esa-sceva/satcom-chunk-collection huggingface dataset:
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Separating Families  
An alternative to the TESLA protocol is to use multiple authentication tags, with each tag created with a separate key shared by the sender and a different subset of receivers. In this section, we assume that an adversary can compromise at most one node. The other case is discussed in Section 4.8.  
For the purposes of this section, we consider a multicast group \(\mathcal{R}\). For ease of notation, we assume that \(\mathcal{R}=\{1,\ldots,r\}\). We assume that there is a master key \(K\) for this group shared by the sender \(S\) and all nodes in \(\mathcal{R}\). The key \(K\) can be used to derive an encryption key for all multicast messages for group \(\mathcal{R}\). Moreover, it can be used to create message authentication codes that can be verified (but also forged) by every node in \(\mathcal{R}\). In this way, we attempt to protect against an external adversary.  
The strategy is to attach not one but \(k\) short message authentication tags \(t_{1},\ldots,t_{k}\) to each message, where:  
* The \(i\)th tag \(t_{i}\) is created with a separate symmetric key \(K_{i}\) shared by the sender and a set \(F_{i}\) of receivers \(F_{i}\subseteq\mathcal{R}\);
* A receiver \(j\) may verify the \(i\)-th tag if and only if it knows \(K_{i}\), i.e., \(j\in F_{i}\).  
The details of how the tags are created are described in Section 4.6. For now, the key point is that if a node \(j\) knows \(K_{i}\), then it can both verify and create a valid tag \(t_{i}\). Consequently, if receiver \(u\) knows all keys \(K_{j}\) known by receiver \(v\), then node \(u\) can impersonate the sender in front of node \(v\). As we do not know in advance which nodes are going to be compromised, we must guarantee that for each pair of nodes \(u,v\in\mathcal{R}\) there is an index \(i\) such that \(v\in F_{i}\) and \(u\
ot\in F_{i}\). This leads to the following concept of separating families:  
**Definition 3**.: _We say that a family \(\mathcal{SF}\) of subsets of \(\mathcal{R}=\{1,\ldots,r\}\) is a separating family for \(\mathcal{R}\) if, for every \(i,j\in\mathcal{R}\), \(i\
eq j\), there exists a subset \(F\in\mathcal{SF}\) such that \(i\in F\) and \(j\
otin F\). We say that \(F\) separates \(i\) from \(j\)._  
Observe that if \(\mathcal{SF}\) is a separating family for \(\mathcal{R}\), then for every \(i,j\in\mathcal{R}\) there exist sets \(F_{1},F_{2}\in\mathcal{SF}\) such that \(F_{1}\) separates \(i\) from \(j\) and \(F_{2}\) separates \(j\) from \(i\). This motivates the notion of two receivers \(i\) and \(j\) being separated in \(\mathcal{SF}\).
## 4 Classifications in Cyber Attacks
### Cyber Attacks on State Estimation  
Voltage and phase angle measurements and state estimation are used to decide the system operation. The state estimation also helps in an efficient and effective operation. The state estimation plays a major role in monitoring and controlling the smart grid. The estimated states are used for estimating the load flow, stability analysis, load forecasting and location pricing. Any attack by the hackers on the states will cause devastating effects on the performance and operation of the smart grid  
State estimation can be classified as AC and DC state estimations. DC state estimations have studied more than AC, as analytical models are simple.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 3 Platform Design and Simulation
### Interface Configuration  
We use STK to establish a common-track satellite constellation that returns to the ground. Since the logical positions of the satellites in the constellation are relatively static within the time slice, as long as the relative positions of the satellites remain unchanged, the antenna can achieve communication as long as the antenna is kept aligned. The set antenna gain is 60 dB. We enable the QualNet interface in STK to configure the interface of the LEO satellite constellation and adopt the Open Shortest Path First routing protocol (OSPFv2) and distance\(-\)vector routing protocol (Bellman-Ford).
# Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications
In this paper, an energy management circuit is presented that combines multiple input energies, and which distributes multiple input energies to dual output loads with low power consumption. By combining input energy from multiple sources, significant energy can be acquired from multiple sources of individually negligible energies. Then, by distributing the output loads, the excess energy (the amount that exceeds that consumed in the load stage) can be utilized to charge a rechargeable battery. These two main functions were implemented in an energy management circuit, and it  
Figure 1: Energy management for a wireless body area network (WBAN). consumed just 1.5 uW with 1.2 Vdd. One of the major contributions of this work is that it shows the potential for utilizing a variety of low-power energy harvesters. In the future, the energy sources that were thought to be useless previously could be utilized in electronic devices, and everlasting wireless sensor nodes without the need for battery charging or replacement might be possible. To present the new energy management circuit, this paper is organized as follows. First, the conventional method used to combine energy from multi-source energy harvesters is analyzed. Then, the newly proposed architecture, circuit design, and novel algorithm for managing energy from multi-source energy harvesters with dual loads are described. Finally, experimental results are presented.
# Effect of Substrates on the Dynamic Properties of Inkjet-Printed Ag Thin Films
## 4 Conclusions  
The proposed method based on the wave approach was introduced to measure the Young's modulus and loss factor of inkjet-printed Ag thin films on various substrates. The effect of the substrate on the properties of the printed films was discussed with respect to the morphology of the film. The vibration transfers of the printed film were compared according to substrate. Alterations of the vibration responses due to the printing process showed different tendencies. The vibration transfers on the inkjet-printed substrates were analyzed using the bilayer beam. The bending stiffness and loss factor of the printed beam were obtained numerically. Through the proposed method, the Young's modulus and loss factor of the ink were determined for various substrates. The dynamic properties of the ink varied considerably according to the substrates. When the thickness of the Ag film  
Figure 6: Young’s moduli and loss factors of the inkjet-printed Ag film on various substrates. The mechanical properties of the ink varied considerably depending on the substrate. These tendencies suggest the effect of the substrate should be considered to accurately obtain the properties of the ink.  
decreased under the identical sintering condition, the Young's modulus and the loss factor increased. The Young's moduli of the Ag films on the substrates were measured to be 46.4-90.8% of that of silver. The loss factor tended to increase with the increment of the modulus. The proposed method reliably determines the dynamic properties of the thin film regardless of the substrate characteristics. The Young's modulus and loss factor were directly determined using the vibration response on the bare substrate and the bilayer without any damage to the thin film. It is also applicable to large structures since the proposed methodology utilizes vibration modal properties. The vibration modal properties are fundamental characteristics and appear for structures of arbitrary geometry. With the rapid advances in printed electronics and 3D printing technologies, the materials used for the construction includes a wide variety of components. For reliability testing or performance evaluations, knowledge of the dynamic material properties is crucial for studying the influence of each components on the whole dynamic behavior. The proposed method allows the rapid laboratory determination of dynamic properties with small vibration measurements and is beneficial when many components need to be characterized.  
This research was supported by a Grant (17 RTRP-B072484-05) from the Railroad Technology Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government.  
[PERSON] and [PERSON] developed the theoretical model, analyzed the data, and performed the experiments; [PERSON] conceived and supervised the research project; all of the authors contributed to the writing.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Organic Geochemical Characteristics of the Shales  
The organic carbon content and bitumen reflectance results obtained for the upper Permian Dalong Formation shale samples from well ED-2 are shown in Figure 2. Among the 26 selected samples, one limestone sample had a low TOC content of 0.25 wt.%, whereas the other 25 siliceous shale samples had TOC contents of 2.6-14.27 wt.%, averaging 6.66 wt.%. These TOC contents are relatively high, especially those with >10 wt.% TOC. The organic matter contents of the Dalong shales were positively correlated with clay mineral and pyrite contents and weakly negatively correlated with quartz and carbonate mineral contents (Figure 6). Solid bitumen reflectances measured in 20 shale samples were in the range \(R_{\rm o}\) = 2.56-2.79%, averaging 2.68%, indicating that the organic matter had entered the over-maturation stage of dry gas generation with a high degree of thermal evolution [43] and the thermal maturity of the shales increased with increasing burial depth. In addition, kerogen microstructures indicated that Dalong shales contain rich sapropelite (86-97%), predominantly type I (sapropelic type) with a small proportion of type II\({}_{1}\) (humic-sapropelic type) [44; 45; 46]. This organic matter was mainly derived from primary marine producers such as algae.  
Figure 4: Lithofacies of the Dalong Formation shales in well ED-2 in Western Hubei (based on the classification of [16]).  
Figure 5: Characteristics of (**a,b**) Siliceous rock, (**c,d**) Carbonate-rich siliceous shale, and (**e,f**) Clay-rich siliceous shale from well ED-2. (**a**) Photograph of black siliceous rock from 1232.9 m depth. (**b**) Microphotograph of black siliceous rock from 1233.1 m depth, rich in organic matter and contains calcite-filled fractures. (**c**) Gray-black carbonate-rich siliceous shale from 1221.9 m depth. (**d**) Microphotograph of carbonate-rich siliceous shale from 1222.2 m depth, with calcite, quartz, and organic matter distributed throughout. (**e**) Black clay-rich siliceous shale from 1242.0 m depth. (**f**) Microphotograph of clay-rich siliceous shale from 1242.2 m depth, rich in organic matter.  
Figure 6: Correlations between TOC content and (**a**) quartz, (**b**) clay mineral, (**c**) carbonate mineral, and (**d**) pyrite contents in the Dalong Formation shales.
## 3 Methodology
### Calibration Methods
#### 3.2.4 Venn-Abers  
Venn-Abers calibration provides a transductive framework for probability calibration rooted in CP, offering distribution-free validity guarantees under the assumption of exchangeability. Unlike parametric or isotonic methods, it outputs calibrated probability intervals rather than point estimates, making it uniquely suited for applications requiring rigorous UQ.  
Given a base model \(M\), Venn-Abers calibration operates on the calibration set \(\mathcal{D}_{\text{cal}}=\{(\mathbf{x}_{j},y_{j})\}_{j=1}^{m}\) by first defining a conformity score \(s(\mathbf{x},y)\), often chosen as \(s(\mathbf{x},y)=y\cdot\hat{p}_{i}+(1-y)\cdot(1-\hat{p}_{i})\), where \(\hat{p}_{i}=M(\mathbf{x}_{i})\). For each insurance \(\mathbf{x}_{\text{new}}\), it computes two smoothed probability estimates:  
\[p_{0}=\frac{|\{j\in\mathcal{D}_{\text{cal}}\cup(\mathbf{x}_{\text{new}},0)\ |\ s(\mathbf{x}_{j},y_{j})\leq s(\mathbf{x}_{\text{new}},0)\}|}{m+1}, \tag{10}\]  
\[p_{1}=\frac{|\{j\in\mathcal{D}_{\text{cal}}\cup(\mathbf{x}_{\text{new}},1)\ |\ s( \mathbf{x}_{j},y_{j})\leq s(\mathbf{x}_{\text{new}},1)\}|}{m+1}, \tag{11}\]  
where \(p_{0}\) and \(p_{1}\) represent the empirical probabilities of observing conformity scores at least as extreme as the hypothetical labels \(y_{\text{new}}=0\) and \(y_{\text{new}}=1\), respectively. The calibrated probability interval is then \([\min(p_{0},p_{1}),\max(p_{0},p_{1})]\), with the point estimate \(\hat{p}_{\text{VA}}=\frac{p_{1}}{p_{0}+p_{1}}\).
# Numerical Study of Mode Conversion between Fundamental Lamb and Rayleigh Waves
## 3 Results
### Lamb Wave to Rayleigh Wave Conversion
#### 3.1.2 Incident \(S_{0}\) Mode  
The nodal displacement vectors for the \(S_{0}\) mode interaction with discontinuity are shown in Figure 5a. Unlike the \(A_{0}\) case, the P waves have a higher amplitude compared to SV, and a distinct shear window (with phase reversal) is not observed. Interestingly, a sub-surface longitudinal wave can be seen propagating ahead of the R wave on the top surface. The mode conversion efficiencies calculated from the numerical model are presented in Figure 5b. About 0.37% of the incident \(S_{0}\) mode is reflected in the thin plate as an \(S_{0}\) mode, and less than 0.1% is reflected as an \(A_{0}\) mode. The Rayleigh mode conversion efficiency for the \(S_{0}\to R\) mode was calculated to be 1.19%, which is very small compared to the \(A_{0}\to R\) mode conversion efficiency. Once again, using the power flux balance, \(P_{s}\) was calculated to be 98.44%. Additionally, a sub-surface longitudinal wave was also observed, as shown in Figure 5a.
## 3 Proposed DACR Model
### Other Details
#### 3.5.3 Loss Function  
Model-based methods usually assume that the data can be generated by the underlying model as \(\hat{y}_{ui}=f\left(u,i|\theta\right)^{\prime}\), where \(\hat{y}_{ui}\) denotes the prediction of \(y_{ui}\), \(\theta\) denotes model parameters, and f denotes the mapping function. The definition of the function f and the estimation of the parameters \(\theta\) are related to the learning ability of the model.  
For parameter estimation, the parameters are usually estimated by optimizing the objective function. There are generally two types of objective functions, namely point-wise loss [23] and pair-wise loss [25]. Point-wise loss, especially the squared loss function, is mostly used in CF with explicit feedback [43]. However, the squared loss does not work with implicit feedback because it assumes that the error between the real rating and predicted rating obeys a normal distribution. We assume that \(y_{ui}\) obeys a Bernoulli distribution in implicit feedback, i.e., \(y_{ui}\sim Bern(p_{ui})\). Since \(p_{ui}\) is also understood as the probability that u matches \(i\), the interactive prediction is simplified into a matching score prediction. Therefore, using the maximum likelihood estimation to determine the model parameters \(\theta\) is the same as minimizing the error function between \(y_{ui}\) and \(\hat{y}_{ui}\). By replacing \(p_{ui}\) with \(\hat{y}_{ui}\) in Equation (8), we can define the likelihood function [13] as:  
\[\begin{array}{l}\mathrm{L}(\theta)=\prod\limits_{(u,i)\in y^{+}\cup y^{-}}P (y_{ui}|\theta)\\ =\prod\limits_{(u,i)\in y^{+}\cup y^{-}}\hat{y}_{ui}^{y_{ui}}(1-\hat{y}_{ui})^ {1-y_{ui}}\end{array} \tag{9}\]  
where \(y^{+}\) represents the set with the value 1 in \(Y\). \(y^{-}\) represents the set of samples with a value of 0 in \(Y\), i.e., negative samples. After taking the negative logarithm of the likelihood function, we obtain the following.  
\[L_{BCE}=-\sum\limits_{(u,i)\in y^{+}\cup y^{-}}y_{ui}\log\hat{y}_{ui}+(1-y_{ui} )\log(1-\hat{y}_{ui}) \tag{10}\]  
In conclusion, we use the binary cross-entropy loss function which is good at learning parameters from implicit feedback as the objective function [44].
## 2 Optimization Problems of Equalizers with Traditional Structures  
Since linear transfer function can be generated by the FFE with the feature of easy implementation, it played a very significant role in compensating the channel impairments [22, 23]. As depicted in Figure 1, the optimal tap coefficients are estimated by the optimization algorithms. As an application of stochastic gradient descent (SGD) method, LMS algorithm aims to minimize the current square error between the training sample \(y_{t}\) and output of the equalizer \(s_{t}\), which can be expressed as [24]  
\[\underset{\omega_{i}}{\text{minimize}}(\sum_{i=1}^{N}x_{j-i+1}\times\omega_{i }-y_{t})^{2} \tag{1}\]  
where \(N\) is the number of taps and \(\omega_{i}\) is the tap coefficient, for \(i=1,2,...,N\). In addition, \(x_{j}\) is the received signal.
## 2 System's Measured Data  
Hourly data is required to run the optimization algorithms for the RES shown in Figure 1. The data was taken in 2018 for Harta city in the north of Irbid, Jordan, as shown in Figures 2 and 3, respectively. The sample time of the data acquisition is one hour. Note that the optimization algorithms are hourly testing the system in order to find the optimal system's configuration. Further, the duration of campaign acquisition for the measured data was one year, i.e., 8760 h.  
The measured load demand (in MW) was obtained from the Jordanian National Electric Power Company (NEPCO), which is responsible for the availability and safety of power transmission. Further, the volumetric flow rate (in m\({}^{3}\)/s) input values into the PHS unit for Al-Wehda Dam was obtained from the Jordan Valley Authority (JVA). JVA is responsible for the public water supply, wastewater services, and development of water resources and dams in Jordan. Moreover, the solar incident irradiance, \(G\)(\(t\)) in W/m\({}^{2}\), and the solar ambient temperature, \(T_{amb}\)(\(t\)) in \({}^{\circ}\)C, were obtained from the royal scientific society (RSS), which is responsible for testing services locally and internationally. Note that \(G\)(\(t\)) and \(T_{amb}\)(\(t\)) are used to compute the photo current (\(I_{Ph}\)) of the solar module as discussed in [6]. It is worth noting that the solar ambient temperature is the temperature of the solar panel surroundings. They were measured in front of the solar panel. Furthermore, RSS performs research studies related to RES in Jordan, such as the optimal tilt angle of a fixed PV array. Note that the designed PV array is to be positioned toward south in a fixed position. A tilt angle of 28\({}^{\circ}\) is optimal for PV installation in Jordan [26]. Note that the three institutions provided the hourly data, shown in Figures 2 and 3, after requesting official letters from the employer of the authors.
## 4 Experimental Analysis
### Degradation of Stiffness  
The shear walls exhibited inelastic behavior under reversed lateral cyclic loading, and the envelope curves showed that the strength and stiffness of the specimens decreased in the inelastic stage, which indicated that the walls experienced accumulated damage. In this study, secant stiffness was applied to investigate the stiffness degradation of the specimens. The secant stiffness at each loading cycle (\(K_{s,i}\)) can be defined as Equation (2):  
\[K_{s,i}=\frac{\left|+Q_{i}\right|+\left|-Q_{i}\right|}{\left|+\Delta_{i} \right|+\left|-\Delta_{i}\right|} \tag{2}\]  
where \(+Q_{i}\) and \(-Q_{i}\) are the maximum lateral loads of the first \(i\)th loading cycle in the push and pull directions, respectively; and \(+\Delta_{i}\) and \(-\Delta_{i}\) are the displacements corresponding to \(+Q_{i}\) and \(-Q_{i}\), respectively.  
Figure 12 shows the relationship between the secant stiffness and the lateral displacement, as well as the drift of the specimens. During the loading process, the stiffness of all specimens decreased following a power law as the displacement increased. The initial stiffness of PMW-2 and PMW-4 was larger than that of PMW-1 and PMW-3A/B, which revealed that the initial stiffness of the shear wall increased with higher axial compression load, as reported by [PERSON] [29]. Nonetheless, compared with PMW-1 and PMW-3A/B, PMW-2 and PMW-4 exhibited faster stiffness degradation and relatively more brittle behavior. The axial stress demonstrated a significant effect on the specimens' stiffness. For specimens with the same compression ratio and vertical reinforcement ratio, but different distributions, the stiffness declined in a similar trend, proving that the stiffness degradation of the PRMSW was not obviously influenced by the vertical rebar distribution.
## 2 Materials and Methods
### Cell Viability  
Raw264.7 cells were seeded at a density of 1 \(\times\) 10\({}^{5}\) cells/mL into 48-well plates, containing DMEM supplemented with 10% FBS and 1% penicillin-streptomycin, and incubated at 37 \({}^{\circ}\)C. After 24 h (about 80-90% confluence), the medium was removed and washed with PBS buffer. LPS (1 \(\upmu\)g/mL) and the sample were exposed to each treatment concentration (1.0, 5.0, and 10.0 mg/mL MSM in 1.0 mg/mL sericin) and cultured for 1 day. Cell viability was analyzed using the CCK-8 reagent, and absorbance was measured at 450 nm. And it was visualized through LIVE/DEAD analysis. The cells were treated by dissolving 1 \(\upmu\)M Calcein-AM and 2 \(\upmu\)M EthD-1 in PBS, and then they were left at room temperature for 20 min. Additionally, results were confirmed with a fluorescence microscope (Leica DMIL LED Fluo, Leica Microsystems, Wetzlar, Germany).
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 6 Security Analysis
### Defence against Compromised \(Rsui\)  
In the proposed system, we assume that the CAs are fully trusted while the \(RSU_{i}\) and the vehicles are semi-trusted which means that they are trusted but cannot know some sensitive and private data of the vehicles. Thus, \(LTCA\) generates the private key \(sk_{RSU_{i}}\) and the \(Cert_{RSU_{i}}\) based on its master key. Therefore, if \(RSU_{i}\)s corrupted, it can be detected through the credentials generated by \(LTCA\) which can immediately revoke the \(RSU_{i}\)'s certificate. Moreover, the corrupted \(RSU_{i}\) can also manipulate the \(ACF\). This manipulation impairs the safety application and can cause accidents. However, in our scheme, \(RSU_{i}\) signs the fingerprinted Chaff \(Sign_{RSU_{i}}(FP(Chaff_{PS_{j}}))\), which helps to detect the \(RSU_{i}\) in case it has malicious activities.
# Underwater Target Recognition Based on Multi-Decision
## 2 System Model  
In this paper, we consider a deep-learning underwater target-recognition framework based on multi-step decision LOFAR line spectrum enhancement which is shown in Figure 1. It is divided into four modules: sampling, feature preprocessing, offline training and online testing.