esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 2 Results
### Important Special Cases
#### 2.5.1 No ICM  
In the degenerate case where there is no ICM, i.e., the surface motion is small relative to the platform motion, then the localized Doppler shifts \(\rho_{\phi,\theta}\) are deterministic and only depend on the look angle relative to platform motion. This means that the local Doppler spectra are each just an impulse, and the scattering function Equation (20) reduces to [42]:  
\[\begin{split} S_{h}^{\text{(no ICM)}}(\tau,\rho)&=\frac{ \lambda^{2}\sigma^{0}}{16\pi^{3}c^{2}\tau^{3}}\int_{(2\pi)}G^{2}(\phi,\theta) \delta(\rho-\beta(\phi,\theta))d\phi\\ &=\begin{cases}\frac{\lambda^{2}\sigma^{0}(\alpha(\tau))}{16\pi^ {3}c^{2}\tau^{3}}\frac{G^{2}(\phi^{\prime},\theta)+G^{2}(-\phi^{\prime},\theta )}{\sqrt{\left(\rho_{X}^{\prime}(\tau)\right)^{2}-\left(\rho-\rho_{Z}^{\prime} (\tau)\right)^{2}}},&|\rho-\rho_{Z}^{\prime}(\tau)|<\rho_{X}^{\prime}(\tau)\\ 0,&\text{else},\end{cases}\end{split} \tag{21}\]  
where the nominal Doppler shift \(\beta(\phi,\theta)\) due to platform motion is given as:  
\[\beta(\phi,\theta)=\rho_{X}\cos\theta\cos\phi+\rho_{Z}\sin\theta, \tag{22}\]  
where \(\rho_{X}~{}=~{}2v_{X}/\lambda\) and \(\rho_{Z}~{}=~{}2v_{Z}/\lambda\) are the Doppler contributions due to motion in the \(X-Z\) plane, \(\rho_{X}^{\prime}(\tau)=\rho_{X}\cos(\theta(\tau))\) and \(\rho_{Z}^{\prime}(\tau)=\rho_{Z}\sin(\theta(\tau))\) are the effective Doppler contributions in the observation direction, and \(\phi^{\prime}(\tau,\rho)~{}=~{}\arccos((\rho-\rho_{Z}^{\prime}(\tau))/\rho_{X} ^{\prime}(\tau))\) is the nominal azimuth angle. The result in Equation (21) is a frequency-selective generalization of the Clarke/Jakes spectrum for airborne radar geometries [9; 10].  
A constant-\(\tau\) cut of Equation (21) without the antenna pattern is plotted in Figure 4, with \(G=1\) and \(\rho_{Z}^{\prime}=0\). The singularity of the scattering function at \(\rho-\rho_{Z}^{\prime}(\tau)\approx\rho_{X}^{\prime}(\tau)\) is extremely critical for modeling nose-aspect clutter, as it serves to narrow the Doppler spread of the antenna pattern. For side-looking antennas, however, the singularities are irrelevant as the Jakes spectrum is flat near \(|\rho-\rho_{Z}^{\prime}(\tau)|\approx 0\).
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### Power Consumption
#### 5.2.1 Active CPU Power Consumption  
The power consumption of the active CPU can be calculated using Equation (6) and the data presented in Table 1:  
\[Active\text{{PU}}power=\frac{Runtime\times 1.8\text{ mA}\times 3\text{ V}}{32,768}. \tag{7}\]  
Equation (7) was used to calculate the active CPU power consumption for both the single-node and two-node attack scenarios. This calculation was performed for three cases: the baseline, an unmitigated attack, and a mitigated attack. The results of this comparison are presented in Figures 8 and 9.  
The proposed mitigation strategy demonstrates significant energy savings in terms of CPU activity compared to the unmitigated attack scenario with a single attacker node (Figure 8). On average, a 59% reduction in active CPU energy consumption was observed across the various message frequency increments. The effectiveness of the mitigation strategy diminished when the number of attacking nodes increased to two, as is clear from Figure 9. In this scenario, the average power savings in terms of active CPU energy were only 6.4%. This observation suggests that as the number of attacker nodes continues to rise, the mitigated scenario's active CPU power consumption might eventually surpass that of the unmitigated scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{One Attacking Node Mitigated} \\ \hline Message frequency every & 8 s & 4 s & 2 s & 1 s \\ \hline Avg filtered messages dropped & 28 & 56.8 & 116.8 & 232.8 \\ Avg false positives & 1 & 2.8 & 2.4 & 6 \\ \hline \multicolumn{5}{c}{Two Attacking Nodes Mitigated} \\ \hline Message frequency every & 8 s & 4 s & 2 s & 1 s \\ \hline Avg filtered messages dropped & 57 & 116 & 235 & 465 \\ Avg false positives & 1.6 & 2 & 4 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dropped messages vs. false positives.
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 4 Model Training and Experimental Results
### Performance Evaluation
Row (a), the DeepSOCIAL, shows 10 more true positive detections (highlighted by vertical arrows) comparing to the second best approach. Although the DeepSOCIAL model showed superior results even in challenging scenarios such as partial visibility and truncated objects, there could be some further challenges such as detections in extreme lighting conditions and lens distortion effects that may affect the performance of the model. This requires further investigations and experiments. Unfortunately, at the time of this research, there were no such datasets publicly available for our assessment. Figure 11: Detection performance of the DeepSOCIAL model in three different datasets, from a low resolution of \(640\times 480\) to an HD resolution of \(1920\times 1080\). (**a**) Oxford Town Centre [31]; (**b**) Mall Dataset [109]; (**c**) Train Station [110]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **Precision** & **Recall** & **FPS** \\ \hline DeepSOCIAL-\(\alpha\) & CSPResNeXt50-PANet-SPP-SAM & 99.8\% & 96.7 & 23.8 \\ DeepSOCIAL-\(\beta\) & CSPDarkNet53-PANet-SPP & 99.5\% & 97.1 & 24.1 \\ DeepSOCIAL-\(\gamma\) & CSPResNeXt50-PANet-SPP & 99.6\% & 96.7 & 23.8 \\
**DeepSOCIAL (DS)** & CSPDarkNet53-PANet-SPP-SAM & 99.8\% & 97.6 & 24.1 \\ YOLOv3 & DarkNet53 & 84.6\% & 68.2 & 23 \\ SSD & VGG-16 & 69.1\% & 60.5 & 10 \\ Faster R-CNN & ResNet-50 & 96.9\% & 83.0 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy, recall-rate, and speed comparison for seven Deep Neural Network (DNN) models on the Oxford Town Centre dataset.
## 5 Conclusions and Future Work  
This paper presents an optimized DCC scheme that utilizes a hybrid approach, combining TRC and TPC algorithms to create a more effective and robust congestion control solution. The HPR-DCC method dynamically allocates transmission power and rate according to the degree of congestion, which improves overall network performance and efficiently allocates available bandwidth. By incorporating PL and CBR metrics, the DCC scheme can make joint state-switching decisions, enhancing its flexibility and adaptability to various network scenarios and congestion situations. Achieving such flexibility entails real-time monitoring and detection of current network conditions, which allows for improved responsiveness to network demands and enhanced network performance. By introducing additional state-switching conditions, the DCC algorithm can be more adaptive, enabling it to better manage network instability and fluctuations. Simulation results substantiate that the HPR-DCC effectively controls the maximum CBR value within 64%, with a 6% enhancement compared to the original DCC approach and an extension of the effective signal reception distance by 20 m while maintaining a PRR of 90%.  
In our future research, we intend to place greater emphasis on algorithmic complexity. This is because the coordination and synchronization between TPC and TRC necessitate implementation and tuning, which can potentially increase computational and processing overhead when compared to existing methods. Subsequently, we will evaluate the performance of the proposed hybrid control scheme in more intricate network environments, such as urban scenarios. Additionally, we aim to explore the possibility of incorporating machine learning techniques into the design of the hybrid control scheme, which could potentially enhance its adaptability and robustness. Furthermore, we will investigate the integration of other advanced technologies to improve the performance and security of the hybrid control scheme.  
Conceptualization, J.T. and K.C.; methodology, J.T., S.A., A.I. and K.C.; validation, J.T., S.A., A.I. and K.C.; formal analysis, J.T., S.A., A.I. and K.C.; investigation, J.T. and K.C.; writing--original draft preparation, J.T. and K.C.; writing--review and editing, J.T., S.A., A.I. and K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-01140, Development of ultra high-speed V2X communication-based autonomous driving service technologies).  
Not applicable.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 1 Introduction  
### Motivation  
Many natural and synthetic quasi-periodic signals, including speech, singing, physiological signals such as ECG, music, and acoustic waves from mechanical system vibrations, have a harmonic structure of sinusoids whose magnitudes, phases, and underlying fundamental frequencies vary over time.  
Harmonic phases are crucial in defining the waveform shape of quasi-periodic signals and are therefore immensely informative about the physical phenomena that generate them. Examples include the periodic glottal excitation signal, which illuminates the physiological processes governing vocal fold vibrations in the larynx; and periodic acoustic signals from mechanical systems, which provide insights into whether these systems are operating correctly within predefined safety margins.  
Given that harmonic phases depend explicitly on time, they vary much faster than harmonic magnitudes and fundamental frequencies; this is challenging from the perspectives of signal analysis, estimation, interpretation, modeling, transformation, and synthesis [1, 2, 3, 4].  
Since accurate frequency and magnitude estimation of sinusoids have been extensively discussed in the literature [5; 6; 7; 8; 9; 10; 11; 12; 13], in this paper, we assume that these factors have been addressed and will instead focus on two problems related to phase. The first concerns the practical and accurate DFT-based phase estimation of individual sinusoids. This is instrumental in addressing a second problem: the parametric modeling of sinusoid phases within a harmonic structure in a way that is time-shift invariant, interpretable, insightful, and simplifies harmonic signal processing. To the best of our knowledge, this is the first time such a combined perspective is presented in a manner that is easily apprehensible and extensively illustrated.  
This paper demonstrates that these problems can be tackled practically, facilitated by Matlab code that replicates the main results and illustrations presented.
## 3 Results and Discussion
### Effect of Inlet Flow Rate  
The inlet flow rate is another important operation parameter in the downhole electric heater. On the one hand, the large flow rate can increase the heating capacity of the heater, making the kerogen decompose in a more efficient way. On the other, the large flow rate can decrease the outlet temperature of the heating fluid, and the pressure loss of the heater can increase dramatically. Figure 9a-c show the changes in the heat-transfer coefficient, pressure drop, and comprehensive indexes of \(h/\Delta p^{1/3}\) versus the flow rate, \(G\), respectively. It is obvious that the larger the inlet flow rate is, the higher the heat-transfer coefficient and the pressure drop will be. As shown in Figure 9c, the comprehensive index of \(h/\Delta p^{1/3}\) increases monotonically with the increase of the mass flow rate. Therefore, under the studied conditions, the large flow rate benefits the performance of the heater. Figure 9d shows the temperature of the electric heater at different flow rates, and the corresponding temperature contours are shown in Figure 10. It can be found that the large flow rate will lead to a decrease in the temperature of the heating tube. Even for the largest flow rate, \(G\) = 10 Nm\({}^{3}\)/min, the outlet temperature of the fluid flow (~618 K) is still within the pyrolysis temperature range of the oil shale [18].  
Figure 8: Temperature nephogram of the electric heater at different lengths of heating pipes. In the simulation, the flow rate is \(G\) = 10 Nm\({}^{3}\)/min, tube diameter is \(d\) = 6 mm, and tube thickness is \(\delta\) = 1 mm.  
Figure 7: Heat transfer and flow performance versus flow rate and the length of heating pipe: (**a**) \(h\), (**b**) \(\Delta p\), and (**c**) \(h/\Delta p^{1/3}\). (**d**) The variation of the wall temperature along the length of the heating pipe; the flow rate is \(G\) = 10 Nm\({}^{3}\)/min.
## 2 Concept of Operations
### Robustness and Integrity
#### 2.3.1 Robustness  
One essential prerequisite for achieving ICaC is conducting a feasibility analysis. In this context, appropriate power allocation is framed as a nonlinear tracking problem that aims to achieve the desired signal-to-noise ratio, denoted as \(\gamma_{clk}^{si}\), for the clock data transfer service. This problem can be transformed into a linear power reference tracking issue by selecting a suitable error function  
\[e_{clk}^{i}(k)=\left[1-\frac{\gamma_{clk}^{si}}{\gamma_{clk}^{I}(k)}\right]CN0 _{clk}^{i}(k)\,. \tag{10}\]  
In view of (4), one can obtain  
\[e_{clk}^{i}(k) = CN0_{clk}^{i}(k)-\frac{\gamma_{clk}^{si}}{B^{i}}CN0_{c}^{i}(k)- \gamma_{clk}^{si} \tag{11}\] \[= CN0_{clk}^{i}(k)-\overline{CN0}_{clk}^{i}(k)\,, \tag{12}\]  
where in fact, the power allocation associated with the clock data transfer service is employed to emulate a time-varying virtual power reference \(\overline{CN0}_{clk}^{i}(k)\) to achieve the desired \(\gamma_{clk}^{si}\); i.e.,  
\[\overline{CN0}_{clk}^{i}(k)=\gamma_{clk}^{si}\left[\frac{1}{B^{i}}CN0_{c}^{i }(k)+1\right]. \tag{13}\]  
It is important to recognize that there is a fundamental trade-off between robustness and integrity. By enhancing the paradigm's robustness through an increase in the value of the virtual power reference (13), we improve the effectiveness of communication services. However, this can inadvertently affect clock data receivers that are not aware of these changes. Furthermore, the resulting linear feedback loop (10) or (12) for the clock data transfer service can be deduced to the dynamic update of the step size; i.e.,  
\[e_{clk}^{i}(k+1)=e_{clk}^{i}(k)+CN0_{total}^{i}u_{clk}^{i}(k)-\frac{\gamma_{clk }^{si}}{B^{i}}CN0_{total}^{i}u_{c}^{i}(k) \tag{14}\]  
by leveraging the results stated in (8) and (9).
## 4 Evaluation Proposal and Analysis
### Results and Analysis  
The results regarding energy consumption and node losses were compared in different scenarios with two metrics (ETX and HOP) and two transmission/interference ranges (50/100 m and 70/90 m). In all cases, the relation among the transmission/interference range from 50/100 m to 70/90 m and its significant effect on the RPL's ability to create and maintain a DODAG instance was clear. This demonstrated that when the nodes were just within each other's interference range, it caused great difficulty in exchanging messages to the degree that a DODAG could not be maintained. In the case of the ETX metric in the 50/100 m scenarios, it always had the highest energy consumption compared to the 70/90 m scenarios due to the smaller transmission area and the higher number of parents to change, so it needed to make more hops to reach the sink node. Furthermore, it always exchanged parents to maintain a good quality of the link.  
Another critical point was the influence of disconnected nodes on network behavior. Disconnected nodes were nodes out of range, or offline (either due to a lack of battery or a duty-cycle strategy). Comments about the results will be presented during the text, and final remarks about it are presented in the last part of this section.
# Automated Assessment of Inferences Using Pre-Trained Language Models
## 5 Conclusions  
In this study, we investigated the feasibility of using language models to classify the inferences of sentence-response pairs. The proposed method achieved high F1 scores by fine-tuning a Transformer-based pre-trained language models. Specifically, the highest F1 score, 0.92, was achieved by fine-tuning RoBERTa-base, which was higher than that of a model with the same size fine-tuned from BERT-base. A larger language model (RoBERTa-large) did not increase the classification accuracy. This suggests that choosing pre-trained language models of high quality and appropriate size is important.  
The proposed method would allow the automated quantification of reader responses to a given text and improve the effectiveness of the think-aloud protocol. In the think-aloud protocol, reader responses are open ended and provide rich information which requires a trained expert to evaluate. This study opens the possibility of simulating trained experts in evaluating inferences, which is one of the key qualities of an effective reader.  
Further work could be conducted in three ways. First, expanding the genre of the stimulus text would be a natural next step. Different genres have unique structures, styles, and conventions that would affect reading behaviors. Therefore, confirming the feasibility of our method for different genres would test the generality of our method. Second, classifying inference subtypes would be an interesting future work. This requires a larger dataset, so that enough samples are collected for each inference subtype. Third, another future research direction is to adapt the proposed method to educational settings. For example, the automated inference classification of user responses could enrich interactive learning by tailoring content to meet individual learning needs and preferences. We are eager to further explore applications and improve learning outcomes.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Chonnam National University (1040198-170920-HR-074-02) on 13 October 2017.  
A comprehensive written consent form was provided to all participants and their parents. These forms outlined the objectives of the research, the extent of participation, potential benefits and risks, assurances of confidentiality, and the right of the participants to withdraw from the study at their discretion.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The author declares no conflicts of interest.
## 3 Transmission Experiment Comparing Analog and Digitized RF-over-Fiber Transport  
Numerous transmission demonstrations have been carried out for different transport schemes to quantify the performance of the fiber-wireless link with the main focus on increasing the transmission capacity, improving the link dynamic range and transmission distance [23; 24; 25; 26; 27]. One strategy to increase the transmission capacity is to efficiently utilize the wireless bandwidth by incorporating advanced modulation format in conjunction with broadband orthogonal frequency-division-multiplexing (OFDM). The demand for larger transmission capacity is driven by the emergence and proliferation of bandwidth-hungry applications such as real-time high-definition video streaming and graphic intensive online gaming.  
OFDM with its inherent advantages of spectral efficiency, immunity to multipath fading and dispersion tolerance, is currently the leading modulation for current wireless standards ranging from  
Figure 3: Possible base station configuration for (**a**) digitized IF-over-Fiber transport and (**b**) digitized IF-over-Fiber transport incorporating bandpass sampling technique.  
3 GPP Long Term Evolution (LTE) to personal area network ultra-wideband (UWB). The multi-carrier nature of OFDM demands for highly linear transmission link with the ability to handle large peak-to-average power (PAPR). This requirement becomes more stringent for wideband (\(>\)1 GHz) signals. Although many different demonstrations have been carried out for the distribution of OFDM signals over fiber-wireless links using analog optical transport [28; 29; 30; 31; 32; 33], the linearity requirement has necessitates the use of low modulation depth which leads to large received DC power at the photodetector and hence, power wastage. On the other hand, digitized transport may emerge as an ideal candidate for the distribution of broadband OFDM signals in fiber-wireless links which overcomes the nonlinearity issue associated with analog transport. However, the needs for highspeed ADC and DAC coupled with excessive optical bit rate after digitization may render the digitized transport scheme unattractive. Given the pros and cons of the analog and digitized transport, we have previously carried out an experimental comparison for the distribution of a 1 GHz OFDM signal using both analog and digitized RF-over-fiber transport schemes [34].
## 2 Literature Review  
### Concepts and Components of CPTED  
CPTED was proposed by [PERSON] and [PERSON] [1; 2] in the 1960s as crime problems arising from urban redevelopment emerged across the United States. [PERSON] [1] raised comprehensive questions about the urban environment, including the relationship between the diversity of urban space and the occurrence of crime. To solve this problem, he suggested improvements, including the complex use of space, the use of streets by hour, and density. [PERSON] [2] proposed the defensible space theory, which states that a crime can be prevented with changes in the physical environment of the city, such as natural surveillance and individual management of owned spaces. These two studies are considered to be the early studies of CPTED, which recognized the relationship between urban environment, crime, and safety.  
Previous studies proposed six components of CPTED based on the defensible space theory: natural surveillance, territorial reinforcement, natural access control, target hardening, activity support, and maintenance and management. The principles are not used independent of each other but constitute an overlapped and complementary relationship [8]. Natural surveillance is the principle of discouraging criminal activities by ensuring visual access to public spaces, providing lighting to maximize exposure, arranging buildings, and introducing landscaping. Territorial reinforcement marks ownership of an area through landscaping, fences, signs, and lighting as a hypothetical territory where residents can assert their rights. It discourages potential criminals from committing a crime as they realize they can be monitored or deterred. Natural access control is the principle of preventing a crime by blocking the entry and exit of potential criminals and controlling the entry and exit of others to a certain protected space with appropriately installed facilities, including entrances, exits, lights, and fences. Activity support is the principle of reducing crimes and promoting a sense of safety by encouraging and stimulating the active use of a certain area by residents and subsequently reinforcing natural surveillance. Maintenance and management is the principle of planning and designing any facility or public space in a way that is conducive to the management for its continuous operation to discourage users' deviant behaviors. Target hardening is intended to provide clear indications and information so that a space or facility can be used as intended, such as signage [8]. These six components of CPTED, if applied harmoniously, would be very effective and practical. In this context, this study aims to develop an implementation plan for each of the architectural urban planning elements based on the CPTED components and ensure a systematic process of combining with intelligent crime prevention technologies.