esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It seems that cone-beam computerized tomography might be very useful in the diagnosis of tumors and teeth-like masses [14; 15; 16; 17; 18]. The presented lesion is also quite often tooth eruption related. In most cases, ameloblastic cells are present with various fibrous tissues and parts of calcified masses. Additionally, types of extraosseous odontogenic tumors can occur. Those findings are very similar to our case, presenting combined radiolucent-radiopaque images on radiographs [19]. The most common occurrence of this lesion is the posterior mandible and the area of the third molar; however, other mandible areas might be also affected [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. [PERSON] et al. performed a study on 114 cases of AFO worldwide [20]. The authors concluded that the mandible was involved in 65%, with 80% occurrence in the posterior part of the mandible and more than 92% was associated with impacted and/or unerupted teeth [21; 22]. The maxillary location remains somehow rare [19; 20; 21; 22; 23]. Studies performed worldwide suggest that hybrid odontogenic tumors can be found [15; 16; 17; 18; 19; 20]. AFO in most cases is a small lesion; however, cases with a great variety of shapes and sizes are describing occurrence both in the maxilla and mandible [8; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. The recurrence rate is highly related to the surgical approach, localization, and structures involved in its composition [1; 2; 3; 4; 5; 18; 19; 20; 21; 22; 23; 24; 25]. Differential diagnostics should include pathologies with similar mixed radiological appearances such as calcifying epithelial odontogenic tumor (CEOT), calcifying odontogenic cyst (COC), immature polydomata, adenomatoid odontogenic tumor (AOT), ameloblastic fibrodentinoma (AFD) [7; 8; 9; 10; 11; 12; 13; 14; 15]. During clinical differentiation, bone swelling should be distinguished from odontogenic cysts (OC), eruption cysts, OKC (odontogenic keratocysts), cyst-related pathologies, and rarely with PHP, primary hyperparathyroidismtumors, namely Brown tumors related to the parathyroid gland adenomas or related lesions [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. After the removal of each pathology from the bone, an empty intraosseous bony cavity is present. Not all cases require bone grafting or GBR (guided bone regeneration) procedures. A bone graft could be performed with calvarial, iliac, or fibula graft, while GBR is performed with autogenous and/or synthetic biomaterials. Recovery after regenerative interventions may be related to a period after surgery of delayed or complicated healing [26; 27; 28]. Shape, size, localization, and close proximity to an esthetic area in the oral cavity, along with the ability to support bone ridge/stable teeth stability in the oral cavity are considered the most important factors. In the presented paper, a rare case of AFO lesion and its treatment with blood cloths/PRF will be presented and discussed
## 3 Results and Discussion
### Effect of Chitosan-Licorice Edible Coatings on Strawberries' Quality
#### 3.4.2 Total Phenolic Compounds Content and Microbiology Analysis  
As stated before, the phenol content of a vegetal sample can be related with its antioxidant activity. Thus, the TPC content of the uncoated and coated strawberries was evaluated after 10 storing days, as an indicator of the coating antioxidant functional capacity (Table 6). The TPC values of uncoated strawberry on day 1 are in accordance with the values reported in the literature [75], which indicated that TPC values of 27 strawberry cultivars varied from 0.57 to 1.33 GAE mg/g of fresh weight.  
The results given in Table 6 indicate that, on day 10 and for all cases (uncoated and coated fruit), the TPC values were lower after the cold storage. But, the type of coating had a significant effect on the observed TPC decrease, as is depicted in Figure 9, and the control samples (uncoated fruit) presented the highest TPC loss percentage (30.93 mg GAE/g), followed by chitosan coating (22.65 mg GAE/g), chitosan + 1% LE (13.1 mg GAE/g) and chitosan + 5% LE (13.3 mg GAE/g). Then, LE extract contributed significantly to preserve the phenolic compounds of samples.  
Concerning the microbiological analysis, Table 7 shows the aerobic bacteria and yeast determined in coated and uncoated strawberries after 6 days of cold storage. No initial growth of bacteria or yeasts was observed in the uncoated and coated strawberries. But after 6 days of storage, viable count of  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{**TPC (mg GAE/g)**} \\ \cline{2-3}  & **Day 1** & **Day 10** \\ \hline Uncoated & \(1.41\pm 0.12\) & \(0.97\pm 0.04\) \\ Ch & \(1.28\pm 0.06\) & \(0.99\pm 0.08\) \\ Ch + LE-1 & \(1.52\pm 0.10\) & \(1.32\pm 0.05\) \\ Ch + LE-5 & \(1.44\pm 0.02\) & \(1.38\pm 0.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Total phenolic compounds (TPC) of strawberries treated with different coatings. Ch = Chitosan; Ch + LE-1 = Chitosan + 1% of licorice extract; Ch + LE-5 = Chitosan + 5% of licorice extract.  
Figure 9: Effect of chitosan-licorice coatings on total phenolic compounds (TPC) of strawberries stored for 10 days at 4 \({}^{\circ}\)C: C = uncoated; Ch = chitosan; LE1 = Chitosan + 1% of licorice extract; LE5 = Chitosan + 5% of licorice extract.  
bacteria and yeasts were determined in uncoated samples and in berries coated with chitosan, while in the samples coated with chitosan-licorice mixtures, no growth was observed. Then, the addition of phytochemicals of licorice in order to prepared edible coating improved the antimicrobial properties of chitosan.
## 4 Results and Discussions
### Adsorption Characteristics  
Based on the results of isothermal adsorption experiments under the conditions of equilibrium water and 30 \({}^{\circ}\)C, we observe that the Langmuir volumes (\(V_{\rm L}\)) of coal samples in the Dacun Block range from 22.67 to 36.84 m\({}^{3}\)/t (avg. 31.36 m\({}^{3}\)/t), and the Langmuir pressures (\(p_{\rm L}\)) vary from 0.52 to 1.25 MPa (avg. 1.02 MPa). [PERSON] et al. [51] found that the \(V_{\rm L}\) of coals is rank-dependent. When R\({}_{o,max}\) < 4.0%, the \(V_{\rm L}\) rises with the increase in R\({}_{o,max}\), and when R\({}_{o,max}\) > 4.0%, the \(V_{\rm L}\) decreases with the increase in R\({}_{o,max}\). However, in this study, the \(V_{\rm L}\) of coal samples seems uncorrelated with the R\({}_{o,max}\) (Figure 11a), suggesting that the coal rank is not the main controlling factor on the \(V_{\rm L}\) of coals in the Dacun Block.  
Regardless of coal rank, the \(V_{\rm L}\) of coals in the Dacun Block presents a parabolic change of first increasing and then decreasing with the increase in depth (Figure 11b), and there is a turning depth (critical depth of \(V_{\rm L}\)), which is around 700 m, which is basically consistent with the turning depth of gas content-depth relationship. To be specific, when the depth of the coal seam is shallower than the critical depth, the \(V_{\rm L}\) rises with the increase in depth; once the depth of coal seam exceeds the critical depth, the \(V_{\rm L}\) decreases with the increase in depth. The existence of the critical depth of \(V_{\rm L}\) is an inevitable result of deep geothermal field warming, which is one of the main controlling factors on the adsorptivity of coal under the formation condition [41].  
Figure 10: The relationship between the (**a**) reservoir pressure, (**b**) reservoir pressure coefficient, and depth of coal seams in the Dacun Block.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 4 Proposed IoT System Architecture for Aquaponics Monitoring
The data link from the node to the farmhouse is made using the Message Queuing Telemetry Transport (MQTT) protocol. This protocol can be configured with 3 levels (i.e., 0, 1, and 2), but for our purpose, it has been configured with level 2 for both subscribers and publishers to ensure information delivery and no data duplication. The security issues were tackled by configuring the Transport Layer Security (TLS) encryption to ensure secure communication between clients and brokers. For the sensor to monitor metrics, different water probes will be attached to the sensors. The design of the sensors should be flexible enough to allow the building of a system which can be customised with sensor combinations described in Table 3 as type A, type B, type A + B, or a minimal type A + B in order to control production costs. Each of these sensors has attached a wireless link which is able to provide coverage of up to 1 km\({}^{2}\) in order to ensure that the solution is flexible enough to accommodate farmland extensions dedicated to aquaponics, such that the farmer can have a view of all his/her farmlands on the same monitoring system. LoRa/LoRaWAN, NB-IoT (5G), and LTE-M technologies will provide the capabilities to support this coverage. In the farmhouse, a special sensor called the gateway sensor will be deployed. The main purpose of this sensor is to act as a receiver for all the metrics periodically sent by all other devices, and it is also responsible for sending the calibration information to the associated sensor deployed in the field. The gateway sensor is connected to two different wireless networks, and it acts as a forwarder between them. Any information received by the LoRa/LoRaWAN network will be forwarded into the WiFi network and vice versa. Finally, a small computer with a monitor, keyboard, mouse, and WiFi connection will be installed in the farmhouse to allow the farmers see the graphical interface where all the information is displayed in real-time. In our proposed architecture, we have designed and prototyped both the device and gateway capabilities indicated in the IoT Methodology standardised by ITU-T under specification ITU-T Y.2060 [28]. However, at this stage, we have not specified the definition of the functional components defined in ITU-T Y.4115-2017 [29], as our IoT system is an industrially relevant, lab-based development, so the IoT Device Capability Exposure (IoT DCE) is summarised in a Database and a Dashboard (in the Farmhouse subsystem) but could be replaced by a complete IoT ecosystem, such as Thingsboard, FIWARE, or any other platform.
## 2 Literature Review
### Related Works
Wavelet packet decomposition and CSP are used to extract the number of selected features from generated artificial EEG signals. Their implantation was evaluated using EEG data from 23 patients in CHB-MIT dataset. It achieved a sensitivity of 92.2% with the cross-validation method. [PERSON] [46] developed a classification model for recognizing the emotions of Alzheimer's disease (AD) patients. EEG data from 30 Korean patients were used to compare the performance of three machine learning techniques: multilayer perceptron, support vector machine, and recurrent neural network. The best result was gained by using the multilayer perceptron, in which its average accuracy was 70.97%. [PERSON] [47] combined data from two datasets using the domain generalization method. Transfer component analysis was utilized for extracted data from CHB-MIT and TUSZ EEG datasets based on reproducing the kernel Hilbert space method. The transformation of 3D electrode coordinates into a 2D space is applied, then Azimuthal Equidistant Projection is used, followed by the Clough-Tocher technique. A recurrent CNN was trained using the generated vectors with 128 units, and the testing result was a sensitivity of 74.5%. Ref. [48] proposed a fall detection model for the elderly by utilizing the accelerometer and gyroscope in personal smart devices. They used machine learning in the local edge device and in the cloud to classify fall status from the collected sensors data. Their model reduced the amount of transmitted data and thereby the network traffic. A selective model for image classification using Deep Neural Network DNN is proposed by [49]. They presented a federate aggregation approach by evaluating the local DNN models and sent the best to the central cloud server. The implantation of their method showed an improvement in terms of efficiency and accuracy compared with the federated averaging method. The applications of graph theory in neuroeconomics using EEG data were reviewed systematically by [50]. They proposed a method for building an EEG data-based brain network by utilizing sensor and source methods. Their results showed that the majority of 57 research articles aimed at cognitive tasks. In [51], a review of 38 articles that utilized the two-photon imaging (2 PI) technique for epilepsy research was introduced. They concluded that 2 PI is a suitable imaging technique for the research of epilepsy, and it can extend the incoming research on human brains. In [52], a hybrid classification model was proposed for predicting epileptic seizures. Their model used DWT to obtain time-frequency domain data from the original EEG signals. The training process in their model is performed using both Long Short-Term Memory and a Dense Convolutional Network, and the evaluation used the CHB-MIT dataset.
# A Review on Local Failure Probability Sensitivity Analysis
## 3 Local RSA in the Original Input Space
### Score Function Method with Advanced Sampling Techniques
#### 3.3.1 Score Function Method with Subset Sampling
The MC estimates of \(\mathbb{E}_{f_{\mathbf{Z}}}\big{[}\mathbb{I}_{F_{0}}(\mathbf{Z})Q(\mathbf{Z}) \big{]}\) and \(\mathbb{E}_{f_{\mathbf{Z}|F_{i-1}}}\big{[}\mathbb{I}_{F_{i}}(\mathbf{Z})Q_{i} (\mathbf{Z})\big{]}\) for \(i>0\) are each unbiased. However, there is a small bias induced by the dependence between the samples generated at each step, if one uses the Metropolis algorithms for the Markov chain MC procedure. This bias affects \(\hat{P}_{f}^{\text{SS}}\) and the SS sensitivity estimate [27]. The dependence makes the variance of the sensitivity estimate along with the mean square error hard to derive. Local sensitivity computation has also been studied in a special case of subset sampling called moving particles method [28].
# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 7 Use Case Discussion and Results  
Our use case was performed at one controlled room related to the identified IoT system. From May 2019 to October 2019, six-month data from an area wherein an average of 20 persons performed their work daily. In these six months of testing, the 20 persons went to a web application (alternative to the mobile device) when they arrived each day. The web applications show the information of sensors collected data (interior room and external temperature). These data are represented in dashboards with history and real-time and also 3D representation (BIM) parameters collected in a Things2 People approach; this representation is made with colours taking into account internationally defined comfort values. After taking this information, the user expresses his 'feeling' of the temperature, light level, via the web application, in a pre-defined template with three levels--cold, regular, and hot. Then, we receive visual information about the other people in the place (see Figure 9). Finally, we ask the user if we intend to change room temperature and how many degrees and lights and receive the prediction information of energy that will be used or saved with their intended action as an example of Things2 People interaction.  
As illustrated in Figure 9, over time, the warning was significantly reduced due to our implemented approach where people interaction was achieved. People react before the system generate alerts. As the output of our approach, we can say that we give the user tools to have a real perception of the environment and take actions as an individual or as a group by understanding the overall room indicators and energy consumptions, leading to more conscious decisions and less energy waste.  
Figure 10 presents energy consumption for that building section for the research period and previous years. From the collected data, it is possible to determine an average energy consumption reduction of \(11\%\) compared with 2017 and 3% compared with 2018. The results compared with 2018 were less obvious because of the lack of frequent heat waves during that year, which prevented air conditioning from being used.  
Figure 9: Number of warnings evolution during the last months.  
It is important to point out that the system was a localized test case, which could have much better results if implemented throughout the building.  
The overall assessment of users (20 employees) collected as a yes or no type of question demonstrated that the reaction to the UI was positive, and the framework is flexible enough to be implemented in more rooms and structures, a very good answer for lessening temperature issues during winter and summer seasons. Taking into account the collected feedback, 65% of changes in temperature were reconsidered based on provided predictive energy consumption. Fewer temperature changes on heating/cooling systems were performed, and our results show an average savings of around 4% of total energy consumed in this location.  
We are extending this approach to other cases, involving students and classrooms, (Figure 11).
## 3 Results
### Trypsin Impedance Results  
Impedance spectra were recorded after film stabilization under PBS flow with a 3D-printed setup. The absolute impedance at 10 kHz revealed to be most sensitive to the presence of trypsin during flow due to the largest changes that occurred relative to the initial signal saturation. Figure 3 presents data comparing the effect of different concentrations of trypsin, which show that 0.5 mg/mL trypsin reduced the film \(\Delta\)Z rate to 0.114 k\(\Omega\)/min, a significant drop compared to the 2.88 k\(\Omega\)/min rate of the 1 mg/mL concentration. The curve for \"Trypsin--No Film\" essentially represents the impedance of PBS over the sensor since trypsin is not significantly polar and thus does not alter the dielectric properties of the buffer, while \"Control--Film\" represents the impedance of the film after equilibrating with the PBS.  
Figure 2: SEM of resulting drop-cast crosslinked gelatin films. Edge views of film (**a**) without exposure to enzyme solutions and (**b**) after exposure to trypsin, and top views of film (**c**) after exposure to trypsin and (**d**) after exposure to all three enzymes in buffer.  
Interestingly, the impedance of the film alone is expected to be higher than that of PBS due to the gelatin's relatively neutral isoelectric point [24]. It is likely, however, that equilibration with the PBS flow allowed salt ions to accumulate, thereby increasing the ion concentration in the gelatin matrix over the sensor.  
In Figure 4, we report a representative sequence of Bode plots at different exposure times that convey changes in the electronic properties of equivalent circuit models, such as the double-layer capacitance and solution resistance.  
Figure 4: Representative bode and Nyquist plots for progressive exposure of (**a,c**) uncoated and (**b,d**) gelatin-coated sensors to 1 mg/mL trypsin in buffer. Solid (-) and dotted (-) lines indicate \(\mid\!Z\!\mid\) and the phase, respectively.  
Figure 3: Representative impedance responses at 10 kHz of sensors, either uncoated or coated with a gelatin film, to trypsin at 1 (both film and no film), 0.5 (film only) and 0 mg/mL in phosphate-buffered saline (Control). The error bars plot the temporal change in impedance at respective time points (span = 3).  
Here, we see over time, both the phase and impedance spectra of the coated sensor approach those of the uncoated sensor, indicating a decrease in film coverage that reflects trypsin degradation of the film due to increased exposure of the sensors to the buffer. We also observe an increase in the frequency range possessing a phase approaching \(90^{\circ}\), further evidence that capacitive elements become more dominating in the circuit. Additionally, there is an increase in the charge-transfer resistance (\(R_{\text{CT}}\)) in the Nyquist plot, illustrated by the increase in diameter of the semicircles (Figure 4d) as trypsin continues to remove film from the sensor surface. This is opposite to what is observed in uncoated sensors (Figure 4c), which further supports the likelihood of ion accumulation in the film.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 3 Cellular V2X: A Primer  
The role of 3 GPP and cellular networks in supporting vehicular communications has been steadily and rapidly growing. Granted the technological feasibility and the undisputed business opportunities  
Figure 1: Two-level controller for the predecessor-leader control strategy.  
for network operators, 3 GPP started a feasibility study to support C-V2X communication in the first quarter of 2015. V2X is part of 3 GPP LTE starting from Release 14 [17; 18], with a clear roadmap for 5G networks to provide the ultra-high reliability and ultra-low latency demands of tomorrow V2X applications (e.g., autonomous driving, car platooning) [22].  
There are two ways to support C-V2X communications specified in Release 14, namely over the LTE-Uu conventional cellular radio interface and over the PC5 radio interface. Communications over the LTE-Uu interface can be unicast and/or leverage eMBMS. In the latter case, a road-side unit (RSU)/eNodeB can disseminate V2X safety data towards multiple vehicular user equipments (VUEs) in a given area via eMBMS. V2V communications are enabled by the new Evolved Universal Terrestrial Radio Access (E-UTRA) capability designated as PC5, also known as _sidelink_ at the physical layer, as opposed to the conventional uplink (UE-to-eNodeB) and downlink (eNodeB-to-UE). The specifications of this interface, which is the focus of this paper, have evolved since Release 12, as described in the next subsection.
## 3 Measurement Setup
### Measurement Settings and Scenarios  
The receiver was installed on the roof of the train engine, which had a height of approximately 5 m. The channel-sounder transmitter was attached to the top of a 3 m tall tripod, which was placed about 4-7 m away from the crossings. The implementation is shown in Figure 1b. The transmitter used a 12 dBi omnidirectional antenna. The receiver used a 12 dBi omnidirectional and a 23 dBi bidirectional antenna. The antenna beam patterns for an omnidirectional and a bidirectional antenna are shown in Figure 4.  
Prior to the measurements, we conducted an Electro-Magnetic Interference (EMI) test at 5.85-5.93 GHz along the test tracks. The purpose of the EMI test was to check the existence of any interference signals with a higher than \(-110\) dBm signal strength. We did not observe any interference signals from the results along the test track.  
The train makes three passes through the measuring regions; three sets of measured data were collected and analyzed for each crossing. The train accelerates to the desired test speed of 10 mph for each pass. The 10 mph is the maximum operating speed permitted due to the SVRR's track regulations. For all the test scenarios, the train maintained a constant speed through the tested range, which was 200 m before and after the crossing and decelerated after passing the end of the testing point, 200 m after the crossing. In Figure 3, each transmitter displacement is marked as a red bar. The testing coverage range for each crossing is marked as a blue bar and labeled as either '\(-200\) [crossing #]' or '\(200\) [crossing #]', which represents 200 m before and after the certain crossing #. The tested train track is colored yellow.  
Figure 3: Test sites for five crossings of the SVRR track (**a**) crossing #1, (**b**) crossing #2, (**c**) crossing #3, (**d**) crossing #4, (**e**) crossing #5.