esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry
## 3 Rear Projection-Based Counting Machine Prototype  
Though the preliminary tests performed using the seven different item typologies, shown in Figure 12a, were deemed representative, a prototypal rear projection-based counting machine was designed in order to perform extensive testing in an industrial environment. As shown in Figure 13, the system comprises:  
* An image acquisition device (industrial monochrome camera IDS UI 3200-SE-M with a 6 mm lens with 12-megapixel resolution (4104 \(\times\) 3006 pixels);
* An LCD overhead light projector, to assure uniform lighting;
* A couple of orientable mirrors (used to extend the light path up to the 1.8 m, mentioned in Section 3). Such mirrors are used to reduce the overall dimensions of the counting machine, which must not exceed \(1.5\times 1.0\times 1.0\) m, in order to not to be excessively cumbersome for an industrial environment;
* An enclosure system, to assure the environmental light does not affect the scene.  
The projector is placed backward, on the frontal part of the machine. Light is reflected by the first mirror upwards towards the second one; this last mirror reflects it forward to hit the galvanic frame. Its shadow is projected on the rear-projection canvas, which is arranged parallel to the frame. In the prototypal implementation of the system, the setup described above did not show any unevenness in the screen illumination using a perfectly white projected image. However, in case this should happen, it is possible to compensate by projecting an image appositely designed in order to feature slightly darker or lighter regions in correspondence to more or less illuminated areas of the screen, respectively. This is a major advantage entailed by the use of the overhead LCD projector rather than a conventional light source (i.e., a lamp).  
In Figure 14a, a rendering of the designed counting machine architecture shows the arrangement of the above-mentioned components. The final design of the machine is shown in Figure 14b.  
A Surface Go tablet (Microsoft Corporation, Washington, U.S.)--on which ran the designed application (developed in Matlab(r), MathWorks, Inc., Natick, Massachusetts, U.S., 2019)--was then used to command the industrial camera. By means of a dedicated Graphical User Interface (GUI), the operator could check the position of the frame and can start the acquisition when such a position was considered correct (see Figure 15).  
Figure 13: Counting system main components.  
The procedure was then able, in approximately 0.3s, to provide the number of the detected items. Simultaneously, it showed a control picture, on which clusters that had been considered were colored red, while those that were ignored were white. In this way, the operator could rapidly check the effectiveness of the procedure and make corrections if needed.
## 2 Results
(**E**) Multiple myeloma (MM) cell lines were pre-incubated with indicated concentrations of 10058-F4 (12.5-150 uM) for 48 h (MM1.S) or 24 h (RPM18226) and thereafter incubated with predetermined concentrations of daratumumab (dara, 10-1000 ng/mL) in the presence of PBMCs obtained from healthy donors as effector cells (E:T ratio of 25:1) for 16 h. Details as in (**B**). (**F**) DLBCL cell lines were pre-incubated with indicated concentrations of 10058-F4 (12.5-150 uM) for 48 h (OCI-LY18) or 96 h (WSU-DLCL2, OCI-LY7 and SU-DHL-6) and thereafter incubated with predetermined concentrations of blinatumomab (blina, 0.1-1 ng/mL) in the presence of PBMCs obtained from healthy donors as effector cells (E:T ratio 10:1) for 24 h. Details as in (**B**). (**G**) Representative flow-cytometry histograms depict CD19 expression in DLBCL cell lines after 24 h of treatment with indicated concentrations (0-50 uM) of 10058-F4. Dotted line represents isotype staining control. Data are represented as mean \(\pm\) SEM of 2-3 technical replicates. * \(p<0.05\); ** \(p<0.01\); *** \(p<0.001\); *** \(p\leq 0.0001\); ns not significant. The pre-incubation with 10058-F4 synergistically enhanced blinatumomab-mediated T cell-dependent cytotoxicity in WSU-DLCL2 and showed additivity in other DLBCL cell lines (Figure 1F). Remarkably, the enhanced blinatumomab-mediated cytotoxicity in MYC downregulated clones was not associated with the upregulation of target antigen expression (Figures 1G and S1E). Hence, these data indicated that MYC-associated escape mechanisms from immunotherapy extend beyond simple antigen modulation on the cell surface.
## 6 Results and Discussion
### Number of Communication Rounds vs. F-Measure  
Figure 6 compares the F-measure of the proposed FT-CID model with that of the neural network models in local IIoT environments (i.e., AMI). Figure 6b shows the F-measure of the proposed intrusion detection model, the FT-CID, with various communication rounds (5, 10, 15, 20, and 25) for 80% of the training data size. Based on analysis of the results shown in Figure 6a,b, it can be determined that the centralised and federated intrusion detection models tend to converge after a certain number of communication rounds. Thus, the proposed intrusion detection model has a 16.83% higher F-measure than the baseline of the CNN in the independent AMI IIoT environment due to the advantage of collaboratively utilising the knowledge from the heterogeneous IIoT environments for 20 communication rounds. As the number of communication rounds increases from 5 to 25, the F-measure performance of each intrusion detection model increases and gradually stabilises, and the performance is reduced after reaching a sufficiently large number of iterations or communication rounds. Moreover, Figure 6b shows the F-measure performance of the FT-CID model with the average AMI heterogeneous IIoT environment score for the various numbers of communication rounds. The data values from the implementation F-measure results are provided in Table 3.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 3 Proposed Search Algorithm, a Set of Behaviors: A1
### Search Behavior
#### 3.1.4 Evaluating Modes  
Each time an agent reaches the center of a search cell, it makes the decision of which search cell to visit next, and the search behavior will be taken into account, depending on the concentration of pheromones on the surrounding cells at each layer.  
At each time step, each discretization cell \(G_{D_{c}}\) has associated a concentration of pheromones \(\phi_{c}^{l}=\phi^{l}(G_{D_{c}})\) at each layer \(l\). The total quantity of the pheromones concentration is then:  
\[\phi_{c}=\phi(G_{D_{c}})=\sum_{l=1}^{3}\phi^{l}(G_{D_{c}}) \tag{27}\]  
Each search cell \(G_{S_{g}}\) has associated a set of discretization cells, so that their centers lie inside it. Furthermore, if the final search cell \(g\) is in a diagonal position (with respect to the agent), half of the contiguous cells will be observed (see Figure 1), and therefore, the discretization cells inside these halves will be accounted for.  
We propose two methods for accounting for the pheromone level at each search cell, for candidates to be visited next:  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline  & \(\phi_{0}\) & \(S_{no}\) & \(S_{o}\) & \(D_{no}\) & \(D_{o}\) & \(\delta_{ro}\) & \(\rho_{\Delta\theta}\) & \(\rho_{\theta}\) \\ \hline L1 & \(\phi_{0}  
* Mean values: The mean values of the pheromone concentration of the cells inside each search cell are considered. The income for each surrounding cell is then calculated: \[I_{\phi_{mmg}}=\frac{\sum\limits_{\forall c|G_{D_{c}}\in G_{Sg}}\phi(G_{D_{c}})}{ \sum\limits_{\forall c|G_{D_{c}}\in G_{Sg}}c}\] (28)
* Maximum values: The maximum value of the concentration inside each search cell is considered. \[I_{\phi_{mmg}}=\max\limits_{c|G_{D_{c}}\in G_{Sg}}\phi\left(G_{D_{c}}\right)\] (29)  
To consider both solutions, the total income of each surrounding cell \(g\) will be calculated as a weighted sum of both:  
\[I_{\phi_{S}}=\beta_{\phi}I_{\phi_{mmg}}+(1-\beta_{\phi})I_{\phi_{mmg}} \tag{30}\]  
where \(\beta_{\phi}\in[0,\;1]\) is the weighting parameter, to be set.
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 2 Adsorption of NPs by Algae
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Nanoparticles**} & \multirow{2}{*}{**Algae**} & **Exposure** & **Exposure** & **Concentration of NPs** & **Reference** \\ \cline{3-6}  & & **Concentration** & **Duration** & **in Algae (Dry Weight)** & **[30]** \\ \hline \multirow{3}{*}{Silver (Ag)} & _Chlorella vulgaris_ & 2 mg/L & 4 h & 1200–3300 \(\upmu\)g/g & [30] \\ \cline{2-6}  & _Raphidocelis subcapitata_ & 15 \(\upmu\)g/L & 24 h & 45.0 \(\upmu\)g 1  & [31] \\ \cline{2-6}  & _Scendesmus obliquus_ & 10 mg/L & 72 h & 59.51 \(\pm\) 2.16 \(\upmu\)g/g & [32] \\ \cline{2-6}  & & 0.3 mg/L & & 490 \(\pm\) 143 \(\upmu\)g/g & \\ \cline{2-6}  & _Scendesmus acutatus_ & 1.2 mg/L & 72 h & 1714 \(\pm\) 488 \(\upmu\)g/g & [33] \\ \cline{2-6}  & & 4.8 mg/L & & 5701 \(\pm\) 3163 \(\upmu\)g/g & \\ \hline \multirow{2}{*}{Carbon nanotubes (CNT)} & _Desmodesmus_ & 24 h & (1.30 \(\pm\) 0.42) \(\times\) 103 \(\upmu\)g/g & \\  & _subspicatus_ & 1 mg/L & 48 h & (2.11 \(\pm\) 1.76) \(\times\) 103 \(\upmu\)g/g & [34] \\ \cline{2-6}  & _subspicatus_ & 72 h & (4.98 \(\pm\) 1.63) \(\times\) 103 \(\upmu\)g/g & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Adsorption capacity of algae to nanoparticles (NPs).
# Advances in Piezoelectric Jet and Atomization Devices
## 4 Advances in the Application of Piezoelectric Jets and Atomizers  
### Cell Printing  
In order to satisfy the demands in the biomedical field for high-precision control of cell culture and experiments, the researchers have made considerable efforts to achieve micro fluid control of piezoelectric micro jet technology. It is expected to improve the accuracy of experimental research by the application of such technology. [PERSON] et al. [67] printed porcine Schwann cells and neuron cells by a piezoelectric micro jet mechanism, and proved that the activity of cells printed into the culture dish is similar to cells cultured by the standard method. [PERSON] et al. [68] established the printing of rat retinal ganglion cells and glial cells by piezoelectric micro jet mechanism. It was found that there was no significant difference in cell activity obtained by piezoelectric micro jet printing. In addition, [PERSON] et al. [69] successfully printed human retinal ganglion cells and glial cells using the piezoelectric micro jet mechanism.  
In 2013, [PERSON] et al. [70] completed the printing of a single cell using a high-pressure micro jet mechanism, and analyzed the cell activity after printing. In 2015, [PERSON] et al. [71] printed a single human B cell into a microtubule by a piezoelectric micro jet mechanism, and the cell activity using corresponding equipment was observed. [PERSON] et al. [72] established the printing of ultra-high density transfected cell microarray. In 2016, [PERSON] et al. [73] proposed the usage of a piezoelectric micro jet mechanism for three-dimensional cell printing, and proved that the cell survival rate produced by this method is quite high In 2017, [PERSON] et al. [74] proposed a method of non-contact production of epithelial cell micro population using a piezoelectric micro jet mechanism, and verified that this method is universal in its printing application.
## 4 Conclusions  
The video feedback method presented in the paper makes it possible to monitor the water flow in the RAS fish-farming tanks and even avoid the use of water flow meters in some configurations. The possibility of the detection of potential failures of the water pumps or water-flow meters makes the proposed solution not only an interesting alternative to typical flow measurements but also a relatively cheap addition due to the increasing availability of affordable high-resolution video cameras.  
The proposed solution may also limit the necessity of mounting some underwater sensors which may injure individual fish. The correctness of the water-flow estimation, verified for typical velocities, also allows its application in some other tanks after the necessary calibration of the algorithm's parameters. As the proposed method is based on the analysis of the local gradients, it is not sensitive to typical changes in lighting and weather conditions.  
Further research on this topic will concentrate on the verification of sensitivity-to-lighting conditions as well as some other constraints, to achieve the higher universality of the proposed approach, allowing it to work in some unexpected conditions. Another direction of research will be related to extensions related to the combination of the proposed method with fish-counting and fish-tracking methods.  
Conceptualization, P.L., K.O., A.T. and K.F.; methodology, P.L. and K.O.; software, K.O.; validation, D.A., D.N., A.T. and K.F.; formal analysis, P.L. and K.O.; investigation, P.L. and K.O.; resources, A.T., A.K.-O. and K.F.; data curation, P.L. and K.O.; writing--original draft preparation, P.L. and K.O.; writing--review and editing, K.O. and K.F.; visualization, D.A., D.N., A.T. and K.O.; supervision, K.F.; project administration, A.K.-O. and K.F.; funding acquisition, K.F. All authors have read and agreed to the published version of the manuscript.  
The research was conducted within the project no 00002-6521.1-OR1600001/17/20 financed by the \"Fisheries and the Sea\" program.  
Not applicable.  
Figure 7: The relationships between the uncalibrated estimates achieved using the proposed simplified approach utilizing the Lucas–Kanade method and the velocities measured using the water-flow meter in the presence of fish.  
Conflicts of Interest: 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.
# Botanicals in Functional Foods and Food Supplements: Tradition, Efficacy and Regulatory Aspects
## 4 Botanicals in Functional Foods
### Soluble and Insoluble Fibers
#### 4.1.3 Beta-Glucans  
Beta-glucans are soluble fibers contained in cereals, such as barley, oat, rye, wheat and some mushrooms [21]. They are linear polymers of glucose linked by \(\beta\)-(1-4) and \(\beta\)-(1-3) bounds. Two claims for beta-glucans received positive opinion by EFSA and were included in the list of wordings allowed in the EU (Table 1) [22].  
The first claim is \"consumption of beta-glucans from oats or barley as part of a meal contributes to the reduction of the blood glucose rise after that meal\" (Table 1). In fact, beta-glucans modulate the post-prandial glycemic response by increasing the meal bolus viscosity, with a consequent delayed absorption of nutrients (including glucose) in the small intestine [23].  
On the basis of intervention studies performed in healthy subjects, EFSA concluded that quantities of about 4 g of beta-glucans/30 g of available carbohydrates are capable to decrease the post-prandial glycemic response without modifying the insulin release [22]. The claim must be associated with the recommendation to use beta glucans during meals.  
The EFSA received from the European Commission a second request for an opinion regarding the claim associating beta-glucans with the positive modulation of normal blood cholesterol levels [24]. On the basis of a certain number of randomized-controlled trials in adults with normal or mildly increased cholesterolemia, the EFSA Panel formulated the following wording \"regular consumption of beta-glucans contributes to the maintenance of normal blood cholesterol levels\". The beneficial effect is linked to a daily consumption of 3 g of beta-glucans in one or more servings. The proposed mechanism of action is based on the fact that dietary beta-glucan forms, in the small intestine, a viscous mixture that reduces the intestinal absorption of dietary cholesterol and the re-uptake of bile acids. As a consequence, new bile acids are synthetized from circulating cholesterol [25].
## 3 Rolling Bearing Remaining Useful Life (RUL) Prediction Method
### Remaining Useful Life (RUL) Evaluation Metrics  
This paper employs three evaluation metrics as the final assessment indicators for the model's prediction results: root mean square error (RMSE), mean absolute error (MAE) [20], and the average score (Score) defined by the IEEE PHM2012 Challenge [21]. Specifically, lower RMSE and MAE values, along with a higher score, indicate superior prediction performance of the model. The calculation formulas are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}d_{i}^{2}} \tag{14}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|d_{i}| \tag{15}\]  
\[A_{i}=\begin{cases}e^{-\ln(0.5)\cdot(E_{ii}/5)}\text{ if }\text{E}_{ri}\leq 0\\ e^{+\ln(0.5)\cdot(E_{ri}/20)}\text{ if }\text{E}_{ri}>0\end{cases} \tag{16}\]  
\[\text{Score}=\frac{1}{N}\sum_{i=1}^{11}\left(A_{i}\right) \tag{17}\]  
Figure 6: Feature set. Black (peak value); Red (variance); Blue (waveform factor); Pink (kurtosis); Green (RMS value); Magenta (root amplitude).  
where \(d_{i}=y-\hat{y}\) denotes the difference between the actual value and the predicted value, \(E_{\text{ri}}=(d_{i}/y)\times 100\%\). When \(d_{i}>0\), it is a lagging prediction, which is prone to causing risks. When \(d_{i}\leq 0\), it is an advanced prediction, which is beneficial for equipment maintenance. For \(N\) data points, \(A_{i}\) denotes the score value of each individual data point, while _Score_ represents the mean value of the scores across all data points.
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
###### Abstract  
From 5000 m underground to the surface, there is a multi-layer hydrocarbon reservoir under the Wuerhe nose uplift in the Junggar Basin. Light oil, heavy oil, oil sand, and solid bitumen are found in Permian to Cretaceous strata. The normal crude oil present under heavy oil and solid bitumen reservoir can easily ignored by explorers. To effectively exploit the petroleum and bitumen mineral resources in the Junggar Basin, geochemical characteristics of crude oils in the different layers were analyzed. It is concluded that the crude oils and bitumen minerals came from Permian source rocks of alkaline lacustrine facies. Combined with tectonic movement analysis, two stages of accumulation occurred in research area. During the Indonesian Tectonic movement, the crude oil generated from Permian source rocks first migrated upwards along large faults and then accumulated in the Permian, Triassic, and Jurassic reservoirs. The crude oil of the Jurassic reservoir was seriously biodegraded and a high abundance of 25-norhopane was detected. At the end of the Yanshan movement, small normal faults were developed to connect the oil in the Triassic reservoirs to the surface. The light components of the oil in the fault system quickly volatilized and left solid bitumen minerals in the faults. Due to the plugging effect of \"bitumen plug\", the oil and gas in the lower part cannot migrate upward and be damaged. Therefore, light oil-heavy oil-oil sand and bitumen minerals were formed from the bottom to the top. The research result will also have a guiding significance for oil and gas exploration in the northwest margin of the Junggar Basin.  
biomarkers; crude oil alteration; oil-source correlation; multi-layer accumulation; geological movement +
Footnote †: journal: Applied Sciences
## 4 Review of Existing Methods and Capabilities
### Safety Controls
#### 4.2.4 Test Safety  
The Range Safety Group of the Range Commanders Council has provided guidelines for Flight Safety Systems for UAS Operations within defined range locations [144]. This standard provides guidelines for safe recovery of UASs that are recommendations for flying on these ranges. Johns Hopkins Advanced Physics Laboratory has proposed a framework for safe testing, Safe Testing of Autonomy in Complex, Interactive Environments (TACE), that can provide both safety features and complex interactive environments in a virtual fashion enabling improved safety and testability [145]. Emergency recovery and flight termination requirements are a necessary capability of UASs, especially larger class UASs that can cause danger to property or personnel. A technology survey of these systems by [PERSON] _et al._[146] provide a good overview of the recovery and termination systems. The need to terminate or provide emergency recovery is critical, especially when testing unproven systems. Integrating emergency recovery and/or flight termination capabilities with systems such as TACE for testing of vehicles will be a critical enabler to safety of the system and any property and personnel close to the test area. Including enabling features like geofencing/boundary control, collision avoidance techniques, reversionary modes to disable autonomy algorithms, and the ability to take over the vehicle during undesirable operations will be critical for safe testing of these systems. The size and level of implementation of recovery and termination systems along with systems like TACE and EVAA along with considerations of run time assurance will be dependent upon vehicle size and risk.
## 3 Methodology
### Validation of Proposed Method
#### 3.2.2 Target Driving Scenario  
This study focuses on a scenario in which an ego vehicle, in left-hand traffic, makes a right turn at a signalized intersection while another vehicle approaches the intersection (Figure 2). The accident in this scenario is a collision between a right-turning ego vehicle and an oncoming vehicle.  
In right turns (and left turns in right-hand traffic), drivers often make safety confirmation errors while driving [46], and the workload of drivers is higher than that in other scenarios [47]. In other words, the target scenario is one in which a driver error is more likely to occur. In addition, this scenario has been identified as a critical scenario in studies identifying the scenarios based on accident data [32; 33; 35] and the driving data of an NDS [37].  
Furthermore, it is difficult for ADSs to plan their behavior in scenarios involving intersections, including right turns. This difficulty results from the presence of multiple vehicles, pedestrians, and other traffic participants. Moreover, the system must predict its behavior based on inaccurate measurements [48; 49].