esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Analysis of the Interaction of Anchor Bolts and Concrete Foundation
### Measurement at Operational Loading
It can be seen that a positive increase in axial forces occurred in Bolts No. 19 and No. 24, which is caused by the fact that, after loading the empty ladle, the preload in Bolts No. 19 and No. 24 is reduced, and this is reflected by an increase in the deformation part of the dynamometer, resulting in a positive reading on the strain gauge apparatus. Simply put, if the preload in the bolts were not set, it would not be possible to register a decrease in the axial force. At a time of about 100 s, the rotation of the casting pedestal with an empty ladle in Basket 1 started from the reserve position to the casting position, which caused changes in the polarity of the force increments in the bolts when Basket 1 was moved to the casting position. It is evident from the courses that in this operation the axial force range in Anchor Bolt No. 8 is 25 kN, in Anchor Bolt No. 19 it is 13 kN, and in Anchor Bolt No. 24 it is 19 kN. At the end of the time period (Figure 10), Basket 1 with an empty ladle was in the casting position. Figure 9: Recording of time changes of axial forces when rotating the casting pedestal with empty baskets. Figure 11 shows the process of rotating the casting pedestal with an empty ladle in Basket 1 to the reserve position, when the empty ladle was removed and the pedestal with a full ladle was rotated to the reserve position again. In order to assess the safe operation of the casting pedestal, the following operational modes were examined:  
* Rotation of the pedestal without a ladle in Baskets 1 and 2;
* Loading the empty ladle with subsequent rotation, and lifting of the ladle in the casting position in Basket 1 and subsequently in Basket 2;
* Loading a full ladle weighing 241,600 kg into Basket 1 and subsequently into Basket 2, rotating the pedestal, and lifting and launching the basket with the ladle;
* Loading the empty and full ladle and handling the pedestal, including the replaced ladles in baskets. After analysing the time records of changes in the axial forces in the bolts in the above-mentioned operational modes of the pedestal, it was found that the maximum range of changes in the axial forces \(\Delta N_{i}\) in the measured bolts is the following: \(\Delta N_{8}=61\,\mathrm{kN},\Delta N_{19}=60\,\mathrm{kN},\Delta N_{24}=61\, \mathrm{kN}\).
## 4 Discussion
### Gross Calorific Value  
The calorific power values of all treatments were similar to those reported by [PERSON] et al. [47] for _Eucalyptus_ wood (19.5 MJ kg\({}^{-1}\)) and higher than those reported by [PERSON] et al. [9] (17.5 MJ kg\({}^{-1}\)), but lower than those reported by [PERSON] et al. [49] (17.35-19,774 MJ kg\({}^{-1}\)) from briquettes made with fresh and weathered pine sawdust.  
The values of calorific power for all treatments were higher than 15.5 MJ kg\({}^{-1}\) established in EN 17225-3 [35], and all briquettes were classified as class A1. The large particle size was suited for the production of briquettes with high calorific power values, as was shown in treatment T1 (100:0.0). Mixtures with large and medium particle size percentages, such as T9 (33:67:0) and T5 (67:33:0), presented calorific power values higher than those obtained with mixtures containing higher proportions of small particles, as in T3 (0:0:100), T6 (67:0:33), and T8 (0:33:67). [PERSON] et al. [50] obtained similar results by studying the effects of particle size and the addition of binder on calorific values in briquettes, showing that small particles (1.75 mm and 2.00 mm) had the lowest calorific values, while the larger size of 3.35 mm had high calorific values.  
Similar calorific power values in briquettes made with palm kernel shell were reported by [PERSON] et al. [51]; when they increased the particle size, the calorific value increased. This may be because there is more space and consequently more air between larger particles, which makes the combustion process more efficient, producing higher calorific power.  
According to the response surface and contour graphs (Figure 8), the highest gross calorific values (>21 MJ Kg\({}^{-1}\)) are yielded when the percentage of large particle sizes is from 95 to 100%, and medium and small particles are from 0 to 5%. The same gross calorific values (>21 MJ Kg\({}^{-1}\)) are obtained when the percentage of large particles is from 30 to 35% and the percentages of medium and small particles are from 65 to 70% and 0 to 5%, respectively.
## 3 Results and Discussion  
### Characterization of Materials  
Figure 6 shows images of gold film before annealing, heat-treated gold films, and TiO2/Au nanocomposite film on a quartz substrate, captured by scanning electron microscopy (SEM; Hitachi S-4800, Tokyo, Japan). Figure 6a-d indicate that the gold nanoparticles exhibited irregular island-like structures and were evenly distributed on the quartz substrate. After heat treatment (600 \({}^{\circ}\)C, 1 h), the shape of the gold nanoparticles changed to nearly spherical. The average size of the gold nanoparticles increased with the increase in the thickness of the gold film (Figure 6e-h), primarily due to the enhanced aggregation effect of gold nanoparticles during heat treatment [41; 42]. The SEM images of the TiO2/Au nanocomposite film (Figure 6i-l) show that the deposited TiO2 film formed a conformal pinhole-free layer, and that heat treatment did not affect the size and distribution of the gold nanoparticles.  
Figure 7 shows the X-ray diffraction (XRD; Bruker D8 Discover, Karlsruhe, Germany) patterns of the annealed TiO\({}_{2}\) and TiO\({}_{2}\)/Au nanocomposite films. Figure 7a indicates that the pure TiO\({}_{2}\) annealed at a high temperature (500 \({}^{\circ}\)C, 1 h) presented an anatase phase with high photocatalytic activity [43,44]. The peaks of the diffraction patterns appeared at 20 = 25.24\({}^{\circ}\), 38.24\({}^{\circ}\), 49.52\({}^{\circ}\), 53.92\({}^{\circ}\), and 64.16\({}^{\circ}\), corresponding to the (101), (004), (200), (105), and (116) anatase structure planes, respectively (JCPDS file no. 21-1271).  
Figure 6: SEM images of gold film before annealing (**a–d**) and gold film (**e–h**) and TiO\({}_{2}\)/Au nanocomposite film (**i–l**) after annealing. (Au: 600 \({}^{\circ}\)C, 1 h. TiO\({}_{2}\)/Au: 500 \({}^{\circ}\)C, 1 h).  
Figure 7: XRD patterns of the pure TiO\({}_{2}\) (**a**) and TiO\({}_{2}\)/Au nanocomposite films, (**b**): TiO\({}_{2}\)/Au (1 nm), (**c**): TiO\({}_{2}\)/Au (2 nm), (**d**): TiO\({}_{2}\)/Au (3 nm), (**e**): TiO\({}_{2}\)/Au (4 nm) after annealing.  
Moreover, despite being doped with gold films of varying thicknesses, the TiO\({}_{2}\) in the doped TiO\({}_{2}\)/Au nanocomposite film exhibited an anatase structure with high photocatalytic activity (Figure 7b-e). The crystal structure of TiO\({}_{2}\) was not affected by the deposition of the gold films with different thicknesses. For the TiO\({}_{2}\)/Au nanocomposite film with a thickness of 2 nm, diffraction peaks of Au crystal planes appeared at 20 = 38.12\({}^{\circ}\), 44.44\({}^{\circ}\), and 64.16\({}^{\circ}\) (JCPDS file no. 04-0784). The other TiO\({}_{2}\)/Au nanocomposite films also exhibited similar diffraction peaks at these 20 angles because the difference in the thickness of the deposited gold film led to variations in the peak diffraction height. The specific trend is that the diffraction peak of the gold film increased with the increase in deposition thickness [45; 46].
## 4 Toxicological Effects of Glyphosate and AMPA
### Carcinogenicity  
When exposure is within the allowable NOAEL, studies have shown that glyphosate is not carcinogenic [23]. According to [PERSON] et al. [159] and [PERSON] et al. [160]. They reported glyphosate could promote tumour growth in skin cells (in vivo mouse) and breast cell proliferation (in vitro human models). In [PERSON]'s [160] study, glyphosate residue concentration in drinking water as low as \(10^{-12}\) M stimulated hormone-induced breast cancer. The International Agency for Research on Cancer recently determined that glyphosate is most likely human carcinogenic [161]. It has been reported that non-Hodgkin lymphoma (NHL), cancer derived from lymphocytes of the regular human immune system, is caused by GBH [162]. Different subtypes of NHL exist depending on where the cell originates, for example, in T and B cells, or natural killer cells. The distinct stages of lymphocyte development are related to the different NHL subtypes. Typically, lymph nodes and the spleen are enlarged, as well as other tissues such as the blood and bone marrow are affected [162]. A variety of infection agents, chemical agents, and deficiencies in the immune system are among the causes of NHL [162]. [PERSON] et al. [163] discovered that being exposed to GBH raises the risk of NHL among workers that are highly exposed to glyphosate. In 2020, NHL accounted for approximately 77,000 new cases and caused almost 20,000 deaths in the United States alone [164]. There were many lawsuits in court by the time Bayer bought Monsanto in 2018, alleging that Roundup is linked to NHL [165; 166; 167]. Researchers from the University of Washington found that Roundup exposure increases NHL risk by 41%, a clear link between the herbicide and cancer [166].  
Even though glyphosate and glyphosate-based herbicides are valuable and significant weed-management tools for agricultural and forestry practices, long-term appropriateprocedures and control of glyphosate usage are still needed to ensure efficacy, minimize pollution, and avoid adverse human health effects.
## 2 System Model
### Network Model  
The integrated satellite terrestrial network we investigate is depicted in Figure 2. In the downlink scenario, a satellite serves multiple users within its coverage. It could be a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite or a geosynchronous (GEO) satellite. It is equipped with \(L\) antennas to provide extensive coverage. Each user is equipped with a single antenna. In this network, the RSMA scheme is adopted for multi-user transmission. Using different precoders, it can simultaneously serve multiple users and reuse the entire spectrum effectively.  
To better describe the link between satellite and terrestrial users in real life, we employ the shadowed-Rician fading as our channel model [33]. Assume \(h_{k}\) is the channel fading coefficient between satellite and \(k\)-th user. The probability density function (PDF) is listed below:  
\[f_{|h_{k}|^{2}}(x)=\alpha_{k}e^{-\beta_{k}x}{{}_{1}F_{1}(m_{k};1;\delta_{k}x)}, \tag{1}\]  
where \(\alpha_{k}=(2b_{k}m_{k}/(2b_{k}m_{k}+\Omega_{k}))^{m_{k}}/2b_{k}\), \(\beta_{k}=1/2b_{k}\), and \(\delta_{k}=\Omega_{k}/2b_{k}(2b_{k}m_{k}+\Omega_{k})\). \(\Omega_{k}\) and \(2b_{k}\) indicates the average power of line-of-sight (LoS) and multipath components, respectively. \(m_{k}\) is the Nakagami-\(m\) parameter with range \([0,\infty)\). \({{}_{1}F_{1}(a;b;x)}\) is the confluent hypergeometric function given by  
\[{{}_{1}F_{1}(a;b;x)}=\sum\limits_{i=0}^{\infty}\frac{(a)_{i}}{(b)_{i}}\frac{x^ {i}}{i!}, \tag{2}\]  
where \((a)_{i}=\Gamma(a+i)/\Gamma(a)\) is the Pochhammer symbol and \(\Gamma(a)\) is the Gamma function [34].  
According to [8], RSMA's performance relies on the quality of CSI. In ISTN, the CSIs of satellites are continuously collected to monitor network condition and adjust precoders. However, perfect CSIs are unlikely to be obtained due to various reasons. A sensor could be introduced to assist CSI collection, but its placement will affect the results. Several papers [35; 36; 37] have studied this problem and proposed various algorithms. In this paper, for simplicity, we assume CSIs are acquired by terrestrial terminals.  
The errors in CSI have various sources, such as Doppler shift [38] and satellite attitude dynamics [39; 40]. Taking all these factors into account will cause rapid expansion of state space in DRL, which will lead to unstable learning and incapability to converge. Fortunately, the influence of these factors can be reflected in CSI. To achieve a tradeoff between complexity and accuracy, we add noise sampled from complex Gaussian distribution into the channel to simulate errors.  
Figure 2: Network model.  
The satellite in this network divides the messages for all users and encodes them with RSMA precoders. After broadcasting, terrestrial users use a shared public precoder and their own private ones to restore the messages for them. To maximize the capacity with minimum power consumption, we will investigate the energy efficiency optimization in the next section.
## 3 Results and Discussion
### Load Profile  
As shown in Figure 6, the power consumption of the olive oil mill during normal operation is around 12-15 kW\({}_{\rm e}\). The peak power consumption is near 55 kW\({}_{\rm e}\). It is also very important that the engine-generator set operates with a certain offset from the full load. Therefore, the power output of the engine-generator set should be about 80 kW\({}_{\rm e}\). This is because the gasification plant has slow load dynamics, which means that it cannot adapt the generated producer gas flow rate to meet abrupt changes in the power demand of the engine-generator set. Accordingly, if sudden load surges occur, the generator will require more gas from the gasifier than the gasifier can provide, resulting in disconnection of the generator.  
The relatively low value of the power factor in Figure 6 indicates that the mill does not include a capacitor bank. During the monitoring test, it can be seen that the power factor drops to values as low as 0.4, while the average value remains around 0.55. This power factor is not suitable for grid-connected operation but, in this case, the olive oil mill operates in off-grid mode. It is noteworthy that, when sizing the power lines, a power factor much lower than normally estimated should be used.  
Figure 5: Process simulation flowsheet of the engine-generator set in the Thermoflex environment.  
Figure 7 shows the consumption of each part of the mill. The most critical motors from the olive oil mill are the shredder, decanter, and the centrifuge. These motors should have installed variable frequency drive to adjust the power consumption with the needed load factor, or at least a soft starter in order to decrease the peak consumption. The motor starting procedure will be described; in the first rectangle, the washer pump was started up and in the second rectangle, a no load start of the plant was carried out. The third shows a sequential start-up of the entire plant. After that, all the plant was stopped, and the fourth rectangle shows the shredder motor. Following, the next motor started was the centrifuge and soon after, the wash pump and the wash system were restarted. The final motor started was the decanter.  
Figure 6: Power consumption and power factor of the olive mill under continuous operation.  
Figure 7: Power consumption of the olive mill under intermittent operation of each section.  
In the case of the power factor under intermittent operation, a very irregular trend was detected, and in some points, the power factor falls to values under 0.3. This low power factor causes the engine-generator set of the olive oil mill to consume more diesel than it should under normal operating conditions with a power factor of around 0.8.  
The rated power consumption of the all electric motors used during the operation of the olive oil mill are included in Table A1 of Appendix A.
# 5G Positioning: An Analysis of Early Datasets
## 3 Methods
### TDoA Analysis  
In order to verify the accuracy and reliability of the 5G observations, the measured \(TDoA\) are compared with the reference \(TDoA\) obtained from the known distances between the base stations (\(BS\)s) and the receiver, as determined by the coordinates provided by the total station benchmark \(TDoA\)).  
In each trajectory, different approaches are tested for choosing the reference station for \(TDoA\) computation:  
* use one \(BS\) as reference for all the epochs;
* use the pivot method, e.g., following a scheme \(BS1\)-\(BS2\), \(BS2\)-\(BS3\), and so on, for each epoch;
* choose as reference station for each epoch the station with the best SNR: in this case, the configuration can change between epochs.  
It is worth noting that, in pivoting, the selection of a specific scheme is not important. Indeed, by a proper propagation of the covariance matrix (as in Equation (10)), the solution of the least squares method for different pivoting strategies should be identical up to numerical rounding [34]. The results of this analysis are discussed in Section 4.1.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 2 Materials and Methods  
### Plant Material and Site  
Experiments were carried out in a high density (513 trees per hectare), 17-year old, irrigated olive orchard at the experimental farm of the University of Pisa, in Venturina (Italy). The orchard included six cultivars, but the work was carried out only on cv. Frantoio, which was the most abundant (61% of the total). A complete description of the plant material and orchard management has been previously published [5; 7].  
Climatic conditions during the study period were monitored using an iMETOS IMT 300 weather station (Pessl Instruments GmbH, Weiz, Austria). The annual reference evapotranspiration (ET0), calculated according to the Penman-Monteith equation, was 952 mm. Annual and summer (21 June-22 September) precipitations were 809 and 39 mm, respectively. Cultural practices and monitoring of phenological parameters, including the estimation of the full bloom date (DOY 148), were performed as previously reported [5; 7].
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 4 Controller Switching Mechanism  
In this section, we present a codesign approach for the switching controllers. The process of the proposed controller switching mechanism is illustrated in Figure 3. The proposed mechanism detects controller failures combined with a backup controller to maintain stability in case of a primary controller is failed. First, a plant is controlled through the primary controller as shown Figure 3a. The sensor information of the plant is delivered to the primary controller, and the primary controller generates control information and transmits it to the plant. A host node in WSANs generates network scheduling information and passes it to each node in the network. The backup controller frequently checks the primary controller for failures by monitoring the communication status between the plant and the primary controller. As shown in Figure 3b, the backup controller detects failures in the current controller and replaces the current controller in the case of an emergency. In this controller switching mechanism, the primary and backup controllers have the same hardware and control functions to calculate control input signals for plant actuation, where roles of these controllers can be determined by a CPS administrator. The only difference between primary and backup controller is that the backup controller has a fault-checking function of the primary controller. Furthermore, we assumed that the backup controller was reliable and would not break down.  
To ensure the stability of the control system, we designed the controller switching to occur under the maximal allowable delay bound (MADB) conditions. In this section, we analyze the LWB network delay and MADB. Then, we designed the WSAN scheduling of the LWB protocol and algorithm for switching controller, so that the delay did not exceed MADB conditions.
# A Hybrid Multi-Objective Optimization Method and Its Application to Electromagnetic Device Designs
## 3 A Hybrid Multi-Objective Optimization Method  
Numerical results demonstrate that the original non-dominated sorting genetic algorithm (NSGA-II) is difficult to converge to the true Pareto frontier when dealing with benchmark test functions with numerous local optima. To improve the global search ability, the population update mechanism of multi-objective particle swarm optimization method (MOPSO) is introduced in the proposed hybrid algorithm. This also contributes to a more uniform distribution of obtained solutions. Moreover, adaptive operators, including crossover and mutation, are proposed to achieve a better balance between global and local searches.
# Knowledge Sharing in Security-Sensitive Communities
## 2 Literature Review
### The Spread of Knowledge  
The first category includes global metrics representing the entire network, enabling analysis of end-to-end knowledge-sharing. For instance, a network's average path length allows comparison between networks, with a lower path length indicating faster knowledge spread. This study's knowledge-sharing metrics include network diameter, radius, and density.  
Diameter and radiusIn Social Network Analysis (SNA), diameter and radius differ from their geometric meanings. The diameter is the shortest path between the two furthest nodes, while the radius is the minimum eccentricity across all vertices [53]. Both are non-directional. In communication networks, a small diameter indicates faster information spread [54; 55]. A community structure that maximizes knowledge transfer results in a smaller diameter. Trust among members influences cooperative behavior, which can affect a network's radius [56]. Communication efficiency is measured by the shortest possible path between all nodes, with knowledge flow linked to trust and commitment [57; 58]. For instance, al-Qaeda's network had a long average path length, suggesting secrecy over efficiency [59]. In security-sensitive organizations, when operational tasks are prioritized, the radius and diameter are expected to be smaller than in non-sensitive organizations.  
DensityNetwork density is the ratio of actual to possible ties, indicating the level of knowledge sharing and node interactivity [60]. In communication networks, density measures a node's embeddedness in discussions. Dense structures foster commitment and trust among members [61]. Given the importance of trust in security-sensitive organizations, we expect their density to be higher than that of non-sensitive organizations.
## 3 Progress in Grating Fabrication Technology  
Grating design and production are very important in semiconductor and spectroscopic applications. Grating morphology is particularly important, hence many researchers have focused on grating fabrication. To date, mechanical scribing has been widely used, and there are also methods for producing target diffraction gratings by duplicating master gratings, such as holographic lithography, electron-beam lithography, nanoimprinting, and others, combined with etching technology (including dry and wet etching) to prepare gratings with various morphologies. In what follows, an overview and discussion of the recent evolution of the grating fabrication technologies mentioned above is presented.