esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Destructive pXRF on Prehistoric Obsidian Artifacts from the Central Mediterranean
## 5 Applications of pXRF on Obsidian Artifacts from Central Mediterranean Sites
### Obsidian Artifacts from Sites around the Adriatic and in Croatia  
Prior to its political breakup, no obsidian artifacts found in Yugoslavia had been analyzed. Since then, however, a significant amount of archaeological research was conducted in Croatia, with obsidian found at many prehistoric sites along the Dalmatian coast, on islands in the Adriatic Sea, and as far north as Istria. At first, the results of analyses by pXRF, which showed that most were from Lipari, with very little reaching there over land from the Carpathian sources, were not expected [53; 59]. In addition, the presence of some from Palmarola, on the island of Susac and on the mainland at Lok. Musa, was surprising considering our previous thoughts of it having limited distribution [47] (Figure 12). Obsidian from Palmarola, however, was identified at three Neolithic sites along the Italian coast as well as at sites in the Tavoliere and near the Gargano Peninsula from where travel by island hopping over the Adriatic likely occurred [47; 53; 60]. Entirely unexpected was the identification of four obsidian artifacts on the island of Palagruza coming from Melos  
Figure 11: Map with sites in southern Italy with Sardinia obsidian artifacts. From north to south, in red circles: Poggio Olivastro 20/100 (20%); Casale del Dolce 1/35 (3%); Venafro 1/132 (<1%); Pulo di Molfetta 1/37 (2.7%); M. Di Gioia 12/12 (100%); Ausino 3/21 (14%); Saracena 4/842 (<1%); Bova Marina 1/200 (<1%); Valdesi 1/41 (2.5%).  
(Sta Nychia subsource) since no others have been securely identified in prehistoric contexts west of Albania [59; 61]. The Palagruza site, however, dates to the Copper Age, when increasing social and economic complexity began in the Aegean.
## 2 Ensemble Neural Network and Signal Graph-Based SEI
### The Proposed Ensemble Neural Network
#### 2.2.2 Loss Function  
The ENN has three outputs which are from the sequence classifier, graph classifier, and ensemble classifier, respectively. In this paper, three sub-loss functions \(L_{S}\), \(L_{G}\) and \(L_{E}\) are set for supervising the sequence classifier, graph classifier, and ensemble classifier, respectively. They are all cross-entropy loss functions as expressed in Formulas (11), (12), and (13), respectively:\[L_{S} =-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{k}y_{C_{i}}\log[p_{y_{C_{i}}}(j)] \tag{11}\] \[L_{G} =-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{k}y_{G_{i}}\log[p_{y_{G_{i}}} (j)]\] (12) \[L_{E} =-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{k}y_{E_{i}}\log[p_{y_{E_{i}} }(j)] \tag{13}\]  
where \(N\) is the number of samples; \(k\) is the number of classes; \(y\) is the sample label; \(p\) is the result predicted by the model.
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 2 Methods
### Numerical Simulation Method  
#### 2.2.1 Turbulence Model  
The RNG \(k\)-\(\epsilon\) turbulence model is used to simulate the airflow organization inside the aircraft cabin in this paper. The specific expressions of the RNG \(k\)-\(\epsilon\) turbulence model are depicted as follows.  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_ {i}}=\frac{\partial}{\partial x_{j}}(\alpha_{k}\mu_{eff}\frac{\partial k}{ \partial x_{j}})+G_{k}+G_{b}-\rho\epsilon-Y_{M}+S_{k} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Boundary Name** & **Boundary Type** & **Velocity** & **Temperature** & **Droplet** \\ \hline Air supply diffusers & Velocity-inlet & 10 L/(s-person) & 292.45 K & Escape \\ Air exhaust diffusers & Pressure-outlet & - & - & Escape \\ Ceiling & Wall & - & 296.15 K & Reflect \\ Sidewall & Wall & - & 294.15 K & Reflect \\ Floor & Wall & - & 296.15 K & Reflect \\ Windows & Wall & - & 290.15 K & Reflect \\ Human & Wall & - & 304.15 K & Trap \\ Mouth of infectious source & Velocity-inlet & \(1.25\,\mathrm{m/s}\) & 306.15 K [34] & Escape \\ Mouth of susceptible person & Velocity-inlet & \(-1.25\,\mathrm{m/s}\) & - & Escape \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions of numerical simulation.  
Figure 2: Schematic of mouth and breathing area of passengers.  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u_{i})}{ \partial x_{i}}=\frac{\partial}{\partial x_{j}}(\alpha_{\varepsilon}l_{eff} \frac{\partial\varepsilon}{\partial x_{j}})+C_{1\varepsilon}\frac{\varepsilon }{\bar{k}}(G_{k}+C_{3\varepsilon}G_{b})-C_{2\varepsilon}\rho\frac{ \varepsilon^{2}}{\bar{k}}-R_{\varepsilon}+S_{\varepsilon} \tag{2}\]  
where \(\alpha_{k}\) and \(\alpha_{\varepsilon}\) are the inverse effective Prandtl numbers for \(k\) and \(\varepsilon\), respectively, \(G_{k}\) represents the generation of turbulence kinetic energy due to the mean velocity gradients, \(G_{b}\) represents the generation of turbulence kinetic energy due to buoyancy, \(Y_{M}\) represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, \(C_{1\varepsilon}\), \(C_{2\varepsilon}\) and \(C_{3\varepsilon}\) are constant, \(S_{k}\) and \(S_{\varepsilon}\) are user-defined source terms, \(R_{\varepsilon}\) represents the additional term of the RNG \(k\)-\(\varepsilon\) Model to Standard RNG Model and can be expressed as Equation (3).  
\[R_{\varepsilon}=\frac{C_{\varepsilon}\rho\eta^{3}(1-\eta/\eta_{0})}{1+\beta \eta^{3}}\frac{\varepsilon^{2}}{\bar{k}} \tag{3}\]  
where \(\eta=S_{k}/\varepsilon\), \(\eta_{0}=4.38\) and \(\beta=0.012\).  
In order to make the model better handle low-Reynolds and near-wall flows, Equation (4) is integrated to obtain an accurate description of how the effective turbulent transport varies with the effective Reynolds number.  
\[d\bigg{(}\frac{\rho^{2}k}{\sqrt{\varepsilon\mu}}\bigg{)}=1.72\frac{\mu_{t}}{ \sqrt{{\mu_{t}}^{3}-1+C_{v}}}d\mu_{t} \tag{4}\]  
where \(\mu_{t}=\mu_{\mathit{eff}}/\mu\), \(C_{v}\approx 100\).
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
###### Abstract  
Grid-connected photovoltaic (PV) systems are designed to provide energy to the grid. This energy transfer must fulfil some requirements such as system stability, power quality and reliability. Thus, the aim of this work is to design and control a grid-connected PV system via wireless to guarantee the correct operation of the system. It is crucial to monitor and supervise the system to control and/or detect faults in real time and in a remote way. To do that, the DC/DC converter and the DC/AC converter of the grid-connected PV system are controlled wirelessly, reducing costs in cabling installations. The used control methods are the sliding for the DC/DC converter and the Proportional-Integral (PI) for the inverter. The sliding control is robust, ensures system stability under perturbations, and is proven to work well via wireless. The PI control is simple and effective, proving its validity through wireless too. In addition, the effect of the communications is analysed in both controllers. An experimental platform has been built to conduct the experiments to verify the operation of the grid-connected PV system remotely. The results show that the system operates well, achieving the desired values for the maximum power point tracker (MPPT) sliding control and the energy transfer from the inverter to the grid.  
grid-connected photovoltaic system; maximum power point tracking (MPPT); sliding mode control; wireless communication 202114193390/en14071931
###### Abstract
However, those methods require the definition of landmarks or meshes. Moreover, several delineated CBCT scans must be available to build a patient-specific shape model. That thwarts the application of such methods at the start of treatment. Therefore, none of these methods accomplishes the challenging task of pelvic organ segmentation on CBCT scans. In parallel, recent advances in computing capabilities, the availability of representative datasets, and the great versatility of deep-learning (DL) approaches have enabled DL algorithms to achieve impressive segmentation performance. Unlike the aforementioned techniques, DL algorithms are supposed to be robust to variations in shape and appearance if those variations are captured in the training database and do not require landmark definition. DL algorithms have already been used successfully to segment pelvic organs on CT scans [19,20]. The 3D U-net fully convolutional neural network [21] has been used to segment female pelvic organs on CBCT scans [22,23]. Concurrently, we showed that adding annotated CT scans to the training set improved bladder segmentation on CBCT scans [24]. This approach was motivated by the scarcity of annotated CBCT scans, compared with annotated CT scans, and the fact that CBCT scans can be roughly considered to be noisy, distorted CT scans from a segmentation perspective, hence sharing shape and contextual information with the CT scans. The current paper extends our previous conference paper [24] in that it considers additional male pelvic organs (rectum and prostate) and presents more comparative results (including the morphons' deformable registration algorithm). It also involves data from an additional hospital and provides a more detailed discussion. Segmentation of male pelvic organs (bladder, rectum, prostate, and seminal vesicles) on CBCT and CT scans using a DL approach was the subject of a recent paper [25]. These authors' contribution consists mainly of the use of artificially-generated pseudo CBCT scans in the training set along with a high segmentation quality. Our approach adds training on real CBCT scans and provides a new and larger test set, as well as more extensive comparison with clinically-used registration tools. The main contributions of this work are to provide (i) a DL-based segmentation method for male pelvic organs on CBCT scans and (ii) a detailed comparison of state-of-the-art segmentation tools in order to guide the choice of method in clinical practice. The impacts of the number of training scans and the addition of CT scans to the training database are studied in order to provide detailed information on the amount of annotations required for use in clinical practice.
## 6 Conclusions  
A novel battery modeling approach using an LSTM is proposed for a lithium-ion battery in vehicle applications. The calculated \(U_{trend}\) and the balanced input dataset give the possibility to train a model without using the SOC and the included difficulties. Two steps were carried out to achieve a valid model: First, the raw experimental data were pre-processed to obtain a useful input vector. With under- and oversampling, redundant data were reduced and under-represented areas were reproduced, respectively. Sequentializing and normalizing permitted adequate training. Second, hyperparameter tuning was carried out during training, in order to find the optimal model architecture. Validation showed that the model accuracy was within an appropriate range. The maximum error in the validation set was below 1% (or 3.9 V) with an MSE of 0.40 V\({}^{2}\) over a Temperature range from \(-\)23 to 56 \({}^{\circ}\)C and power up to 11 kW.  
The proposed battery model can be used in real-time applications, as the model inputs are physically measurable parameters. This ensures the possibility of simple and accurate implementation in a BMS. Its accuracy and transferability to other battery types mean that the developed modeling method can be used in a variety of mild-hybrid vehicles.  
Future work may involve battery SOP estimation based on the proposed voltage prediction. A more robust voltage prediction in the peripheral SOC areas must, thus, be trained.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Profile** & **MAE [V]** & **MaxError [V]** & **MSE [V\({}^{2}\)]** & **Mean Temp [\({}^{\circ}\)C]** & **Mean Voltage [V]** \\ \hline
1 & 0.734785 & 2.963551 & 0.818735 & \(-\)2.578630 & 44.894085 \\
2 & 0.345099 & 1.590984 & 0.291348 & \(-\)17.880545 & 45.090210 \\
3 & 0.221103 & 1.251659 & 0.108364 & \(-\)17.000000 & 47.619461 \\
4 & 0.430389 & 2.482880 & 0.343608 & \(-\)0.494065 & 43.953995 \\
5 & 0.611864 & 2.738808 & 0.712661 & 1.473041 & 43.313877 \\
6 & 0.308964 & 1.174629 & 0.162599 & 53.152065 & 43.104599 \\
7 & 0.402310 & 1.196079 & 0.249962 & 56.111210 & 44.732620 \\
8 & 0.339628 & 1.021107 & 0.187283 & 27.318338 & 47.641869 \\
9 & 0.321065 & 1.027824 & 0.169096 & 29.902288 & 44.442562 \\
10 & 0.498891 & 1.313484 & 0.403387 & 31.953856 & 42.876999 \\
11 & 0.365947 & 1.216549 & 0.223687 & 48.910248 & 46.265839 \\
12 & 0.678125 & 3.916935 & 1.175681 & \(-\)23.398380 & 44.776375 \\ \hline Mean & 0.438181 & 1.82454 & 0.403868 & 15.6225 & 44.8927 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation errors of the test set profiles.  
Figure 8: Voltage (a) and error (b) validation result for a current profile at 27 \({}^{\circ}\)C and a SOC range from 84% to 89%.  
**Author Contributions:** Conceptualization and validation, D.J. and O.T.; methodology, software and investigation, D.J.; review and editing, D.J., O.T., R.K. and A.T.; supervision, R.K. and A.T.; and funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Higher Layer Protocol Evaluation
### MAC Protocols
The CSMA/CA protocol shows a more stable behavior in terms of goodput performance in OH network scenarios, but environmental conditions and the influence of propagation time in the overall packet delay dramatically affect handshaking protocols. On the other hand, the CSMA protocol maintains the average packet delay low and stable in both OH and MH strategies, since no handshaking is performed to complete one packet delivery. In Figure 12(b), CSMA protocols exhibit higher average packet delay at very low network loads, decreasing as network loads increase. This behavior is due to the hot-spot traffic pattern, since as network load increases the nodes closer to the gateway are the ones with higher delivery rates and, at the same time, lower packet delay (signal propagation delay), resulting in a reduction of the average packet delay. Finally, another gauge to measure the power consumption in the network is packet collision statistic. In Figure 14, we show CSMA and CSMA/CA protocols with both OH and MH network configurations. As expected, CSMA shows a much higher number of collisions, leading to an increasing number of  
Figure 14: Collisions one hop _vs_. Multihop. Figure 13: Signal propagation times: OH _vs_. MH. packets lost, increasing the overall wasted energy. However, CSMA/CA shows better performance arriving to a constant number of collisions just after the network reached saturation. In both cases, the number of collisions is highly reduced with the MH approach.
## 4 Part II: The Impact of Head Tracking on the Localization Performance
### Experiment Design
#### 4.1.2 Experimental Setup, Subjects and Test Signal  
Both listening tests, TestDynamic and TestStatic, were performed in the control room of the recording studio at Jade University of Applied Sciences (4.7 m \(\times\) 5.1 m \(\times\) 3 m, average reverberation time: 0.35 s). The choice of using different recording and listening environments was in line with practical applications, since in general it is not always straightforward to listen to binaural signals in the same environment as where they were captured, e.g., signals recorded in a moving vehicle or during a concert. During listening tests, the room was normally illuminated and subjects could see the room and the objects in it (desk, mixing console, loudspeakers, etc.). Subjects were informed that there was no association between the virtual target sources and any objects in the room. Subjects were in addition informed that the signals had been captured in an anechoic environment, and that they should imagine themselves in an anechoic room to judge the distances from the presented sources. The GUI was presented to the subjects via a laptop, connected to the audio interface and headphone amplifier. In each test, each of the 15 target source positions was presented four times, i.e., once with each of the four BRIRs (V11, V21, V23 and HTK in TestDynamic and V11s, V21s, V23s and HTKs in TestStatic). This resulted in a total of 60 virtual sources for each test, presented in a randomized order. Prior to each test, five of these 60 virtual sources were chosen randomly to be presented for familiarization and were discarded from the evaluations. No feedback was given to the subjects during the familiarization and during the listening test.  
The same 14 subjects who participated in TestReal and TestVR in Part I, took part in the new listening tests. Seven subjects started with TestDynamic, whereas the otherseven subjects started with TestStatic. For each subject, there was a pause of at least one day between both tests. During TestDynamic, subjects were encouraged to move their heads within the allowable range of \(\pm 90^{\circ}\) horizontally and \(\pm 15^{\circ}\) vertically. During TestStatic, subjects were made aware that head tracking was switched off.  
The test signal was the same speech signal spoken by a female speaker as in TestReal and TestVR in Part I.
## 2 CNN-LSTM Based Automatic Multi-Class Classification Network
### Preliminary of LSTM
#### 2.1.1 The Gating Structure of LSTM  
Since particle identification task involves analyzing the temporal features of particle waveform, LSTM has substantial advantages in the task of particle identification based on pulse waveform analysis. Figure 2a illustrates the gating structure of LSTM, and the corresponding computational formulas are as follows:  
\[\begin{split} f_{t}&=\sigma\Big{(}W_{f}\cdot[h_{t -1},x_{t}]+b_{f}\Big{)}\\ g_{t}&=\sigma\big{(}W_{g}\cdot[h_{t-1},x_{t}]+b_{g} \big{)}\\ \tilde{C}_{t}&=\tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C })\\ C_{t}&=f\cdot C_{t-1}+g_{t}\cdot\tilde{C}_{t}\\ o_{t}&=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o})\\ h_{t}&=o_{t}\cdot\tanh(C_{t})\end{split} \tag{1}\]  
where \(f_{t}\), \(g_{t}\), and \(o_{t}\) represent the forget gate, input gate, and output gate at time step \(t\), respectively. \(\tilde{C}_{t}\), \(C_{t}\), and \(h_{t}\) denote the candidate cell state, the cell state, and the hidden state at time step \(t\), respectively. \(W\) and \(b\) represent the weights and biases associated with the respective gate functions, respectively. \(\sigma\) and \(\tanh\) represent the sigmoid activation function and the \(\tanh\) activation function, respectively.  
Figure 2: The visualization of (**a**) the gating structure of LSTM and (**b**) the forward propagation process of LSTM with N layers.
## 3 Results and Discussions
### Spatial Illumination Pattern  
In this section, we investigate the spatial distribution of illumination at a square receiver plane with an area of \(3\times 3\) m\({}^{2}\) to better describe the channel spatial dispersion characteristic. The whole area is divided into 50 \(\times\) 50 pixels, with each occupying the area of 6 \(\times\) 6 cm\({}^{2}\). Then we compute the normalized intensity of the strike photons within each pixel over a specific time slot after performing PTA. As Figure 7a-c shows, for LED-based links, the received intensity tends to spread uniformly from the center to its periphery and become weaker with the increase of water turbidity. For laser-based paths, the energy is concentrated on the center spot, and there is almost no such beam spread in coastal water (Figure 7d). However, in the case of turbid harbor water, the received light intensity always exhibits significant spatial spread whether LED or laser (Figure 7e,f). Although spatial dispersion phenomenon can aggravate the energy loss and time-domain broadening effect, a divergent link also reduces the tracking and alignment difficulties, making it feasible to cover more mobile users.  
Figure 5: Illustration of the CIR curve for different water types under various system configurations.  
Figure 6: Illustration of CIR histogram of diffusive and collimated links for coastal & harbor water.
# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the RGB-D Object Detection Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Video**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Var**} & \multicolumn{2}{c}{**Phase**} & \multicolumn{2}{c}{**SGBM**} & \multicolumn{2}{c}{**RGB**} \\  & & \(\text{F}_{1}\) & \(\sigma_{\text{F}_{1}}\) & \(\text{F}_{1}\) & \(\sigma_{\text{F}_{2}}\) & \(\text{F}_{1}\) & \(\sigma_{\text{F}_{1}}\) & \(\sigma_{\text{F}_{1}}\) & \(\sigma_{\text{F}_{1}}\) \\ \hline \multirow{8}{*}{Suitcase} & CB & \multicolumn{1}{c}{} & & & & & 0.520 & 0.261 \\  & C-KDE & & & & & & 0.688 & 0.087 \\  & CB-D & 0.728 & 0.106 & 0.409 & 0.118 & 0.560 & 0.381 & \\  & D-KDE & 0.755 & **0.068** & 0.701 & **0.102** & 0.783 & **0.080** & \\  & DEOR-PCA & 0.826 & - & 0.431 & - & 0.413 & - & \\  & DECB & 0.766 & 0.089 & 0.499 & 0.147 & 0.790 & 0.105 & \\  & DECB-LF & 0.750 & 0.116 & 0.724 & 0.116 & 0.765 & 0.143 & \\  & RGBD-KDE & **0.847** & 0.084 & **0.769** & 0.057 & **0.865** & 0.091 & \\ \hline \multirow{8}{*}{LCDScreen} & CB & \multicolumn{1}{c}{} & & & & 0.745 & 0.132 \\  & C-KDE & & & & & 0.723 & 0.074 \\  & CB-D & 0.400 & 0.226 & 0.535 & 0.099 & 0.094 & 0.115 & \\  & D-KDE & 0.666 & 0.172 & 0.600 & 0.199 & 0.782 & 0.090 & \\  & DEOR-PCA & 0.764 & - & 0.684 & - & 0.668 & - & \\  & DECB & 0.784 & 0.084 & 0.639 & **0.032** & 0.061 & \\  & DECB-LF & 0.803 & 0.074 & 0.691 & 0.071 & 0.832 & 0.075 & \\  & RGBD-KDE & **0.904** & **0.042** & **0.820** & 0.064 & **0.911** & **0.045** & \\ \hline \multirow{8}{*}{Crossing} & CB & \multicolumn{1}{c}{} & & & & 0.653 & 0.112 \\  & C-KDE & & & & & 0.741 & 0.203 \\  & CB-D & 0.278 & 0.334 & 0.259 & 0.294 & 0.387 & 0.366 & \\  & D-KDE & 0.479 & 0.280 & 0.507 & 0.122 & 0.538 & 0.252 & \\  & DEOR-PCA & **0.906** & - & 0.620 & - & 0.416 & - & \\  & DECB & 0.780 & **0.082** & 0.636 & 0.051 & 0.804 & 0.048 & \\  & DECB-LF & 0.791 & **0.082** & 0.765 & 0.111 & 0.851 & 0.038 & \\  & RGBD-KDE & 0.807 & 0.089 & **0.821** & **0.042** & **0.872** & **0.011** & \\ \hline \multirow{8}{*}{LabDoor} & CB & \multicolumn{1}{c}{} & & & & 0.601 & 0.176 \\  & C-KDE & & & & & 0.527 & 0.081 \\ \cline{1-1}  & CB-D & 0.303 & 0.310 & 0.217 & 0.283 & 0.201 & 0.321 & \\ \cline{1-1}  & D-KDE & 0.573 & **0.144** & 0.499 & 0.297 & 0.476 & 0.220 & \\ \cline{1-1}  & DEOR-PCA & **0.780** & - & 0.547 & - & 0.572 & - & \\ \cline{1-1}  & DECB & 0.548 & 0.190 & 0.658 & 0.137 & 0.661 & 0.156 & \\ \cline{1-1}  & DECB-LF & 0.674 & 0.176 & **0.673** & 0.140 & 0.691 & **0.145** & \\ \cline{1-1}  & RGBD-KDE & 0.614 & 0.191 & 0.552 & **0.099** & **0.759** & 0.182 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance results of various background subtraction methods on the RGBD videos of the MULTIVISION Stereo dataset, using depth from three disparity estimation algorithms (Var [103], Phase [104], and SGBM [105]) or using only color (RGB). \(\text{F}_{1}\) and \(\sigma_{\text{F}_{1}}\) are the mean and the standard deviation over four GT masks for each video. In boldface, the best results for each metric and each video.