esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 DRFM Systems
### Sources of Distortion  
Nothing in the previous section is unusual from the design of an RF transceiver for a radar or communications system, the difference with a DRFM is the wide bandwidth of the receiver, which significantly exceeds the signal and the requirement to repeat the signal, instead of demodulating it for a radio or performing detection in a radar. This presents a problem, as the signal replicated by the jammer has been distorted at every stage of the reception and retransmission process.  
The factors that could affect its accuracy in recreating the received waveform and that will be studied in this chapter include the thermal noise, quantization noise, the type of DRFM (amplitude or in-phase/quadrature), and the modulation of the waveform itself. While outside the scope of this paper, the RF front-end would also create non-linear effects, particularly while re-transmitting the signal at high power.  
To replicate a modern DRFM, a Field Programmable Gate Array (FPGA) implementation was performed. This FPGA implementation not only simplifies the development, it largely mirrors the modern jammer architecture, as the cost of the development cost of Application Specific Integrated Circuits (ASIC) has driven EW systems to increasingly capable FPGAs [6]. The second benefit is it allowed the system not to be modeled but to actually be implemented on the device, and to do so in numerous iterations quickly. The flexible nature of a software defined EW system on an FPGA, effectively means the factors affecting DRFM performance were tested against actual jammers, implemented digitally, with only the RF front-end modeled.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### Object Localization and Tracking Algorithm
#### 2.4.1 NRLM Algorithm for Object Localization  
Suppose \(v\) is the wave propagation speed of ultrasonic, \(\mathbf{x}\) is the target object position, which is unknown, \(\mathbf{x}_{t}\) is the position of the ultrasonic transmitter, \(\mathbf{x}_{ri}\) is the position of the ultrasonic receiver where \(i=1\sim m\), and \(m\) is the number of receivers, and \(\mathbf{x}\), \(\mathbf{x}_{t}\) and \(\mathbf{x}_{ri}\in\mathbb{R}^{n\times 1}\) (in general applications, \(n=2\) or \(3\)), and \(\tau_{i}\) is the traveling time, which can be measured from the transmitter to the object and back to the receiver \(i\). The round trip equation can be formulated as follows:  
\[h_{ti}(\mathbf{x})=\frac{1}{v}(|\mathbf{x}-\mathbf{x}_{ri}|+|\mathbf{x}- \mathbf{x}_{t}|) \tag{29}\]  
which presents the traveling time from the transmitter to the object and back to the receiver \(i\). Therefore, the problem can be constructed as the minimum summation of the squared errors between measured traveling times with the corresponding receivers such that:  
\[\operatorname*{argmin}_{\mathbf{x}}\sum_{i=1}^{m}(\tau_{i}-h_{ti}(\mathbf{x}) )^{2} \tag{30}\]  
From Section 2.3.2, a similar procedure can be applied to solve this problem by a recursive formula to estimate the object position \(\mathbf{x}\).
## 3 Results
### Effect of MSS and DF Supplementation on the Abundance of Selected Microbes  
MSS and DF addition, alone or in combination, significantly increased the population density of _F. succinogenes_ and _S. ruminantium_ compared to control cultures with different substrates (\(p<0.05\)) but decreased the population density of methanogenic archaea (\(p<0.05\)) (Table 3). The lowest density of methanogens was found in cultures with 14 mmol/L added DF in F as the substrate, but when V was the substrate, the lowest density of methanogens was found in cultures added with the combination of 7 mmol/L DF and 80 mg/kg MSS. The population density of total bacteria, methanogenic archaea, ciliate protozoa and _S. ruminantium_ with F as the substrate were lower than V, but the number of fibolytic bacteria (e.g., _R. flavefaciens_, _F. succinogenes_ and _R. albus_) with F as the substrate were higher than V, demonstrating that F can provide more material for fibolytic bacteria growth.  
Figure 4: Redundancy analysis (RDA) of ruminal fermentation parameters and prokaryotic community compositions at the phylum level. Arrows indicate the direction and magnitude of parameters associated with prokaryotic community structures. A:P, acetate and propionic ratio; TVFAs, total volatile fatty acids.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 3 System Model  
Figure 2 depicts a MEC server assisted platooning in our paper. The platoon controller controls and manages the whole platoon. When the platoon members communicate with the BS, they need to transmit the messages to the BS through the platoon controller, and the members in the platoon can directly communicate with each other [25; 26]. Position of the controller in the platoon is not clearly defined in the current research. Most of the articles directly select the leader as platoon controller for the sake of simplification. In this paper, we do not discuss in detail how to choose the controller, which can be selected according to the signal intensity, platoon length and channel multiplexing.  
We next introduce the system model of the optimal offloading decision for collaborative task execution, including task model, path loss model and task execution model.
## 3 Results and Discussion
### SEM Analysis of TOCgel/ZnO Composites' Characterization
In comparison with the heterogeneous nucleation observed in the presence of cellulosic fibers, SEM images demonstrated the formation of rod-like ZnO microstructures which could be obtained through the homogeneous nucleation directly in the solution (Figure 3C). According to the literature [53], the adsorption of HMT chains onto the non-polar facets of zincite crystal (_i.e._, 100, 110 and 010) promotes the epitaxial growth of ZnO along the \(c\)-axis and leads to the formation of rod-like ZnO microstructures as shown in Figure 3C. In addition, an alignment phenomenon of flower-like ZnO superstructures has been observed in the TOCgel/ZnO composite film. This is further supported by high magnification SEM, which demonstrated the alignment state of ZnO superstructures along the main cellulosic fibers axis (Figure 4). In fact, the physical interaction (_i.e._, hydrogen bonding, electrostatic and van der Waals forces) between polar moieties (carboxylate and hydroxyl) of TOCgel on the one hand, and polar species of ZnO crystal (basal plane 001) and Zn\({}^{2+}\) ions on the other hand could explain the incorporation state of ZnO superstructures along the cellulose fiber axis. As shown in Figure 4, the alignment of flower-like ZnO superstructures towards the composite film is well-characterized by a 5 \(\upmu\)m width, which is similar to the diameter of a single flower-like ZnO structure observed in this composite. This indicates that the former TOCgel/ZnO (flower-like) possess specific anisotropic microstructures. Figure 3: SEM images of samples films synthetized with two types of cellulose fibers: (**A**) native cellulose fibers; and (**B**) oxidized cellulose (TOCgel); and without cellulose (**C**). In order to explain the growth mechanism involved in the formation of these superstructures, a physical and chemical approach will be discussed in detail later in this section.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 4 Network Flow Programming Models
### Special Classes of NFPs
#### 4.2.4 Shortest Path Problems  
The length of a path in a network is the sum of the arc costs along the path from a source node to a destination node. The problem is to find the shortest path from some specified node, to some other node or perhaps to all other nodes. The latter problem is called the _shortest path tree_ problem because the collection of all shortest paths from a specified node forms a graph structure called a _tree_. Since it is not much more difficult to find the paths to all nodes than it is to find the path to one node, the shortest path tree problem is usually solved. The NFP equivalent of the shortest path tree problem is formed by equating arc distance to arc cost, assigning a fixed external flow of \(m-1\) (\(m\) is the number of nodes) to the source node, and assigning fixed external flows of \(-1\) to every other node. The _[PERSON]'s algorithm_ is a classic algorithm that can be used to solve this problem to optimality in a worst cast time complexity \(O\left(n^{2}\right)\), where \(n\) is the number of nodes. There are many references to understand how the algorithm operates, amongst those is [24].  
**Example for a Possible Application to WSN**: In WSNs, a shortest path problem can be used to solve a routing problem were the arc costs could be the energy expended on a link and an objective of minimizing the overall energy in an entire path from a source node to each and every node in the network. Assuming constant pre-set transmission power for each sensor node, the energy that has to be expended over a link can be approximated (in a simple scenario) to be linearly proportional to the attenuation on the link. The problem would then be a routing problem, in which the set of arcs that comprise a complete path from the source node to each node has to be found. Each non-source sensor node would be receiving a separate stream of packets that are unique to each node (unicasting). The path between each pair of nodes that minimizes the transmission energy would be obtained by the [PERSON]'s algorithm.
# Promising Approaches for Heat Utilization in Agricultural Machinery Engines
## 1 Introduction  
Modern agricultural machinery is equipped with thermal piston engines of internal combustion, which use engines with relatively low efficiency: diesel engines reach up to 42...44%; gasoline engines reach up to 27%. Thus, exhaust gases, the heat of the cooling system, lubrication, and air cooling result in energy equivalent to approximately 875,000 tons of diesel fuel and 292,000 tons of gasoline being lost annually [1].  
At least 50% of the heat of fuel combustion is lost to exhaust gases and coolant. Therefore, there are significant reserves for improving the efficiency of diesel power plants. Work to reduce these heat losses has been carried out in two main directions: the creation of engines with a higher values of efficiency and the development of heat recovery systems for existing engines. The latter direction does not require the radical modernization of the engine [2].  
A number of leading machine-building companies (Scania, Ford, BMW, Cummins, Navistar Advanced Technology, etc.) and scientific centers of universities are dealing withissues of heat utilization in mobile energy vehicles. For instance, specialists at the Navistar Advanced Technology company consider the use of heat recovery systems to be one of the main components of achieving their ambitious goal of 50% diesel efficiency [3].  
The diesel engines of agricultural machinery have their own characteristic features during operation that significantly distinguish them from other vehicles. For instance, the engines of tractors and combines are used at load factors of 60...90%, while most trucks and passenger cars operate at 17%. When performing technological operations, the engine of an agricultural power tool works at a constant frequency of the rotation of the crankshaft.  
Today, disposal systems are developed mainly for trucks equipped with powerful engines. Thus, at the present time, there are no proven circuit solutions and equipment that could be used in agricultural machinery for efficient heat utilization. The theoretical aspects of the specified direction of increasing efficiency have been insufficiently worked out. In addition, there is no classification of recycling systems, which complicates the general idea and analysis of this direction of increasing efficiency [4].  
The purpose of this work is to analyze and classify heat utilization systems and to determine promising areas of application in mobile energy vehicles for agricultural purposes.
## 3 Analysis and Design of the Force Control System
### Stability Analysis
#### 3.2.2 Stability in Terms of \(K_{p}\)  
For the computation of the gain margin in terms of \(K_{p}\), the stiffness \(K\) is considered constant and the frequency response \(G_{c}(j\omega)\) is divided in real and imaginary parts, \(G_{c}(j\omega)=K_{p}+\left(\frac{K_{d}\omega^{2}-K_{i}}{\omega}\right)j\). Adding the multiplicative gain \(K_{GM}\) to \(G_{c}(j\omega)\), the magnitude \(|G_{c}(j\omega)|_{K_{GM}}\) is defined as  
\[|G_{c}(j\omega)|_{K_{GM}} = K_{GM}|G_{c}(j\omega)|=\bigg{|}K_{GM}K_{p}+\bigg{(}\frac{K_{GM}K _{d}\omega^{2}-K_{GM}K_{i}}{\omega}\bigg{)}j\bigg{|},\] \[= K_{GM}\bigg{|}K_{p}+\bigg{(}\frac{K_{d}\omega^{2}-K_{i}}{\omega }\bigg{)}j\bigg{|}.\]  
Note that the gain \(K_{GM}\) is directly proportional to the control magnitude \(|G_{c}(j\omega)|_{K_{GM}}\) or directly proportional to each control gain \(K_{p}\),\(K_{d}\), and \(K_{i}\).  
Using \(|G_{c}(j\omega)|_{K_{GM}}\), the transfer function to test and compute the gain margin is  
\[|L(j\omega)|_{test}=K_{GM}|G_{c}(j\omega)|K|G(j\omega)||G_{f}(j\omega)||G_{d} (j\omega)|,\]  
where \(K_{GM}\) represents an increment/change in the magnitude \(|G_{c}(j\omega)|\). Since the stability condition is \(|L(j\omega)|_{test}<1\), the gain margin is the gain \(K_{GM}=K_{GM_{max}}\) that produces \(|L(j\omega)|_{test}=1\). Therefore, the gain margin computation gives an estimate of how much one can modify the control gain/magnitude without producing instability.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results
### Individual Sensor off Probability  
As mentioned in Section 3.1, when the SINR received on the collector side is below the SINR threshold \(\gamma_{max}\), no RR will be assigned to this sensor. In other words, this active sensor is forced to  
Figure 10: Total number of required RRs in a typical cell with respect to maximal access delay \(\tau_{max}\) with \(\lambda_{a}=5.5\) nodes/km\({}^{2}\) and \(\lambda_{b}=0.5\) nodes/km\({}^{2}\).  
Figure 9: Comparison between theoretical and empirical \(N_{t}\) values in which \(\lambda_{a}=5.5\) nodes/km\({}^{2}\) and \(\lambda_{b}=0.5\) nodes/km\({}^{2}\).  
be off in this trial. Table 4 illustrates the individual off probability in the typical cell according to the different antenna configurations, including SISO, \(1\times 8\) SIMO with antenna selection and MRC, 2 users and 4 users \(1\times 8\) MU-MIMO. From the table, we can see the \(1\times 8\) SIMO with MRC outperforms other modes in the individual off probability. Although it does not reduce the number of RRs required in the typical cell significantly compared to the SIMO with antenna selection or the MU-MIMO, it does reduce the sensor individual off probability, ensuring more sensors to be served. This result gives us a new way of thinking about resource planning, i.e., we need to take into account both the total number of RRs assigned to the network and the individual off probability to improve network service.
## 3 The Proposed Method
### Secret Data Hiding  
As a data hider \(ID_{i}\), the secret data \(\mathbf{m}_{i}=0,1^{N}\) is first encrypted using the data hiding key \(K_{i}\) to produce the encrypted data \(\mathbf{m}^{\prime}_{i}=0,1^{N}\). Subsequently, the partial edge information can be extracted from the first row and column of the encrypted image, containing parameters such as \(WL\), \(B\), \(\mathbf{ARP}\), HCR with the partial \(\mathbf{WS}\). By converting pixel values to 8-bit binary according to Equation (7), based on the partial \(\mathbf{WS}\), and referencing the HCR, the labeled values of the partial pixels can be determined, indicating the embedding positions of these pixels, specifically the high \((w+1)\) bits of the pixel values. By iteratively extracting and processing the edge information, all embedded edge information can be obtained.  
Then we divide the image \(\mathbf{IE}_{k}\) into part A, part B\({}_{k}\), and part C, and replace the pixel values of part C with \(\mathbf{m}^{\prime}_{i}\) to complete the embedding operation to obtain the embedded image \(\mathbf{IA}_{k}\), and then we perform the double encryption of scrambling encryption and XOR encryption to further improve the security of the embedded image. We first divide the image \(\mathbf{IA}_{k}\) into pixel blocks of size \(B\times B\), use the key \(K_{i}\) to generate a random number sequence, and then sort the random number sequence to obtain the index vector of the pixel blocks. Finally, the image blocks are scrambled according to the index vector to obtain the image \(\mathbf{IE}^{\prime}_{k}\). Then, we perform XOR encryption on the image: generate an \(M\times N\) randommatrix \(P_{k}\) according to the key \(K_{i}\), and encrypt the image \(\mathbf{IE}_{k}^{\prime}\) according to the following equation, finally obtaining marked encrypted images \(\mathbf{IM}_{k}\):  
\[\mathbf{IM}_{k}(i,j)=\mathbf{P}_{k}(i,j)\oplus\mathbf{IE}_{k}^{\prime}(i,j) \tag{12}\]  
The operation denoted by \(\oplus\) corresponds to modulo 256 addition, with \(i,j\) representing the position index where \(1<i\leq M\), \(1<j\leq N\).
# An Experimental Study on the Large-Volume Liquid Hydrogen Release in an Open Space
[18] performed an experiment to release 1.4 tons of liquid methane and obtained the visible clouds during the initial stage, middle stage, and final stage. Based on the experiment, they numerically studied the leakage and diffusion characteristics of liquid methane. Another popular method to study the evolution of the hydrogen cloud in the atmosphere is numerical simulation [19; 20; 21], which can rapidly obtain the diffusion characteristics under different environment conditions [22; 23], different space types [24; 25; 26; 27], different release states [28; 29; 30; 31], and so on. In terms of theoretical study, [PERSON] et al. [32] analyzed the diffusion characteristics of the hydrogen cloud during a liquid hydrogen leakage accident based on the Gaussian diffusion theory. The results show that the wind velocity helps the diffusion of the hydrogen cloud, and the dilution of the hydrogen cloud can be enhanced under higher wind velocities. [PERSON] et al. [33] employed the jet integral model to calculate the diffusion of the hydrogen cloud that arises from the release of liquid hydrogen under a crossflow field, and the buoyant effect was concluded. They found that increasing the crossflow field velocity can inhibit the vertical diffusion of the hydrogen cloud, thus increasing the flammable region owing to a lower central trajectory of the flammable cloud. Although numerical simulations and theoretical derivations are efficient means to study the leakage and diffusion characteristics of liquid hydrogen, there are often many assumptions in the models, which make it impossible to reflect the real situation. In addition, the use of liquid nitrogen as a substitute for liquid hydrogen in existing experiments can reduce the safety hazard of the experiments, but the huge difference in physical properties makes it difficult to generalize the experimental results to the leakage-diffusion of liquid hydrogen. Only a few release experiments used liquid hydrogen in large spaces to observe the diffusion characteristics directly. In order to reveal the diffusion mechanism of liquid hydrogen in a large open space and to complement the relative experiments, a series of liquid hydrogen release experiments are carried out out outdoors, with the total release volume ranging from 0.5 m\({}^{3}\) to 3 m\({}^{3}\). Meanwhile, arrays of hydrogen concentration sensors and temperature sensors are set up to monitor and investigate the diffusion of the cryogenic hydrogen clouds formed by the evaporation of liquid hydrogen in an open space, which contributes to the development of safety guidelines, risk assessment protocols, and emergency response strategies.
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
###### Abstract  
Coordination of a hydropower, combined heat and power (CHP), and battery energy storage system (BESS) with multiple renewable energy sources (RES) can effectively reduce the adverse effects of large-scale renewable energy integration in power systems. This paper proposes a concept of a renewable-based hybrid energy system and puts forward an optimal scheduling model of this system, taking into account the cost of operation and risk. An optimization method is proposed based on Latin hypercube sampling, scene reduction, and piecewise linearization. Firstly, a large number of samples were generated with the Latin hypercube sampling method according to the uncertainties, including the renewable resources availability, the load demand, and the risk aversion coefficients, and the generated samples were reduced with a scene reduction method. Secondly, the piecewise linearization method was applied to convert nonlinear constraints into linear to obtain the best results of each scene. Finally, the performance of the proposed model and method was evaluated based on case studies with real-life data. Results showed that the renewable-based hybrid system can not only reduce the intermittent and volatility of renewable resources but also ensure the smooth of tie-line power as much as possible. The proposed model and method are universal, feasible, and effective.  
hydro-PV-wind; hybrid energy system; uncertainty; Latin hypercube sampling and scene reduction; risk aversion coefficients +
Footnote †: journal: Applied Sciences
## 4 Discussion  
### Spatial-Temporal Determinations of NAIs  
Previously, based on site measurements, the environmental factors (such as RH, T) and air quality (such as PM\({}_{2.5}\), CO, NO\({}_{2}\), etc.) were mostly related to NAI dynamics [15; 16]. However, the conclusion had never been validated spatially across different sites. In this study, with a large amount of site-based NAI measurements, spatial variation in NAI was found mostly related to topographic factors, such as DEM, slope, and aspect (Figure 6). The contribution of environmental factors and air quality followed behind topography (Figure 6). Path analysis indicated that the determining role of topography came from the integrated effects of multiple factors, among which SIF had the greatest direct effects on the NAI dynamics (Figure 7a\({}_{1}\),a\({}_{2}\)).  
The significance of NAIs was also presented by site-based studies where forest canopy SIF accurately captured the dynamic changes in NAI concentrations [10; 16; 24]. These results once again proved the opinion that plant photosynthesis plays a vital role in regulating NAI concentrations [15; 20]. It is noted that the contribution of SIF tended to be varied among different land-use types: forest land tended to have larger SIF contributions than croplands, while urban regions had quite weak SIF contributions (Figure 7b\({}_{1}\),b\({}_{2}\)-d\({}_{1}\),d\({}_{2}\)). In other words, the higher the vegetation coverage, the more important the SIF. These results imply that ignoring SIF in NAI inversion would lead to dramatic uncertainties, especially in regions with high vegetation coverage [15; 17; 20].  
Even though plant photosynthesis was proven to be crucial for NAI inversion, the other indicators such as GPP, NPP, ET, etc., had weak relation to NAI variations (Figure 7). These factors were reported to be highly related to NAI variations from the site-based studies [15; 20]. The decoupling between SIF and plant productivity should be responsible for their weak correlation. Many studies have argued that plants' photoelectric effect in the photosynthesis process was actually the direct source of NAI formation [28; 29; 30]. Meanwhile, as a direct product of the plant photoelectric process, the SIF was not always closely related to GPP, NPP, etc., and sometimes, negative relations could also be observed in the GPP-SIF relations [31]. Thus, inaccurate predictions of NAI would be expected when GPP, NPP, ET, etc., were incorporated into the model. Evidence could be found in the weak correlation between GPP, NPP, and ET with the NAI in the summer of 2020 (\(p>0.05\)), when heat waves frequently occurred in Zhejiang Province [32].