esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Surgically Assisted Rapid Palatal Expansion on Facial Soft Tissues: A Systematic Review
## 3 Results
### Study Characteristics
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Outcomes** & **Outcome Measures** & **Time of Measuring** & **Main Findings** \\ \hline [PERSON] et al., 2011 [26] & Transversal nasal measurements & Alar width measured directly with a caliper & T0: preoperative T1: at 2 months T2: at 6 months T3: at 12 months postoperatively & Postoperative growth of the alar base ranging from 3.12 to 3.48 mm for females and from 3.43 to 3.65 mm for males. The nasolabial angle grew by about 2 degrees. \\ \hline [PERSON] et al., 2008 [25] & Facial soft tissue measurements on all 3 planes of space & Various linear and angular measures via 3D facial scanning & T0: before transverse palatation (TPD) & TPD produces mean facial changes (1-2 mm) in the cheek and paranaasal areas as well as in the nasal base in the transverse plane only. \\ \hline [PERSON] et al., 2014 [23] & Nasolabial measurements on all 3 planes of space & Various liner and angular measurements via 3D photogrammetry & T0: preoperative T1: at least 6 months postoperatively & TPD produces mean facial changes (1-2 mm) in the cheek and paranaasal areas as well as in the nasal base in the transverse plane only. \\ \hline [PERSON] et al., 2010 [27] & Transversal nasal measurements & Alar width measured directly with a caliper & T0: preoperative T1: 2 months T2: 6 months T3: 24 months T4: 36 months postoperatively & No statistically significant differences. \\ \hline [PERSON] et al., 2012 [21] & Sagittal measurements & Measurements on lateral cephalometric radiographs & T0: before expansion T1: after expansion (unspecified) & Significant postoperative decrease in soft tissue convexity angle; significant increases in upper and lower nasal width as well as in the anterior face height. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Outcomes, outcome measures, time of measuring, and main findings of the included studies.
## 4 Experiments and Results  
### Experiments  
The first layer in our model is an embedding layer. This layer receives the previously tokenized words using a dense vector representation. The order of the words processed is taken into consideration. The next layer is an LSTM layer with 100 neurons. A dropout layer is used for regulating the network and keeping it away, as much as possible, from any bias. The final dense layer is the output layer, which has four classes representing the four distinct themes in this experiment. All of the data processed for training are balanced among the four themes. The models are compiled using the Adam optimizer and sparse_categorical_crossentropy. The Adam optimizer is an ideal deep learning optimizer for handling classification problems [26; 27]. For training and testing, we split our dataset into 80% training and 20% testing (849 lines for training and 213 lines for testing). The model was implemented in Keras, an open-source library that provides a high-level neural network API [28]. Table 3 shows the experimental configuration setting of our LSTM models.  
Furthermore, in order to verify the effectiveness of choosing the LSTM model, we compared the results obtained with another deep learning model using a recurrent neural network (RNN). The experiment was performed using the same dataset and settings mentioned in Table 3. However, instead of using LSTM layers, we used the RNN layer (SimpleRNN), which handles information over time using a memory cell.
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Materials  
On the southeastern coast of Madeira, the largest island in the Madeira archipelago in the subtropical eastern North Atlantic, sits the Madeira International Airport, as shown in Figure 1. This unique geographic location presents a set of specific challenges due to the island's complex topography, notably a significant mountain range. This characteristic significantly influences the climatic conditions at the airport, contributing to an intricate pattern of air flows and denoting substantial wind complexity, as the prevailing wind regime from the N and NE sectors is modified by the island topography [24; 25].  
Figure 1: Madeira Island hypsometric map (10 m resolution digital terrain model—Direcão Regional do Ordenamento do Territiório, IP-RAM), depicting the airport runway and the examined wind sensors sites (ROSARIO, RWY05, MID, and RWY23).  
The wind data utilized for this study are not publicly available, yet they were provided, upon request, by NAV Portugal E.P.E., a public corporate entity entrusted with the responsibility of providing air traffic services in Portugal.  
Wind observations at Madeira Airport are recorded by a wind set from Vaisala, model WA15, comprised of a Vaisala Anemometer WAA151 and a Vaisala Wind Vane WAV151. This system is deployed in four different sites, one at the short-final of runway 05 (ROSARIO) and three others at different sectors of the airport: RWY05, MID, and RWY23, as illustrated in Figure 1. In their unprocessed form, collected data are captured every three seconds and stored on a server in a comma-separated values (CSV) format. This format includes a timestamp, the wind speed expressed in m/s, and the wind direction in degrees.  
The provided dataset comprises complete CSV records collected from the four deployed sensors, covering the unprocessed three-second interval data for the years 2018 and 2019 and accounting for 84,096,000 potential observations, of which 84,040,836 contained valid and usable data. Figures 2 and 3 display the distribution of the raw data for the entire period, showcasing the prevailing wind speed and direction for each sensor. It is noticeable that the predominant wind in the data occurs from the N and NE, averaging between 4.5 m/s and 4.9 m/s on ROSARIO and RWY05, respectively.
# Smart Collaborative Caching for Information-Centric IoT in Fog Computing
## 4 Validation and Discussion  
A topology (shown in Figure 3) is carefully built to compare and analyze the overall performance of different schemes. Multiple IoT nodes are gathered to form a cluster, which is the basic element of fog computing. A cluster is composed of a leader (marked by the circles with red) and a group of members (marked by the circles with blue). The enlarged view of a cluster is illustrated at the bottom left corner. There are totally \(N\) clusters (located in the center area), 100 subscribers and 100 publishers (located at the left and right sides, respectively) in the network. The clusters are numbered from 1 to \(N\), according to the distance between the cluster and subscriber (\(N\) means the farthest cluster). A rendezvous server with aggregation function belongs to one cluster that is near to the publishers. Let us assume that each cluster has caching ability to store a complete packet and one message will not be separated into multiple packets. The period interval of requests is stable.  
Four kinds of schemes are enabled inside multiple clusters between subscribers and publishers. The first one is a traditional unicast scheme in the IP network. The second one is a flooding scheme in ICN, which spread the packets to all neighbors. The third one is our SCC scheme, which aims to significantly reduce the number of packets and the transmission latency. The last one is ICN SCC-E, which is an enhanced version of SCC based on one hop searching [59]. It could maintain one hop feature during the cluster searching process by storing more items in their finger tables.  
Two scenarios (simple status inquiry and complex content sharing) are established, and two experiments (calculating packet number and transmission latency during the requests and content exchanging) are executed by considering four candidates. In the first experiment, the subscribers will generate and send requests. The best case and the worst case, regarding overall packet number, will be calculated respectively. Then the average number of them are marked with different signs in figures. In the second experiment, similar to the first one, we calculate the average transmission latency between sending the requests and receiving the content.  
Figure 3: Topology establishment for SCC validation.
# Evaluating the Utility of UV Lamps to Mitigate the Spread of Pathogens in the ICU
## 3 Results  
### Mosaic Virus Transfer  
When analyzing the data of the mosaic virus dissemination in the absence of UV-C treatment, the virus disseminated to 2.00% (n = 2 of 100) of surfaces after 24 h, peaked at 10.10% (n = 10 of 99) after 48 h, and was found on 5.13% (n = 4 of 78) of tested surfaces after 96 h (Figure 1). Post UV deployment, 0.00% of surfaces tested positive for the mosaic virus DNA after 24 h, 1.20% (n = 1 of 83) of sites tested positive for the mosaic virus DNA after 48 h, and 2.50% (n = 2 of 80) tested positive after 96 h (Figure 1).  
The greatest reduction in mosaic virus transfer post UV implementation was observed at 48 h with a 90% relative reduction (\(p<0.0001\)) in mosaic virus spread across surfaces in the ICU (Table 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device** & **Average 24-h Touches** \\ \hline IV Pump & 48.8 \\ Keyboard & 111.2 \\ Pyxis & 50.5 \\ Ventilator & 28.5 \\ Vtals Monitor & 29.5 \\ Worksurface on Cart & 112.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimated Number of Unique Device Touches per 24-h Period.  
Figure 1: Percent of intensive care unit (ICU) Surfaces Contaminated with Mosaic Virus at Sequential Samplings. \({}^{*}\) Denotes a statistically significant reduction.
## 4 Results and Discussion
### Determination of Back-Calculated Defect Size  
Taking the PDR simulation in Figure 5b as an example, all predicted defect diameter values versus time are plotted in Figure 8. It can be seen that most of the predicted defect diameter values are distributed around the original defect needle size (0.358 mm) before 27 s, proving that the model has certain applicative scope to fit the defect size. Here, the average back-calculated diameter was 0.376 mm, a little more than the defect diameter of 0.358, and the size modified coefficient was 0.975.  
All the back-calculated defect diameter and size-modified coefficients are shown in Table 5. It can be seen that the effect of different parameters on back-calculated accuracy was different. The product of 1-dimensional Monte Carlo analysis is the cumulative distribution function, which can express all information as variability in the theoretic \(d_{eq}\). To determine the back-calculated diameter resolution (BDR), analysis was conducted using @RISK software (V7.5, Palisade Corporation, Ithaca, NY, USA and 2017). Data used in the analysis was collected from experiments. The results of the analysis regarding the correlation coefficients between parameters and B  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(H\) (mm) & 10 & 20 & 30 \\ \hline \multirow{2}{*}{\(r\)} & Pressure & 0.998 & 0.999 & 0.999 \\  & PDR & 0.985 & 0.987 & 0.981 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation between measured data and predicted values with different water depths.  
Figure 8: The predicted defect diameter versus time.  
From Figure 9, it can be seen that the most influential factors for the BDR were \(V_{g}\) and the PDR. The correlation coefficients of the membrane gas chamber volume \(V_{g}\) and PDR were 0.735 and 0.545, respectively. The correlation coefficient of the gas pressure in the membrane gas chamber \(P_{g}\) was \(-0.267\). The correlation coefficients of water depth on the membrane H and elastic pressure \(P_{e}\) were very low. The BDR decreased with the increase in \(V_{g}\) and PDR as well as with the decrease in \(P_{g}\).
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 3 Methods
### Machine Learning Models  
In the study of predicting the maximum magnitude of earthquakes in the coming year, we are confronted with a complex task that combines the properties of time series prediction and regression analysis. Seismic activity is a dynamic process over time, and current seismic events are strongly influenced by historical seismic activity, which requires our model to capture the long-term dependence and dynamics in the time series. At the same time, predicting the magnitude as a continuous-valued regression problem, in turn, requires the model to be able to resolve the complex nonlinear relationship between seismic activity parameters and future magnitude. Therefore, we selected several machine learning models commonly used in earthquake prediction for experiments based on these conditions: the random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), light gradient boosting machine (LightGBM), LSTM, and bi-directional long short-term memory (BiLSTM) models.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 5 Application of Probiotics in Combination Therapy  
Probiotics have shown promising therapeutic effects on CRC, yet relying solely on single-probiotic therapy remains impractical for comprehensive tumor treatment. Excellent chemotherapy drugs (5-Fu) are widely used in clinical applications [113]. Nonetheless, certain studies have utilized probiotics as a complement to drugs to achieve superior outcomes. For example, as an approved antitumor agent, celecoxib exhibited a preventive effect on CRC. In contrast, the combination therapy of _L. rhamnous_ GG and celecoxib resulted in a greater reduction (56%) in the number of aberrant crypt foci than either treatment alone (_L. rhamnous_ GG: 27%; celecoxib: 40%) in DMH-induced CRC Sprague-Dawley rats [114]. Another study involved the combined administration of capecitabineand probiotics [_L. plantarum_, _L. casei_, _Streptococcus faecalis_, and _B. brevis_] in Wistar-Lewis rats with DMH-induced CRC. It was found that the tumor burden in the combination treatment group (1.25) was lower than in the capecitabine group and probiotics group (1.81 and 3.9) [115].  
Cancer monotherapies are insufficient to produce satisfactory results due to tumor heterogeneity, which is associated with acquired drug resistance [82; 116]. Engineered probiotic-based combinatorial therapies can be individually designed based on multiple characteristics of cancer to deal with dilemma at hand. For instance, the efficacy of anti-PD-L1 antibody-mediated immunotherapy is reduced due to lower L-arginine levels in solid tumors [117]. To solve this problem, probiotic Ech was developed to be L-Arg bacteria, which can colonize the tumor site and continuously synthesize L-arginine. Notably, the substrate used by L-Arg bacteria is ammonia, the metabolic waste product in tumors, which represents a strategy that turns waste into treasure. The results indicated that in MC38 colon cancer mice, the combination therapy of L-Arg bacteria with anti-PD-L1 antibodies increased the efficacy of alone anti-PD-L1 therapy by nearly 30% [103].  
Additionally, some in vitro studies also proved the advantage of combination therapy [118; 119]. For example, the combination administration of 5-Fu and an _L. plantarum_ culture supernatant promoted apoptosis in the chemotherapy-resistant colon cancer cells HT-29 and HCT-116 via enhancing cellular hypersensitivity to 5-Fu [120]. Although the direct anticancer effects of probiotics are generally weaker than chemotherapy drugs, probiotics offer a variety of advantages such as regulating gut microbiota and alleviating chemotherapy side effects. Combining them with chemical drugs for CRC treatment may possess significant therapeutic benefits in clinical applications because they complement each other's strengths and weaknesses.
## 2 Materials and Methods
### Volatile Composition by Gas Chromatography  
A solid-phase microextraction (SPME) followed by the gas chromatography-mass spectrometry (GC-MS) method was carried out to determine the volatile compounds in halophyte plants [11]. Briefly, the fresh plants were crushed with a mortar and pestle until a paste was formed; after 1.5 g of paste was transferred to a 20 mL headspace vial (La-Pha-Pack(r), Langerwehe, Germany), it was capped with a white PTFE silicone septum (Specanalitica, Carcavelos, Portugal GC-MS vial). The SPME conditions were as follows: extraction temperature was at 40 \({}^{\circ}\)C for 40 min, agitation of 10 s, rotating speed of 250 rpm, and desorption time of 3 min at 250 \({}^{\circ}\)C. For headspace SPME sampling, a divinylbenzene/carboxen/polydimethylsiloxane (DVB/Car/PDMS) fibre was used (Supelco Analytical, Bellefonte, PA, USA). Analyses were performed on a GCMS QP2010 Plus (Shimadzu(r), Kyoto, Japan) equipped with an AOC-5000 autosampler (Shimadzu(r), Kyoto, Japan). The volatile compounds were separated using a capillary column Sapiens (5-MS, Teknokroma, Barcelona, Spain) with dimensions (30 m \(\times\) 0.25 mm (IS); 0.25 \(\mu\)m (film thickness). The GC-MS conditions were as follows: the injector and detector temperature were maintained at 250 \({}^{\circ}\)C; the injection mode was accomplished in the splitless mode for 1.5 min; and high-purity helium (\(\geq\)99.999%) was used as the carrier gas. The column oven temperature was maintained at an initial temperature of 40 \({}^{\circ}\)C for 5 min, increased to 170 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\), and then increased to 230 \({}^{\circ}\)C at 30 \({}^{\circ}\)C min\({}^{-1}\) and kept for 4 min. The carrier gas (He) was maintained with a flow of 2.00 mL min\({}^{-1}\), and the MS interface temperature was kept at 250 \({}^{\circ}\)C, as was the ion source temperature. Mass spectra were acquired in the electron ionization (EI) mode at 70 eV in an \(m/z\) range between 29 and 300 with a scan speed of 588 scans s\({}^{-1}\). The Linear Retention Index (RI) and mass spectra library (NIST 2005 mass spectra database, Boulder, CO, USA) [27; 28] were used to perform the putative identification of compounds. The volatile profile of samples was analyzed in duplicates.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 4 System Model
### Network Architecture of ERPMSA-UWSN  
In this section, the network architecture of our proposed protocol is presented. We describe the masters-slave technique of forwarding packets that aims to achieve low energy consumption.  
The network architecture of ERPMSA-UWSN is constituted of anchor nodes, relay nodes, and sink nodes as depicted in Figure 2. Sink nodes float on the surface bearing radio and acoustic modems to communicate with each other and relay nodes, respectively. These nodes can receive signals from water and perform the transmission/reception in the external environment without any boundaries. Relay nodes are deployed at different levels of depth, act as the 3 rd party between the transmitters (anchor nodes) and receivers (sink nodes), and carry the transmitted data towards the receiver. Anchor nodes are tethered alongside seabed and can move with water currents or with other environmental disruption. Their main task is to gather environmental data and pass it to the sink nodes via relay nodes. The centralized stations are the sink nodes that enable communication with other sink nodes as well as relay nodes. The reception of the data packet on any of the sink nodes is measured as a successful packet delivery.
## 2 Materials and Methods
### Contrast Limited Adaptive Histogram Equalization (CLAHE)  
Contrast limited adaptive histogram equalization is a classic method in image contrast enhancement [15]. To perform CLAHE, the original image is split into equally-sized rectangular partitions, while on each partition, the same transformation function will be applied.  
Contrast-limited gray level transformation is the main procedure in CLAHE, which is similar to the traditional histogram equalization (HE), but limits the contrast by a clip point to cut off the peak value in the histogram. For a given image \(I\) with L discrete gray levels, denoted as \(g_{0},g_{1}\dots g_{L-1}\), the probability density function (PDF) \(p(i)\) in each partition is defined as:  
\[p(i)=\frac{n_{i}}{N}\;for\;i=0,1\dots L-1 \tag{4}\]  
where \(n_{i}\) denotes the number of pixels with the gray level \(g_{i}\) and \(N\) is the total number of pixels in the partition. Crop \(p(i)\) above the clip point and distribute the cropped part evenly to \(p(i)\) to get a new \(p^{\prime}(i)\), as shown in Figure 0(a). The gray level transformation function \(T(g_{k})\) is showed in Equation (6). It should be noted that the transformation functions on different partitions will be different.  
\[c(i)=\sum\
olimits_{k=0}^{i}p^{\prime}(i) \tag{5}\]  
\[T(g_{k})=g_{0}+(g_{L-1}-g_{0})c(k)) \tag{6}\]  
where \(c(i)\) is the probability distribution function, which is the integral of \(p^{\prime}(i)\).  
To remove the possible block artifacts, a bilinear interpolation between the partitions was used to smooth the final pixel values. The transformation is appropriate for the center pixel of each partition, while the other pixel values are interpolated from the transformation functions in the surrounding partitions.  
\[I_{CLAHE}(x,y)=\left\{\begin{array}{c}T_{B_{k}}(I(x,y))\;I(x,y)in\;the\; centre\;of\;block\;B_{k}\\ \sum_{k=1}^{4}\frac{(L-\left\lvert x_{B_{k}}-x\right\rvert)\cdot(L-\left\lvert y _{B_{k}}-y\right\rvert)}{L^{2}}T_{B_{k}}(I(x,y))\;otherwise\end{array}\right. \tag{7}\]  
Figure 1: The probability density function (PDF) redistribution and bilinear interpolation within blocks. (**a**) Clip point and redistribution; (**b**) Bilinear interpolation.  
where \(I(x,y)\) denotes the value of the pixel at \((x,y)\) and \(I_{CLAHE}\) is the transformed image. Pixel \((x,y)\) is surrounded by the center of four partitions \(B_{k}(k=1,2,3,4)\). \(T_{B_{k}}(g)\) is transform function in the block \(B_{k}\) (\(x_{B_{k}},y_{B_{k}}\)) is the center of the block \(B_{k}\) and \(L\) is the length of the partitions (see Figure 1b).
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 3 Numerical Modeling
### Analysis of Simulation Results
#### 3.2.2 Detection Results of Different Defects at Different Locations  
The simulation calculation was carried out by changing the defect diameter from 0.4 mm, 0.6 mm, 0.8 mm, 1.0 mm, 1.2 mm and the defect depth from 7 mm, 14 mm, 21 mm, 28 mm, and 35 mm. The simulation results are shown in Figure 4. The range of the first signal peak of the transmitted energy is integrated by the square of the sound pressure. It can be seen that when the defect position is unchanged, the transmission detection results of each depth independently indicated that the larger the defect diameter, the smaller the detected transmission amplitude and energy. There is a significant linear relationship between the ultrasonic energy attenuation and the size of the defect, which is consistent with the theoretical calculation results in Section 2. The principle of flooding ultrasonic transmission method for defect detection is based on this law, and the size of the defect is determined by the attenuation of energy.  
Figure 2: Internal defect detection model by liquid immersion ultrasonic transmission.  
Figure 4: Ultrasonic transmission simulation results: (**a**) simulation signal in time domain (**b**) Energy map of the simulation.  
Figure 3: Sound pressure distribution of ultrasonic wave in the medium at different times: (**a**) 1.4 μs; (**b**) 4 μs; (**c**) 6.4 μs; (**d**) 9.0 μs; (**e**) 11.8 μs. Rayleigh waves (R wave), longitudinal waves (L wave).