esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Empirical Results
### Oil prices
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Forecasting Horizons** & \multicolumn{3}{c}{**1 Month**} & \multicolumn{3}{c}{**3 Months**} & \multicolumn{3}{c}{**6 Months**} & \multicolumn{3}{c}{**12 Months**} & \multicolumn{3}{c}{**24 Months**} \\ \hline Country & OLS & SVR & RW & OLS & SVR & RW & OLS & SVR & RW & OLS & SVR & RW & OLS & SVR & RW \\ Turkey & 6.86 & 6.86 & 6.69 & 15.74 & 15.63 & 13.98 & 28.27 & 27.34 & 21.06 & 42.76 & 43.64 & 27.58 & 50.70 & 46.98 & 36.74 \\ Mexico & 7.02 & 7.02 & 6.68 & 16.27 & 16.30 & 14.68 & 29.47 & 28.28 & 22.65 & 43.25 & 42.27 & 30.05 & 49.12 & 47.58 & 39.64 \\ Korea & 6.73 & 6.68 & 6.55 & 15.13 & 15.49 & 13.33 & 25.96 & 25.79 & 20.16 & 36.70 & 36.32 & 27.59 & 44.59 & 42.95 & 36.56 \\ Russia & 7.21 & 7.26 & 6.98 & 17.77 & 17.84 & 16.27 & 31.68 & 29.24 & 25.10 & 45.89 & 42.39 & 32.44 & 49.35 & 46.98 & 43.24 \\ India & 6.88 & 6.77 & 6.55 & 15.39 & 15.12 & 13.33 & 25.85 & 25.15 & 20.16 & 35.83 & 36.56 & 27.59 & 42.98 & 46.78 & 36.56 \\ Brazil & 6.88 & 6.85 & 6.77 & 16.44 & 16.53 & 14.97 & 20.75 & 29.41 & 23.05 & 43.30 & 44.36 & 29.97 & 47.43 & 51.48 & 39.16 \\ China & 6.82 & 6.85 & 6.66 & 15.55 & 15.78 & 14.23 & 27.95 & 26.48 & 21.53 & 39.65 & 40.02 & 28.45 & 51.47 & 55.24 & 37.54 \\ Indonesia & 6.71 & 6.82 & 6.57 & 15.31 & 15.66 & 13.41 & 26.23 & 25.80 & 20.24 & 35.51 & 36.63 & 27.97 & 41.83 & 45.70 & 36.64 \\ Saudi Arabia & 6.89 & 6.98 & 6.71 & 17.37 & 16.96 & 15.25 & 33.32 & 30.54 & 23.70 & 48.40 & 47.32 & 30.54 & 47.91 & 42.01 & 40.81 \\ Argentina & 6.91 & 7.02 & 6.82 & 17.75 & 17.81 & 15.70 & 34.02 & 32.23 & 24.51 & 47.70 & 46.60 & 32.15 & 49.75 & 52.64 & 41.60 \\ Thailand & 7.02 & 7.15 & 6.56 & 18.83 & 18.41 & 14.64 & 37.63 & 35.45 & 21.86 & 62.67 & 66.68 & 35.20 & 58.87 & 89.41 & 77.78 \\ Israel & 6.87 & 6.83 & 6.73 & 15.73 & 15.63 & 14.43 & 29.08 & 27.73 & 21.73 & 43.46 & 43.92 & 28.90 & 51.54 & 55.64 & 39.69 \\ Malaysia & 6.44 & 6.53 & 6.46 & 14.61 & 15.15 & 13.95 & 27.38 & 25.30 & 19.71 & 54.06 & 53.62 & 31.25 & 78.61 & 78.91 & 64.72 \\ Philippines & 6.84 & 6.84 & 6.71 & 16.25 & 16.58 & 15.25 & 31.92 & 30.11 & 23.70 & 45.95 & 45.07 & 30.54 & 50.95 & 56.62 & 40.81 \\ \hline \hline \end{tabular} Note: All values are percentages. Although the oil prices (dependent variable) used for all countries are the same, the different MAPE of the RW models is attributed to the different number of out-of-sample forecasts for each country, according to the availability of the data so that the comparison between the dependent variables could be feasible.  
\end{table} 
Table 2: Oil prices out-of-sample forecasting accuracy—MAPE. As expected, in the shorter one-month horizon, both models adhere closely to the actual oil prices, while in the longer 12-month forecasts they fail to capture the \"true\" data generating mechanism. This fact could be justified by the larger uncertainty in forecasting oil prices in longer horizons, given that the macroeconomic conditions and especially the demand for oil that significantly affects its price is volatile in longer forecasting horizons.
## 2 Related Work  
To reduce the energy consumption and prolong the network lifetime, many studies have been done. This section presents related works concerning the clustering routing protocol, the data fusion technique, and the GA. In Section 2.1, some clustering routing protocols are presented and the difference between these protocols and the proposed underwater clustering routing protocol in this paper is provided. In Sections 2.2 and 2.3, some researches about the data fusion technique and the GA are reviewed. Because the proposed underwater clustering routing protocol cannot be comparable to the data fusion technique or the GA, we just summarize the advantages of the data fusion technique and the GA, and present that they can reduce energy dissipation in UWSNs.
###### Abstract  
Machine-type communications (MTC) enable the connectivity and control of a vast category of devices without human intervention. This study considers a hybrid coexisting wireless cellular network for traditional and MTC devices along with the need for an energy efficient power allocation mechanism for MTC devices. A model is presented for the interference and battery lifetime of MTC devices and a battery lifetime maximization problem is formulated. Conventional game designs are unable to address the demands of a densified user environment because of the dimensional difficulty presented when attempting to achieve a converged solution that would lead to a stable equilibrium. The MTC power control problem is modeled as a differential game and a mean field game (MFG) for massive number of MTC nodes estimates the power allocation policy with system utility defined in terms of the experienced interference and reliability. The formulated power control MFG is solved using a finite difference method and analyzed using extensive simulations. The solution provides an optimal power control strategy for MTC devices, enabling them to prolong their battery lives with the implemented energy efficient power allocation scheme.  
Keywords:game theory; Internet of Things; machine-type communications (MTC); mean field games (MFG); power control (PC) +
Footnote †: journal:
## 4 Datasets and Experimental Settings  
### Dataset  
**DIOR-R**: The DIOR-R dataset is a large-scale dataset designed for remote sensing object detection, featuring a wide range of object classes commonly found in satellite and aerial imagery. This dataset contains 20 object categories across 23,463 images with 192,472 labeled instances. It includes 5862 images for training, 5863 for verification, and 11,738 for testing, allowing researchers to systematically evaluate the generalization and robustness of detection models.  
**HRSC2016**: The HRSC2016 dataset focuses on ship detection in high-resolution remote sensing images, includes 1061 annotated images--617 for training and 444 for testing. Image resolutions range from 300 \(\times\) 300 to 1500 \(\times\) 900 pixels, yielding 2976 labeled instances within a single \"ship\" category, which captures ships of varying sizes, shapes, and orientations.
## 2 Results and Discussion
### Expression of VMPSTS in _L. lactis_  
Expression of VMPSTS in _L. lactis_ was analyzed by SDS-PAGE and Western blot analyses. In the crude protein extracts comparing induced and uninduced cultures for pNZ:VMPSTS as well as the negative control (clones harboring empty pNZ8048 plasmid), the SDS-PAGE did not show any distinct bands corresponding to an expected protein size of 63 kDa which was predicted to be exclusively visible in the induced culture (data not shown). However, subsequent analysis by Western Blot showed a clear expected band size of 63 kDa which was only present in the induced cultures but not in the uninduced cultures or the negative control (Figure 1). This shows that the recombinant protein was successfully expressed in _L. lactis_ but the protein of interest may be masked by other host proteins of the same size in the crude protein extracts when seen on the SDS-PAGE gel. Optimization of the induction conditions showed that _VMPSTS_ expression can be induced using 10-60 ng/mL nisin with no distinct differences in expression from 40 ng/mL nisin onwards, based on the intensities of the bands in the Western blot. However, 2 h induction was preferable compared to 4 h as VMPSTS expression seemed to decrease or perhaps degrade as the induction time was increased. Since growth rates of induced and uninduced cultures were similar (data not shown), this could possibly be associated with the production of isoprenoids _in vivo_ which have been shown to follow exponential growth and to cease at the stationary phase owing to the FDP substrate being produced during growth for primary metabolism [24]. Therefore in subsequent studies, 40 ng/mL nisin was used to induce expression for 2 h prior to harvesting the cells. Purification of the crude protein using a Ni-NTA column to bind the N-terminal histidine tag of the recombinant protein produced clear protein band of the expected size of 63 kDa as observed through SDS-PAGE analysis confirming successful expression of the recombinant plant terpene synthase in _L. lactis_ (Figure 2).  
Figure 1: Western blot of VMPSTS expression profiles showing the expected band size of 63 kDa. (**A**) Clones harboring pNZ:VMPSTS were grown and induced with nisin at different concentrations and durations. Lanes 1 and 5: 10 ng/mL nisin; 2 and 6: 20 ng/mL nisin; 3 and 7: 40 ng/mL nisin; 4 and 8: 60 ng/mL nisin; (**B**) Comparison between induced, uninduced and negative control samples. M: PageRank’Prestained Plus Protein Ladder; (-): Empty pNZ8048 plasmid; I: 40 ng/mL nisin for 2 h; UI: uninduced.
## 3 Results
Our study is based on experiments with a more robust dataset, provided by increasing to 45 the number of BE patients, which allows the DCNN to learn a wider range of features. Moreover, we show that using a multi-frame approach for classifying an ROI increases the confidence of our algorithm, compared to single-frame analysis (0.90 vs 0.86 with [PERSON] et al. [15] method, and 0.96 vs 0.91 with our proposed DCNN). \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Accuracy** & **Specificity** & **Sensitivity** & **AUC** \\ \hline
**Single-frame** & & & & \\ Training set & 0.87 (95\% CI, 0.82–0.92) & 0.87 (95\% CI, 0.82–0.92) & 0.87 (95\% CI, 0.82–0.92) & 0.95 (95\% CI, 0.90–0.99) \\ Testing set & 0.83 (95\% CI, 0.77–0.89) & 0.84 (95\% CI, 0.77–0.89) & 0.83 (95\% CI, 0.77–0.90) & 0.90 (95\% CI, 0.85–0.95) \\ \hline
**Multi-frame** & & & & \\ Training set & 0.92 (95\% CI, 0.89–0.96) & 0.92 (95\% CI, 0.88–0.96) & 0.95 (95\% CI, 0.91–0.98) & 0.98 (95\% CI, 0.96–0.99) \\ Testing set & 0.88 (95\% CI, 0.83–0.94) & 0.85 (95\% CI, 0.79–0.91) & 0.95 (95\% CI, 0.92–0.99) & 0.96 (95\% CI, 0.93–0.99) \\ \hline \hline \end{tabular} Confidence intervals (CIs) reported between brackets.  
\end{table} 
Table 2: Comparison of single-frame and multi-frame analysis using the ensemble deep convolutional neural network (DCNN) for both the training and the test dataset.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 2 Modern Radar and Phased Antenna Arrays
Phased-array radars enable electronic beam steering, where the beam can be directed in any direction by electronically adjusting the phase of each array element. This allows for faster scanning compared to traditional mechanical antenna radars, as well as real-time adaptability to track multiple targets simultaneously, which is essential for modern radar systems used in defense, airborne surveillance, weather radars, and autonomous vehicles. By adjusting the phases, \(\beta\), of the array elements, the radar can track multiple targets, perform frequency hopping across different angles, and provide real-time information on the position and velocity of targets [28]. Phased array radars are used in critical applications where speed and accuracy are essential [29; 30; 31]. For example, in missile defense, phased array radars such as the AN/SPY-1 enable the real-time detection and tracking of missiles. Similarly, in airborne radar systems, AESA (active electronically scanned array) radars provide comprehensive surveillance of airspace, enabling precise flight path control and the better detection of stealth targets. In meteorology, these radars allow for the detailed monitoring of weather phenomena, such as storms and precipitation, with high spatial resolution and the ability to scan large areas quickly. Phased-array radars offer advanced capabilities in electronic beam steering and multi-target detection, enabling them to be used in a wide range of complex applications. The precise control of the beam, enabled by the array factor, and the ability to adapt to dynamic tracking needs make phased array radars an essential technology in modern defense, airborne surveillance, and intelligent transportation systems. Figure 1 illustrates the structure of a smart antenna, which consists of an array of antennas, including both analog and digital low noise amplifiers (LNAs), a high-power  
amplifier (HPA), a power amplifier (PA), an analog-to-digital Converter (ADC), a digital-to-analog converter (DAC), a local oscillator (LO) for baseband transmission (Tx) and reception (Rx), and the weights for antenna array synthesis. The antenna array synthesis involves adjusting the weights applied to each antenna element to optimize the overall radiation pattern and performance of the system for various communication or sensing applications, based on the architecture proposed by Prof. [PERSON] [32].
## 3 Performance Evaluation of the VFA-SF  
In the present study, a novel performance metric based on the pair correlation function within the crystalline structure was introduced to evaluate the topology of the sensor network. Pair correlation function \(g(r)=(N(r,\Delta S))/(2\pi r\Delta N)\) within the network structure represents the probability of finding two nodes separated by a distance \(r\), where \(S\) is the area of coverage, \(N\) is the number of sensor nodes contained within \(S\), and \(N(r,\Delta)\) indicates the number of nodes located between \(r-\Delta/2\) and \(r+\Delta/2\). Therefore, the pair correlation diversion is  
\[\delta(\Omega,\Omega_{H})=\frac{\int_{0}^{r_{T}}\parallel g_{\Omega}(r)-g_{ \Omega_{H}}(r)\parallel^{2}dr}{\int_{0}^{r_{T}}\parallel g_{\Omega_{H}}(r) \parallel^{2}dr}\, \tag{8}\]  
where \(\Omega\) is the network topology to be analyzed, \(\Omega_{H}\) is the perfect hexagonal topology, and \(r_{T}\) is the bound of the radial distance.  
[PERSON] et al. already compared pair correlation function \(g(r)\) for network topology resulting from the original VFA-SF with that of a perfect hexagon. The positions of sensor nodes from the VFA-SF were consistent with the required locations from the perfect hexagon [14]. Therefore, pair correlation diversion (PCD) provides an objective metric for evaluating the virtual-force algorithms. It quantitatively analyzes the proximity from any network topology to a perfect hexagon configuration [11; 12]. This metric indicates the uniformity for the sensor network. Moreover, it can be used to characterize the convergence rate of any virtual-force algorithm. The slope of the pair-correlation-function curve, as a function of the simulation steps, indicates how fast the virtual-force algorithm converges.  
For a wireless sensor network, when the value of \(\delta(\Omega,\Omega_{H})\) is small, the network coverage topology approximates a hexagonal structure. Generally, a perfect deployment solution has a PCD value of 0. In addition, the convergence rate can be obtained according to the decrease of the PCD curve. When a network reaches its steady state, the PCD value should also become stable.
# Design and Testing of a Wearable System for Monitoring Car Drivers
## 4 Discussion
* Cost Considerations: As a prototype, the current system involves significant costs, primarily due to the design activity, used sensors, built additional parts, and testing setup and lab experiences. The expense may be a barrier to widespread adoption andimplementation, highlighting the need for cost-effective alternatives or improvements in manufacturing processes. Despite the abovementioned challenges, the proposed monitoring system and the data evaluation through the aggressive driving criterion can be considered a contribution to improve safe driving. Using low-cost, comfortable, wearable sensors on the driver, fairly simple data collection can be carried out to analyze aggressive driving when compared with existing solutions with complex structures, limited tasks referring mainly to drowsiness detection, or heavy computation for data analysis of driving behavior. In summary, while the prototype demonstrates significant potential for real-time detection of aggressive driving by using different device units, practical implementation will address issues related to device management, user comfort, calibration, and cost. Future developments should focus on enhancing ease of use, improving sensor accuracy, and reducing costs to make the system more viable for practical applications. Additionally, while the experiments demonstrate the feasibility of the proposed aggressive driving criterion (ADC), the numerical value of weights and the threshold can be improved with an extensive experimental campaign, since current values have been selected according to a limited number of tests and subjects. Future testing should include different driving situations, analyzing how these thresholds vary according to road conditions, the average speed of driving, the environment (e.g., city, suburbs, countryside, highways), and travel duration (e.g., short daily commute, long-distance driving, driving with frequent stops, etc. ).
# Spectrum Demand Forecasting for IoT Services
## 4 Spectrum Forecasting Model
### Offered Traffic  
To calculate the offered traffic per cell or per gateway, three additional parameters are needed:  
* The average transmission rate (in bits/s): Most of the modern wireless technologies perform some type of link adaptation mechanism, hence the transmission rate of any connected device depends primarily on its distance to the gateway, and additionally on the load factor of the gateway. We use the average between the maximum and minimum transmission rates according to each technology specification.
* The link spectral efficiency (in bits/s/Hz): Based on the average transmission rate and the total bandwidth used for that link.
* The activity factor: Considering that any device is modeled as an on-off source, \(0<\gamma_{s,i}\leq 1\) defines the percentage of activity created by the average device during a connection using the service \(s\) in scenario \(i\).  
The average transmission rate can be calculated as \(\tilde{r}_{s,i}=\sum_{t\in T_{i}}\rho_{s,t}r_{s,t,i}\). Therefore, the offered traffic per service \(s\) per scenario \(i\) is:  
\[A_{s,i}=u_{s,i}\gamma_{s,i}\tilde{r}_{s,i} \tag{3}\]  
and the total offered traffic per scenario is:  
\[A_{i}=\sum_{s\in S_{i}}u_{s,i}\gamma_{s,i}\tilde{r}_{s,i} \tag{4}\]
## 3 Results and Discussion  
### Use of Tablets in Fieldwork  
The creation of the app that was used on tablets had the following application context:  
* Duration: each intervention (fieldwork) lasted around four hours, and the duration was dependant on the users' movement between the heritage landmarks of each heritage experience;
* Modality: the fieldwork corresponding to the E zones of Santiago de Chile was carried out;
* Objectives of the activity: the objectives of the educational intervention pertained to three main areas: \(\circ\) To spatially identify the relationship between legacy and material heritage based on a tour of the city's historic centre;
* To relate the concept of historical legacy to the architectural and functional legacy of buildings, churches, and neighbourhoods, considering each work's social and design aspects;
* To understand how tangible heritage is associated with the idea of national monuments as symbols of identity and historical legacy.  
In a complementary way, fieldwork was proposed as a teaching resource and a unique learning space for understanding the historic city's environment and its heritage elements. Concrete knowledge of the environment and its elements, where the user visualises and knows their surrounding reality, is possible.  
* Contents: the contents of the activities are related to the heritage elements of the city and are established as an area in the next stage (Table 4);
* Activities: the users (\(n=54\)) used tablets to explore the study areas, visualising the digital resources of the heritage elements to be studied. The teacher served as a general guide for the activity and supplemented the device information. At the same time, the users were urged to reflect on and exchange ideas about the phenomena visualised on the devices in the context of the actual scenario in which the activity took place;
* Resources: the educational resource used corresponds to the AR-MPN application implemented on the tablets for visualisation during the fieldwork.  
To determine the heritage quality values of each building, a scale from 1 (low) to 10 (high) was established using the criteria presented in Table 3 and spatialized in Figure 3. In this way, this heritage assessment allowed the definition of the route zones according to common characteristics within the scope of their heritage value.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 3 Application
### Dimensional Reduction of Flight Data Based on CIPCA  
Because the interval matrix \(T_{m\times p}\) of the original data is obtained based on the interval compression of samples, the dimension reduction method for interval data is needed when reducing variable dimensions. According to the CIPCA in Section 2.2, first, standardize the interval variable \((T_{1},T_{2},\ldots,T_{p})\) and calculate the covariance matrix of \(T_{m\times p}\) to obtain the eigenvalues and the corresponding standard orthogonal eigenvectors, retaining the first \(q\) eigenvalues and eigenvectors. The principal component score \(P_{m\times q}\) of the interval data is calculated. \(P_{m\times q}\) is the interval type flight data, \(X_{n\times p}\), obtained by the original massive high-dimensional flying parameter data after sample size compression and variable dimension reduction.
## 3 Results and Discussion
### Structural Characterization
#### 3.1.2 Xrd  
The XRD test result for the untreated and treated layered silicates is shown in Figure 3. It can be seen that in Figure 3a, the layered silicates all had the intercalated structure with different \(d_{001}\) interlayer spaces, where REC had a remarkably larger \(d_{001}\) interlayer space as compared to MMT,  
Figure 2: The microstructure of untreated and treated layered silicates: (**a**) Montmorillonite (MMT); (**b**) rectorite (REC) [17]; (**c**) organic montmorillonite (OMMT); (**d**) organic rectorite (OREC).  
irrespective of the organic treatment. This is ascribed to the nature of the pristine layered silicate. Moreover, Figure 3a also shows that the \(d_{002}\) interlayer space of REC was 1.10 nm, which increased to 1.34 nm after the organic treatment. From Figure 3b, MMT had a greater increment in \(d_{001}\) value than REC, which can be attributed to the nature of the layered silicates. As mentioned in the Introduction, MMT has a larger cation exchange capacity than REC. As a result, more organic reagent molecules can insert MMT interlayers through the cation exchange reaction, and correspondingly, the interlayer space of MMT is largely increased.
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
## 2 TEM Horn Antenna Design
### TEM Horn Antenna  
Figure 6 shows a 3D view of the TEM horn antenna. The TEM horn antenna is implemented by assembling the adaptor and the horn aperture. The simulated reflection coefficient and realized gain of the horn antenna are described in Figure 7. The gain is observed in the +z-direction. The reflection coefficient and gain levels of the antenna are less than \(-13\) dB and greater than 5.47 dBi, respectively, within the range of 6-18 GHz.  
Figure 5: Cross-sectional views of the horn aperture: (**a**) \(xz\)-plane and (**b**) \(yz\)-plane.  
Figure 6: 3D view of the TEM horn antenna.