esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nanostructured binary copper selenides have wide applications in various fields, such as solar cells, field-effect transistors, gas-sensing devices, Li-ion batteries, and biological and medical appliances [1; 2; 3]. Copper selenides have various stoichiometric (Cu\({}_{2}\)Se, CuSe, Cu\({}_{3}\)Se\({}_{2}\), CuSe\({}_{5}\), CuSe\({}_{4}\)) and nonstoichiometric (Cu\({}_{2\times}\)Se) phases. There is special interest in the nonstoichiometric phase, owing to its unique properties. Cu\({}_{2\times}\)Se is an intrinsic p-type semiconductor with direct and indirect bandgap energies in the range of 2.1-2.3 eV and 1.2-1.4 eV, respectively [4]. Non-stoichiometry affects the crystalline structure and the electronic and cation exchange properties. Nonstoichiometric nanostructured copper selenide may exhibit various morphologies, such as nanocubes [5], nanotubes [6], nanowires [7], nanosheets [8], nanodiscs [9], nanocages [10], and nanocrystalline films with complex architectures [11]. The type of nanostructure is determined by the formation methods and applied technological conditions. A detailed analysis of all the methods for Cu\({}_{2\times}\)Se nanostructure creation is presented in [1]. Additionally, the properties of Cu\({}_{2\times}\)Se nanostructures can be changed by thermal annealing or doping with various chemical elements. The plasma treatment of metal chalcogenides in argon plasma results in new phenomena associated with effective surface modification [12; 13; 14]. However, currently, no information about the effect of plasma treatment on the parameters of copper selenides exists in the literature. This study aims to analyze the effects of treatment by argon plasma with low ion energy on the structural parameters of nanocrystalline Cu\({}_{2\times}\)Se films with complex architectures. An additional important physical problem investigated here is the poorly studied process of the plasma sputtering of discontinuous layers with pores.
# Process Automation in an IoT-Fog-Cloud Ecosystem: A Survey and Taxonomy
## 5 Fog Intelligence and Automation
### Automatic Data Transfer  
Any IoT task or request needs to be hosted firstly. Each layer in the ecosystem has its capability in terms of hosting IoT tasks. The IoT layer with cheap and distributed resources is close to users. This layer, however, cannot handle complex, resource-demanding tasks. The Fog has more capabilities and efficiencies but still not suitable for high computation such as Machine learning (ML) applications. The Cloud is enriched with sophisticated resources and components such as servers and clusters but far away from users. Choosing the best hosting for any IoT task for satisfying their SLAs is the responsibility of the automatic data transfer. [PERSON] et al. [48] suggest that every task hosts in the Fog layer by default, and if extra resources are needed, they will be transferred to the Cloud. The tasks are classified as critical(time-sensitive) and non-critical ones. The automation of data transfer is then carried out based on the classification. If a task is classified as a time-sensitive task and needs extra resources, resources should be provided by Fog nodes rather than Cloud hosts. Similar to previous studies, [PERSON] et al. [49] show another aspect of data classification for events in a health monitoring application. The information is classified into event data, and raw data and event data is sent to the Cloud for further computation. This classification determines when tasks need to be transferred to the Cloud for storing or to the servers for computation. [PERSON] et al. [50] propose the Virtual Machine (VM) migration as a data transfer solution for failure handling and fault tolerance. The VMs need to be moved between different processing hosts to meet response time SLAs. Many times VM migration is to meet energy efficiency. Their study shows a classification algorithm on data types. [PERSON] et al. [16] notes that computational time-sensitive tasks are better to be hosted in the Fog layer. By any QoS violation or resource overutilization, the tasks can be moved to the Cloud. By this method, any IoT task which requires a computational resource has the chance to be hosted in the Fog layer before migration to the Cloud. [PERSON] and [PERSON] [51] show the problem of data overflow by devices that are connected to IoT sensors. Data transmission latency between the IoT and the Cloud is addressed as a problem, which makes the applications' QoS unacceptable. They call Fog nodes private Clouds and depicts them as a potential solution for QoS response time improvement.
## 1 Introduction
Hence, the installation of the proposed ASFRM overlay can be completed via merely plastering and is easier to accomplish around the opening compared with the installation of meshes. In previous studies by the authors, a recommended mixing ratio for an ASFRM was derived, and the corresponding strengthening effects were demonstrated for masonry prisms, which were created with good workmanship, i.e., both bed and head joints were filled compactly with mortar [20]. However, in actual bricklaying, sufficient mortar is spread on the bed joints only, and efforts to fill head joints are insufficient. In this study, masonry prisms in hollow red clay bricks whose head joints were intentionally not filled were strengthened and tested to reproduce the bricklaying practice. Various test variables were considered, including the number and thickness of mortar overlays, fiber volume fraction, and additional reinforcement using mesh or shear connectors. In addition, tests for the bond strength between the ASFRM overlay and masonry surface were conducted to evaluate the contribution of the bond strength to the diagonal tension strength. The test results were combined with other experimental results of masonry in solid concrete bricks reported by [PERSON] et al. [21; 22] to derive design equations for the compressive and diagonal tension strengths. The design equations consider the asymmetrical stress distribution and the contribution of bond strength to the compressive strength based on experimental observations. The remainder of this paper is organized as follows: First, a test program including the experimental setup, material properties, and specimen characteristics is described. Second, the test results for compressive, diagonal tension, and bond strengths are described in terms of improved strength and damage characteristics. Third, design equations to predict enhanced strength are proposed based on experimental observations and verified with the test results. Finally, strength modification factors based on the statistics of the test results are presented.
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 3 Feed Mechanism  
Horizontal slots induce vertical polarization as they can quickly interrupt the longitudinal surface current on the antenna's outer surface [36], as seen in Figure 2. Conversely, the longitudinal slots in the antenna's outer surface cannot be stimulated due to their orientations that are in line with the surface current, and even a short circuit would not modify the flow of the surface current [28]. So it is not easy to produce horizontal polarization using a slot configuration on a coaxial cylinder. In our design, feed pins are inserted to excite the vertical slots, which connect the outer conductor of the oversized coaxial cable with its inner conductor, as shown in Figure 1b. Thus, these slot apertures are energized sideways while the opposing sides are kept floating. The slot is regarded as a dipole having a magnetic current source [29], so the slot is \(\lambda\)g/2 long. Normally, the external feed has a built-in problem where it radiates along with the main radiating elements and causes a significant gain ripple in the omnidirectional pattern. Here, we have designed an internal feed that runs inside the radiating part and does not interfere or radiate. As for the actual feeding of the antenna, a standard SMA connector is used for feeding. The SMA connector is a coaxial structure and the antenna designed in this section is also based on coaxial structure, so the matching structure is designed and inserted between the radiation part and the feed part according to impedance transformation of coaxial transmission line [37],  
\[Z_{oversize}=Z_{match}(Z_{sma}+jZ_{match}tan\beta T)/(Z_{match}+jZ_{sma}tan \beta T)\]  
where the \(T\) is the length of the matching pin and \(Z_{sma}\), \(Z_{match}\), and \(Z_{oversize}\) are the characteristic impedances of the SMA connector, matching pin, and oversize antenna assembly, respectively. The SMA connector's inner pin's optimized height ensures a seamless transition from normal Coaxial TEM mode to oversized TEM cable mode. The slot excitation of the proposed antenna is simple and easy without involving baluns or impedance transformers. Four pins join the inner cylinder to the vertical slots in the antenna's outer cylinder. The feed and antenna can be conveniently integrated by arranging the manufactured parts together around the central axis.  
Figure 2: Slot configurations: (**a**) vertical and horizontal slot, (**b**) vertical slot feed.
## 4 Simulations and Results Analysis
### Simulation Environment  
We have performed the simulations using Castalia [36], a discrete event simulator suitable for sensor networks, based on OMNeT++ [37], a platform for building event-driven simulators. Castalia is a powerful tool able to simulate realistic wireless channel and radio models, with a realistic node behavior.  
We have deployed five differently sized WSNs consisting of \(200\), \(400\), \(600\), \(800\) and \(1000\) nodes. In each of these networks, there is only one base station, and the nodes are static and are uniformly distributed in a square region. The node density was set to \(8\) sensors per \(100\) m\({}^{2}\), while increasing the region size to fit \(200\) to \(1000\) nodes. Each sensor was set up with a radio range of \(50\) m, assuming bidirectional links. The radio used in the simulations was CC2420, which is used in several real sensors, such as MICAz and TelosB [38].  
The MAC layer protocol used is T-MAC [39], a contention-based medium access control protocol suitable for WSNs. In the network layer, we have used the multipath ring routing protocol (provided by Castalia), a simple implementation of the synopsis diffusion protocol [40]. Each network is configured so that every 1 s, for 20 min, each node sends a message to the base station containing four readings (temperature, humidity, pressure and light).  
Finally, we highlight that the settings described above have been used in all simulation runs, unless otherwise specified.
## 3 Air Pressure Compensation Algorithm
### A-ILC Algorithm
Assumption 2: For the air pressure compensation system, there is a reasonable input sequence \(u_{d}(t)\in\big{[}u_{d}^{min},u_{d}^{max}\big{]}\), which makes the air pressure curve inside the car completely track the IAPC \(P_{d}(t)\) when the high-speed elevator runs in a limited time \(t\in[0,T]\). \[\dot{P}_{d}(t)=\frac{q_{mi}(u(t))-q_{mo}(u(t))}{\beta} \tag{42}\]  
Assumption 3: There is an unknown random disturbance \(d(t)\) during the operation of the high-speed elevator, and the air leakage flow \(q_{ml}(t)\) is also considered to be included in it. Assuming that the disturbance is bounded in a finite time \(t\in[0,T]\), which is:  
\[|d(t)|\leq D, \tag{43}\]  
where \(D\) is the upper bound of unknown disturbance \(d(t)\). To introduce the unknown random disturbance and assume that the intake and exhaust fans are being affected by the same controlled variable, Equation (21) can be rewritten as:  
\[\dot{P}_{k}^{i}(t)=\eta\cdot\Big{(}P_{k}^{i}(t)-P_{k}^{o}(t)\Big{)}+\delta \cdot u_{k}(t)^{2}+d(t), \tag{44}\]  
where \(k\) is number of times of the high-speed elevator runs; \(P_{k}^{i}(t)\), \(P_{k}^{o}(t)\) and \(u_{k}(t)\) are, respectively, the air pressure inside the car, the air pressure outside the car and the input frequency of the control fan at the \(k\)-th iteration; \(d(t)\) is the unknown random bounded disturbance; and \(\eta\) and \(\delta\) are the flow coefficients. The tracking error of the high-speed elevator car air pressure adjustment curve is defined as \(e_{k}(t)=P_{d}(t)-P_{k}^{i}(t)\), and the dynamic characteristics of the tracking error of the system at the \(k\)-th iteration can be obtained as:  
\[\dot{e}_{k}(t)=\dot{P}_{d}(t)-\dot{P}_{k}^{i}(t) \tag{45}\] \[=\dot{P}_{d}(t)-\eta\cdot\Big{(}P_{k}^{i}(t)-P_{k}^{o}(t)\Big{)}- \delta\cdot u_{k}(t)^{2}-d(t)\] \[=\dot{P}_{d}(t)+\theta_{k}^{T}(t)\xi_{k}(t)-\delta\cdot u_{k}(t)^ {2}\]  
Figure 8: A-ILC system for the air pressure compensation system. where \(\theta_{K}^{T}(t)=[-\eta,d(t)]^{T}\) is the estimated value of the system parameters at the \(k\)-th iteration; and \(\zeta_{k}(t)=[\Delta P_{k}(t),1]^{T}\) is the status information of the system at the \(k\)-th iteration. To consider the time-varying parameters of the elevator car air pressure compensation system, the following adaptive iterative learning law and system parameter update law are designed. \[\left\{ \begin{array}{c}u_{k}(t)=K_{p}e_{k}(t)+K_{d}\dot{e}_{k}(t)+\hat{\theta }_{K}^{T}(t)\zeta_{k}(t)\\ \hat{\theta}_{k}(t)=\hat{\theta}_{k-1}(t)+q\cdot\zeta_{k-1}(t)e_{k}(t)\end{array} \right. \tag{46}\]  
where \(q>0\) is the learning gain of the parameters.
## 3 Results and Discussion
### Effects of Resin Type
#### 3.3.2 Internal Bond Strength  
The results reveal that the influence of the resin type on internal bond strength is essential. As shown in Figure 12a, rice straw particleboards with cornstarch resin with a content of 10% as  
Figure 11: The influence of resin type on bending properties of particleboard.  
adhesive, as well as particleboards without any resin, showed much poorer internal bond properties than particleboards bonded using UF resin with a content of 12%. With the use of urea formaldehyde resin, the internal bond significantly increased from 0.003 to 0.02 MPa. Contrarily, with the use of cornstarch resin, the internal bond slightly decreased from 0.003 to 0.002 MPa.
## 4 Experimental Results and Analysis  
### Speech Corpora for Experiments  
To verify the efficiency of the R-vector and the R-vector-based LID approach, we conducted several experiments using several speech corpora consisting of multiple languages. The type of languages used for evaluation was firstly considered. We preferentially selected four target languages including English, Korean, Chinese, and Spanish, considering the language distribution property introduced in a well-known linguistics study [28]. As described in Figure 5, English is known as a prototypical stress-timed language, whereas Spanish is a representative syllable-timed language. On the other hand, Korean and Chinese belong to intermediate languages between stress-timed and syllable-timed languages, but Chinese has language properties that are highly pertinent to stress-timed languages.  
Unfortunately, there are no individual speech corpora containing these four types of languages. Hence, we conducted experiments separately on two corpora published by different organizations, as presented in Table 2. The first corpus released by the Speech information TEchnology & industry promotion Center (SiTEC) contains English and Korean language data. Another corpus called Common Voice contains open source speech data and was released by Mozilla in 2019. Two corpora have different recording environments. The SiTEC corpus consists of read speech data recorded in the silent studio, whereas the Mozilla corpus retains speech data of natural and dialogic voice recorded in real-life environments. For this reason, experiments using all the data of two corpora may not provide a fair evaluation. Thus, we firstly performed evaluation for English and Korean using the SiTEC corpus and then investigated the performance for English, Chinese, and Spanish on the Mozilla corpus.  
Figure 5: Language distribution property.
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 3 Results
### Further Findings  
When considering multiple trains (cf. Section 2.4), the resulting positions of the infill balise groups are more likely than not a compromise of the individual optimal positions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Initial Speed [km/h]** & **Positions [m]** & **Weighted Additional Runtime [s]** & **Delta to Optimum [s]** \\ \hline optimum & 120 & 194 – 483 – 1187 & 39.5 & — \\
1 & 120 & 250 – 1000 – 1187 & 43.2 & 3.7 \\
2 & 120 & 250 – 700 – 1187 & 40.5 & 1.0 \\
3 & 120 & 396 – 791 – 1187 & 42.8 & 3.3 \\ \hline optimum & 160 & 243 – 705 – 1581 & 52.0 & — \\
1 & 160 & 250 – 1000 – 1581 & 53.1 & 1.1 \\
2 & 160 & 250 – 700 – 1581 & 52.0 & 0.0 \\
3 & 160 & 527 – 1054 – 1581 & 55.6 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results from evaluating different strategies for the manual placement of three infill balise groups for the test train.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Initial Speed [km/h]** & **Positions [m]** & **Weighted Additional Runtime [s]** & **Delta to Optimum [s]** \\ \hline optimum & 120 & 322 – 1187 & 44.7 & — \\
4 & 120 & 594 – 1187 & 48.4 & 3.7 \\
5 & 120 & 400 – 1187 & 45.2 & 0.5 \\ \hline optimum & 160 & 420 – 1581 & 56.7 & — \\
4 & 160 & 791 – 1581 & 62.4 & 5.7 \\
5 & 160 & 400 – 1581 & 58.7 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results from evaluating different strategies for the manual placement of two infill balise groups for the test train.  
However, as the weighted additional runtime only increases marginally in an envelope of about \(\pm 50\,\)m around the optimal positions, this compromise has minor negative effects on the overall performance. This was also confirmed by testing different, non-optimal placements in the previous paragraphs. Obviously, the downsides increase the more the driving characteristics of the trains in question differ because the infill balise groups are not able to provide infill information at the optimal location for each individual train. One could then increase the number of infill balise groups, but technical and economic reasons as well as diminishing returns limit this number.  
During testing, it also became clear that the algorithm can handle occurring discontinuities in the additional runtime very well. Those discontinuities--which could also be observed in various test cases and are perceivable in Figure 5 on the left-hand side of the plot--stem from taking system latencies into account. In our case, these are the processing time of the OBU as well as the cruising phase between deceleration and acceleration. Such discontinuities can produce local minima of the weighted additional runtime but do not present themselves as the global minimum where the absolute lowest weighted additional runtime can be found. The algorithm always identifies the global minimum and does not erroneously confuse a non-optimal local minimum as the global minimum. The global minimum has been correctly detected by all sensible input parameters tested to date.
# Novel Technologies Used in the Assessment of Patellofemoral Pain: A Scoping Review
## 4 Discussion
### Recommendations for Future Studies
#### 4.4.5 Myotonometry/Reaction Time  
In the present search, three new technologies for PFP assessments were found. However, these were not exactly related to the patellofemoral joint but somehow to relevant muscular conditions. One is tensiomyography, which evaluates the muscular mechanical and contractile properties by means of an external electrical stimulus of controlled intensity. It provides information on fatigue, muscle activation, and tone [90]. Another one, used in the same study, is a myotonometry device that measures the stiffness of the muscle at rest [90]. Both of these assess biomechanical muscular properties in an objective way and have been shown to be of value. Muscle stiffness and fatigue are mentioned in fact to be among possible risk factors for knee-joint-related injuries [91]; thus, these new methods may provide original information in addition to EMG.  
The third method can evaluate the muscle reaction time, i.e., the time from the appearance of unpredictable stimuli to the start of the selected motor response. It includes central processing and motor time that is necessary for motor response. The reaction time was found higher in PFP patients compared to healthy populations, with this being a valuable representation of the central process and motor reaction delay [92; 93]. This assessment can be used as a non-invasive and easy alternative for central deficit assessments.
## 3 Results and Discussion
### Physico-Chemical Characterization of the Hydrolysates
#### 3.1.4 pH-Protein Solubility  
The inclusion of functional proteins in food formulations depends on its solubility, which affects other protein functional properties, such as foaming and emulsifying [73]. The isoelectric pH (lowest solubility) of WKH, RKH, and BKH ranged 4-6 (Figure 2B), which is not different from the intact mother protein. Generally, the solubility of protein significantly increased \(p\leq 0.05\) when the pH moves away from the isoelectric point. The solubility of WKH, RKH, and BKH after 30 min hydrolysis by Alcalase was (53, 66, and 75%) at pH 2 and recorded the highest levels at pH 10, i.e., 70, 75, and 90%, respectively. The order of solubility levels BKH > RKH> WKH, is a reflection of the same order of the degree of hydrolysis, which understandably enhances it. The observed higher solubility at the basic side than the acidic side agrees with [PERSON], et al. [32] who reported 35.13% solubility values of carioca bean and soybean protein hydrolysate at pH 3.0 against 100% at pH 7.0. [PERSON], et al. [74] found that white kidney bean protein pepsin hydrolysate has maximum solubility at pH 10 with 82%, also, [PERSON] and [PERSON] [75] found that white kidney bean protein papsin hydrolysate has 78% solubility at pH 10.
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 2 A CubeSX-HSE Nanosatellite-Based Remote Sensing Platform
### Satellite Flight Characteristics  
The CubeSX-HSE nanosatellite launched into a 570 km orbit on 22 March 2021, carried by a Soyuz-2.1a rocket with a Fregat upper stage. Figure 2 shows the 3.5 kg satellite after the assembly. The camera unit with an aperture opening can be seen on the right side of the satellite assembly in the second picture below.  
The satellite three-axis orientation and stabilization system has solar sensors, a four-motor flywheel module, and a microcontroller located on the motherboard.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Baselines
#### 3.4.1 SiNet  
Consistent with the most typical paradigm [4], we employ a single speech prediction branch for SE, abbreviated as SiNet. SiNet has exactly the same network structure and parameter settings as the causal speech branch in the proposed UniInterNet, and it also adopts the masking method as Equation (8) to predict the enhanced speech.