esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction: State of the Art
### Surface Roughness in Milling
The study concluded that the most effective machining data configuration was a spindle speed of 2000 rev/min, feed per tooth of 0.1 mm/rev, and axial depth of cut of 1.0 mm, as it produces the most advantageous surface roughness: Ra approximately 0.3 \(\upmu\)m.  
Recently, researchers have been highlighting the fact that description of the surface that is limited to the analysis of Ra is insufficient, as it fails to provide information on numerous relevant characteristics of the roughness profile; therefore, additional 2D roughness parameters must be included when describing the condition of the surface. According to [PERSON] [4], the amplitude parameters, Rt and Rz, provide a better indication of the fatigue strength than Ra. These parameters were also analyzed by [PERSON] et al. [14]. [PERSON] [4] points out that the horizontal parameter, Rsm, is also important from the point of view of the utilitarian characteristics of surfaces. The behavior of surfaces in operating conditions is described by, e.g., Rku and Rsk. The Rku parameter describes surface defects, while from the results of Rsk measurements, the conductivity and load-carrying capacity of a given surface may be inferred. These parameters have been analyzed in the literature, e.g., in the study of precise cylindrical milling [15]. The surface roughness is among the key indicators describing numerous machining processes, e.g., plastic forming (pre-treatment), the Abrasive Water Jet Machining (AWJM) method (preparation of semi-finished products for other processes) [16], or brushing treatment (deburring) [7]. It is thus necessary to supply more detail in the description of the surface finish quality after machining. Nevertheless, the specialist literature tends to limit the scope of analyses to the Ra parameter, which appears broadly insufficient considering the applicability of Mg alloys. Although the spiral tool path is employed in the machining of the hardest-to-machine materials, in the case of light alloys, such as Mg, trochoidal milling is used to reduce the cutting forces, heat, and chatter in the area of the cut [17; 18]. The research works devoted to trochoidal milling of light alloys tend to seek the real values of machinability indexes, e.g., the cutting force components [19; 20; 21], and their simulation with ANN/MES solutions [22]. What is more, these analyses focused on Al alloys, e.g., 7075-T651 [20], 5086 Al alloy [21], or Al6061-T6 alloy [22], which indicates that there is a distinct paucity of research into the machinability of Mg alloys using a trochoidal path.
## 2 Materials and Methods  
### Description of an Amplitude-Phase Probe  
It should be noted that the solving the discussed problem by implementing a self-oscillating ECP is practically impossible because the output signal \(f\) is one-parameter, but it is influenced by both \(d\) and \(\sigma\), and their influences cannot be separated.  
We propose to use a three-coil transformer compensated amplitude-phase eddy-current probe (AP ECP), the block diagram of which is shown in Figure 5[10].  
Using the built-in DAC, the microcontroller synthesizes a harmonic excitation signal of a given frequency. The excitation signal amplifier (AE) is the source of the required excitation voltage. The excitation voltage applied to the excitation coil (We) creates a magnetic field that interacts with the base metal of the test object. The resulting magnetic field, derived from the interaction of the magnetic field of the excitation coil and the eddy currents field in the base metal of the test object, is picked by the measuring coil (Wm). The compensating coil (Wc) senses the magnetic field We but does not pick the magnetic field of the eddy currents. Wm and Wc are connected in-series in opposite directions, which results in compensating the constant component of the Wm signal. The signal of the measuring coil output corresponds to the added voltage of the AP ECP. This voltage is applied to the added voltage amplifier (AM), and then, the signal arrives to the ADC. The ADC signal is transmitted through the communication unit for further processing. The output signal of the AP ECP is a complex value containing two independent signal  
Figure 5: Block diagram of three-coil transformer compensated amplitude-phase eddy-current probe. Here, We—excitation coil, Wc—compensating coil, Wm—measuring coil, AE—excitation signal amplifier, AM—added voltage amplifier.  
parameters (Re; Im)--the real and imaginary components of the added voltage of the AP ECP [11].
# FBG-Based Monitoring of Geohazards: Current Status and Trends
## 3 Development of FBG-Based Geohazard Monitoring Systems
### Development of FBG Temperature Sensors  
In the past two decades, different types of FBG sensors have been developed for geohazard monitoring. The simplest sensor is the FBG temperature sensors. A commonly used FBG temperature sensor consists of a loose FBG sensor encapsulated in a tube (Figure 3). When applied in the field, this sensor is mechanically uncoupled from but in thermal contact with the host material. In general, the temperature resolution is 0.1 \({}^{\circ}\)C or less. [PERSON] et al. installed FBG thermal probes in a borehole surrounding a central heating borehole to conduct in-situ thermal measurements of argillaceous rocks [43]. The FBG results were in good agreement with those from conventional resistive probes. [PERSON] et al. pointed out that gratings written in tin-doped silica fibers exhibited great thermal stability up to about 800 \({}^{\circ}\)C, surpassing those written in borogermanosilicate, germanosilicate or hydrogen loaded telecom fibers, which were potentially suitable for high temperature applications such as in volcanic areas [44].  
Figure 3: Photograph of the tube packaged FBG temperature sensor [12].
# Graphic Applications of Unmanned Aerial Vehicles (UAVs) in the Study of Industrial Heritage Assets
## 2 SWOT Analysis Applied to UAV
### Weaknesses  
Regarding weaknesses, the use of UAVs has the following negative aspects:  
W1. Short battery life: The usual battery lifetime during flight is around 15 or 20 min, depending on the flight conditions. This reduces the possibility to record long duration videos or sufficient inspection time, requiring frequent landings to perform battery exchanges. At least four batteries and a previous flight plan are required to complete a heritage asset analysis. In spite of this, or 3D generation models using photogrammetry techniques, a battery can be considered enough to capture the required images. In addition, the batteries can be recharged approximately 3000 times, without reducing their efficiency.  
W2. Device fragility: Short battery life or flying in reduced areas or with objects around can cause the aircraft to crash. Occasionally, depending on the speed and height of the flight, the UAV can become unusable. To reduce the possibility of crashes, UAVs have proximity sensors and battery life monitoring systems.  
W3. High computational cost of information processing: Due to the high definition image capture and high definition video recording capacity, the treatment and manipulation of this information requires high capacity software and hardware. The application of photogrammetry techniques to the captured images require high performance hardware systems and usually involves a lengthy computational time cost.  
W4. Complexity of flight in dark spaces: In addition to the possibility of an aircraft crash during flight, high performance camera systems are often required to generate images in dark spaces, increasing the UAV cost. Furthermore, open space night flights are submitted to restrictive legal conditions for safety reasons.  
W5. Limitations under adverse weather conditions: High wind speeds and rain preclude flights for safety reasons. Even if the wind speed is not high enough to prevent the UAV from flying, the stability of the aircraft may be affected and the results obtained may not be satisfactory. In addition, it is necessary to consider the possibility of drafts in reduced space flights, which can cause the aircraft to fall unexpectedly.  
W6. Maintenance costs: In spite of the fact preventive aircraft maintenance is not necessary for basic UAV, a civil liability insurance is required to cover any possible damage to third parties in the case of a crash. Additionally, replacement insurance is recommended at least during the two first years of the aircraft's life. This represents a maintenance cost of around $500 per year.
# Dark Solitons and Grey Solitons in Waveguide Arrays with Long-Range Linear Coupling Effects
## 5 Conclusions  
In conclusion, we performed numerical studies of dark solitons and grey solitons in a waveguide array with long-range linear coupling effect. This system is described by the discrete nonlinear Schrodinger equation with off-diagonal elements in the linear coupling matrix filled with non-zero interaction terms. The stabilities of dark solitons and grey solitons are studied. The features of solitons such as the widths of dark solitons and the grey levels of grey solitons are comprehensively studied. Our results may fill the gap in the understanding of discrete solitons in long-range linear coupling waveguide arrays, and our design may have potential applications in optical communications and all-optical networks.  
This work was supported, in part, by the National Natural Science Foundation of China through Grant Nos. 61571197, 11575063, and 61172011.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived of and designed the model. [PERSON] performed the numerical simulations. [PERSON] and [PERSON] analysed the data. [PERSON] and [PERSON] provided theoretical explanations. [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 4: Grey levels of grey solitons with different \(d\) and \(P\) values.  
Figure 3: Grey solitons in a 512-waveguide array. **(a)** The imaginary part of the eigenvalues \(\lambda\) (\(\text{Im}(\lambda)\)) for different \(d\) values; **(b)** The imaginary part of the eigenvalues \(\lambda\) (\(\text{Im}(\lambda)\)) for different \(P\) values; **(c)** The amplitude of the fundamental solution for grey solitons (\(P=1\) and \(d=3\)); **(d)** The intensity of the fundamental solution for grey solitons (\(P=1\) and \(d=3\)); **(e)** The evolution of a stable solution for grey solitons (\(P=1\) and \(d=3\)); **(f)** The evolution of an unstable solution for grey solitons (\(P=0.5\) and \(d=0.8\)).
## 10 Discussion and Future Developments
### Autolocalization Tools  
Equally important is the development of technologies that allow the instrument to automatically estimate its position with increasing ease in the phases following the initial survey, once the first data collection has already been completed. Currently, such capabilities are already present in systems such as Heron(r), which primarily rely on real time comparison between the instrument's 3D survey and a \"reference\" 3D model derived from a previous survey or a parametric three-dimensional model of the building. However, these systems tend to fail, particularly in modern buildings where spaces are repeated cyclically, making it impossible for simple geometric comparisons to automatically determine the surveyor's correct location.  
New approaches based on artificial intelligence algorithms allow a system that simultaneously captures 3D scenes and images to recognize objects within 3D models, thus accurately determining its position. This is achieved not only by comparing the 3D model with reality but also by identifying the location based on the size, position, and characteristics of objects detected in real time. These technologies, known as Visual Place Recognition (VPR), often rely on the integration of 3D measurements, typically LiDAR-based, with RGB photographic acquisitions [100; 101; 102].
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
###### Abstract
Meanwhile, a nonlinear frequency adjustment module (NFAM) is adopted to improve the broadband adaptability of the APD circuit within the frequency range of 25.1\(\sim\)27.5 GHz. Section 4 presents the experimental results of the proposed APD MMIC and the nonlinear characteristics of a Ka-band TWTA combined with the APD MMIC. The results are discussed in Section 5, along with potential future research advances. The paper is concluded in Section 6.
# Online Distributed User Association for Heterogeneous Radio Access Network
### HetNet and User Association  
To boost areal spectral efficiency, reuse of radio resources is indispensable. However, the deployment of more macro BSs, forming a dense homogeneous network, is an unattractive solution as the deployment costs, along with the operational and maintenance costs, are very high. An alternative to this homogeneous deployment is to have a heterogeneous one, where many low-powered small cells are deployed within the coverage area of a macro BS. A HetNet cellular network consists of randomly located BSs forming a multi-tier cellular network. Here, each tier differs in the average transmitting power, data rate, and deployment density. The HetNet provides big capacity, high performance, ease of operation, and fast deployment, but with the cost of additional revenue, interference, resource management, and backhaul management [13]. HetNet also has the potential to degrade load imbalance among different BSs due to disparate transmit powers and capabilities [14]. However, with HetNet environment, there are some critical challenges, such as _user association, power control,_ and _resource allocation_ that need to be addressed [15]. The traditional cell-selection strategy, max-SINR(signal-to-interference-plus-noise ratio) [16], used by user equipment (UEs) causes load imbalance in HetNet [17]. This is mainly because UEs in the coverage area of small BSs still receive the strongest signal from the macro BS, and also because the varying data-traffic pattern in the network is not being considered during the cell-selection procedure. To overcome this problem, UEs should be effectively pushed onto the small BSs, but currently, cellular standards support max-SINR, max-RSSI (received signal strength indicator) [16], and biased received power-based user association [18] schemes. In the first two schemes, the UE will be associated with the BS from which it receives maximum signal strength among all the BSs. From the UE's perspective, this scheme should give a better data rate. In a  
Figure 1: Heterogeneous radio access network (RAN) system architecture.  
biased received power-based user association scheme, UEs are offloaded to low-powered small cells using association bias [14]. The UE adds a bias value to the receiving power of the low-powered small cells; however, it is difficult to prescribe the optimal biased value. These schemes perform well in the traditional cellular network [14], where the macro BSs have uniform transmitting power and are deployed uniformly. However, the HetNet environment is completely different, as the heterogeneous BSs are not deployed uniformly.
## 3 Methods and Tools
### Particle Interaction Study  
[PERSON]'s second law is used to describe the interaction particles. The general expression for the forces acting on a particle can be given as follows:  
\[m_{i}\frac{d^{2}\mathbf{x}_{i}}{dt^{2}}=\mathbf{F}_{i}(t) \tag{1}\]  
where \(m_{i}\) and vector \(\mathbf{x}_{i}\) (bold letter means vector) are the mass and position of the \(i\)-th particle. The vector \(\mathbf{F}_{i}(t)=\sum_{j}\mathbf{F}_{i,j}(t)\) corresponds to the resultant forces acting on particle \(i\) in the event of possible interaction.  
If we define the normal orientation of contact by the unit vector \(\mathbf{N}\), the force vector \(\mathbf{F}^{N}=F^{N}\mathbf{N}\) may be described by the time-dependent scalar variable \(F^{N}(t)\)[31]. The interaction of a particle in the normal direction can be described by five forces of different nature:  
\[F^{N}(t)=F^{N}_{deform}(t)+F^{N}_{attr}(t)+F^{N}_{attr-diss}(t) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Values/Method Type** & **References, Source** \\ \hline Density of the material (mycelium) & 300 kg/m\({}^{3}\) & [13; 14; 15] \\ Initial gauge pressure & 0 Pa & - \\ Gauge pressure in the discharge channel & 0 Pa & - \\ Direction of the reverse flow & Normal to the boundary & - \\ Solution of the pressure–velocity dependence & Second-order discretization of pressure and momentum & - \\  & Relaxation factors & - \\ Pressure & 0.5 & - \\ Momentum & 0.5 & - \\ Density & 1.0 & - \\ Volume forces & 1.0 & - \\ Initialization & Hybrid & - \\ \hline \end{tabular}
\end{table}
Table 1: Main model parameters.  
where \(F_{deform}^{N}(t)\) is the particle deformation force in the normal direction (Hertz model); \(F_{altr}^{N}(t)\) is the force of attraction of a particle in the normal direction; and \(F_{altr-diss}^{N}(t)\) is the dissipative force of a particle in the normal direction associated with adhesion [32]. A more detailed description of the model can be found in [31; 32; 33; 34].  
Estimating the action of the normal force \(F^{N}(t)\), the adhesive-dissipative interaction of a particle with a surface can be divided into two parts (Figure 2).  
The first part comprises approach (S-L) and detachment (A-D'). When approaching and detaching, the particle experiences the action of the adhesive force and, accordingly, moves a certain distance from the interacting surface, and its displacement is considered negative. The second part includes loading (L-U) and unloading (U-A). During loading and unloading, the particle comes into contact with the surface, it is deformed, and the displacement is considered positive. As seen in Figure 2, a negative normal force corresponds to attraction, and a positive normal force corresponds to repulsion, according to [PERSON] and [PERSON] [42; 43]. If it does not have enough energy to detach at point D', it returns, and sticking process occurs. In this work, the sticking process was not analyzed.  
The initial data are presented in Table 2.
## 5 IETF ROLL Working Group
### Implementation and Evaluation  
#### 6.2.1 CoAP Implementations  
At the time of writing this article, the CoAP protocol still is not yet finalized. However it is considered in its final stages before being finalized. Nevertheless several implementations of the CoAP protocol for various platforms and programing languages already exist. Some of these implementations are open source and others have commercial licenses. Interoperability between many of these implementations has been formally tested by the _European Telecommunications Standards Institute_ (ETSI), a non-profit standards organization. ETSI organizes a series of events called ETSI Plugtests to test interoperability of telecommunication technologies in a multi-vendor, multi-network or multi-service environment. ETSI Plugtests, the IPSO Alliance and the FP7 Probe-it project have organized two IoT CoAP Plugtests. In addition to assessing the interoperability of participating products, these Plugtests events aimed to validate the CORE base standards. The first Plugtests event (March 2012) was attended by 18 companies testing the world's first CoAP client and server implementations. The features tested at this event included the base CoAP specification, CoAP Block Transfer, CoAP Observation and the CoRE Link Format. More than 90% of 3,000 tests executed in this event were successful. According to ETSI Plugtests this result is classified as a high level of interoperability [90]. At the second IoT CoAP Plugtests event (November 2012), some additional tests were added to cover previously untested aspects of CoAP in addition to introducing new optional tests for proxy functionality and M2M communication. In this event 1,775 test cases were performed, in 60 pairing test sessions, with a success rate of 97.8% [91]. The improvement in interoperability compared to the first IoT CoAP Plugtests event indicates that the base CoAP protocol and its main options are getting more robust. With insights of the first IoT Plugtests a survey on the current state of the art of lightweight REST implementations was presented in [92]. In Table 7 we present an adapted version of the survey results. In this adapted version we concentrate on publicly available implementations and servers and commercial implementations that are publicly announced (See [92] for other implementations). This table uses device classes as defined in [34]. Class 1 devices have \(\sim\)10 KiB of RAM, and \(\sim\)100 KiB of ROM. Class 2 devices have \(\sim\)50 KiB of RAM, and \(\sim\)250 KiB of ROM.
## 2 Materials and Methods  
### Raw Materials  
The recently cut elm (_Ulmus laevis_ P.) and poplar (_Populus nigra_ L.) samples, measuring 45 mm \(\times\) 90 mm \(\times\) 4000 mm (L \(\times\) T \(\times\) R) boards, were collected from the northern part of Iran, Caspian Hyrcanian mixed forest. The region is located in the southern part of the Caspian Sea in Iran and the western regions in the Republic of Azerbaijan. The defect-free samples (no knot, cracks, and reaction wood) were cut from heartwood. The moisture contents of both recently cut samples were 9.9%. The density of recently cut elm and poplar were, respectively, 516.2 kg/m\({}^{-3}\) and 506.3 kg/m\({}^{-3}\).  
The archaeological elm sample was part of a historical building in the city of Iasi, Romania. This specimen was a flooring element, and the building was built about 350 years ago. The house had been abandoned for a long time and most of it was destroyed. These samples were buried under the soil and were excavated during the excavation operation in September 2009. The type of soil was sand and gravel. The moisture content of the sample during analysis was 11.6%, and the density of the sample was 330.0 kg/m\({}^{-3}\).  
The archaeological poplar sample was a part of a boat in the Moldavian region of Romania. The boat was buried in the Moldova riverbed. The sample was not waterlogged because the river had changed course over a long period, and the soil at the sample site had low moisture content. Thus, it is classified as dry archaeological wood. After the discovery of this boat, it was transferred to the history museum in the city of Iasi, and it was kept in storage at the museum for one year until the exhibition hall with proper conditions (50% RH and 18-20 \({}^{\circ}\)C) became ready. The sample was removed from the boat during the restoration process. The approximate age of this specimen is 1000 to 1200 years. The moisture content and the density of the sample during analysis were 10.5% and 288.5 kg/m\({}^{-3}\), respectively.  
It should be noted that the ages of the archaeological wood samples were determined by the stratigraphic evaluation method [6; 34]. The recently cut and archaeological specimens are shown in Figure 1. Both samples were chosen from the cracked-, knot-, and dust-free part of the wooden elements. A few millimeters of wood surfaces were removed carefully, and then the samples were cut randomly from the inner part of the subjects. The samples were not conserved with any chemicals. Since both archaeological specimens were found buried underground and in a dry environment, and the samples were found in their place of use, in their original context and undisturbed, the samples can be considered as a primary context group.  
Figure 1: Recently cut (**a**) and archaeological elm (**b**) and recently cut (**c**) and archaeological poplar (**d**).