esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 3 The Proposed Gesture Recognition Algorithms and Systems
### PairNet  
As shown in Figure 4, the PairNet is a 7-layer CNN, where layers 1-5 are convolutional layers. Layer 1 is the convolutional layer with stride size 1 and kernel size 1 \(\times\) 3. We can observe from Figure 4 that the input data to layer 1 is \(X_{t}\) with length \(N=50\). Recall that \(X_{t}\) is a window from the sensory data \(X\), where each sample contains \(M=6\) elements. We therefore view \(X_{t}\) as a set of six 1D sequences, where the \(i\)-th 1D sequence is formed by the \(i\)-th element of samples of the sensory data in \(X_{t}\), \(i=1,\ldots,6\). In this study, each 1D sequence is termed a channel. Therefore, the number of input channels is six. Layer 1 produces a set of 128 1D sequences. The number of output channels for layer 1 is then 128. From Figure 4, we see that the length of each 1D sequence (i.e., channel) is 48.  
The output sequences produced by layer 1 then serve as the input sequences to layer 2, which in turns propagates its computation results to subsequent layers. For layers 2-5, the stride size is 2, and kernel size is 1 \(\times\) 2. The corresponding convolution operations can be viewed as a pairing operations, where each of the input 1D sequences are separated into disjoint pairs (due to a stride size of 2), and each pair operates independently in accordance with the kernel weights (due to kernel size \(1\times 2\)). The pairing operations then produces output channels half the length of the input channels to that layer. Although the length of channels decrease as the data propagate through the network, we retain or increase the number of channels so that sufficient features can be extracted for final classification. In fact, there are 128 output channels at layers 2 and 3, and there are 256 output channels at layers 4 and 5.  
When we only consider the length of channels at each layer, the structure of PairNet therefore is pyramid-like, as shown in Figure 5. The output channels produced by the layer 5 are located on the top of the pyramid. At the layer 6, the average pooling operations are then carried out over these output channels. The resulting data are subsequently flattened, and served as inputs to the fully connected layer with softmax activation function for the final classification results at layer 7.  
Figure 4: The parameters of the PairNet. The dimension of each sample of \(X_{t}\) is 6 (\(M=6\)). Therefore, there are six channels. Each channel has a length of 50. Layer 1 is a convolution layer with kernel size \(1\times 3\) and stride size 1. The output length therefore is 48. Layers 2–5 are also convolution layers with kernel size \(1\times 2\) and stride size 2 for pairing operations. The output lengths of Layers 2–5 are then 24, 12, 6, and 3, respectively. Layer 6 is an average pooling layer averaging three elements to a single one. The number of gestures to be classified is \(Q=11\). Layer 7 is a fully connected layer producing 11 outputs.
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 4 Conclusions  
The study conducted demonstrates that microwave treatment plays a crucial role in modifying the properties of acorn flour, directly influencing the antioxidant capacity and overall quality of muffins. The application of microwave heating enhances the content of free phenolic compounds and flavonoids while reducing tannin levels, leading to an improved antioxidant potential in the final product. These changes in antioxidant properties and overall quality were dependent on microwave power and exposure time.  
Muffins prepared with wet microwave-treated flour at lower power and shorter durations demonstrated the most desirable characteristics, including improved sensory acceptance, optimal texture, lighter crumb and crust color, and enhanced antioxidant properties. In contrast, muffins made with flour subjected to high microwave power or prolonged exposure were associated with diminished quality attributes, such as increased firmness, reduced volume and porosity, and lower consumer acceptability. These findings suggest that moderate microwave processing, particularly under moist conditions, can be an effective method for enhancing both the functional and technological performance of acorn flour in bakery applications while minimizing negative effects on product quality.  
Overall, the results underscore the potential of microwave processing as a valuable technique for improving the nutritional quality of acorn flour-based products. Future research should focus on optimizing microwave treatment parameters to maximize antioxidant retention while further evaluating sensory and textural attributes to ensure consumer acceptance.  
Conceptualization, E.S. and M.T.; methodology, E.S. and M.T.; validation, E.S.; formal analysis, E.S.; investigation, E.S.; resources, E.S. and M.T.; data curation, E.S.; writing--original draft preparation, E.S. and M.T.; writing--review and editing, E.S. and M.T.; visualization, E.S. and M.T.; supervision, M.T.; project administration, E.S.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Centre, Poland (project number 2021/41/N/NZ9/02668).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Cross-Section Shape and Asymmetric Support Technology of Steeply Inclined Thick Coal Seam Roadway
## 2 Engineering Background
### Analysis of Deformation and Failure of Roadways with Different Cross-Sectional Shapes  
#### 3.2.1 Establishment of Numerical Model  
To further study the stability of roadways in steeply inclined thick coal seams with different cross-sectional shapes, based on the geological surrounding rock column diagram of the 1502 transportation gateway, a finite element mesh model with tetrahedron-based elements was established using RHINO, and simulated calculations were performed using FLAC3D. Since the 1502 transportation gateway is a mine district main roadway generally arranged in areas with stable geological conditions, only the deformation and failure of the roadway surrounding the rock after roadway excavation were simulated, without considering the influence of geological structures, mining-induced effects, etc., on roadway stability. Therefore, the model size was set as 50 m \(\times\) 50 m \(\times\) 50 m. The numerical model of the arched cross-section roadway is shown in Figure 6, which contains 439,274 zones and 96,762 gridpoints (numerical simulation studies conducted with this model size are sufficient to avoid the influence of boundary effects on the roadway surrounding rock [16; 24]).  
The physicochemical parameters of the roof and floor of the 1502 level roadway are used for the coal and rock properties of the roadway surrounding rock (see Table 2). The Mohr-Coulomb elastoplastic criterion based on elastoplastic theory is adopted in the simulation. Considering the self-weight of the overlying rock, a uniform vertical stress of \(\gamma\)H = 25 kN\(\cdot\)m\({}^{-3}\)\(\times\) 280 m = 7.0 MPa is applied to the top boundary of the model, and the  
\begin{table}
\begin{tabular}{c c c} \hline
**Cross-Sectional Shape** & **Roof Stress** & **Calculation Results** \\ \hline \multirow{2}{*}{Arched} & \(F_{1}\) & \(ql/2\) \\  & \(T_{1}\) & \(ql^{2}/8 lh_{1}\) \\ \hline \multirow{2}{*}{Rectangular} & \(F_{2}\) & \(ql/2\) \\  & \(T_{2}\) & \(ql^{2}/4h_{2}\) \\ \hline \multirow{2}{*}{InclinedRoof Trapezoidal} & \(F_{3}\) & \(ql/2+R_{3}\) \\  & \(T_{3}\) & \(ql^{2}/8 lh_{3}\) \\  & \(P_{3}\) & \(\frac{ql^{2}}{8 lh_{3}}+\frac{\sqrt{1+\tan^{2}\theta_{0}}}{2 lh_{3}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Calculation results of stress on roadways with different cross-sections.  
lateral boundaries and bottom of the model are set as fixed constraints. Taking the influence of roadway cross-sectional dimensions into account, the dimensional parameters of the arched cross-section are adopted from those used in the engineering site, with different cross-sectional shapes maintaining an equal width and equal cross-sectional area.
# Brief Review of Recent Developments in Fiber Lasers
## 6 Fiber Laser Applications
### Military Sector  
Fiber lasers have high temperature and high corrosion resistance, which is especially suitable for military applications. Fiber lasers can serve as one of the main light sources of tactical laser weapons [105]. In fiber optic radar, fiber lasers can be used as laser sources to provide high-power and high-stability laser signals for target detection and tracking.
# Quantum Stream Cipher Based on Holevo-Yuen Theory
## 2 Current Status of Quantum Communication Security Technology
### Comparison of Services Based on Each Quantum Cryptosystem  
QKD and Y-00 are about 40 and 20 years old, respectively. At the time of their invention, the principle models of both quantum cryptography technologies were not very attractive in terms of security and communication performance. However, nowadays, the systems and security assurance technologies of both technologies have evolved dramatically. Based on the results, business models for security services using these quantum cryptography technologies have been proposed. Figure 3 shows the current status.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 2 Wireless Sensor Deployment Optimization
### IHACA-COpSPIEL Deployment Method  
[PERSON] et al. proposed the pSPIEL algorithm, which is an improved greedy algorithm, but with both a larger number of sensors and a longer communication distance. The ant colony algorithm is easy to combine with other methods and performs well in path optimization. Therefore, this paper combines the improved ant colony algorithm with the chaotic operator improved pSPIEL algorithm, and proposes the IHACA-COpSPIEL.
## 4 Methodology  
The proposed study applied two stages of analysis methods, namely variance-based structural equation modeling (PLS-SEM) and artificial neural networks (ANN). The reliability and validity of the measurement model and also hypotheses were tested by SEM, while the neural network was employed for the predictors and antecedents of CC adoption [45]. As indicated by [PERSON] and [PERSON] [169], to validate the relationship of hypotheses in behavioral and social science, PLS-SEM is frequently used; however, it is seldom integrated with other artificial intelligence algorithms. As PLS-SEM is employed for the linear model, it may often simplify the complications in making technology adoption decisions [170]. To solve this issue, the neural network method was used to recognize the non-linear relationships between predictors in the proposed research model. According to [PERSON] and [PERSON] [169], ANN helps to learn complex linear and non-linear associations between the factors of technology adoption and adoption decisions. Moreover, the ANN makes it possible to perform more precise anticipation in comparison to the general regression procedure [171]. However, some researchers applied a combination of ANN and PLS-SEM analysis in diverse adoption settings, such as CRM adoption [PERSON], [PERSON] [172], [PERSON], [PERSON] [66], and wearable healthcare devices and IOS adoption [PERSON] and [PERSON] [173]. Therefore, at the main phase, to find the factors that have an important influence on the adoption of CC, this study applied SEM. The ANN approach was employed in the  
Figure 1: Proposed model.  
second phase for predicting the CC adoption, based on the considerable factors resulted from the PLS-SEM analysis.
## 3 Numerical Simulation Methods and Validation  
### Finite Element Model  
The scaled-down experiments on the cabin structures subjected to internal explosions were performed by [PERSON] [6]. The test specimens are square-shaped single cabin structures with extended boundaries, with lengths of 60 cm, 45 cm, and 30 cm, thicknesses of 4 mm, 3 mm, and 2 mm, respectively, and a round hole in the middle of the top plate for placing explosives in the center of the cabin. In numerical simulations, using the finite element software LS-DYNA, the finite element models were established, as shown in Figure 1. The structure was meshed using the SHELL163 element type, while the explosive and air were meshed using the SOLID164 element type. The mesh size of the structure and air is 4 cm. The explosive is filled in the centre of the air using a fluid-solid coupling algorithm. The surface of the air is set up with no-reflection boundary conditions, as in Figure 1.  
Figure 1: Calculation model [6]. (**a**) Box structure and coordinate; (**b**) Air domain.
## 4 Conclusions
Conceptualization, A.M., M.B. and S.D. ; methodology, A.M., G.L. and S.D. ; validation, M.B., D.C., P.M.R.R. and S.R.A.K. ; formal analysis, A.M. and G.L. ; investigation, A.M. and S.D. ; resources, P.M.R.R. and S.R.A.K. ; data curation, M.B. and S.D. ; writing--original draft preparation, A.M., G.L. and S.D. ; writing--review and editing, M.B. and D.C.; visualization, P.M.R.R. and S.R.A.K. ; supervision, D.C. and S.R.A.K. ; All authors have read and agreed to the published version of the manuscript. This research received no external funding, since it consisted in a research collaboration between University of Florence and the University of Twente. This study did not require ethical approval. Not applicable. The authors declare no conflict of interest. **Nomenclature**  
**Nomenclature**  
A  
Area (m\({}^{2}\))  
C\({}_{\rm y}\)  
Carbon Yield (%)  
c\({}_{\rm p}\)  
Specific Heat at Constant Pressure (kJ kg\({}^{-1}\) K\({}^{-1}\))  
FC\(\_\)ESP  
experiment code: Electrostatic Precipitator setup  
FC\(\_\)SC  
experiment code: Spray Condensers  
h  
Specific Enthalpy (kJ kg\({}^{-1}\))  
H & Entalpy (kW)  
HHV  
Higher Heating Value (MJ kg\({}^{-1}\)), (MJ Nm\({}^{-3}\))  
LHV  
Lower Heating Value (MJ kg\({}^{-1}\)), (MJ Nm\({}^{-3}\))  
M & Mass (kg)  
\(m\)  
Mass flow rate (kg s\({}^{-1}\))  
n  
molar flow rate (kmol s\({}^{-1}\))  
P  
Pressure (bar)  
Q  
Heat stream (kW)  
T  
Temperature (\({}^{\circ}\)C)  
t  
experimental time (s)  
U  
Superficial velocity (m s\({}^{-1}\))  
w  
Weight  
W  
Power (kW)  
Y  
Yield (kg kg\({}^{-1}\))  
**Greek Symbols**  
\(\varepsilon\)  
Recovered energy (%)  
\(\eta\)  
Efficiency (\(-\))  
\(\tau\)  
Residence time of vapors and gases (s)  
**Subscripts**  
bm  
biomass  
cond  
condenser  
\(f\)  
formation  
feed  
hot  
hot part of the setup  
i  
pyrolysis product (char, bio-oil, gases)  
j  
component  
k  
process element  
mf  
minimal fluidization velocity  
pr  
product  
py
pyrolysis  
r  
reactor bed  
re  
reactant  
react  
s  
sensible heat  
t  
total  
w  
reactant component  
y  
yield  
**Acronyms**  
BFBR  
Bubbling Fluidized Bed Reactor  
daf  
dry  
dry feedstock basis  
EU  
European Union  
EPS  
Electrostatic Precipitator  
FBR  
Fluidized Bed Reactor  
FPBO  
Fast Pyrolysis Bio Oil  
GHG  
Greenhouse Gases  
TRL  
Technology Readiness Level  
WV  
Wood Vinegar
## 5 Numerical Results
### The Influence of Waiting Queue Length  
Figure 8a,b shows \(P_{b}\) and \(P_{r}\) as a function of \(B_{2}\) for different waiting queue lengths of a first-tier MEC server \(r_{1}\). It is apparent that the influence of the bias factor \(B_{2}\) on \(P_{b}\) and \(P_{r}\) is remarkably similar to what was observed in Figure 3. We can also see that  
Figure 6: Experimental results of different user density and MEC server density. (**a**) \(P_{r}\) of HetMECNet as a function of \(B_{2}\) for different user density and MEC server density. (**b**) \(P_{b}\) of HetMECNet as a function of \(B_{2}\) for different user density and MEC server density.  
Figure 7: Experimental results of different number of CPU cores. (**a**) \(P_{r}\) of HetMECNet as a function of \(B_{2}\) for different number of CPU cores. (**b**) \(P_{b}\) of HetMECNet as a function of \(B_{2}\) for different number of CPU cores.  
significantly increases as the waiting queue length \(r_{1}\) is increased and \(P_{b}\) decreases sharply with the increase in \(r_{1}\). This increase can be attributed to the fact that a larger queue length allows for more user tasks to be offloaded to the MEC servers, thereby improving the overall performance of the system. Equations (20) and (21) demonstrate that longer queues reduce blocking by absorbing transient loads. Waiting queue length optimization balances task backlogs and processing capacity, providing a practical lever for real-time performance tuning.
## 4 Empirical Evaluation
### Dataset  
In the experiment, we used all 13 target library classes from [8; 9; 13; 14] as the benchmark to evaluate MCLSM.  
In this benchmark, ArrayList serves as the implementation of a variable-size array of list interfaces, while LinkedList is primarily employed for creating linked list data structures. HashSet, implementing the set interface, ensures the absence of duplicate elements and does not guarantee the order of elements. HashMap, functioning as a Hashtable, facilitates key-value mapping with rapid access speeds. Hashtable, originating from the original java.util, is a concrete implementation of a dictionary. The Signature class in Java is utilized to provide a digital signature algorithm for applications, while the Socket class offers a comprehensive set of network communication methods and properties. ZipOutputStream allows direct content writing to the zip package. The StringTokenizer class implements both the Iterator and Enumeration interfaces. These nine target class libraries are integral components of the Java Development Kit. Additionally, four other class libraries are used, namely, StackAr from the Daikon project, NFST (NumberFormatStringTokenizer) and ToHTMLStream from Apache, and SMTPProtocol from Columbia.ristretto.  
Table 1 shows the information of these classes. The column \"Target Library Class\" displays the names of the target classes. The column \"M\" lists the number of class methods analyzed in the target classes. The column \"Generated Test Cases\" is the number of test cases generated by Randoop, and the column \"Recorded Method Calls\" gives the number of method calls recorded in all execution traces of the target classes. The data in Table 1 consist of 13 target classes, which are both complex and simple; thus, we chose to use these data to evaluate the effectiveness of our method on different sizes of data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target Library Class1** & **M** & **Generated Test Case** & **Recorded Method Calls** \\ \hline ArrayList & 18 & 42,865 & 22,996 \\ LinkedList & 7 & 13,731 & 4847 \\ HashSet & 8 & 23,181 & 257,428 \\ HashMap & 11 & 53,396 & 67,942 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target library classes.  
Figure 6: Example of detection and refinement.
## 1 Introduction
In this setting, one of the most promising communications technologies is represented by the Long Range (LoRa) one, together with the associated LoRa Wide Area Network (LoRaWAN) protocol. When designing a LoRa-based network infrastructure, the adoption of LoRaWAN provides several advantages with respect to other customized Media Access Control (MAC) protocols. First of all, there is a large availability of open source LoRaWAN servers which can be easily installed and employed to rapidly set-up LoRaWAN networks. As a matter of fact, most of the Gateways (GWs) currently available on the market support the LoRaWAN protocol. In terms of range and coverage, this technology provides a far longer range than Wireless Fidelity (WiFi) or Bluetooth connections [5], applicable for indoor as well as outdoor scenarios, especially in remote areas where the cellular networks have poor connections. Moreover, when setting up dense network infrastructures, upper layer protocols like MAC ones are developed in LoRaWAN with the aim of managing the whole network infrastructure as well as the coexistence of large quantity of end devices (EDs). Moreover, LoRaWAN provides several built-in features that can be very important for the scenario at hand such as: (i) security in the transmission by means of Advanced Encryption Standard (AES-128) end-to-end data encryption; (ii) Possibility of boosting the capacity through the use of multiple channels; (iii) Adaptive Data Rate (ADR) and power consumption by controlling the Spreading Factor (SF), the Bandwidth (BW) and the Coding Rate (CR). Finally, LoRaWAN provides a number of different message types which allow to set up unconfirmed (UNCONF) or confirmed (CONF) data transmissions (by means of Acknowledgement (ACK)) and a downlink (DL) channel to send information back to ED as well as the Over-The-Air Authentication (OTAA), a procedure that simplifies the association of an ED to the network by means of Join request messages. The rest of the paper is structured as follows. In Section 2, the state of the art of LoRa solutions for reliable communication and related works are presented. In Section 3, we introduce the sensing and communication parts describing the main sensor characteristics, power consumption analysis and server architecture. Then, the proposed approach is described in Section 4. The main experimental results and discussions are presented in Section 5. Finally, concluding remarks are given in Section 6.
## Appendix A Search String  
**Pubmed**  
((\"Scoliosis\"[MeSH Terms] AND \"Adolescent\"[MeSH Terms]) OR \"adolescent idiopathic scoliosis\"[All Fields] OR \"AIS\"[All Fields]) AND (\"Spinal Fusion\"[MeSH Terms] OR \"surgery\"[MeSH Subheading] OR \"surgical treatment\"[All Fields] OR \"operative treatment\"[All Fields]) AND (\"Biomechanical Phenomena\"[MeSH Terms] OR \"Gait Analysis\"[MeSH Terms] OR \"Motion Capture\"[All Fields] OR \"kinematic analysis\"[All Fields] OR \"movement analysis\"[All Fields])) AND (2013:2024[pdat])  
**Scopus**  
(TITLE-ABS-KEY(\"adolescent idiopathic scoliosis\" OR AIS) AND  
TITLE-ABS-KEY(\"surgical treatment\" OR surgery OR \"spinal fusion\" OR \"surgical correction\" OR \"operative treatment\") AND  
TITLE-ABS-KEY(\"motion capture analysis\" OR \"motion analysis\" OR \"gait analysis\" OR \"biomechanical analysis\" OR \"kinematic analysis\" OR \"movement analysis\" OR \"3D analysis\" OR \"three-dimensional analysis\"))  
AND PUBYEAR > 2012  
AND (LIMIT-TO(LANGUAGE, \"English\"))  
AND (LIMIT-TO(DOCTYPE, \"ar\"))  
**Embase**  
('adolescent idiopathic scoliosis'/exp OR 'AIS' OR'scoliosis'/exp) AND  
('surgical procedure'/exp OR surgery OR'spinal fusion'/exp OR'surgical correction' OR 'operative treatment') AND  
('biomechanics'/exp OR 'gait analysis'/exp OR'motion capture analysis' OR 'kinematic analysis' OR'movement analysis') AND