esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Satellite Communication System Security
###### Abstract  
In recent years, satellite communication systems (SCSs) have rapidly developed in terms of their role and capabilities, promoted by advancements in space launch technologies. However, this rapid development has also led to the emergence of significant security vulnerabilities, demonstrated through real-world targeted attacks such as AcidRain and AcidPour that demand immediate attention from the security community. In response, various countermeasures, encompassing both technological and policy-based approaches, have been proposed to mitigate these threats. However, the multitude and diversity of these proposals make their comparison complex, requiring a systemized view of the landscape. In this paper, we systematically categorize and analyze both attacks and defenses within the framework of confidentiality, integrity, and availability, focusing on specific threats that pose substantial risks to SCSs. Furthermore, we evaluate existing countermeasures against potential threats in SCS environments and offer insights into the security policies of different nations, recognizing the strategic importance of satellite communications as a national asset. Finally, we present prospective security challenges and solutions for future SCSs, including full quantum communication, AI-integrated SCSs, and standardized protocols for the next generation of terrestrial-space communication.  
s _Sensors_2024, 24, 2897. [[https://doi.org/10.3390/s24092897](https://doi.org/10.3390/s24092897)]([https://doi.org/10.3390/s24092897](https://doi.org/10.3390/s24092897))
## 4 GPS/BDS Combined RTK Implementation
(5) Kalman filter  
Given the DD measurements as \(\mathbf{Z}\) in Equation (27), and the updated \(\mathbf{P}_{k}\), \(\mathbf{N}_{\mathrm{float}}\) and \(\mathbf{b}_{\mathrm{rb}}\) from the pre-filter module, Equations (23)-(27) can be solved to produce the baseline, float SD ambiguities and the SD VC matrix for the current epoch. Here, \(h\)() in Equation (27) can be written as:  
\[h\left(\mathbf{X}_{k+1k}\right)=\left[\mathbf{\phi}_{\mathrm{DD}}+\mathbf{D} \mathbf{N}_{\mathit{float}},\mathbf{\phi}_{\mathrm{DD}}\right]^{T} \tag{32}\]  
where \(\mathbf{D}\) is the transformation matrix from single to double-differenced measurements for either GPS or BDS as given by Equation (22), or to their combination as given by Equation (33):  
\[\mathbf{D}=\left[ \begin{matrix}\mathbf{D}^{\mathrm{GPS}}\\ &\mathbf{D}^{\mathrm{BDS}}\end{matrix} \right] \tag{33}\]  
(6) LAMBDA algorithm  
The SD \(\mathbf{N}_{\mathrm{float}}\) and its VC matrix \(\mathbf{P}_{\mathrm{SD}}\) (the upper-left 4 \(\char 126\) 3 + m + n rows and 4 \(\char 126\) 3 + m + n column square matrix of \(\mathbf{P}_{k}\)) from the Kalman filter can be transformed to their DD counterparts by \(\mathbf{D}\mathbf{N}_{\mathrm{float}}\) and \(\mathbf{D}\mathbf{P}_{\mathrm{SD}}\mathbf{D}\) that become the input to the LAMBDA algorithm [12]. The integer DD ambiguity \(\mathbf{N}_{\mathrm{int}}\) is obtained as the output of this module. (7) Baseline output  
We use the popular ratio test [20] with a fixed threshold to decide whether to accept the integer ambiguity solution from LAMBDA. The final output of the baseline is given in Equation (34):  
\[\mathbf{X}_{\text{bt}}=\mathbf{X}_{\text{r}}-\mathbf{X}_{\text{b}}+\left( \mathbf{H}_{\Phi}^{\text{7}}\mathbf{R}_{\Phi}^{-}\mathbf{H}_{\Phi}\right)^{-1} \mathbf{H}_{\Phi}^{\text{7}}\mathbf{R}_{\Phi}^{-1}\left(\boldsymbol{\phi}_{ \text{m,DD}}-\boldsymbol{\phi}_{\text{DD}}-\mathbf{N}\right) \tag{34}\]  
where \(\mathbf{H}_{\Phi}= \begin{bmatrix}\mathbf{H}_{\text{bt}}^{\text{GPS}}\\ \mathbf{H}_{\text{bt,L}}^{\text{HDS}}\end{bmatrix} \) is the coefficient matrix for baseline, \(\mathbf{R}_{\Phi}\) is the DD carrier phase measurement VC matrix, \(\boldsymbol{\phi}_{\text{m,DD}}\) is the DD carrier phase measurement vector, and \(\boldsymbol{\phi}_{\text{DD}}\) is its estimation. If ratio test is passed, then \(\mathbf{N}=\mathbf{N}_{\text{int}}\) and the fixed baseline solution with higher precision is output, else \(\mathbf{N}=\mathbf{N}_{\text{float}}\) and the float baseline is output.
## 3 Analysis of Factors Affecting Shear Behavior of Reinforced Concrete Beams
### Effect of the Elastic Modulus of the FRP Grid  
To investigate the effect of the elastic modulus of the FRP grid on the shear behavior of strengthened beams, taking the strengthened beam B-I as the reference, keeping the original parameters unchanged, the elastic modulus was changed to 10, 15, 30, 40, and 50 GPa. The specific parameters are shown in Table 3. The elastic modulus of the FRP grid described in this paper refers to the elastic modulus of the FRP grid calculated based on the cross-sectional area of a single limb, rather than the elastic modulus of the FRP material. Figure 10 is a comparison diagram of the load-midspan deflection curves of the strengthened beams under different elastic modulus values. The detailed data and comparison are listed in Table 6. As the elastic modulus increases, the ultimate load of the strengthened beam increases slightly, and the effective strain of the FRP grid decreases significantly, which intuitively reflects that the elastic modulus of the grid is positively correlated with the shear behavior capacity and negatively correlated with the grid strain. Further analysis shows that when the elastic modulus increases, the grid stiffness increases and its ability to resist deformation improves. Therefore, the overall stiffness of the strengthened beam is  
Figure 9: Effect of FRP grid spacing.  
enhanced, and at the same time, the ductility of the beam deteriorates, and the midspan deflection at failure becomes smaller. Compared with the reference beam B-1 with an elastic modulus of 21 GPa, the strengthened beams B-E2, B-E3, and B-E4 with an elastic modulus of 15, 30, and 40 GPa in Table 6 have a change rate of the ultimate load less than 1%, but the change in the grid effective strain is relatively significant, ranging from 10% to 40%. This indicates that, under the same shear behavior capacity level, choosing a more suitable elastic modulus, that is, a more appropriate type of FRP, can increase its utilization rate.
## 4 Validation and Evaluation
### Experimental Results and Analysis Based on Self-Collected Data Sets  
In this section, three aspects are verified on the self-picked dataset. First, the recognition activity accuracy results and analysis when TX and RX were at different distances. Second, the recognition activity accuracy results and analysis when TX and RX were at a certain distance and the human body was connected to the two at different distances. Third, the results and analysis of the LOS and NLOS cases when TX and RX are at a certain distance.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
###### Abstract  
The use of a Controller Area Network (CAN) bus in the automotive industry for connecting electronic control units (ECUs) poses security vulnerabilities due to the lack of built-in security features. Intrusion Detection Systems (IDSs) have emerged as a practical solution for safeguarding the CAN bus. However, developing an effective IDS for in-vehicle CAN buses encounters challenges in achieving high precision for detecting attacks and meeting real-time requirements with limited computational resources. To address these challenges, we propose a novel method for anomaly detection on CAN data using windowed Hamming distance. Our approach utilizes sliding windows and Hamming distance to extract features from time series data. By creating benchmark windows that span at least one cycle of data, we compare newly generated windows with recorded benchmarks using the Hamming distance to identify abnormal CAN messages. During the experimental phase, we conduct extensive testing on both the public car-hack dataset and a proprietary dataset. The experimental results indicate that our method achieves an impressive accuracy of up to 99.67% in detecting Denial of Service (DoS) attacks and an accuracy of 98.66% for fuzzing attacks. In terms of two types of spoofing attacks, our method achieves detection accuracies of 99.48% and 99.61%, respectively, significantly outperforming the methods relying solely on the Hamming distance. Furthermore, in terms of detection time, our method significantly reduces the time consumption by nearly 20-fold compared to the approach using deep convolutional neural networks (DCNN), decreasing it from 6.7 ms to 0.37 ms.  
H  
amming distance; controller area network (CAN); intrusion detection system; in-vehicle network; security
## 5 Interfacial Strength Parameters of Fiber-Reinforced Iron Tailings Powder
### Interfacial Strength Parameters of Fiber-Reinforced Iron Tailings Powder  
The direct shear energy dissipation \(W_{fs}\) of fiber-reinforced iron tailings powder can be expressed as the sum of the direct shear energy dissipation \(W\) of iron tailings powder and the interfacial energy dissipation \(W_{f}\) of fiber, as shown in Equation (19).  
\[W_{fs}=W_{s}+W_{f} \tag{19}\]  
According to Equations (6), (18), and (19), the fiber interfacial strength parameters \(a_{sf}\) and \(\delta_{sf}\) can be obtained by the programming solution, and the mathematical model is shown in Equation (20).  
\[\begin{array}{l}\min z=\sum\left|(W_{fs}^{i}-W_{s}^{i})-W_{f}^{i}\right|\\ s.t.\left\{\begin{array}{l}W_{f}^{i}=\frac{2}{3}C_{1}^{i}(a_{sf}+(C_{2}^{i}+C_ {3}^{i}C_{4}^{i})\tan\delta_{sf})\\ W_{fs}^{i}=\int_{0}^{\max}F_{fs}^{i}ds\\ W_{s}^{i}=\int_{0}^{\max}F_{s}^{i}ds\\ a_{sf}\geq 0,\delta_{sf}\geq 0\end{array}\right.\end{array} \tag{20}\]  
where \(i\) represents the test number, \(i=1,2,...,16\). Substituting the data of Tables 2 and 3 into Equation (20), using the programming solver to obtain when \(z\) is the minimum, \(a_{sf}=0.29\) kPa, \(\delta_{sf}=0.57^{\circ}\). Substituting the calculation results of \(a_{sf}\) and \(\delta_{sf}\) into Equation (18) can obtain the fiber interfacial failure energy dissipation as shown in Figure 12.  
It can be seen from Figure 12 that under the same normal stress, as the fiber dosage increases, \(W_{f}\) increases linearly but not obviously. In the case of the same fiber dosage, \(W_{f}\) increases substantially and linearly with increasing normal stress. When the fiber dosage is between 0% and 1%, and the normal stress is in the range 100-400 kPa, the maximum value of \(W_{f}\) is 0.8 J.  
It can be concluded from the above analysis that the interfacial energy dissipation of polypropylene fiber and iron tailings powder increases the direct shear energy dissipation of polypropylene-fiber-reinforced iron tailings powder to a certain extent, thereby improving the shearing performance of polypropylene fiber.
# An Algorithm for Producing Fuzzy Implications via Conical Sections
## 1 Introduction  
Fuzzy implications have been developed into an integral and crucial part of fuzzy logic. They generalize the classical logic implication operator and enable \"if-then\" rules to be applied in a fuzzy inference system [1; 2]. Apart from the basic properties that a fuzzy implication has to satisfy by definition, researchers have proposed many additional properties, e.g., [3; 4; 5], each usually satisfied by a particular subclass of implications. Depending on the specific context of the application, a different set of these properties might be desired. Even within a fuzzy implication subclass, it is often desirable to find the one that fits a given dataset best, i.e., to finetune the fuzzy inference system [6; 7].  
Thus, it becomes clear that, in such applications, it is imperative for the researcher to have an algorithm capable of producing a wide class of implications from which to select the one that best fits their particular problem. Among the many most commonly used ones, we focus in this paper on the so-called \((S,N)\) implications [8]. \((S,N)\) implications are produced using a fuzzy distinction \(S\) (fuzzy \"or\") in conjunction with a fuzzy negation \(N\) based on the well-known logical truth \"if \(P\) then \(Q^{\prime}\equiv\)\"if NOT \(P\) then NOT \(Q^{\prime}\).  
In the present study, an algorithmic approach for producing a parametric family of \((S,N)\) implications is presented. As a basis for the developed algorithm, we have utilized the strong fuzzy negation introduced in [9], which is derived via the parametric form of the conical sections equation. There are other families of parametric negations in the literature, such as the Sugeno or Yager classes [10]. However, as the authors in [9] demonstrated, the examined methodology is more general since it produces some of these classes as a subset of the produced negations.  
The introduced parameter enables, as mentioned, many different implications to be chosen from for a particular problem. This is an advantage compared to other methodologies. For example, Yager's \(f\)- and \(g\)-generated implications [11] or the implicationspresented in [12] are not produced by a parametric generator. In addition, the fact that the implications are produced via a strong negation leads to many additional desirable properties being satisfied, as indicated in the present study. Finally, this family of implications generalizes other well-known implications, such as the Lukasiewicz implication, as, for special choices of the parameter, these are produced by our algorithm.  
Thus, the contributions of the study presented herein are focused in two main directions. On the one hand, we introduce an algorithm to effortlessly produce an infinite variety of implications, which can be used in an integrated decision-making system in a fuzzy environment. On the other hand, the theoretical mathematical properties of this family of implications are studied, which is a necessary step towards their successful implementation in a practical application.
# Body-to-Body Cooperation in Internet of Medical Things: Toward Energy Efficiency Improvement
## 2 WBSN Networks Architecture  
### WBSN in IoMT-based Health Network  
A new architecture of an IoMT health-based paradigm is shown in the Figure 1 which can be divided into four tiers, where every layer of this proposed architecture clarified as:  
* **The WBSN tier (tier 1)**: In this tier, sensors might be attached directly to the human body or sewed into fabric, or embedded inside the human body. Such sensors can be EEG, ECG, or EMG, etc. The data recorded via sensors are transmitted to the coordinator node via wireless 802.15.6 standard, after that the _CN_ transfer what were transmitted by the sensors to the next tier over one of the wireless technology or cables.
* **Smart\(\backslash\)wireless technology interface tier (tier 2)**: In this tier, smart devices are utilized, (i.e., smart phone, laptop or tablet). In this tier, data are inspected and analysed, then the data  
Figure 1: WBSN in IoMT-based health networks.  
transferred to tier 3 over one of the smart devices or one of the chosen wireless communication technologies (i.e., Bluetooth, Wi-Fi or cellular base-station). Tier 2 represents the bridge tier that is join the WBSN to the infrastructure internet, and some time located within WBSN area.
* **Infrastructure internet tier (tier 3)**: This level bridge the gap between the tier 2 and tier 4 via exiting backbone communication technology.
* **Care-Services tier (tier 4)**: In this tier, the data is received and forwarded to the intelligent server (IS), the intelligent server is stored the data, analysed and forwarded to the suitable service, such as emergency response, a physician, or family.  
It is clear from the above discussion that the journey of the information is entirely dependent upon received data from the first tier; if incorrect or damaged data is received from the first tier, the decision at the intelligent server might be incorrect and hazardous to the life of the patient. Therefore, we proposed a new protocol to ensure and enhance data delivery to the intelligent server.
# BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
## 2 Related Work  
### Structural Health Monitoring System  
The structural condition of large-span bridges can be significantly affected by various internal and external factors over time, such as material degradation, temperature fluctuations, wind speeds, and vehicle loads. Ensuring the healthy operation of bridges has been a hot and challenging issue in civil engineering [10; 11; 12; 13]. With advancements in data acquisition, signal processing, data management and analysis techniques, a large number of systems have been deployed on bridges around the world, such as the Tsing Ma Bridge in Hong Kong, China [14], the Wuhan Yangtze River Bridge, China [15], the Z-24 Bridge in Switzerland [16] and the Seohae Bridge in Korea [17]. These systems generate large-scale, heterogeneous SHM data that exhibit clear big data characteristics [18].  
After long-term operation, these monitoring systems have accumulated vast amounts of data, encompassing environmental factors, operational loads, and structural responses. For example, the SHM system on the Jiubao Bridge in Hangzhou can monitor the bridge's displacement and external environmental data in real time. Preliminary results from this system reveal the relationship between structural temperature and the deformation of the main girder [19]. However, compared to the hardware, the software component of these monitoring systems has received insufficient attention. Most current SHM systems have relatively simple data analysis functions, making it difficult to efficiently process large volumes of high-dimensional data. This limitation has become a bottleneck restricting the practical application of SHM technology.  
This paper employs visual analytics of big data technology to develop the BOPVis system, which integrates human cognitive abilities into the data analysis process and improves the efficiency of traditional data analysis methods.
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Time Synchronization Requirements  
Time synchronization is important in some distributed systems or in all types of networks. The other systems, especially without outer dependencies, do not require time synchronization. Recently, various mechanisms and algorithms for time synchronization have been proposed and used; however, WSNs have many characteristics that make these mechanisms and algorithms unsuitable for WSNs. For example, limited battery power  
Figure 1: Packet delay components.  
of sensors and limited bandwidth availability make it impossible to run synchronization messages frequently, and miniature hardware reduces the computational power and storage space [23]. Therefore, the traditional synchronization techniques, i.e., NTP and GPS are unsuitable for WSNs because their general requirements cannot be fulfilled with limited resources. In the previous section, we analyzed current challenges that are affecting the different methods of time synchronization and making them unsuitable to be used for time synchronization. The following requirements need to be fulfilled by the time synchronization method to achieve time synchronization in the network [35]:
## 4 Key Enabling Techniques
### Full Duplex (FD)  
The name \"duplex\" in communication system denotes the ability of two systems are able to transmit and receive data. However, simultaneous data flow capability of the system is termed as Full-Duplex (FD). FD wireless has gained substantial research interest recently, its numerous benefits at higher layers, and doubling the network capacity by simultaneous transmission and reception on a single carrier without acquiring a new spectrum. Thus, data loss can be minimize and uninterrupted users transmission for channel sensing is achieved by using FD. Hence, FD increase the utilization of spectrum and in the process, improves network capacity but by paying the cost of increased hardware complexity and energy consumption. However, another challenge related to FD receivers is the transmitter's self-interference (SI) which is so powerful (a billion to a trillion times) and ranges from 90 to 20 dB. This high power at the transmitter side suppresses the desired signal at the receiver. So, to have full utilization of the FD network, it needs to be redesigned not only at the physical layer but also at the medium access control layer [104; 105; 106; 107; 108]. To reduce the SI on an acceptable level to the thermal noise researchers have developed several efficient techniques in the past such as a combination of passive and active cancellation methods. Columbia University conducted recent research on integrated full duplex radios by using non-magnetic complementary metal oxide semi-conductor circulators [109]. The use of a single antenna and recent evolution's in SI cancellation/suppression methods turned FD communications a promising candidate to increase spectrum efficiency and channel capacity for 5G and B5G networks.
## 4 Complexity Analysis of the VVC Decoder
### Complexity Analysis of the Adaptive Loop Filter  
The ALF is one of the most intensive computational modules of the decoder, and it consumed, on average, 16% of the total decoding time and 37% of the in-loop filtering time. It was composed of the following most relevant sub-modules: copy reconstructed YUV (CRY), virtual boundaries check (VBC), derivative classification (DeC), luma component filtering (LCF), chroma component filtering (CHCF), cross-component filtering (CRCF) and others (OT). ALF profiling was performed in the same fashion as for the DBF.  
The ALF profiling results are shown in Table 7, where average processing times (in %) for ALF block profiling over the HGPP and EGPP are presented. The most time-consuming functions of the ALF over the HGPP were for LCF, with 37.7% of the total time consumed for the AI sequences and 43.2% for the RA sequences, and CRCF, with 22.9% of the total time consumed for the AI sequences and 11.6% for the RA sequences. The percentage of computational load of the DeC was 17.5% for the AI sequences and 21.6% for the RA sequences, and the computational load of the CHCF was 10.7% for the AI sequences and 8.0% for the RA sequences. However, ALF profiling over the EGPP revealed that the LCF, CHCF and DeC functions consumed approximately 90% of the total time, where LCF alone consumed approximately 70% of the time used for the ALF process.  
Detailed ALF profiling for QPs 22 to 37 with all the configurations over HGPP is displayed in Figure 11. The average computational load of the CRY, DeC, LCF, CHCF, and CRCF varied maximum 2% for the AI sequences with QPs 22, 27 and 32. But for QP 37, CRY, DeC, LCF, and CHCF increased 3% to 14% with respect to other QPs and CRCF decreased to 0%. Moreover, all the ALF functions for the RA sequences showed a pattern similar to that of the ALF function for the AI sequences. However, the time consumed by the CHCF and CRCF decreased by 5% and 16%, respectively with QP 37 compared to QP 22.  
Figure 12 presents the average time distribution for different parts of the ALF (in %) over the EGPP. The time consumed by all the functions remained the same or varied slightly,  
Figure 11: Average time distribution for different parts of the adaptive loop filter (in %) over the HGPP.  
less than 4% for all cases, with the change in QP. Therefore, it can be summarized that the existence of a large difference in the average processing time distribution of different submodules of ALF is observed between HGPP and EGPP. It can be stated that different strategies can be used to accelerate EGPP, mainly focusing on the acceleration in LCF, DeC, and CHCF functions.