esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
### Related Studies
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol Type** & **Limitations to Adopt in FANETs** \\ \hline Proactive & High control overhead and bandwidth consumption to maintain an updated neighbor table, and slow reaction to rapid topology changes. \\ \hline OLSR [33] & High control overhead, routing loop, and link breakage. \\ \hline DSDV [34] & Requires periodic updates, high bandwidth, and control overhead \\ \hline Reactive & Higher delay and latency in routing discovery, and no link quality assessment \\ \hline DSR [36] & Produces high overhead during route discovery in large-scale networks, and high delays \\ \hline AODV [34] & Higher delay, high bandwidth consumption, and link breakage \\ \hline Hybrid [3] & High computational complexity to construct and maintain the cluster, cluster head, and cluster member. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of topology-based routing protocols and their limitations in FANETs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Path Strategy** & **Protocol** & \multicolumn{3}{c}{**Limitations to Adopt in FANETs**} \\ \hline \multirow{4}{*}{Single-path strategy} & \multirow{4}{*}{Greedy forwarding} & \(\bullet\) & Always seeks progress in transmission distance; thus, it cannot ensure the desired link quality. \\  & & \(\bullet\) & Encounters link breakages, blind paths, and routing loops. \\  & & \(\bullet\) & Not energy efficient \\ \cline{2-4}  & \multirow{2}{*}{Compass} & \(\bullet\) & High possibility to trap in routing loops \\  & & \(\bullet\) & Not energy efficient \\ \cline{2-4}  & \multirow{2}{*}{Most forward} & \(\bullet\) & Trapped in local minimum (no further node within transmission range to forward toward the destination) \\  & & \(\bullet\) & Encounters higher link breakages and blind paths \\  & & \(\bullet\) & Not energy efficient \\ \hline \multirow{4}{*}{Multipath strategy} & Restricted directional & \(\bullet\) & Deterministic decision to select the direction of broadcasting packets \\  & & \(\bullet\) & Broadcast multiple copies of the same packet to the selected direction \\  & & \(\bullet\) & Provides excessive overhead and is not energy efficient \\ \cline{2-4}  & Randomized & \(\bullet\) & Randomized decision to select the flooding direction \\  & directional flooding & \(\bullet\) & Provides excessive overhead and high contention \\  & & \(\bullet\) & Not energy efficient \\ \hline \multirow{2}{*}{Simple Flooding} & \(\bullet\) & Excessive overhead and high contention \\  & & \(\bullet\) & Not energy efficient \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of position-based routing protocols and their limitations in FANETs [37].
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Hardware Architecture
#### 3.1.6 Device Assembly and PCBs  
We need to mention that all of the electrical and electronic components, enclosures, and other parts used in building the presented device were supplied by Mouser Electronics, Inc., Mansfield, Texas, USA and Transfer Multisort Elektronik S.R.L., Timisoara, Romania and were shipped at our laboratory in Bucharest.  
The device is designed for both industrial and home use to be easily installed into an existing electrical cabinet without requiring substantial changes to the electrical circuit. As we aimed for a compact device, we searched for a DIN rail-mountable enclosure that occupies a maximum of three slots.  
A model that caught our attention was the Italtronix MODULBOX DIN 3M H53 (05.0302530), especially for its interior modularity. It has an exterior dimension of 90 mm \(\times\) 53 mm \(\times\) 58 mm (Lx \(\times\) W \(\times\) H) and can accommodate three stacked PCBs with the following sizes: 50.2 mm \(\times\) 87 mm at the bottom, 49.7 mm \(\times\) 58.7 mm in the middle, and 42 mm \(\times\) 49.3 mm at the top of the case.  
Considering this, the resulting physical circuit of the device was split into three boards, presented in Figure 2, one for each level of the case (from left to right):  
* Bottom: contains the grid power supply, the voltage and current conditioning circuits, and the ADC module along with their positive and negative power supplies. On both short edges of this board, we placed a high-current screw terminal block, which allows for the connection of the grid or consumer conductors.
* Middle: includes the battery charger, power switching circuit, main power supply, the RS-485 transceiver, and the microcontroller.
* Top: contains the elements that need to be accessible to the user: the microSD card slot, the RS-485 wires connector and dipswitches for line termination and biasing, two push-buttons and two LEDs for user interaction, and a debug port for programming and testing purposes.
## 4 Experiments
### DL on BUZZ2  
The raw audio ConvNets (RawConvNet, ConvNets 1, 2, and 3) were trained on the training dataset of BUZZ2 (7582 audio samples) with the same parameters as on BUZZ1 (see Section 4.1) and tested on the testing dataset of BUZZ2 (2332 audio samples). Recall that, in BUZZ2, the training dataset was completely separated from the testing dataset by beehive and location. Table 14 gives the performance results comparing RawConvNet with ConvNets 1, 2, and 3 on the BUZZ2 train/test dataset. The size of the receptive field was set to 80 for all ConvNets. The training accuracies of RawConvNet (99.98%) and ConvNet 1 (99.99%) were higher than those of ConvNet 2 (69.11%) and ConvNet 3 (69.62%). The testing accuracies of all ConvNets were on par. However, the testing loss of RawConvNet (0.14259) was considerably lower than the testing losses of ConvNet 1 (0.55976), ConvNet 2 (0.57461), and ConvNet 3 (0.58836). Thus, RawConvNet, a shallower ConvNet with a custom layer, is still preferable overall to the three deeper ConvNets without custom layers. It is noteworthy that there is a performance gap between the training and testing accuracies of ConvNets 2 and 3 similar to the gap between the same ConvNets on the BUZZ1 train/test dataset (see Table 4).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{_True_} & \multicolumn{5}{c}{_Predicted_} \\ \cline{2-6}  & **Bee** & **Noise** & **Cricket** & **Total** & **Validation Accuracy** \\ \hline Bee & 299 & 1 & 0 & 300 & 99.66\% \\ Noise & 41 & 309 & 0 & 350 & 95.42\% \\ Cricket & \(1\) & 19 & 480 & 500 & 96.00\% \\ Total Accuracy & & & & & 94.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Confusion matrix for M1 (logistic regression) on BUZZ1 validation dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Validation Accuracy** \\ \hline RawConvNet (\(n\,=\,80\)) & 95.21\% \\ ConvNet 1 (\(n\,=\,80\)) & 74.00\% \\ ConvNet 2 (\(n\,=\,80\)) & 78.08\% \\ ConvNet 3 (\(n\,=\,80\)) & 75.04\% \\ Logistic regression & 94.60\% \\ Random forests & 93.21\% \\ KNN (\(n\,=\,5\)) & 85.47\% \\ SVM OVR & 83.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Performance summary for DL and ML models on BUZZ1 validation dataset.
# Surface Coatings and Treatments for Controlled Hydrate Formation: A Mini Review
## 3 Hydrate-Public Surface Treatments and Coatings
### Polymeric Coatings
The tests were carried out under low driving force (subcooling) and the induction time in the coated system was considerably higher compared to the uncoated system for reference solution. In the presence of inhibitors in a coated system, the nucleation rate was very low; thus, it was difficult to distinguish the temperature and pressure changes due to nucleation. The coated system also performed very well in reducing the hydrate growth. The coated system with just 0.02 wt.% LuvicapBio reduced the hydrate growth, equivalent to an uncoated system with 0.20 wt.% LuvicapBio. [PERSON] et al. [48] characterized several commercial surface coatings that can be used to avoid hydrate adhesion to a pipeline wall for their roughness, contact angles (water and diiodomethane), and THF hydrate adhesion force measurements. The authors pointed out that the tests performed on model systems at atmospheric pressure may lead to erroneous conclusions. Therefore, a novel experimental methodology was proposed to truly assess the efficiency of this technology in realistic conditions of high pressure. The authors carried out two distinct adhesion tests with a H\({}_{2}\)O: THF solution at atmospheric pressure and with a natural gas system at high pressures. Two commercial polymeric coatings produced by two different manufacturers, namely, polytetrafluoroethylene (PTFE) and fluorinated ethylene propylene (FEP), were applied on a carbon steel substrate to assess their effectiveness in hydrate inhibition. Hydrophobicity and oleophobicity of the surfaces under study were assessed by measuring water and diiodomethane contact angles, respectively. The authors made five repetitions of adhesion measurements in the case of the THF systems. They faced partial hydrate detachment and out-of-range issues while measuring adhesion on carbon steel and B samples; therefore, those results were not reported. Tests performed on methane hydrates in autoclave were repeated thrice for each system. The results of the performance of both the commercial coatings from both the methods were compared, indicating that the THF method can underestimate their performance in comparison to the test performed in realistic conditions of high pressure, which may lead to erroneous rankings. However, the authors did not provide the temperature and pressure conditions at which the experiments were carried out and paid attention to the fact that the hydrates formed by THF (type sII) and methane (type sI) presented distinct structures.
## 2 State of the Art
Also the average per hop delivery latency of Crankshaft rises faster than that of Y-MAC as the traffic load increases and Y-MAC outperforms Crankshaft in terms of data reception rates. Y-MAC exploits multiple channels in order to handle burst messages. Authors proved that Y-MAC achieves effective transmission of burst messages under high traffic conditions (1 frame/node/sec). On the other hand, the channel allocation method is not detailed in the paper, authors only insist on not using the same channel in 1-hop neighborhood which leads in high interference due to 2-hop interference (interference caused by transmissions received from nodes that are 2-hop away from the receiver). We argue that 2-hop neighborhood is not enough to avoid collisions and interference, and that 3-hop neighborhood should be considered when allocating channels. In addition, neighborhood discovery causes traffic congestion when the network is dense, and thus exchanging control traffic results in a lot of collisions using CSMA/CA. In order to avoid this, we propose to use bitmaps and TDMA for representing and exchanging neighboring information. Very few protocols use multi-interface nodes and thus most protocols reach network saturation very fast. In our protocol we propose to use multi-interface nodes for the sink and show how our protocol copes better with high data rates. This paper is an extension to the short paper presented in [18]. In the short paper, the state of the art was very brief and did not exceed one column in double column format. In this paper, we extended the state of the art and discussed the performance results of cited papers. We also explained in more details the HMC protocol. In addition, only aggregate throughput and packet loss were evaluated in the short paper. In this paper, more significant results are discussed such as packet delivery ratio and number of frame repetitions.
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 6 Results and Discussion
### Impact of Ordering Metric  
Finally, the accuracy results of the third experiment are shown in Table 4. Here, we show the average top 5 accuracy of the configuration with the specific ordering metric shown, \(\alpha=128\) and \(C_{f}=64\), sorted from lowest to highest accuracy. We can observe statistically significant improvement in all the configurations, but the largest improvements appear with the _total variation_ metric (2% higher than the reference).  
To further explore the differences between the metrics, we show the average orness and dispersion profiles (Figure 5), obtained in the same fashion as in Sections 6.1 and 6.2. We also show a single example of the resulting weighting matrix of each configuration in Figure 6, each matrix with its features (rows) sorted top to bottom from lowest to highest orness value. These figures show how different ordering metrics tend to converge to different learned features, with different preference for _or_-like or _and_-like operators.  
We can highlight the two entropy metrics, which both learn a similar distribution of orness slanted to the _and_-like operators.  
A particularly relevant example is the case of the _median_activation_ ordering, which in the matrix shows weight patterns that appear unrelated to the sorting of the layers, and in the orness figure shows the most limited range of all the metrics. This indicates that the _median_activation_ is not adequate to induce the ordering of the channels, making the learned OWAs closer to a regular weighted aggregation. This highlights the importance of the metric selection on the OWA layer to obtain meaningful OWA operators.  
Figure 4: Orness and dispersion profiles of learned features depending on \(C_{f}\), the number of learned features.  
Figure 3: Final accuracies of the system depending on the number of learned features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Top 5 acc.** & \(p\)**-Value** \\ \hline Reference & \(52.17\pm 1.5\) & \\ \(H\) & \(53.78\pm 1.4\) & \(<\)0.001 * \\ \(H_{sv}\) & \(53.87\pm 1.4\) & \(<\)0.001 * \\ Sum & \(53.93\pm 1.4\) & \(<\)0.001 * \\ Max & \(53.99\pm 1.2\) & \(<\)0.001 * \\ Median & \(54.11\pm 1.1\) & \(<\)0.001 * \\ TV & \(54.27\pm 1.5\) & \(<\)0.001 * \\ \hline \hline \end{tabular}  
* Results marked with
* have better top 5 accuracy than the reference with \(p\)-value \(<\) 0.05.  
\end{table}
Table 4: Accuracy results for different metrics.  
Figure 5: Ormess and dispersion profiles of learned features for the different ordering metrics.  
Figure 6: Example OWA weighting matrices for different ordering metrics. The OWA weighting vectors in each matrix are sorted according to the orness for better comparison.
## 3 Results and Discussion
### Unconfined Compressive Strength and Curing Period
Like curing period versus UCS, it could be considered a limitation of the study that the UCS tests were not performed for 30%, 45%, and 55% fly ash content to make sure that UCS did not peak for one of these missing fly ash contents. However, to avoid this limitation, polynomial trend lines were added to see any shift of max UCS, presented Figure 9.  
Figure 8: Variation of UCS with fly ash content. Figure 9: Variation of UCS with fly ash content with trend lines. Like the optimum curing period, the maxima and minima concepts were also applied to estimate the maximum UCS and the corresponding optimum fly ash content for all curing periods using the trend lines (Figure 9). The corresponding estimated data are presented in Table 4.  
From Figure 9 and Table 4, it was observed that the optimum fly ash content varied from 32% to 46%, which seemed to be a wide range. Since the 8-day curing period showed the maximum UCS of 2036 kPa for a fly ash content of 32%, it is reasonable to conclude that an optimum fly ash content could be 32% based on the trend analysis. However, the experimental data concluded that 50% fly ash content is an optimum. If strength was not the primary consideration, 46% fly ash content could be considered as an optimum based on the trend analysis for a curing period of 28 days, which seemed to closely coincide with the optimum fly ash content based on the experimental data. Therefore, it is difficult to select a single fly ash content to be used as an optimum rather than the range. In this case, conducting additional experiments for soil-fly ash mixtures of 30%, 45%, and 55% fly ash contents to select a single optimum fly ash content is recommended.
## 2 Materials and Methods
### Design Consideration of Method I  
In Figure 4, the phasor diagram of the converter using Method I at different operation points is illustrated, which includes the three voltage phasors \(\mathbf{V}_{p}\), \(\mathbf{V}^{\prime}_{s}\) and \(\mathbf{V}_{T}\) and one current phasor \(\mathbf{I}_{r}\). According to Kirchhoff Voltage Law, it is seen that  
\[\mathbf{V}_{p}-\mathbf{V}^{\prime}_{s}=\mathbf{V}_{T}=\mathbf{I}_{r}\cdot jX _{t} \tag{7}\]  
In the CC stage (from A to C), the phase angle \(\phi\) is fixed as discussed before, and the length of \(\mathbf{V}^{\prime}_{s}\) increases continuously. Geometrically, the resonant current phasor \(\mathbf{I}_{r}\) will rotate anti-clockwise with the expanding \(\mathbf{V}^{\prime}_{s}\). Meanwhile, the length of \(\mathbf{I}_{r}\) increases because of a constant \(I_{o}\) and an increasing \(\beta\) according to (5). \(\mathbf{V}_{T}\) becomes longer too with a constant \(X_{t}\) according to (7). It is known that the necessary ZVS condition for both bridges is that  
Figure 4: Phasor diagram of the converter of Method I.  
the current \(i_{t}\) lags \(v_{p}\) and leads \(v_{s}\). As the operation point moves from A to C, the ZVS margin of the secondary switches becomes larger. To satisfy the ZVS condition in the whole CC stage, this is designed so that the secondary switches work at the boundary of the ZVS range at the beginning of the charging process (point A). As a result, the resonant current \(\mathbf{I}_{t}\) is in phase with \(\mathbf{V}^{\prime}_{s}\) at the minimum load of the CC stage (point A), at which the voltage phasor triangle is a right-angle triangle.  
In the CV stage (from C to D), the converter output current is expected to vary between 100% and 10% of the rated current, which is equivalent to between a full load to 10% of a load. It is well proved in the literature that a DBRC can maintain full ZVS operation of all switches from a full load to zero load only at unity gain theoretically when using phase-shift modulation [26]. Therefore, in order to achieve ZVS operation in the CV stage, the converter voltage gain is fixed at unity, which is also the maximum gain in the whole charging procedure. At the point C which is the boundary between CC and CV stage, the converter is working at maximum output power and voltage. The phasor diagram is an isosceles triangle as \(\mathbf{I}_{t}\) is right between two voltage phasors with same length, i.e., \(G_{v}=1\) as expected, and \(\beta\) reaches its maximum as \(\beta_{max}=\phi_{max}/2\). In the CV stage, the load level decreases with a fixed output voltage. The phasor diagram remains an isosceles triangle with a decreasing \(\phi\). The resonant current phasor is always between the two voltage phasors \(\mathbf{V}_{p}\) and \(\mathbf{V}^{\prime}_{s}\), which satisfies the necessary ZVS condition.
## 2 Materials and Methods
### Bacterial Cultures for Adhesion and Invasion Assays
#### 2.4.2 Invasion Assays  
The enumeration of cells in each of the three representative wells was performed by trypsinizing each respective well with 0.3 mL of trypsin-EDTA, incubating at 37 \({}^{\circ}\)C in an incubator (5% CO\({}_{2}\) atmosphere) for 5 min, and adding 0.7 mL of D10F. Overnight bacterial cultures were washed 3 times with PBS and subsequently diluted to obtain an MOI ratio of 10:1 (_Salmonella_: animal cells). Washed bacteria were diluted 10\({}^{-6}\) with PBS, followed by plating 100 \(\upmu\)L on LB agar plates for CFU/mL enumeration. The diluted bacteria were added to the cell lines, Caco-2 or HD11, and the plates were incubated at 37 \({}^{\circ}\)C in an incubator (5% CO\({}_{2}\) atmosphere) for two hours. The cells were washed 3 times with cell PBS followed by treatment with 1 mL of DMEM containing 100 \(\upmu\)g/mL gentamicin per well in order to kill extracellular bacteria considered to be adherent. The plate was incubated for 2 h at 37 \({}^{\circ}\)C in an incubator (5% CO\({}_{2}\) atmosphere). The cells were subsequently washed 3 times with cell PBS and subjected to treatment with 1 mL of 0.1% Triton X-100 in cell PBS. The plate was incubated for 10 min at 37 \({}^{\circ}\)C in an incubator (5% CO\({}_{2}\) atmosphere). Serial dilutions of suspensions were conducted in PBS followed by inoculation onto LB agar plates, in duplicate, to determine the number of organisms that survived treatment  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Adhesion and Invasion to Caco-2 Cells** & **Adhesion and Invasion to HD11 Cells** \\ \hline _Salmonella_ TyphimuriumATCC 14028 & _Salmonella_ TyphimuriumATCC 14028 \\ _Salmonella_ MethylaminIVUK-1 \\ _Salmonella_ Heidelberg ARI-14 & _Salmonella_ Heidelberg ARI-14 \\ _Salmonella_ Enteritidis ATCC 13076 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Salmonella strains used in this study.  
with gentamicin and, hence, had invaded the Caco-2 or HD11 cells. Plates were incubated at 37 \({}^{\circ}\)C for 16 h. Each strain was tested in triplicate in three independent experiments.
# The Impact of Silver Nanoparticles on Detntinal Tubule Penetration of Endodontic Bioceramic Sealer
## 2 Material and Methods
### Experimental Design  
Each sample was assigned a number that was randomly allocated to one of the four experimental groups (n = 25) as follows:  
Group 1 (BC/SC): BC sealer obtutated with the SC technique;  
Group 2 (BC+AgNPs/SC): BC sealer mixed with AgNPs obtutated with the SC technique;  
Group 3 (BC/CWC): BC sealer obtutated with the CWC technique;  
Group 4 (BC+AgNPs/CWC): BC sealer mixed with AgNPs obtutated with the CWC technique.  
The BC sealer (TotalFill(r) Hillow BC Sealer(tm), FKG, La Chauv-de-Fonds, Switzerland) was used for all experimental groups and was mixed with rhodamine B (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) at an approximate ratio of 0.1% weight to generate fluorescence under CLSM for microscopic penetrability analysis in line with earlier research [13]. Additionally, the BC sealer used in Group 2 and 4 was modified with AgNPs incorporation. AgNPs with a concentration of 0.06% AgNPs were prepared using a digital analytical balance to measure 0.06 mg of nanoparticles for every 1 g of sealer, in accordance with a previous study [24]. Ultrasonication using a probe sonicator (Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) was applied to the modified sealer for 1 h to improve the dispersion of nanoparticles.  
Following the preparation of sealers, the root canals were filled according to the designated group. The obturation procedure using the SC technique involved the injection of the BC sealer into the root canal, followed by coating a TotalFill(r) BC Point (FKG, La Chauv-de-Fonds, Switzerland) (ISO size 30/0.06) with the sealer and inserting it along the full length of the canal. The cone was then trimmed at the orifice level using activated System B pluggers (Sybron Endo, Orange, CA, USA), and a coronal seal was applied using Cavit G (3M ESPE, Seefeld, Germany). The CWC technique was performed in the same manner as the SC technique, with the key difference being that the GP cone was trimmed 5 mm shorter than the full length, and the canal was subsequently backfilled with thermoplasticized GP using TotalFill(r) BC pellets (FKG, La Chauv-de-Fonds, Switzerland). A single operator executed all treatment procedures. In order to facilitate the complete setting process of sealers, samples were stored in an incubator at 37 \({}^{\circ}\)C under humidified condition for two weeks. Subsequently, 1 mm thick horizontal dentin slices were sectioned from the middle third of each root using an IsoMet 4000 slow-speed diamond blade (Buehler, Lake Bluff, IL, USA), with water coolant to minimize heat generation. To ensure consistency in the specimens' thickness, each slice was measured with a digital caliper.