esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We report on the development of an optical frequency domain reflectometry (OFDR) system based on a continuous-wave Er-doped self-sweeping fiber laser. In this work, we investigate the influence of the input data processing procedure in an OFDR system on the resulting reflectograms and noise level. In particular, several types of signal averaging (in time and frequency domain) and Fourier analysis are applied. We demonstrate that the averaging in the frequency domain can be applied to evaluate absolute values of the local scattering amplitudes related to the Rayleigh light scattering (RLS), which is associated with the interference of scattering signals on microscopic inhomogeneities in optical fibers. We found that the RLS signal remains unchanged in the case of signal averaging in time domain, while the noise floor level decreases by 30 dB with an increasing number of points from 1 to \(\sim\)450. At the same time, it becomes possible to detect the spectral composition of the scattering at each point of the fiber using windowed Fourier transform. As a result, the sensitivity of the developed system allows us to measure the RLS signal at a level of about 20 dB above the noise floor. The described analysis methods can be useful in the development of distributed sensors based on Rayleigh OFDR systems.  
self-sweeping laser; optical frequency domain reflectometry; noise floor level; Rayleigh scattering; fiber optics
## 4 Experiments and Analysis of Results
### Analysis of Multi-Interest Channels  
Multiple items for the session in the MGMI-CEHCN model imply potential user interests, and the number of channel counts has a large impact on modeling multiple interests in a session using a decentralized GRU-based interest extraction network. Because of the limitations of the model structure, the number of channels is an integer multiple of its embedding size relative to the model embedding size. Therefore, representative values were selected, including 2, 4, 8, and 16, to analyze the performance.  
According to the observations in Figure 4, both P@20 and MRR@20 were best at a k of 8 on the Cosmetics and Diginetica-buy datasets, while for the Amazon dataset, the performance was best at a k of 4. To explain this discrepancy, we believe that shorter session average lengths may mean that users are involved in less information per session, and thus the model may require fewer channels to efficiently capture features and patterns in the session. Conversely, longer session average lengths may contain more information and interactions, and thus this may require more channels to capture richer and more complex  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Cosmetics**} & \multicolumn{2}{c}{**Diginetica-Buy**} & \multicolumn{2}{c}{**Amazon**} \\ \cline{2-7}  & **P@20** & **MRR@20** & **P@20** & **MRR@20** & **P@20** & **MRR@20** \\ \hline MGMI-CEHCN\({}^{\text{C}}\) & 54.85 & 37.86 & 65.01 & 26.96 & 68.12 & 59.43 \\ MGMI-CEHCN\({}^{\text{P}}\) & 54.12 & 37.32 & 64.88 & 26.38 & 67.95 & 59.26 \\ MGMI-CEHCNCM-G & 53.88 & 37.05 & 64.42 & 25.96 & 67.89 & 59.11 \\ MGMI-CEHCNCM-I & 53.72 & 37.02 & 64.28 & 26.05 & 68.32 & 59.48 \\ MGMI-CEHCNC\({}^{\text{L}}\) & 54.35 & 37.55 & 64.95 & 26.85 & 68.01 & 59.22 \\ MGMI-CEHCNC & 55.25 & 38.26 & 65.60 & 27.47 & 68.39 & 59.59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of the MGMI-CEHCN model ablation experiment. unit: %.  
features in the session. For all three datasets, the performance showed an increase and then a decrease as the number of channels increased. Too few channels may lead to underfitting of the model, and too many channels may lead to overfitting of the model.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 3 Ground Resource Scheduling Model
### Symbol Definition  
To facilitate the description of ground resource scheduling issues, the symbol definitions are first provided, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definition** \\ \hline AL & List of ground station antennas. \\ WL & List of visible time windows between satellite and the ground. \\  & Task set that converts all satellite-to-ground visible time windows \\ T & satisfying the user-specified minimum data transmission time \\  & into data transmission tasks. \\ SDCL & List of SDCs at the ground station. \\ DL & List of demodulators at the ground station. \\ RDL & List of RDs at the ground station. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbol definitions.  
Figure 3: Resource usage conflict diagram.  
Based on the aforementioned symbol definitions, we establish the following ground resource scheduling model for a single task:  
\[\mathrm{T_{k}}=\left[p_{k},time_{s}^{k},time_{e}^{k},x_{k},T_{A}^{k},T_{SDC}^{k},T _{D}^{k},T_{RD}^{k}\right] \tag{1}\]  
\(p_{k}\) represents the priority of task \(k\); \(time_{s}^{k}\) and \(time_{e}^{k}\) represent the valid start and end execution times of task \(k\), respectively; \(x_{k}\) indicates whether task \(k\) is executed; \(T_{A}^{k}\), \(T_{SDC}^{k}\), \(T_{D}^{k}\), and \(T_{RD}^{k}\) correspond to the parameters of the antenna, SDC, demodulator, and RD required for task \(k\), respectively.
## 7 Bird Nest Deployment
### System Evaluation
#### 7.3.4 Lessons Learned  
The main lessons learned from our bird nest deployment are as follows.  
* It is feasible and practical to use an image-based wireless sensor network for effective bird nest monitoring. Our system is simple to use, scalable, flexible, supports tiered architecture and multihop and satisfies the application requirements described in Section 3. We have captured important biological events, and our biologist users found our system useful for their research purposes.
* However, it is challenging to implement sophisticated image compression algorithms on the resource-constrained sensor platforms. Simple run-length encoding or PackBits algorithms can be effective alternatives, but their compression ratios are limited by the image quality.
* It is possible to use solar panels for providing energy to camera sensors indefinitely without worrying about battery life. However, for long-term deployments such as ours, the growth of foliage and their shade must be considered when placing solar panels for powering the sensor nodes.
* Rate and congestion control is very effective, if not a must, for achieving a high image delivery ratio in a highly dynamic network environment.
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 3 Proposed Method
### Theories
#### 3.2.2 Linear Multi-Classification Task  
For a multi-class classifier, the predicted label is chosen by the maximal logit attained over all classes:  
\[\hat{y}=\arg\max_{k}f_{k}(\mathbf{x})\]  
where \(f_{k}(\mathbf{x})\) is the output of \(f(\mathbf{x})\) corresponds to the \(k\)th class. In linear case, \(f_{k}(\mathbf{x})=\mathbf{\omega}_{k}^{T}\mathbf{x}+b_{k}\).  
For any two classes \((p,q)\), the decision boundary between them is called \(l_{p,q}\).  
\[\begin{split} l_{p,q}&=\Big{\{}\mathbf{x}\mid\mathbf{ \omega}_{p}^{T}\mathbf{x}+b_{p}=\mathbf{\omega}_{q}^{T}\mathbf{x}+b_{q}\Big{\}}\\ &=\Big{\{}\mathbf{x}\mid\Big{(}\mathbf{\omega}_{p}^{T}-\mathbf{\omega}_{q}^{T }\Big{)}\mathbf{x}+b_{p}-b_{q}=0\Big{\}}\end{split} \tag{6}\]  
The geometric distance of a point \(\mathbf{x}\) from \(l_{p,q}\) is:  
\[d_{p,q}(\mathbf{x})=\frac{\Big{(}\mathbf{\omega}_{p}^{T}-\mathbf{\omega}_{q}^{T}\Big{)} \mathbf{x}+b_{p}-b_{q}}{\Big{\|}\Big{(}\mathbf{\omega}_{p}^{T}-\mathbf{\omega}_{q}^{T} \Big{)}\Big{\|}_{2}} \tag{7}\]  
Some margin-based methods only consider the margin between the ground truth class and the most easily misclassified class, i.e., setting \(p=y,q=\arg\max_{i\
eq y}f_{i}(\mathbf{x})\). Instead, our method considers all margins between each class and the other classes. In other words, we regard a multi-classification task as a multiply binary classification task. Equation (5) can be generalized to the following optimization problem in Equation (8).  
\[\min\sum_{i=1}^{m}\sum_{j=1}^{n}\max\Big{(}0,1-y_{j}^{(i)}\Big{(}\mathbf{\omega}_{ j}^{T}\mathbf{x}^{(i)}+b_{j}^{T}\Big{)}\Big{)}+C^{\prime}\|\mathbf{\omega}_{j}\|_{2}^{2} \tag{8}\]where \(n\) is the class number, \(y_{j}^{(i)}=\pm 1\) indicate \(\mathbf{x}^{(i)}\) is/not belong to class \(j\).
# The Contribution of Near-Surface Geophysics for the Site Characterization of Seismological Stations
## 3 Application of the Geophysical Surveys and Processing Results
### Seismological Station at Loutraki, Korinthia (LOUT)  
Station _LOUT_ was installed on 27 May 2010 at the monastery of Prophet [PERSON] (Figure 9), near Loutraki town, south of Gerania Mt. Station _LOUT_ is currently using a _Guralp CMG-DM2456-EAM_ datalogger, employing a _CMG-40T/30s_ seismometer. The station is based on old cemented talus cones and scree (_Q.cn_) (Figure 7), which overlie in places the Middle Jurassic shales-chert formation (_sl_) and limestones (_JmL.k_) [71].  
Figure 8: SRT and MASW (**top**) and ERT (**bottom**) sections at the site of the seismological station _VILL_.  
Figure 9: Location of the seismological station _LOUT_ and the geophysical measurements along with the geology of the area.  
The HVSR results at station _LOUT_ (Figure 10) indicate some peak frequencies, but two of them seem the most important. A first frequency peak appears to be at \(f\) = 1.4 \(\pm\) 0.1 Hz, with \(A\) = 2.3 \(\pm\) 0.5, and a secondary frequency peak at \(f\) = 5.1 Hz, with \(A\) = 2.2 \(\pm\) 0.4. The base level of the amplification factor is equal to 1.8. Station LOUT has the same sensor as the old one of station VILL (_CMG-40T/30s_), which may be the cause of the observed gradual decrease of HVSR for frequencies >5.0 Hz, likely masking any possible higher peak frequencies.  
At _LOUT_ seismological station, the results of the SRT technique (Figure 11) revealed three seismic layers with the following characteristics: _V\({}_{P1}\)_ = 700 m/s, _V\({}_{P2}\)_ = 900 m/s, _V\({}_{P3}\)_ > 1100 m/s, and an average thickness of 2.5 m and 1.0 m for the first and second layer, correspondingly. According to the MASW technique (Figure 11), the shear-wave velocity of the seismic bedrock, below the first 5.0 m, was determined to be greater than 500 m/s. Finally, the V\({}_{S0}\) value of the subsurface for the _LOUT_ seismological station was calculated as 638 m/s. In Figure 11, the results derived from the processing of the ERT data are also presented. In the first 3.0 m, on average, a relatively resistant (>200 Ohm.m) formation is present across the section. The resistivity value decreases (<100 Ohm.m) with depth, as a result of the possible increase in the ambient moisture.  
Figure 10: Average HVSR curves (solid lines) determined for station _LOUT_ on different days, from 60 s long windows selected after the application of STA/LTA anti-triggering on 6 h long records. Circles denote the global frequency on each curve, while diamond symbols mark the average of the frequency band denoting the range of peak frequencies of the HVSR lines from the individually selected windows (light/dark gray band in Figure 2b). Squares mark the prominent global and secondary peak frequencies recognized from the HVSR spectra, with error bars indicating the uncertainty of the amplification factor (Table A1).
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 4 Optimization Key Parameters by Particle Swarm Optimization (PSO)
\(E\) and \(G\) denote the visual signal at the output of the HVS model from the original and watermarked images respectively, from which the brain extracts cognitive information. The terms \(I(\overset{\rightarrow}{C};\overset{\rightarrow}{E}|s^{Z,k})\) and \(I(\overset{\rightarrow}{C};\overset{\rightarrow}{G}|s^{Z,k})\) represent the mutual information that can ideally be extracted by the brain from a particular channel in the original and the watermarked images, respectively, given a reference image [29]. As is known in the literature, the VIF value reflects perceptual distortions more precisely than peak signal to noise ratio (PSNR). The range of VIF is [0, 1] where the closer value to 1 represents the better fidelity with respect to the original image. On the other hand, BCR is defined by (15) as:  
\[BCR=\frac{L-\sum_{b=1}^{L}W_{b}\oplus W_{b}^{\prime}}{L} \tag{15}\]  
where \(W\) and \(W^{\prime}\) are the original and extracted watermarks respectively, \(L\) is the watermark length and \(\oplus\) denotes a module 2 addition, or XOR operation. In this paper, we employ five attacks (\(n=5\)) in (13). These signal processing and geometric distortions are shown in Table 2.  
The fitness value increases when VIF and BCR values grow. The VIF [29] and BCR values have been multiplied by 100, since its normal values fall in the range [0, 1]. In training of the PSO algorithm, the watermark strength factor \(\alpha\in[0.5-10.5]\) and the pair of radiuses \(r_{1}\) and \(r_{2}\) in the magnitude \(M(u,r)\) are \(r_{1}\in[10-100]\) and \(r_{2}\in[105-250]\). The parameter values in the PSO algorithm are given in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Distortion** & **Tolerance** \\ \hline Centered cropping & 100 \(\times\) 100 for images with spatial resolution of 640 \(\times\) 480 \\ Box filter & Kernel of 5 \(\times\) 5 \\ Downsizing & Scaling factor of 0.6 \\ JPEG compression & Quality factor 30 \\ Gaussian noise & \(\mu=0\), \(\sigma^{2}=0.01\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Signal processing and geometric distortions considered in the fitness function of the PSO algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Maximum Number** & **Population Size N** & **Cognitive and Social** & **Inertia Coefficient \(\omega\)** \\ \hline
100 & 10 & 2.0 & 1.0–0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter setting of PSO algorithm.
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 4 LoRaWAN Medium Access Control Protocols
### Pure ALOHA  
The Pure ALOHA MAC protocol is a random medium access strategy that allows an end-device to transmit at any time, i.e., whenever it has a message to be transmitted.  
Each end-device using Pure ALOHA-based medium access strategies must comply with duty cycle constraints (\(\mathcal{D}\mathcal{C}\)) and maximum transmission power, where \(\mathcal{D}\mathcal{C}\) is defined as the maximum percent of time per hour that an end-device can transmit in a sub-band. For example, with a \(\mathcal{D}\mathcal{C}\) equal to 5%, an end-device can transmit 180 s per hour.  
Table 1 shows the duty cycle constraints (\(\mathcal{D}\mathcal{C}^{max}\)) and maximum transmission power (\(Tx\mathit{Pow}^{max}\)) in the EU863-870 MHz ISM band relevant to the sub-bands that match the ERC Recommendation 70-03.  
Since the ETSI limitations apply to the sub-bands, in the case that a number of 125 kHz-channels higher than one is available for transmission in a given sub-band, the transmission times on these channels must be jointly considered.  
The current LoRaWAN specification exclusively uses transmissions with limited duty cycle to conform to the ETSI regulation [36].
## 6 Discussion  
### Discussion on Engineering Practice Applications in Metro Tunnel Maintenance  
The YOLOv8-based detection model for apparent defects in metro tunnel linings proposed in this study holds significant practical value for metro tunnel engineering. Its high defect-detection accuracy, coupled with its overall performance in detection speed and model size, makes it particularly well-suited for metro tunnel scenarios. The enhanced detection accuracy, particularly in identifying small defects such as fine cracks and minor exfoliation, directly improves the safety and reliability of metro tunnel operations. By detecting defects at an earlier stage, this model enables timely maintenance interventions, thereby reducing the risk of catastrophic failures, such as tunnel collapse or severe water leakage, which could have devastating consequences. Early detection not only enhances the safety of tunnel operations but also prolongs the lifespan of infrastructure by preventing minor issues from escalating into major structural problems. Moreover, the application of super-resolution technology significantly improves the clarity and detail of crack images, which is crucial for accurate crack segmentation and geometric feature extraction. By enhancing image quality, super-resolution not only increases the accuracy of subsequent analyses but also provides more reliable data support for high-precision engineering assessments. This is of great importance for decision-making in tunnel maintenance, enabling more effective guidance for actual repairs and preventive maintenance work.  
However, despite the model's excellent performance in controlled experimental environments, its application in more diverse and complex real-world tunnel environments may reveal certain limitations. For instance, the model's effectiveness could be challenged by varying lighting conditions, different tunnel materials, or more complex defect patterns that may not have been fully represented in the training dataset. Additionally, the processing time and computational resource demands of super-resolution technology could pose a bottleneck in practical deployment, particularly on resource-constrained mobile devices.
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
## 1 Introduction
A common example of such a regime change is in the scope of economic growth and decline, as first demonstrated by [PERSON] in [14]. In our crowd context, we encounter situations containing periodically introduced changes to the environment in subway stations, where a rail vehicle will enter or leave the station, thereby creating huge temporary changes to the environment from a signal strength perspective. In prior research [15], a solution was proposed to detect the arrival of rail vehicles in the environment by using a Time-of-Flight (ToF) sensor-based solution. In this paper, we will demonstrate a system using only a DFWS network to both estimate the number of people present in the environment and detect changes in the environment. Based on the measured environment's state, we switch the active model between two trained models in order to provide a more reliable crowd size estimation. In this study, we aim to address the following research question: Can the accuracy of DFWS-based crowd size estimation systems be improved by incorporating a switching model to mitigate the impact of dynamic environmental factors? This paper makes the following novel contributions:  
* We demonstrate the use of Received Signal Strength Indicator (RSSI) values in a wireless sensor network to detect the presence of a rail vehicle in a subway station. * We demonstrate the use of two separately trained polynomial regression models, when a rail vehicle is present and absent, in order to perform crowd size estimation using the change in the RSSI between wireless sensor nodes in the different environment states. * We evaluated our proposed switching model in an uncontrolled environment and used a single DFWS network to perform the detection of the environment's state and switch between models accordingly. This paper is structured as follows: In Section 2, we will provide a brief overview of related research. We will dedicate Section 3 to describing the system and the chosen parameters for our experiments. In Section 4, we will discuss the algorithms used to detect the presence of a rail vehicle in the environment, as well as the models used for crowd size estimation. In Section 5, we present the achieved accuracy of both the independent components and the full system and discuss these results. Finally, in Section 6, we conclude this paper.
## 3 Results and Discussion
### Antiviral Activity of the SeNPs  
To detect the antiviral activity of USeNPs against HAV HM175, MTT assay was utilized (Table 1) along with Vero cells (Figure 10). Figure 10 shows the cytotoxic activity of USeNPs with Vero cells with CC\({}_{50}\) = 331.76 \(\pm\) 4.79 ug/mL in order to evaluate the risk of these nanoparticles. [PERSON] et al. [63] found that SeNPs inhibit Madin-Darby canine kidney cells with H1N1 by preventing chromatin condensation and DNA fragmentation. Table 1 shows the antiviral effects of the tested compounds on the hepatitis A virus (HAV-10) when tested at maximum noncytotoxic concentration of 100 ug/mL. USeNPs have good antiviral activity (++) against the tested virus with 74.13 \(\pm\) 3.79% inhibition, and EC\({}_{50}\) = 57.41 \(\pm\) 2.37. The ratio of CC\({}_{50}\) to EC\({}_{50}\) was used to calculate the selectivity index ((SI) = 5.78)) to indicate whether USeNPs had sufficient antiviral activity which exceeded its toxicity [43]. This index is referred to as a therapeutic index, and it illustrated that USeNPs is an active antiviral agent which warrants further study since compounds with SI \(\geq\) 2 are described as active43. The antiviral activity of SeNPs against various other types of viruses is likely due to the SeNPs' direct binding to viral envelope glycoproteins which prevents viral entry into host cells, even though the mechanism of action has not been well-studied [64; 65].  
Figure 12: Effect of USeNPs on epidermal growth factor receptors enzyme (EGFR enzyme) activity with IC\({}_{50}\) = 230.79 \(\pm\) 3.47 ug/mL. and IC\({}_{50}\) = 0.76 \(\pm\) 0.18 ug/mL. For USeNPs and Sorafenib (ref. drug).