esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Security Adaptation for Low-Power IoT Applications
###### Abstract  
The constant evolution in communication infrastructures will enable new Internet of Things (IoT) applications, particularly in areas that, up to today, have been mostly enabled by closed or proprietary technologies. Such applications will be enabled by a myriad of wireless communication technologies designed for all types of IoT devices, among which are the Long-Range Wide-Area Network (LoRaWAN) or other Low-power and Wide-Area Networks (LPWAN) communication technologies. This applies to many critical environments, such as industrial control and healthcare, where wireless communications are yet to be broadly adopted. Two fundamental requirements to effectively support upcoming critical IoT applications are those of energy management and security. We may note that those are, in fact, contradictory goals. On the one hand, many IoT devices depend on the usage of batteries while, on the other hand, adequate security mechanisms need to be in place to protect devices and communications from threats against their stability and security. With this motivation in mind, we propose a solution to address the management, in tandem, of security and energy in LoRaWAN IoT communication environments. We propose and evaluate an architecture in the context of which adaptation logic is used to manage security and energy dynamically, with the goal of guaranteeing appropriate security, while promoting the lifetime of constrained sensing devices. The proposed solution was implemented and experimentally evaluated and was observed to successfully manage security and energy. Security and energy are managed in line with the requirements of the application at hand, the characteristics of the constrained sensing devices employed and the detection, as well as the threat, of particular types of attacks.  
IoT; LoRaWAN; LPWAN; end-to-end security; energy and security; dynamic security adaptation +
Footnote †: journal:  
Article
## 3 Results
### Stratified Analysis of FMA-UE Score by Severity Level  
Statistical results are shown in Table 5. For patients with severe impairment (\(n=34\)), the intervention group showed a median change of 1.0 [\(-\)0.25, 1.0], while the control group's was 0.0 [0.0, 1.25]. The differences were not statistically significant, with U-values of 16.00 (\(p=0.22\), r = 0.43) for the intervention group and 21.50 (\(p=0.09\), r = 0.00) for the control group (Figure 3B). In the moderate impairment category (\(n=50\)), the shaking device group exhibited a median change of 1.0 [0.0, 4.0], significantly greater than the control group's change of 0.0 [0.0, 1.0] (U = 59.50, \(p=0.01\), r = 0.25) (Figure 3C). The control group's result was not significant, with a U-value of 25.50 (\(p=0.50\), r = 0.00). Among mildly impaired patients (\(n=9\)), the intervention group showed a median change of 2.0 [0.5, 3.5], compared with the control group's 1.5 [\(-\)0.5, 2.75]. Neither group's results were statistically significant for the intervention group, and they were 3.50 (\(p=0.63\), r = 0.36) for the control group (U = 1.50, \(p=0.25\), r = 0.77) (Figure 3D). These findings indicate that the shaking device intervention significantly improved FMA-UE scores in patients with moderate impairment, while no significant improvements were observed in patients with severe or mild impairments.
## 3 Development of Cyclic-di-GMP PDEs Inhibitors  
The traditional view of c-di-GMP signaling suggests that diguanylate cyclases (DGCs, GGDEF/GGEEF-domain proteins) increase intracellular c-di-GMP levels, promoting biofilm formation, while phosphodiesterase (PDEs, EAL/HD-GYP domain proteins) degrade c-di-GMP, reducing biofilm formation and virulence [64]. This has led to the assumption that inhibiting PDEs would unintentionally enhance biofilm formation, an undesirable effect. However, recent findings reveal a more complex regulation system where overall intracellular c-di-GMP levels alone do not dictate bacterial phenotypes [56; 65]. Instead, localized micro-concentrations of c-di-GMP, along with the spatial distribution of regulatory enzymes, adaptor proteins, and binding RNAs, play a crucial role in determining bacterial behavior. The presence of multiple DGCs and PDEs within a single bacterium further contributes to this complexity.  
Studies on PDE mutants and overexpressing clones indicate that certain virulence-associated traits are modulated by multiple PDEs through localized changes in c-di-GMP levels [64; 65; 66]. Mutants of specific PDE-encoding genes exhibited attenuated virulence in mouse infection models, suggesting that c-di-GMP degradation is highly localized and directly linked to specific regulatory targets [66]. Furthermore, not all c-di-GMP PDEs influence the global c-di-GMP pool, indicating that selective PDE inhibition could have therapeutic applications without broadly altering bacterial signaling. A stark example of this was displayed by the Sintim group, which used computational high-throughput screening to identify a RocR (one of many PDEs in _P. aeruginosa_) inhibitor, Compound **1** (Figure 7A). The inhibitor was able to significantly reduce the swarming ability of the _P. aeruginosa_ PAO1 strain (Figure 7B) [65].
## 4 Discussion
Similar findings were presented by [PERSON] et al. [62]; they stated that the combination of nadir and oblique imagery can effectively be used for geomorphological mapping in areas with complex topography and steep slope (>60\({}^{\circ}\)), to increase the geometric accuracy of UAV data. Accuracies of PPK solution up to 0.100 cm should provide a satisfactory photogrammetric-derived product at a representational scale [28; 31; 33; 39]. [PERSON] et al. [31] mentionedthe use of BBA approaches without GCPs at the scale of 1:500. [PERSON] and [PERSON] [69] proposed to use RTK/PPK, depending on the application intended for the results. For topography mapping, where the accuracy range is between 0.010 and 0.020 m, they proposed the use of RTK/PPK; with cadaster mapping having an accuracy range between 0.010 and 0.030 m, they proposed the use of RTK/PPK with the GCPs; and, for engineering surveying (accuracy range 0.010 m), the use of GCPs was recommended. [PERSON] et al. [37] stated that the PPK method is consistent enough in generating a large-scale mapping with less effort than the GCP method, and that highly detailed maps (spatial resolution \(<\)0.05 m) can be achieved. [PERSON] et al. [28] concluded that the BBA_PPK method can provide an optimal solution for mapping in remote areas. Based on the results of our study, we can come to similar conclusions. Regarding the use of BBA_traditional or BBA_PPK, the decision regarding the georeferencing method must follow the accuracy requirements of data application and the scale needed, and the conditions for a safely conducting of the field survey. In the event of extreme rockfall events, where rockfall activity is high and the conditions for field work are not safe, we would recommend using the BBA_PPK method only. When the results will be used at a regional level (decimeter accuracy), such as for rockfall mapping of release areas, or the modeling of potential rockfall propagation and deposit areas, the use of the BBA_PPK method could prove to be satisfactory (achieving horizontal accuracies around 0.040 m and vertical up to 0.080 m). On the other hand, when the results of the rockfall modeling are to be used for the planning of the technical protection measures, additional GCPs should be included, to increase the accuracy of the results (up to 0.010-0.020 m) and their reliability. To find an optimal ratio between the accuracy of the photogrammetric products, the work load related to georeferencing and, most importantly, the safety of the field operations, we will continue our research by finding an optimal combination of BBA_traditional and BBA_PPK methods. The following research will, therefore, investigate the number of GCPs needed besides the BBA_PPK method, and the importance of the location where they are situated, especially taking into consideration that they are not located on the exposed and hazardous areas.
## 1 Introduction
By employing high -efficiency modulation technologies, such as orthogonal frequency division multiplexing (OFDM) or discrete multi-tone (DMT), data rates can also be effectively increased [23]. [PERSON] et al. adopted the OFDM technology with 64-quadrature amplitude modulation (QAM) to realize 127.07 Mbps of offline communication over a 2-m underwater channel [24]. Another approach is to decrease the area size of the LEDs by using micro-LEDs as the light source. In [25], [PERSON] et al. utilized a GaN-based micro-LED to establish a UOWC link with a data rate of 800 Mbps over a 0.6-m length, and the used micro-LED had a maximum modulation bandwidth of 160 MHz. However, the optical output power of the micro-LED is insufficient for long-distance transmission. [PERSON] et al. proposed a 4 \(\times\) 4 LED array to increase the output power and realize underwater data rates up to 200 Mbps [26]. Although the transmission data rates  
Figure 1: Conception of AUV swarm collaborative operation. of the LEDs have been significantly improved, considering the underwater attenuation effect, most UOWC systems need to use a Fresnel lens at the receiving end to improve the receiver's sensitivity for a longer transmission distance. However, the convergence ability of the lens is sensitive to the incident angle of the received optical signal, which means that the alignment is still a restriction [27]. Especially for the optical communication links between the AUVs, a transceiver alignment system with high dynamics is essential to alleviate the influence of underwater waves on the attitude of the AUVs. Hence, there are still challenges in making full use of the large divergence angle of the LED to enhance the mobility of the UOWC systems. In this paper, we propose and experimentally demonstrate a multi-degree-of-freedom (MDOF) UOWC system to achieve higher flexibility based on the off-the-shelf LED source. Firstly, a Fresnel lens array is constructed at the receiver terminal to enhance the convergence and sensitivity of the received optical signals with different incident angles. Then, we design an additional digital signal recovery module (DSR) after the traditional trans-impedance amplifier (TIA) to improve the underwater link impairments introduced during signal transmission, including attenuation and timing jitter. Furthermore, we employ a pre-equalization circuit at the transmitter terminal to enhance the \(-3\) dB bandwidth of commercial off-the-shelf LEDs, which enables the proposed system to achieve high-speed data transmission. Moreover, the cost, power consumption, and complexity of the system can be reduced by its practical compatibility with underwater lighting equipment. Finally, we establish an experimental verification system with a 100-Mbps data signal using the non-return-to-zero on-off-keying (NRZ-OOK) modulation format over a 1.2-m water tank. The transmission reliability for different incident signals is demonstrated by the experimental results of the eye diagrams and BER curves.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 3 Proposed Method
### Faster R-CNN for Fire Object Detection  
As in DTA, Faster R-CNN results in the bounding boxes of flame, smoke, and non-fire regions in an image, as shown in Figure 3. The ConvNet backbone ResNet152 extracts visual features for location classification of the fire scene. The samples of training images are shown in Figure 3, which includes flame, smoke, and non-fire objects. As in Figure 4, there are some hard-negative examples included in non-fire objects that resemble real fire objects, such as sunset, misty street, and cloud. The bounding boxes enclosing flame and smoke objects were treated individually as SRoFs. A non-fire object may have been enclosed by a bounding box if it was a hard-negative example, otherwise, it included the whole frame as a bounding box.  
Figure 2: Timing diagram of fire detection [26].  
Smoke objects were detected at every frame and merged to decide the state of smoke during \(T_{Bayes.}\). The smoke detection result was \"_S = yes_\" if the number of frames which detect the smoke object with a threshold of confidence of 0.5 was more than half of the total frames during \(T_{Bayes.}\).
## 4 Validation
### Cogging Torque
#### 4.3.2. Rotor-Segmented Case  
In the case of a rotor segmentation, the cogging torque amplitude is due to the stator configuration (slotting and tooth without tooth tips). In this way, the estimation of the cogging torque results from the classical method computation. Figure 25 shows the waveforms and harmonic contents for the RS-machines with different gap ratios, \(p_{gap}\). In this case the peak value of the cogging torque is about \(2.5\,\%\) of the rated torque of the machine. For the three presented cases the results are similar.  
To validate more closely the analytical model to compute cogging torque, Figure 26 proposes to compare peak values and waveform correlation values for each cases.  
The analytical estimation of the cogging torque for a rotor-segmented machine offers very good correlation results (Figure 26b) with numerical calculation. Regarding the maximum values presented in Figure 26a, analytical and numerical models lead to the same evolution in function of the gap proportion \(p_{gap}\) with a difference of around \(7\%\).  
Figure 23: Comparison between numerical and analytical calculation of cogging torque for the SS-machine with \(n_{gap}=7\) gaps and with different \(p_{gap}\). (**a**) Peak values. (**b**) Correlation.  
Figure 24: Comparison between numerical and analytical calculation of cogging torque for the SS-machine with \(p_{gap}=0.50\) and with different \(n_{gap}\). (**a**) Peak values. (**b**) Correlation.  
by 2D FEM and analytical model for one of the phase (the EMF at 1 rd/s corresponds to the angular position derivative of the no-load flux in a phase using (4) and (5)).  
In the case of magnetic gaps introduced in the stator, the used analytical model leads to a very good accurate estimation of the EMF. It can be notices that only \(p_{gap}\) has an influence on the EMF. The results are the same for different \(n_{gap}\) if the gap ratio is kept constant.  
Figure 28a shows the decrease of the peak values, for both analytical and numerical methods. That decrease is due to the corrected axial length. In other words, when the correction factor is taking into account, the increase of the segmentation rate (and so the increase of the axial length) leads to a small decrease of the electromotive forces peak value. Analytical peak values of EMF is less than 1% overestimated compared to numerical results.  
In term of correlation between waveforms (Figure 28b), the electromotive forces obtained by analytical and numerical methods have a correlation around 0.999 for the three \(p_{gap}\) values.  
Figure 27: Waveforms of electromotive forces in one phase (neutral phase) at 1 rd/s for the SS-machine with different \(p_{gap}\). (**a**) Reference machine. (**b**) SS-machine with \(p_{gap}=0.25\) and \(n_{gap}=7\) gaps. (**c**) SS-machine with \(p_{gap}=0.50\) and \(n_{gap}=7\) gaps.
## 4 Results
### Information of the Fitted Super Learner  
SL is the prediction model that employs six trained base learners, namely SVR, KNN, DT, RF, GBM, and XGB. Each base learner stores the predictions of five-fold cross-validation to determine the coefficient in step 3 of Algorithm 1. The stored predictions are utilized to calculate the coefficients of base learners in step 4. These coefficients are then combined with the predicted values of the base learners on the original dataset in step 6. In this study, SL uses base learners trained with the optimal hyper-parameters previously obtained.  
Figure 3 shows the coefficients of base learners for 10 validation folds, which were created using two-fold cross-validation on each of five files. The sum of the coefficients for each fold remains at 1. There is a difference in coefficients for each fold, but XGB has the highest coefficient for all folds, followed by GBM. In several folds, SVR, KNN, and RF have coefficients of 0, and DT has all coefficients at 0. SL is more significantly affected by XGB and GBM than by the other models and is hardly affected by DT.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Parameter** & **Description** & **Range** \\ \hline \multirow{2}{*}{SVR} & C & Regularization parameter & [0.1, 1, 10, 100] \\  & gamma & Kernel coefficient & [1, 0.1, 0.01, 0.001] \\ \hline \multirow{3}{*}{KNN} & n\_neighbors & The number of neighbors & range(3, 20, 2) \\  & P & Distance method (1: Manhattan, 2: Euclidean) & [1, 2] \\  & weights & Weight function used in prediction & [uniform, distance] \\ \hline DT & min\_samples\_leaf & The minimal number of samples at the leaf node & range(5, 50) \\ \hline \multirow{2}{*}{RF} & max\_features & The number of random features when setting node & range(1, 5) \\  & n\_estimators & The number of trees & range(100, 301, 100) \\ \hline \multirow{3}{*}{GBM} & learning\_rate & Learning rate & range(0.01, 0.5, 0.05) \\  & max\_depth & The maximum depth of the tree & range(3, 11) \\  & n\_estimators & The number of trees & range(100, 301, 100) \\ \hline \multirow{3}{*}{XGB} & learning\_rate & Learning rate & range(0.01, 0.5, 0.05) \\  & max\_depth & The maximum depth of the tree & range(3, 11) \\  & n\_estimators & The number of trees & range(100, 301, 100) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameter information of the machine learning models.  
Figure 3: The coefficient-stacked bar displays the optimal weight combinations calculated from the SL for each of the 10-fold cross-validations. Coefficients below 0.02 are not displayed in the text.
# A Review on SIW and Its Applications to Microwave Components
## 4 Transitions in SIW  
As SIW components are always integrated with other circuit components within a system, it is important to discuss the interconnection between devices. The transition sandwiched between planar transmission line technologies such as microstrips, and the substrate-integrated waveguide structure is a crucial part of the design of SIW devices [14]. A few published papers have reported SIW components with different types of input/output (i/o) connections. A microstrip to substrate-integrated waveguide transition constructed on straightforward taper was reported [18; 19]. The tapered segment attaches a 50\(\Omega\) microstrip signal line to the substrate-integrated waveguide. The taper is utilised by converting the quasi-TEM mode in the microstrip technology to the TE\({}_{10}\) mode of the substrate-integrated waveguide. Coplanar waveguides (CPWs) have also played a vital role in this research area. Coplanar waveguide to substrate-integrated waveguide conversion established with a ninety-degree bend was reported in [20]. In another research paper, a kind of conversion involving a grounded CPW and substrate-integrated waveguide established on a current probe was proposed [21]. Current flow across the probe produces a magnetic field which corresponds to the magnetic field within the substrate-integrated waveguide. Transition between the traditional rectangular waveguide and the substrate-integrated waveguide structure has also been proposed in [22; 23]. Microstrip to substrate-integrated waveguide conversions within multiple layered substrates were researched/reported [24]. [PERSON], [PERSON], and [PERSON] proposed a new microstrip-CPW-SIW transition in [10]. The new transition technique exploits the step impedance on a 50\(\Omega\) microstrip signal path, to the small impedance grounded coplanar waveguide, then linking to the SIW component through the brief small impedance grounded coplanar waveguide signal path. The conversion method is effective since it facilitates double flexibilities in managing i/o couplings; hence, the external quality factor may be altered by both altering the stepped-impedance of the coplanar waveguide and the size of the brief coplanar waveguide signal path. Figure 5 shows some of the popular transition techniques that have been employed in the design of substrate-integrated waveguide components.  
Figure 5: _Cont._
## 3 Case Study
### Data Preprocessing
#### 3.2.3 Data Normalization  
The mean, unit, and value of each physical quantity in EPB tunneling parameters are different. If the parameters are directly sent to the model for training, the prediction accuracy of the model will be reduced. Thus, we normalized the tunneling parameters according to the standard deviation standardization method [57].  
\[\overline{x}=\frac{x-u}{\sigma}, \tag{13}\]  
where \(x\) is the original value; \(\overline{x}\) is the normalized value; \(u\) and \(\sigma\) are the mean and the standard deviation of a dimension of the tunneling parameters, respectively. After the training process, the results needed to be transformed into the original data.  
After data preprocessing, a total of 5607 drilling cycles were segmented from the raw data, which constituted our AI learning database. The dataset was further split into two groups: the first group contained 4800 drilling cycles (EPB1\(\sim\)EPB4) for training the model, and the second group contained 807 drilling cycles (EPB5) for verifying the predictive ability of the model.
# Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
## 1 Introduction
The advantage of using this privacy definition is that it enables generating data of higher fidelity, compared to previous work on GANs with DP, allowing for finer-grained inspection of data. While some problems with data or data pipelines can be discovered using very coarse samples (e.g., pixel intensity inversion in [8]), more subtle bugs, such as partial data corruption, would require samples of much better quality, rendering the DP guarantee too loose to be meaningful. Moreover, if fidelity is high enough, synthetic data can be used for annotation and training itself, removing the related privacy concerns and extending applicability of FL. We evaluate our solution in these two aspects in Section 3. The main contributions of this paper are as follows:  
* We use Bayesian DP to enable higher quality GAN samples, while still providing a strong privacy guarantee;
* We demonstrate that this technique can be used to discover finer data errors than has been previously reported;
* We also show that for some tasks synthetic data are of high enough quality to be used for labelling and training.
# Continuous-Wave THz Imaging for Biomedical Samples
## 2 CW THz Single-Point Scanning Imaging
### CW THz Reflection Single-Point Scanning Imaging
The THz images showed that a tumour could be accurately distinguished from normal tissue and background. The biggest advantage of the reflection imaging method is that it is more suitable for the actual preparation of biological samples, but the SNR and imaging contrast are lower than the transmission measurement. Therefore, both transmission and reflection measurement possess their own advantages and are complementary with each other. The appropriate imaging method should be chosen according to the characteristics of the tested sample and experimental conditions.