esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Automatic Deviation Correction Control Strategy
#### 2.2.2 Path Planning Research
Adjusting the distances from the control points (C\({}_{1}\) and C\({}_{2}\)) to the starting point and the endpoint changes the shape of the curve accordingly. The distance from the control point to the starting point  
Figure 7: The cubic polynomial path when \(\left|e_{1}\right|>\left|e_{0}\right|\) and \(\left|e_{0}\right|>\left[e\right]\). (endpoint) is defined as \(K_{\text{s}}L_{\text{c}}\), where \(L_{\text{c}}\) is the driving cycle footage and \(K_{\text{s}}\) is the shape coefficient of Bessel curve, which is taken from 0 to 1. Different shape coefficients correspond to different curve shapes. The classification and discussion according to different cycle endpoints are as follows:  
1. \(\left|e_{1}\right|\leq\left|e_{0}\right|\)  
The yaw angle of the roadheader at the start point on the target path is the same as that at the endpoint.The Bezier curve degenerates into a straight line, and the target path is a straight line between the start point \(P_{0}\) and the endpoint \(P_{1}\). 1. \(\left|e_{1}\right|>\left|e_{0}\right|\) and \(\left|e_{0}\right|\leq\left[e\right]\)  
In order to meet the requirements of the automatic deviation correction trajectory constraint of the roadheader, the control point \(C_{1}\) is selected on the tangent of the curve starting point, that is, the extension line of the initial speed direction. The control point \(C_{2}\) is selected on the tangent line at the end of the curve, that is, on the \(Y\)-axis, as shown in Figure 8.  
The start and end coordinates are \(P_{0}(e_{0},0)\) and \(P_{1}(0,L_{\text{c}})\), respectively. The distance between the control point \(C_{1}\) and the starting point \(P_{0}\), and the distance from control point \(C_{2}\) to endpoint \(O_{1}\) are \(K_{\text{s}}L_{\text{c}}\). The coordinate of the control point is \((C_{1}\) (\(K=L_{\text{c}}e_{0}+K_{\text{s}}L_{\text{c}}\text{tan}a_{0}\)), \(C_{2}\) (\(L_{\text{c}}-K_{\text{s}}L_{\text{c}}\),0)). 1. \(\left|e_{1}\right|>\left|e_{0}\right|\) and \(\left|e_{0}\right|>\left[e\right]\)  
Similarly to the second case, the control point \(C_{1}\) is selected on the tangent of the curve starting point, that is, the extension line of the initial speed direction. The control point \(C_{2}\) is selected on the parallel line between point \(P_{1}\) and the Y axis, as shown in Figure 9.  
The coordinate of the control point \(C_{2}\) is \((L_{c}-K_{s}L_{c},e_{0}\pm[e])\), and the rest is the same as the second case.
# Faster Force-Directed Graph Drawing
## 1 Introduction
### Our Contribution  
We use the WSPD in order to speed up the force-directed algorithm of Fruchterman and Reingold (FR). Instead of computing the repulsive forces for every pair of points, we represent every set \(A_{1},\ldots,A_{k},B_{1},\ldots,B_{k}\) in the decomposition by its barycenter and use the barycenter of a set, say \(A_{i}\), as an approximation when computing the forces between this set and a point in \(B_{i}\). For laying out a graph with \(n\) vertices and \(m\) edges, the resulting runtime is \(O(m+n\log n)\) per iteration, instead of \(\Omega(n^{2})\) for the classical algorithm.  
Our method is very simple and allows the user to define forces arbitrarily, as long as the total force on a point \(p\) is the sum of the forces of point pairs in which \(p\) is involved. Hence, our approach can be applied to any algorithm whose force model fulfills this requirement, which holds for all force-directed methods of which we know. For example, [PERSON] (2014) suggests to define the repulsive forces as \(F_{\text{repulsive}}(u,v)=c/d^{p}(u,v)\) for any \(p>0\). In the sequel, he uses \(p\in\{2,3\}\) for some graph classes such as trees, in order to reduce peripheral effects, but he also gives an example with \(p=1.3\)(([PERSON], 2014), Figure 14). We do not consider other techniques, such as multidimensional scaling (MDS), as we only want to show that we can speed up force-directed graph layout algorithms using the WSPD. Our method combines well with the multilevel approach.  
In contrast to our WSPD-based implementation of FR, the above-mentioned fast multipole method uses an approximation of the repulsive forces that is quite complicated (as [PERSON] and [PERSON] (2010) point out); it requires the expansion of a Laurent series. Furthermore, it is restricted to repulsive forces that decay inversely proportionally with the node-node distance. In contrast to the work of [PERSON] et al. (2014), our speed-up technique does not assume access to the GPU. Other than [PERSON] (2014), we do not round vertex positions to an integer grid, and our speed-up technique does not rely on certain types of hardware architectures.  
We first detail how we use the WSPD to speed up FR; see Section 2. Then, we test our modified algorithm on six types of real-world and synthetic data sets. We compare several versions of FR in terms of running time and two commonly-used quality metrics (namely number of crossings and deviation of edge lengths); see Section 3.  
Our algorithm is implemented in Java based on JUNG (2014). While this makes it difficult to compare running times to the algorithms implemented in C++ in the Open Graph Drawing Framework (OGDF (2014)), we have found and fixed bugs in the only existing Java implementation of FR (in JUNG). Our speed-up technique will serve the Java community and will potentially popularize graph drawing methods in that community. The source code for our Java implementation and our test data sets can be found on our webpage ([PERSON] et al., 2015).
# Phytoplankton as CO\({}_{2}\) Sinks: Redirecting the Carbon Cycle
## 3 Phytoplankton Culture
### Photobioreactors (PBRs)
#### 3.2.2 Closed PBRs  
In contrast to open ponds, closed PBRs minimise contact between the culture and the atmosphere, thereby reducing the likelihood of contamination and evaporation rates. Consequently, closed PBRs allow for several geometries and designs [33]. A typical example of closed PBRs is a flat panel, where the culture stays vertically arranged between two transparent surfaces spaced several centimetres apart [37]. Other functional geometries include tubular PBRs, where the culture flows through transparent tubes (glass, polymethyl methacrylate, polycarbonate, etc.) of variable diameter arranged vertically, horizontally, or in a helical configuration [38; 39]. A particular variant of tubular PBRs is the annular system, where the culture grows in the cavity between two concentric tubes of different diameters that are vertically disposed [40]. Closed PBRs typically include a robust control system that continuously monitors and, if necessary, adjusts key culture parameters. Therefore, the volumetric productivity is usually higher or much higher than in open ponds. In contrast,  
Figure 2: Comparative diagram between closed and open photobioreactors and the main differences in their implementation and running parameters.  
closed PBRs are also much more expensive to implement, operate and maintain due to the complex geometry and materials required and the implementation of control systems.
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 4 Code Design and Advantages
### NB-LDPC Code Design
In the case of \(R=2/3\) and \(\eta=0.5\) bit/s/Hz, based on (14) and (15), the EXIT chart of the resulting scheme using 8M2 RC concatenated with designed NB-LDPC code was drawn in Figure 6, where the convergence threshold is 0.81 dB.
## 4 Mixed-Flow Load-Balanced Scheduling (MFLBS)
### Dynamic Routing Network
#### 4.1.2 Dynamic Routing Mechanism  
In each time slot, the switch will transmit data according to the priority. If the bandwidth on the initial path in this time slot has been allocated, \(f_{k}\) will wait until the higher-priority flow transmission task in the queue ends, and the excess bandwidth is left before its transmission can be started.  
This situation seriously affects the performance of the network and will further aggravate the local congestion of the network. Therefore, we need to reroute the paths of these flows. First, we need to maintain two tables (Table 1. Remaining bandwidth table and Table 2. Maximum flow allocation table) to obtain global information and update the contents of the table in real time during the scheduling process. The remaining bandwidth table records the unallocated traffic on the path between the two shortest connection switches, and the maximum allocated flow table records the current flow allocated to the most traffic on the shortest link.  
Finally, we determine the new route for these marked flows according to the network state and the Dijkstra algorithm in the next time slot. In the following section, we outline the important steps of dynamic routing network scheduling in a time slot.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Spectral Interpretation and Identification  
The mass spectrum generated from each microorganism serves as a proteomic profile, which is reproducible under consistent growth and preparation conditions. These profiles are compared against a reference database using proprietary algorithms (e.g., Bruker Biotypper, VITEK MS). Matching is based on the presence, intensity, and position of peaks within the spectrum. A successful match provides identification at the genus or species level, and in some cases, subspecies or strain level, depending on database resolution [55; 56; 57].
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 4 Simulation Results  
The experimental setup described in Section 3 was simulated in MATLAB/Simulink. Figure 15 shows a screenshot of the simulation model.  
The model of the PV module was configured from Simulink/Simscape/Electrical/Specialized Power Systems/Sources/PV Array. In order to simulate the delay in LoRa communications, a discrete sample and hold block was used with a sample time of 1 s. The P&O and SMC controllers used in the simulations were implemented as per Algorithms 1 and 2.  
Figures 16 and 17 show the simulation results when there was a step change in irradiance, from 400 W/m\({}^{2}\) to 1000 W/m\({}^{2}\), while keeping the temperature constant at 40 \({}^{\circ}\)C. This abrupt change in irradiance was introduced as an extreme case, knowing that it will never happen in reality, but it served to test the response of the simulation model.  
Figure 16: Simulation of the P&O controller in response to a step change in irradiance. (**a**) Cell temperature. (**b**) Solar irradiance. (**c**) PV power. (**d**) Duty cycle.  
Figure 15: Screenshot of the simulation model in MATLAB/Simulink.  
Both the P&O (Figure 16) and the SMC (Figure 17) algorithms responded well and in a similar way to a sudden variation in the irradiance, with approximate response times of 25 s in both cases. This behavior was expected from these controllers. However, the experiments show that the response time under real rapid changes in irradiance is much shorter (see Section 5).
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.8 Efficient Planning and Resource Allocation Systems  
Mapping containers to physical nodes is a complex problem, in which the aim is to maximize the performance of the applications (i.e., their execution time) while taking into account factors such as interference reduction (i.e., performance isolation), maximization of the use of computational resources, use of bandwidth, and reduction of power consumption. This requires the implementation of intelligent resource allocation algorithms. In the case of cooperative microservices, efficient resource allocation mechanisms must be implemented that consider the locations of the data and reduce the communication time between services.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology
### Accuracy Evaluation  
To quantitatively evaluate the classification performance of different models, the overall accuracy (OA) and kappa coefficient are utilized as model evaluation indicators. OA is defined as the ratio of the number of correctly classified data to the total test data. It provides an intuitive measure to assess the overall classification performance of the test data. The kappa coefficient is considered to be a more robust measure compared to a simple percent agreement calculation because it takes into account the possibility of the agreement occurring by chance.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 2 Preliminary Studies
### The Energy Evaluation  
Many ideas and calculations (see Equations (9)-(12)) have been applied to the energy calculations from [20], in some cases with a slightly different interpretation. Generalizing, the drone's energy consumption can be summarized as follows:  
\[E_{total}=E_{processors}+E_{mators}+E_{communication}+E_{peripherials} \tag{9}\]  
where \(E_{(\text{anything})}=P_{(\text{anything})}\)*t.  
Some constant and very low energy for (\(E_{communication}+E_{peripherials}\)) is assumed. The energy consumed by the processor(s) is specified depending on control system complexity:  
\[E_{processors}=\sum_{i=1}^{nr\,of\,proc.}\int_{t0}^{t1}P_{processor(i)}(t)*dt \tag{10}\]  
Furthermore, the energy consumed by motors is as follows:  
\[E_{mators}=E_{takeoff}\left(a,w\right)+E_{hover}\left(a,w,t\right)+E_{move} \left(w,s,t,\rho\right) \tag{11}\]  
where the dominant energy is the following:  
\[E_{move}\left(w,s,t,\rho\right)=E_{dng}\left(d,\rho,k_{dng},S_{off}\right)+E_ {kinetic}\left(w,s\right) \tag{12}\]  
This \(E_{move}\left(w,s,t,\rho\right)\) is dominant and vital in different atmospheric conditions (this will be modelled later in the space model). Having completed the calculations for a particular case, the authors created the following \"average energy map\" of the drone, as seen in the diagram below (Figure 5, Phantom 4 drone pro/pro+ series).  
Figure 4: The idealized UAV flight pattern, where \(C_{1}\) and \(C_{2}\) areas denote energy consumption, while \(S\) area is the energy saving region [19].  
Figure 5: The generalized energy consumption of the drone (hover—blue, move—orange, idle—blue, peak—orange).  
These calculations were based on a \"Phantom 4 Pro\" drone. For energy calculations, the simple mathematical relations were used, where voltages are known and currents were measured: \(P=U^{\intercal}I=E/t\). For thrust force calculations, see Equations (1)-(3), above. The two different colors of bars represent the \"Hover/Move\" energy of motors, and the \"Idle/Peak\" energy of the \"Powerful\" and \"Weakly\" loaded processors.
## Appendix C Nucleic Acid Solutions  
The first sample was small interfering (si) RNA containing three fragments of length 17, 21, and 25 base pairs with 2-base, 3\({}^{\prime}\) overhangs on each end [21]. Including the overhangs, the molecular weight of the three fragments was 10,789, 13,369, and 15,941 Da. Assuming equal occurrence of each fragment, the total mass concentration of RNA was 9.0 \(\upmu\)g/mL, which corresponds to a molarity of 6.7 \(\times\) 10\({}^{-10}\) mol/mL. The solvent consisted of a 5% glycerol, 1 mM EDTA buffer solution with a pH = 7.2. From a biological standpoint, siRNA has attracted much interest because of its involvement in RNAi pathways, where it interferes with the expression of specific genes [39]. As such, it could play a key role in gene therapy [40]. It has also been found to be a potent inhibitor of influenza virus replication in embryonated chicken eggs and promises to help prevent and treat influenza virus infections in humans [41]. It has also been found to counteract the Human Immunodeficiency Virus (HIV) [42].  
The second sample was a 50-bp DNA ladder containing a distribution of 17 fragments of length 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 766, 916, and 1350 bp, with the 200 and 1350 bp being slightly predominant (to enhance the intensity for electrophoresis applications). Taking a weighted average amongst all fragments, the total mass concentration of DNA was \(\approx\)1.0 \(\times\) 10\({}^{-3}\) gm/mL, which corresponds to an average molarity of 3.2 \(\times\) 10\({}^{-9}\) mol/mL. The third sample was a 1-bp DNA ladder containing a distribution of 10 strands of length 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0 8.0, and 10.0 kbp, with the 3.0-kbp sample being slightly predominant. Again, taking a weighted average the total mass concentration of DNA was 0.5 \(\times\) 10\({}^{-3}\) gm/mL, which corresponds to an average molarity of 2.1 \(\times\) 10\({}^{-10}\) mol/mL. Both the 50-bp and 1-kbp samples came in a buffer solution of 10 mM Tris-HCL and 1 mM EDTA with a pH of 8.0.  
The largest nucleic acid specimen we investigated was lambda DNA--the phage for the _Escherichia coli_ bacterium. It consists of 48,502 base pairs [43] and is one of the best understood of all naturally occurring and physiologically relevant DNA species. Lambda DNA did not flow as readily into the submicron channels as the smaller species, so a longer waiting time (roughly 20 min) was taken to allow their full penetration. However, all four samples tested are considered biologically relevant. Like the 50-bp and 1-kbp DNA samples, the lambda DNA was obtained commercially, and dissolved in EDTA buffer solution at a pH of 8.0.
## 3 Results
### Microsurfacing Test Results
#### 3.3.2 Consistency Test Results  
The consistency test results for the microsurfacing mixtures produced with both asphalt emulsions studied in this work are presented below. The initial compositions studied at this stage were based on the preliminary test results for Emulsions 1 and 2.  
Firstly, 400 g of microsurfacing mixture was produced with emulsion 1, using 52% fine aggregates and 48% coarse aggregates, 2.5% cement, 12% emulsion, and 11% water. The average flow value of the mixture measured at four points in perpendicular directions was 25 mm, confirming that 11% water assures adequate workability for mixtures produced with Emulsion 1. Figure 10 shows the test preparation and its final result.  
The initial mixture produced with Emulsion 2 used 52% fine aggregates and 48% coarse aggregates, 2.5% cement, 12% emulsion, and 10% water. However, the average flow of the mixture was higher than 35 mm, showing that 10% water was excessive for mixtures produced with Emulsion 2. Therefore, a new microsurfacing mixture was designed with less water (9%), which presented an average flow of 30 mm measured at four points in  
Figure 9: Samples used for preliminary design of microsurfacing asphalt mixtures: (**a**) pizza sample just after molding, (**b**) pizza sample after 60 min, (**c**) sample for ball test.  
perpendicular directions, thus improving the workability of the mixture produced with Emulsion 2.  
The results of this test demonstrated that microsurfacing mixtures can use varying amounts of added water to guarantee adequate workability depending on the type of emulsion used. The results of this work suggest that emulsions with lower residual binder contents, such as Emulsion 2, may result in microsurfacing mixtures demanding lower amounts of added water.
## 3 Enhanced Distributed Fountain Coding for Multiple-Access Relay Networks with Cooperative Relay  
Figure 3 depicts the proposed fountain coding scheme for multiple-access relay networks with cooperative relay shown in Figure 2, named enhanced distributed fountain codes (EDFC). We assume that each source \(S_{i}\) includes \(k_{i}\) independent information symbols. Source \(S_{i}\) performs LT encoding on \(k_{i}\) information symbols with degree distribution \(\Phi_{i}(x_{i})=\sum_{d=1}^{k_{i}}\Phi_{id}x_{i}^{d}\). The encoded symbols of each source are forwarded to  
Figure 1: Communication system based on multiple-access multiple-relay networks.  
Figure 2: Multiple-access relay network model with cooperative relay.  
relay \(R_{1}\) and re-encoded according to degree distribution \(\Gamma_{1}(x)=\sum_{d=1}^{n}\Gamma_{1,d}x^{d}\), which means that \(R_{1}\) chooses \(d\) encoded symbols from \(n\) sources with probability \(\Gamma_{1,d}\) according to the probability distribution \((\Gamma_{1,1},\Gamma_{1,2},\ldots,\Gamma_{1,n})\). Moreover, we assume that \(t\) sources \(\{S_{n-t+1},S_{n-t+2},\ldots,S_{n}\}\) may be in poor channel conditions, and the encoded symbols from these sources are forwarded to relay \(R_{2}\) additionally. Then \(R_{2}\) performs re-encoding on incoming symbols with degree distribution \(\Gamma_{2}(x)=\sum_{d=1}^{t}\Gamma_{2,d}x^{d}\). The re-encoded symbols from \(R_{1}\) and \(R_{2}\) are sent to destination \(D\), which starts to decode using the BP algorithm when it successfully receives sufficient symbols. The entire encoding process can be regarded as a two-layer concatenated fountain code.  
We assume that each source generates the same number of encoded symbols, denoted by \(N\). Assuming that the code overhead of source \(S_{i}\) is \(\varepsilon_{i}\), we have  
\[N=k_{1}(1+\varepsilon_{1})=k_{2}(1+\varepsilon_{2})=\cdots=k_{n}(1+\varepsilon _{n})=(1+\varepsilon)\sum_{i=1}^{n}k_{i}, \tag{1}\]  
where \(\varepsilon\) is the overall coding overhead. We divide the transmission of the coded symbols between the source and relay into a number of time slots. In each time slot, each source independently generates a coded symbol and sends it to the relay at the same time. In order to ensure that the relays can receive the coded symbols sent by each source at the same time, each symbol will be stored in buffers of each source-relay link unless it is erased on the erasure channels. The size of each buffer is set to \(N\). At the destination, symbols from both relays are received alternately. We assume that half of the symbols received by the destination come from relay \(R_{1}\) and the other half from relay \(R_{2}\). Similarly, the relay-sink links are set up with similar buffers. In summary, the above coding scheme can be denoted as \(\text{EDFC}(\{\Phi_{i}(x_{i})\}_{i=1}^{n},\{\Gamma_{i}(x)\}_{i=1}^{2},n,t,k_{i})\).