esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
## 2 Materials and Methods
### Device Failure Mechanisms  
The Si power MOSFET failed when the parasitic bipolar junction transistor (BJT) was activated. The body diode and the parasitic BJT are shown in Figure 3. The voltage drop between the base-emitter (\(V_{BE}\)) for the parasitic Si NPN BJT was approximately 0.7 V. When the temperature increased, the \(V_{BE}\) decreased gradually until the parasitic BJT was activated [11]; however, the \(V_{BE}\) for the parasitic SiC NPN BJT was approximately 2-3 V, and the BJT activation due to the temperature increasing was not a main reason for the device failure. As discussed in Section 2.1, when the DUT was turned-off, the blocking voltage increased until the recombination was completed; however, if the DUT had been turned on for a longer time, too many electrons could have remained in the p-base region. Then, the avalanche breakdown that caused the failure of the DUT may have occurred before the electrons remaining in the p-base region were completely recombinated with the holes. As shown in Figure 4a, the inductor current increased when the DUT failed, and the \(V_{DS}\) went to zero. Additionally, the temperature of the device increased, as shown in Figure 4b [12,13].  
Figure 1: Unclamped inductive switching (UIS) test circuit.  
Figure 2: (a) Simulated UIS single gate pulse (\(t_{on}\)) waveform; (b) simulated UIS temperature waveform (before failure); (c) simulated UIS \(V_{DS}\) and \(I_{D}\) waveforms (before failure); \(V_{DD}=400\) V; \(L=1\) mH.
# Nanovised Control Flow Attestation
## 4 Background  
C-FLAT is a dynamic attestation and complements static attestation. It measures the program's execution path at the opcode level by capturing its runtime behavior. Figure 2 (taken from the original C-FLAT paper) presents the C-FLAT. _Prv_ is a prover, and _Ver_ is the verifier. The Prover usually runs on a resource-limited device, and, therefore, cannot do the verification while in execution. Both the Verifier and the Prover must have access to the program's binary at any time.  
First step 1 is that _Ver_ generates a control-flow-graph offline. It also measures each possible control-flow path using function \(H\), and saves the results 2. In the original C-FLAT, _Ver_ stores and generates CFG's results in _Ver_, because _Prv_ is a low resources' computer. In C-FLAT Linux the CFG is stored in _Ver_ itself. Reading the CFG is required only once per program. We do it on the process start-up, so it does not endanger the performance. In 3, _Ver_ challenges _Prv_. Then, _Prv_ starts executing the program 4, while our trusted software computes the program's path 5. In the original C-FLAT, code running in TrustZone performed this computation. In our system code, running in hypervisor mode performs the calculation. Lastly, _Prv_ generates the attestation report \(r=SigK(Auth,c)\), computed as a digital signature over the challenge c and Auth using a key K known only to the measurement engine. Lastly, _Prv_ transmits r to _Ver_ 6 to validate it 2.  
C-FLAT model is designed for small embedded devices, and therefore does not fit to a general-purpose OS on a general-purpose processor which is common today in many embedded setups. C-FLAT Linux is suitable for these setups.  
In addition to the above, for C-FLAT to trace the program's flow, it is essential to intervene in the program binary (Original). For this, we replace the program's branches and returns-from-function. For example, in Figure 3, the \"br x0\" opcode is replaced by \"br hook_b\" opcode (Instrumented). Hook_b is an address of a procedure that searches for the address of the original branch opcode, and once found (Trampoline), it sends (via the SMC command) it to computation in the TrustZone. The computation records the address and returns to perform the original branch command.
## 1 Introduction
### Contributions  
This study shows the successful implementation of the Interval Type-2 Fuzzy PID (IT2-FPID) controller under a nonlinear dynamic model and unknown/uncertain parameters of the UAV with a payload in simulation and real-time systems. The implementation comes from model-based tuned parameters to overcome an unknown weighted flexible cable-connected payload. It is aimed to eliminate the oscillating payload effect on DJI Tello UAV and to follow the given references by using these controllers. PID, T1-FPID, and IT2-FPID are tuned using the Big Bang-Big Crunch (BB-BC) optimisation method [31] on the dynamical model of the UAV without a payload. Due to the unmodelled dynamics, such as inaccessible parameters or uncertainties, the controller coefficients are fine-tuned on a real-time UAV without the payload before being tested with the flexible cable-connected payload. After that, it is expected that the UAV will be able to eliminate disturbing payload effects and demonstrate efficient tracking of the designed trajectory. Moreover, the performance of the IT2-FPID is compared with T1-FPID and PID under the same scenarios. The presented results of the study clearly show that the designed IT2-FPID control system is superior in comparison to its T1 and conventional counterparts. The video file provided in the Supplementary Material shows the real-time control performance of the deployed IT2-FPID.  
The rest of the study is organised as follows: Section 2 provides the UAV model, including the DJI Tello model and the problem statement and challenges. Section 3 presents implemented controller methods, which are PID, T1-FPID and IT2-FPID, and the simulation results. Then, Section 4 shows the indoor testing area and the designed offline path. Moreover, the process of position localisation of the UAV is explained in this section. The real-time results of the PID, T1-FPID and IT2-FPID controllers are given in Section 5. Finally; Section 6 presents the conclusions and a perspective on future work.
## 3 Results
### Exploratory Part: Aggregate Data for Patients Initially Diagnosed with Stage I-IV NSCLC
\({}^{\int}\) Any adjuvant treatment (including TT, ICI or TT + ICI). Abbreviations: adj., adjuvant; ChT, Chemotherapy; CRT, Chemoradiotherapytherapy; cur., curative; Investig., Investigational; ICI, Immune Checkpoint Inhibitor; N, number of patients with available data; n, number of patients with variable; neoadjuvant., neoadjuvant; NSCLC, Non-Small Cell Lung Cancer; RT, Radiotherapy; ST, Systemic Therapy, SUR, Surgery; T, Therapy; TT, Targeted Therapy. Among Stage IV patients, 10.3% (351/3396) received non-pharmacologic treatment only, and 76.7% (2606/3396) received ST. Of the latter, 61.9% (1614/2606) received ICI (\(\pm\) ChT and/or TT), 17.9% (467/2606) ChT-alone, 14.9% (387/2606) TT (\(\pm\) ChT), and 5.3% (138/2606) investigational ST. Treatments utilized as part of ITS in the pre-COVID-19 and COVID-19 periods in the Stage I/II/III/IV NSCLC subpopulations are illustrated in Figure 4D. For those receiving ST-alone, the ST rate was statistically significantly lower in the pandemic versus pre-pandemic period among Stage I NSCLC [1.4% (13/920) vs. 3.0% (24/805); \(p\) = 0.025]. The opposite was observed for Stage IV patients with statistically significantly higher ST rates in the pandemic versus pre-pandemic period [78.5% (1352/1722) vs. 74.9% (1254/1674); \(p\) = 0.013]. ST rate did not statistically differ between the aforementioned periods for Stage II and III patients. In terms of ST pharmacologic categories, the frequency did not statistically significantly differ between two periods, neither for Stage I nor for Stage II patients. Conversely, a statistically significant decrease was noted in the rate of ChT-alone and a statistically significant increase in the rate of ICI \(\pm\) ChT, for both Stage III and IV subpopulations (Figure 4D).
## 4 Data Validation
### Evaluation Indicators  
The confusion matrix is used to visualize the performance of the classification model. The predicted categories of data images in the confusion matrix correspond to the actual categories, and the data reflected in each cell of the matrix can show how each category of bridge anomaly data is classified by the model. The evaluation metrics Accuracy, Precision, and Recall of the confusion matrix are defined as shown in Equations (1)-(3), respectively [32].  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
True positive (TP) indicates that the actual value is the same as the predicted value and correctly classifies the target category samples into the belonging categories; false positive (FP) indicates that the actual value is negative, but the model predicts a positive value that misclassifies samples from other categories into the target category; true negative (TN) indicates that the actual and predicted values are the same, which means that the samples from other categories are correctly classified into the other categories to which they belong; false negative (FN) indicates that the actual value is positive but is predicted by the model to be the negative value, meaning that the target category samples are misclassified into other categories. Precision is the probability of the samples which the model considers correct and are indeed correct as a percentage of all samples considered correct by the model. Recall is the probability for the samples that the model considers correct and are indeed correct as a percentage of all samples which are actually correct. Accuracy is the percentage of correctly predicted results in the total sample.  
Accuracy and Recall will be used as the main evaluation analysis metrics to evaluate the effectiveness of data migration when data is not balanced among these metrics. Accuracy reflects the overall performance of the model. Recall is more realistic for bridge data anomaly detection than Precision, which can find each category of abnormal data as much as possible.
# A Survey on Cybersecurity in IoT
## 4 Network Security and Communication Protocols
### Emerging Trends and Protocol Innovations  
As the IoT landscape continues to evolve, new communication protocols and security paradigms are emerging. One significant trend is the integration of Software-Defined Networking (SDN) with IoT, where SDN's centralized control model can provide greater visibility and control over network traffic, allowing for more dynamic and responsive security measures. By decoupling the control and data planes, SDN enables the real-time monitoring of network conditions and the application of security policies that can adapt to emerging threats [192; 193; 194; 195].  
Another emerging trend is the use of blockchain technology to enhance the security and integrity of IoT networks. Blockchain's decentralized and tamper-resistant nature makes it an attractive option for managing IoT device identities and securing communications. Protocols such as IOTA, designed specifically for IoT, leverage the Tangle architecture--a DAG that allows for scalable and secure transactions without the need for miners, which is resource-intensive. This approach is particularly suited for IoT environments, where resources are constrained and traditional blockchain solutions may be impractical [196; 197; 198].  
Additionally, the development of protocol-agnostic security frameworks is gaining traction, where security measures are abstracted from the specific protocol and instead applied at a higher layer of the network stack. This allows for a more uniform application of security policies across heterogeneous networks, addressing one of the key challenges in IoT network security [199; 200; 201].  
A summary of the above works that analyze network security and communication protocols is captured in Table 4.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **References** & **Summary** \\ \hline Communication Protocols Overview & [146; 147; 148] & Surveys and reviews on IoT communication protocols and their performance. \\ \hline MQTT Protocol & [149; 150; 151] & Studies on the MQTT protocol, including use cases, security challenges, and implementations. \\ \hline CoAP Protocol & [152; 153; 154] & Research on the CoAP protocol, focusing on securing communications and performance analysis. \\ \hline Zigbee Protocol & [155; 156; 157] & Analysis of the Zigbee protocol, including its application in various networks and security aspects. \\ \hline Bluetooth Protocol & [158; 173; 174] & Overview of Bluetooth vulnerabilities, such as eavesdropping, replay attacks, and pairing security issues. \\ \hline \hline \end{tabular}
\end{table}
Table 4: A list of topics and related studies dedicated to network security and communication protocols.
# Microbially Induced Desaturation and Carbonate Precipitation through Denitrification: A Review
## 2 The Denitrification Mechanism
### Inducing Calcium Carbonate Precipitation through Denitrification  
Denitrifying bacteria can use a diverse range of electron donors in natural environments, including pure compounds (methanol, acetone, acetate, glucose, methane, and amino acids), sugars, wastewater, food industry waste, and sludge [23], which favor the generation of dissolved inorganic carbon (DIC). Inorganic carbon dissociates into CO2, bicarbonate HCO3-, and CO2- in aqueous solutions. If a solution with a high pH and total inorganic carbon content contains excess dissolved Ca2+, CaCO3 will precipitate [8], and the system will transfer to the solid phase (Figure 2). The denitrification process can be expressed by the equations using acetate (CH3 COO-) as the electron donor (Table 1). Furthermore, the production of CaCO3, the generation of biogas, and the growth of bacteria result in biofilm and biomass accumulation, which favors the formation of a bio-barrier and decreases the permeability of the medium [28]. The full reaction system of the denitrification-based MICP is given in Figure 2.
## 5 Experimental Results and Discussion  
In this section, the results of various usage scenarios on pattern identification and scene augmentation along with an extensive evaluation testing during the execution of the ModAR prototype on low-specification devices are presented. The input testing dataset of pattern images and 3D models is described before proceeding to the main evaluation phase. Then, the results obtained by profiling CPU usage, timing, frame rate and memory allocation separately on the native side during pattern recognition and on the Java side for the 3D graphics engine are reported and discussed.
# Low-Profile High-Efficiency Transmitarray Antenna for Beamforming Applications
###### Abstract
An FSS, that is, any thin and repetitive surface designed to reflect, transmit, or absorb electromagnetic waves, is employed to control the phase and amplitude of the source antenna [19; 20]. In particular, the FSS is designed by a square patch array based on a metallo-dielectric FSS [21] and is formed by four dielectric layers with equally spaced capacitive printed patches. Each unit cell of the FSS is fed by each slot of a partial H-plane waveguide slot array antenna and is symmetrically located above the slot. The phase set required for beamforming is controlled by the unit cell dimension of the FSS located above each slot. To confirm its beamforming performance, three types of FSSs to operate a forming angle of \(-40^{\circ}\), \(-20^{\circ}\), \(0^{\circ}\), \(+20^{\circ}\), and \(+40^{\circ}\) are designed and integrated into the slot array antenna at 12 GHz. The organization of this paper is as follows. In Section 2, the design procedure of the two-dimensional (2D) partial H-plane waveguide slot array antenna is described. In Section 3, the unit cell of the FSS is theoretically analyzed, and its transmittance characteristic is confirmed by a full-wave simulation. Moreover, the beamforming performance of the partial H-plane waveguide slot array antenna integrated with the FSS is verified by simulated and measured results. Finally, the conclusion is presented in Section 4.
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 4 Results and Validation  
The presented approach was applied to the city of Munich. Table 1 lists the simulation parameters used to generate artificial workday mobility plans based on the presented methodology. In order to obtain a significant amount of mobility plans for evaluation, plans for 50,000 cars were generated for a 7-day period, of which 2 days were considered padding. This padding was used to eliminate the impact of edge effects on the beginning and end of the plans by removing the first and last day. Since a first test revealed a slight overestimation of the work activities, small nudges were applied using the \(p_{\text{no\_worker}}\) parameter. Consequently, a car with work information provided was drawn to not belong to a working person with a probability of \(p_{\text{no\_worker}}=0.05\). The inverse probability remained \(p_{\text{worker}}=0\) to not counteract the intended reduction of the working population. Intermediary test runs showed that the daily driving behavior of cars best matched the general mobility behavior judged by the MID study results when the remaining hyperparameters were set to \(p_{\text{drive\_every\_day}}=0.59\) and \(p_{\text{car\_driven}}=0.4\). This means that the simulation assumed that approximately \(59\%\) of all cars within Munich were driven daily, whereas cars that were not driven daily were used on \(40\%\) of the days, or once every \(2.5\,\text{d}\).  
In the following, the obtained simulation results are shown in two separate sections. First, Section 4.1 provides an overview of the general mobility behavior reflected in the generated mobility plans, and the metrics employed in this section evaluate whether the order, frequency, and travel distances of the generated mobility plans were macroscopically realistic, as imposed by \(\text{Req}_{1b}\) in Section 3.1.1.  
Indicators of the general mobility behavior were validated by comparison with data from the employed general mobility survey MID. Subsequently, Section 4.2 assesses the spa tiotemporal quality of the generated mobility plans based on a comparison with the results from MITO and MID. The herein conducted comparison of locations, times, and durations of activities concludes the evaluation of \(\text{Req}_{1b}\).
## 3 Results and Discussion
### Mechanism Involved in the Transmission of ARGs
#### 3.2.2 Correlations Between ARGs, LRGs and the Microbial Community
An increase in pH reduces metal bioavailability [11], reshaping microbial community structures. Our results suggested that variations in soil physicochemical properties, particularly pH and Pb concentration, were key drivers of changes in microbial diversity and community composition during the synergistic remediation of Pb-contaminated soils using composite agents combined with BES. Since Pb-regulated LRGs and ARGs often coexist within bacterial hosts, shifted in host populations may lead to changes in the relative abundances of co-selected LRGs and ARGs.
## 4 Experimental Result and Discussion
### Phenology Analysis Based on Backscatter Coefficients  
We selected a test dataset with the distribution shown in Figure 4 for phenology analysis based on the land-use map of the RRD in 2012. Data, collected in the year of 2018, includes 7, 4, 8, 6, 10 points for water, bare land, urban, rice and forest, respectively. The time-series of backscatter coefficients for land classes are presented in Figure 5.  
It was observed that the average backscatter coefficients of urban and forest areas remain high and stable with low standard deviation. Backscatter values in urban areas are in the range of \(-12.47\) to \(-11.45\) dB for the VH channel and \(-4.92\) to \(-3.32\) dB for VV channel. Meanwhile, the backscatter coefficient of the forest is slightly slower in a range of \(-14.8\) to \(-13.3\) dB with VH channel and \(-8.67\) to \(-7.24\) dB with VV channel. Bare land has lower backscatter values in comparison with urban and forest (i.e., \(-19.3\) to \(-15\) dB for VH and \(-12.6\) to \(-9\) dB for VV). Entirely bare soil will have stable backscatter values but these areas may include grass and wild plants which make unstable signals sometimes. Besides, some bare land areas at low elevation may be temporarily inundated if heavy rains occur. The bare land's backscatter value was sharply reduced on 22 July 2018, when there were heavy rains caused by the Son-Tinh storm (see Figure 5). Because of changes in the bare land surface, its standard deviation value is slightly higher than the stand  
Figure 4: Test sizes for phenology analysis in the Red River Delta (RRD).  
and urban classes. Regarding paddy rice, there is an extremely large change in backscatter signals during its growth, which is presented by the high standard deviation from 3.67 dB with VH and 2.6 dB with VV. Based on this time-series characteristic, rice might be separated from different land surfaces. Water is the layer with the lowest backscatter average from \(-28.5\) to \(-24.8\) dB with VH and \(-21.3\) to \(-17\) dB with VV. Therefore, both VH and VV can be used to distinguish water surface from other surfaces [68; 69]. In addition, in the calm wind condition, radar signals may be considered as reflections causing low-intensity equivalent to the dark pixel in SAR imagery [70]. In this case, VV will give better accuracy than VH polarization [71]. The statistical backscatter coefficients of VV and VH channels are summarized in Table 3.
###### Abstract  
The problem of single-channel reception of global positioning system (GPS) communication waveforms makes passive sensing of aerial target difficult because of forward scatter. This paper proposes a novel aerial target passive sensing method based on linear canonical transformation (LCT) using the forward scattered satellite communication waveforms. The proposed method firstly preprocesses the received signal based on the characteristics of the traditional satellite tracking loop and the forward scattered satellite communication waveforms to effectively suppress the interference of the direct wave through DC removal. Then, the Gaussian noise and multipath interference in the channel are suppressed by applying a rectangular window to its linear canonical domain. Finally, aerial target sensing is performed based on the peak value of signals in the linear canonical transform domain. The characteristic signal is constructed by analyzing the satellite communication waveforms. Combining the linear canonical transform with the matched filter (MF) to estimate the target parameter. Simulation results show that the proposed method can effectively perform the aerial target sensing by using satellite communication waveforms in the forward scatter scenario.  
forward scatter; target parameter estimation; target passive sensing satellite communication waveforms 2022141375[[https://doi.org/10.3390/rs14061375](https://doi.org/10.3390/rs14061375)]([https://doi.org/10.3390/rs14061375](https://doi.org/10.3390/rs14061375))