esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 2 Model  
### Micro-Energy-Grid Model  
In the greenhouse micro-energy-grid, electricity comes from CHP by burning biogas and PV power generation devices set up on the roof of the greenhouse. Besides, it is connected with the power distribution network: when the power is insufficient, it can purchase electricity directly from the upper level. Thermal energy in the greenhouse micro-energy-grid also comes from CHP by burning methane and air source heat pumps (ASHPs). Thus, the electricity and heat in the micro-energy-grid are coupled. Batteries and phase change thermal storage (PCTS) as energy storage components can store surplus electricity and heat, and affect time-shifting. The greenhouse also has biogas digester, which uses heat pump in the digester to consume electricity and promote microbial fermentation producing biogas. The basic on-line diagram of the greenhouse micro grid is shown in Figure 1.  
CHP, ASHPs and transformer are the main components of the agricultural greenhouse micro-energy-grid. CHP includes microturbine, biogas digester and absorption heat pump (AHP). All parts work together to realize conversion and the process can be seen in Table 1. Micro-energy-grid's energy is supplied from biogas, PV and upper level power grid. Under the condition that energy hub (EH) meets the electricity and heat demands of the greenhouse, the surplus PV power generation can be processed on the grid.
## 2 Data and Methods
### ERA5 Reanalysis Data  
ERA5 is the fifth generation of atmospheric reanalysis to be produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 replaces the outgoing ERA-Interim reanalysis, which was stopped at the end of August 2019. ERA5 now provides an enhanced number of output parameters, hourly temporal resolution, a horizontal resolution of 31 km, a vertical resolution of 137 levels up to 1 Pa, and the benefit of over 10 years of model and data assimilation (DA) developments relative to ERA-Interim [32]. Note that COSMIC-2 RO soundings had not yet been operationally assimilated into ERA5 during the study period (September-December 2019).
## 3 Results and Discussion
### Effect of Compressor Speed and Ambient Temperature on Optimal Gas Cooler Air Volume Flow Rate  
#### 3.3.1 Influence of Compressor Speed on Optimal Gas Cooler Air Volume Flow Rate  
At the ambient temperature of 35 \({}^{\circ}\)C, the influence of the compressor speed on the optimal air flow rate was studied in detail (Figure 9). The air flow rate and air temperature at the evaporator inlet were 340 m\({}^{3}\).h\({}^{-1}\) and 27 \({}^{\circ}\)C, respectively. As the compressor speed was 6500 rpm, the optimal discharge pressures were not found due to the safety limit regarding the discharge temperature (i.e., 135 \({}^{\circ}\)C). Therefore, all data were recorded at 135 \({}^{\circ}\)C, at which the highest COP values were observed. As the compressor speed was 5000 rpm, the optimal discharge was not found under the air flow rate of 500 m\({}^{3}\).h\({}^{-1}\) and 1000 m\({}^{3}\).h\({}^{-1}\). Therefore, the data were recorded at the discharge temperature of 135 \({}^{\circ}\)C. All other data were recorded under the optimal discharge pressure.  
Figure 9 shows how the cooling capacity and the maximum/highest COP are affected by the compressor speed. As the speed was 3500 rpm, the cooling capacity increased at first and then decreased with the rise in air flow rate. As the compressor speed was 5000 rpm, the cooling capacity first increased and then there were no marked variations. As the compressor speed was 6500 rpm, the cooling capacity basically showed a gradually increasing trend as the air flow rate increased. The CO\({}_{2}\) gas cooler outlet temperature decreased, leading to an increase in cooling capacity. By contrast, the optimal discharge pressure gradually decreased, which had a negative effect on the increase in cooling capacity. As the air flow rate was smaller than 1500 m\({}^{3}\).h\({}^{-1}\), the role of the CO\({}_{2}\) gas cooler outlet temperature was in a dominant position, so the cooling capacity increased first. As the air volume exceeded 1500 m\({}^{3}\).h\({}^{-1}\), the role of the optimal discharge pressure was dominant, leading to the decrease in the cooling capacity.  
Based on the above data analysis, it could be concluded that the optimal air flow rate would increase slightly as the compressor speed increases. However, the increase in air volume flow rate slowed down as the speed of the compressor increases.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 2 Positioning Foundation of the Roadheader and Modeling
### Definition of Coordinate System and Pose
Specifically, point \(P_{\rm w}(x_{\rm w},\,y_{\rm w},\,z_{\rm w})\) in the O\({}_{\rm w}\) system is rotated and transformed to point \(P_{\rm c}(x_{\rm c},\,y_{\rm c},\,z_{\rm c})\) in the O\({}_{\rm c}\) system, which can be described as: (1) rotating at angle \(\alpha\) along the Z\({}_{\rm w}\) axis, as shown in Equation (1); (2) rotating at angle \(\beta\) along the X\({}_{\rm w}\) axis, as shown in Equation (2); and (3) rotating at angle \(\gamma\) along the Y\({}_{\rm w}\) axis, as shown in Equation (3). \(R\) has a definite transformation relationship with the \(\alpha\), \(\beta\), and \(\gamma\), as shown in Equation (4) [22]. \[\left[ \begin{array}{c}x_{\rm c}\\ y_{\rm c}\\ z_{\rm c}\end{array} \right]=\left[ \begin{array}{ccc}\cos\alpha&-\sin\alpha&0 \\ \sin\alpha&\cos\alpha&0\\ 0&0&1\end{array} \right]\left[ \begin{array}{c}x_{\rm w}\\ y_{\rm w}\\ z_{\rm w}\end{array} \right] \tag{1}\]  
\[\left[ \begin{array}{c}x_{\rm c}\\ y_{\rm c}\\ z_{\rm c}\end{array} \right]=\left[ \begin{array}{ccc}1&0&0\\ 0&\cos\beta&\sin\beta\\ 0&-\sin\beta&\cos\beta\end{array} \right]\left[ \begin{array}{c}x_{\rm w}\\ y_{\rm w}\\ z_{\rm w}\end{array} \right] \tag{2}\]  
\[\left[ \begin{array}{c}x_{\rm c}\\ y_{\rm c}\\ z_{\rm c}\end{array} \right]=\left[ \begin{array}{ccc}\cos\gamma&0&-\sin \gamma\\ 0&1&0\\ \sin\gamma&0&\cos\gamma\end{array} \right]\left[ \begin{array}{c}x_{\rm w}\\ y_{\rm w}\\ z_{\rm w}\end{array} \right] \tag{3}\]  
\[ \begin{array}{c}R_{\rm cw}=\rm{Rot}(Z_{\rm w},\,\alpha)\cdot\rm{Rot}(X_{\rm w },\,\beta)\cdot\rm{Rot}(Y_{\rm w},\,\gamma)\\ =\left[\begin{array}{ccc}\cos\alpha\cos\gamma-\sin\alpha\sin\beta\sin\gamma&- \sin\alpha\cos\beta&\cos\alpha\sin\gamma+\sin\alpha\sin\beta\cos\gamma\\ \sin\alpha\cos\gamma+\cos\alpha\sin\beta\sin\gamma&\cos\alpha\cos\beta&\sin \alpha\sin\gamma-\cos\alpha\sin\beta\cos\gamma\\ -\cos\beta\sin\gamma&\sin\beta&\cos\beta\cos\gamma\end{array}\right]\end{array}  \tag{4}\]  
The translation of the body (camera) is described by 3D vector \(t\), and \(t_{\rm cw}\) represents the translation transformation from the O\({}_{\rm w}\) system to the O\({}_{\rm c}\) system, as shown in Equation (5). After homogeneous transformation, \(R_{\rm cw}\) and \(t_{\rm cw}\) can be unified into transformation matrix \(T_{\rm cw}\), as shown in Equation (6) [22]. In the absence of ambiguity, \(R_{\rm cw}\), \(t_{\rm cw}\), and T\({}_{\rm cw}\) below are referred to as \(R\), \(t\), and \(T\) without subscripts. \[ \begin{array}{ccc}t_{\rm cw}=\left[\begin{array}{ccc}t_{\rm x}&t_{\rm y}&t _{\rm z}\end{array}\right]^{\rm T}\end{array}  \tag{5}\]  
\[ \begin{array}{ccc}T_{\rm cw}=\left[\begin{array}{ccc}R_{\rm cw}&t_{\rm cw }\\ 0^{\rm T}&1\end{array}\right]\end{array}  \tag{6}\]
## 3 Results
### Torrefaction Performance and Severity Index
Similar mass yield was observed in the calculated energy yields, which decreased with torrefaction temperature and time (Figure 3). For torrefaction time 10 min, the calculated energy yields were 87.96-34.57%, 74.19-50.62%, 88.71-60.25%, 85.34-53.48%, and 89.23-47.66%, respectively, for each biomass, for torrefaction time 30 min, the calculated energy yields were 83.1-34.28%, 70.59-49.89%, 85.56-60.75%, 80.83-51.09%, and 85.15-45.63%, and for torrefaction time 60 min those values were varying between 80.79-34.49%, 68.70-53.10%, 83.60-62.13%, 80.38-50.97%, and 82.32-47.88%, respectively. Again, the most significant decrease was seen during the torrefaction of miscanthus and hops biomass samples, whereas during the torrefaction of other biomass samples minor decreases were observed. The decrease in energy yield was smaller than the decrease in mass yield, because the increased torrefaction temperature helped to improve the energy density of torrefied biomass samples. In this work, TSI showed the degree of weight loss of the biomass at the different torrefaction conditions. The profiles of TSI and torrefaction temperature are shown in Figure 4 for specific torrefaction times. TSI was determined at the highest torrefaction temperature studied in this work (350 \({}^{\circ}\)C) and the longest torrefaction time (60 min). Based on the literature and as already mentioned, these values were in the range of 0-1 [19], where value 0 presented that the torrefaction process had not yet begun, and value 1 meant that the torrefaction process reached the highest torrefaction temperature and time. The results of this work are in agreement with other works [40; 47]. In addition, the same was stated for the correlation between TSI and energy yield, which is presented in Figure 5.  
Figure 4: TSI with respect to torrefaction temperature and time for all five materials. Additionally, the energy-mass co-benefit index (EMCI) was calculated. The results for each biomass sample are presented in Table 4. EMCI is an index that could describe the economic aspect of the torrefraction process, and its definition says that it represents the difference between the energy yield and the mass yield at the lowest possible torrefraction severity. The higher the EMCI, the higher the energy density, and the lower the volume of the torrefied biomass [62]. Our results show that the highest EMCI was calculated for SS and the mixture of SS and miscanthus, 14.46 and 11.57 at 300 \({}^{\circ}\)C, respectively. Under other torrefraction conditions, the calculated EMCI indexes were slightly lower. From the obtained results, sewage sludge stands out the most. It is believed this is due to the lack of lignocellulosic components in the sample.
# Interplanetary Magnetic Field Bx Effect on Field-Aligned Currents in Different Local Times
## 4 Discussion
These differences primarily stem from distinct physical processes: the effect of IMF By can be attributed to interhemispheric FACs, whereas IMF Bx influences FACs through its regulation of antiparallel reconnection efficiency in the magnetotail lobes.
## 4 Robotic Arm Position Detection  
To establish the best possible model for predicting hand positioning over a keyboard, we used a controllable robotic arm. This model allowed us to understand the ceiling on accuracy achievable for real case recognition, since the human hand is always less accurate than a robot arm, which can reliably control the position above a keyboard without any shaking or jittering. This section describes the structure of the robotic arm, along with the Proportional-Integral-Derivative (PID) control system used to regulate its position over the keyboard.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 4 Static Network Performance Evaluation of a TFBN
### Node Degree  
A node of an MPC system is consisting of a processing component, a router, and a memory. Many nodes are interconnected together using an interconnection network topology. The router of a node is used to connect to its neighboring node. The node degree or degree of a node is described as the number of links instigating from a node to connect all of its neighbor nodes. Different nodes of an interconnection network topology will have different degrees of the node. The maximum value of the degree of nodes of an interconnection network is called the node degree. Node degree is very important for the router of a node. A constant node degree is desirable for the good scalability of the massively parallel computer system. If the node degree is changing with the size of the network, then the scalability is difficult and expensive. Vice versa, it can be said that the scalability is not possible for the MPC system.  
On the other hand, the cost of a router is directly proportional to the degree of a node. The lower node degree lowers the router cost. Usually, the number of the core of the processing element and the size of the memory of a node is constant in the chip-level design. Therefore, the degree of a node is directly proportional to the cost of that node. The lower the node, the lower the cost of a node. The degree of a node in different networks is tabulated in Table 1. It is illustrated that the node degree of proposed TFBN is constant and it is 8. The degree of a node the proposed TFBN is higher than that of mesh, torus, TESH, and TTN networks. However, this high cost of an individual node will result in a high performance of the proposed TFBN. It is shown in the next subsequent sub-section about the performance improvement and in the next section about the cost-performance effectiveness of the proposed hierarchical interconnection network TFBN.
# Chaos and Stability in a New Iterative Family for Solving Nonlinear Equations
## 2 Convergence of the New Family  
In this section, we perform the convergence analysis of the new triparametric iterative family. Furthermore, we propose a strategy to reduce the triparametric scheme to an uniparametric scheme in order to accelerate the convergence. **Theorem 1**. : _Let \(f:I\subseteq\mathbb{R}\rightarrow\mathbb{R}\) be a sufficiently differentiable function on an open interval \(I\) and \(\xi\in I\) a simple root of the nonlinear equation \(f(x)=0\). Suppose that \(f(x)\) is continuous and sufficiently differentiable in an environment of the simple root \(\xi\), and \(x_{0}\) is an initial estimate close enough to \(\xi\). Then, the sequence \(\{x_{k}\}_{k\geq 0}\) obtained by using the expression (1) converges to \(\xi\) with an order of convergence of four, being its error equation_  
\[e_{k+1}=(1-\alpha-\gamma)C_{2}\Big{(}C_{2}^{2}-C_{3}\Big{)}e_{k}^{4}+\mathcal{ O}\Big{(}e_{k}^{5}\Big{)},\]  
_where \(e_{k}=x_{k}-\xi\), \(C_{q}=\frac{1}{q! }\frac{f^{(q)}(\xi)}{f^{\prime}(\xi)}\) and \(q=2,3,...\)_  
Proof. : Let \(\xi\) be a simple root of \(f(x)\) (that is, \(f(\xi)=0\) and \(f^{\prime}(\xi)\
eq 0\)) and \(x_{k}=\xi+e_{k}\). Using Taylor expansion of \(f(x_{k})\) and \(f^{\prime}(x_{k})\) around \(\xi\), we have  
\[ \begin{aligned} f(x_{k})&=f(\xi+e_{k})\\ &=f(\xi)+f^{\prime}(\xi)e_{k}+\frac{1}{2!}f^{\prime\prime}(\xi)e_ {k}^{2}+\frac{1}{3!}f^{\prime\prime\prime\prime}(\xi)e_{k}^{2}+\frac{1}{4!}f^{ (iv)}(\xi)e_{k}^{4}+\mathcal{O}(e_{k}^{5})\\ &=f^{\prime}(\xi)\bigg{[}e_{k}+\frac{1}{2!}\frac{f^{\prime\prime \prime}(\xi)}{f^{\prime}(\xi)}e_{k}^{2}+\frac{1}{3!}\frac{f^{\prime\prime \prime}(\xi)}{f^{\prime}(\xi)}e_{k}^{3}+\frac{1}{4!}\frac{f^{(iv)}(\xi)}{f^{ \prime}(\xi)}e_{k}^{4}+\mathcal{O}(e_{k}^{5})\bigg{]}\\ &=f^{\prime}(\xi)\Big{[}e_{k}+C_{2}e_{k}^{2}+C_{3}e_{k}^{3}+C_{4}e _{k}^{4}+\mathcal{O}(e_{k}^{5})\Big{]},\end{aligned}  \tag{3}\]  
and\[f^{\prime}(x_{k}) =f^{\prime}(\xi+e_{k}) \tag{4}\] \[=f^{\prime}(\xi)+f^{\prime\prime}(\xi)e_{k}+\frac{1}{2!}f^{\prime \prime\prime}(\xi)e_{k}^{2}+\frac{1}{3!}f^{(iv)}(\xi)e_{k}^{3}+\mathcal{O}(e_{k }^{4})\] \[=f^{\prime}(\xi)\left[1+\frac{f^{\prime\prime}(\xi)}{f^{\prime}( \xi)}e_{k}+\frac{1}{2!}\frac{f^{\prime\prime\prime}(\xi)}{f^{\prime}(\xi)}e_{k} ^{2}+\frac{1}{3!}\frac{f^{(iv)}(\xi)}{f^{\prime}(\xi)}e_{k}^{3}+\mathcal{O}(e_ {k}^{4})\right]\] \[=f^{\prime}(\xi)\left[1+2C_{2}e_{k}+3C_{3}e_{k}^{2}+4C_{4}e_{k}^{ 3}+\mathcal{O}(e_{k}^{4})\right],\]  
where \(C_{q}=\frac{1}{q!
## 4 Simulation Results
### Evaluation of the BER Performance  
For the signal restorer, the number of \(\mathbf{h}_{2}[n]\) branches was five and the FIR filter length for each branch was 64. The resulting BER performance is shown in Figure 9. We previously demonstrated that cyclic frequency offset can significantly degrade the performance of a FRESH-filter receiver. Failure to compensate for such errors degrades BER performance to below that of the case without CSIN mitigation. At BER = \(10^{-4}\), the \(E_{b}/N_{0}\) required for the various cases was as follows: \(\Delta=0\) Hz (22.6 dB), \(\Delta=\pm 4.8\) Hz with cyclic frequency offset compensation (23.3 dB), without applying CSIN mitigation (25.2 dB), and \(\Delta=\pm 4.8\) Hz without compensation (26 dB). These results show that the proposed method imposed a loss of only 0.7 dB, compared with the ideal case (\(\Delta=0\) Hz). Note however that the proposed method enabled gains of 1.9 dB and 2.7 dB, compared with the case without CSIN mitigation and the case without cyclic frequency offset compensation. Nevertheless, the required \(E_{b}/N_{0}\) to reach BER = \(10^{-4}\) is only about 8 dB when there is no CSIN in our simulation model. This resulted from  
Figure 7: The signal-to-interference-plus-noise ratio (SINR) versus frequency at \(E_{b}/N_{0}=0\) dB.  
the fact that the averaged power of the CSIN is 100 times of the averaged power of the background AWGN and the CSIN cannot completely be removed by the receiver.  
Figure 8: Performance evaluation for CSIN estimation module: (**a**) error-to-signal power ratio (ESR) and (**b**) cyclic frequency relative error (CFRE).
# Molecular Response of Bacteria Exposed to Wastewaters-Borne Nanoparticles
###### Abstract  
The increasing release of nanoparticles into aquatic environments, particularly via wastewater, raises concerns about their biological effects on microbial communities. This study investigated the molecular response of _Pseudomonas putida_ to aluminum oxide nanoparticles (Al2O3 NPs) under controlled conditions and in synthetic wastewater, both before and after biological treatment. Acute toxicity was evaluated using growth inhibition assays, while the expression of _katE_, _alpC_, and _ctaD_--genes associated with oxidative stress and energy metabolism--was quantified via RT-qPCR. Exposure to pristine Al2O3 NPs induced a strong, time-dependent upregulation of all tested genes (e.g., _katE_ and _alpC_ up to 4.5-fold). In untreated wastewater, this effect persisted but at a lower intensity; bulk Al2O3 caused only moderate changes. Treated wastewater samples showed markedly reduced gene expression, indicating partial detoxification. Nanoparticles elicited stronger biological responses than their bulk counterparts, confirming the material form-specific effects. Comparative analysis with _Daphnia magna_ revealed similar patterns of oxidative stress gene activation. These findings highlight the influence of nanoparticle form and environmental matrix on microbial responses and support the use of gene expression analysis as a sensitive biomarker for nanoparticle-induced stress in environmental risk assessment.  
wastewater; Al2O3; nanoparticles; gene expression; RT-qPCR; genetic markers; bacteria Article
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 1 Introduction
And the second one is proposed in the framework of the basis expansion model (BEM) [16; 17; 18; 19]. We will show in this paper that the transform domain matrix channel of UP-OCDM can be represented as a sum of the product of several diagonal and circulant matrices. By directly performing the LSQR algorithm [20] on the time-domain receive signal, low complexity iterative equalization is achieved. This paper is organized as follows. In Section 2, the UP-OCDM signal model, the circulant structure of the transform matrix, and the transmission model over the doubly selective channel is presented. In Section 3, firstly, the minimum mean square error (MMSE) equalizer for UP-OCDM is given. Then based on the circulant structure of the transform matrix, the band MMSE block equalization and the iterative LSQR equalization are proposed, and the complexities are presented. Numerical simulation results are proposed in Section 4. Finally, conclusions are drawn in Section 5. The notation used in this paper is summarized as follows. Bold upper (lower) letters denote matrices (column vectors); \((\cdot)^{*},(\cdot)^{T}\) and \((\cdot)^{H}\) stand for conjugate, transpose, and Hermitian transpose respectively. \(\odot\) denotes the element-wise product. We define \(x[n]\) as the \(n\)th entry of the vector \(\mathbf{x}\), and \(X(m,n)\) as the \((m,n)\)th entry of the matrix \(\mathbf{X}\), where all indices are starting from 0. \(\mathbf{F}\) stands for the \(N\times N\) unitary DFT matrix, that is, \(\mathbf{F}(p,q)=N^{-\frac{1}{2}}e^{-j2\pi\frac{m}{N}}\); \(\mathbf{I}_{N}\) refer to the \(N\times N\) identity matrix; \(\mathcal{D}(\mathbf{x})\) is denoted as a diagonal matrix created from \(\mathbf{x}\).
## 2 Related Work
### Delivery Service Routing (DSR)  
The DSR problem is a combinatorial optimization problem seeking to serve a number of customers with a fleet of vehicles [1,2]. Let \(G=\langle V,C\rangle\) denote a graph, where \(V\) denotes the nodes, say customers that need service, \(c_{ij}\in C\) denotes the time distance between nodes \(v_{i}\) and \(v_{j}\). Each customer \(v_{i}\) has a time window specified by an interval \([e_{i},l_{i}]\), corresponding to the earliest and latest possible serving times, respectively. A vehicle route starts at the depot \(v_{0}\), serving some number of customers at most once within their windows. [PERSON] [1] proposes a heuristic of inserting a new un-routed customer into the current partial route, between two adjacent customers with the minimum insertion cost. To further improve routing efficiency, [PERSON] and [PERSON] [11] propose a 2-opt exchange heuristic where the initial route is improved by exchanging two existing route links for another two new route links--being aware that some lateness customers can be tolerated, and the soft time window is introduced and a tabu search heuristic of swapping sequences of consecutive customers between two routes is proposed in [12]. To minimize the number of vehicles and traveling cost, a two-stage hybrid algorithm is proposed in [13], in which the simulated annealing heuristic is used to minimize the number of vehicles, and the neighborhood search is used to minimize travel costs. Considering the necessity of visiting recharging stations in electric DSR problem, meta-heuristic search [14] and branch-price-and-cut-based exact algorithms [15] are proposed to minimize the route cost. To minimize the average distance traveled per customer, the weighted DSR is introduced [16]. For weighted DSR, the regret-insertion heuristic is proposed to construct the initial solution and iterative local link exchange heuristics is proposed to improve the routing efficiency. In the above DSR literature, customers have the same pickup location (i.e., the depot), and each drop-off location is modeled as a node, which can be served successfully once it is visited by a vehicle. In contrast to the existing DSR with the same pickup location, this paper studies the delivery service sharing (DSS) variant where the customer is specified by the origin, destination, and flexible pickup/drop-off time windows, and the customer can be served successfully if and only if he is picked up from its origin and delivered to its destination with the time window.