esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty Theory-Based Resilience Analysis for LEO Satellite Communication Systems
## 4 LEO-SCS and Its Uncertain Evolution Model  
### A Short Introduction to LEO-SCS  
An LEO-SCS is composed of LEO satellites, user segments, and gateways [19], see Figure 1. The user segment includes various user terminals, such as fixed terminals, hand-held devices, and vehicle-based and airborne terminals. The gateway is a ground station that transmits data between the satellite and terrestrial networks. Links between LEO satellites are inter-satellite links (ISLs), links between LEO satellites and user terminals are user links (ULs), and links between LEO satellites and the gateways are feeder links (FLs).  
There are both intra-plane and inter-plane ISLs. For example, each satellite in Figure 2 has four adjacent ISLs. In this figure, LEO12, LEO16, and LEO11 are in the same orbit plane, and between them are intra-plane ISLs. LEO61, LEO21, and LEO11 are in different orbit planes, and between them are inter-plane ISLs.  
As the number of user terminals is quite large, the Earth's surface is divided into grids of equal area, and each uses a virtual node (VN) [20; 21] to provide services for all user terminals in the grid. Messages are transmitted between the VN and the satellite node through the UL. In an LEO-SCS, ULs and FLs can be built between satellites and connectable gateways/VNs according to the access criteria. A connectable gateway/VN of a satellite is located within its coverage area. In Figure 3, \(g1\), \(g2\), and \(g3\) are connectable gateways of LEO11, and \(v1\), \(v2\), \(v3\), and \(v4\) are connectable VNs of LEO12. A satellite connects to a specific gateway/VN according to an access criterion. Access criteria for an LEO-SCS include load balancing, minimum delay, maximum service time, strongest signal, and maximum available channels [22; 23].  
There are two types of communication methods in LEO-SCS [24]:  
Figure 1: LEO-SCS.  
Figure 3: Schematic diagram of connectable nodes.  
Figure 2: Schematic diagram of adjacent satellites.  
Between user terminals: user terminals can connect directly with an LEO satellite, and the messages are transmitted through one or more LEO satellites from the source to the destination;
2. Between user terminal and terrestrial users: a terrestrial user can only connect with its gateway; the gateway and user terminal transfer messages through one or more LEO satellites.
## 4 Construction of the Retaining Structure  
For the soil nail-prestressed anchor composite retaining structure, soil nails and prestressed anchors were distributed in a \"square\" layout with an equal vertical and horizontal spacing of 1.4 m. Boreholes with a diameter of 120 mm and an inclination of 10\({}^{\circ}\) were predrilled manually. After the installation of the steel reinforcement bars into the boreholes, two-stage grouting was applied. In stage one, conventional gravity grouting was used to seal the annular space between the steel bar and the hole with cement grout. In stage two, a predefined length of the soil nail was grouted using a grout pressure of approximately 1.5 MPa. The objective of the pressured grouting was to fabricate the annular cement grout and permeate the surrounding soil. Two-stage grouting has been used successfully to reinforce cut slopes, excavations, tunnels, etc. to increase the performance of soil nails and therefore reduce the number of required soil nails in many countries and areas [22; 23; 24].  
Each soil nail used in the experiment consists of a ribbed steel reinforcement bar of 18/22 mm diameter, the elastic modulus of which is 200 GPa. The soil nails were embedded in a grout mixture with a water/cement ratio of 0.5. The facing was made up of 200 mm by 200 mm thin steel mesh grids (6 mm in diameter). The facing was enhanced with two reinforcement bars (12 mm in diameter) in both horizontal and vertical directions. The detailed enhancement configuration is shown in Figure 3.  
The concrete slurry, with a thickness of 80 mm and a compression strength of 20 MPa, was sprayed to fill the gap between the steel mesh and the soil behind and form the retaining wall. A steel plate, which was 200 mm in length, 200 mm in width, and 20 mm in thickness, was installed at the conjunction between the prestressed anchor and the retaining wall to reduce the possible stress concentration. The designed value of prestressing force of the anchor was 50 kN. The design parameters of the soil nails and prestressed anchors in different retaining structures are summarized in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil** & **D** & \(\gamma\) & \(c\) & \(\varphi\) & \(\tau\) \\
**Layer** & **(m)** & **(kN/m3)** & **(kPa)** & (\({}^{\circ}\)) & **(kPa)** \\ \hline \hline
1 & 2.20 & 18.1 & 14.0 & 20.0 & 52.0 \\
2 & 2.30 & 17.9 & 20.0 & 15.0 & 50.0 \\
3 & 1.10 & 18.2 & 15.0 & 21.0 & 60.0 \\
4 & 2.30 & 18.2 & 21.0 & 16.0 & 56.0 \\ \hline \end{tabular} Note: D is thickness, \(\gamma\) is unit weight, \(c\) is cohesion, \(\tau\) is shear stress of soil/grout interface, and \(\varphi\) is internal friction angle.  
\end{table}
Table 2: Design parameters of soil nailed retaining structure.  
Figure 3: The enhancement configuration of the facing.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 5 Real Scenarios Evaluation
### Analysis of Results
#### 5.2.4 Background Traffic Conditions with FEC Protection
Taking this observation into consideration, we can state that for moderate network load conditions (62 pps), the 10% protection level is considered enough protection  
Figure 12: PSNR for every til/rm layout and every encoding mode. (FEC protection level of 10%). for layouts equal or greater than 4 tiles per frame, as can be seen in Figure 12c. We can also conclude that, for tight conditions (125 pps), the combination of a high number of tiles per frame (6, 8, 10) with high a level of protection (30%), may also provide some robustness, even for this hard-to-manage zone, as can be seen in Figure 12d.
## 3 Methodology
### Performance Parameters
**Latency (s)**: Measuring latency in an edge-fog-cloud architecture involves assessing the time it takes for data or a computational task to travel through the various layers of the architecture. Latency in this context refers to the delay introduced at different stages of processing, from the edge devices to the fog nodes and ultimately to the cloud servers. For network latency measurement, this research uses the following Formula (12) [33]: \[\text{Latency}=\frac{\text{Data\ Size}}{\text{Transmission\ Speed}}\] (12) where _Latency_ is the round-trip time it takes for data to travel from the source to the destination and back. It is measured in seconds. _DataSize_ is the size of the data being transmitted, expressed in kilobytes (KB). The measurement ensures that the size is consistent with the units used for network transmission speed. _TransmissionSpeed_ represents the capacity of the network to transmit data and is typically measured in kilobits per second (Kbps) [34]. CPU and memory (%)**: Measuring the average CPU and memory usage during a learning process is crucial for assessing the computational demands of a machine learning model. This research utilizes system monitoring tools like 'psutil' in Python to provide real-time insights into CPU and memory usage [35].
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 7 Energy-Wave Problem and High Power Communication
### Communication Setup and Test Findings
Direct connectivity case even outperforms hop-by-hop communication in energy consumption on all data rates despite using much high transmission power. Figure 13: Results comparisons with respect to increasing message sending rates. The greater is the sending rate of requests, the greater is the difference and benefit per message exchange because, at high rates, data flow as a stream and control packets are fewer. At one request per second, direct connectivity consumes six times lower energy per message than multi-hop communication. **Duty Cycling Faced:** Average duty cycling faced per message exchange Figure 13d shows the second main reasons for less energy usage. In hop-by-hop case, sender has to perform more duty cycling on all the sending rates. The highest case is at message rate 2 where it goes to 33.87% in comparison 5.9% by direct communicating sender. Other reasons for high energy consumption and duty cycling in multi-hop case are packet losses, more control packets and channel contention problems due to more no of communicating devices. Packet losses and resultant retransmissions are obvious from the fact that calculated packet delivery ratio (PDR) dropped up to 0.8 on high data rates, which means 20% extra data communication and hence extra energy usage, duty cycling and total time for data delivery. On all the metrics, the direct connectivity high power case outperforms the hop-by-hop connectivity despite using about 10 times lower transmission power by sender in multi-hop communication. By analysing these preliminary results, high power transmissions-based direct one hop communication appears to be a promising option for IoT connectivity extending. Application quality of service is expected to improve in comparison to multi-hop communication and even energy consumption is reduced due to in-time, loss free delivery and reduced duty cycling. We have further verification plans for extensive long range outdoor experiments about 100 m distance and testing the case with multiple IoT application protocols.
# On the Use of Importance Measures in the Reliability of Inventory Systems, Considering the Cost
## 2 Reliability Analysis in Inventory Systems
According to the mathematical reliability model of series systems, the reliability of the first series system is  
\[M_{T1}=M_{Y1}M_{C1}M_{L1}M_{Z1}M_{P1}\]  
The second group of series systems consists of five logistics units: transportation \(Y2\), storage \(C2\), circulation processing \(L2\), loading and unloading handling \(Z2\) and distribution \(P2\). Therefore, according to the mathematical reliability model of series system, the reliability of the second series system is  
\[M_{T2}=M_{Y2}M_{C2}M_{L2}M_{Z2}M_{P2}\]  
Figure 4: Structural diagram of a typical series parallel logistics system. Figure 3: Structural diagram of a typical parallel logistics system. The reliability of two series systems in parallel is  
\[ \begin{array}{ll}M_{T}=1-(1-M_{T1})(1-M_{T2})=M_{T1}+M_{T2}-M_{T1}M_{T2}\\ =M_{Y1}M_{C1}M_{L1}M_{Z1}M_{P1}+M_{Y2}M_{C2}M_{L2}M_{Z2}M_{P2}-M_{Y1}M_{C1}M_{L1 }M_{Z1}M_{P1}M_{Y2}M_{C2}M_{L2}M_{Z2}M_{P2}\end{array} \]  
In a typical series parallel logistics system, as shown in Figure 5, the reliability of transportation \(Y1\), transportation \(Y2\), storage \(C1\), storage \(C2\), circulation processing \(L1\), circulation processing \(L2\), loading and unloading handling \(Z1\), loading and unloading handling \(Z2\), distribution \(P1\) and distribution \(P2\) are \(M_{Y1}\), \(M_{Y2}\), \(M_{C1}\), \(M_{C2}\), \(M_{L1}\), \(M_{L2}\), \(M_{Z1}\), \(M_{Z2}\), \(M_{P1}\), and \(M_{P2}\), respectively. The first group of parallel subsystems is composed of transportation \(Y1\) and transportation \(Y2\) in parallel. According to the mathematical reliability model of the parallel system, the reliability of the first group of parallel subsystems is  
\[M_{Y}=1-(1-M_{Y1})(1-M_{Y2})=M_{Y1}+M_{Y2}-M_{Y1}M_{Y2}\]  
Similarly, the reliability of the second group of parallel subsystems is  
\[M_{C}=1-(1-M_{C1})(1-M_{C2})=M_{C1}+M_{C2}-M_{C1}M_{C2}\]  
The reliability of the third group of parallel subsystems is  
\[M_{L}=1-(1-M_{L1})(1-M_{L2})=M_{L1}+M_{L2}-M_{L1}M_{L2}\]  
The reliability of the fourth group of parallel subsystems is  
\[M_{Z}=1-(1-M_{Z1})(1-M_{Z2})=M_{Z1}+M_{Z2}-M_{Z1}M_{Z2}\]  
The reliability of the fifth group of parallel subsystems is  
\[M_{P}=1-(1-M_{P1})(1-M_{P2})=M_{P1}+M_{P2}-M_{P1}M_{P2}\]  
The reliability of a typical parallel series logistics system is the reliability of the five parallel subsystems in series. \[ \begin{array}{ll}M_{T}&=M_{Y}M_{C}M_{L}M_{Z}M_{P}\\ &=(M_{Y1}+M_{Y2}-M_{Y1}M_{Y2})(M_{Y1}+M_{Y2}-M_{Y1}M_{Y2})(M_{L1}+M_{L2}-M_{L1 }M_{L2})\\ &(M_{Z1}+M_{Z2}-M_{Z1}M_{Z2})(M_{P1}+M_{P2}-M_{P1}M_{P2})\end{array} \]  
Figure 5: Structural diagram of a typical parallel series logistics system.
## 2 Preliminaries
### D-H Key Exchange Mechanism  
The D-H key exchange mechanism was introduced in 1976 [22] and has become a fundamental milestone in modern cryptography. D-H is one of the pioneering protocols in public key cryptography, enabling two parties to securely establish a shared symmetric key without sharing a prior key over an insecure channel. Many communication protocols utilize D-H to establish a secure channel by exchanging information over a public network. There are several variations of D-H, including the Elliptic Curve D-H Key Exchange (ECDH). Given the hardware limitations in IoT environments, the D-H implementation in this study was chosen for its minimal power consumption, ensuring compatibility with power-limited IoT devices.  
The lightweight version of D-H employed in this study is based on the discrete logarithm problem, mathematically expressed as \(f(x)=\mathrm{G}^{x}\bmod\mathrm{P}\), where \(\mathrm{G}\) is the base and \(P\) is a prime modulus.  
* Private Key Generation: The IoT device and the server independently select their private keys, denoted as \(a\) and \(b\), respectively. These private keys are large random integers chosen securely to ensure robustness against attacks.
* Public Key Calculation: Using the private keys, the respective public keys are computed as follows: For the IoT device: \(PK_{A}=\mathrm{G}^{a}\bmod\mathrm{P}\) For the server: \(PK_{B}=\mathrm{G}^{b}\bmod\mathrm{P}\) These public keys are derived from the exponential function over a finite field and are securely exchanged over an insecure channel.
* Shared Key Derivation: Upon receiving the other party's public key, the IoT device and the server calculate the shared symmetric key \(SK\) as follows: For the IoT device: \(SK=PK_{B}^{a}\bmod\mathrm{P}\) For the server: \(SK=PK_{A}^{b}\bmod\mathrm{P}\)  
Figure 2: Hamming Weight grouping results.  
Due to the commutative property of modular exponentiation, both calculations yield the same shared secret key \(SK\), which forms the basis of secure communication.  
As illustrated in Figure 3, in order to derive the final shared key \(SK\) from the public values, an attacker would need to solve for the private keys \(PK_{A}\) or \(PK_{B}\), which is computationally infeasible without access to those private keys. D-H ensures strong security by relying on the exchange of public keys and the difficulty of solving the discrete logarithm problem, even over an insecure channel. This mechanism is leveraged in our study to implement dynamic key replacement, providing protection against SCAs.
## 2 Preprocessing
### Features Selection
#### 2.2.5 Calculated Amplitude of Signals  
Signal amplitude refers to the magnitude or strength of a signal's oscillation or variation over time. It represents the maximum deviation of the signal from its baseline or mean value and is a critical characteristic in analyzing and understanding various types of signals, such as electrical, audio, and biological signals. Key aspects of signal amplitude Measurement: Amplitude can be measured in various units depending on the type of signal: Voltage (V) for electrical signals. Decibels (dB) for sound intensity. Arbitrary units in specific applications. Types of amplitude: Peak amplitude: The maximum positive or negative value of the signal from the center line. Root mean square (RMS) amplitude: A statistical measure that reflects the effective value of a varying signal, often used to compute power in alternating current (AC) circuits. Peak-to-peak amplitude: The total range of the signal, calculated as the difference between the maximum positive and maximum negative peaks. Signal strength: Amplitude indicates the strength of the signal, with higher amplitude correlating to greater intensity or energy. Quality: In communication systems, a higher amplitude generally improves the signal-to-noise ratio (SNR), enhancing clarity and quality. Biological insights: In medical signals (e.g., EEG or ECG), amplitude variations can indicate neural or cardiac activity levels, revealing important health information.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
###### Abstract  
This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones' navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system's robustness in evolving drone environments.  
U 2024 8 743 123201  
advanced UAVs that use alternative communication technologies such as 4G, and discusses future directions for developing multi-frequency jamming strategies.
## 4 Simulation Verification and Analysis
### Steady-State Operational Validation
#### 4.2.2 GSMMC AC-Side Two-Phase Ground Fault  
To analyze the transient characteristics of a two-phase ground fault, a B-C phase ground fault with a duration of 0.5 s was set at \(t=4.5\) s. When a two-phase ground fault occurred in the GSMMC, the current characteristics of it were similar to those under the A-phase ground fault, and the AC voltages of phase B and phase C were the same during  
Figure 7: Simulated waveform of the system under a short-circuit fault to phase A: (**a**) voltage on the AC side of the GSMMC; (**b**) current on the AC side of the GSMMC; (**c**) the active power output of the GSMMC; (**d**) the power input of the GSMMC and the output power of the wind turbines; (**e**) DC voltage of the MMC-HVDC system.  
the fault. As the wind energy continued to be delivered to the WFMMC, the DC voltage of the MMC-HVDC continued to rise. As can be seen from Figure 8, the DC voltage was not out of the safe range (1.05 p.u.), as the GSMMC could still deliver close to 150 MW of active power.  
If only the transmission power of the wind farm was increased to 280 MW, all other conditions remained the same. The simulation waveform for a two-phase ground fault is shown in Figure 9. Compared to the fault with a transmission power of 150 MW, the DC voltage during the fault exceeded 1.05 p.u. This indicates that the transient overvoltage not only changed with the type of fault but also increased with the increase in the power transfer of the system. The short-circuit current on the AC side of the GSMMC is was greater than when transmitting 150 MW.  
Figure 8: Simulated waveform under a two-phase ground short-circuit: (**a**) voltage on the AC side of the GSMMC; (**b**) current on the AC side of the GSMMC; (**c**) the active power output of the GSMMC; (**d**) the power input of the GSMMC and the output power of the wind turbines; (**e**) DC voltage of the MMC-HVDC system.
## 2 Materials and Methods
### Proposed Method Application for Evaluation with Clinical Data
#### 2.2.2 Detection of Lung Nodules in Chest CT Images with Proposed Feature Extraction  
Lung nodule detection is applicable to chest CT images scaled to 1.25 mm isotropic voxels. After extracting the lung field regions through thresholding and hole filling, the vascular regions within the lung field are extracted using a combination of thresholding, morphological processing, and small-area removal [18].  
Similar to the cerebral aneurysm detection described above, a cubic window of 32 voxels per side is scanned with a stride of 16 voxels on the vascular region's binary image to extract lesion candidate patches overlapping with the pulmonary vascular regions by more than 10% of the window area. An Ada-Boosted classifier ensemble classifies and eliminates FP candidates with the image features of the proposed unsupervised feature extraction.  
Normal vascular image patches automatically detected via the above lesion candidate detection process from 300 cases of normal chest CT image data are used to learn the feature extraction process. These images were scanned for screening purposes at the University of Tokyo Hospital. A cost-sensitive method is introduced for training the classifier ensemble, as in the classification process for cerebral aneurysm candidates. The weak classifiers in the ensemble also use decision stumps.  
The pulmonary nodule detection performance was evaluated via three-fold cross-validation on a dataset of 450 chest CT images containing at least one pulmonary nodule. These images were also scanned for screening purposes at the University of Tokyo Hospital. The nodule teacher labels were also determined by a consensus between at least two radiologists who performed CT image readings as part of their daily work. Both radiologists had over 10 years of CT-reading experience.
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
## 4 Discussion
### Volatilome and Bacterial Contribution to Fruit Aroma
nectarea_ suggests that this species may affect fruit quality by releasing terpenoids and by degrading pectins [86], thus confirming a possible role in volatile emission diversity. _Methylobacterium_, _Brevibacillus_ and _Sphingomonas_ are more abundant in IPM grown fruits and positively correlate with aldehydes emission. Among these bacterial genera, _Methylobacterium_ (in particular _M. extorquens_) has been thoroughly studied for its ability to enhance strawberry flavour [105; 106]. In fact, _M. extorquens_ possesses alcohol dehydrogenase enzymes capable of oxidizing a wide range of alcohols to aldehydes and ketones. Volatiles produced by bacterial isolates on raspberry juice were used for the assembly of in silico fruit volatilome of organic and IPM fruits. Although authors are aware of the limitations imposed by such an artificial system, a clear separation among VOC typical of microbiomes of differently grown berries was observed (Figure 8). Fragments of \(m/z\) 59.049, 87.08, 97.0274 and 107.0349 were identified as belonging to the aldehyde or ketone class, and were typical of the IPM in silico volatilome. Interestingly, this result is in accordance with volatile emissions of the real fruits and, together with correlations between microbiome and VOC emissions, pave the way for further studies about the role of bacteria in shaping raspberry aroma.