esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Transformer-Based Approaches for Image Captioning
## 3 Method Selection  
In this section, we describe the strategy that we used to select the models reviewed. There have been many approaches to carrying out image captioning. However, in this review we are mainly interested in transformer-based approaches since these constitute the current state-of-the-art. We, therefore, do not include in our review models which were developed prior to the design of the transformer model in 2017 [36]. Furthermore, there have already been other reviews which have amply studied the previous approaches of image captioning [41; 42; 43].  
We divide the transformer-based approaches into two broad categories: those prior to vision-language pre-training (vanilla transformer-based models) and those involving vision-language pre-training (VLP models). The former methods were developed earlier in time (shortly after the initial design of the transformer model). The latter models arose after the recent advancements in pre-training. Vision-language pre-training has had a significant impact on multi-modal models involving the visual and language domains and the current state-of-the-art models employ some form of vision-language pre-training. To illustrate this, we first study a number of vanilla transformer-based models and thereafter we assess a number of VLP models.  
We are interested in models that employ publicly available datasets, especially MS COCO [75], since this has for a long time served as a good basis for comparing various works. We are particularly interested in models that have performance metrics based on the online and public MS COCO leaderboard [69] since this adds an additional level of objectivity. We are also interested in models that at least give performance using the CIDEr metric [73], due to its high correlation with human judgements. Nocaps [57] is a more recent dataset and interesting for out-of-domain captioning but we preferred to leave the study wider and include methods that do not necessarily report on Nocaps.  
Given the plethora of models that have sprung up and the clear limitation of not being able to include them all in our review, we mainly focus on those with a significant design component over and above the initial design of the transformer. This means that we have not included models that have only very slight adjustments, for instance mere changes in the parameters such as size.  
The selection strategy has resulted in the models reviewed being representative of the main recent approaches of transformer-based models used in image captioning.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Description of the Uvsq-Sat NG Satellite and Its Payloads
Therefore, the position and velocity vectors calculated from TLEs will be compared to the measurements conducted via the UVsq-Sat NG onboard GPS receiver, which is capable of reaching a precision close to 2 m.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Spectrometer** & **NanoCam** & **ERSs** \\ \hline Field of view & 0.15\({}^{\circ}\) & 13\({}^{\circ}\) & 180\({}^{\circ}\) \\ Aperture/surface & 15 mm & 32 mm & 10 \(\times\) 10 mm \\ Spectral range & 1200–2000 nm & 390–690 nm & 0.1–30 μm \(\mid\) 0.1–100.0 μm \\ Image size & 1 \(\times\)
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
###### Abstract  
Recently, wireless communication systems use a multi-hop transmission scheme using a relay to expand the cell coverage of the system. The multi-hop transmission scheme can expand the cell coverage of wireless communication systems. However, if an appropriate relay is not selected, errors generated during signal processing in the relay are transmitted to the receiver. Therefore, a relay selection scheme is essential for reliable multi-hop transmission. This paper proposes a relay selection scheme for reliable multi-hop transmission in a multi user-multiple input multiple output (MU-MIMO) system. The proposed relay selection scheme uses a MIMO channel matrix between the transmitter and relays to select an appropriate relay. The proposed relay selection scheme obtains the singular values of the MIMO channel matrix using the singular value decomposition (SVD). Then, the sum of the singular values is calculated, and the relay having the largest value is selected. Therefore, in the proposed relay selection scheme, although the transmitter only knows the channel information between the transmitter and relays, the transmitter can select an appropriate relay for reliable multi-hop transmission.  
B 2018 8 1747 10.3390/app8101747 10.3390/app8101747
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 2 \({}^{68}\)Gallium
### \({}^{68}\)Ge/\({}^{68}\)Ga Generators
Obninsk Cyclotron Ltd. (Obninsk, Russia) also commercialized these type of generators, however \({}^{68}\)Ge breakthrough is less than 0.005% and elution yield is higher than 75% for each elution. Generator is eluted with 0.1 M HCl and eluate volume is 5 mL. Ithemba Labs (Cape Town, South Africa) produced SnO\({}_{2}\) column-based generators that can be eluted with 0.6-1 M HCl. Elution volume stands in 6 mL. They present low specific volume, high metallic ion impurities content in the eluate and require a stronger acidic solution for the elution in comparison to other generator systems. At calibration time, \({}^{68}\)Ga yield is higher than 80% and \({}^{68}\)Ge breakthrough less than 0.001% [33]. PARS Isotope Company (Tehran, Iran), commercializes a \({}^{68}\)Ga/\({}^{68}\)Ge sterile generator named PARS-Gallugen\({}^{\circledR}\), with a SnO\({}_{2}\) column which is eluted with 1 M HCl with more than 99% radiochemical purity and radiochemical yield. Silica gel-based (SiO\({}_{2}\), organic) generators were commercialized by ITG Isotope Technologies (Garching bei Munchen, Germany). They are eluted with 0.05 M HCl and is GMP certified. Elution volume is 4 mL and elution yield is greater than 80%. \({}^{68}\)Ge breakthrough is expected to be less than 0.005% [33]. In 2015, IRE ELiT (Fleurus, Belgium) started commercializing Galli Eo\({}^{\circledR}\), \({}^{68}\)Ga/\({}^{68}\)Ge generator eluted with 0.1 M HCl, with more than 67% yield and less than 0.001% \({}^{68}\)Ge breakthrough. Column matrix material is unspecified. It is produced under GMP conditions but is only intended for research and laboratory purposes in the European Union.
# New Developments and Progress in Absorption Chillers for Solar Cooling Applications
## 4 Prototypes of Absorption Chillers
### Single-Stage Absorption Chillers
#### 4.1.1 Ammonia-Based Working Pairs
It was significantly smaller (by approximately 57% [62] and 71% [63]) than any previous single-effect ammonia/LiNO\({}_{3}\) prototype that used PHEs for some of their components (i.e., condenser, evaporator, and solution heat exchanger). An air-cooled single-effect ammonia/LiNO\({}_{3}\) chiller (illustrated in Figure 15a) was developed at the Universidad Nacional Autonoma de Mexico (Mexico) [64]. In this prototype, the heat rejected during the absorption and condensation processes was removed by using finned tubes. An electric motor driven axial fan was used to circulate the air through the absorber and condenser. The desorber and solution heat exchanger were made of 40 and 20 Alfanova 52 plates, respectively, whereas the evaporator was made of 20 Alfanova 27 plates. The overall size of the prototype (shown in Figure 15b) was approximately 2.5 m\({}^{3}\). The prototype delivered a cooling output of up to 3.4 kW and a thermal COP of up to 0.33 when operated at a driving hot water temperature of between 80 \({}^{\circ}\)C and 100 \({}^{\circ}\)C. It supplied chilled water as low as 2.6 \({}^{\circ}\)C and was operated without a cooling tower due to the use of an air-cooled heat rejection system. Figure 14: Small-capacity, air-cooled, single-effect ammonia/LiNO\({}_{3}\) absorption chillers manufactured by CIATESA. Adapted from Reference [60].
# Universal Voltage Conveyor and its Novel Dual-Output Fully-Cascadable VM APF Application
## 4 Experimental Verification  
In order to confirm the theory and prove the real behavior of the proposed VM APF using the readily available UVC-N1C 0520 [22; 23; 24] chip, a printed circuit board (PCB) was developed, shown in Figure 3. The experimental measurement results were carried out using an Agilent 4395A Network/Spectrum/Impedance Analyzer (Agilent Technologies: Penang, Malaysia). In all measurements, the passive component values of the filter were selected as \(C_{1}=C_{2}=560\) pF and \(R_{1}=R_{2}=1\) k\(\Omega\), which ensures a 90\({}^{\circ}\) phase shift at \(f_{\mathrm{p\_teor}}\cong 284.2\) kHz. Figure 4a,b illustrate measured gain and phase responses for both inverting and non-inverting outputs, respectively, from which it can be observed that the obtained pole frequency has a value \(f_{\mathrm{p\_meas}}\cong 277.8\) kHz and there is an attenuation of about 2.55 dB \(\pm 6\%\). Output noise behavior for both responses with respect to frequency are shown in Figure 5. As it can be seen, the noise behavior of both responses are likely the same. For instance, the output noise for \(V_{\mathrm{02}}\) response at the operating pole frequency was found as \(1.5089\)\(\upmu\mathrm{V}/\sqrt{\mathrm{Hz}}\). Measured time-domain responses are depicted in Figure 6, in which a sine-wave input signal of 200 mV\({}_{\mathrm{PP}}\) and frequency of 275 kHz was applied to the filter. Subsequently, the Fourier spectrum of both output signals, showing a high selectivity for the applied signal frequency, is shown in Figure 7a,b, respectively. The total harmonic distortions (THDs) at pole frequency were found as 2.078% and 2.080% for the outputs \(V_{\mathrm{01}}\) and \(V_{\mathrm{02}}\) of the proposed filter, respectively. Note that the minor deviations are caused by real behavior of the UVC-N1C 0520 chip and extra parasitic capacitances of the fabricated PCB. The real behavior of the filter is very satisfactory.  
Figure 3: Developed printed circuit board (PCB) prototype.  
Figure 4: Measured magnitude (blue line) and phase (red line) responses of the VM APF for outputs: (a) \(V_{01}\); (b) \(V_{02}\) with cursor position at \(f_{\text{p\_meas}}\).  
Figure 5: Measured noise variations for both voltage outputs versus frequency (red line—\(V_{01}\), blue line—\(V_{02}\)).  
Figure 6: Measured time-domain waveforms (blue line—input, orange line—\(V_{01}\), pink line—\(V_{02}\)).
# Development and Evaluation of an Image Processing-Based
## 2 Materials and Methods
### First Phase
#### Optimization of the Data in the System  
In the design of the projection-based kinesthetic English language learning module, the data received from the Xbox Kinect One sensor were converted into coordinate data through the Kinect for the Windows SDK v1.8 program. This plugin made the data meaningful, and the limb positions on the x, y, and z axes were accurately transferred to the computer environment. The x data from the Kinect sensor define the position of the selected limb on the horizontal axis, the data on the \(y\)-axis depict the position of the selected limb on the vertical axis, and the data on the \(z\)-axis define the distance between the selected limb and the sensor. The communication between the program plugin and the data received in the Unity game engine used in the realization of the games was provided by the Kinect for the Windows Unity Pro 2.0 library.  
Figure 2 shows the skeletal system structure that can be obtained from the Kinect sensor. In this study, after creating the test setup, we predicted that the data from joints 0, 3, 15, and 19 in Figure 2 could carry the appropriate characteristics for the system requirements. However, after the experiments, the data from limbs 0 and 3 caused incorrect responses. These limbs may have remained in front or behind when the person bended or jumped. As a result, the best coordinates were obtained from the right and left foot joints numbered 15 and 19, respectively. In this way, the margin of error between the data received from the sensor and the position of the person in the game was reduced to \(\pm\)8 cm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Menu** & **Sub-Menu** & **Rate** & **Preference** \\ \hline \multirow{6}{*}{Screen} & & 4:3 & \\  & & 16:9 & \\  & & LBX & 16:9 \\  & & Natural & \\  & & Auto & \\ \cline{2-3}  & \multicolumn{2}{c}{Edge masks} & 0\(\sim\)10 & \(1\) \\ \cline{2-3}  & \multicolumn{2}{c}{Keystone correction} & \(-\)40\(-\)40 & \(-\)17 \\ \hline \multirow{2}{*}{Audio} & \multicolumn{2}{c}{Silent} & Open & \\  & \multicolumn{2}{c}{Closed} & Open \\ \cline{2-3}  & \multicolumn{2}{c}{Audio} & 0\(\sim\)10 & 10 \\ \hline \multirow{3}{*}{Settings} & \multicolumn{2}{c}{Front} & \\  & \multicolumn{2}{c}{Rear} & \\ \cline{1-1}  & \multicolumn{2}{c}{Cailing} & \\ \cline{1-1}  & \multicolumn{2}{c}{Rear top} & \\ \cline{1-1} \cline{2-3} \end{tabular}
\end{table}
Table 1: Technical details of the projection device.  
Figure 1: Physical layout of the system (Yellow; Playing Field, Brown; Floor, White; Wall).
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 3 Proposed Image Encryption Algorithm
### Recipient's Decryption Process  
Figure 3 is the image decryption flowchart of the proposed algorithm. [PERSON] decrypts the encrypted image \(E\) with the steps as follows.  
Step 1: Chaotic Sequence Generation  
[PERSON] iterates Equation (1) \(m\times n\) times with the variables \(x_{0}\) and \(q\) of PWLCM, and obtains a chaotic sequence \(X=\left\{x_{\text{i}}\right\}_{m\times n}\).  
Step 2: Chaotic Image Generation  
[PERSON] can get the \(Y=\left\{y_{\text{i}}\right\}_{m\times n}\) with Equation (5), and convert it into a matrix \(C_{m\times n}\).  
Step 3: Group Pixels  
Figure 3: The decryption flowchart of the proposed algorithm.  
Figure 2: Converting an encrypted big integer into pixel values.  
[PERSON] groups 16 pixel values together to form a big integer number with the 128-bit length. For the encrypted image \(E\), [PERSON] can obtain \((m\times n)/16\) encrypted big integers \(\{c_{i}\}\). For the chaotic image \(C\), [PERSON] can also obtain \((m\times n)/16\) chaotic big integers \(\{r_{i}\}\).  
Step 4: AES Decryption  
[PERSON] computes:  
\[d_{1}=\text{AES}_{\text{d}}\Big{(}K_{1},c_{1}\bigoplus r_{1}\Big{)} \tag{9}\]  
\[d_{\text{i}}=c_{1}\bigoplus c_{1-1}\ \bigoplus r_{\text{i}},\quad i=2,\ 3,\ \cdots,\ (m \times n)/16 \tag{10}\]  
where \(\{d_{\text{i}}\}\) are decrypted big integers, and \(\text{AES}_{\text{d}}(\cdot)\) is the decryption function of AES, whose input parameters are the key \(K_{1}\) and \(c_{1}\bigoplus r_{1}\).  
Step 5: Recovering Pixels  
[PERSON] divides \(d_{\text{i}}\), \(i=1,\ 2,\ \cdots,\ (m\times n)/16\) into 16 parts, and converts them into pixel values. According to the pixel positions, he rebuilds these pixel values into a decrypted image \(D\) with the size \(m\times n\).
## 5 Results and Discussion  
Several empirical relations of shear wave velocity (V\({}_{s}\)) and penetration resistance (N) have been developed by a variety of researchers. In some references, corrected N-values were used in the regression analysis. These empirical correlations are summarized in Table 2. These correlations have a basic power law form V\({}_{s}\) = a \(\times\) N\({}^{b}\), where a and b are coefficients that govern the shape of the empirical function. From Table 2, it is observable that for all previously proposed empirical correlations, a typically ranges from 19 to 121 and b ranges from 0.24 to 0.85.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 4 Experiment
### Comparison Experiments  
#### 4.2.1 Experiments on Dirty MVTec Dataset  
We compared our method with three others: CutPaste, PaDim, DFR. The image was resized to 256 \(\times\) 256 for training and inference. In formula (3), we set \(k=10\), \(s=4\). In Equations (8) and (9), we set \(\alpha=0.05\), \(\beta=3\). In Equation (15), we set \(\tau=1.15\) and \(\eta=2.3\).  
We tested the Image-level detection performance of the compared methods at noise ratios of 5%, 10%, 15%, 20%, 25%, and 30%, respectively. As shown in Figure 9, the y-axis is the mean image-level AUROC value for all categories, and the x-axis is the different noise ratios. Our proposed PaIRE method outperforms all comparison methods when the dataset contains noise. PaDim and CutPaste are sensitive to noise and suffered from severe performance degradation. Detailed comparison data with different noise ratios are shown in Table 2. At each noise ratio, the bold format indicates the best performance.  
The anomaly segmentation performance of PaIRE at different noise ratios is shown in Figure 10. As shown in Table 2 and Figure 10a, it can be seen that both image-level detection and pixel-level detection have only a slight performance degradation when the noise ratio is larger than 15%. As shown in Figure 10b, PaIRE can precisely locate the anomaly area in the image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise Ratio** & **PaIRE** & **PaDim** & **DFR** & **CutPaste** \\ \hline
0\% & 92.19 & 95.06 & 94.90 & **95.2** \\
5\% & **93.86** & 82.11 & 92.51 & 80.48 \\
10\% & **93.50** & 69.95 & 91.49 & 61.06 \\
15\% & **93.79** & 59.27 & 90.25 & 50.58 \\
20\% & **92.94** & 42.75 & 89.30 & 34.71 \\
25\% & **92.05** & 33.31 & 88.70 & 22.94 \\
30\% & **91.36** & 30.5 & 88.26 & 19.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with SOTA methods at different noise ratios.  
Figure 9: Comparison on MVTec dataset.
## 3 Experiment and Analysis
The histogram of the errors in Figure 12 shows that the average error was about 1.99 cm, and all the positioning errors were within 3.85 cm. Therefore, it is obvious that the proposed double-light positioning algorithm could accomplish highly accurate positioning. Finally, the average positioning computing time of double-light positioning algorithm was 56.28 ms.  
Figure 9: The positioning results of double-light positioning system: (**a–d**) the 3-D positioning results at the height of 0 m, 0.1 m, 0.2 m and 0.3 m, respectively; and (**e–h**) the horizontal view of the 3-D positioning results at the height of 0 m, 0.1 m, 0.2 m and 0.3 m, respectively. Through the experiment above, it can be clearly seen that the proposed double-light positioning algorithm not only achieved a high positioning accuracy, but was also fully real-time capable. Compared with the methods in [16; 17], as representative for the existing research on two LEDs  
Figure 11: The CDF curves of positioning error in double-light positioning system. Figure 10: The positioning error varies with height in double-light positioning system. in the VLP, the proposed double-light positioning algorithm had higher positioning accuracy (6.5 cm in [16] and 7.5 cm in [17]). Moreover, neither of the methods [16; 17] takes the symmetry problem of angle into consideration. As for the positioning accuracy, the errors of the experiment were caused by the following reasons. First, some errors were caused by human measurement. The coordinates of the receiver position were determined by artificially drawing the grid, which easily caused the position coordinates of the receiver on the grid to deviate from the true position coordinates of the receiver. Second, the installation of LEDs and the placement of the camera could also cause some errors, i.e., the position of the luminaire had a certain coordinate deviation between the reality and the measured value. Finally, additional errors were generated by the algorithm itself. These errors in measurement, lamps installation, and camera placement were not available, but our algorithm ensured that good positioning accuracy could be achieved in the presence of these errors. In the case of considering all experimental errors, the positioning error was within 3.85 cm, indicating that the error of our algorithm was theoretically lower. As for the real-time ability, most of the research on VLP only describes the positioning accuracy of their positioning algorithms, but does not express the real-time ability of their positioning algorithms. The proposed double-light positioning algorithm could realize positioning 17 times in 1 s, which represents good real-time performance. Moreover, the double-light positioning algorithm could be applied to robot positioning, as 17 positions in 1 s is sufficient.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 6 Results
### Multi-Service Adaptive SPS Algorithm
Beyond this point, the PUSCH utilization for the dynamic scheduler does not increase more than 83% (as seen in Figure 17) due to the unavailability of enough PDCCH resources to transmit the uplink grants. Therefore, the capacity bottleneck of the dynamic scheduler for M2M traffic comes from the control channel, by overcoming which the adaptive schedulers can exploit the full capacity of the data channel.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### RFI Power Affecting the Pixel after SAR Processing
#### 2.4.2 LEO-TO-GEO RFI
Therefore, the gain obtained by LEO-to-GEO RFI in SAR processing can be written as  
\[G_{RFI}^{L-G}=G_{r}^{L-G}G_{a}^{L-G}L_{1}^{L-G}/L_{2}^{L-G} \tag{23}\]
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 3 Measurement Results and Analysis
### The Power Spectral Density  
Another method of characterizing dynamic channels is by observing their power spectral density (PSD). The PSD provides useful information on the frequency composition of the signal. It describes how the power of the signal is distributed over the frequency range. Figures 6 and 7 show the PSD for running activity and cycling activities, respectively. We can observe that there is a strong spectral components with high power at 0 Hz followed by other components at higher frequencies in both activities. In addition to that, the PSD tends to decrease exponentially in the logarithmic scale. This lowpass characteristic of the spectrum is consistent with a radio channel having moving scatterers [14], and has been observed in on-body WBAN [15]. As for the case of cycling activity (see Figure 7),  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{2}{c}{**Coherence Time (ms)**} \\ \cline{2-3}
**Scenario** & **Running** & **Cycling** \\ \hline Scenario 1 & 48 & 80 \\ Scenario 2 & 48 & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coherence time: Time before auto-correlation crosses the value of 0.7.  
Figure 5: The auto-correlation function of all measured scenarios with a delay of up to 3 s.  
the PSD tends to settle between 20 and 50 Hz before it start to decrease again, resulting in a local turning point at around 50 Hz. This phenomenon can be explained by the presence of the off-body scatterer (bicycle) in the cycling activity, which was absent in the running activity. Similar effects of off-body scatterers have been observed for on-body radio channels in [16].