esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Nitric Oxide on the Surface Properties of Selected Polymers
## 2 Experimental Section
### Zeta-Potential Measurements  
The zeta-potential measurements were conducted using a SurPASS 3 ([PERSON], Graz, Austria). An adjustable gap cell was used for the MABS and the PU samples. The gap was set at approximately 100 \(\upmu\)m. The pressure range utilized extended from 600 to 200 mbar. Care was taken for both materials to analyze the same side of the samples to avoid inconsistencies. These measures were taken as surface morphology can impact zeta-potential measurements [26]. The PMP samples were analyzed using a cylindrical cell. A total of 150 mg of PMP was used in these measurements. The permeability index of the cell was approximately 100. A KCl solution with a concentration of 10\({}^{-3}\) mol/L was used as the electrolyte. Measurements were conducted over a pH range of three to ten using HCl and KOH solutions with a concentration of 0.05 mol/L to adjust the pH. Two separate sample pairs were used per measurement to cover the pH range. Measurements of both treated and untreated polymer samples were conducted. The zeta-potential was calculated using the Helmholtz-Smoluchowski equation [27]:  
\[\zeta=\frac{\mathrm{d}\mathrm{U}_{\mathrm{S}}}{\mathrm{d}\Delta\mathrm{p}} \frac{\uppi\kappa}{\varepsilon\epsilon_{0}} \tag{1}\]  
with \(\zeta\) being the zeta-potential, \(\mathrm{U}_{\mathrm{S}}\) being the change in the streaming potential, \(\mathrm{d}\Delta\mathrm{p}\) being the change in pressure difference, \(\uppi\) being the dynamic viscosity of the liquid, \(\kappa\) being the specific conductivity of the bulk liquid, \(\varepsilon\) being the relative permittivity of the liquid, and \(\varepsilon_{0}\) being the electrical permittivity of the vacuum.
# Design and Test of Seeding-Picking Mechanism of Fully Automatic Transplanting Machine
## 4 Control System Scheme Design  
### Control System Composition  
The demand analysis of the control system reveals that the control system discussed in this article comprises a controller, driver, host computer, actuator, and sensors, as shown in Figure 6. The controller employed is a Programmable Logic Controller (PLC) that manages the operation of the entire control system. It receives and processes information from external sensors, coordinates the control output to each actuator, and executes the designated work project. The host computer communicates with the PLC via a USB interface, enabling real-time monitoring of the system's operational status. The stepper motor driver converts electrical pulses into angular displacement, thereby controlling the rotation angle and speed of the stepper motors. Additionally, the servo motor driver regulates position, speed, and torque, offering three methods to control the servo motor for achieving high-precision positioning within the transmission system. The actuator components consist of four stepper motors, five servo motors, six electromagnetic seedling lifting mechanisms, and six pneumatic clamping jaws, which work in coordination according to the operational process to facilitate the mechanical movement of the system. Sensors and relays play a critical role in detecting the position of each execution component and transmitting the collected information to the PLC for coordinated control [22; 23; 24].
# Coverage Extension for the UK Smart Meter Implementation Programme Using Mesh Connectivity
## 2 Methodology  
The measurement system for the 868 MHz mesh radio system consisted of a narrow band ZigBee transmitter and receiver. The transmitter was mounted in a box on top of tripod as shown in Figure 1a; the receiver was mounted in a backpack with a GPS, laptop, and battery as shown in Figure 1b. The system was then powered up and synchronised so that we could measure both signal level and a proxy packet for service management. The height of the surrounding roof tops was noted, using a laser inclinometer, as was the road width in each direction (near side and far side).  
Two sets of experiments were carried out in this stage: test 1 and test 2. These tests were carried out in the vicinity of Newbury, UK (Figure 2) which is a typical UK town with a mix of buildings. In test 1, we used the following equipment:  
* 1 \(\times\) 868 MHz mesh Transmitter (26.9 dBm max output, 250 KHz bandwidth, 869.5 MHz centre frequency)
* 2 \(\times\) uBlox 1 m accuracy GPS
* 2 \(\times\) 0 dBi antenna
* Tripod Stand for the TX
* Backpack for the RX  
Figure 1: (**a**) Mesh transmitter setup; (**b**) mesh receiver setup.  
The LOS 2.4 GHz Zigbee measurements used a similar setup to the 868 MHz mesh measurements, but focussed on 2.4 GHz frequency, with a bandwidth of 15 MHz. This was measured using the TSMU Continuous Wave (CW) receiver, using the same antenna as the coordinator. The same route was walked as Test 1.  
In test 1, we used ZigBee dongles (PRoBeeZU10) with the 1.6 standard transmission distance. One unit was set up as a coordinator and the other was set up as the endpoint. A unicast broadcast was used to send data from the coordinator to the endpoint. The data packet size was 100 bytes, with 90 bytes of data and 10 bytes of addressing. The coordinator was mounted in a rucksack with a GPS. The ZigBee antenna was mounted clear of obstructions. A custom program monitored the bytes sent from the endpoint. If the bytes do not arrive within 1.5 s or are less than 100 bytes in size, then the sent packet is considered a failure.  
In test 2, we set up a CW signal source which transmitted through the same antenna as the receive point. For this test, we used the following equipment:  
* 2 \(\times\) ProBee ZU10 with ZigBee standard 1.6 (+18 dBm max output, 2.44 GHz centre frequency, 15 MHz)
* 1 \(\times\) uBlox 1 m accuracy GPS
* 1 \(\times\) TSMU CW receiver and ROMES software
* 1 \(\times\) 2.4 GHz Signal source
* 2 \(\times\) 2.4 GHz 0 dB antennas  
A single set of measurements was made on Greenham Common Airbase, shown in Figure 3, which consists mainly of open scrubland and gravel paths.  
Figure 2: Map of Newbury, a typical UK town, with a mix of 1- and 2-storey buildings. (Source: Google Earth Maps).
## Experiments
### Results and Discussion
#### 4.3.3 Hyper-Parameters Analysis  
The performance comparison of hyperparameters \(K_{1}\), \(K_{2}\) is shown in Table 4. \(K_{1}\), \(K_{2}\) do not affect the number of parameters of the model, but have a certain impact on the inference speed of the model. Models (a) and (b) illustrate that performance can be improved by  
Figure 4: The interaction recognition accuracy on the NTU60 dataset: ST-TR, MS-G3D and our Joint-Contrast.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**NTU60**} & \multicolumn{2}{c}{**NTU120**} \\ \hline
**Methods** & **CS acc (\%)** & **CV acc (\%)** & **CS acc (\%)** & **CV acc (\%)** \\ \hline ST-LSTM [1] & 83.0 & 87.3 & 63.0 & 66.6 \\ LSTM-IRN [4] & 90.5 & 93.5 & 77.7 & 79.6 \\ AGC-LSTM [39] & 89.2 & 95.0 & 73.0 & 73.3 \\ \hline SAN [47] & 88.2 & 93.5 & - & - \\ VACNN [48] & 88.9 & 94.7 & - & - \\ \hline ST-GCN [17] & 83.3 & 88.7 & - & - \\ AS-GCN [18] & 87.6 & 95.2 & 82.9 & 83.7 \\ ST-TR [49] & 90.8 & 96.5 & 85.7 & 87.1 \\
2 sshift-GCN [50] & 90.3 & 96.0 & 86.1 & 86.7 \\ MS-G3D [51] & 91.7 & 96.1 & - & - \\
2 sKA-AGTN [20] & 90.4 & 96.1 & 86.7 & 88.2 \\ \hline JointContrast (ours) & 94.1 & 96.8 & 88.2 & 88.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The interaction recognition accuracy under Cross-Subject (CS) and Cross-View (CV) accuracy evaluation on NTU60 and NTU120.  
increasing \(K_{2}\) when \(K_{1}\) is in a certain region; Comparing models (a) and (c), we improve the performance of the model by increasing the receptive field controlled by \(K_{1}\). However, \(K_{1}\) and \(K_{2}\) are coupled, and the redundant features increase as \(K_{1}\) and \(K_{2}\) increase, which leads to no performance improvement of model (d).
# Wireless Sensor Networks for Enabling Smart Production Lines in Industry 4.0
## 2 Methodology
### Coverage Map Calibration  
From the initial network-planning result, we have a coverage map with theoretically received power at every location. The coverage map is based on a statistical PL model. Therefore, the calculated received power can differ from actual received power, for example, due to different material characteristics resulting in different attenuation, or when the floor plan is not fully accurate. From the monitoring system discussed in Section 2.3, we gather measurement data such as latency and RSSI from the client devices. It is also expected that reported RSSI values show an offset compared to the received power \(P_{\text{RX}}\), as the RSSI depends on the actual chipset.  
To be able to make decisions based on the reported RSSI values in the monitoring loop, a calibration of the calculated coverage map is needed. This calibration is performed for each receiver and is visualized as step 2 in Figure 2. Figure 3 shows the algorithm for the coverage map calibration algorithm.  
After the initial network-planning, the APs are installed in the factory hall and the network is set up. When the clients start sending and receiving data, the mean value of a number of RSSI samples of the data packets is calculated and compared to the theoretically received power value from the initial network-planning. For each client, we store the offset between \(P_{RX}\) and RSSI and we will take this offset into account when making decisions based on RSSI.  
Note that the location of the clients is assumed to be known, which is an acceptable assumption due to the extensive localization methods that are available for indoor environments [44], including visible light positioning [45; 46; 47; 48; 49] or the time difference of arrival techniques [50]. Localization based on RSSI is also possible [51] and can be integrated into the network-planning framework.
## 4 Planetary Gear Train Meshing Frequency and Characteristic Frequencies
### Analysis of the Simulated Signal  
The number of teeth in the gearbox. the operating speed of the transmission and the characteristic frequency of fault gear are calculated using Equations (11)-(18) for local or distributed damage.  
Gear faults are detected and located by examining the sideband structure and determining whether the sideband position is related to characteristic frequencies.  
To verify this theoretical derivation and demonstrate the spectral structure of the vibration signal, two signals that simulate vibration caused by local damage to a planetary gear tooth are generated, one of which is calculated using (4). The other is calculated using (9) and includes the AM effect caused by the rotating planet carrier. The parameters for this simulation are listed in Table 2. The signal is sampled at a frequency of 500 Hz.  
If the waveform of the modulated signal is a sine wave, Figures 11 and 12 show the time domain waveform and the frequency spectrum. The spectral peaks are significantly larger at frequencies of \(f_{m}\pm kf_{p}^{H},\ (k\ \in\ Z)\) and other spectral lines are relatively small. This is consistent with the result of (7). The corresponding characteristic frequency of the planet gear with a local fault is \(K\cdot f_{p}^{H}\), which is consistent with (14). The second signal includes the AM effect, so there are more spectral peaks as sidebands, when Figures 11 and 12 are compared. These sidebands are centered beside the mesh frequency and the rotation frequency of the planet carrier plus and minus the mesh frequency, which is \(f
## 2 Materials and Methods
### Classifiers  
From the feature extraction and selection process, we obtained a total of four datasets (one for each selection method) per vocal task. With two vocal tasks (vowel/e/ and sentence), we have a grand total of eight datasets, used to train three classifiers:  
* Naive Bayes (NB).
* Random Forest (RF), with 100 iterations/bags created by sampling with repetition up to a dimension as big as the original set (100% bags).
* Multi-layer Perceptron (MLP), i.e., a fully connected artificial Neural Network with a number of hidden layers equal to the number of features + number of classes divided by two, trained with a learning rate of 0.3 and a momentum of 0.2.  
With four selection methods (Correlation, IG, GR, and GA) and three classifiers (NB, RF, and MLP), there are twelve ML models for each vocal task, so 24 in total. We considered as the maximum test accuracy the \"saturation value\", that is, a value that does not meaningfully increase on increasing the number of features. For the wrapper method, the maximum accuracy result was guaranteed since it was the evaluator of the GA. Ten-fold cross-validation was used in all ML models: this leads to a data split of 90-10, chosen due to the dataset being limited in dimensions. Using 90% of the data allows us to retain a consistent (big enough) training set. On the other hand, although the test set consists of a small number of subjects, the ten cross-validation folds cover the whole dataset, thus training ten slightly different versions of each classifier, whose results on the test sets are averaged.  
Receiving Operating Characteristic (ROC) curves are also produced, to assess the performance of the model over all its operating range for a given label or class. The Area Under ROC (AUROC) curve analysis of each class in every ML model developed is computed and used as a metric to assess the vocal test, classifiers, and feature selection methods.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 7 Drawbacks and Future Direction  
Various studies have been performed on EMI applications, but there are still a few limitations and drawbacks in the carried-out work which requires vital attention. Researchers have tried different methods to synthesize composites, however, adoption criteria for the different material synthesis methods is still missing in the literature. Although graphene is a significant material in composite formation, its dispersion is not easy. Similar is the case of polymers, where researchers usually did not properly quote the appropriate amount of solvent and curing agent which is required to mix polymers with graphene and iron. Besides that, in EMI applications, few researchers have calculated the reflection properties, few find the absorption and few have calculated the overall EMI shielding value.  
Bringing the discussion into a net shell, researchers did not provide complete information about the composite synthesis process which makes it difficult to understand the role of different methods, amount of added solvent and curing agent, and the temperature effect during the whole composite-formation process. Considering the above statement, besides exploring the new combinations, there is a need to evaluate the comparison of methods for composites formation with their required parameters. Moreover, it needs to be stated that even in EMI shielding effectiveness which material performs better in absorption, and which performs better in reflection, as the composite applications may vary according to the influenced EMI working field. Moreover, new combinations of graphene-based polymers need further exploration in the higher frequency range as most of them currently dealing within the X-band frequency range.
# Subpath Queries on Compressed Graphs: A Survey
## 3 Indexing Labeled Graphs and Regular Languages
### Wheeler Graphs
#### 3.7.1 Subpath Queries  
When a Wheeler order exists, all the power of suffix arrays can be lifted to labeled graphs: (1) the nodes reached by a path labeled with a given pattern \(\Pi\) form a consecutive range in Wheeler order, and (2) such a range can be found in linear time as a function of the pattern's length. In fact, the search algorithm generalizes those devised for the particular graphs discussed in the previous sections. Figure 11 depicts the process of searching all nodes reached by a path labeled with pattern \(\Pi=``\text{aba}\"\). The algorithm starts with \(\Pi=\epsilon\) (empty string, all nodes) and right-extends it by one letter step by step, following the edges labeled with the corresponding character of \(\Pi\) from the current set of nodes. Note the following crucial property: at each step, the nodes reached by the current pattern form a _consecutive range_. This makes it possible to represent the range in constant space (by specifying just the first and last node in the range). Similarly to what we briefly discussed in Section 3.5 (_Subpath Queries on the XBWT_), by using appropriate compressed data structures supporting rank and select on strings, each character extension can be implemented efficiently (i.e., in log-logarithmic time). The resulting data structure can be stored in entropy-compressed space [50].  
The algorithm we just described allows us to find the number of nodes reached by a given pattern, i.e., to solve _count_ queries on the graph. As far as _locate_ queries are concerned, [PERSON] et al. in Reference [76] proposed a sampling scheme that returns the labels of all nodes reached by the query pattern, provided that the labeling scheme satisfies a  
Figure 11: Searching nodes reached by a path labeled “aba” in a Wheeler graph. Top left: we begin with the nodes reached by the empty string (full range). Top right: range obtained from the previous one following edges labeled ’a’. Bottom left: range obtained from the previous one following edges labeled ’b’. Bottom right: range obtained from the previous one following edges labeled ’a’. This last range contains all nodes reached by a path labeled “aba”  
Figure 10: The automaton of Figure 9, sorted according to the three Wheeler properties.  
particular monotonicity property (that is, the labels of a node are larger than those of its predecessors). In practical situations, such as genome-graph indexing, such labels can be equal to an absolute position on a reference genome representing all the paths of the graph. In the worst case, however, this scheme requires to store \(\Theta(n)\) labels of \(O(\log n)\) bits each. As another option, the sampling strategy described by [PERSON] et al. [70] Section 5.1 and [PERSON] [49] (see Section 3.5.4 for more details) can be directly applied to the DFS spanning forest of any Wheeler graph, thus yielding locate queries in polylogarithmic time and small additiona redundancy.
## 4 Numerical Simulation and Result Analysis
### Setting Up a Numerical Simulation System  
The awareness combat network simulation system should have the following functions: simulation scenario establishment, simulation process control and management, network topology, information generation simulation, network interconnection, routing maintenance, data forwarding, node movement, parameter setting, and statistical analysis.  
The awareness combat network simulation system refers to the Opportunistic Network Environment (abbreviated as ONE) [16; 17], a DTN special network simulation tool developed by the Nokia Research Center of Helsinki University of Technology in Finland. It is developed with the JAVA programming language and adopts the agent-based discrete event simulation mechanism, which mainly includes the modeling of node movement, node communication, node routing, and message processing between nodes, viewing node movements and message transmissions in real time via the GUI, and collecting and analyzing visual data results. In this paper, the wireless link is abstracted into a communication range and bit rate. The parameters are pre-configured with multiple options before simulation, and the parameters are then selected accordingly during simulation according to the data transmission conditions.  
In this paper, the AODV protocol is implemented with Java programming language, and then the AODV.java class is rewritten, the message-related events and resource management callback functions of the Message Router module in ONE are called, the location of the node and the current time values are read, the space-time routing table is established, the message events are processed according to the DTN routing mechanism, and method functions in the ActiveRouter class (handling message transmission between neighboring nodes, successful transmission/interrupted transmission, etc.) are extended. In this way, an ActiveRouter subclass DTN-AODV.java of the DTN overlay is added to the AODV protocol. DTN-AODV.java is placed in the routing package and compiled together with other routing models. When the program is run, the awareness combat network routing protocol designed in this paper can be implemented.
_Applied_  
_sciences_  
_Atomic Vacancy Defect, Frenkel Defect and Transition Metals (Sc, V, Zr) Doping in Ti\({}_{4}\)N\({}_{3}\) MXene Nanosheet: A First-Principles Investigation_  
**[PERSON] 1,2**, [PERSON] 3, [PERSON] 2, [PERSON] 1, [PERSON] 2, [PERSON] 2,4 and [PERSON] 1,*  
Received: 8 March 2020; Accepted: 31 March 2020; Published: 3 April 2020  
**Abstract:** Using first-principles calculations based on the density functional theory, the effects of atomic vacancy defect, Frenkel-type defect and transition metal \(Z\) (\(Z\) = Sc, V and Zr) doping on magnetic and electric properties of the Ti\({}_{4}\)N\({}_{3}\) MXene nanosheet were investigated comprehensively. The surface Ti and subsurface N atomic vacancies are both energetically stable based on the calculated binding energy and formation energy. In addition, the former appears easier than the latter. They can both enhance the magnetism of the Ti\({}_{4}\)N\({}_{3}\) nanosheet. For atom-swapped disordering, the surface Ti-N swapped disordering is unstable, and then the Frenkel-type defect will happen. In the Frenkel-type defect system, the total magnetic moment decreases due to the enhancement of indirect magnetic exchange between surface Ti atoms bridged by the N atom. A relatively high spin polarizability of approximately 70% was detected. Furthermore, the doping effects of transition metal \(Z\) (\(Z\) = Sc, V and Zr) on Ti\({}_{4}\)N\({}_{3}\) nanosheet are explored. All doped systems are structurally stable and have relatively large magnetism, which is mainly induced by the directed magnetic exchange between surface \(Z\) and Ti atoms. Especially in the doped Ti\({}_{4}\)N\({}_{3}\)-Sc system, the high spin polarizability is still reserved, suggesting that this doped system can be a potential candidate for application in spintronics.  
**Keywords:** first-principles; Ti\({}_{4}\)N\({}_{3}\) nanosheet; vacancy defect; doping; magnetism