34; 35], the contribution of deep learning in time series forecasting is becoming more and more valuable. For multivariate time series, researchers use Convolutional Neural Networks (CNNs) [36] to extract features for prediction. Recurrent Neural Networks (RNNs) [37] can learn sequence contexts and are very helpful for time series forecasting problems. However, the flexibility of machine learning might lead to overfitting, which makes traditional linear methods more dominant when the amount of data is small [38]. One research trend is to use mixed models for time series forecasting. Hybrid models combine the advantages of traditional linear models and machine learning models to achieve better results [39; 40; 41].  
In the past, researchers predicted future traffic with statistical methods and indicators to get regression equations. Most recent studies attempted to replace statistical methods with emerging deep learning methods to predict network traffic. [PERSON] [15] provided a comprehensive evaluation of deep learning methods for network traffic prediction and demonstrated that deep learning methods outperform statistical methods based on an Internet bandwidth usage dataset. [PERSON] et al. [16] used LSTM to predict cellular network traffic and indicated the superior performance of LSTM over ARIMA. [PERSON] et al. [17] proposed a hybrid forecasting method that combines neural networks with fractionally integrated ARIMA and generalized autoregressive conditional heteroskedasticity (GARCH).  
Although previous studies have made efforts on network traffic prediction, to the best of our knowledge, there is still a lack of an approach to predict IPv6 traffic accurately. In particular, we are the first to utilize the correlation between IPv6 traffic and IPv4 traffic to propose a novel IPv6 traffic prediction model called LS6, which combines the advantages of the statistical method and deep learning method and makes use of the information from both IPv4 and IPv6.
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 2 Background
### Related Work
ADAPT is suitable for resource-constrained sensor nodes because of its low complexity, as it uses only local information on sensor nodes for network configuration and operation. The IEEE 802.15.5 standard provides mesh capabilities for both high- and low-rate wireless personal area networks [64]. Low-rate meshes can be built on IEEE 802.15.4 MAC, with high-speed meshes being built on IEEE 802.15.3 MAC. The IEEE 802.15.5 standard provides basic mesh capabilities, including tree formation, address block allocation, and mesh routing. For low-rate sections, several important advanced features, including multicasting, power saving, and portability, have been presented. High-rate sections provide server-guided routing to support QoS provision. However, this standard involves changing the MAC superframe structure, which affects interoperability with the current MAC standard. [PERSON] et al. [65] proposed a methodology based on the automatic generation of fault trees to evaluate the reliability and availability of WSNs when permanent faults occurred in network devices. The proposed methodology supported all topologies, varying levels of redundancy, network reconfigurations, device importance, and arbitrary failure conditions. The proposed methodology was particularly suitable for the design and verification of WSNs when attempting to optimize the reliability and availability requirements. In [66], the authors proposed a novel multi-path routing protocol method for event-driven WSNs that provided reliability, load balancing, and energy efficiency for routing paths. Furthermore, using residual energy as the main parameter for dynamic switching between two alternative paths, the algorithm could be deployed for the virtualization of WSNs. [PERSON] et al. [67] proposed a low-power grid-based cluster-routing algorithm for WSNs. A feature of the algorithm was that it configured nodes in the grid in a clustering manner. The clustering head was dynamically selected considering the energy consumption of the cluster node, before communicating with the base station through the relay node. [PERSON] et al. [68] proposed a smart Zigbee/IEEE 802.15.4 MAC protocol for wireless sensor multi-hop mesh networks based on a beacon-enabled mode. The proposed algorithm could build a smart Zigbee/IEEE 802.15.4 MAC-based beacon-enabled mesh network while improving the performance of the network in terms of time delay, duty cycle, energy saving, and reliability. Compared with existing studies, our proposed algorithm has the following characteristics: (1) The TSMR deals with the mobility of multi-hop WSNs based on the IEEE 802.15.4 standard, a still unresolved problem to the best of our knowledge. (2) The TSMR can find the optimal path with reduced computational overhead. (3) The TSMR provides an efficient and distributed address-allocation algorithm. (4) The TSMR improves the network reliability.
# Biomethane Potential of Sludges from a Brackish Water Fish Hatchery
## 2 Material and Methods  
### Substrate and Inoculum  
The sludges used as substrate for the methane potential determination in this study were collected from a fish hatchery located in Puglia (Southern Italy), where fingerlings of sea bream (_Sparus aurata_) and sea bass (_Dicentfrachus labrax_) are produced. Wastewater was discharged from the bottom of the tanks where sea breams were raised from 5 to 45 g l.w. and were filtered by a rotating drum filter (Hydrotech Microscreen 1603, Veolia) with a 30 mm mesh, having a treating capacity of 120 L/s. The thickened fraction from the filter was settled in a circular tank with a conic shaped bottom and the sludges were collected after 1 h of sedimentation for the anaerobic tests.  
The inoculum was collected from a digestate of a full-scale mesophilic anaerobic digester (CSTR) treating dairy cow manure and corn silage, located in Padova, Italy. The main physicochemical characteristics of the actual substrate and inoculum are shown in Table 1. Before the beginning of the anaerobic assays, the inoculum was mixed and pre-incubated for a week at ambient temperature in order to volatilize the residual biogas and to deplete the residual readily available organic material.
## 5 Incremental Cooperative Routing Protocols for WBANs
The time division multiple access (TDMA) scheme is utilized, and the channel is accessed by nodes in different time slots: being energy efficient, TDMA as a channel access scheme is utilized by nodes in different time slots to avoid data collision.
## 5 Evaluation and Performance  
The simulation environment employed to evaluate the efficiency of our proposed technique is presented first in this section. Following that, we identify the performance criteria, which is crucial to compute the performance of any network clustering technique. Finally, we give an extensive analysis of our simulation results.
# Financial Optimization of a Solar-Driven Organic Rankine Cycle
## 2 Material and Methods
### The Examined System
#### 2.1.2 Organic Rankine Cycle Modeling  
The work production in the ORC turbine shaft (\(W_{T}\)) is given as:  
\[W_{T}=m_{orc}\cdot(h_{4}-h_{5}) \tag{11}\]  
The power demand of the pumping work (\(W_{p}\)) is given below:  
\[W_{p}=\frac{m_{orc}\cdot\Delta P}{\rho_{1}\cdot\eta_{motor}} \tag{12}\]  
The motor efficiency (\(\eta_{motor}\)) is selected at 80% in this work.  
The isentropic efficiency of the turbine (\(\eta_{is\cdot T}\)) is selected at 85% [27] and its definition is given below. It has to be said that the selected value is a relatively high value and it corresponds to a well-designed expansion device in order to achieve high system efficiency.  
\[\eta_{is,T}=\frac{h_{4}-h_{5}}{h_{4}-h_{5,is}} \tag{13}\]  
The ORC net electricity production (\(P_{el}\)) is given below:  
\[P_{el}=\eta_{g}\cdot\eta_{m}\cdot W_{T}-W_{p} \tag{14}\]  
The electrical generator efficiency (\(\eta_{g}\)) is selected at 98% and the mechanical efficiency (\(\eta_{m}\)) at 99% which are reasonable values.  
The ORC efficiency is defined as below:  
\[\eta_{orc}=\frac{P_{d}}{Q_{ltrs}} \tag{15}\]The system instantaneous energy efficiency (\(\eta_{\text{sys}}\)) is defined as:  
\[\eta_{en}=\frac{P_{el}}{Q_{sol}} \tag{16}\]  
Moreover, the yearly energy efficiency of the system (\(\eta_{en-y}\)) is defined as below:  
\[\eta_{en-y}=\frac{E_{el}}{E_{sol}} \tag{17}\]  
The yearly electricity production (\(E_{el}\)) and the yearly solar potential (\(E_{sol}\)) are defined as:  
\[E_{el}=\sum_{i=1}^{12}\left[SD_{i}\cdot\int_{0}^{24\;h}P_{el}\cdot dt\right] \tag{18}\]  
\[E_{sol}=\sum_{i=1}^{12}\left[SD_{i}\cdot\int_{0}^{24\;h}Q_{sol}\cdot dt\right] \tag{19}\]  
Practically, in this work, every month is examined by using a typical day. For every month, a specific number of sunny days (\(SD\)) are used in order to take into account only the days with the potential for exploitation of the beam irradiation. The sunny days from January up to December are the following: 13, 10, 15, 18, 20, 22, 29, 20, 18, 17 and 15, respectively [28]. In total, there are 218 sunny days during the year, which is a reasonable number of days. These data have been taken by using statistical results for Athens, Greece [29; 30; 31]. More specifically, the used number of sunny days for every month has been found after calculating the average monthly number of sunny days for the last five years [30].
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 2 Photoacoustic Signal Generation
### Plasma Size and Shape
Thus, the pulse-to-pulse repeatability is significantly improved with a factor ~2.7 and the plasma becomes more compact with a stronger emission with a factor ~3.0. Moreover, the double-lens combinations with shorter effective focal length can generate more condensed plasma,equating to a shorter plasma using the same laser energy [60]. As a result of this behavior, the moving-breakdown range can be effectively decreased while the total emission intensity of plasma is increased. In [61], it has been shown that spherical aberrations increase the breakdown threshold energy, resulting in the formation of multiple elongated plasmas with weak emissions. Using fast imaging and spectroscopic techniques, it has been demonstrated that a compact plasma with stronger emissions could be generated by aberration minimized focusing with larger focusing angles. [PERSON] et al. [62] have studied the plasma sphericity index (\(\xi\)) as a function of laser pulse energy (\(E\)) and focusing angle (\(\theta\)), where \(\zeta(E,\theta)=\frac{\pi\
u}{z_{max}}\). The results, which are redrawn in Figure 3, demonstrate that the plasma is more spherical for higher focusing angles, and the sphericity index is around 0.7-0.8 at threshold energy. However, the sphericity index is capped by 0.4 at large energies, regardless of the focusing angle. A very small focusing angle means focusing with a high \(f\)-number generates filament-like plasma. For example, [PERSON] et al. observed filament formation at 1.8\({}^{\circ}\) focusing angle for the 6-ns pulses and at 1.7\({}^{\circ}\) focusing angle for the 30-ps pulses because of self-focusing [55]. They also reported filament formation at larger focusing angles (4\({}^{\circ}\) and 8.5\({}^{\circ}\)) with pulse energies far above the breakdown threshold. Figure 2: Comparison of the calculated plasma length using Equation (6) to the experimentally measured values in [55], while using 6 ns and 30 ps pulses with focusing angle 22\({}^{\circ}\) using 1064 nm Nd:YAG laser.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 4 Observation and Results
### ROC Curve  
To evaluate the performance of our proposed model, we used the receiver operating characteristic (ROC) curve, which is a widely accepted evaluation metric for AI models. The ROC curve plots the true positive rate against the false positive rate, and the area under the ROC (AUROC) is a measure of the model's overall performance. The higher the AUROC, the better the model's performance compared to other methods. In the context of detecting DDoS attacks in SDN environments, the ROC curve and AUROC are useful metrics for assessing the effectiveness of the model in identifying and mitigating attack traffic while minimizing false alarms and correctly identifying normal traffic. Overall, the ROC curve and AUROC provide valuable insights into the performance of our proposed model and can be used to compare its performance with other approaches.  
Loss: In deep learning and machine learning, an inaccurate prediction is known as a loss. The magnitude of the loss indicates how accurate or inaccurate the model's prediction is. If the loss is high, the model is not providing satisfactory results on the test data; on the other hand, if the loss is low, the model is providing satisfactory results on the test data. In this work, we used Colab and Google Drive to compare the performance of our proposed model on two different datasets that underwent the same pre-processing steps. By using these tools, we were able to effectively evaluate the accuracy and loss of the model and determine its effectiveness in detecting DDoS attacks in SDN networks.
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 2 Medical Data Management Platform
### Blockchain and Medical Data Privacy  
To address privacy protection issues in blockchain-based medical data management solutions, Ref. [37] proposed a medical data privacy protection scheme based on blockchain and group signatures, and asymmetric encryption was proposed to achieve reliable medical data sharing among medical institutions while protecting patient data privacy. To solve the problems of traditional EHR systems being vulnerable to attacks and lacking privacy protection, Ref. [38] established a trusted platform for sharing encrypted EHRs based on blockchain and smart contracts. The platform uses ABE (Attribute-Based Encryption) scheme with fixed-size attributes to achieve fine-grained access control. It also features an efficient multi-keyword Boolean search scheme to facilitate users in searching encrypted EHRs. Ref. [39] introduced an authorization mechanism and Attribute-Based Encryption (ABE) algorithm to protect data privacy while sharing data among multiple medical institutions. To enhance the security and privacy of IoT medical devices, Ref. [40] utilized blockchain technology and re-encryption proxies on the basis of data encryption. They also adopted the PoA (Proof of Activity) protocol to accelerate data storage speed. Ref. [41] combined federated learning and blockchain technology to ensure the security of IoMT data transmitted to the cloud server and completed model training while ensuring data privacy. Ref. [42] proposed a blockchain-based privacy-protected distributed application to create and maintain medical certificates, and used smart contracts to establish security rules to prevent various attacks such as collusion, phishing, and impersonation in traditional medical schemes.
## 3 Results and Discussion  
### Analysis of Longitudinal Grain Spacing Stability Test Results  
The results and analysis of the longitudinal grain spacing stability test are presented in Figure 6. When no disturbance signal is applied, regardless of whether the machine utilizes the position compensation function, the coefficient of variation of the longitudinal grain spacing remains consistent at 2.6% \(\pm\) 0.2%, suggesting that the longitudinal grain spacing is relatively stable.  
Figure 5: Laser-assisted measurement of seed spacing. (**a**) Experiment on stability of longitudinal grain distance at sowing. (**b**) Experiment on stability of transverse grain spacing at sowing.  
Figure 6: Experiment on the influence of disturbance signals on the stability of seeding grain spacing. (**a**) Undisturbed signal. (**b**) Random disturbance signal. (**c**) Sine wave disturbance signal. (**d**) Random disturbance signal plus. (**e**) Undisturbed signal. (**f**) Random disturbance signal. (**g**) Sine wave disturbance signal. (**h**) Random disturbance signal plus sine wave disturbance signal.  
However, when a disturbance signal is applied, there is a noticeable increase in the coefficient of variation of the grain distance, and the longitudinal grain distance divergence also increases. It was found that the use of the dynamic compensation function significantly reduces the variation coefficient of seed spacing by an average of 6.94% compared to when it is not utilized, which meets the requirement of JB/T 10293-2013 \"Technical Conditions of Single Seed (Precision) Seeder\" that states the variation coefficient of seed spacing should be less than or equal to 30%. Furthermore, the seed spacing falls within the range of 0.5 to 1.5 times the theoretical grain spacing, meeting the requirements of the NY/T 1628-2008 \"working quality of no-tillage corn planter\" and ensuring good sowing plant spacing.
# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 3 Methodology
### Description of Microservices Used  
This project utilized four main services on both cloud providers, namely, storage, computing, machine learning, and web app services. The description of each service can be summarized as follows:  
1. Storage: the storage resource used for this project is mainly object-storage services, used to store the data. * AWS: AWS offers object-storage services through the Amazon S3 service, which provides a platform for storing and retrieving large amounts of unstructured data. * Azure: Azure offers object-storage service through the Azure Blob Storage service, which offers a scalable platform for unstructured data.
2. Compute: The compute resource used for this project is mainly virtual machines that are used to host the infrastructure components of the client application. These include the frontend application code base and its corresponding web-server engine. * AWS: AWS offers compute instances through the Amazon EC2 service, which provides a resizable compute capacity in the cloud. * Azure: Azure offers compute instances through the Azure Virtual Machine service, which offers a scalable compute capacity for running applications.
3. Machine learning: The machine learning resources used in this project mainly pertain to AWS because of the reasons discussed in the previous section. The resources provided by the machine learning services include the building, training, and endpoint deployment of machine learning and deep learning models. * AWS: AWS offers machine learning services via its native machine learning platform as a service (PaaS) known as Amazon SageMaker. It is a fully managed machine learning platform that enables you to build, train, and deploy machine learning models.
4. Web application: The web application resources used in this project mainly pertain to Azure because of the reasons discussed in the previous section. The main aim of web application deployment is to expose a machine learning microservice that can be called by using a Representational State Transfer (REST) Application Programming Interface (API). The microservice accepts the Hypertext Transfer Protocol (HTTP) and Hypertext Transfer Protocol Secure (HTTPS) requests in JavaScript Object Notation (JSON) format while returning a JSON response containing the inference results. * Azure: Azure offers web application services via Azure App Services, which is a fully managed PaaS for building and deploying web applications. It supports multiple programming languages and frameworks.