esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 2 System Model  
### Network Model  
Consider a mmWave UAV system consisting of one source (S), one destination (\(D\)), multiple UAV relays and multiple eavesdroppers, as shown in Figure 1. We assume that the direct link from \(S\) to \(D\) is disconnected since mmWave signals are easy to be blockaded on the ground, and the communication occurs via a selected UAV relay (\(R_{0}\)). To be specific, \(S\) first sends the messages to \(R_{0}\), and \(R_{0}\) employs the decode-and-forward protocol to forward its received signals to \(D\). Meanwhile, there are multiple ground eavesdroppers randomly distributed around \(D\) and out of the coverage area of \(S\), which means that the eavesdroppers only wiretap the transmission from \(R_{0}\) to \(D\), similar to [25; 26; 27]. In addition, the distribution of UAV relays follows a homogeneous PPP \(\Phi_{U}\) with density \(\lambda_{U}\), and we assume that all UAV relays are deployed inside a circular disc with radius \(Q\) and at the same altitude \(H\)[16; 28]. The ground eavesdroppers are modeled as an independent homogeneous PPP \(\Phi_{E}\) with density \(\lambda_{E}\).  
For facilitating the analysis, a 3D polar coordinate system is adopted, in which \(S\) and \(D\) locate at \((d_{SD},\pi,0)\) and \((0,0,0)\) respectively. Then, for the UAV relay \(R_{x}\) at \((r,\theta,H)\), the distance from \(S\) to \(R_{x}\) and \(R_{x}\) to \(D\) are represented as \(d_{SR_{x}}=\sqrt{d_{SD}^{2}+r^{2}-2 rd_{SD}\cos(\theta-\pi)+H^{2}}\), and \(d_{R_{x}D}=\sqrt{r^{2}+H^{2}}\).
## 4 Materials and Methods
### Volatiles Collection and GCMS Analysis using HS-SPME
#### 4.3.2 Samples Analysis with GC-MS  
The analysis of VOCs obtained by HS-SPME was performed on a gas chromatography mass spectrometer (GC Agilent GCMS 7820A) equipped with MS detector 5977E (Agilent Technologies, USA) and a DB-35 ms column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) (Santa Clara, CA, USA). The fiber was desorbed in the splitless injector 270 \({}^{\circ}\)C of GCMS with other operation conditions. The initial temperature of the column was 50 \({}^{\circ}\)C and held for 2 min, then increased to 250 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\) and held for 5 min at 250 \({}^{\circ}\)C. Helium gas (He) was used as a carrier and supplied by (BOC Gas, Sydney, Australia) and the flow rate of the column was 1:1 mL/min, while the splitless was 20 mL/min at 1.5 min and the total GC-MS run time was 45 min. The calibration of the SPME fiber was performed by injecting the \(n\)-alkanes standard C7-C30.  
HS-SPME/GC-MS analysis of the VOCs were identified by using AMDIS software version 2.72 and the US National Institute of Standards and Technology (NIST) 2014 MS database. The VOCs were confirmed by comparing GC retention time data with those of authentic standards or from the published literature [44].
## 2 Literature Review
### Healthcare 4.0  
The integration of IoT technologies into healthcare has ushered in the era of Health 4.0, where interconnected networks link patients, healthcare professionals, medical facilities, and suppliers to create a cohesive and intelligent healthcare ecosystem [45; 46]. Despite these advancements, the sector faces key challenges such as patient dissatisfaction, escalating costs, medication traceability, and the imperative to safeguard sensitive medical data. The decentralized nature of healthcare systems further complicates the execution of processes in a manner that is reliable, secure, efficient, and transparent.  
The convergence of DLTs and the IoT offers a promising solution by enabling secure tracking of medical products and protecting patient data through decentralized P2P networks. However, traditional blockchain implementations often introduce significant computational overhead, rendering them less suitable for resource-constrained IoT healthcare environments [47]. Holochain presents itself as a viable alternative for secure, decentralized, and scalable healthcare systems capable of managing sensitive data and complex, multi-stakeholder interactions. For instance, [PERSON]. [48] introduced a Holochain-based IoT healthcare framework that effectively addresses data security and confidentiality while reducing complexity and resource demands. Their comparative analysis indicates that Holochain achieves security and data integrity levels comparable to blockchain-based solutions but with diminished computational overhead.  
Further research [47] proposed a hybrid DLT model that integrates both blockchain and Holochain to enhance privacy and security within IoT healthcare ecosystems. This approach mitigates the individual limitations of each technology, such as recognition delays, memory consumption, and computational costs, while leveraging their respective strengths.
## 4 Parallel Multi-Objective Energy Valley Optimizer
### Knowledge-Driven Neighborhood Structure  
Energy production scheduling requires the design of multiple decision variables for optimization over a long time horizon, with each variable having a relatively large range of values. As a result, the solution space of the optimization model is likely to expand. When applying a versatile algorithm to solve a specific problem, a feasible solution is often obtained, but the solution's quality and convergence speed may be suboptimal. To improve convergence speed, one approach is to embed existing knowledge related to the problem into the algorithm design. During iterative optimization, existing knowledge should guide the changes in the solution candidate set.  
This paper designs two neighborhood structures for the search of solution candidates:  
(1) Reduce production for solutions that exceed predicted demand to minimize waste.  
(2) For solutions requiring electricity purchase at a given time, increase the operating time of the wind turbine and solar panel within the feasible region.  
The two neighborhood structures are illustrated in Algorithm 3.  
```
Input: Solution candidate X\({}_{t}\), Electricity demand forecast value for t = 1 \(\rightarrow\) Ddo if Total electricity generation > Electricity demand forecast value then if satisfying the constraints of battery then Excess electricity = Total electricity generation - Electricity demand forecast value - The amount stored in the battery if The amount stored in the battery \(\geq\) 0 then Reduce the electricity generation of SP, Excess electricity- 0* Excess electricity = excess electricity - The reduction of SP generation if Excess electricity>0 then Reduce the electricity generation of WT, Excess electricity- 0* end if else Extract power from the battery and calculate the purchased electricity amount. if the purchased electricity amount >0 then Increase the electricity generation of SP, The purchased electricity amount = The purchased electricity amount - The increasing SP generation if The purchased electricity amount >0 then Increase the electricity generation of WT, Excess electricity- 0* end if end end if
```  
**Algorithm 3** Neighborhood structure  
In Algorithm 3, when the electricity generation of a solution candidate exceeds the predicted demand, the priority is to reduce electricity generation from solar energy. Conversely, when the electricity generation falls below the predicted demand, the priority is to increase electricity generation from solar energy. This is due to the fact that the operating and maintenance expenses for solar panels are typically lower compared to those of wind turbines, and the priority for adjustments to the neighborhood structure changes with cost fluctuations under specific conditions.
## 5 Experiment Results
### Aerial Remote Sensing Map Construction  
A drone is a useful way to monitor vast areas of interest, and it offers low costs and efficient data collection with high-resolution images for remote sensing. During drone surveillance tasks, it flies at a high altitude--sometimes higher than 100 meters--and may encounter a strong breeze. A strong breeze or moderate gate occurring at a high  
Figure 8: The results for the FPS when the deep learning model was running on the onboard computer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MSE** & **MAE** \\ \hline VGG16 & 0.0394 & 0.0761 \\ VGG19 & 0.033 & 0.0652 \\ ResNet50 & 0.0698 & 0.1036 \\ ResNet101 & 0.0662 & 0.1032 \\ MobileNetV2 & 0.0842 & 0.131 \\ Xception & 0.0553 & 0.1125 \\ DenseNet121 & 0.068 & 0.1078 \\ InceptionV3 & 0.066 & 0.1297 \\ LMFM & 0.0627 & 0.1143 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance comparison when performing on the onboard computer.  
altitude causes the drone to jitter. Figure 9a shows the results of the aerial SWIR image map construction, which applied the drone control strategy from [24] and contained several blurry areas. Due to the drone jittering in a high-altitude environment, the SWIR image became blurred and generated a blurred area on the SWIR image map. The blurred aerial SWIR image map cannot be used for any aerial remote-sensing tasks.  
Our proposed LMFM relatively controlled the drone action using the captured SWIR image quality. Through a lightweight CNN with a small size and small parameters, the drone can predict the next action and calculate a new waypoint to capture a clear SWIR image, as shown in Figure 9b. The resulting aerial SWIR image map shows the full information in the SWIR spectrum and helps analyze the items or objects in it. The industrial area was close to the sea, and the drone flew at 300 m above it in the sky, with a strong breeze always available. However, the aerial map in Figure 9b is clear and has no blurred areas, because our proposed method could capture high-quality SWIR images in real time via controlling the drone's action using a lightweight deep learning model.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 4 Performance Analysis
### Outage Probability  
**Proposition 2**.: _The time-varying channel \(h(t)\) between BS and MR follows complex-valued Gaussian distribution with mean \(\mu_{h}(t)\) and variance \(\sigma_{h}^{2}(t)\), namely,_  
\[h(t)\sim\mathcal{CN}\big{(}\mu_{h}(t),\sigma_{h}^{2}(t)\big{)}, \tag{36}\]  
_where_  
\[\begin{split}\mu_{h}(t)&\triangleq\mathbb{E}\{h(t )\}\\ &=A_{0}(t)K_{1}(t)h_{d}^{\rm LoS}(t)+\sum_{m=-1,n=1}^{\sqrt{M} \sqrt{M}}A_{nm}(t)\exp\big{(}-J\phi_{nm}(t)\big{)}K_{3}(t)G_{1}h_{mn}^{\rm LoS}( t)\phi_{mn}^{\rm LoS},\end{split} \tag{37}\]_and_  
\[\sigma_{h}^{2}(t)\triangleq\mathbb{V}\{h(t)\}=A_{0}^{2}(t)K_{2}^{2}(t)+K_{4}^{2}(t )G_{2}^{2}\sum_{m=1,n=1}^{\sqrt{M},\sqrt{M}}A_{mn}^{2}(t). \tag{38}\]  
Proof.: See Appendix B.  
**Remark 8**.: _It is shown that the expectation of \(h(t)\), i.e., \(\mu_{h}(t)\), relies on the LoS Rician factors, \(K_{1}(t)\), \(K_{3}(t)\), and \(G_{1}\). Therefore, when the MR, the BS, and the RIS are close enough, \(\mu_{h}(t)\) would be maximized. However, the variance \(\sigma_{h}^{2}(t)\) depends on the NLoS Rician factors, namely, \(K_{2}(t)\), \(K_{4}(t)\), and \(G_{2}\)._  
**Proposition 3**.: _The outage probability \(\mathrm{Prob}_{\mathrm{out}}(t)\) of the \(h(t)\) is given by_  
\[\mathrm{Prob}_{\mathrm{out}}(t)=1-Q_{\frac{1}{2}}\bigg{(}\sqrt{\xi(t)},\sqrt{ \gamma_{0}(t)}\bigg{)}, \tag{39}\]  
_where \(\xi(t)=|\mu_{h}(t)|^{2}/\sigma_{h}^{2}(t)\) and \(\gamma_{0}(t)=\gamma_{\mathrm{th}}/(\bar{\gamma}\sigma_{h}^{2}(t))\) with a given SNR threshold \(\gamma_{\mathrm{th}}\) and \(\bar{\gamma}=P_{t}/\sigma^{2}\). Besides, \(Q_{u}(a,b)\) is the Marcum \(Q\)-function with the order \(u\)._  
Proof.: See Appendix C.  
**Remark 9**.: _It can be seen that \(\mathrm{Prob}_{\mathrm{out}}(t)\) is also related to \(\mu_{h}(t)\) in (37) and \(\sigma_{h}^{2}(t)\) in (38), which means that the distance-dependent Rician factors affect the outage probability of the proposed system. Besides, due to the square power law, increasing the number of elements can bring a lower outage probability compared to increasing the number of antennas in traditional relays._
## 3 Results
### Gene Expression of Skeletal Muscle  
In the TA muscle, MyoD gene expression levels showed no significant differences between the control and CKD groups at both 12 and 20 weeks (Figure 4A). At 12 weeks, muscle anabolic-related gene expression (myogenin) was significantly more elevated in the CKD group than in the control group, but this difference was not observed at 20 weeks (Figure 4B). The mRNA levels of genes associated with muscle catabolism, atrogin-1 and MuRF-1, were markedly higher in the CKD group at 12 weeks (both \(p\) < 0.01), with no significant differences at 20 weeks (Figure 4C,D). The significant increases in myogenin, atrogin-1, and MuRF-1 expression observed in the CKD group at 12 weeks were notably decreased by 20 weeks (all \(p\) < 0.01) (Figure 4B-D). The expression of the PGC-1\(\alpha\) mRNA indicated a potential increase in the CKD group at 12 weeks, although it did not reach statistical significance between the groups (Figure 4E).  
Figure 3: Histological section of the kidney with Elastica–Masson stain control and chronic kidney disease (CKD) rats. (**A**) Control group at 12 weeks. (**B**) CKD group at 12 weeks. (**C**) Control group at 20 weeks. (**D**) CKD group at 20 weeks. CKD group causes fibrosis of the renal interstitium at both 12 weeks and 20 weeks.
## 4 Simulation Results and Analysis  
### Formatting of Mathematical Components  
After the mining of the 43 upper01 working face, as shown in Figure 3, significant bending, subsidence, and fracturing of the overlying strata were observed. The simulation results indicate that the initial fracture lengths of the primary and secondary key strata were 97 m and 87.2 m, respectively, which are in close agreement with theoretical calculations. Following the initial fracturing of the secondary key stratum, the subsequent collapse length shortened with ongoing mining but generally remained stable. At this point, the fractures were primarily macroscopic and distributed within the primary and secondary key strata above the 43 upper01 working face.  
After mining of the 43 upper02 working face, as depicted in Figure 4, it was observed that the overlying strata of the 43 upper01 working face had largely collapsed to the floor. The A and B regions within the primary and secondary key strata showed a significant increase in damage-induced fractures due to tensile and compressive effects, which were easily distinguishable. In contrast, only a small number of fractures developed in the overlying strata of the 43 upper02 working face, which exhibited an overall tilting state. At location C, distinct layer misalignment and extrusion were observed. It is noteworthy that due to mining activities in the coal seam above, the 3 lower coal seam had already been affected, with prominent damage fractures developed in the D and E sections, while the interbedded rock layer between the two coal seams remained largely unaffected.  
Upon completion of mining at the 43 lower01 working face, as shown in Figure 5, the siltstone interlayer between the two coal seams was largely in a state of continuous collapse due to ongoing mining. The overlying primary and secondary key strata showed significantly increased damage levels from secondary mining, with a notable reduction in their integrity. Subsidence effects from the overlying strata induced by fracturing in the previous coal seam were evident. After mining of the 43 lower02 working face, aside from slight enhancement of fracturing in the primary and secondary key strata, the siltstone interlayer maintained its integrity.  
Figure 4: Broken status after finishing 43 upper02 working face excavation.  
Figure 3: Broken status after finishing 43 upper01 working face excavation.  
After the mining of the 43 lower02 working face, as shown in Figure 6, the damage to the overlying strata from multiple mining activities was evident, with a high degree of rock fragmentation. However, the damage to the surrounding rock was largely confined to the overlying strata, and the integrity of the side and floor rocks of the mining area was scarcely affected.
## 1 Introduction
It should be noted that the above analyses have one major limitation, i.e., the vehicles were simplified as vertical load and only one vehicle was considered during the analysis. Even if the traffic flow is considered, critical parameters in the traffic flow, e.g., the vehicle type, vehicle speed, gross vehicle weight, and vehicle lane occupancy ratio, were not or only partially considered. Such simplification ignores the coupling effects between the traffic flow and the bridge, which may have significant influence on the dynamic behavior of the suspender and the resultant fatigue performance [16]. Additionally, there has been limited literature specifically focused on the effects of wind loads and the fatigue performance of the suspender considering site-specific wind load and traffic load has not been quantitatively evaluated. In the present study, a probabilistic fatigue framework is proposed to evaluate the fatigue performance of the suspenders under stochastic wind and traffic loads, through the integration of in-situ monitoring data and the linear fatigue damage rule. First, the fully coupled traffic-bridge-wind (TBW) simulation is introduced. Subsequently, the stochastic wind and traffic load conditions at the bridge site are introduced, in which the associated key parameters are presented in detail. Furthermore, a probabilistic numerical framework is established to predict the time-dependent fatigue reliability of the suspenders based on the linear fatigue damage rule. As a demonstration, the proposed numerical framework is applied to a long-span suspension bridge located in a mountainous canyon. The stress response characteristics of both the long suspended and the short suspender under individual wind load, individual traffic load, as well as combined wind and traffic loads are investigated thoroughly. Finally, the fatigue life of the suspenders is estimated using Monte-Carlo simulations.
# Theory of Faults (ToF): Numerical Quality Management in Complex Systems
## 3 Development
### Information Constraint (IC)  
Definition: Information constraint is a system's limit of fault load processing capacity. Rather than directly calculating that limit, we counted the number of faults causing the load and noted the IC = the number of faults at threshold points, acknowledging the oversimplification in the underlying assumption of an equal load per fault. The IC is a stability threshold--when IC thresholds are traversed, systems avalanche. There is little redundancy in information systems, because resources are optimized for minimum cost [17]. We case-studied IC in design and construction projects. In Case Study 1, all three projects the IC < 7; IC = 5 was found in our case study of design projects [36].  
The phenomenon of a second IC provisioned in [22] was validated in Case Study 1. On site, the second IC is overseen by foremen.  
Related factors that aggravate the information load on info-systems are as follows:  
_Allocation_: The convolution of fault vectors in info-systems causes stacking (Figure 7) and resource idling. Designers and contractors optimize resources by undertaking multiple projects, thus attempting to attain full capacity employment at the expense of constantly delaying and deferring tasks, transferring resource idling and inefficiency to their associates.  
_Morphology_: Information systems are scale-free networks. Scale-free network morphologies propagate data faster due to small-world effects and centrality.  
_Scale-free networks_ are characterized by preferential attachment. Preferential attachment manifests in rank (Experiment A.4). Rank causes a higher burden on the higher ranked nodes; the burden has a power function magnitude distribution function. Experiment A.4 showed that the degree of centrality grows with time and that not only do hub centralities grow, but new hubs appear.
* _Morphological changes_: Info-system networks' morphology evolves with time and by faults, most notably generating higher-order loops, upsetting the effects of buffers and planning, causing more rework, and causing vexation and apathy. An in-depth study of networks evolution was carried out in [36].
* _Liability and prestige_ (Experiment A.4): High-ranked individuals not only carry a burden scaled by centrality but also must deal with contractual, litigation, insurance, and prestige implications. Experiment A.4 highlights the effects of liability and prestige on severing triadic connections, the formation of bridges, and the inflation of degree.
# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
## 4 Interfaces
### Interface for Pilot-Controller Communication  
The pilot-controller communication interface enables changes in aircraft trajectories, directly influencing the traffic situation and providing a valuable foundation for evaluating controller workload. Numerous systems estimate controller workload through specific properties of the traffic, such as coordination tasks. A widely used example of such task-based methodology is EUROCONTROL's CAPAN studies [30]. In real-world applications, pilot-controller communication is typically triggered by events necessitating trajectory changes--such as adverse weather, flight parameter optimization, emergencies or non-nominal situations, frequency handoffs, or misunderstandings in issued clearances.  
This communication interface involves three interconnected layers: the aircraft simulation layer, the traffic simulation layer, and the controller layer. This architecture is illustrated in Figure 7, while the detailed interface structure is shown in Figure 8.  
Figure 5: Functionality of Interface 1.  
Figure 6: Structure of position estimation interface.  
Figure 7: Functionality of Interface 2.  
Pilot requests can be generated in a simple, randomized manner. The simulation accepts a user-defined parameter that specifies the proportion of the aircraft generating requests, allowing traffic complexity to be adjusted. Both the selection of aircraft and the content of the requests are randomized.  
Alternatively, the aircraft simulation layer can be assigned the responsibility of request generation, aligning with its role as an aircraft behavior model. This is facilitated via Interface 2, which defines communication between aircraft agent and controller agent. In this arrangement, the traffic simulation layer functions primarily as a messenger, relaying information between layers.  
The process initiates with pilot requests, which are transmitted to the controller agent. Based on the controller's decisions, new instructions or approvals/denials are returned. Given that the simulation assumes class C airspace [31], aircraft are expected to follow ATC instructions. However, non-compliance or alternate proposals from the pilot side are possible in the subsequent iteration. Here the memory between iterations has to be maintained by the aircraft layer. Time progression is modeled via successive loop iterations, during which the aircraft positions are updated and new communication rounds are triggered.  
The data structures used for this communication are described in Figure 9. The relevant fields associated with Flight Parameters are explicitly marked in Table 2.  
An example scenario illustrating the use of Interface 2 is provided in Figure 9, demonstrating how the simulation framework captures communication between ATC and aircraft crew over time.