esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanical Design of CDHRM  
### Overall Design of CDHRM  
To meet the task requirements in a typical confined underwater scenario, the characteristics which are required to be considered in the design of the CDHRM include being lightweight, strong obstacle avoidance, high flexibility and strong load capacity.  
A general mechanical design of a CDHRM is shown in Figure 1a. It is an integrated structure consisting of a driving subsystem and manipulator. The driving subsystem contains the cables and actuators mounted on the base, and the adjacent joints in the manipulator are coupled with each other by a universal joint. The joint number can be extended with regard to practical tasks, which can greatly improve the flexibility of the CDHRM.  
Figure 1: _Cont_.  
Each cable is used to pass through the joints to connect the end-effector and driving subsystem. The rotational motions of each joint can be achieved by pulling and pushing cables passing through the holes pierced in the tubular structure, which is controlled by the rotation of the actuator. And the driving subsystem (shown in Figure 1b) is used to convert the rotation of actuator to the rectilinear motion of cables. The linear magnetic encoders are mounted on the driving subsystem to measure the cable's displacement.
## 2 Materials and Methods
### Assumptions  
In order to analyze the drying system from a scientific perspective, several assumptions were made in the present work, which are listed as follows:  
Figure 2: The schematic diagram of the flows for different fluxes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{6}{c}{**Flue Gas Valve Openings \(\alpha\)\({}^{o}\)**} \\ \cline{2-7}  & **15** & **30** & **45** & **60** & **75** & **90** \\ \hline _v\({}_{s}\)_ & 2.72 & 2.68 & 2.65 & 2.62 & 2.60 & 2.59 \\ m\(\cdot\)s\({}^{-1}\) & 2.70–2.74 & 2.66–2.69 & 2.63–2.68 & 2.59–2.64 & 2.58–2.61 & 2.57–2.62 \\ _v\({}_{b}\)_ & 5.68 & 7.26 & 9.21 & 11.83 & 15.35 & 20.47 \\ m\(\cdot\)s\({}^{-1}\) & 5.21–6.85 & 5.96–9.32 & 7.65–11.32 & 9.75–13.25 & 14.32–18.63 & 1.79–23.2 \\ _Tfg\({}_{c}\)_ & 336.6 & 462.5 & 632.3 & 795.6 & 936.5 & 1024.3 \\ _Tc_ & 296.3–362 & 428.6–486.8 & 601.5–663.8 & 765.8–816.3 & 904.8–968.9 & 987.6–1086.3 \\ _Tfg\({}_{c,out}\)_ & 77.3 & 82.6 & 96.3 & 105.6 & 115.6 & 128.5 \\ _Tc_ & 74.3–80.2 & 79.8–83.9 & 92.1–99.6 & 100.3–108.9 & 112.3–119.6 & 11.61–132.3 \\ _Tg\({}_{b,0}\)_ & 62.2 & 65.3 & 73.9 & 78.5 & 82.2 & 85.6 \\ _Tc_ & 57.9–64.8 & 62.3–69.2 & 69.8–76.1 & 77.2–81.9 & 80.2–85.6 & 83.6–89.3 \\ _T\({}_{air}\)_ & 32.6 & 56.4 & 76.5 & 86.2 & 95.2 & 100.3 \\ _Tc_ & 30.6–35.2 & 54.2–58.9 & 73.5–79.6 & 82.1–89.3 & 92.6–99.8 & 97.6–105.9 \\ _Tc_ & 65.2 & 73.5 & 84.2 & 96.3 & 98.8 & 105.6 \\ _v\({}^{\circ}\)_C_ & 63.2–69.8 & 71.6–75.6 & 82.6–87.9 & 93.6–98.3 & 95.6–102.3 & 101.6–109.6 \\ _m\({}_{cal}\)_ & 155.4 & 188.7 & 206.5 & 275.4 & 334.8 & 425.6 \\ kg\(\cdot\)h\({}^{-1}\) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental data of the parameters.  
1. The fluxes were in the steady flow state.
2. The heat loss on the FGP was neglected.
3. The influence of moisture content variations on the hot air velocity was neglected.
4. The temperature and the relative humidity of ambient air in the experimental period were considered to be constant at 8 \({}^{\circ}\)C and 85%, respectively.
# How to Make Sense of Team Sport Data:
## 2 Team Sport Data
### Video and Sensor Data
#### 2.1.1 Movement Data  
Movement data describes where an actor or game object (e.g., a player or the ball) is located at a specific point in time. Locations are usually measured by local coordinate systems with reference to the game pitch. These measurements contain as a minimum the \(x\)- and \(y\)-coordinates and sometimes also the \(z\)-coordinate. Depending on the acquisition technique, the positions are usually sampled around 10 to 25 times per second (Hz). In the sample dataset of the ACM DEBS 2013 Grand Challenge, each player has two sensors (one in each shoe; the keeper additionally has sensors in gloves) that each transmit position reports at 200 Hz. The ball contains one sensor that transmits at 2 kHz. For each sensor, a timestamp (\(X\), \(Y\), \(Z\))-position, as well as both overall and component-based velocity and acceleration is retrievable. Storing a full game results in approximately 10 GB of data. Body postures are not recorded in the sample dataset due to the large amount of active sensors needed for robust posture recognition. Nevertheless, we expect these data arising in the foreseeable future especially with passive recognition from video data [11; 12].
## 2 Materials and Methods
### Determination of Antibiotic Activity  
Antibiofilm activity was determined as described by [PERSON] et al. [25] with some modifications. Briefly, an extract was dissolved in TSB supplemented with 1% (\(w/v\)) glucose (Sigma, St. Louis, MO, USA) at three different concentrations, namely the MIC, one-half of the MIC, and one-fourth of the MIC. These concentrations are commonly employed to explore both direct antimicrobial effects (at MIC) and sub-inhibitory effects (at half and quarter of MIC) that may influence biofilm formation and bacterial behavior without completely inhibiting growth. Additionally, these concentration ranges ensured the solubility and stability of the extracts throughout the assay. In cases where no MIC was observed, the three highest concentrations tested were used (40, 20, and 10 mg/mL). The microorganisms used to screen for antibiofilm activity were _E. coli, L. monocytogenes, P. aeruginosa, P. agarici, P. fluorescens, P. tolaasii_, and S. aureus. Test solutions were inoculated at 2% (\(v/v\)) using an overnight inoculum (ca. 10\({}^{8}\) CFU/mL), transferred into 96-well microtiters (Nunc, Darmstadt, Germany), and incubated for 24 h at 37 \({}^{\circ}\)C or 25 \({}^{\circ}\)C depending on the microorganism. Afterwards, the contents of the plate were discarded, and each well was carefully washed three times with sterile distilled water to remove any adhered cells. The adhered cells were then fixed with 200 mL/well of methanol for 15 min. Afterward, the methanol was discarded, the plates were left to dry, and then the fixed biofilm was stained with 200 \(\upmu\)L/well of crystal violet (CV) for 5 min. Excess staining was rinsed out by placing the plate under slow-running tap water. After this, the plates were air-dried, and the dye that bound to the adhered cells was resolubilized by adding 200 \(\upmu\)L/well of 33% (\(v/v\)) glacial acetic acid. The optical density of the solution obtained was measured at 570 nm using a microtiter plate reader. All assays were performed in triplicate, a positive control was prepared using inoculated culture media, and a negative control was prepared using only sterile media. The results were given in biomass formation inhibition percentages and calculated according to the following formula, in which ODassay is the OD measured in the presence of the extract and ODpositive control is the OD measured for the positive control.  
\[\%\text{biomass formation inhibition}=100-\left(\frac{\text{OD}_{\text{assay}}}{\text{OD}_{\text{positive control}}}\right)\times 100\]
## 5 Computational Experiments
### Experiment Results
#### 5.2.1 Effectiveness Validation under Small-Batch Instances
As shown in Figure 5, the values of \(Act\) in \(SPFH\) are better than those of \(LL\), especially at the fill rate of \(67\%\). Intuitively, the \(SPFH\) algorithm outperforms the \(LL\) heuristic. Still, the relocation rules \(MSS\) and \(FSS\), which allow moving other containers unrelated to the target container, may prolong the loading operation of a few ETs during some operation rounds.
## 2 Background Work
### Path Determination
Our proposed approach is different from the above as the ferry does not have to visit each node in the network to collect sensed information. Instead, the sensing field area will be divided into virtual grids and a checkpoint will be placed in each grid. The ferry will only visit these checkpoints to collect data. Our approach also uses TSP and genetic algorithm to choose the optimum path of the ferry which consists of visiting a list of sequenced checkpoints. The sequence of checkpoints that will be visited will be decided by assigning a weight to each checkpoint and deciding which checkpoint will be visited first. Moreover, the NRCA algorithm will be applied in each virtual grid to decide on the best placement position for each checkpoint in order to preserve the energy of the whole network. Our aim is to minimize the overall round trip travelling time of the ferry and to minimize consumed energy in the network. This is achieved by modifying NRCA to be applied in reference to the position of the checkpoint rather than the position of the base station _i.e._, the sink. By doing this, each checkpoint will act like a virtual sink within each virtual grid. We refer to the new modified NRAC algorithm as ferry based NRCA or FNRCA. In FNRCA distance used to rank the nodes is in reference to the checkpoint position rather than the position of the base station as was done in NRCA.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band OFF and K/Ka-Band ON
#### 7.3.3 Reflection Coefficients  
In Figure 27, the input and output reflection coefficients remain better than \(-5\) dB throughout 23-28 GHz. This indicates a suitably matched design for the K/Ka-band.
# Optical Interference Suppression Based on LCD-Filtering
## 2 Fundamentals and Experimental Set-Up  
The dynamic filter approach presented can be considered as an analogon to receiver-side beamforming, well known in RF communication, allowing for steering the receive beam pattern. This is accomplished by mounting a transmissive LCD in front of a PD and controlling the individual LCD pixels in order to maximize the SNR. The arrangement under consideration is depicted in Figure 1. By switching the transmissivity of the individual pixels the LCD can be adjusted such that light from specific origins is able to pass the LCD while the interfering signals are mostly blocked. In the simplest case, the LCD filter is targeted at a single transmitter by setting one area of LCD pixels to transmissive state while all other pixels are blocking incident light rays.
## 1 Introduction
[46] developed a single cell with a La\({}_{1.7}\)Ca\({}_{0.3}\)Ni\({}_{0.75}\)Cu\({}_{0.25}\)O\({}_{4-\delta}\) cathode, a gadolinium doped cera (GDC) electrolyte, and a NiO-GDC anode, demonstrating a maximum power density of 0.75 W cm\({}^{-2}\) and the total polarization resistance \(Rp<0.1\)\(\Omega\) cm\({}^{2}\) at 700 \({}^{\circ}\)C. In our recent study [48], we observed the minimal \(Rp\) value in the La\({}_{1.7}\)Ca\({}_{0.3}\)Ni\({}_{1-x}\)Cu\({}_{x}\)O\({}_{4-\delta}\) series at x = 0.4, while in the La\({}_{2}\)Ni\({}_{1-x}\)Cu\({}_{x}\)O\({}_{4+\delta}\), it was found at x = 0.2 [40]. The polarization resistance of the electrodes in the Nd\({}_{2}\)Ni\({}_{1-x}\)Cu\({}_{x}\)O\({}_{4+\delta}\) series showed a minimum at x = 0.2 [41]. All these studies demonstrate that the effect of Cu doping on polarization resistance is complicated, and the observed phenomena may be associated with such properties as electrical conductivity, excess oxygen content, oxygen transport properties, and crystal structure changes with doping. Recently, we presented a study of the synthesis features and structural, thermo-mechanical, and electrical properties of electrode materials in the Nd\({}_{1.6}\)Ca\({}_{0.4}\)Ni\({}_{1-x}\)Cu\({}_{y}\)O\({}_{4-\delta}\) series [49]. However, the influence of Cu substitution on the high-temperature behavior, the oxygen transport properties, and, finally, the performance of the related electrode, all of which are critical for the practical application of these materials, were not investigated. Thus, the focus of this study was concentrated in this direction, and the above-mentioned properties were investigated using advanced methods such as high-resolution neutron and in situ X-ray powder diffraction, isotope exchange, and impedance spectroscopy with analysis of the distribution of the relaxation times (DRT) to identify the electrode processes and select the electrode with superior properties for the cells with Ce\({}_{0.8}\)Sm\({}_{0.2}\)O\({}_{1.9}\), BaCe\({}_{0.80}\)Sm\({}_{0.19}\)Cu\({}_{0.10}\)O\({}_{3-\delta}\), BaCe\({}_{0.8}\)Gd\({}_{0.19}\)Cu\({}_{0.1}\)O\({}_{3-\delta}\), and BaCe\({}_{0.5}\)Zr\({}_{0.3}\)Y\({}_{0.1}\)Yb\({}_{0.1}\)O\({}_{3-\delta}\) solid electrolytes, potentially for application in the IT range.
## 3 Antenna Performance Enhancement Techniques
### Role of Parasitic Patches in the Band and Gain Enhancement  
The use of parasitic patches has shown an outstanding role in the enhancement of performance parameters, with especially effective behavior in the bandwidth enhancement. In [25], the authors presented a simple low-profile spiral monopole antenna upgraded with vias in the ground plane in the inverted-\"U\"-shaped sequence. A 11.5 dBi peak gain with more than 83% radiation efficiency is achieved. The back side of the antenna is introduced with parasitic patches in the hexagonal shape to restrain the flow of surface waves and to minimize the interelement mutual coupling. Bandwidth ranging from 23.76 to 42.15 GHz is achieved. In the geometry of the antenna, two spiral arms are lengths L1 and L2, respectively. These arms are then connected to the feed line. A rectangular slot is created in the back side of the antenna. The width of the slot is the same as in the feed line. RT/Duriod Rogers 5880 is used with dielectric constant \(\varepsilon\tau=2.2\), and the thickness of substrate is 0.254 mm. A low-profile parasitic antenna in G shape for 5G is presented in [27]. The antenna offers the impedance bandwidth with two separate ranges. For the result-oriented geometry of the parasitic design, the antenna is optimized and a wide bandwidth due to the parasitic patch was observed, as in Step 1, and as 7.46 to 8.42 GHz in the second step from 4.43 to 8.82 GHz.  
The authors of [27] have cross-verified the results by cross-checking on both software CST microwave studio and high-frequency structured simulator HFSS results from both software are well agreed. For converting the end fire direction and pattern of beam to the broadside, the extension in the U-shape have a key role.  
Similarly, in [28] the parasitic elements are placed on either side of the monopole radiator to improve the bore-sight gain. The mutual coupling between of two adjacent elements is reduced by the arrangement of radiating patches and their feed lines with a rotation of 180 degrees out-of-phase alternating sequence discouraging the flow of surface waves, which directly results in improving the symmetry of the radiation pattern, and mutual coupling of 20 dB with uniform distribution of amplitude and a 19.8 dBi maximum broadside gain is achieved with a side-lobe level of \(-12.1\) dB. These characteristics rendering the antenna would be an excellent choice for 5G wireless applications. The geometry of the parasitic patch is shown in Figure 12.  
The coupling of 20 dB with uniform distribution of amplitude and a 2 dBi maximum broadside gain is achieved and a side-lobe level of \(-12.1\) dB is achieved. All simulated parameters well agree with the measured one.
## 3 Experiments and Functioning Tests
### Microrobot AR Missing Object Detection Test  
The purpose of this experiment is to test the effectiveness of the proposed framework in identifying missing tools for maintenance or objects that might obstruct narrow passages, namely, pipes, and display the abstracted information in the AR headset. To carry this process out, low-cost HEXBUG nano or HEXBUG Spider devices mounted with ESP32-CAM boards collect image data, pass the data to a nearby single-board processor on the network, and identify the objects of interest using ML. As an example, a baseball is used as the target for analysis. The Wi-Fi-enabled microrobot units transfer images of the target back to a Raspberry Pi acting as a base station for ML processing. An abstraction of the detected object is then sent to the AR headset for the human user to view. The HoloLens 2 is capable of taking screenshots that emulate the user's first-person view while wearing the device. Screenshots on the HoloLens 2 include any running programs in user view, superimposed over real-world surroundings.  
Many important maintenance tools can easily misplaced within a home. Locating such objects can be difficult and time-consuming, especially for occupants with special needs or those living in cluttered dwellings. Hammers and screwdrivers are used as example targets of missing tools in this functioning test. The tools are are placed at different angles and orientations relative to the view of the microrobot camera to determine the system's ability to detect objects and provide associated confidence levels.
## 3 Method
### Integration with Strong Weak Faster R-CNN  
We took the state-of-the-art Strong Weak Faster R-CNN as our baseline detector. Since CAS-DA focuses on strengthening the learning ability of RPN classifier, we only needed to add extra RPNs without making any changes to the other processes. The implementation details of CAS-DA are as follows: First, the structure and the data flow of RPN1 and RPN2 were designed similarly to those of the RPN in Strong Weak Faster R-CNN, i.e., RPN3 in Figure 1. To make sure that the feature maps with the same size were available for the three RPNs, we added convolutional blocks composed of a 1\(\times\)1 convolutional layer and an average pooling layer between the first two cascade RPNs and the feature extractor. Specifically, the convolutional layer size was \(1\times 1\times 1024\), the pooling layer output feature map was \(38\times 38\), and finally, a feature map of \(38\times 38\times 1024\) was fed to RPNs. For the convenience of joint training, the batch size of each stage, i.e., \(N_{cls}^{r}\) (r\(\in\){1,2,3}), was set to 1024, 512, and 256, respectively, in which the batch size of RPN3 was the same as that of the RPN in Strong Weak Faster R-CNN. Furthermore, a one-dimensional tensor, i.e., \(\alpha_{r}\), was introduced to evaluate whether the sample was rejected in previous stages.  
Finally, we can train our CAS-DA in an end-to-end manner by backward propagation. The training loss \(L_{CAS-DA}\) was designed to compose the loss of detection and domain adaptation, they were balanced by the trade-off parameter \(\lambda\). Specifically:  
\[L_{CAS-DA}=L_{Cascade-RPNs}+L_{Fast-RCNN}+\lambda\Big{(}L_{loc-img}+L_{global- img}\Big{)}, \tag{7}\]  
where \(L_{Cascade-RPNs}\) and \(L_{Fast-RCNN}\) are both composed of classification loss and regression loss.
## 4 Illustrative Example
### Experiments and Results
We directly add the failure on the flow between operational activities. That is, multiple communication links fail in the physical architecture. Based on the plot, the solutions on the Pareto front have a fair amount of surviving capability and recovered capability, but lower than the values in Figure 8 due to the functional link disruption. The plot also demonstrates the process of output moves to a better Pareto set after 100 generations. In the Pareto set of the 100 th generation, most of the solutions share the same set of initial system mixes that includes Destroyer Class C, Destroyer Class D, Destroyer Class E, EW Aircraft Class C, and EW Aircraft Class E. Again, this is reasonable under the current experiment setup because the fighters and UAVs are not adequately cheap or useful. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**System Type**} & \multicolumn{6}{c}{**Functions**} \\ \cline{2-7}  & **F\_S1** & **F\_S2** & **F\_S3** & **F\_D1** & **F\_D2** & **F\_I1** & **F\_I2** \\ \hline Destroyer Class B & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\ Destroyer Class C & 0 & 3 & 0 & 0 & 1 & 2 & 1 \\ Destroyer Class D & 0 & 0 & 0 & 0 & 4 & 0 & 3 \\ Destroyer Class E & 0 & 8 & 0 & 0 & 3 & 3 & 1 \\ EW Aircraft Class B & 0 & 2 & 1 & 2 & 0 & 0 & 0 \\ EW Aircraft Class C & 1 & 3 & 1 & 2 & 0 & 0 & 0 \\ EW Aircraft Class D & 3 & 3 & 0 & 0 & 0 & 0 & 0 \\ EW Aircraft Class E & 1 & 3 & 3 & 1 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: An example solution of initial architecture configuration. Figure 10: Pareto fronts of the experiment with communication link disruption. The results in this synthetic example demonstrate that the proposed method is able to provide decision-makers in the defense domain with a quantitative, integrated tool to find the initial architecture selection and configuration that can handle disruptions more effectively, as well as reconfiguration decisions that can help recover more capability. The results also imply the importance of incorporating reconfigurability in the constituent systems and SoSs to achieve higher resilience. When transitioning from the theoretical method to real-world examples, we need to pay careful attention to the normalized values in the mathematical formulations of some reconfiguration enablers (e.g., function capacity). We need to apply techniques like multi-attribute utility theory to convert the concrete parameters to normalized values.
## 4 Monomode Interband Cascade Lasers in the 3 \(\upmu\)m to 4 \(\upmu\)m Spectral Range  
The wavelength range from 3-4 \(\upmu\)m is particularly interesting for several sensing applications, such as the detection of hydrocarbons. As shown in the preceding sections, several well established types of laser sources approach this wavelength range from both ends. However, decreasing hole confinement and increasing Auger recombination in GaSb-based type I interband diodes limit their usability towards higher wavelengths. Intersubband QCL on the other hand approach the mentioned wavelength range from above but suffers from fast phonon scattering losses. Due to these issues, there is a need for a more efficient laser source in the 3-4 \(\upmu\)m regime.  
The discussed issues can be circumvented by using interband cascade lasers (ICL) [57, 58]. These relatively new and technologically demanding laser sources utilize optical transitions between an electron state in the conduction band and a hole state in the valence band in a cascade of Sb-based type-II QW structures. A broken-gap band edge alignment enables the tailoring of the emission wavelength by altering the cascade structures.  
Continuous wave operation above room temperature as well as monomode lasing have been observed in ICLs [59], making the concept very feasible for sensing applications between 3 \(\upmu\)m and 4 \(\upmu\)m.  
Figure 9: Tuning range, output power and average current at various pulse repetition frequencies (PRF) of two double channel DFB QCL (15 \(\upmu\)m ridge width, 2 mm length) at 11.43 \(\upmu\)m. They are driven in inter-pulse tuning mode with a pulse length of 8 ns, a cycle frequency of PRF/1000, and ramp duty-cycles of 94.1% (device A) and 99.8% (device B). The heat sink temperature was 10 \({}^{\circ}\)C for device A and 23 \({}^{\circ}\)C for device B to yield the same spectral position of the DFB laser mode.