esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating the Privacy and Security of the SimpliSafe Security System on Android and iOS
## 4 Results
### Network Findings  
The analysis of network captures did not show any unexpected or inexplainable patterns in the network traffic. Figure 24 shows the I/O graph from the first Wireshark capture. All spikes in network traffic were consistent with regular network traffic. TCP error packets increased only when the number of total transferred packets increased, which was consistent with normal traffic patterns.  
As mentioned earlier, during the data acquisition phase, we changed the state from OFF to Away while capturing the network traffic and repeated this scenario ten times. Using PCAP Analyzer for Splunk, we were able to automatically generate various visual representations of the ten repeated packet captures and combine them onto a dashboard. Figure 25 shows a section of the PCAP Analyzer for the Splunk dashboard.  
Based on the ten packet captures, there were a total of 1491 total packets transferred, with a packet loss of 3.89%. Regarding the bytes transferred by protocol and the packet  
Figure 24: I/O Graph for the action of changing the state from OFF to Away in the SimpliSafe app.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Artifact** & **Android** & **iOS** \\ \hline User’s Name & No & Yes \\ \hline User’s Email & Yes & Yes \\ \hline System Location & Yes & Yes \\ \hline User Interactions & No & Yes \\ \hline App Usage & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of recovered artifacts from Android and iOS  
Figure 25: PCAP Analyzer for Splunk’s dashboard showing network traffic graphs from OFF to Away packet captures repeated 10 times.  
count by protocol, most bytes and packets were transferred using the TCP and TLSv1 protocols. The majority of the TCP flags over time were ACK flags. TCP errors over time increased and decreased, depending on the number of packets transferred, which was consistent with normal network traffic. Nothing unusual from the maximum round trip times and window sizes was detected.  
The \"tls & e ip.src=192.168.137.141\" was used to filter only Transport Layer Security (TLS) packets that came from the 192.168.137.141 source IP address (Android phone) to inspect the encrypted packets further. The Wireshark analysis showed that SimpliSafe was using TLSv1.2 to authenticate and encrypt data over the network. It was found that the key-exchange protocol used elliptic-curve Diffie-Hellman with a 65-bit public key. The Change Cipher Spec protocol was used to generate a session key and let the other party know that the message was encrypted. A 256-bit signature was used.  
Given that SimpliSafe uses secure TLS, elliptic-curve cryptography, and best key-exchange practices, this may be a reason why most network traffic analysis findings were as expected. SimpliSafe follows the best practices for network security as determined by [22] when it comes to safeguarding its network traffic. As such, a substantial amount of the SimpliSafe network traffic that was analyzed was encrypted.
## 4 Experimental Analysis
### Evaluation Metrics  
To verify the effectiveness of the proposed MSCAE to extract HI in this paper, three evaluation metrics, monotonicity, correlation and robustness, were used to quantify the performance of HI [31]. It is worth noting that the range of all three metrics is within [0, 1].  
* Monotonicity: It aims to assess the tendency of HI to increase monotonically or decrease monotonically as the running time increases. The stronger the monotonicity of HI, the closer it is to 1. The specific formula for monotonicity can be expressed as: \[Mon(H)=\left|\frac{\text{No. of }dH>0}{T-1}-\frac{\text{No. of }dH<0}{T-1}\right|\] (25)  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training Set** & **Test Set** \\ \hline \multirow{3}{*}{Condition 1} & Bearing1\_2 Bearing1\_4 & \multirow{3}{*}{Bearing1\_1 Bearing1\_3} \\  & Bearing1\_5 Bearing1\_6 & \\  & Bearing1\_7 & \\  & Bearing2\_1 Bearing2\_2 & \\  & Bearing2\_3 Bearing2\_4 & \\  & Bearing2\_5 Bearing2\_7 & \\  & Bearing3\_1 Bearing3\_2 & Bearing3\_3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training set and test set division.  
Figure 6: PRONOSTIA test bench.  
where \(dH\) denotes the first-order derivative between two HI values and \(T\) denotes the number of HI and also the number of sensor samples.
* Correlation: It aims to measure the correlation between HI and runtime. The more correlated the two are, the closer the value of correlation is to 1, and vice versa. The formula for correlation can be expressed as: \[Corr(H)=\frac{\left|\sum\limits_{i=1}^{T}{(h_{i}-\bar{H})(t_{i}-\bar{T})}\right| }{\sqrt{\sum\limits_{i=1}^{T}{(h_{i}-\bar{H})^{2}\sum\limits_{i=1}^{T}{(t_{i}- \bar{T})^{2}}}}}\] (26) where \(\bar{H}=\sum_{i=1}^{T}{h_{i}/T}\) and \(\bar{T}=\sum_{i=1}^{T}{t_{i}/T}\).
* Robustness: It aims to measure the ability of HI to resist outlier interference; the stronger its ability, the closer the robustness is to 1, and vice versa. The extracted HI can be seen as a superposition of the average trend and noise, whereby \(H\) can be expressed as: \[H(t_{i})=H_{T}(t_{i})+H_{R}(t_{i})\] (27) where \(H_{T}(t_{i})\) denotes the average trend of HI at the moment \(T\), and \(H_{R}(t_{i})\) denotes the noise disturbance of HI at the moment \(T\). Then, the robustness is calculated by the formula: \[Rob(H)=\frac{1}{T}\sum\limits_{i=1}^{T}{\exp(-\left|\frac{H_{R}(t_{i})}{H(t_{i })}\right|)}\] (28)  
In order to comprehensively evaluate the advantages and disadvantages of the extracting HI, a Composite Indicator (CI) containing the above three indicators is proposed, which is defined as:  
\[\text{CI}=\frac{1}{3}(\text{Mon}+\text{Corr}+\text{Rob}) \tag{29}\]
# Optimized Sonar Broadband Focused Beamforming Algorithm
## 3 Simulation and Performance Analysis
### Optimized Broadband Focused Beamforming Algorithm Based on RCB Algorithm  
First, the RCB algorithm was used to correct the focus matrix. Second, the 21 sub-band signals were focused on the optimal focusing frequency 2760 Hz by the corrected focusing matrix \(\hat{T}(f_{j})\). Finally, the conventional delay-and-sum beamforming can complete the constant beam-width beamforming. The time complexity of this algorithm is \(O(n^{3})\).  
Figure 6 shows the main lobe width of the broadband focusing beam is 21.4\({}^{\circ}\), and the sidelobe level of the broadband focusing beam is 7.5 dB.  
Compared to Figure 1b, a narrower main lobe is obtained, and it can keep the main lobe constant and make the sidelobe lower. Therefore, an optimized broadband focused beamforming algorithm based on the RCB algorithm is effective.
## 1 Introduction  
### Wireless Sensor Networks  
A wireless sensor network (WSN) is a network of thousands of resource-constrained sensors whose communications with a central station are conveyed by means of wireless signals. A sensor node is generally comprised of four basic elements, including a sensing unit, a processing unit, a transceiver unit, and a power unit. The WSN is frequently deployed for sensing the area of interest where data captured encompass light, pressure, sound, and others. Sensor nodes in WSN mainly use a broadcast communication paradigm where the sensor signals are used in further analyses of the sensed environment. WSN is preferred as the sensor system architecture with regard to its inherent redundancy but is susceptible to disadvantages caused by limited operation life-time. Differ from other wired networks, the use of WSNs are usually restricted by energy stored, computation capability, memory, plethoric information flow, and short communication distance [1]. Since the sensor nodes are often densely deployed in a sensing field, it is difficult and costly to replace faulty sensor nodes manually. Furthermore, sensor nodes may have no global information of the whole network and the topology of a WSN varies frequently [2,3].
## 3 Node Distribution with LoS Links
### Distance Distribution of LoS Links  
We note that Node \(i\) becomes the receiver candidate, (i.e., the relaying candidate for the next hop), only if \(U_{i}=1\). Thus, let \(d_{i}\) be the distance to Node \(i\) given that the corresponding LoS path is not blocked (i.e., \(d_{i}\triangleq r_{i}|U_{i}=1\)). Its PDF is then derived as  
\[f_{d_{i}}(x) =\frac{\Pr[U_{i}=1|r_{i}=x]f_{r_{i}}(x)}{\Pr[U_{i}=1]}=\frac{e^{- \beta x}\cdot\frac{2x}{R^{2}}}{\int_{0}^{R}e^{-\beta x}\cdot\frac{2x}{R^{2}}dx}\] \[=\frac{e^{-\beta x}\cdot\frac{2x}{R^{2}}}{\frac{2\left(1-e^{-\beta R }(1+\beta R)\right)}{\beta^{2}R^{2}}}=\frac{\beta^{2}xe^{-\beta x}}{1-e^{- \beta R}(1+\beta R)}, \tag{9}\]  
where \(0\leq x\leq R\). Hence, the cumulative distribution function (CDF) is given by  
\[F_{d_{i}}(x)=\int_{0}^{x}f_{d_{i}}(x)dx=\frac{e^{\beta(R-x)}(-1+e^{\beta x}- \beta x)}{e^{\beta R}-1-\beta R}, \tag{10}\]  
where \(0\leq x\leq R\). Based on the probability distributions, the \(m\)-th moment is  
\[\mathbb{E}\{d_{i}^{m}\} =\int_{0}^{R}x^{m}\frac{\beta^{2}xe^{-\beta x}}{1-e^{-\beta R}(1+ \beta R)}dx\] \[=\frac{e^{\beta R}[\Gamma(m+2,0)-\Gamma(m+2,\beta R)]}{\beta^{m}( e^{\beta R}-\beta R-1)}, \tag{11}\]  
where \(m>-2\) and \(\mathbb{E}\{\cdot\}\) denotes the expected value. Moreover, \(\Gamma(a,z)=\int_{z}^{x}t^{a-1}e^{-t}dt\) is the incomplete gamma function.
## 3 Results and Discussions
### Durability
#### 3.6.3 Drying Shrinkage  
The contraction of the overall mass upon moisture loss causes shrinkage, a common phenomenon that affects practically all cementitious products. Figure 12 is a graphical presentation of the drying shrinkage results for the control and ternary blended cement mixes cured for 3, 7, 14, 28 and 91 days. Drying shrinkage was found to be more severe in the first 14 days of the curing. By 14 days, the samples had obtained about 75% of the total shrinkage recorded after 91 days of curing. This is about 10% of the total shrinkage of ordinary Portland cement which can record a potential drying shrinkage up to 1000 microstrain [55]. From Figure 13, drying shrinkage was found to be less severe in the control mortar than the blended cement mixes. Different blended cement mixes impacted drying shrinkage differently. Results recorded for 20 CC10 SF, 10 CC20 SF, 15 CC15 SF, 30 CC and 30 SF after 91 days curing were 31.54, 45.8, 33.45, 50.67 and 54.5 microstrain, respectively. Drying shrinkage in the blended cement mixes containing silica fume obtained higher values than the ones without. [PERSON] [56], in his study on the long-term influence of silica fume, reported an increase of about 10% in drying shrinkage with the addition of silica fume. This was supported by studies conducted by [PERSON] et al. [57]. Mortar mixes containing calcined clay outperformed silica fume-rich mixes in withstanding drying shrinkage. Increased drying shrinkage in silica fume blended mortars is due to pozzolanic reactivity and pore refinement mechanisms within the cementitious matrix [56; 58].  
Figure 12: Drying shrinkage of ternary blended cement.
## 3 Results and Discussions  
### Compressive Strength  
The compression test was conducted using a compression testing cm\({}^{3}\). Cube specimens were 15 cm in size. Aggregate size did not exceed 20 mm. For different replacement levels of NS and RHA, the compressive strength of all the mixes were determined at the ages of 7, 14, and 28 days. For traditional categories, three samples were measured, and the mean compressive strength of the three samples was considered to be the compressive strength of the specified category. Table 6 and Figure 3 show the compressive strength test results. Figure 4 shows a comparison of the compressive strength of concrete with different proportions of NS and RHA. Here we can observe that the addition of NS and RHA increased the compressive strength for 28 days up to 10% and decreased at further increase. The pozzolanic reaction between nano silica and calcium hydroxide, which promotes the development of hydrated calcium silicate, is the fundamental reason for the increase in concrete compressive strength.  
On the other hand, concrete without NS can only hydrate to a minimum of calcium silicate hydrate by relying on cement. One of the essential components that contribute to strength is hydrated calcium silicate. As a result, concrete without NS has a low compressive strength [22; 23]. [PERSON] and [PERSON] et al. [24; 25] discovered that the early strength improvement effect of nano-silica modified concrete is more prominent due to the greater pozzolan activity of nano-silica particles [26; 27; 28; 29]. In natural pozzolan-based polymers, nanosilica not only speeds up the polymerization reaction, but also encourages  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Sample**} & \multirow{2}{*}{**NS and RHA in Concrete (\%)**} & \multicolumn{3}{c}{**Compressive Strength (N/mm\({}^{2}\))**} \\ \cline{3-5}  & & **7 Days** & **14 Days** & **28 Days** \\ \hline
1 & 0\% & 17.8 & 20.7 & 24.5 \\
2 & 0.5\% NS \& 5\% RHA & 18.3 & 23.1 & 27.3 \\
3 & 1.0\% NS \& 10\% RHA & 19.9 & 26.2 & 31.3 \\
4 & 1.5\% NS \& 15\% RHA & 19.1 & 25.5 & 30.7 \\
5 & 2.0\% NS \& 20\% RHA & 18.6 & 22.8 & 26.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Compressive strength of concrete.  
Figure 4: Comparison of compressive strength of concrete with different proportions of NS and RHA.  
the formation of calcium silicate hydrates (C-S-H) and sodium aluminosilicate hydrates (N-A-S-H) [30].
## 3 Results and Discussion
### Distribution of Electric Field around Tip without NP
Figure 4 provides detailed distributions along the \(y\) direction (the locations in \(z\) direction are the same as for the white dash lines in Figure 3). For the HP laser, although the intensities of the enhancement field around ODPs and CDPs with small distances are higher than for the VP laser, they quickly decay as the distance increases. This phenomenon is consistent with previous works on the optical enhancement using nano-gaps [25]. So, it can be concluded that the conditions of forming desirable fields in these two schemes are stricter. However, despite relatively lower intensity, ODPs with different tip distances irradiated by the HP laser give a stable enhancement because of the separated two evanescent fields, as shown in Figure 4b. Moreover, the fields located at the bottom of metallic tips may be better than between two tips (induced by the VP laser), since the target nonmaterial is located at the bottom of AFM probes with high probability during the manipulation process, as Figure 1c,d shows. Hence, the electric field and trapping force around an NP should be investigated further. Figure 4: Distributions of electric field along \(y\) direction around dual probe with different distances. (**a**) ODPs irradiated by HP laser; (**b**) ODPs irradiated by VP laser; (**c**) CDPs irradiated by HP laser. Figure 3: Distributions of electric fields in \(y\)-\(z\) plane (top diagram) and along \(y\) direction (bottom diagram; the white dash lines in top diagram show the paths) around dual probe irradiated by polarized near-field laser. (**a**) ODPs irradiated by HP laser; (**b**) ODPs irradiated by VP laser; (**c**) CDPs irradiated by HP laser; (**d**) CDPs irradiated by VP laser.
## 1 Introduction  
Permanent magnet linear synchronous motors (PMLSM) are arguably best suited for use in ultra-precision positioning, numerical control machining, and processing areas. PMLSMs have many advantages such as high force density, high dynamic performance, low thermal losses, and most importantly, the high precision and accuracy associated with the simplicity in mechanical structure. Traditionally, the iron-core PMLSM gained more attraction because of the highly-developed motor thrust [1]. However, the thrust ripple, which is the main disadvantage of an iron-core permanent magnet linear synchronous motor, results in periodic force oscillation with respect to the mover position.  
For some special applications, low thrust ripple and high precision position control are needed. In this case, the permanent magnet linear synchronous motor with an ironless winding seems to be the most suitable electrical machine. However, in order to achieve high force density, PMLSMs with ironless windings are usually supplied with high current density, which may generate excessive heat in windings and lead to a rapid and high temperature increase. The heat induced by the winding can cause insulation deterioration, structure deformation, and permanent magnet demagnetization, or even lead to the failure of the machine [2], which seriously restricts the thrust density and limits the application of PMLSM. Meanwhile, the temperature increase can influence the resistance of windings and magnetic performance of permanent magnets, which will lead to a decrease of thrust output and make the machine unable to reach the desired characteristics [3; 4; 5]. Therefore, it is essential to investigate the PMLSM with a cooling system in order to inhibit the effects of the temperature increase.  
Many studies on the cooling system and thermal analysis have been carried out for permanent magnet motors [3; 5; 6; 7; 8]. However, analysis of the influence of the cooling system on the PMLSM hasreceived less attention. In this work, a novel ironless linear synchronous motor with a cooling system is proposed. Compared to other PMLSMs with a cooling system, the primary winding of the novel ironless PMLSM is divided into two layers, and the cooling system is located between the two layers. Flux density is obtained analytically by the separation of variables method according to the boundary conditions. For the linear synchronous motor with a cooling system, the eddy current braking force induced in the cooling system is a main reason for the thrust ripple. In this paper, analysis and calculation of the eddy current braking force of an ironless linear synchronous motor with a cooling system is investigated. At last, a prototype is manufactured and some experiments are carried out with the prototype. Furthermore, the experiment results are compared with the finite element analyzed results for validation.
## 2 Method Principles
### Bidirectional Gated Recursive Unit  
The upper channel extracts the spatial features of the sample data, but the temporal features in the sample data cannot be processed. Therefore, the lower channel adopts a Bidirectional Gated Recurrent Unit (BiGRU) neural network to extract features from data in both forward and reverse directions [19]. Its specific structure is shown in Figure 1.  
The BiGRU neural network compensates for the GRU's inability to encode information from back to front. In the BiGRU neural network, information has two transmission routes, so you can extract more before and after information, and have a stronger learning ability. The BiGRU is composed of two GRUs stacked on top of each other, and its output depends on the state of the two GRUs. At time t, the hidden state of the BiGRU is obtained by the weighted summing of the forward hidden state \(\overset{\rightarrow}{h}_{t}\) and the reverse hidden state \(\overset{\leftarrow}{h}_{t}\), as shown in Equations (2)-(5).  
\[\overset{\rightarrow}{h}_{t}=GRU(x_{t},\overset{\rightarrow}{h}_{t-1}) \tag{2}\]  
\[\overset{\leftarrow}{h}_{t}=GRU(x_{t},\overset{\leftarrow}{h}_{t-1}) \tag{3}\]  
Figure 1: A diagram of the BiGRU’s structure.  
\[h_{t}^{\prime}=w_{t}\overrightarrow{h_{t}}+v_{t}\overleftarrow{h_{t}}+b_{t} \tag{4}\]  
\[q_{t}=\sigma(w_{O}\bullet h_{t}^{\prime}) \tag{5}\]  
where \(x_{t}\) represents the input information, the \(GRU(\cdot)\) function is used to perform a nonlinear transformation on the input vector, thereby encoding the vector into the corresponding GRU hidden state. In this process, \(w_{t}\) and \(v_{t}\) represent the weights corresponding to the forward hidden state and the reverse hidden state of the BiGRU at time \(t\), respectively, \(b_{t}\) represents the bias of the corresponding hidden state at time \(t\), and \(q_{t}\) representing the output at time \(t\).
## 3 Challenges in Terahertz-Based FSO Communication
### Atmospheric Absorption
#### 3.1.3 Advanced Modeling for Atmospheric Effects  
The atmospheric attenuation also depends on additional environmental factors like the temperature (\(T\)) and pressure (\(P\)). The following advanced equation models these dependencies:  
\[\alpha(\mathrm{f},\mathrm{T},\mathrm{P})=\mathrm{k}\cdot\mathrm{P}\cdot\left( \frac{300}{\mathrm{T}}\right)^{\mathrm{n}} \tag{9}\]  
where \(\mathrm{k}\): the proportionality constant (depends on gas type); \(\mathrm{P}\): the tmospheric pressure (\(\mathrm{Pa}\)); \(\mathrm{T}\): the temperature (\(\mathrm{K}\)); \(\mathrm{n}\): the temperature dependence factor  
This formula allows for the dynamic adjustment of THz communication parameters based on real-time atmospheric conditions, improving the link performance.  
Table 5 summarizes the challenges in THz communication due to the atmospheric absorption of water vapor, scattering, and environmental sensitivity. Possible solutions, including the use of photonic crystal fibers, low-loss materials, and adaptive beam steering, are presented, and their barriers, including their high costs and complex system integration, are discussed. The reduced transmission quality due to atmospheric absorption is depicted in Figure 5.
## 4 Free Surface RANS Solver and Validation  
### Governing Equations  
The CFD solver employed in this study was WAVIS ver. 2.2 [44; 45] which was developed in KRISO. The governing equations for 3-D unsteady incompressible viscous turbulent flow are the Reynolds-Averaged Navier-Stokes (RANS) equations, which are expressed in Cartesian coordinates as follows:  
\[\frac{\partial U_{i}}{\partial x_{i}}=0, \tag{6}\]  
\[\frac{\partial U_{i}}{\partial t}+U_{j}\frac{\partial U_{i}}{\partial x_{j}}= -\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+\frac{1}{Rn}\frac{\partial^{ 2}U_{i}}{\partial x_{j}\partial x_{j}}-\frac{\partial\left(\overline{u_{i}^{ \prime}u_{j}^{\prime}}\right)}{\partial x_{j}}+F_{i}. \tag{7}\]  
Here, \(U_{i}=(U,V,W)\) is the average velocity component in the \(x,y,z\) direction. \(u_{i}^{\prime}=(u^{\prime},v^{\prime},w^{\prime})\) is the fluctuating velocity component. \(-\overline{u_{i}^{\prime}u_{j}^{\prime}}\) is the Reynolds stress defined as  
\[-\overline{u_{i}^{\prime}u_{j}^{\prime}}=v_{t}\left(\frac{\partial U_{i}}{ \partial x_{j}}+\frac{\partial U_{j}}{\partial x_{i}}\right)-\frac{2}{3} \delta_{ij}k. \tag{8}\]  
\(k\) is the turbulent kinetic energy, \(v_{t}\) is the eddy viscosity calculated by Realizable \(k-\epsilon\) turbulence model. The WAVIS ver. 2.2 is the FVM (Finite Volume Method) based RANS solver which is widespread among Korean shipyards. Discretization of convective term and diffusion term were carried out using the 3 rd order MUSCL method and the central difference, respectively. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm was applied to the velocity-pressure coupling. A level-set method was employed to compute the evolution of free surface.
# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 4 Results and Analysis
### Analysis and Findings
#### 4.3.1 Analysis by Collaboration
Similarly, overlooking security considerations can have severe consequences, as demonstrated by the attack on the UK's National Health Service (NHS) during the WannaCry ransomware incident. The outdated architecture, based on a legacy Windows version [54], allowed the ransomware to propagate rapidly across the network, disrupting healthcare services and compromising patient safety [55]. Therefore, integrating technologies such as Blockchain could enhance trust in data exchange, especially in critical IoT infrastructures like healthcare and transportation. Moreover, for practitioners of Smart City planning, ensuring that communication channels between citizens and urban management systems remain open and secure will be key to fostering community participation and real-time decision-making.