esa-sceva/satcom-chunk-collection huggingface dataset:
# A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate
## 6 System Measurement Results  
The system test of the wireless communication link is shown in Figure 9, in which the transmitter and receiver are positioned within a line-of-sight range. Figure 10 shows the constellation of the wireless link, and the measured EVM is 12.7%, which is slightly higher than the result from the IF loopback test, 10.3%, due to the inclusion of the THz front-end and the path loss. The measurement result suggests a high quality of the wireless communication link. A comparison was made between the proposed communication system and recent work reported at similar frequencies, as shown in Table 2. It is shown that the proposed system has achieved a high speed with a relatively lower sampling rate, owing to the large bandwidth utility. The performance of the system can be further improved in terms of the communication distance with the application of antennas of higher gain and dual polarization and power combining at the transmitter end.  
Figure 8: (**a**) Design and fabricated photo of the proposed THz BPF, and (**b**) its simulation and measurement results.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 4 Experimental Study
### Experiment 1: Decomposing a Multi-Class Problem  
In this experiment, OVO and OVA binary decomposition strategies are considered and compared to the standard multi-class semantic segmentation approach. Table 2 presents the results obtained in terms of the IoU and F-score not only for each label, but also for each city in the testing set. Additionally, the averaged metrics across the testing cities are also included. The best results achieved for each metric are presented in **boldface**. The results show that decomposing a multi-class problem is an effective way to reduce the impact that the separability of the classes has on the overall performance. On the one hand, the OVO decomposition strategy increases the metrics from 0.6320, 0.3994 and 0.3565 to 0.8279, 0.5629 and 0.5427, in terms of averaged IoU, and from 0.7742, 0.5666 and 0.5252 to 0.9057, 0.7193 and 0.7021, in terms of averaged F-score, for each class (background, building and road), respectively. On the other hand, the OVA decomposition strategy increases the metrics from 0.6320, 0.3994 and 0.3565 to 0.8670, 0.5789 and 0.5828, in terms of averaged IoU, and from 0.7742, 0.5666 and 0.5252 to 0.9287, 0.7325 and 0.7347, in terms of averaged F-score, for each class (background, building and road), respectively. Moreover, the OVA decomposition strategy leads to better results than the OVO (0.8670, 0.5789 and 0.5828 vs. 0.8279, 5629 and 0.5427, in terms of averaged IoU, and 0.9287, 0.7325 and 7347 vs. 0.9057, 0.7193 and 0.7093, in terms of averaged F-score, for each class (background, building and road), respectively). It must be noted that the OVO decomposition strategy achieves higher metrics in the building class for the Granada city than OVA (0.5745 vs. 0.5660 and 0.7297 vs. 0.7228, in terms of averaged IoU and F-score, respectively). The quantitative results can be complemented with the qualitative results presented in Figures 7 and 8. Here, the different approaches are contrasted, not only comparing their multi-class predictions but also through visual inspection of the IoU (vIoU) for each class in terms of true positives (TP) in green, false positives (FP) in blue, true negatives (TN) in white, and false negatives (FN) in red. Additionally, the averaged IoU and F-score metrics have been included. A great deal of false positives (FP) can be observed in the final multi-class prediction for the direct multi-class approach. This is due to the limited spatial resolution since building and road classes may coexist within the same pixel. Accordingly, the direct multi-class approach optimizes the overall performance, choosing between both classes on detriment of the not selected one. However, as can be seen in the multi-class predictions generated by the OVO and OVA decomposition strategies, this issue is addressed. Even though OVO and OVA strategies perform similarly for the building class, it must be noted a major decrease in the false positives for the road class when using the latter one.
## 3 Data and Methods
### Data  
This paper utilizes seismic, wireline log, drill core, outcrop, and related geochemical test data. The 82.5 km\({}^{2}\) Taidong S-wave 3D seismic dataset, acquired west of the Sebei-1 gas field in the Taidong area, is not influenced by biogenic gas in the strata (Figure 2). The seismic survey was acquired by EV56S shear-wave vibrator and processed into fast and slow S-wave data [15]. The survey has a bin spacing of 20 m in the inline direction and 10 m in the crossline direction, with a time sampling rate of 2 milliseconds. The predominant frequency varies across different strata: 26 Hz from K13 to K10, 25 Hz from K9 to K6, 33 Hz from K5 to K3, 18 Hz from K2 to K0, and 31 Hz from K0 to K0\({}^{\prime\prime}\). Well SE39, which includes S-wave acoustic wireline logs (DTS) and density logs (DEN), was used for tying the well data to the S-wave 3D seismic data. A decent well-seismic tie shows that the average interval velocity from K13 to K0\({}^{\prime}\) is 2917 m/s to 801 m/s for P-wave data and 1406 m/s to 343 m/s for S-wave data. As a result, the S-wave 3D seismic data offer better vertical resolution (3\(\sim\)13 m, 28 Hz) compared to P-wave data (7\(\sim\)24 m, 30 Hz) from K13 upward, particularly in the shallower standard zones for their low velocity.  
Geochemical data from boreholes SG-1, SG-1b, and SG-5 in the western part of the Qaidam Basin were referenced and analyzed from published literature to interpret the evolution of paleoclimate from a geochemical perspective. The modern evaporitic analog from northeast Lake Chad, a present key to the past, was used as a geological model to analyze Quaternary sedimentary facies in the Taidong area.
## 4 Evaluation Methodology
### Experiment Results of Feature Selection with Binary Decoding  
In the binary decoding experiment, the performances were compared and analyzed using 41 KDD Cup '99 features for analysis of the performances and using the wrapper feature-selection method based on simple detection rate (detection precision) and the proposed feature-selection method for the performance comparison of feature selection. PCA was excluded from the experiment since it is impossible to assign the number of features differently from permutations. The algorithm was applied to each of the 10 models in the same way to compare average performances, and an NB classifier-based detection model was used.
# Can IMU Provide an Accurate Vertical Jump Height Estimate?
###### Abstract  
The aim of the present study was to determine if an inertial measurement unit placed on the metatarsal part of the foot can provide valid and reliable data for an accurate estimate of vertical jump height. Thirteen female volleyball players participated in the study. All players were members of the Republic of Serbia national team. Measurement of the vertical jump height was performed for the two exemplary jumping tasks, squat jump and counter-movement jump. Vertical jump height estimation was performed using the flight time method for both devices. The presented results support a high level of concurrent validity of an inertial measurement unit in relation to a force plate for estimating vertical jump height (CM) t = 0.897, \(p\) = 379; ICC = 0.975; SQI t = \(-\)0.564, \(p\) = 0.578; ICC = 0.921) as well as a high level of reliability (ICC > 0.872) for inertial measurement unit results. The proposed inertial measurement unit positioning may provide an accurate vertical jump height estimate for in-field measurement of jump height as an alternative to other devices. The principal advantages include the small size of the sensor unit and possible simultaneous monitoring of multiple athletes.  
IMU; force plate; squat jump; counter-movement jump; vertical jump Article
# Lightweight Violence Detection Model Based on 2D CNN with Bi-Directional Motion Attention
## 4 Experiments
### Ablation Studies
#### 4.3.2 Ablation Studies on The Bi-LTMA Module  
We conduct ablation studies to evaluate the effectiveness of the architecture design and the fusion method proposed for the Bi-LTMA module on the RWF-2000 dataset. The TSM module and the auxiliary classifier with optimized coefficient \(\lambda=0.5\) are employed in our experiments.  
Compared with the ME module [41], which focuses on the channel dimension of the input feature, the novelty of Bi-LTMA is twofold: (1) a bidirectional architecture is adopted to capture motion information in both forward and backward directions; and (2) both the spatial dimension and channel dimension of the input feature are used to capture motion information. Experiment results are given in Table 3 to illustrate the contributions of the bidirectional architecture, channel dimension, and spatial dimension of the input feature. As can be seen, the accuracy is 87.75% when the channel dimension of the input feature is used alone, as in the ME module [41]. If the bidirectional architecture is adopted together with the channel dimension of the input feature, the accuracy is improved to 89.75%. This means that the bidirectional architecture brings an improvement of 2% in accuracy. Moreover, if the bidirectional architecture is adopted and both the spatial and channel dimensions of the input feature are used as in Bi-LTMA, the best accuracy of 90.25% is achieved. It reveals that motion information lying in the spatial dimension of the input feature can be utilized to improve the accuracy effectively. These observations confirm the advantages of the proposed Bi-LTMA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bi-LTMA** & **TSM** & **Auxiliary Classifier** & **Accuracy** & **GFLOPs** & **Params** \\ \hline \multirow{3}{*}{\(\surd\)} & & & 87.5\% & 0.23 & 4.26 M \\  & & & 88.5\% & 1.21 & 4.48 M \\  & \(\surd\) & & 88.25\% & 0.23 & 4.26 M \\  & & \(\surd\) & 88.0\% & 0.23 & 4.26 M \\ \(\surd\) & \(\surd\) & & 89.25\% & 1.21 & 4.48 M \\ \(\surd\) & & \(\surd\) & 88.75\% & 1.21 & 4.48 M \\  & \(\surd\) & \(\surd\) & 89.25\% & 0.23 & 4.26 M \\ \(\surd\) & \(\surd\) & \(\surd\) & 90.25\% & 1.21 & 4.48 M \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation studies on the proposed modules on the RWF-2000 dataset.  
As shown in Figure 5, a DropConnect layer and a shortcut connection are employed to realize feature fusion in the Bi-LTMA module. Experiment results are given in Table 4 to highlight the contributions of the DropConnect layer and the shortcut connection. One can find that the accuracy is 87% if neither the DropConnect layer nor the shortcut connection is employed. Notably, the accuracy is improved to 87.75% when the DropConnect layer is employed. Moreover, if both the DropConnect layer and the shortcut connection are employed, as in the Bi-LTMA module, the best accuracy of 90.25% is achieved. This suggests that the shortcut connection can be used to improve the accuracy further. These observations confirm the effectiveness of the proposed fusion method.
## 7 Discussion
### Feasibility of Sentinel-1 PS-InSAR for Ground-Motion Detection in Ireland
Thus, future work will also consist of using more sophisticated methods for the estimation of the atmospheric delays and the correction of the InSAR-derived time-series. Overall, the obtained results demonstrate that Ireland, even if geologically very stable, is still characterized by a dynamic and mutable landscape where gravity-driven or water-related ground movements are common at scales of between \(10^{-3}\)-\(10^{1}\) km\({}^{2}\) and represent the main geo-hazards of the country. The success here of PS-InSAR with Sentinel-1 data in detecting such ground motions makes the technique a good candidate for upscaling such surveying to an island-wide SAR monitoring, as tested recently for neighboring Great Britain [67].
## 2 Related Work and Foundational Concepts
### Software and System Development Life Cycles
#### 2.1.5 DevSecOps Method  
A common limitation across these foundational models is the tendency to treat security as a separate, often late-stage, concern. This gap led to the rise of Development, Security, and Operations (DevSecOps), a paradigm that integrates security practices directly into the entire development life cycle [7; 16]. By leveraging automation, continuous integration and delivery (CI/CD), and a culture of shared responsibility, DevSecOps aims to make security an intrinsic component of the software pipeline [17]. Core principles include \"Security as Code,\" where security policies and controls are managed as versioned, auditable code, and continuous monitoring throughout the life cycle [18]. As illustrated in Figure 1, the DevSecOps life cycle embeds security touchpoints, from threat modeling in the planning phase to security analysis in the feedback phase, across the entire process. While DevSecOps provides a critical cultural and procedural foundation, it does not, by itself, prescribe a specific risk management structure or access control philosophy, leaving room for more comprehensive, integrated frameworks.
## 3 Proposed Universal Filter
### SIMO Filter Configuration  
Figure 3 shows the SIMO filter topology. It can be seen from the figure that the core circuit remain unchanged and the resistor \(R_{1}\) is no longer required leading to resistorless implementation. The only drawback is the availability of the HP output through the capacitor. The HP current can be sensed using a current follower. In the SIMO configuration, the filter can function in CM mode by providing all five filter responses.  
For the current-mode of operation, \(V_{\text{IN}}\) is set to zero and current \(I_{\text{IN}}\) is applied to the filter topology. The transfer functions in the CM are given in Equations (9)-(13), while a corresponding quality factor and pole frequency are given in Equations (14) and (15).  
\[\frac{I_{\text{LP}}}{I_{\text{IN}}}=\frac{g_{\text{m1}}g_{\text{m3}}}{s^{2}C_{ 1}C_{2}+sC_{1}g_{\text{m2}}+g_{\text{m2}}g_{\text{m3}}}, \tag{9}\]  
\[\frac{I_{\text{HP}}}{I_{\text{IN}}}=\frac{s^{2}C_{1}C_{2}}{s^{2}C_{1}C_{2}+sC_ {1}g_{\text{m2}}+g_{\text{m2}}g_{\text{m3}}}, \tag{10}\]  
\[\frac{I_{\text{BP}}}{I_{\text{IN}}}=\frac{-sC_{1}g_{\text{m2}}}{s^{2}C_{1}C_{ 2}+sC_{1}g_{\text{m2}}+g_{\text{m2}}g_{\text{m3}}}. \tag{11}\]  
\[\frac{I_{\text{BR}}}{I_{\text{IN}}}=\frac{g_{\text{m1}}g_{\text{m3}}+s^{2}C_{ 1}C_{2}}{s^{2}C_{1}C_{2}+sC_{1}g_{\text{m2}}+g_{\text{m2}}g_{\text{m3}}}, \tag{12}\]  
\[\frac{I_{\text{AP}}}{I_{\text{IN}}}=\frac{g_{\text{m1}}g_{\text{m3}}-sC_{1}g_{ \text{m2}}+s^{2}C_{1}C_{2}}{s^{2}C_{1}C_{2}+sC_{1}g_{\text{m2}}+g_{\text{m2}}g _{\text{m3}}}, \tag{13}\]  
\[\omega_{0}=\sqrt{\frac{g_{\text{m2}}g_{\text{m3}}}{C_{1}C_{2}}}, \tag{14}\]  
\[Q=\sqrt{\frac{g_{\text{m3}}C_{2}}{g_{\text{m2}}C_{1}}}. \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Response**} & \multicolumn{5}{c}{**Inputs**} \\ \cline{2-5}  & \(\mathbf{I_{1}}\) & \(\mathbf{I_{2}}\) & \(\mathbf{I_{3}}\) & \(\mathbf{I_{4}}\) \\ \hline LP & \(l_{\text{m}}\) & 0 & 0 & 0 \\ \hline HP & \(l_{\text{m}}\) & \(l_{\text{in}}\) & \(l_{\text{in}}\) & \(l_{\text{in}}\) \\ \hline BP & 0 & \(l_{\text{in}}\) & 0 & 0 \\ \hline BR & 0 & \(l_{\text{in}}\) & \(l_{\text{in}}\) & \(l_{\text{in}}\) \\ \hline AP & \(l_{\text{in}}\) & 2\(l_{\text{in}}\) & \(l_{\text{in}}\) & \(l_{\text{in}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Input current excitation sequence.  
Figure 3: Proposed SIMO dual-mode universal filter.  
Note that the BR and AP responses can be obtained by summing the LP, HP, and BP currents, \(I_{\text{BR}}=I_{\text{LP}}+I_{\text{HP}}\) and \(I_{\text{AP}}=I_{\text{LP}}+I_{\text{HP}}+I_{\text{BP}}\).
## 3 Research Design
### Fixation Point Coordinate Transformation  
The main advantage of this method lies in its high flexibility and adaptability, allowing it to handle various image transformations, including rotation, scaling, and perspective distortion. This not only improves the accuracy of eye-tracking data analysis but also provides greater flexibility in experimental design. To evaluate the accuracy of the proposed method, we conducted a comparative analysis with a standard homography-based transformation approach. Specifically, three representative video frames (frames 126, 350, and 2710) were selected from the experimental dataset, and both methods were applied to the same fixation point (1190, 865) for coordinate mapping. The resulting transformed coordinates revealed spatial deviations, calculated as Euclidean distances, of 130.9, 141.6, and 239.4 pixels, respectively. The root-mean-square error (RMSE) across the three frames was 177.45 pixels. Given the resolution of the reference image (10,000 \(\times\) 10,000 pixels), this error accounts for less than 2% of the total image dimensions. Such precision is considered acceptable for visualization-based structural modeling and exploratory analysis in eye-tracking studies, especially in contexts where real-time performance and operational flexibility are prioritized over sub-pixel geometric accuracy. These results support the spatial consistency of the proposed method and confirm its applicability for analyzing gaze distributions across complex visual stimuli.
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## VLC for Vehicular Services  
Vehicular networks applications are envisioned to benefit of communication opportunity given by visible light [20; 21]. Visible light communications (VLC) show advantages which are not inherent in RF-based technology, e.g., huge unlicensed spectrum, robustness versus jamming, and much less interference. VLC is also a green technology since the same energy used to light or for road signalling can be used to communicate with vehicles.  
A preliminary experiment has been conducted at the University of Florence for I2V communications which involves VLC (Figure 3) signals. A real traffic-light, located in a real urban road, was used as VLC transmitter. A VLC receiver was moved over a grid of points, at different distances, in front of the traffic light. The experiments provided a 200 kbps data rate up to 40 m. The prototype included a custom LED driver installed in a regular traffic light. The digital information was inserted into the light of the red lamp by the LED driver which modulated the light based on the source information bits. In particular, the intensity of the LED lamp moves from +A (bit 1) to \(-\)A (bit 0) around a mean value, which is the nominal intensity of the traffic-light. The lens inside the traffic-light is a regular traffic-light lens. The traffic-light was located in an urban road in the industrial part of the city. The receiver is composed by a photodiode and a lens collecting the light. A high precision oscilloscope was connected to the VLC receiver for the display and record of the incoming VLC signal. The VLC signal was recorded for 400 us in each point of a virtual grid from 2 to 40 m along the lanes of the road. These measurements and the subsequent analysis led us to develop an accurate propagation model as well as a performance evaluation [22; 23].  
As depicted in Figure 1, the framework envisioned to implement a 5G modem into each traffic light of the (smart) city, while each vehicle should include a VLC receiver. Computational complexity mainly depends on the type of information provided by the infrastructure to the incoming vehicles. As in large vehicular networks, information sent to vehicles or exchanged by vehicles is typically not \"heavy\", also due to the dynamic topology of the network. Messages to vehicles do not usually require a large amount of bits (alarms, traffic information, etc.), thus the computational complexity or the scalability of the network does not seem to be a hard task.
## 4 Demonstration
### Example 2: \(CO_{2}\) Dataset  
This example demonstrates the use of the DPSF algorithm. The dataset consisted of atmospheric concentrations of CO\({}_{2}\) expressed in parts per million (ppm) and reported in the preliminary 1997 SIO manometric mole fraction scale [46]. The values for February, March, and April of 1964 were missing and were obtained by interpolating linearly between the values for January and May of 1964. Table 3 contains the statistical characteristics of the time series.  
The time series data were divided into training and testing datasets. The training set contained the time series data, excluding the last 12 values. The testing dataset contained the last 12 values. A Dpsf model was created using the training dataset, and the future 12 values were forecasted as shown in Figure 5. The corresponding commands are shown in Listing 7. These  
```
#ImportDpsfget_ts(),anddpsf_plot()
#fromPSF_Pypackage >>>>fromPSF_PyimportDpsf,dpsf_plot,get_ts
#LoadthetimeseriesCO2 >>>>ts=get_ts('co2')
#Dividethetimeseriesintotrainingandtesting >>>train,test=ts[:len(ts)-12],ts[len(ts)-12:]
#CreateDpsfmodel >>>>a=Dpsf(data=train,cycle=12)
#Thecreatedmodelcanbedisplayedusing #model_print()method >>>>a.model_print() Originaltime-series:
0 315.42
1 316.31
2 316.50
3 317.56
4 318.13
5 318.00
6 316.39
7 314.65
8 313.68
9 313.18
10 314.66

\dots 448 365.45
449 365.01
450 363.70
451 361.54
452 359.51
453 359.65
454 360.80
455 362.38

Length:456,dtype:float64 k=2 w=18 cycle=12 dim=313.18 dmx=365.45 type='class'PSF_Py.dpsf.Dpsf'>
#Performpredictionusingpredict()method >>>>b=a.predict(n_ahead=12) >>>b array(1363.35347826,364.19434783,365.03521739,366.27304348,366.79696552,366.19913043,364.75913043,362.73458498,361.06708498,361.03143281,362.2740415,363.51445817))
#Plotthemodelandpredictedvalues >>>dpsf_plot(a,b)
```  
**Listing 7**. Commands to create and use Dpsf model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Function**} & **Dpsf()** & **Psf()** & **psf()** & **auto.arima()** \\  & **(Python)** & **(Python)** & **(R)** & **(R)** \\ \hline
**Models** & \((\mathbf{k,w})=(\mathbf{2,18})\) & \((\mathbf{k,w})=(\mathbf{9,19})\) & \((\mathbf{k,w})=(\mathbf{2,10})\) & **-** \\ \hline RMSE & 0.41 & 1.48 & 5.93 & 1.63 \\ MAE & 0.32 & 9.27 & 5.91 & 1.40 \\ MAPE & 0.08 & 2.67 & 1.62 & 0.38 \\ NSE & 0.95 & 0.32 & \(-8.15\) & 0.302 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of forecast methods with different error metrics for the “CO\({}_{2}\)” dataset.