esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Drone Path Planning for PFBs Inspection
#### 5.1.3 Collecting Targeted PFB Images  
Finally, following each of the necessary experiments to determine the PFB detection model fit, distance calculation accuracy, and battery life optimization, aerial imagery of the PFBs was collected for dataset creation. Images of the PFBs were saved into 1 of 7 directories that corresponded with the fan the images were taken of. For example, if an image was taken from the fan identified as _fan_4_, the image would be saved into the folder named _fan_4_. The folders were located in a One-Drive cloud to allow seamless collaboration with the DL models used for crack and anomaly detection. The QR codes scanned by the drone verify in real-time which fan is being visited and decide which folder to save the images. In these experiments, _fan_4_ illustrated in Figure 16 is the only fan with faulty PFBs, while all the PFBs of the six other fans are healthy.  
Figure 17 shows the flight path for image collection. Once the QR code is scanned, the drone can be assumed to be about 100 cm in front of the fan; thus, the first steps are to elevate the drone and move it towards the fan to prepare the rectangular pattern for image collection. Once the images are taken at the six designated locations of the front, the drone moves to the right, forward past the fan, left to align with the fan rotor, and turns around to face the back of the fan. Images are taken in the same rectangular pattern of the fan rear  
Figure 14: Battery usage across different experiments.  
Figure 15: Flight times across different experiments.  
to finish the mission around the fan. The conservatively large U-bracket shape around the fan allows for a safe margin of error during the flight.  
Figure 17 shows the planned path in a perfect scenario where the fan is equidistant from the inspection points. However, this case is not always present in the experiments. With a smaller U-bracket-shaped flight and the position of the fan being farther than expected, the drone could collide once it is time to go behind the fan. Alternatively, in the reverse case, a resulting collision is probable when it is time for the drone to approach the fan front. With collisions observed in preliminary experiments, the image collection path was conservatively defined so that the worst case scenarios would look like those shown in Figure 18. This safety measure results in the drone being capable of only one sided close-up  
Figure 16: The back and front of the faulty blades of _fan_4_.  
Figure 17: The planned flight path of the drone around the fan PFBs in an ideal scenario, which is used to collect PFB images. The drone’s starting location represents the instant the drone identifies the QR code.  
images, but not both, without risking collision. A sample of the resulting collected images via the autonomous drone can be observed in Figure 19.
## 1 Introduction  
Wireless sensor network (WSN) is a multihop self-organizing network composed of multiple sensor nodes. Due to its small size of nodes, strong networking capability, and convenient deployment and maintenance, it is widely deployed for environmental monitoring [1; 2; 3; 4], intelligent transportation [5; 6; 7; 8], military defense [9; 10] and medicine and health [11; 12]. In WSN applications for asset (e.g., rare animal) monitoring, the location information is routed from the source node that detects the asset to the base station. Attackers can find out the source node (asset) location by using a hop-by-hop backtracking strategy for illegal purposes. Therefore, how to ensure the privacy of the source location is a problem that urgently needs to be solved.  
The existing source location privacy (SLP) protection methods [13; 14; 15; 16; 17; 18] mainly protect the SLP by increasing the variety of routing paths, such as phantom routing, multi-path routing, and false information source injection and other secure routing mechanisms. Most of these technologies sacrifice network performance for a certain level of security and are not suitable for applications that require high query efficiency. Additionally, the existing MAC-layer SLP protection algorithm SLP-TDMA-DAS (STD) [19] protects the SLP by generating a decoy single-path and adjusting the priority of its broadcast slot to interfere with the attacker's hop-by-hop traceback. However, if the listening range does not cover the decoy path, no matter how high the time slot priority of the nodes on the decoy path, it will not attract the attacker to move to that path.  
In order to solve these problems, this paper proposes an efficient algorithm based on Circular Trap (CT) which integrates the routing layer and MAC layer protocol to provide SLP protection. This algorithm constructs a circular route using the geographic routing protocol and adjusts the time slot of its nodes to be lower than that of its neighbors. When an attacker eavesdrops on a network, it will first receive messages from the circular route and circulate through it. If the attacker starts from the base station to track the source node location, it is inevitable for the attacker to receive the message broadcast by the circular trap path, thus extending the capture time of the attacker. If an attacker listens in a random location in the network, the circular trap can also increase the probability of the attacker being disturbed. Compared with the STD algorithm, the CT algorithm has higher stability and security. We make the following novel contributions: (i) we introduce the concept of Circular Trap upon TDMA to protect SLP; (ii)we propose a 3-phase distributed algorithm using the Circular Trap method. (iii) we propose a routing protocol to adjust the time slots of the nodes when constructing a circular route.  
The rest of this paper is organized as follows: We summarize the related work in Section 2 and introduce the preliminaries in Section 3. Section 4 illustrates the basic idea of CT algorithm. The evaluation of CT algorithm is conducted in Section 5. Finally, we conclude this paper in Section 6.
## 4 Results and Discussion  
### Soil Resonance Frequencies and Amplitudes  
The soil resonance frequencies and the corresponding amplitudes for the cities of El-Attaf, El-Abadia, and Ain-Defla, are mapped in Figure 6. In El-Attaf city, the resonance frequencies are between 1.2 and 8.3 Hz. In the major part of the city, the predominant frequencies are between 1.2 and 5 Hz. The obtained values are related to impedance contrasts in the subsoil between the Quaternary alluviums and the Miocene marks and sandstones. Near Ouled Moussa, south of the city, higher frequencies are observed (between 5 and 8.3 Hz). This increase is most likely related to the Cretaceous marks outcropping in the south [21]. Since the buildings in this area have between 1 and 5 floors, the resonance frequencies of the ground are close to the buildings' frequencies, which can be damaging for the structures during strong shaking. The predominant amplitudes of the frequency peaks vary between 2.2 and 8.7. The amplitudes are lower to the northern areas of the city. The highest amplitudes (between 5 and 8.7) are observed in \"Cite Bouzar\" neighborhood, in the western areas of the city.  
In the El-Abadia city, the resonance frequencies vary between 1.4 and 4.2 Hz, while the corresponding amplitudes vary between 2 and 4. The obtained resonance frequencies are related to impedance contrasts between the Quaternary alluviums and the Pliocene sandstones. The amplitudes slightly increase to the southwest towards the Middle-Chelif plain, where the Quaternary stiff and soft soils are thicker. Finally, in the Ain-Defla city, the resonance frequencies vary between 1.4 and 4.5. These frequencies are related to the impedance contrasts between the Quaternary deposits and the Cretaceous-Jurassic bedrock. The increase in the resonance frequency peak from north to south is related to the presence of the Doui Massif and its hard Jurassic limestones [22] to the south of the city. The predominant amplitude of the frequency peaks varies between 2 and 7.1. The amplitudes are relatively low in the central part of the city. However, in \"Mohamad Khiat\"  
Figure 6: Results of the single\(-\)station measurements analysis.  
neighborhood, in the SW, the amplitudes are relatively higher (5-7.1). In the northern areas, it reaches a value of 6.8.
## 5 Conclusions and Future Works
### Future Recommendations  
Several possible improvements of this work can be possible, which can be the subject of future works. Firstly, a more general scenario with multiple NUs and FUs with the aid of multiple D2D pairs is anticipated to be studied. Secondly, hybrid DF- and AF-based cooperative D2D aided protocols can be investigated. Lastly, the proposed network models can be extended to the multi-cell scenario.  
Although multiple technologies can be combined without too much effort in the simulation environment, there are many challenges to implement in a real environment. Some of them are as follows:  
* There should be both technical and policy requirements agreement among different service providers. For example, in the proposed technique, the cellular users and D2D users should be in consensus for their mutual benefits.
* Whereas a simulation environment can assume perfect synchronization among the antennas or users and perfect channel estimation, the real environment introduces various challenging tasks.
* During software simulation, it is considered that software algorithm can be realized perfectly by hardware design. However, algorithm design is often bounded by the hardware condition in real environment [56].
* With the current 3 rd generation partnership project (3 GPP) cellular standards, different approaches need to be complied.  
**Author Contributions:** Conceptualization, M.F.K.; Writing--original draft, M.S.K. and M.F.K.; Investigation, M.F.K. and S.M.R.I.; Writing--review and editing, M.F.K., S.M.R.I. and H.Y.; Supervision, M.F.K. All authors have read and agreed to the possible publication of the manuscript.  
**Funding:** This research was supported by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; 2019R1A2C1083988), by the Ministry of Science and ICT, Korea, under the Information Technology Research Center support program (IITP-2020-2016-0-00313) supervised by the Institute for Information & Communications Technology Planning & Evaluation, and by Sejong University through its faculty research program (20192021).  
**Acknowledgments:** This work was technically supported by the University of Chittagong.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Scenario** & **Antenna Configuration** & **D2D-CRS-SM** & **VBLAST-ZF** \\ \hline \multirow{2}{*}{S\({}_{1}\)} & A\({}_{\text{BS}}\) = 4, \(\text{A}_{\text{NU}}\) = 4, \(\text{A}_{\text{D}_{1}}\) = & Modulation order 4/16/32/64 & Modulation order 4-QAM: 6 \\  & 4, \(\text{A}_{\text{FU}}\) = 4, \(\text{A}_{\text{D}_{2}}\) = 4 & -QAM: 3/4/4.5/5 bits/s/Hz & bits/s/Hz \\ \hline \multirow{2}{*}{S\({}_{2}\)} & A\({}_{\text{BS}}\) = 4, \(\text{A}_{\text{NU}}\) = 4, \(\text{A}_{\text{D}_{1}}\) = & Modulation order 4/16/32/64 & Modulation order 4-QAM: 6 \\  & 4, \(\text{A}_{\text{FU}}\) = 4, \(\text{A}_{\text{D}_{2}}\) = 4 & -QAM at BS: 3/4/4.5/5 bits/s/Hz & bits/s/Hz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum achievable theoretical sum SE comparison between D2D-CRS-SM and VBLAST-ZF.
###### Abstract  
The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset.  
control moment gyroscope; abnormal detection; LSTM; transfer learning +
Footnote †: journal: Medical Imaging
## 6 Algorithms and Frameworks in Federated Learning
### CO-OP Algorithm  
The asynchronous CO-OP protocol arises as a novel approach to the complexities of distributed machine learning, particularly in dynamic environments. Instead of relying on static datasets, as is common with many existing FL frameworks, CO-OP is designed to adapt to dynamically generated data. As users interact with their mobile devices, the individual datasets of these users grow in real time, introducing an added layer of complexity.  
Consider a system whereby \(K\) mobile clients are actively participating in a cooperative learning process. Each client, governed by the unique interaction patterns of its user, gathers its own set of data. Once a pre-defined threshold (\(B\) samples, known as the local batch size) is reached, the client begins the process of refining its local model, employing gradient descent techniques based on these recently accumulated data.  
Traditional FL frameworks often employ a more centralized approach, wherein the server periodically prompts specific clients to contribute to model updates. CO-OP, however, champions decentralization by empowering clients to initiate model updates asynchronously. This autonomy allows clients to choose the best time and environment for local training, such as during optimal network conditions. After refining their local models, clients can then liaise with the central server, merging their updates and subsequently obtaining the revised global model.  
The global model parameters on the server are denoted as \(w\), with each client's parameters represented as \(w_{k}\). Within the CO-OP framework, a critical metric is the age of the global model, indicating the update frequencies of clients. Clients monitor their own model's age, recalibrated using the CO-OP protocol. Starting with a common model \(w\), clients gather data, fine-tune their models, and synchronize them with the server. CO-OP's age filter ensures timely and relevant client integration, avoiding outdated updates. As FL evolves, CO-OP addresses both IID and non-IID data, but empirical studies show that it faces strong competition from established algorithms like FSVRG.
### Organization of Project Based Company and Ore Procurement  
Due to the enormity of required investment, most Ni smelting operations require a special purpose company (SPC). The SPC is a legal entity encompassing a combination of organizations involved in the manufacturing of Ni who share all risks and project profitability. For this paper, the SPC consists of an equity provider, mining company, smelter, and steel manufacturer. Of the many agreements required to create this arrangement is the procurement of raw materials for stainless steel manufacturing. This is the focus of this paper and is also known as the feedstock agreement. Figure 1 displays the SPC used for this paper, specifically identifying the feed stock supply agreement.  
At the inception of the project, the feedstock agreement is executed, ensuring a stable supply of mined ones throughout the duration of the project. Theoretically any losses due to low-grade ones is borne by all organizations. However, often, the SPC contract does not provide for fluctuations in the ore Fe and Ni content which becomes the smelting company's responsibility [5]. Often, a set Ni ore price is determined for the duration of the project based on a bilateral negotiation executed at the start of the project. The price calculation considers the supply period, quantity, and market price without a global standard. The supply period, quantity and price of the ore, ore supply rate, and quotation are specified within the feedstock supply agreement. An \"as-is\" practice is the most common practice for calculating the Ni purchasing price, with the following formula [5]:  
\[\text{Ni Ore }Price=Net\ Ni\ Weight*LME\ Ni\ Price*Price\ Ratio \tag{1}\]  
where  
\[\text{LME Ni Price = London Metal Exchange (LME) Ni price as a reference point  
\[\text{Price Ratio}=\frac{Ni\ Content\ Grade}{Ni\ Price\ in\ Commodity\ Exchange\ Market}\]  
Equation (1) uses a constant price ratio is a poor practice due to both the potential for variation in the ore's Ni content and price within the current market [5]. Equation (1) also does not consider the Fe content which has a negative impact on the manufacturing price. With these inadequacies of the current process, this paper suggests an alternative solution: sharing the smelter's profitability fluctuation risk depending on the changes in the Ni and Fe content.
# Annealing Process-Induced Microstructural Variation in NiV/B4C Multilayers
## 3 Results and Discussion
### X-Ray Diffraction Analysis  
The results of GIXR and X-ray scattering measurements showed that an increase in interface width was observed when the multilayers were annealed at different temperatures. We conjectured that the annealing process changed the microstructure of the multilayers, leading to increased interface roughness and period thickness.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Time (min)**} & \multicolumn{4}{c}{**Interface Width \(\sigma\) (nm)**} \\ \cline{2-7}  & \multicolumn{2}{c}{**As-Deposited**} & \multicolumn{2}{c}{**Annealed**} & \multirow{2}{*}{\(\Delta\sigma_{\text{NiV-on-B}_{\text{t}}C}\) (nm)**} & \multirow{2}{*}{\(\Delta\sigma_{\text{B}_{\text{t}}C-\text{NiV (nm)}}\)} \\ \cline{2-7}  & \(\sigma_{\text{NiV-on-B}_{\text{t}}\text{c}}\) (nm) & \(\sigma_{\text{NiV-on-B}_{\text{t}}\text{c}}\) (nm) & & \(\sigma_{\text{B}_{\text{t}}\text{C-on-NiV (nm)}}\) & \\ \hline
10 & 0.270 & 0.291 & 0.273 & 0.293 & 0.003 & 0.002 \\ \hline
30 & 0.260 & 0.284 & 0.270 & 0.292 & 0.011 & 0.008 \\ \hline
60 & 0.270 & 0.275 & 0.291 & 0.293 & 0.021 & 0.017 \\ \hline
120 & 0.290 & 0.295 & 0.353 & 0.351 & 0.063 & 0.056 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative fitting results of the interface width before and after annealing at 340 \({}^{\circ}\)C.  
Figure 5: Rocking curve scans of NiV/B4C multilayers: (**a**) measurements and fitted curves, (**b**) set of measurements at different temperatures, (**c**) measurements and fitted curves, (**d**) set of measurements for different times.  
The crystallization was confirmed by XRD measurements, and the results are shown in Figure 6. In Figure 6, a (0 0 1) crystal plane diffraction peak of V5O9 appeared at 20 = 11.453\({}^{\circ}\), and no other sharp peaks were present when the annealing time was less than 120 min. This is consistent with previous research [22]. The crystal plane diffraction peaks of B2O (0 0 1) (PDF#41-0624) appeared at 20 = 12.530\({}^{\circ}\) and a crystalline phase of Ni5V appeared at a smaller angle after annealing for 120 min [23]. With the increase in annealing temperatures, the (0 0 1) peak position of V5O9 moved to a lower 20, indicating that the crystallinity increased.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 3 The Proposed Channel Synthesizer  
Now we have enough information to build the new channel synthesizer for UAVs in the presence of tree canopies. The purpose of this synthesizer is to generate a time series of received signal levels for the scenario of interest in the site-specific way, i.e., respecting the mutual positions of the UAV, its ground terminal, and trees, while accounting for physical parameters of the trees in terms of their dimensions, shapes, and types of branches and leaves.
## 2 Background and the Related Work  
### Software-Defined Networking (SDN)  
SDN is a modern networking approach that is flexible, manageable, cost-effective, and adaptable. The objective of SDN is to simplify network management, promote innovation, improve network resource utilization, and optimize network performance. It is well suited for high-bandwidth, dynamic applications. In large networks with hundreds of switches and routers, network management is a highly difficult and time-consuming procedure. Thus, SDN, a novel approach, seeks to improve the present unfavorable scenarios for building, developing, and maintaining networks. The core concept of SDN is to logically centralize network control in an SDN controller, which controls and monitors network behavior [8].  
Data traffic in our society is increasing exponentially as a result of the recent rapid growth of intelligent devices and network technologies. Networks are growing more heterogeneous and advanced in order to manage higher numbers of devices and optimize traffic distribution. One possible approach to solving these issues is to increase network intelligence. Fortunately, SDN makes learning easier [9]. The traditional methods of configuring, optimizing, and troubleshooting computer networks may not be sufficient or efficient due to the large size, heterogeneity, and complexity of current and future networks [10]. SDN offers security, energy efficiency, and network virtualization for enhanced network performance as a result of the rising prevalence of smart programmable devices in networks [11]. Even though SDN provides many benefits, such as scalability, agility, and ease of administration, security is one of the most important aspects to take into account while using it [12]. The benefits of SDN and the explanations for its rapid acceptance are depicted in Figure 1.  
Since the SDN architecture is centralized, programmable, and able to collect real-time data from the controller, it is conceivable to use some \"intelligence\" (ML techniques) for efficient routing and QoS provisioning [9]. By utilizing SDN's programmability, ML could  
Figure 1: SDN Benefits.  
be used to provide real-time QoS solutions. In SDN, the control and forwarding functions of a network are separated, enabling the control of the network to be programmable and the infrastructure for applications and network services to be abstracted. This design makes SDN a suitable choice for current and future networking needs. SDN architecture majorly consists of three layers, namely (a) the application layer, (b) the control layer, and (c) the infrastructure layer. Each of these layers performs certain functions and communicates with the others via interfaces [13].