esa-sceva/satcom-chunk-collection huggingface dataset:
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 3 Results and Discussion
### Real Time Testing  
Once models 4, 5 and 6 were selected as candidates for the gesture classifier, real time testing was performed according to the proposed validation in Section 2.6. Tables 2 and 3 show the accuracy and F1-Score of the selected models obtained in different real time testing sessions along with their standard deviation (\(\sigma\)), Table 4 shows the summary of Tables 2 and 3.  
Looking at Table 4 it can be seen that LSTM-RNN model 6 performed the best among the candidates during the real time application of the classifier. It is important to note that this model ended up being extremely sensitive. Looking at Tables 2 and 3 it can be seen that the models vary their performance across different acquisition sessions. This sensitivity was caused by the variation in the data distribution across training/validation and real time testing sessions, this change in data distribution is mainly caused by variations in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} \\ \cline{3-6}  & & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline
1 & LSTM (8) & 0.6575 & 0.6173 & 0.6000 & 0.5392 \\ \hline
2 & LSTM (16) & 0.9075 & 0.9068 & 0.8500 & 0.8360 \\ \hline
3 & LSTM (32) & 0.9700 & 0.9700 & 0.9600 & 0.9589 \\ \hline
4 & Dense (8) & & & & \\  & LSTM (16) & 0.9850 & 0.9849 & 0.9900 & 0.9894 \\  & Dense (8) & & & & \\ \hline
5 & Dense (16) & & & & \\  & LSTM (16) & 0.9875 & 0.9874 & 0.9800 & 0,9798 \\  & Dense (16) & & & & \\ \hline
6 & Dense (32) & & & & \\  & LSTM (16) & 0.9925 & 0.9925 & 0.9900 & 0.9896 \\  & Dense (32) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: LSTM-RNN models performance in training and validation sets during the hyperparameter tuning.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Accuracy in Session**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**SD (\(\sigma\))**} \\ \cline{2-2} \cline{5-6}  & **1** & **2** & **3** & **4** & \\ \hline
4 & 0.7500 & 0.8443 & 0.8521 & 0.7456 & 0.7980 & 0.0581 \\ \hline
5 & 0.8724 & 0.7455 & 0.7895 & 0.8406 & 0.8120 & 0.0559 \\ \hline
6 & 0.8640 & 0.9660 & 0.7979 & 0.8640 & 0.8729 & 0.0694 \\ \hlinethe EMG armband location; this model sensitivity to position variations of the acquisition device is one of the current challenges when working with EMG signals and real time EMG classifiers [40; 77; 78; 79]. In order to try to solve this, the three candidate models were fed with more data and trained again for 150 epochs, aiming to make the models more robust and more capable of generalizing to new data; however, this approach failed and the three models presented similar accuracy to the one shown in Table 4.
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 3 Simulation Algorithm of the 2D Profile
### Calculation of the Real Profile  
The real profile refers to the line of intersection between the real surface and the plane \(P\), which is presented in Figure 9. We found that the simulation model can be considered a series of scallops, each defined by the \(CC\) point, the principal direction, and the principal curvature.  
In a free-form surface as an example, line \(L_{p_{i}}\) passes through point \(p_{i}\), and it is parallel to the measuring direction \(V_{M}\). Figure 10 shows that the intersection of line \(L_{p_{i}}\) and the real profile is point \(p_{i}^{R}\), which corresponds to the nominal point \(p_{i}\).  
Line \(L_{p_{i}}\) intersects some scallops, and the distance between the intersection point \(p_{i}^{m}\) and the point \(p_{i}\) is denoted \(l_{i}^{m}\), where m is the index of the \(CC\) point corresponding to the  
Figure 8: Point \(p_{i}\) on the nominal profile.  
Figure 10: The point on the real profile of the free-form surface.  
Figure 9: Configuration of real profiles. (**a**) The real profile of plane surface. (**b**) The real profile of free-form surface.  
sallop, as shown in Figure 11. The point corresponding to the minimum distance value \(l_{i,\text{min}}\) is the intersection point \(p_{i}^{R}\) of \(L_{p_{i}}\) with the real profile.  
The flowchart for calculating the 2D profile is shown in Figure 12. The 2D profile can be obtained through the following steps:  
1. Input the CL data, tool parameters, and the workpiece design model. Input points \(P_{S}\) and \(P_{E}\) for a plane surface, or input points \(P_{1}\) and \(P_{2}\) for a free-form surface.
2. Calculate plane \(P\) according to the 2D profile measuring line \(V_{L}\) and the normal vector \(V_{n}\) of the nominal surface at the start point \(P_{S}\). Then calculate the measuring direction \(V_{M}\) by using Equation (6).
3. Calculate the points \(p_{i}\) on the nominal profile.
4. Calculate the intersection of \(L_{p_{i}}\) and the scallop marked m according to the point \(p_{i}\) and the measurement direction \(V_{M}\). Then calculate the distance \(l_{i}^{m}\).
5. Calculate the intersection of \(L_{p_{i}}\) and the next scallop, and update the minimum distance \(l_{i,\text{min}}\).
6. Repeat Step (5) for each scallop to obtain the minimum distance \(l_{i,\text{min}}\) and the real profile's intersection point \(p_{i}^{R}\).
7. Repeat Steps (3) to (6) for each point \(p_{i}\) to get the real profile.
8. The main objective of 2D profile simulation is to find the difference between the real profile and the nominal profile. Hence, the simulation algorithm of the 2D profile can be obtained.  
Figure 11: Intersection of line \(L_{p_{i}}\) with scallops.
## 1 Introduction
This decoder maintains the fine-grained details required for high-quality reconstructions while balancing the reconstruction efficiency during the up-sampling process. The proposed method addresses a significant advancement to increase the multi-view 3D reconstruction capabilities while addressing the main issue related to feature richness and representation efficiency. The experimental results on the ShapeNet and Pix3D datasets demonstrated that our model established a SOTA performance in the area of 3D object reconstruction. The contributions of our work are as follows:  
1. We present a Window Grouping Attention (WGA) mechanism, which can segment tokens into groups from individual window attentions and efficiently establish both inter-view and intra-view feature extraction. A novel encoder is introduced for operating multi-view inputs by integrating WGA into a swin transformer architecture to enhance 3D reconstruction accuracy. 2. We introduce a progressive hierarchical decoder to integrate a 3D CNN and a swin transformer block to address the dependencies of working on the comparatively high-resolution voxel that can enhance reconstruction capability. 3. The experimental results on the datasets ShapeNet and Pix3D represent that our work outperforms other SOTA methods in both multi-view and single-view 3D object reconstruction. The main sections of this article are organized as follows. In Section 2, we present a review of related work, including multi-view 3D reconstruction, especially transformer-based 3D reconstruction methods. Section 3 introduces the proposed WGA-SWIN framework, including the Window Grouping Attention (WGA) mechanism, an encoder based on a swin transformer, and a progressive hierarchical decoder. To demonstrate the effectiveness of our proposed approach, extensive experiments, results, and ablation analysis are conducted within Section 4. Finally, the conclusion and suggestions for further research are presented in Section 5.
## 2 Physical and Mathematical Models
### Mathematical Model  
#### 2.2.1 Assumptions of the Model  
The following assumptions were made to construct the mathematical model.  
1. Temperature and pressure in the beds, the condenser and the evaporator are uniform.
2. Thermodynamic equilibrium exists in the adsorbent bed at any given time.
3. The activated carbon granules are treated as a continuous medium.
4. Gaseous methanol is treated as an ideal gas.
5. Thermal conductivity and specific heats of various components of the system are assumed to be constant in the working temperature range of 293 K to 423 K.
## 2 Design and Analysis  
The proposed antenna is designed on a Rogers RT/Duriod 6202 with relative permittivity 2.94 and loss tangent of 0.0015. All the patch elements are laminated on the top of the substrate, and the ground plane is on the opposite side of the patch where four rectangular slots are placed as DGS elements. The overall dimension is 33.5 mm \(\times\) 33.5 mm \(\times\) 1.52 mm which is depicted in Figure 1. The corresponding values are listed in Table 1.
## 2 Materials and Methods
### Determination of Survey Line Spacing  
One of the most critical requirements for hydrographic survey work using a multi-beam echosounder is that we ensure full bottom bathymetric coverage. To meet the above condition, the hydrographer is obliged to determine, calculate, or accept, based on expert knowledge and experience, the distance between survey profiles the \(L_{s}\) (line spacing). Based on the average depth of survey area and the swath angle of the multi-beam echosounder, the distance between the survey lines \(L_{s}\) can be determined from the formula:\[L_{s}=2\cdot D\cdot\text{tg}\left(\frac{\theta}{2}\right), \tag{2}\]  
where \(D\)-- average depth of the survey area [m]; \(\theta\)-- swath angle of the multi-beam echosounder [\({}^{\circ}\)].  
However, Formula (2) does not take into account the possibility of the vessel deviating from the track line and the potential measurement errors on the edge of the MBES swath width. The resulting gaps in data recording can always be covered by performing additional, complementary transitions along the lines, which comes with extra time.  
The distance between the survey lines \(L_{s}\) should consider the zone of double depth measurements between two adjacent lines. This zone is called \"overlap\" and the bottom coverage in this area equals 200% (Figure 1). Overlap is crucial as it allows for the maintenance of depth measurement continuity in cases where the helmsman cannot keep the track line. This zone can also be used as a data quality control measure. The size of overlap mostly depends on the MBES operator's experience, the specification of the contract requirements, or the order of hydrographic survey work.  
Based on Figure 1,  
\[\frac{L_{s}}{2}=\frac{SW_{u}}{2}-\frac{p}{2}, \tag{3}\]  
where \(L_{s}\)--distance between the survey lines [m]; \(SW_{u}\)--useful swath width of the MBES [m]; and \(p\)--overlap [m].  
Simplifying the dependence (3), we obtain  
\[L_{s}=SW_{u}-p. \tag{4}\]  
Since  
\[SW_{u}=2\cdot D\cdot\text{tg}\left(\frac{\theta_{u}}{2}\right), \tag{5}\]  
where \(\theta_{u}\) is the useful swath angle of the multi-beam echosounder [\({}^{\circ}\)], and assuming that the overlap \(p\) is a percentage of the entire swath width, the distance between the survey lines (line spacing) is calculated from the following relationship:  
\[L_{s}=2\cdot D\cdot\text{tg}\left(\frac{\theta_{u}}{2}\right)-SW_{u}\cdot p, \tag{6}\]  
where \(p\) is the overlap, being a percentage of the useful swath width of the MBES, taking values from 10 to 25%.  
Finally,  
\[L_{s}=2\cdot D\cdot\text{tg}\left(\frac{\theta_{u}}{2}\right)\cdot(1-p). \tag{7}\]  
Figure 1: Survey planning parameters using multi-beam echosounder (\(p\)—overlap; \(SW_{u}\)—useful swath width of MBES; \(W\)—width of the survey area; \(D\)—average depth of the survey area).
## 2 Materials and Methods
### Data Collection  
The patients were recruited retrospectively from the department's database from 2000 to 2018. The inclusion criteria were patients with a confirmed diagnosis of oral cancer with epithelial origin (OSCC) and age \(\geq\)18 years. Patients with cancers outside the oral cavity, with other types of oral cancers, and/or with age \(<\)18 years were excluded from the study.  
A total of 262 patients were found and were fulfilling the inclusion criteria of the study. The medical recordings of twenty-three patients were incomplete. Therefore, these patients were excluded from the study. The remaining 239 patients (125 females and 114 males) with an average age of 65.56 years, were recruited in the Test Group (TG). The site of OSCC was registered according to the 10 th edition of the International Classification of Diseases (ICD-10) version 2010 [23]. The oral cavity was described as follow; tongue (C02.3), superior alveolar ridge mucosa (C03.0), palate (C05.9), inferior alveolar ridge mucosa (C03.1), tonsillar pillar (C09.1), buccal mucosa (C06.0), the floor of the mouth (C04.9), oral vestibule (C06.1) and lips (C00.5).  
A total of 239 patients (123 females and 116 males) with no history and/or diagnosis of oral cancer, were recruited in the Control Group (CG). The patients were selected from the department database with the same age interval, average age, age distribution and time window of the TG to compare the obtained results. The collected data for both groups were as follows; age, gender, tobacco status and consumption amount, alcohol status and consumption amount and FHC.  
For the analysis of tobacco, the patients were categorized into non-smokers and smokers. Patients who stopped smoking for more than 10 years, were considered non-smokers. The tobacco consumption amount was measured in pack years. The tobacco consumption amount was divided into three categories; \(\leq\)20 pack years, 21-40 pack years and \(>\)40 pack years.  
To analyze the alcohol, the patients were categorized into non-drinkers and drinkers. The type of alcohol was described in three categories; wine, beer and spirit. The alcohol consumption amount was measured in drinks/week. The patients were divided into three groups according to the quantity of alcohol consumption; \(<\)7 drinks/week, 7-10 drinks/week and \(>\)10 drinks/week. Patients with alcohol abuse issues were documented.  
The FHC was categorized into General Family History of Cancer (GFHC) and Family History of Head and Neck Cancer (FHHNC). The degree of kinship was also considered, where the GFHC was categorized into First-Degree Family History (FDFH) and Second-Degree Family History (SDFH). The FDFH included parents, full siblings and children. The SDFH included grandparents, aunts/uncles and cousins.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 4 System Description
### Architecture  
The network is based on LoRa technology, i.e., the network topology used in this case will be an infrastructure architecture where a gateway will be in charge of collecting data from nodes and, several nodes will be spread along within the scenario.  
To implement our system, it is necessary to design and develop the end devices using the LoRaclass A specification [7] since it is the option that consumes less energy. The LoRa Gateway will be used to collect data from the LoRa nodes and to forward them to a server for storing and/or processing the data from the sensors.  
The data will be stored in a network storage server. In this case, an already developed service provided by The Things Network (TTN) was used. It offers the necessary tools to collect and store the data in a database (DB) with the minimum cost.  
Finally, a web application will be the user's interface to visualize the data from sensors and for the estimations of warning areas. Figure 6 shows the network architecture of our proposal.  
Figure 5: Main features of these technologies.
# Design Exploration of Bamboo Shells Structures by Using Parametric Tools
## 2 Methods
### Calculations Based on the NSR-10 Code
#### 2.1.3 Analysis of Bending Elements  
The analysis of bending elements was driven by the allowable deflections modified by the coefficients for Guadua (Table 4); likewise, the bending stress (\(f_{b}\)) must not exceed the value of the allowable bending stress (\(F_{b}^{\prime}\)) shown in Equation (2).  
\[f_{b}\leq\frac{M}{S}F_{b}^{\prime} \tag{2}\]  
knowing that  
\[S=\frac{\pi}{32D_{e}}\left(D_{e}^{4}-\left[D_{e}-2t\right]^{4}\right) \tag{3}\]  
where  
\(f_{b}=\) bending stress actuator;  
\(M=\) bending moment actuator;  
\(D_{e}=\) exterior diameter of the Guadua;  
\(t=\) thickness;  
\(F_{b}^{\prime}=\) modified allowable bending stress;  
\(S=\) elastic section modulus.  
Equation (3) satisfies the sections composed of only one culm. To verify sections with 2 or more Guadua, the elastic section modulus should be calculated for each section in accordance with [23].  
Allowable deflection \(\delta\) is determined using Equation (4)  
\[\delta=\frac{l}{K} \tag{4}\]  
where  
\(l=\) beam length;  
\(K=\) bending factor;  
(both values can be seen in Figure 2 and Table 3).  
While the load for immediate deflections \(W_{i}\) and creep deflections \(W_{f}\) are defined as follows:  
\[W_{i}=D+L \tag{5}\]  
\[W_{f}=2.8D+1.3L \tag{6}\]  
In Equations (5) and (6), values were considered for a variable climate, with a moisture content of \(CH\geq 19\%\) and a temperature of \(t\leq 37\)\({}^{\circ}\)C, as proposed in [23].  
In this way, we calculated the deflections for a distributed load as follows:  
\[\Delta=\frac{5 wl^{4}}{384\ EI} \tag{7}\]  
where \(w\), is the load that can be substituted by Equation (5) to obtain the immediate deflections, and by Equation (6) to obtain creep deflections.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Coefficient** & **Remarks** \\ \hline \(C_{D}=0.9\) & The value of 0.9 indicates a permanent load. \\ \(C_{m}=0.7\), 0.8 & A moisture content of \(\geq 19\%\), a \(C_{m}=0.7\) for bending, and a \(C_{m}=0.8\) for shear were determined. \\ \(C_{t}=1\) & A temperature of \(\leq 37\)\({}^{\circ}\)C was considered in this work. \\ \(C_{L}=1\) & The value of 1 corresponds to beams formed by a single Guadua. \\ \(C_{r}=1.1\) & For four or more equally rigid elements, the allowable stresses should be increased by 10\%. \\ \(C_{c}=1\) & Modification coefficient by shear. \\ \hline \hline \end{tabular} Note: For more information, please refer to [23].  
\end{table}
Table 4: Modification coefficient values.
# Design of a Computed Tomography Automation Architecture
## 2 Computed Tomography Setup and Initial Methods
### Prior Restriction of View Angles  
[PERSON] et al. [4] incorporated CAD data to help perform fast in-line defect inspection. They assumed that there is prior knowledge of both the CAD data of the object and the  
Figure 2: Generic computed tomography (CT) procedure that is completed manually.  
inspected object itself. This is not always the case because part drawings may not always be available. [PERSON] et al. built CAD-simulated projections from the 3D CAD model and eliminated much of the time involved in the CT process by restricting imaging to optimal projection angles. This was carried out by defining regions of interest (ROIs) assuming the position and extent of relevant defects are approximately known. Optimal projection angles for seeing defects were found for these ROIs based on image contrast in the CAD model. The orientation and alignment of the sample were then determined from these simulated CAD projection data. Only a few projection angles were selected for which a potential defect is most visible. The projections were then acquired and directly compared with their CAD-simulated counterparts. A chosen threshold for root-mean-squares error difference between the simulated and real projections was used to classify the sample as defective or not. By directly identifying defects in the projection-space, they eliminated the need to perform 3D reconstruction and reduced the time required for CT analysis. Figure 3 is an example of a partial architecture that has been modified by introducing initial data that change the setup and procedure compared to the generic CT procedure in Figure 2 and represent the method used by [PERSON] et al. [4].  
[PERSON] and [PERSON] [5] applied robot kinematic modeling for controlling the positioning of parts in beamlines. The focus was to help with parts of complex geometries to be rapidly and accurately oriented in the beam using arbitrary serial robotic manipulators. The aim of their study was to provide a framework allowing for simulation and control of various types of positioning systems, and automatic alignment of parts. Count time minimization was also a focus of this study, which was accomplished by determining the incident beam paths that undergo less attenuation through the material.
## 4 Experimental Results and Analysis
### Experimental Setup  
To evaluate the effectiveness and robustness of the proposed Fed-DTB framework, we implemented it in Google Colab, leveraging its cloud-based GPU resources to simulate a distributed IoV environment. The implementation utilised TensorFlow 2.7.0, along with the Keras API for model construction, to ensure efficient distributed training. A custom TensorFlow federated aggregator was used to represent the central server, while individual TensorFlow clients represented vehicles in the IoV environment. This setup closely mimics the dynamics of federated learning in vehicular networks.  
Figure 4 illustrates the experimental setup, showing the data preparation phase, adversarial data injection into a subset of clients, and the federated learning process, including trust computation and model aggregation at the central server.
# Availability of Primary Closure for Resection of Oral Cavity Cancer
###### Abstract  
Resection of malignant lesions in the oral and maxillofacial area causes functional and morphological defects. In order to recover from these defects, reconstruction surgery is needed, such as a primary closure or a flap. There are advantages and disadvantages to the method of using the primary closure and the flap, and the choice of procedure is entirely up to the surgeon. The purpose of this study is to evaluate the availability of primary closure in patients. For 10 years, from January 2010 to May 2020, patients who recovered using flaps after removing malignant lesions from the oral cavity and those who closed the lesion using primary closure were investigated. The investigation was conducted by searching the database of the hospital. There were 85 patients who removed malignant lesions in the oral and maxillofacial area, and among them, 16 patients closed the soft tissue defects using primary closure. Operation time, histologic types, stages, wound status, and period of hospitalization according to the patient's medical records were investigated retrospectively. Between the staging and the primary closure, statistically significant differences were observed between stage 1 and stage II patients in 13 patients using the primary closure. (\(p\) = 0.046). Statistically significant differences were identified between operation time and primary closure, with an average of 4.02 h (\(p\) = 0.015). The average period of hospitality for patients who underwent primary closure was 13.4 days, compared to 26.7 days for those who underwent flap surgery. This difference was statistically significant (\(p\) = 0.0003). As a result of three-way ANOVA analysis on operation time and hospital stay, only neck incision, reconstruction method ( flap or primary suture), and intrastage reconstruction method had a statistically significant effect on operation time and period of hospitality. This study evaluated the use of primary closure in patients who underwent resection of malignant lesions in the oral and maxillofacial area. Although the majority of patients underwent reconstruction with flaps, our results showed that primary closure could be a viable option for certain patients with early-stage lesions.  
reconstruction; oral cancer; primary closure; free flap; local flap 2023  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Availability of Primary Closure for Resection of Oral Cavity Cancer _Appl. Sci._**2023**, 13, 5919. [[https://doi.org/10.3390/appl3105919](https://doi.org/10.3390/appl3105919)]([https://doi.org/10.3390/appl3105919](https://doi.org/10.3390/appl3105919))