esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Analysis
### Computational Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instance** & **zOpt** & **GVNS\_1** & **GVNS\_2** & **GVNS\_3** & **GAP\_1** & **GAP\_2** & **GAP\_3** \\ \hline a280.tsp & 2579 & 2672 & 2728 & 2706 & 3.61 & 5.78 & 4.92 \\ att48.tsp & 10628 & 10628 & 10628 & 10724 & 0.00 & 0.00 & 0.90 \\ bayg29.tsp & 1610 & 1610 & 1610 & 1615 & 0.00 & 0.00 & 0.31 \\ bayg29.tsp & 2020 & 2020 & 2020 & 2028 & 0.00 & 0.00 & 0.40 \\ bier127.tsp & 118282 & 118518 & 119737 & 119860 & 0.20 & 1.23 & 1.33 \\ kroA100.tsp & 21282 & 21283 & 21337 & 21699 & 0.00 & 0.26 & 1.96 \\ burma14.tsp & 3323 & 3323 & 3323 & 3323 & 0.00 & 0.00 & 0.00 \\ ch130.tsp & 6110 & 6157 & 6218 & 6289 & 0.77 & 1.77 & 2.93 \\ ch150.tsp & 6528 & 6583 & 6680 & 6649 & 0.84 & 2.33 & 1.85 \\ d493.tsp & 35002 & 36659 & 37040 & 36557 & 4.73 & 5.82 & 4.44 \\ kroB100.tsp & 22141 & 22168 & 22317 & 22557 & 0.12 & 0.79 & 1.88 \\ kroC100.tsp & 20749 & 20757 & 20806 & 21321 & 0.04 & 0.27 & 2.76 \\ kroD100.tsp & 21294 & 21329 & 21567 & 21904 & 0.16 & 1.28 & 2.86 \\ kroE100.tsp & 22068 & 22144 & 22250 & 22407 & 0.34 & 0.82 & 1.54 \\ kroA150.tsp & 26524 & 26630 & 27050 & 27738 & 0.40 & 1.98 & 4.58 \\ kroB150.tsp & 26130 & 26232 & 26701 & 26742 & 0.39 & 2.19 & 2.34 \\ kroA200.tsp & 29368 & 29570 & 30177 & 29718 & 0.69 & 2.75 & 1.19 \\ kroB200.tsp & 29437 & 29801 & 30459 & 30205 & 1.24 & 3.47 & 2.61 \\ d198.tsp & 15780 & 15871 & 16077 & 15964 & 0.58 & 1.88 & 1.17 \\ hpg180.tsp & 1950 & 1956 & 2026 & 2153 & 0.31 & 3.90 & 10.41 \\ berlin52.tsp & 7542 & 7542 & 7542 & 7591 & 0.00 & 0.00 & 0.65 \\ dantzig42.tsp & 699 & 699 & 699 & 706 & 0.00 & 0.00 & 1.00 \\ eil51.tsp & 426 & 426 & 426 & 430 & 0.00 & 0.00 & 0.94 \\ eil76.tsp & 538 & 538 & 543 & 544 & 0.00 & 0.93 & 1.12 \\ eil101.tsp & 629 & 629 & 642 & 649 & 0.00 & 2.07 & 3.18 \\ frz126.tsp & 937 & 937 & 937 & 948 & 0.00 & 0.00 & 1.17 \\ gil1262.tsp & 2378 & 2444 & 2505 & 2542 & 2.78 & 5.34 & 6.90 \\ grl7.tsp & 2085 & 2085 & 2085 & 2085 & 0.00 & 0.00 & 0.00 \\ gr21.tsp & 2707 & 2707 & 2707 & 2707 & 0.00 & 0.00 & 0.00 \\ gr24.tsp & 1272 & 1272 & 1272 & 1272 & 0.00 & 0.00 & 0.00 \\ gr48.tsp & 5046 & 5046 & 5046 & 5063 & 0.00 & 0.00 & 0.34 \\ gr96.tsp & 55209 & 55247 & 55549 & 56109 & 0.07 & 0.62 & 1.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results using the First Improvement search strategy and an execution time limit of 2 min. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Instance** & **zOpt** & **GVNS\_1** & **GVNS\_2** & **GVNS\_3** & **GAP\_1** & **GAP\_2** & **GAP\_3** \\ \hline gr120.tsp & 6942 & 6960 & 7063 & 7103 & 0.26 & 1.74 & 2.32 \\ gr137.tsp & 69853 & 70005 & 71002 & 71  
In some cases, GVNS_3 with a time limit of 1 min produced better results than using the 2 min time limit in solving sTSP instances. This might be happened due to the use of pure random diversification method such as the shuffle operator. More precisely, by executing more times GVNS_3, the shuffle operator is also executed more times and consequently it can shift the search into not so promising areas. Thus, the search may be trapped into low quality local optima.
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
###### Abstract  
Linear networks (sometimes called chain-type networks) occur frequently in Internet of Things (IoT) applications, where sensors or actuators are deployed along pipelines, roads, railways, mines, and international borders. LoRa, short for Long Range, is an increasingly important technology for the IoT with great potential for linear networking. Despite its potential, limited research has explored LoRa's implementation in such networks. In this paper, we addressed two important issues related to LoRa linear networks. The first is contention, when multiple nodes attempt to access a shared channel. Although originally designed to deal with interference, LoRa's technique of synchronisation with a transmission node permits a novel approach to contention, which we explored. The second issue revolves around routing, where linear networks permit simpler strategies, in contrast to the common routing complexities of mesh networks. We present gossip routing as a very lightweight approach to routing. All our evaluations were carried out using real equipment by developing real networks. We constructed networks of up to three hops in length and up to three nodes in width. We carried out experiments looking at contention and routing. We demonstrate using the novel approach that we could achieve up to 98% throughput. We compared its performance considering collocated scenarios that achieved 84% and 89% throughputy using relay widths of two and three at each hop, respectively. Lastly, we demonstrate the effectiveness of gossip routing by using various transmission probabilities. We noticed high performance up to 98% throughput \(T_{prob}=0.90\) and \(T_{prob}=0.80\) by employing two and three active relay nodes, respectively. The experimental result showed that, at \(T_{prob}=0.40\), it achieved an average performance of 62.8% and 73.77% by using two and three active relay nodes, respectively. We concluded that LoRa is an excellent technology for Internet of Things applications where sensors and actuators are deployed in an approximately linear fashion.  
LoRa networks; relay networks; linear networks +
Footnote †: journal:  
00 2023 by the authors.  
L
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Gaussian Curvature
#### 4.3.3 Analysis of Wavefront Gaussian Curvature in Link 3  
From Figure 12a, the Gaussian curvature map under sunny conditions shows more pronounced fluctuations in the central and right regions, with curvature values higher than those in Figure 12b (cloudy). The wavefront distortion is more severe and complex, affecting a broader area. In Figure 12b, the Gaussian curvature map under cloudy conditions shows fluctuations in the central region, indicating noticeable distortion. Overall, at a transmission distance of 10.2 km, the wavefront distortion is significant under both sunny and cloudy conditions. However, the curvature values are higher under sunny conditions, with more complex wavefront distortion and a broader affected area.
## 4 Simulation and Analysis
### Application  
In order to test practical performance of the proposed automatic noise cancellation method for the machinery acoustic signal based on EMD-IFOA, an acoustic-based diagnosis system was constructed in the fully-mechanized coal mining working face. The shearer is a kind of essential equipment in modern coal mining, and the operating condition diagnosis for the shearer is of great importance. Conventional diagnosis systems are mainly based on the vibration signal [33] and cutting stress signal [34]. Working lives of the vibration sensors and strain gauges are very short due to the harsh working conditions and contact measurement. The coal output is always restricted due to the frequent maintenance. In January 2016, a working state diagnosis system for coal mining shearers was set up in National Coal Mining Equipment Research and Experiment Center at the China Coal Zhangjiakou Coal Mining Machinery Co., Ltd. (Zhangjiakou, China) to identify whether the shearer was cutting coal seam, rock seam or coal seam-gripping rock. The arrangement of the shearer and the cutting sound collection system were presented in Figure 11. An industrial microphone was installed to collect the sound signal. In order to assure the normal use of the collector under the condition of excessive dust and gas, dust-proof and explosion-proof processing satisfying the coal mining requirements was carried out for the microphone.  
As there existed a large number of noise signals among the cutting signal, eliminating noise from the field collected sound signal was a crucial step for improving the recognition accuracy. In order to validate the effectiveness and superiority of the proposed denoising method, a piece of acoustic signal with the sampling frequency of 44.1 kHz and the time duration of 1 s was collected and analyzed. As the frequency spectrum was often applied to further state diagnosis, the frequency distribution of the original field signal and denoised signal were also calculated and presented in Figure 12. It could be seen from the figure that the original signal saltated sharply in amplitude due to the random noise element and the denoised signal turned out to be smooth. Moreover, the frequency component of the original sound was presented disorderly in the whole domain, which resulted in difficulty determining the cutting state of the shearer. While it became regular for the processed signal, some wave peaks which reflected the working state of the shearer appeared in the spectrogram, and other areas were relatively stable. Furthermore, a zoom on the range of [0, 2000 Hz] was shown in Figure 13. It can be seen from the figure that the amplitude of the denoised signal fluctuates at 50 Hz, 260 Hz and 480 Hz. The working state of the shearer can be diagnosed according to the corresponding frequency of the fluctuation and the value of the spectrum. The denoised signal was more suitable for further processing.  
Figure 10: The percent root mean square difference of the five methods in different SNRs. PRD: percent root mean square difference.
## 2 Related Work  
### Grid Structure Data Forwarding Approach with Mobile Sink  
Several protocols have been proposed to implement query and data dissemination architectures in WSNs [8; 9; 10]. Most of the aforementioned studies on data dissemination protocols still assume both a single sink and knowledge of the sink's location, but mobile sink environments are used by many current applications such as mobile phones and PDAs, so many researchers have recently studied the problem of supporting sink mobility [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24].  
EEDD [14] has proposed an Energy-Efficient Data-Dissemination protocol to solve both the target and inquirer mobility problem. It has adopted a virtual grid-based two-level design to schedule the active sensor nodes. GGR [15] is a proposed Geographic Grid Routing protocol to provide a multi-path approach at all stages of communication. It focuses on the reliability aspects of the routing protocol.  
Perhaps the most similar protocol to SDLS is the Two-Tier Data Dissemination (TTDD) protocol [16], which is source oriented to provide the location information of the source node. TTDD is a well-known grid-based routing protocol to provide query and data dissemination for multiple mobile sinks. Upon detection of an event, the source node proactively creates a virtual grid structure throughout the sensor field with dissemination nodes located at the grid cross points. TTDD operates in a two-tier hierarchical manner to support the mobile sink's routing. First, the lower tier is within the cell of the sink's current location. When the sink wants to obtain the sensed data, it floods its query within the lower tier until it reaches the closest dissemination node, called the immediate dissemination node. Second, the query message is then forwarded through the higher tier, which is made up of either the source node or a dissemination node from the sink's cell to the source's cell. The dissemination node requests a data download to the source node along the reverse grid path with the data announcement. Finally, once the source node receives the query message, packets are forwarded from the source to the immediate dissemination node along grid horizontal and vertical axes. Requested data will flow down in the reverse direction to the sink. Thereafter, the requested data from the source node is forwarded by trajectory forwarding [16]. TTDD solves the sink mobility problem using a grid structure. However, if the number of source nodes is increased, data dissemination point management can considerably increase the communication and storage overhead of the system, due to the grid structures of data dissemination points from each source node [23]. Therefore, an efficient protocol with multiple source nodes is necessary [12; 13; 14; 15; 20; 21; 22; 23; 24].
# Projected Wind Energy Maximum Potential in Lithuania
## 3 Results
### Energy Potential  
In order to assess the energy potential of wind, according to the data in Figure 1, the maximum area of the territory of Lithuania where, at least hypothetically, it would be possible to build wind power plants of the selected size was estimated. The analysis of the wind power regulation map (Figure 1) showed that Lithuania has 23,949 km\({}^{2}\) of land that could be used for wind energy development. According to equation (4), this area could accommodate approximately 12,023 wind turbines of the size described in the previous section. We assume that electricity production in Lithuania is expected to change similarly to wind speed projected for each of the IPCC models over the next 40 years. This period was chosen because it is approximately the lifetime of wind turbines.  
The next four decades (2020-2060) should be the most energy-efficient in the MPI model. Figure 4 shows that energy production could reach up to 150 GWh per decade in the western part of Lithuania. Some decrease in energy production would be expected between 2020 and 2040 under the IPSL and HAD models, but a slight increase is possible between 2040 and 2060--those two decades should be the most energy-effective throughout the century.  
Figure 4: Maps show how much energy can be produced over a particular decade in any location using a selected wind turbine for the SSP2-4.5 scenario. Units—GWh.  
The analysis of the HAD model shows that in 2020-2040, wind generation will be the lowest compared to any other of the four decades (Figure 4). Over the next two decades, the amount of energy produced will increase. An evident change is expected in the western and central parts of the country. This change will lead to an increase in the energy output of up to two times compared to 2020-2040 (Table 3).  
The IPSL model should be similar to the HAD model. The first two decades of the analyzed period should be the worst in terms of energy production, followed by an increase in green energy production and greater stability in the middle of the century (2040-2060) (Figure 4). An ongoing decrease, from the middle of the century until the end of the century, will follow the increase. In the upcoming 20 years, the maximum annual energy production of the country should be around 57.78 TWh, and in the middle of the century, it will increase by around 2.3% (Table 3).  
The MPI model is the most favorable for the wind energy sector compared to the other examined scenarios. The highest electricity production is projected in this scenario; it could reach up to 150 GWh per decade per turbine in the coastal region. One wind turbine's average annual electricity production will be up to 9 GWh. The last two decades have been the least energy-efficient. In addition, the analysis shows that this scenario shows a much larger energy-useful area of the country (with an energy production between 30-45 GWh).
# Enhancing Vibration Isolation Performance by Exploiting Novel Spring-Bar Mechanism
## 1 Introduction
In recent years, the PFA has also been applied to the coupled oscillators with different nonlinear joints [32; 33; 34], with clearance [35; 36], or with nonlinear inerter mechanisms [37]. In our previous study [14], a nonlinear isolation system with a NSM created by a pair of bars under constant compression forces was investigated. It was successfully shown that the NSM can reduce the natural frequency of the linearized system and significantly lower the force and power transmission in the low-frequency range. However, it was also found that the NSM is nonlinear and that it can possibly lead to sub-harmonic resonances resulting in a larger response as well as a higher level of power flows at high excitation frequencies. In addition, the NSM introduced softening nonlinearity into the system, thus limiting the maximum allowable deflection to avoid static instability. Therefore, it is necessary to obtain improved NSM design, which can not only assist in low-frequency vibration isolation but can also avoid undesirable nonlinear effects. To address these issues, this paper develops the previous NSM design by replacing the compression forces with adjustable spring forces dependent on the spring stiffness and the initial deflection. This new configuration based on the spring-bar mechanism (SBM) can better control the stiffness/restoring force characteristics so that effective isolation can be achieved without compromising isolation performance due to nonlinearity. The dynamics and power flow behaviour of the integrated vibration isolation system is investigated thoroughly over the design parameter space so as to make better use of the SBM without introducing undesirable nonlinear effects. In the following content, the SBM and the integrated vibration isolation system model is described. Then, the mathematical model, the stiffness and restoring force characteristics, and bifurcation analysis are presented. The response of the integrated system subject to a harmonic excitation is studied using both analytical approximations based on the averaging method and numerical integrations. Both force transmissibility and power flow characteristics are studied to assess the vibration isolation performance of the system with periodic responses. Moreover, the power flow characteristics of the system exhibiting possible bifurcations and chaotic responses are examined. Some conclusions are provided at the end of the paper.
## 3 Results
### Carbon Mass Balance  
The carbon content of the dry cell of _A. platensis_ during this experiment was found to be \(32\pm 4\%\) from the elemental analysis, and it did not change significantly throughout the experiment. The overall balance between the total incoming carbon mass (\(C_{in}\); absorbed + remained CO\({}_{2}\)) and outgoing carbon mass (\(C_{out}\); differences between initial and final concentrations of DIC, DOC, and POC) was evaluated (Figure 5). Near closure of the balance was observed with the difference of less than 3-23% between \(C_{in}\) and \(C_{out}\). The absorbed CO\({}_{2}\) was converted into DIC, DOC, and POC. Average biological carbon fixation (DOC + POC) rates were 0.114, 0.131, and 0.103 gC L\({}^{-1}\) d\({}^{-1}\) for Runs 1, 2, and 3, respectively, and the conversion efficiencies from absorbed CO\({}_{2}\) to DOC + POC were 58, 38, and 30%, respectively. The difference between CO\({}_{2}\) supply and biological carbon fixation was relatively large in Runs 2 and 3, causing DIC accumulation (Figures S3 and S4) and pH decrease in the absorption column (Figure 2).  
Figure 4: Photosynthesis-based products in the photobioreactors: (**a**) algal growth and (**b**) dissolved organic carbon.  
The carbon mass flux analysis also revealed that out of all photosynthetically fixed carbon (increased and discharged POC + DOC), 16-24% was converted into DOC (Figure 5). The accumulated DIC ultimately escaped from the system in the medium discharge (cf. supplementary data Figure S5 for carbon flux).
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 2 Methodology
### Computational Scheme  
Here, the computational steps required to perform seismic tomography are explained. This subsection is proposed to effectively explain the computational scheme to ensure the repeatability of the experiments. The computational scheme, consisting of 11 interconnectedblocks, is depicted in Figure 4. Block number 1 contains the SAR image in SLC format, processed to obtain tomography, while block number 2 represents the two-dimensional DFT (DFT2) operator. The input of computational stages 3 and 4 represents the copy of the DFT2 of 1, and therefore contains the same data, having a common source. Computational stage 3 is programmed to generate the sub-aperture represented by the black squared Doppler spectrum visible in Figure 2, while block 4 is programmed to generate the blue squared spectrum, also visible in Figure 2. Computational blocks 5 and 6 perform the inverse DFT2 (IDFT2) which is used to return to a lower azimuth resolution SLC SAR image. Computational block 7 performs pixel-tracking for all those pixels for which tomography needs to be trained. The result of pixel-tracking is the generation of complex vectors that will form block 8 (non-computational), which represents the raw tomographic complex data that must be focused on in elevation or depth but in the orthogonal dimension of the slant range. The focus of the raw tomographic signal is effected by computational block 9, representing the DFT mathematical operator. Block 10 (this is also not computational) represents the focused tomographic image. Finally, block 11 performs the geocoding of the tomogram, using a three-dimensional geographic coordinate reference system.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
## 2 Description of the Experimental Setup  
### Scenario  
The scenario for the measurements is a laboratory of the Universidad Politecnica de Cartagena (UPCT) as shown in Figure 1. The dimensions of the lab are \(9.1\times 4.8\times 4.1\) m, and cupboards, desks, chairs, and shelves can be found as furniture. The walls are made of plasterboard, and the floor and ceiling are made of concrete.  
Figure 2 depicts a map of the scenario with all measured positions. For the measurements, one position is assumed as an access point and several user positions are uniformly distributed around the room and marked as 1 to 14. In all the experiments, a line of sight (LoS) exists.  
In Table 1 the distance to the walls of each one of the assumed user positions and the location data of the considered access point are shown.  
Figure 1: Photo of the measurement scenario.  
Figure 2: Map of the measurements.