esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontology-Based Production Simulation with OntologySim
## 9 Summary and Outlook  
In order to enable fully autonomous digital twins that interact with real world entities and allow the digital representation of changing and flexible production systems, an ontology-based simulation model, the OntologySim, is presented. The OntologySim is an open-source fully ontology-based event-discrete simulation, which has high flexibility and modularity due to the developed ontology schema and the defined interfaces. Due to the fully ontology-based approach, it is possible to change and save simulations during runtime. The designed user interface allows a detailed analysis of the agents using KPIs, event, and simulation display. Since OntologySim is published as open source (AGPL-3.0 License), we hope to contribute to the growing collaboration and exchange in the production and simulation community.  
\begin{table}
\begin{tabular}{l l  
Follow up research shall continue the development to enable to join and separate products. This would offer new possibilities to better map production processes in the industry and likewise enable portfolio external products and their influences on the production system to be analyzed [33]. Furthermore, work on the OntologySim is ongoing and feedback from the community is being incorporated. To further close the research gap as outlined before, experimental studies with real world use cases shall be continued to be conducted. Last, but not least, the advantages provided by the ontology core can be researched with the integration of autonomous production control, understandable reinforcement learning [55], and novel production planning approaches that make use of the available real-time data and experimentation ability in further research projects.  
Conceptualization, M.C.M., L.K., A.K. and G.L.; methodology, M.C.M., L.K., A.K. and G.L.; software, L.K. and M.C.M.; validation, L.K. and M.C.M.; formal analysis, L.K. and M.C.M.; investigation, L.K. and M.C.M.; resources, M.C.M., A.K. and G.L.; data curation, L.K. and M.C.M.; writing--original draft preparation, L.K. and M.C.M.; writing--review and editing, M.C.M., L.K., A.K. and G.L.; visualization, L.K. and M.C.M.; supervision,A.K. and G.L.; project administration, M.C.M.; funding acquisition, M.C.M., A.K. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research work was undertaken in the context of DIGIMAN4.0 project (\"DIGItal MANufacturing Technologies for Zero-defect Industry 4.0 Production\", [[http://www.digiman4-0.mek.dtu.dk/](http://www.digiman4-0.mek.dtu.dk/)]([http://www.digiman4-0.mek.dtu.dk/](http://www.digiman4-0.mek.dtu.dk/)), accessed on 27 December 2021). DIGIMAN4.0 is a European Training Network supported by Horizon 2020, the EU Framework Programme for Research and Innovation (Project ID: 814225).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Experiments and Results
### Planning for the One-Resource-One-Agent-Type  
The first experiment considered Wi-Fi as the only resource requested by agents (i.e., \(A=\{A_{1}:``WiFi\"\}\)). Only one agent type exists, and all agents use sigmoid-based function \(f_{1}\) as the collision CDF.  
We first studied the influence of the collision threshold \(T\) and the offset parameter \(\delta\) on performance. Figure 5a shows the success rate of the one-resource-one-type SC-M* with different thresholds \(T=\)0 (equivalent to the basic M*), 0.2, 0.4, and 0.45, while the offset parameter is fixed to \(\delta=6.0\). Table 1 (left) shows the run time in seconds for the experiment. The results clearly show that larger thresholds bring improvement in performance with a higher success rate and lower run time for a large system size (\(m>50\)). The improvement in performance results from the property of SC-M* that larger thresholds render more relaxed constraints, and thus, agents are less likely to collide on resources.  
Figure 5b shows the success rate of the SC-M* with different offset parameters \(\delta=0\), 3.0, 6.0, and 9.0, with fixed \(T=0.35\). Table 1 (right) shows the run time for the experiment. The results  
Figure 4: Grid system with 20 \(\times\) 20 stops and 40 bidirectional lines. Square and circle of the same color correspond to the source and destination of an agent, respectively.  
Figure 5: Impact of the collision threshold \(T\) (given \(\delta=6.0\)) and offset parameter \(\delta\) (given \(T=0.35\)) on one-resource-one-type SC-M*.  
illustrate that SC-M* is sensitive to \(\delta\) and can efficiently handle up to 100 agents with \(\delta=9.0\). These results are reasonable because the sigmoid-based CDF is used in the experiments, featuring a surge in the collision probability at the experience value around the offset, and the offset parameter poses a cutoff value on the resource experience, with collision always announced once the resource experience is larger than the offset. The standard M* (\(T=0\)) can only scale to fewer than 30 agents. Taking advantage of this property, one can tune the parameters to trade off the scalability against the tightness of constraints.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 2 Progresses in QKD Networks
### Researches on Interfaces and Protocols of the Networks  
In terms of protocol extension and interface definition, the following related researches have been conducted around the word. In 2017, as shown in Figure 2, [PERSON] et al. introduced a new definition of the control layer to provide SSH and HTTPS interfaces to allow NETCONF RPC to be used in SSL/TLS-based solution through SSH, RESTful API and GMPLS protocols [18]. In their other work [19], a node structure for end-to-end QKD services was also proposed, demonstrating the workflow and protocol extensions in different SDN scenarios. In the same year, [PERSON] et al. proposed a network abstraction model and open table model for software-defined QKD networks [20]. In 2018, [PERSON] et al. used the latest development of OpenFlow protocol in software-defined QKD networks, and realized the control and management of optical networks [21].
# Valorization of Agri-Food Waste into PHA and Bioplastics: From Waste Selection to Transformation
## 2 Production of Valeric- and Propionic-Rich VFA Streams from Agri-Food Wastes
### Agri-Food Waste Typically Used for VFA Production
#### 2.2.4 Production of VFAs Rich in Valeric Acid and Propionic Acid from Expired Juices  
In the context of other waste products typical of the agri-food industry, such as expired juices, the potential for biohydrogen production through acidogenic fermentation has also been the subject of investigation. The findings indicated by [PERSON] et al. [108] in 2014 show that the biohydrogen produced comprised 55% H\({}_{2}\) and 45% CO\({}_{2}\) by volume. Moreover, in that study, the gas mixture was fed directly into a fuel cell without undergoing any purification step. The generation of VFAs was not subject to monitoring in the course of this study. Although the objective of the study was not the production of VFAs, it can be inferred from the results that acidogenic fermentation to generate VFAs from these waste materials is a viable process. In this case, a real effluent of fruit juices with a sugar concentration of 9 g/L was used, comprising 5.4 g/L glucose, 1.9 g/L fructose, and 1.8 g/L sucrose.
## Nomenclature  
\begin{tabular}{l l l l} \(A\) & cross sectional area, m\({}^{2}\) & \(I\) & inclination of surface (-) \\ \(D_{h}\) & hydraulic diameter, m & AR & aspect ratio, (-) \\ \(Re\) & Reynolds number, (-) & \(Q\) & volumetric flow rate, m\({}^{3}\)/s \\ \(\mathbf{u}\) & velocity vector, m/s & St & Stokes number (-) \\ \(\mathbf{I}\) & identity matrix & \(E_{D}\) & hydraulic power, W/m\({}^{2}\) \\ \(U\) & time-averaged velocity, m/s & **Greek symbols** \\ \(L\) & length, m & \(\rho\) & water density, kg/m\({}^{3}\) \\ \(\dot{m}\) & mass flow rate, kg/s & \(\mu\) & water viscosity, Pa. s \\ \(W\) & channel width, m & \(\mu_{t}\) & turbulent viscosity, Pa-s \\ \(M_{l}\) & mixing length, m & \(\rho_{p}\) & algae cell density, kg/m\({}^{3}\) \\ \(R_{t}\) & residence time, s & \(\omega\) & turbulent dissipation rate, 1/s \\ \(F_{d}\) & drag force, N & \(u_{p}\) & chlorella cell velocity, m/s \\ \(d_{p}\) & algae cell diameter, m & \(\tau_{p}\) & relaxation time, s \\ \(d\) & channel depth, m & \(\sigma_{\omega}\) & Prandtl number for dissipation rate \\ \(k\) & turbulent kinetic energy, m\({}^{2}\)/s\({}^{2}\) & \(\sigma_{k}\) & Prandtl number for kinetic energy \\ \end{tabular}
## 2 Materials and Methods
### Laboratory Electron Probe Microanalyses-Energy-Dispersive Spectroscopy (EPMA-EDS) Measurements  
Electron probe microanalyses (EPMA) coupled with energy-dispersive spectroscopy (EDS) were performed on the painted samples' surface to obtain information about the chemical composition (in term of major elements) by a JEOL JXA 8230 equipment coupled with a JEOL EX-94310 FaL1Q silicon drift type EDS. The measurements were performed on samples coated with a thin and highly conductive graphite film.
## 4 Results and Discussion
### Instrument-Level Experiment Results
#### 4.1.2 Instrument-Level Responsivity Temperature Dependence Experiment Results  
The instrument-level experiment was divided into two sub-experiments with different operating environments to ensure the temperature range of the photosensitive surface. The G\({}_{\text{ins}}\)(T) results are shown in Figure 11, which shows that the responsivity of the 2250 nm channel was positively correlated with the temperature of the photosensitive surface. This instrument-level experiment determined that the responsivity was reduced by approximately 1.3% when the PSAC operated on orbit (T \(\approx-30\)\({}^{\circ}\)C) compared with the laboratory-based radiometric test (T \(\approx-65\)\({}^{\circ}\)C), with around 35 \({}^{\circ}\)C PST difference. Therefore, it is necessary to implement RTDC for the PSAC; otherwise, a large error will be introduced in the retrieval results of aerosol parameters. In Figure 11, we can see that the PST distribution was irregular and was affected by the ambient temperature under the active-passive temperature control scheme.  
The error sources of instrument-level experiments were analyzed to determine their impact on the measurement of G\({}_{\text{ins}}\)(T) and the PSAC RTDC results. The random errors include the instability of light source radiance, stray light in the laboratory, instability of the PST, and nonlinearity and instability of the PSAC 2250 nm channel's radiation response. The stability of the integrating sphere radiance was greater than 2% /h. The influence of stray laboratory light was limited to when the field of view of the PSAC was 0.52\({}^{\circ}\). The combined standard uncertainty of nonlinear and unstable radiation response of the PSAC 2250 nm channel was better than 1.17% in the laboratory-based radiation test, and it was smaller when the PST was at an on-orbit operation temperature. Moreover, a systematic error may be caused by the chopper, which cannot provide an ideal dark reference with the stray light in the laboratory. The influence of this error is also limited because the response of the PSAC to the chopper is less than 1% of that of the light source. With multiple measurements and normalizing fitting, the influence of these errors on G\({}_{\text{ins}}\)(T) measurement is further limited, ensuring the accuracy of the instrument-level experiments.
## 2 Materials and Methods
### pH, Titratable Acidity (TA) and Soluble Solids Content (SSC)  
The apple powder (0.5 \(\pm\) 0.1 g) was mixed with 20.0 \(\pm\) 0.5 mL of distilled water. Soluble solids (Brix\({}^{\circ}\)) were determined using a digital handheld refractometer (Vee Gee PDX-1, VeeGee Scientific, Inc.; PDX series; Model PDX-1; Vernon Hills, IL, USA). The pH of samples was measured using a Jenway 3310 pH meter (Bibby Scientific Ltd., Stone, UK). For titratable acidity determination, the prepared mixtures were titrated with 0.1 M NaOHsolution to pH 8.2, using phenolphthalein as an indicator [10; 11; 15]. The experiments were performed in triplicate.  
The results were expressed in g of malic acid per 100 g of dry sample according to the following equation:  
\[TA=\frac{N\times V\times f}{m}\times 100 \tag{3}\]  
where \(N\) is the normality of the NaOH solution, \(V\) is the volume of the NaOH solution, \(f\) is the correction factor of malic acid and \(m\) is the mass of the sample.
## 6 Analysis and Discussion of the Results
#### 6.2.7 FEDtoxic gases  
From Figure 19 in both cases of ventilation investigated, the burning of the BEB--compared to the ICEB fire--causes a significant increase in the values of the FEDtoxic gases parameter along the entire tunnel length.  
Figure 19 also shows that in the case of natural ventilation, the values of the FEDtoxic gases parameter along its entire length are, except near the two tunnel portals, always above the tenability limit of 0.1 [42], reaching a much higher peak during the burning of the BEB rather than the ICEB both downstream (approximatively 0.72 versus about 0.6) and upstream (approximatively 0.75 against about 0.63) of the fire.  
Figure 17: Spatial distributions related to the radiant heat flux at the height of breathing along the escape route downstream and upstream of the bus in flames for both BEB and ICEB fires, in both cases of ventilation investigated.  
Figure 18: Spatial distributions related to the visibility distance at the height of breathing along the escape route downstream and upstream of the bus in flames for both BEB and ICEB fires, in both cases of ventilation investigated.  
In the case of mechanical ventilation, Figure 19 shows that the values of the \(\text{FED}_{\text{toxic gases}}\) parameter upstream of the bus in flames are almost always equal to zero, while they never drop below 0.1 along the entire length of the tunnel portion downstream of the fire, achieving a much higher peak during the combustion of the BEB rather than the ICEB (approximatively 1.75 against about 1.2).  
In summary, the findings related to the naturally ventilated tunnel indicate that the firefighters entering the structure according to the direction of the air flow (i.e., from Portal \(A\)) to extinguish the burning bus and/or assist people still inside the tunnel might find more unsustainable environmental conditions in terms of HF and CO concentrations, temperatures, visibility distances, and \(\text{FED}_{\text{toxic gases}}\) in the event of the BEB fire compared to the ICEB fire. In this circumstance, the firefighters should wear even more appropriate smoke and firefighting equipment because the BEV fire, in addition to worsening the environmental conditions in the tunnel, also requires much more time to be extinguished than the ICEV fire [10].
## 2 System Model
The received signal vector is given by ref. [32]:  
\[re\left[ \begin{array}{c}y_{1}\\ y_{2}^{*}\\ y_{3}\\ y_{4}^{*}\end{array} \right]=\left[ \begin{array}{cccc}h_{1}&h_{2}&h_{3}&h_{4 }\\ h_{2}^{*}&-h_{1}^{*}&h_{4}^{*}&-h_{3}^{*}\\ h_{3}&h_{4}&h_{1}&h_{2}\\ h_{4}^{*}&-h_{3}^{*}&h_{2}^{*}&-h_{1}^{*}\end{array} \right]\left[ \begin{array} []{c}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{array} \right]+\left[ \begin{array}{c}n_{1}\\ n_{2}\\ n_{3}\\ n_{4}^{*}\end{array} \right] \tag{8}\]  
That is equivalent to  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n}=\mathbf{H}(\mathbf{x}^{l}+  
The channel estimation vector is \(\mathbf{H}\), whereas the channel estimation error vector is \(\mathbf{e}\). We suppose that the Nakagami distributions apply to all parts of the channel estimation vector. On the other hand, the complex Gaussian random variables that make up the channel estimation error vector have zero means and variances of \(\sigma_{e}^{2}\)[33].  
\[\hat{\mathbf{x}}=\arg\min_{\mathbf{x}}\|\mathbf{y}-\hat{\mathbf{H}}(\mathbf{x}^{I}+j\mathbf{x}^{Q})\|^{2} \tag{12}\]  
In Section 3, we provide the three different QOSTBC detection methods used in [14] for clarity.
# Fusing Context Features and Spatial Attention to Improve Object Detection
## 6 Performance
### Example Visualization of Experimental Results  
We compared YOLO-v3 with our proposed target detection algorithm. The comparison results are shown in Figure 6. We found that the network with the attention mechanism and context feature extraction module better detected the targets. Our algorithm improved  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class** & **Airplane** & **Bicycle** & **Bird** & **Boot** & **Bots** & **Car** & **Cat** & **Chair** & **Cow** \\ \hline R & 83.33 & 85.92 & 73.17 & 61.47 & 54.09 & 83.06 & 82.17 & 83.61 & 59.37 & 53.16 \\ P & 99.01 & 96.83 & 96.77 & 95.71 & 85.00 & 100 & 93.81 & 95.03 & 79.74 & 58.33 \\ \hline \multirow{2}{*}{**Class**} & **Dining** & \multirow{2}{*}{**Dog**} & \multirow{2}{*}{**Horse**} & \multirow{2}{*}{**Motorbike**} & \multirow{2}{*}{**Person**} & **Potted** & \multirow{2}{*}{**Sheep**} & \multirow{2}{*}{**Sofa**} & \multirow{2}{*}{**Train**} & **TV** \\  & **Table** & & & & & & & & **Monitor** \\ \hline R & 63.89 & 88.70 & 86.47 & 86.05 & 82.98 & 40.44 & 28.42 & 76.47 & 83.47 & 80.45 \\ P & 80.23 & 87.80 & 95.83 & 97.37 & 93.86 & 88.10 & 100 & 85.71 & 91.82 & 91.45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The \(R\) and \(P\) values for all classes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **Precision** & **Recall** & **mAP** & **FPS** \\ \hline Faster RCNN & ResNet-101 & 600 \(\times\) 600 & 77.10 & 64.63 & 73.41 & 10.31 \\ FCOS & Normal Convolution & 600 \(\times\) 600 & 76.38 & 67.06 & 74.53 & 14.53 \\ SSD512 & VGG16 & 512 \(\times\) 512 & 84.15 & 67.58 & 76.80 & 17.21 \\ CenterNet & Hourglass-104 & 600 \(\times\) 600 & 85.88 & 70.11 & 80.23 & 12.80 \\ YOLO-v3 & ResNet53 & 412 \(\times\) 412 & 90.26 & 73.83 & 82.34 & 16.95 \\ Our model & ResNet53+ & 412 \(\times\) 412 & 91.67 & 76.97 & 85.68 & 19.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The experimental results of all models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ResNet53+** & **Depthwise** & & & & & \\
**Segurable** & **Context feature** & **SAM** & **mAP** & **FPS** \\  & **Convolution** & & & & & \\ \hline ✓ & \(\times\) & \(\times\) & \(\times\) & 82.34 & 16.95 \\ ✓ & ✓ & \(\times\) & \(\times\) & 82.13 & 22.01 \\ ✓ & ✓ & ✓ & \(\times\) & 83.73 & 20.60 \\ ✓ & ✓ & ✓ & ✓ & 85.68 & 19.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation study.  
the accuracy of detecting small target objects. For certain large targets and small targets, some overlaps were also better detected.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 7 Simulation Verification  
The practicality of the proposed circuits in Figures 2 and 3 has been evaluated by PSPICE simulation using CMOS VDGA, shown in Figure 1b [30], whereas 0.18 \(\upmu\)m CMOS technology from TSMC was employed. In the simulation, the aspect ratios W/L (in \(\upmu\)m/\(\upmu\)m) of different CMOS transistors are as follows: M\({}_{1\text{k}}-\text{M}_{2\text{k}}=24/0.18\), M\({}_{3\text{k}}-\text{M}_{4\text{k}}=30/0.18\), M\({}_{5\text{k}}-\text{M}_{7\text{k}}=5/0.18\), and M\({}_{8\text{k}}-\text{M}_{9\text{k}}=6/0.18\). They were supplied with DC voltages of \(\pm 0.9\) V. The CMOS VDGA circuit in Figure 1b gives the relationship between the transconductance gain \(g_{mk}\) and the corresponding bias current \(I_{Bk}\), which can be written as [29]:  
\[g_{mk}=\left(\frac{g_{1\text{k}}g_{2\text{k}}}{g_{1\text{k}}+g_{2\text{k}}} \right)+\left(\frac{g_{3\text{k}}g_{4\text{k}}}{g_{3\text{k}}+g_{4\text{k}}} \right), \tag{37}\]  
where:  
\[g_{jk}=\left(\frac{\mu\alpha_{ox}W_{j}I_{Bk}}{L_{j}}\right)^{\frac{1}{2}}, \text{ for }j=1,2,3,\text{ and }4, \tag{38}\]  
and \(\mu\) is the carrier mobility, \(C_{ox}\) is the gate-oxide capacitance per unit area, and \(W_{j}/L_{j}\) is the aspect ratio of transistor M\({}_{jk}\), respectively.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
###### Abstract  
An intracortical visual prosthesis plays a vital role in partially restoring the faculty of sight in visually impaired people. Reliable high date rate wireless links are needed for transcutaneous communication. Such wireless communication should receive stimulation data (downlink) and send out neural recorded data (uplink). Hence, there is a need for an implanted transceiver that is low-power and delivers sufficient data rate for both uplink and downlink. In this paper, we propose an integrated circuit (IC) solution based on impulse radio ultrawideband using on-off keying modulation (OOK IR-UWB) for the uplink transmitter, and binary phase-shift keying (BPSK) with sampling and digital detection for the downlink receiver. To make the solution low-power, predominantly digital components are used in the presented transceiver test-chip. Current-controlled oscillators and an impulse generator provide tunability and complete the on-chip integration. The transceiver test-IC is fabricated in 180 nm CMOS technology and occupies only 0.0272 mm\({}^{2}\). At 1.3 V power supply, only 0.2 mW is consumed for the BPSK receiver and 0.3 mW for the IR-UWB transmitter in the transceiver IC, while delivering 1 Mbps and 50 Mbps, respectively. Our link budget analysis shows that this test chip is suitable for intracortical integration considering the future off-chip antennas/coils transcutaneous 3-7 mm communication with the outer side. Hence, our work will enable realistic wireless links for the intracortical visual prosthesis.  
implanted transceiver; impulse radio ultrawideband (IR-UWB); link budget; low-power; intracortical visual prosthesis; neural recording; non-coherent digital demodulator