esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Procedure of the Proposed Method  
In this section, we explain the key details of the RevAuc procedure executed by a REP (Figure 13). We define an evaluation function to calculate bid scores and assume that both the server and mobile hosts know this function.  
1. Some mobile hosts volunteer to serve as a P-REP or an S-REP that requests bid invitations from a server. The REP sends a Bid Invitations Query message that includes information on whether the sender is a P-REP or an S-REP, query ID, and the NEA.
2. When the server receives a Bid Invitations Query message from a P-REP, the server selects data requests included in bid invitations sent to the P-REP based on the NEA in the Bid Invitations Query message. Then, the server sends the bid invitations to the P-REP with a Bid Invitations message. After receiving a Bid Invitations Query message from an S-REP, the server identifies a Bid Invitations Query message from a P-REP that corresponds to the S-REP by matching the query ID in the Bid Invitations Query message. Then, the server sends the same bid invitations to the S-REP as the bid invitations it sent to the P-REP.
3. After receiving a Bid Invitations message, a REP sends a D2D Bid Invitations message containing the bid invitations received from the server via two-hop D2D broadcast. Two-hop D2D broadcast is used to allow the one-hop neighbors of the P-REP to receive the D2D Bid Invitations message from the S-REP. Note that the one-hop neighbors of the S-REP are not the same as the one-hop neighbors of the P-REP.
4. After receiving a D2D Bid Invitations message, a mobile host determines whether to tender a bid in response to the invitation. If the mobile host tenders a bid, it sends it with a D2D Bids message to the REP via two-hop D2D broadcast.
5. Each REP waits for a configuration parameter time \(T_{w}\) after sending a D2D Bid Invitations message. Then, for each bid invitation, the REP selects the bid with the highest evaluation score among its own bid and other bids received with D2D Bids messages while waiting for time \(T_{w}\). The REP sends the selected bids to the server in a Bids message.
6. The server selects the successful bidder, which is the host that sent the bid with the highest evaluation score among the bids received within time \(T_{q}\) after the data request was received. It sends a Bid Result message to the successful bidder via the cellular network to ask the mobile host to obtain and transmit the requested data.
7. After receiving a Bid Result message, the successful bidder mobile host obtains the requested data and transmits it to the server in a Data message via the cellular network.  
Figure 13: Example of the RevAuc procedure carried out by a P-REP.  
Because the actions in step (1) are a little complicated, we explain more details of this step below.
###### Abstract  
The electric solar wind sail, or E-sail, is a propellantless interplanetary propulsion system concept. By deflecting solar wind particles off their original course, it can generate a propulsive effect with nothing more than an electric charge. The high-voltage charge is applied to one or multiple centrifugally deployed hair-thin tethers, around which an electrostatic sheath is created. Electron emitters are required to compensate for the electron current gathered by the tether. The electric sail can also be utilised in low Earth orbit, or LEO, when passing through the ionosphere, where it serves as a plasma brake for deorbiting--several missions have been dedicated to LEO demonstration. In this article, we propose the ESTCube-LuNa mission concept and the preliminary cubesat design to be launched into the Moon's orbit, where the solar wind is uninterrupted, except for the lunar wake and when the Moon is in the Earth's magnetosphere. This article introduces E-sail demonstration experiments and the preliminary payload design, along with E-sail thrust validation and environment characterisation methods, a cis-lunar cubesat platform solution and an early concept of operations. The proposed lunar nanospacecraft concept is designed without a deep space network, typically used for lunar and deep space operations. Instead, radio telescopes are being repurposed for communications and radio frequency ranging, and celestial optical navigation is developed for on-board orbit determination.  
electric solar wind sail; lunar orbit; cubesat; in-orbit demonstration; interplanetary nanospacecraft
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.2 [PERSON]  
[PERSON] et al. [32] proposed the Cluster Head Election mechanism based on the Fuzzy logic (CHEF). Contrary to LEACH, CHEF uses the distance to the BS and the remaining energy of node for the CH selection. At each round, a node generates a random value between 0 and 1, and thereafter compares the obtained value with a threshold, Popt. If the random value is smaller than Popt, the chance value is calculated by using fuzzy IF-THEN rules. Thus, the node sends a message called Candidate_Message with the calculated chance. The Candidate_Message sent by the node means that it is a candidate for being a CH with the value of its chance. The node which sent a Candidate Message, waits for Candidate Messages and appropriate chance values from its neighbors. If its own chance is bigger than every _chance_ from other nodes, the sensor node notifies the network with a CH-Message which means that the sensor node itself is elected as the CH. If a node which is not a CH receives the _CH_-message, the node selects the closest CH as its CH and joins a cluster by sending a Cluster Join Message to the CH. For the evaluation of CHEF, the authors consider 400 nodes randomly distributed in the area and the BS is not located within the experimental field. The comparison between LEACH and CHEF has shown that the cluster formation on CHEF was more efficient than on LEACH. Thus, CHs are not as closed with the proposed algorithm as with LEACH; there are too many nodes within a cluster that may rapidly decrease the energy of the CH. The results of experimentations have also shown that the lifetime of the network on CHEF is 22.7%, which is better than the lifetime of the network with LEACH according to the FND.
## 3 Results
### Coagulation-Flocculation in Continuous Mode
#### 3.1.3 Nutrients

Table 3: Presence of nutrients in the aquaculture effluent before and after coagulation treatment. sium, the estimated hardness of the raw effluent was approximately 250 mg\(\cdot\)L\({}^{-1}\) as CaCO\({}_{3}\), placing it within the ideal range of 100-400 mg\(\cdot\)L\({}^{-1}\) for aquaculture. After coagulation treatment, water hardness ranged from 99 to 137 mg\(\cdot\)L\({}^{-1}\), 109 to 112 mg\(\cdot\)L\({}^{-1}\), 87 to 123 mg\(\cdot\)L\({}^{-1}\), and 100 to 147 mg\(\cdot\)L\({}^{-1}\) for CS, Tanfloc, aluminum-based, and iron-based coagulants, respectively. These values also comply with Portuguese legal limits for aquaculture water (Annex XI), which allow a hardness range of 10-500 mg\(\cdot\)L\({}^{-1}\)[45]. Iron was detected only in the samples treated with ferric chloride, with concentrations reaching up to 1.1 mg\(\cdot\)L\({}^{-1}\). Iron is essential for various physiological processes in both fish and plants, acting as a crucial cofactor in several enzymatic pathways, including chlorophyll biosynthesis, DNA synthesis, and nitrogen reduction [50]. According to [PERSON], et al. [50], supplementation with iron significantly enhanced the growth of _Pangasianodon hypophthalmus_--increasing from 6.32 g (without supplementation) to 7.66 g after 15 days, and from 9.3 g to 12.3 g after 90 days when exposed to 1.5 mg\(\cdot\)L\({}^{-1}\) of iron. Other authors have confirmed that iron supplementation can be beneficial for fish development [55; 56; 57]. However, its effectiveness is closely dependent on the fish species and the concentration of iron applied. Although iron leaching into treated water can be a concern, in this specific case, the presence of iron resulting from the use of an iron-based coagulant is not considered problematic and may, in fact, provide biological benefits.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 4 Proposed HDLNIDS Model  
Deep learning for the detection of malicious traffic enables the detection of various changes in traffic, which in turn enhances the performance and allows only normal traffic to pass into the system.  
Deep learning can assist in the identification of hostile intrusions for the detection of rising attacks. We proposed a model based on deep learning for an intrusion detection system that is illustrated in Figure 1. It comprises two learning phases, which are represented below. We proposed this method to build an IDS utilizing an HDLNIDS-based deep learning technique. Our suggested HDLNIDS is better in terms of computation while employing full-featured datasets and provides enhanced accuracy with a low likelihood of failure.  
With a massive data processing architecture, HDLNIDS learning concentrates on tackling real-world ID challenges. Solving such a challenge is difficult due to a lack of time and space. Big data now has massive and expanding quantities; however, it requires enormous amounts of energy, resources, and a computing device to help with the training process that can deal with significant data correctly.  
Combining the RNN with a CNN-DL model, HDLNIDS reduces the aforementioned issues. Figure 1 depicts the HDLNIDS in further detail. According to the HDLNIDS overview, a CNN comprises two basic components: a feature extractor comes first, followed by a classifier. The feature extractor comprises two layers: a convolution comprising of the twin set of layers and a pooling layer. The obtained output called the feature map is fed into the second component for classification. CNN adopts the local characteristics in this way. The drawback is that it overlooks the time dependency of essential features. As a result, we added recurrent layers following the CNN layers to capture both spatial and temporal data more adequately. These four layers of RNN were added to make it more efficient in terms of accuracy and computation. Using this, we were able to effectively manage the disappearance and inflating gradient issues, which increases the capacity to capture temporal and spatial correlations and efficient learning from varied extent patterns.  
The CNN first processes the input in the HDLNIDS network, and the output is subsequently sent to the recurrent layers, which produce sequences at each timestep, allowing for the modeling of both spatial and temporal characteristics. The resultant sequential vector is then input into a layer of SoftMax for the probability distribution over the categories after passing through a set of two fully linked layers.
###### Abstract  
A solution to the problem of reduction of available photosynthetically active radiation (PAR) due to the cover with conventional opaque photovoltaics (PV) of greenhouses is the use of semitransparent PV. The question is how dense the semitransparent PV should be and how dense the coverage should be in order not to burden plant growth. The present paper assesses the effect of the use of semitransparent organic photovoltaics (OPV) on the greenhouse roof cover on the available PAR inside the greenhouse. The method used is to simulate the transmission of radiation through the cover and into the greenhouse with computational fluid dynamics (CFD) using the discrete ordinates (DO) model. Three combinations of OPV/cover that give a normal (perpendicular) transmittance to PAR of 30%, 45%, and 60%, defining the required PV covering, were examined. Then the radiation transmission during eight indicative solar days was simulated. The results are given in terms of available PAR radiation inside the greenhouse and of crop photosynthesis rate, comparing them with the results of a polyethylene cover without OPVs and external conditions. The reduction observed to the mean daily PAR radiation integral for the cases with normal PAR transmittance of 30%, 45%, and 60% in relation to the bare polyethylene (PE) was 77%, 66%, and 52%, respectively while the respective simulated reduction to the daily average photosynthesis rate was 33%, 21%, and 12%, respectively. Finally, the yearly power production from the OPV per greenhouse length meter for the cases with normal PAR transmittance of 30%, 45%, and 60% was 323, 242, and 158 kWh m\({}^{-1}\) y\({}^{-1}\), respectively. The results of this work could be further used for the optimization of greenhouse design for maximizing the PAR at the crop level.  
photosynthetically active radiation; computational fluid dynamics; film transparency; covering material; solar day; photosynthesis rate +
Footnote †: journal: Applied Physics Letters
## 3 Theoretical Models for Calculating Breakdown Pressure
### Theoretical Model Based on Elasticity Mechanics
[34] demonstrated that, prior to fracturing, SC-CO\({}_{2}\) infiltrates a significantly larger area of shale compared to water, which fails to penetrate to the characteristic distance defined by [PERSON] [8]. The point-stress model proposed by [PERSON] [8] provides a framework for explaining breakdown pressure during SC-CO\({}_{2}\) fracturing, as expressed in Equations (5) and (6). This model enhances previous approaches by incorporating the fluid pressure distribution outside the wellbore, allowing for a more accurate calculation of the circumferential stress component \(\sigma_{b}^{3}\). Additionally, it considers the effects of wellbore diameter and pressurization rate on breakdown pressure. The model posits that the location where the maximum effective stress surpasses the tensile strength of the rock is not at the wellbore wall, but rather at a point within the rock. The distance from this point to the wellbore wall is defined as the characteristic distance \(d_{c}\), given by \(d_{c}=K_{IC}^{2}/\left(2\pi\sigma_{t}^{2}\right)\). Here, \(K_{IC}\) represents the mode I fracture toughness, MPa\(\cdot\)m\({}^{0.5}\). Thus, the total circumferential stress (\(S_{\theta}\)) and pore pressure distribution can be expressed as follows:  
\[S_{\theta}=\left(\sigma_{\theta}^{1}+\sigma_{\theta}^{2}+\sigma_{\theta}^{3} \right)_{r=r_{w}+d_{c}} \tag{5}\]  
\[p(r,t)=C{\int_{0}^{t}}f(r,t)dt+P_{0} \tag{6}\]  
where \(C\) denotes the wellbore pressurization rate, and \(f(r,t)\) is defined as [8]:  
\[f(r,t)=1+\frac{2}{\pi}{\int_{0}^{\infty}}exp\big{(}-\kappa u^{2}t\big{)} \Bigg{[}\frac{J_{0}(ur)Y_{0}(ua)-Y_{0}(ur)J_{0}(ua)}{J_{0}^{2}(ua)+Y_{0}^{2}(ua )}\Bigg{]}\frac{du}{u}\]  
In this context, \(\kappa=k_{r}/\mu\phi\beta\), where \(k_{r}\) represents the rock permeability, \(\mu\) is the fluid viscosity, \(\phi\) is the porosity of the rock, and \(\beta\) is the fluid compressibility. Additionally, \(J_{0}\) and \(Y_{0}\) are the zero-order Bessel functions of the first and second kind, respectively. For both infinite and zero pressurization rates, [PERSON]'s model provides the upper and lower bounds for breakdown pressure, as shown in Equations (7) and (8) [8]. \[P_{b}=\left(1+\frac{d_{c}}{r_{w}}\right)^{2}\big{(}\sigma_{t}-\sigma_{\theta}^ {1}-P_{0}\big{)}+P_{0} \tag{7}\]  
\[P_{b}=\frac{\sigma_{t}-\sigma_{\theta}^{1}-P_{0}}{\frac{1}{2}\bigg{\{}1+\left( 1+\frac{d_{c}}{r_{w}}\right)^{2}\bigg{\}}\left(2-\frac{\alpha(1-2\
u)}{1-\
u} \right)}+P_{0} \tag{8}\]
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 2 Spatiotemporal Fusion Approaches  
In the next few sections, we will briefly summarize the STARFM, FSDAF, and HCM algorithms. Both Planet and WV images are co-registered and resampled to the same image size and extent according to the requirement of these fusion algorithms.
## 3 Results and Discussions
### Enhanced Fuel Conversion by Catalytic Oxygen Carrier
For the oxygen carrier of LaCu, the CO\({}_{2}\) concentration was observed during the early reaction indicating full oxidation. After that, H\({}_{2}\) concentration still maintained at a low level similar to the reference condition of thermal pyrolysis without catalyst. The toluene conversion was very low, with the tar blocking the pipeline and the experiment was interrupted. It indicates that the reduced LaCu has no catalytic effect on toluene conversion. Figure 3: Gas concentration during thermal toluene pyrolysis at typical temperature without bed material. Figure 5 shows the C\({}_{1}\) yield as a function of time using different oxygen carriers. C\({}_{1}\) yield exhibited a first increasing and then decreasing trend with time. According to C\({}_{1}\) yield peak, the reactivity decreased in the order of LaCo > LaFe > LaNi > LaCu > LaMn. However, Ni exhibited the best catalytic effect on hydrocarbons conversion, resulting in the highest H\({}_{2}\) yield. Instead, after the consumption of lattice oxygen, the LaMn and LaCo exhibited lower catalysis reactivity for toluene conversion with H\({}_{2}\) yield lower than 20%. The LaFe oxygen carrier exhibited a strong oxygen capacity, supporting partial oxidation. The reduced LaFe would also have a catalytic effect on toluene conversion, and H\({}_{2}\) yield  
Figure 4: Gas concentration versus reaction time at 900 \({}^{\circ}\)C with typical oxygen carrier: (**a**) LaNi, (**b**) LaFe, (**c**) LaCo, (**d**) LaMn and (**e**) LaCu. maintained at about 40%. The H2 yield decreased in the order of LaNi\(>\) LaFe > LaCo \(\approx\) LaMn > LaCu. Overall, under a different mechanism, LaFe and LaNi caused the most yield of H2. However, H2 yield was still far below 100%, and serious carbon deposition occurred, affecting the catalytic effect. Therefore, improving the activity of oxygen carriers is very important for the conversion of toluene into C1 and H2.
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
## 3 Overview of Non-Terrestrial Networks
### NTN Scenario  
The user control connection connects the satellites directly to the on-ground handheld user equipment (UE) via the new radio (NR) air interface [4]. The top architecture denotes a solution with a transparent payload, whereas the bottom denotes a solution with a regenerative payload. The gNB is located conceptually at the gateway (GW) in the first scenario, but it is realized on board with regenerative payloads. In both scenarios, the user access link between the satellite and the on-ground UE is implemented using the classic NR-Uu air interface [30]. The position of the gNB determines the solution for the feeder link between the satellite and the GW. A transparent payload serves as a radio-frequency repeater, necessitating the employment of an NR-Uu solution for the feeder connection. In contrast, the gNB is located on the satellite, and the feeder link is implemented via the air interface between a gNB and the next-generation core (NGC) network. This significantly reduces propagation delays for NR PHY and MAC operations, easing the changes required for NTN [31].
# Ion Beam Nanopatterning of Biomaterial Surfaces
## 2 Nanoscale Pattern Formation on Solid Surfaces during Ion Beam Irradiation
### Pattern Dimensions  
The different nanopatterns fabricated by ion irradiation are typically characterized by two quantities: the lateral periodicity of the pattern \(\lambda\) and the vertical peak-to-peak amplitude \(\mathrm{A_{p2p}}\). When an initial pattern is forming in the beginning of irradiation, i.e., in the so-called linear regime, \(\mathrm{A_{p2p}}\) is growing exponentially, while \(\lambda\) remains constant. Only at later stages corresponding to high ion fluences, nonlinear effects become important and result in a saturation of \(\mathrm{A_{p2p}}\)[84]. In this regime, the pattern periodicity \(\lambda\) may start to increase with fluence, typically following a power-law dependence, until it saturates as well at very high fluences. This behavior is called interrupted coarsening [84]. The resulting stationary patterns typically have aspect ratios \(\mathrm{A_{p2p}}/\lambda\) between 0.1 and 1.  
For a given surface, the overall pattern periodicity may depend on a number of experimental parameters, including substrate temperature [87], angle of incidence [88], and most importantly, ion energy. The latter is demonstrated in Figure 2, which displays the dependence of the ripple periodicity \(\lambda\) on the ion energy for the irradiation of Si surfaces with \(2\times 10^{17}\) cm\({}^{-2}\) Xe\({}^{+}\) at room temperature and \(65^{\circ}\) incidence with respect to the surface normal. As can be seen, \(\lambda\) increases linearly with energy from about 60 nm at 5 keV to about 530 nm at 180 keV. This periodicity range covers the dimensions of many cellular and extracellular features such as filopodia tips and collagen fibrils, respectively [89]. It is this possibility of adjusting the dimensions of the generated nanopatterns to match that of certain biological components in such a straightforward manner that makes ion beam nanopatterning very attractive for biomaterials applications.
While much research has focused on mining-related factors such as the stress state, stress path, and loading rate, and their influence on spalling failure and surrounding rock characteristics, the geological environment of deep tunnels has received less attention. During deep mine tunnel excavation, encountering fault zones makes it difficult to detect their width and depth because of complex geological conditions. To minimize resource loss, engineers often adjust the tunnel's excavation direction or fill the fault zone to maximize recovery. The fault zone is located along one side of the tunnel, with its length along the tunnel unknown (Figure 2). Ensuring the safety and stability of underground engineering necessitates a thorough study of how fissures at different depths impact the stability of rectangular tunnels. In response to this, this study was conducted to examine the effects of radial fissures with 0%, 25%, 50%, and 100% penetration on the failure process and features of a rectangular tunnel. The experiments used a true triaxial servo testing system, simulating a stress state corresponding to a depth of 500 m. A micro-camera and acoustic emission monitoring equipment recorded the entire test in real time, capturing the failure process in the rectangular tunnel and the evolution of surrounding rock cracks due to fissure penetration. The test process and results were analyzed in detail, focusing on how varying fissure penetration levels impacted tunnel failure and stability. Figure 2: Deep mine underground engineering encountering structural plane.