esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case-Study Description and Problem Formulation  
The previous section has mentioned a practical example of a collaborative workcell, where one cobot is in cooperation with one worker in a reconfigurable manufacturing scenario. The cobot is responsible for the pick and place operations while the worker assembles the handled products. Due to the speed difference between the worker and the cobot, a buffer space is needed beside the worker to store the loaded jobs by the cobot till the worker is free to process them. In this paper, we will take this example as a case-study to show the solution approach.  
In this case-study the production volume is continuously varying which leads to the fluctuation in the required time from both the cobot and the worker to complete a job. The required time from the cobot to pick and place the parts of an assigned job is unknown, however it can be calculated. Since the cobot speed is reliable, thus we can assume that the required time from the cobot to pick and place one job is a constant and equals to a Robot Time Constant (RTC). Therefore, by multiplying RTC by the required units number, the overall time can be calculated. On the other hand, the required time which is needed from the worker to assemble a job is varying, therefore this time cannot be calculated. However, it can be predicated by recording the needed time from the worker to assemble the previous jobs, then calculating the Worker Mean Time Value (WMTV), which can be used later to predict the overall needed time for assembling the next jobs.  
The research question is very easy to state, yet it is very hard to answer. When a cobot is in coordination with a worker in the mentioned case-study. How can the control solution assign the next production job among many standing jobs to the cobot and the worker to minimize the overall makespan. Taking into consideration the previously mentioned uncertainty in the required time from the worker to accomplish a production job. The difficulty of answering this question stands from the fact that the answer is beyond an efficient scheduling algorithm only, since it needs an intelligent information control and communication solution which can understand the overall status of the workcell and monitor the worker time to be able to take logical decisions and therefore can apply the scheduling algorithm. During this research, we extend the research in [3] by applying [PERSON] algorithm to schedule the cooperation at the case-study. The required solution components as well as Johnson algorithm are going to be explained in details in the next section.
## 2 Materials and Methods
### Shallow Classification Algorithms
#### 2.3.3 k-Nearest Neighbors (k-NNs)  
k-NNs [31] is widely used in classification due to its simplicity, speed, flexibility in understanding and implementation, as well as its high performance and simplicity in the learning process. k-NNs determines the class of a test sample by selecting the most frequently occurring class among its k nearest neighbors. The performance of the k-NN algorithm mainly depends on the distance metric used to identify the k adjacent neighbors of a given point. Let \(X_{1}\) and \(X_{2}\) be represented by feature vectors:  
\[X_{1}=\{x_{11},x_{12},\dots,x_{1m}\} \tag{6}\]  
\[X_{2}=\{x_{21},x_{22},\dots,x_{2m}\} \tag{7}\]  
where \(m\) is the dimensionality of the feature space. The adjacency can be measured by distance and the most frequently used one is the Euclidean distance given by:  
\[dist(X_{1},X_{2})=\sqrt{\sum_{i=1}^{m}(x_{1i}-x_{2i})^{2}} \tag{8}\]  
where \(x_{1i}\) and \(x_{2i}\) are the \(i\)th features of \(X_{1}\) and \(X_{2}\), separately.
## 4 Impact of Burial Depth and Freeze-Thaw Cycles on Rock Mechanical Properties
### Experimental Results and Analysis  
As shown in Figure 5, the uniaxial compressive strength of rocks decreased significantly with increasing freeze-thaw cycles and decreasing burial depth, albeit at a progressively reduced rate. For unfrozen granite, the compressive strength was 98.44 MPa. After 20 freeze-thaw cycles, specimens at 5.27 m and 0.10 m burial depths exhibited compressive strengths of 45.87 MPa (53.40% reduction) and 30.72 MPa (68.79% reduction), respectively.  
A three\(-\)dimensional exponential fitting (Equation (1), fitness coefficient: 0.908) was applied to describe the uniaxial compressive strength (\(\sigma_{\text{c}}\)) of rocks at varying burial depths post\(-\)freeze-thaw cycles. The fitness coefficients confirm that the freeze-thaw cycles exerted a far greater influence on strength degradation compared to burial depth (i.e., low\(-\)temperature extremes).  
\[\sigma_{\text{c}}=98.44e^{(0.00772Y-0.07115)N} \tag{1}\]  
where \(Y\) is the depth of the rock mass (m, positive values indicate burial depth) and \(N\) is the number of freeze-thaw cycles.  
As indicated in Figure 6, the tensile strength variation of rocks with freeze-thaw cycles and burial depth paralleled compressive strength trends, though the magnitude of strength reduction was slightly smaller. Unfrozen granite exhibited a tensile strength of 7.06 MPa. After 20 freeze-thaw cycles, specimens at 5.27 m and 0.10 m burial depths experienced tensile strengths of 3.91 MPa (44.60% reduction) and 3.30 MPa (53.25% reduction), respectively. Strength losses for compressive and tensile properties at 0.10 m depth exceeded those at 5.27 m by 15.39% and 8.65%, respectively.  
A three\(-\)dimensional exponential fitting (Equation (2), fitness coefficient: 0.956) quantified tensile strength (\(\sigma_{\text{t}}\)) dependence on burial depth and freeze-thaw cycles. Analogous to compressive behavior, freeze-thaw cycles dominated strength degradation over burial depth effects (low\(-\)temperature extremes).  
\[\sigma_{\text{t}}=7.059e^{(0.00348Y-0.04739)N} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \begin{tabular}{c} **Freeze–Thaw** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Corresponding** \\ **Bural Depth (m)** \\ \end{tabular} & \begin{tabular}{c} **Freeze–Thaw** \\ **Cycles** \\ \end{tabular} & \
\begin{tabular}{c} **No. of Specimens** \\ **Uniaxial Compression** \\ \end{tabular} &
\begin{tabular}{c} **Brazilian Splitting** \\ \end{tabular} \\ \hline
1 & \(-\)2\(\sim\)7 & 0.10 & & 5 & 5 \\
2 & \(-\)5\(\sim\)10 & 1.88 & 0, 2, 5, 10, 20 & 5 & 5 \\
3 & \(-\)10\(-\)14 & 3.50 & & 5 & 5 \\
4 & \(-\)20\(-\)22 & 5.27 & & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental scheme.  
Figure 4: Granite specimens after cutting and polishing: (**a**) uniaxial compression specimens; (**b**) Brazilian discs.  
Figure 5: Compressive strength in rocks at different burial depths under freeze–thaw cycling.  
Figure 6: Tensile strength in rocks at different burial depths under freeze–thaw cycling.
# Construction, Test and Application of a Tungsten Metallogene Named MGW11: Case Studies in China
## 4 Application of Geochemical Exploration in Lianyang Area
### Geographical and Geological Settings  
The Lianyang area is located in south China with an area of ca. 5400 km\({}^{2}\) ranging from E 111\({}^{\circ}\)47\({}^{\prime}\)24\({}^{\prime\prime}\) to E 112\({}^{\circ}\)40\({}^{\prime}\)12\({}^{\prime\prime}\) with a distance of 90 km from west to east and N 24\({}^{\circ}\)02\({}^{\prime}\)24\({}^{\prime\prime}\) to N 24\({}^{\circ}\)34\({}^{\prime}\)48\({}^{\prime\prime}\) with a distance of 60 km from north to south (Figure 5a), which is situated in a typical subtropical monsoon zone with a humid climate. The mean annual temperature is ca. 15.5\(\sim\)20.4 \({}^{\circ}\)C. The annual rainfall is ca. 1500-2200 mm, most of which falls in summer, according to the public network data. The topography in the Lianyang area is characterized by foothills, and the elevation is low in the middle, high in the north and south, and with a value of 50 to 1900 m a.s.l. Soils are thickly developed, and the regolith thickness commonly varies from 10 to 25 m depending on the relief. The land is commonly covered by crops or arbors.  
The strata in the study area belong to Cambrian, Devonian, Carboniferous, Permian, Triassic, Jurassic, and Quaternary periods, respectively, which petrological descriptions are illustrated briefly in Figure 5a as notes. Faults are mainly trending N-S in the center and NE-SW in the northwestern, and NW-SE in the northeastern. The main intrusion in the study area is the Lianyang granitic complex in which two types of lithology can be recognized gradationally: the medium-grained biotite granite as the main body and the medium-coarse-grained porphyritic-like biotite granite outcropped locally [21].  
Six polymetallic W deposits have been recognized in the study area [22], although they have not been receiving much attention [23]. The polymetallic W deposits are mainly distributed around the Lianyang granitic complex contacting with strata and faults (Figure 5a). Among the six polymetallic W deposits, three deposits are located in the north margin of the Lianyang granitic complex near the Zhainan town, two deposits in the south margin of the Lianyang granitic complex near the Zhongzhou town, and one in the northeast margin of the Lianyang granitic complex near the Yangshan town. In addition, three Cu deposits, one Au deposit, and one Mo-Fe deposit are also recognized in this area [16; 24].
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 5 Operational Requirements and Radar Specifications
### Minimum Update Rate  
The referred standards do not explicitly state the requirements of the update rate and, instead, specify the antenna HPBW as a crucial requirement. To the best of the authors' understanding, the requirement of a wide HPBW implies that the projection of the antenna on the ground surface shall be large enough such that the platform does not cover a longitudinal distance more than the major axis of the elliptical antenna illumination on the surface of the ground between two successive updates from the radar sensor.  
For a symmetric radiation pattern, the choice of antenna orientation is irrelevant. However, UAV platforms displace faster along the longitudinal direction than yaw or roll. Therefore, it is logical to orient the antenna such that the larger HPBW is along the  
Figure 6: (**a**) Concept of AoA estimation and (**b**) maximum angular FOV [28].  
longitudinal motion of the platform. This study considers the specifications of the development platform, IWR1843 BOOST, for the evaluation of waveform parameters. For the given board, the azimuthal HPBW is greater than the elevation HPBW [62] and, therefore, used in subsequent mathematical expressions. From (25), the length of the major axis, \(D\), of the ellipse can be mathematically given in terms of the altitude, \(R\), as  
\[D=\frac{\theta_{a}}{2}R. \tag{41}\]  
To estimate the minimum update rate, for a platform capable of traversing at maximum velocity, \(V_{max}\), and a minimum altitude requirement of \(R_{min}\), the maximum time between successive updates is upper-bound by  
\[\Delta t_{max}\leq\frac{\frac{\theta_{a}}{2}R_{min}}{V_{max}}. \tag{42}\]  
This upper bound signifies that there is a minimum requirement of fetching the successive altitude data before the overlap of elliptical antenna projections on the surface of the ground reaches zero over the time period \(\Delta t_{max}\). The inverse of this duration, the minimum update rate, \(U_{min}\), can then be given as  
\[U_{min}=\frac{1}{\Delta t_{max}}. \tag{43}\]  
The crux of this discussion is that, as the altitude of the platform decreases, the antenna projection becomes smaller and there is a requirement to rapidly update the altitude information, particularly during the landing stage. Furthermore, faster platforms necessitate a rapid update rate, hence an inverse relationship. Figure 7 illustrates this phenomenon.
# Impact Characteristics of a Bidirectional Pneumatic DTH Hammer for Unconsolidated Formations
## 3 Bidirectional Impact Model for the Pneumatic DTH Hammer
### Simulation Model  
During DTH hammer drilling, the piston is driven by compressed air moving forward and backward alternately, which is a complex dynamic process with nonlinear characteristics. It is difficult to obtain an analytical solution via theoretical analysis. To study the dynamic characteristics of the DTH hammer, a dynamic simulation model was established using SimulationX 4.1 student version according to the pneumatic transmission physical model built in Section 3.1, as shown in Figure 4. In the pneumatic module of the SimulationX software, pneumatic components such as cylinders and valves are available. In the simulation model, the pneumatic DTH hammer is divided into four parts, which are the DTH control part (Area I), the dynamic testing part (Area II), the hammer functional part (Area III), and the rock interaction part (Area IV), respectively. In the control part (Area I), position feedback control functions are adopted to output control signals via logical operation by measuring the displacement of the DTH hammer cylinder and piston and then control the opening and closing of air supply valves and exhaust valves dynamically. The dynamic testing part (Area II) is used to measure parameters in the reciprocating motion of the piston, such as displacement, velocity, acceleration, force, and collision. In the DTH hammer functional part (Area III), four cylinders are adopted as the actuator of the DTH hammer and their dimensions are defined based on the structure of the piston, inner cylinder, gas distribution sleeve, and inner tube. By setting a different piston diameter and cylinder diameter, the five chambers of the DTH hammer (Q1, Q2, Q3, Q4, and Q5) are built. These actuators are driven by compressed air and their working status depends on the position signals' output from the control part. In the rock interaction part (Area IV), the interaction between the DTH hammer and rocks is simulated using a spring damper and a mass block.  
What is more, in the simulation model, the four cylinders are rigidly connected. The mass of outer cylinders and pistons are represented by one mass block, respectively. Thus, the working mechanism of these cylinders is consistent with the real DTH hammer.  
Figure 3: Pneumatic transmission model of the bidirectional DTH hammer.
## 2 Measuring System, Materials and Methods
### Measuring System and Samples  
#### 2.2.1 Samples  
The schematic picture of the sample and parameters are presented in Figure 8.  
The RC structures are characterized by three principal parameters: the thickness of the concrete cover (\(h\)), the diameter of the reinforcing bars (\(D\)), and the class of rebar steel. The class parameter delineates the ductility and hardness of the alloy. The samples used in the experiments had the following parameters:  
* Concrete cover thickness--range of 20-70 mm with increments of 10 mm (six classes from \(h_{20}\) (20 mm) to \(h_{70}\) (70 mm)--typical values in RC structures: from 20 to 50 mm;
* Rebar diameters--two classes \(D_{10}\) (10 mm) and \(D_{12}\) (12 mm);
* Alloy classes--three classes: AI (characterized by maximum ductility and minimum hardness), AIII (exhibiting low ductility and high hardness), and AIIIN (featuring minimum ductility and maximum hardness) [78].  
Used materials (like rebars) are standard within the construction industry and are readily available, thereby enhancing the practical relevance of the experiments conducted in civil engineering. The magnetic relative permeability of concrete is influenced by its composition. They typically range from approximately 1.000001 to 1.000003. In contrast, the relative magnetic permeability of steel is usually more than a hundred times higher. As a result, concrete has a negligible effect on the results of magnetic measurements [79; 80]. Consequently, the properties of concrete are not discussed in detail in this paper.
# A Baseline for General Music Object Detection with Deep Learning
## 1 Introduction
The graph formulation essentially re-casts the notation reconstruction algorithms like that of [16] as a problem of recovering binary labels over symbol pairs, therefore also making it amenable to machine learning approaches. Again, other works sometimes refer to the stage after object detection as semantical reconstruction. Note that, in this approach, this stage only attempts to reconstruct the relations between symbols and a large part of the semantics is assigned in the encoding stage. 4. **Encoding**: Given a complete music notation graph, the music can be encoded into any output format unambiguously, e.g., into MIDI for playback or MusicXML/MEI for further editing in a music notation program. Keep in mind that this step potentially has to deal with the subtleties of music notation, such as omitted symbols. Currently, the hardest challenge of this pipeline is posed by the music object detection step. Unfortunately, it is unclear to what extent deep learning has been successful in addressing this stage. Existing studies that focus on music notation objects are dispersed and not comparable with each other in terms of the used algorithms, datasets, and metrics, which has so far made a fair comparison impossible. However, there is no good reason for this state of affairs: music object detection can borrow standard evaluation from generic object detection settings, and the deep learning models are similarly domain-agnostic. Therefore, this work aims to fill an obvious gap: provide a direct comparison between the different general deep learning models for object detection that were recently proposed for the task of music object detection, across the available musical symbol datasets, and thus establish a clear state-of-the-art baseline. We evaluate three competing approaches on three distinct datasets containing both handwritten and typeset music. To compare the different approaches on common ground, we propose a standard bounding-box based data model, usable with multiple OMR datasets, and use an up-to-date standard for evaluating object detection, namely the _Common Objects in Context_ (COCO) evaluation protocol [17]. All scripts for obtaining the test-bed, preprocessing the data and evaluating the results are being made publicly available [18]. To the best of our knowledge, this marks the first time that music object detection methods based on machine learning are directly compared against each other. [PERSON] et at. [19] evaluated a number of commercial OMR applications in 2007, but it was done manually, making it difficult to replicate, and, more importantly, the systems have no published descriptions, which means the comparison has limited value for guiding future developments. The evaluation methodology in [19] also does not correspond to current object detection evaluation protocols.
## 4 Beamforming Based on INCM Reconstruction
### Weight Vector Calculation  
Using the SV of the OFDM and the reconstructed INCM deduced in the previous section, the corrected weight vector is obtained by reapplying Equation (6)  
\[\mathbf{w}=\frac{\mathbf{\hat{R}}_{i+n}^{-1}\hat{\mathbf{a}}_{0}}{\mathbf{\hat{a}}_{0} ^{H}\mathbf{\hat{R}}_{i+n}^{-1}\mathbf{a}_{0}}. \tag{32}\]  
Throughout the above analysis, the proposed algorithm can be summarized as below:  
1. Construct the \(M\times M\) dimensional data matrix \(\mathbf{R}_{1}^{a}(\tau)\) using Equation (11) and the pseudo data matrix \(\mathbf{R}(\alpha)\) using Equation (12);
2. Perform singular value decomposition or eigenvalue decomposition on \(\mathbf{R}(\alpha)\mathbf{R}(\alpha)^{H}\) to obtain the signal and noise subspace. Then, use the MUSIC algorithm for DOA estimation to determine the directions of signals;
3. Based on the preliminary SVs obtained in step (2), construct the corresponding error neighborhood (19), and perform the Capon spectral peak search within the neighborhood to obtain the corrected SVs of each signal;
4. Directly reconstruct the ICM using Equation (25), then use the least squares method to obtain the signal and noise vectors, reconstruct the NCM, and combine ICM and NCM to obtain the reconstructed INCM;
5. Calculate the weight vector \(\mathbf{w}\) using the OFDM signal SV obtained in step (3) and the reconstructed INCM obtained in step (4).
# Microwave SINIS Detectors
## 8 Discussion  
The key features of SINIS detectors and the achieved performance are closely related to the proper modeling microwave absorption, power losses, and the electron and phonon cooling balance. In a recent publication [56], the authors estimated the voltage responsivity as \(dV/dP=2k/(e\Sigma\
u T_{e}\)\({}^{4})\), which brings 2.5 \(\times\) 10\({}^{10}\) V/W at \(T_{e}\) = 100 mK, 1.5 \(\times\) 10\({}^{9}\) V/W at \(T_{e}\) = 200 mK and 3 \(\times\) 10\({}^{8}\) V/W at \(T_{e}\) = 300 mK. According to their experimental curves, the voltage responsivity is 1.5 \(\times\) 10\({}^{9}\) V/W at a phonon temperature of 200 mK, an electron temperature of 120 mK, and a 0.3 pW absorbed power. This means that electron cooling does not improve sensitivity. For an estimated electron temperature of 130 mK, the responsivity (according to a simple estimation from the electron temperature alone) should be over 10\({}^{10}\) V/W. In [56], the NEP = 3 \(\times\) 10\({}^{-17}\) W/Hz\({}^{1/2}\) at a bath temperature of 200 mK and NEP = 6 \(\times\) 10\({}^{-17}\) W/Hz\({}^{1/2}\) at a bath temperature of 200 mK for \(T_{e}\) = 120 mK and 200 mK. The overestimated readout amplifier noise 20 nV/Hz\({}^{1/2}\) should bring a corresponding NEP = 2 \(\times\) 10\({}^{-18}\) and 6 \(\times\) 10\({}^{-18}\) W/Hz\({}^{1/2}\). One order of magnitude difference means that electron cooling does not result in the expected improvement, which confirms our model of a quantum detector.  
The main differences from earlier studies, and the novel insights we offer, are as follows: using a quantum absorption model instead of simple bolometric response; there was no significant improvement caused by direct electron cooling in the same SINIS; using a suspended absorber instead of an absorber on the substrate; and increasing the volume of the superconductor instead of direct N traps for the cooling of superconducting electrodes. Our future research will be oriented towards SINIS samples with suspended heavy normal metal absorbers and thick superconducting antennas and wiring.
## 2 Collisions and the Coefficient of Restitution (\(CoR\), \(e\))
### Methods for Determining the Coefficient of Restitution (CoR, e)  
Several experimental approaches are available, depending on the instrumentation and the material under study. The most common are the following:  
1. Velocity-based method;
2. Height-based (drop-test) method;
3. Impulse-based (force-impulse) method.
## 3 Results and Discussion
### Electrochemical Characterization
These EIS results also agree well with the CV results. Cyclic stability tests were carried out at a scan rate of 100 mV/s for 5000 cycles to evaluate the capacitance retention of \(\text{NDG}/\text{Mn}_{3}\text{O}_{4}/\text{Fe}_{3}\text{O}_{4}\). As shown in Figure 8a, the \(C_{\text{sp}}\) retention of \(\text{NDG}/\text{Mn}_{3}\text{O}_{4}/\text{Fe}_{3}\text{O}_{4}\) initially increases until the 2500\({}^{\text{th}}\) cycle, and then decreases to 103.4% at the 5000\({}^{\text{th}}\) cycle. The initial increase in capacitance of \(\text{NDG}/\text{Mn}_{3}\text{O}_{4}/\text{Fe}_{3}\text{O}_{4}\) may be due to the active site activation of the composites [10; 26]. After activation, the electrochemically active materials will be completely exposed to the electrolyte [27]. This phenomenon can be related to the ESR value of the composite after 2500 cycles (Figure 8b), in which the ESR decreases to 1.83 \(\Omega\) due to the proper wetting of the electrode surface, which can enhance the electrolyte diffusion. From 2500 to 5000 cycles, there is an decrease in \(C_{\text{sp}}\) retention that was caused by activity loss of the metal oxide particles during the charging and discharging processes [26]. This phenomenon is also supported by the EIS data, in which there is an increase in ESR (2.12 \(\Omega\)). Generally, the \(\text{NDG}/\text{Mn}_{3}\text{O}_{4}