esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric and Fog Effects on Ultra-Wide Band Radar Operating at Extremely High Frequencies
## 6 Conclusions  
In this paper we developed a model to study the effect of fog on the distance accuracy measurements using an ultra-wideband FMCW radar operating in the millimeter and sub-millimeter wave regimes. The developed model can be employed to calculate the radar performance at any frequency from 10 GHz up to 1 THz. The study demonstrates the effects of dispersive attenuation and phase on a detected radar signal. It is found that loss and dispersion in the atmospheric medium cause a deviation in the frequency obtained at the output of the FMCW radar, leading to an erroneous estimation of the distance to the target. We found that foggy conditions significantly affect the accuracy of the ultra-wideband radar. If weather conditions are known (fog, mist, high humidity) or the transfer function of the medium is estimated, these effects can be overcome by an equalizer and adaptive system. 'Side information' about the channel behavior may be used for calibration of the system and compensating for the erroneous measurements. One should note that time-dependent variations in the atmospheric medium lead to temporal fading and turbulence scintillation phenomena. However, in this paper we assume that temporal changes in weather conditions are much slower than the travel time of the signal.  
The obtained results are the physical bounds for accuracies in distance measurements as well as the resolution. Technical design issues, such as system non-linearities, can cause further degradation that should be considered. It is also interesting to note that in digital wireless communication wideband links operating in millimeter wavelengths, the dispersion of the atmospheric medium is expected to play an equivalent role. The magnitude and phase response of the atmosphere will affect the transfer of M-ary (multi) phase or frequency-shift keying signals, causing inter-symbol interference in the demodulated data.  
[PERSON] and [PERSON] wrote and performed the simulations; [PERSON], [PERSON] and [PERSON] contributed analysis tools; [PERSON] conceived the theory; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Noise Control in Air Mechanical Ventilation Systems with Three-Dimensional Metamaterials
## 4 Results
### Analysis of acoustic filter consisting of two layers of spheres  
When the acoustic filter is composed of two layers of spheres, the attenuation assumes a different behavior, based on the distance between the layers. In configuration No. 2, a greater attenuation corresponding to 12 dB occurs at 8.0 kHz.  
If the distance between the layers increases by 10 mm, as indicated in configuration No. 3, the highest sound attenuation shifts to 4 kHz, followed by another peak at 6.3 kHz, corresponding to 12 dB and 10 dB, respectively. The graph for configuration No. 3 in Figure 5 highlights negative IL values; this phenomenon is due to the amplification of the sound at specific frequency bands caused by the reflection of some soundwaves that pass through the filter. In the case of configuration No. 3, the amplification occurs at 2.5 kHz.  
If the distance between the layers of spheres increases, the graph related to configuration No. 4 indicates a more pronounced attenuation occurring at 4 kHz, which is 18 dB. This measured value is the maximum sound attenuation compared to the other selected configurations. At 8 kHz, a small peak of 8 dB is visible, but this can be considered negligible.  
When the distance is even greater and is 40 mm, the graph related to configuration No. 5 shows a sound attenuation of 13 dB at 6.3 kHz. This line trend is similar to the measured values for configuration No. 1, but the peak is shifted to a higher frequency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration Type** & **No. of Layers** & **Xs (mm)** & **Xr (mm)** & **Distance between 1 st** & **Distance between 2 nd** \\  & & & **and 2 nd layer (mm)** & **and 3 rd layer (mm)** \\ \hline No. 1 & 1 & & - & - \\ No. 2 & 2 & & 10 & - \\ No. 3 & 2 & & 20 & - \\ No. 4 & 2 & 400 & 400 & 30 & - \\ No. 5 & 2 & & 40 & - \\ No. 6 & 3 & & 10 & 20 \\ No. 7 & 3 & & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters assumed for the configurations during the acoustic measurements, where D is the diameter of scatter, \(\alpha\) is the lattice constant, Xs is the distance between the sound source and the closest edge of the filter, and Xr is the distance between the microphone and the closest edge of the filter.
# On the Modeling of Automotive Security: A Survey of Methods and Perspectives
## 2 Taxonomy of Security Models
### Qualitative Security Methods  
Qualitative methods aim to analytically discover and state the security threats. Different from informal group discussions of the potential threats, qualitative security models can be used as a tool to investigate the vulnerabilities systematically. The models can be built either from the developers' perspective or the attackers'.  
Figure 1: The structure of this article.  
Figure 2: The process of system security engineering [28].  
One of the classical qualitative approaches is the STRIDE model proposed by Microsoft. It is a structured approach for identifying the threats according to the purposes of the attacks. STRIDE model was originally applied in a software system. STRIDE represents the acronym of potential threats: spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege [42]. Firstly, the evaluated system is described with a data flow diagram (DFD). The entities and the data flow of the system are labeled in the DFD as security-related elements. Then the elements are examined to check if their security attributes of confidentiality, integrity and availability are violated. Based on the examination, the threats are identified with associated terms from the potential types of threats. The advantage of the STRIDE model is that the possible attacks are generalized into limited kinds of threats instead of every specific attack. Moreover, it emphasizes the completeness and repeatability of identifying the threats and is applicable for non-security-experts [43]. [PERSON] et al. applied STRIDE model in [28] to derive security requirements for complex systems like networked systems. Three case studies of software applications and computer systems are presented in the paper to show the threat modeling process. There are many similar methods derived from STRIDE. For example, a method named LINDDUN is used for data security to assess privacy [44]. It stands for linkability, identifiability, non-repudiation, detectability, disclosure of information, unawareness, and non-compliance [45; 46].  
Qualitative methods can be used to visualize threats from the attackers' counterpart. It allows to figure out what activities the attackers will perform, the way they carry out the attacks, and how they make decisions [47; 48]. Persona non Grata (PnG) is a method to model the threats by analyzing the motivations and skills of human attackers from an unintended use point of view [49]. It helps the developers to realize the vulnerabilities and compromise spots from the other side [50]. Refs. [51; 52] respectively considered the security problems in the attackers' perspective and from the viewpoint of the misuse cases. While the former one proposed a persona methodology to understand the complex ways attackers might work, the latter one specified a sequences of actions to be avoided to prevent various threats.
## 2 Materials and Methods
### Endothelial Cell Culture  
Immortalized human cerebral microvessel endothelial hCMEC/D3 cell line was used as a model of the human BBB, as previously described [15; 16]. hCMEC/D3 cell expansion was made until passage number 34 in Corning flasks coated with 150 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) rat tail collagen type I (R&D Systems, Bio-techne, Lille, France) in cold sterile water. The culture medium consisted of EBM-2 (Lonza, Basel, Switzerland) supplemented with 5% fetal bovine serum (Eurobio-Ingen, Les Ulis, France), 1% penicillin-streptomycin (Gibco), 1% chemically defined lipid concentrate (Gibco), 10 mM HEPES buffer (ThermoFisher Scientific), 5 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) ascorbic acid (Sigma-Aldrich, Saint Quentin Fallavier, France), and 1.4 \(\upmu\)M hydrocortisone (Sigma-Aldrich). A total of 1 ng\(\cdot\)mL\({}^{-1}\) basic fibroblast growth factor (Sigma-Aldrich) was added exterporaneously.  
The upper channel of the device was coated for one hour at 37 \({}^{\circ}\)C with rat collagen type I (R&D Systems, Bio-techne, Lille, France) diluted at 5 \(\upmu\)g/cm\({}^{2}\) in cold water. Both upper and lower channels were washed with DPBS (Gibco) and filled with culture medium. A total of 50,000 cells/cm\({}^{2}\) (165,000 cells per condition) were seeded on the membrane through the upper channel. Culture medium was renewed in all channels every 24 h to avoid nutrient depletion and waste accumulation. Phase contrast microscope examination was used to monitor cell confluence. After three days in a static condition, the cell monolayer reached about 80% confluence. To establish the dynamic condition, the upper channel was connected to the Ibidi pump system (Ibidi, Munchen, Germany) to unidirectionally recirculate culture medium for four days. A flow rate of 16.3 mL\(\cdot\)min\({}^{-1}\) was applied, corresponding to a shear stress of 10 dyn\(\cdot\)cm\({}^{-2}\). The lower channel was filled with culture medium (no medium change was operated because the channel did not contain co-cultured cells). A static culture represents the control condition, with cells grown in coated devices for a total of seven days without connection to the pump system. Culture medium was renewed every 24 h in both upper and lower compartments. To resume, dynamic culture consists of three days in static to reach cell confluence, plus four days under shear stress, whereas static control involves seven days of culture in static.
# Recent Advances of the BIRALET System about Space Debris Detection
## 4 Conclusions and Future Works  
In the last six years, the BIRALET system has been actively involved in the European space debris monitoring program, following an upgrade schedule that culminated in the  
\begin{table}
\begin{tabular}{c c c c} \hline
**NORAD ID** & **Estimated Doppler (kHz)** & **Measured Doppler (kHz)** & **Error–Absolute Value (kHz)** \\ \hline
26102 & \(-7.96\) & \(-7.44\) & 0.52 \\
36119 & 5.78 & 5.62 & 0.16 \\
39194 & 4.22 & 4.44 & 0.22 \\
15494 & 7.28 & 7.26 & 0.02 \\  
installation of a dedicated receiving channel. Although the capabilities of the newly added channel have already been assessed in a recent paper [13], an extensive measurement campaign to test the real potential of the radar system was still lacking. In this paper, the most substantial radar measurement campaign for the BIRALET system is presented. Based on a list of objects provided by the Italian Air Force, the BIRALET has been able to detect a total of 27 objects with RCS ranging from 0.13 to 13.4 m\({}^{2}\) in a range interval between 459 and 1224 km. In the future, this campaign will stand as a solid foundation for the next important step in the evolution of the BIRALET for space debris monitoring purposes: the tracking system. This new feature will enable the possibility to employ the BIRALET system for orbit determination of known and unknown objects in both single and joint campaigns with other Italian and European radar sensors.  
The following are available online at [[https://www.mdpi.com/2226-431](https://www.mdpi.com/2226-431)]([https://www.mdpi.com/2226-431](https://www.mdpi.com/2226-431)) 0/8/3/86/s1.  
Conceptualization, T.P., G.M. ([PERSON]), and L.S.; methodology, T.P., G.M. ([PERSON]), L.S., and P.O.; validation, T.P., L.S., and P.O.; formal analysis, T.P., G.M. ([PERSON]), and G.M. ([PERSON]); data curation, T.P., G.M. ([PERSON]), and G.M. ([PERSON]); writing--review and editing, T.P., [PERSON] ([PERSON]), [PERSON] ([PERSON]), and [PERSON]; supervision, [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was founded in part by the European Commission Framework Programme H2020 and Copernicus Space Surveillance and Tracking under grants 785257-2-3 SST2016 and 237/G/GRO/COPE/16/8935-ISST2016.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
The AFF module significantly improves the ability to discern and capture complex feature representations, with F1-score increases ranging from 2.2% to 18.7%. This marked improvement highlights the AFF module's exceptional efficacy in leveraging attention mechanisms to integrate and emphasize essential features within encrypted malicious traffic. * Exploiting Malicious IP Aggregation with the CGE Method: This study discovers and verifies the core role of destination IP communication aggregation in accurately distinguishing malicious- and benign-source IP traffic. Building on this pivotal discovery, this paper introduces a novel method called Communication Graph Embedding Learning (CGE). This method represents a significant advancement in malicious traffic detection, achieving performance improvements ranging from 2.3% to 22.4%. The CGE module has proven to be exceptionally effective in capturing the complex and dynamic interaction patterns among traffic entities, which is vital for detecting anomalous communication behaviors in encrypted environments. * Deep Integration of Internal Features and Structural Relationships in Encrypted Traffic: The innovative aspect of the AFF_CGE method lies in its deep integration of internal features and structural relationships in encrypted traffic. Traditional methods often focus on single-dimension feature analysis, whereas AFF_CGE simultaneously considers the internal semantic information of traffic and the structural relationships between traffic flows, constructing a more comprehensive and multi-dimensional feature representation space. This multi-level feature fusion enables the model to more accurately capture the essential characteristics of encrypted traffic, thereby improving the accuracy and robustness of malicious traffic detection. The remainder of this paper is organized as follows: Section 2 presents a comprehensive review of related work on encrypted malicious traffic detection and feature extraction. Section 3 provides a detailed analysis of both session-level and IP-level features, emphasizing the significance of combined features. Section 4 provides a comprehensive explanation of the proposed method, AFF_CGE, detailing the process of analyzing and capturing the enriched feature representations of encrypted malicious traffic. Section 5 outlines the experimental setup, performance evaluation metrics, and comparison results with other methods. Finally, Section 6 concludes the paper and proposes directions for future research.
# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
## 1 Introduction
Future research should focus on refining algorithms, expanding demographic validations, and integrating waveform analysis to improve reliability further and expand applicability to diverse clinical conditions [13; 14; 15]. Portable and cost-effective solutions are needed to enable telerehabilitation and patient monitoring. Therefore, this study aims to address these gaps by evaluating the reliability and validity of smartphone-based accelerometers compared to the GAITRite system across different walking speeds. Unlike prior research, which often focused on static or clinical settings, this study explores the potential of smartphone accelerometers for real-world gait monitoring. The primary research question is whether smartphone-based accelerometers can achieve comparable reliability and validity to GAITRite in assessing key gait parameters. We hypothesize that smartphone accelerometers will demonstrate high agreement with GAITRite, particularly for parameters such as walking speed and cadence, while highlighting areas for improvement in spatial parameter estimation.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 3 Minimum Distance Optimization Using Progressive Chord Edge Growth
### Progressive Chord Edge Growth Optimization for Efficient Encoding  
The proposed method for configuring non-zero entries in a progressive chord edge growth manner can be used to generate NB-LDPC cycle codes with an arbitrary size, as long as a Hamiltonian graph is provided. After slight modification, the proposed method can also be applied to construct NB-LDPC codes with linear-time-encoding.  
Based on the study by [PERSON] et al. [24], a parity-check matrix with a bidiagonal structure in the last \(m\) columns and \(\alpha^{0}\) in the principal diagonal can be efficiently encoded. Therefore, the proposed method can be used to construct NB-LDPC codes with low encoding complexity after two minor modifications. The first one is to modify the Hamiltonian graph, so that it can be mapped to a matrix with a bidiagonal structure, which is, the Hamiltonian graph after disconnecting the first vertex and edge \(e_{m}\) is mapped to be the parity-check base matrix. The second is to modify the non-zero element configuration corresponding to the Hamiltonian cycle. When selecting non-zero elements of the Hamiltonian cycle, the non-zero entry corresponding to edge \(e_{j}\) and vertex \(v_{j}\) is fixed as \(\alpha^{0}\), and the non-zero entries that correspond to edge \(e_{j}\) and vertex \(v_{j+1}\) are randomly selected from \(R\). These two modifications are applied to the initialization step in the method described in Section 3.1, thereby enabling the designed code to have an efficient encoding structure.
## 2 Materials and Methods
### Models for Estimating Reference Crop Evapotranspiration
#### 2.3.2 BP Neural Network Model  
A neural network model with multi-layers is able to approximate any nonlinear continuous function [33]. BP neural network is a nonlinear adaptive learning system, containing lots of parallel interconnected neurons. The advantage of BP neural network is its feed-forward and feed-back structure. A feed-forward network contains an input layer, an output layer and several hidden layers, constructing multiple layers of perceptron network [26]. The BP output results can affect the inputs using the back propagation (BP) principle and, in turn, adjust the weights of the feedback neural network. In this study, we constructed an '\(n\)-7-1' structure of a BP neural network. The letter '\(n\)' was the number of input variables in different input combinations. Owing to the nature of back propagation, the BP model can feedback the input layers either in a positive or a negative direction, which increases or decrease the weights of networks [29].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Climate** & **Station No.** & **Station Name** & **Latitude (N)** & **Longitude** & **Elevation** \\
**Type** & **Station No.** & **Station Name** & **Latitude (E)** & **(m)** \\ \hline TC & 52,203 & Hami & 42\({}^{\circ}\)49\({}^{\prime}\) & 93\({}^{\circ}\)31\({}^{\prime}\) & 737.2 \\ TC & 53,463 & Xilin Hot & 43\({}^{\circ}\)56\({}^{\prime}\) & 116\({}^{\circ}\)10\({}^{\prime}\) & 1065.2 \\ TC & 53,614 & Yinchuan & 38\({}^{\circ}\)28\({}^{\prime}\) & 106\({}^{\circ}\)12\({}^{\prime}\) & 1110.9 \\ TM & 54,823 & Jinan & 36\({}^{\circ}\)36\({}^{\prime}\) & 117\({}^{\circ}\)15\({}^{\prime}\) & 170.3 \\ TM & 54,161 & Changchun & 43\({}^{\circ}\)54\({}^{\prime}\) & 125\({}^{\circ}\)13\({}^{\prime}\) & 236.8 \\ TM & 57,083 & Zhengzhou & 34\({}^{\circ}\)43\({}^{\prime}\) & 113\({}^{\circ}\)39\({}^{\prime}\) & 110.4 \\ MP & 52,818 & Germu & 36\({}^{\circ}\)25\({}^{\prime}\) & 94\({}^{\circ}\)55\({}^{\prime}\) & 2807.6 \\ MP & 52,866 & Xining & 36\({}^{\circ}\)44\({}^{\prime}\) & 10\({}^{\circ}\)45\({}^{\prime}\) & 2295.2 \\ MP & 56,137 & Changdu & 31\({}^{\circ}\)09\({}^{\prime}\) & 97\({}^{\circ}\)10\({}^{\prime}\) & 3315.5 \\ STM & 59,287 & Guangzhou & 23\({}^{\circ}\)13\({}^{\circ}\)13\({}^{\circ}\)29\({}^{\prime}\) & 70.7 \\ STM & 58,238 & Nanjing & 31\({}^{\circ}\)56\({}^{\prime}\) & 118\({}^{\circ}\)54\({}^{\prime}\) & 35.2 \\ STM & 57,516 & Chengdu & 30\({}^{\circ}\)40\({}^{\prime}\) & 104\({}^{\circ}\)04\({}^{\prime}\) & 259.1 \\ TPM & 59,758 & Haikou & 20\({}^{\circ}\)03\({}^{\prime}\) & 110\({}^{\circ}\)35\({}^{\prime}\) & 18.0 \\ TPM & 59,948 & Sanya & 18\({}^{\circ}\)14\({}^{\prime}\) & 109\({}^{\circ}\)31\({}^{\prime}\) & 7.1 \\ \hline \end{tabular} Note: TC, temperate continental zone; TM, temperate monsoon zone; MP, mountain plateau zone; STM, subtropical monsoon zone; TPM, tropical monsoon zone.  
\end{table}
Table 1: Geographic characteristics of the 14 national meteorological stations located in different climate zones.
## 2 System Model
### Cooperative Channels Sensing
Local decision \(D_{\mathrm{z}}=0\) indicates the presence of a PU's signal in the observed channel \(Ch_{\mathrm{z}}\), while \(D_{\mathrm{z}}=1\) denotes the absence of a PU's signal in the considered channel \(Ch_{\mathrm{z}}\). It is assumed that the channel between \(MN_{l}\) and \(CH_{j}\) is a perfect channel since the distance between them is short. The clusterhead \(CH_{j}\) employs \"M-out-of-N majority\" decision counting rule fusion to determine the existence of PU in the channels \(S_{ch}=\{Ch_{\mathrm{z}}|\mathrm{z}=1,2,3,...,n_{\mathrm{z}}\}\) and then broadcasts the outcome. The final cooperative decisions \(CD_{j}\) based on this rule indicates the presence of a PU's signal in the channel when \(\ell\) out of \(m\) sensing results indicate the presence of a PU's signal in the channel [38]. This implies that when the number of cooperative member nodes \(cm_{n}\) that report presence of PU's signal in the considered channel is greater than or equal to half of the total number of cooperative member node \((cm_{n}\geq m_{n}/2)\), then the final cooperative decision indicates the presence of a PU in the observed channels [39]. Otherwise it indicates the absence of a PU and hence the availability of the observed channels. Thus, the cooperative probability of detection \(Q_{d,j}\) is given as [30]:  
\[Q_{d,j}=\sum_{\ell=n}^{m}\binom{m}{\ell}\mathrm{P}_{d}^{\ell}(1-P_{\mathrm{d} })^{m-\ell} \tag{7}\]