esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization Algorithms for Equalizers with Traditional Structures  
### BGD Method  
There are three variants of gradient descent, including SGD, batch gradient descent (BGD) and mini-batch gradient descent (Mini-BGD) [21]. The main difference is that they use different number of training sample every training iteration. SGD updates the gradient of error function with respect to the only one training sample, while BGD uses all training samples to perform the parameters updating every iteration. To solve the problem of sharp increase in computation by using all samples every iteration in BGD, partial training samples are used in Mini-BGD every iteration. However, different from deep learning training, which requires large-scale training set, in practical communication systems, it is necessary to use as few training samples as possible. As a result, there is no need to adopt Mini-BGD but using BGD directly with a relatively small batch size. BGD method aims to minimize the mean square error (MSE) of all training samples, which can be expressed as  
\[\underset{\omega_{i}}{\text{minimize}}\,\frac{1}{M-N+1}\sum_{j=N}^{M}(\sum_{i =1}^{N}x_{j-i+1}\times\omega_{i}-y_{t})^{2} \tag{2}\]  
where \(M\) is the total number of training samples. BGD method using gradients for all training samples to perform just one update of tap coefficients and thus it is a global optimization algorithm. For better convergence, the first \(N-1\) training samples are discarded [24]. Matrix form of Equation (2) can be express as  
\[\underset{\omega}{\text{minimize}}\,\frac{1}{M-N+1}(\mathbf{R}\omega-\mathbf{Y})^{T} (\mathbf{R}\omega-\mathbf{Y}) \tag{3}\]  
Figure 1: Schematic of an FFE with \(N\) taps.  
where \(\mathbf{\omega}=[\begin{array}{cccc}\omega_{1}&\omega_{2}&\ldots&\omega_{N}\end{array}]^ {T}\) is the tap coefficient vector of the FFE and \(\mathbf{Y}=[\begin{array}{cccc}y_{N}&y_{N+1}&\ldots&y_{M}\end{array}]^{T}\) is the desired training vector. The \((M-N+1)\)-by-\(N\) training matrix \(\mathbf{R}\) can be expressed as  
\[\mathbf{R}=\left[\begin{array}{cccc}x_{N}&x_{N-1}&\ldots&x_{1}\\ x_{N+1}&x_{N}&\ldots&x_{2}\\ \vdots&\vdots&\ddots&\vdots\\ x_{M}&x_{M-1}&\ldots&x_{M-N+1}\end{array}\right] \tag{4}\]  
where \(x_{j}\) are the received training samples, for \(j=1,2,...,M\). Gradient of MSE with respect with tap coefficient can be calculated as  
\[\mathbf{g}(\mathbf{\omega})=\frac{2}{M-N+1}\mathbf{R}^{T}(\mathbf{R}\mathbf{\omega}-\mathbf{Y}). \tag{5}\]  
BGD method updates tap coefficient vector \(\mathbf{\omega}\) in the opposite direction of the gradient, which can be expressed as  
\[\mathbf{\omega}_{t}=\mathbf{\omega}_{t-1}-\theta\times\mathbf{g}(\mathbf{\omega}_{t-1}) \tag{6}\]  
where \(\theta\) is a positive step size and subscript \(t\) denotes \(t\)-th iteration.  
NN researchers have long realized the fact that step size is one of the most difficult hyper-parameters to determine but it is critical to model performance and training costs. Then the following three adaptive step size optimization algorithms are successively proposed for NNs. These algorithms can also be used in tap estimation of equalizers with traditional structures.
# Control-Flow Integrity: Attacks and Protections
## 5 Protection Techniques
### CFI Key Features Summary  
As we have previously analyzed, each CFI protection technique can be implemented on hardware and software, use different approaches such as shadow stack, use labels and even be coarse and fine grained. In order to summarize all those features, we present in Table 1 the key features being adopted by the 14 CFI protection techniques analyzed.  
The summary reveals that all CFI protection techniques are based on software and only three of them are also implemented in hardware. In addition, most of them need compilers modifications and perform an offline Control-Flow-Graph (CFG) of the application. Regarding coarse vs fine-grained approaches, around 50% of techniques adopted coarse-grained and the other 50% adopted fine grain. This shows that there is not a clear inclination about any of those by the security community.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**CFI** & \begin{tabular}{c} **Based on** \\ **HW** \\ \end{tabular} & \begin{tabular}{c} **Supplier** \\ **SW** \\ \end{tabular} & \begin{tabular}{c} **Shadow** \\ **Modified** \\ \end{tabular} & \begin{tabular}{c} **CFG** \\ **Stack** \\ \end{tabular} & **CFG** & **Label** & \begin{tabular}{c} **Coarse** \\ **Grained** \\ \end{tabular} & \begin{tabular}{c} **Fine** \\ **Grained** \\ \end{tabular} & \
\begin{tabular}{c} **Backward** \\ **Edge** \\ \end{tabular} &
\begin{tabular}{c} **CFI** \\ **Enforcement** \\ \end{tabular} \\ \hline CFI1 [51; 52] & & ✓ & & ✓ & ✓ & ✓ & & & ✓ & \\ \hline CCFI [46] & ✓ & ✓ & ✓ & & & & ✓ & \\ \hline binCFI [42] & & ✓ & & & ✓ & ✓ & & \\ \hline CCFIR [59] & & ✓ & & & ✓ & ✓ & & ✓ & Binary Rewriting \\ \hline HW-CFI [41] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & Landing Point \\ \hline PICRI [44] & & ✓ & ✓ & & ✓ & & ✓ & ✓ & Static Analysis \\ \hline KCoFI [61] & ✓ & ✓ & & & ✓ & ✓ & & ✓ & SVA Instrumentation \\ \hline Kernel CFI [62] & ✓ & & & ✓ & & ✓ & ✓ & Retrofitting Approach \\ \hline IFCC [43] & ✓ & ✓ & & ✓ & & ✓ & & Dynamic Analysis \\ \hline CFB [45] & ✓ & & ✓ & ✓ & & ✓ & ✓ & Precise Static CFI \\ \hline SAFEDISPATCH [65] & ✓ & ✓ & & & ✓ & & & Static Analysis \\ \hline C-Guard [66] & ✓ & ✓ & & ✓ & ✓ & & & Dynamic Instrumentation \\ \hline RAP [49] & & ✓ & ✓ & & ✓ & & ✓ & & Type Based \\ \hline O-CFI [47] & ✓ & & & & & ✓ & ✓ & Static Rewriting \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key features of CFI techniques.
## 3 Architecture and System Evaluation
### Measurements of the Different Delay Components
#### 3.3.2 Transmission Delay  
Next, we presented the impact of the channel conditions and the encoding rate on the transmission delay of different image sets (batch sizes). The size of the transmitted image sets was between 6-230 MB. Figures 6-8 present how the transmission delay is affected by the encoding rate value. Our measurements were performed with three different channel conditions. The conditions of the three channels as shown in Figures 6-8 are good, medium, and bad channel conditions respectively. As expected the higher the encoding rate value \(q\), the bigger the transmission delay. As mentioned before we proposed to develop a technique in which we minimize the transmission delay by sacrificing the least possible accuracy of the Edge-Assisted object detection system. In more detail, we suggested a method to minimize transmission delay while maintaining high accuracy in the Edge-Assisted object detection system. Through our testing, we have determined that the most effective value for \(q\), which achieves minimal transmission delay without compromising system accuracy is \(q\) = 50 which is clearly shown in Table 1. The accuracy is affected by the compression in a non-deterministic, and a priori unknown, fashion. Indeed, as prior studies have shown, the detection accuracy depends on how well the training data match the test data, the efficacy of the particular ML library we use, and several other latent factors. Hence, whether the compression affects significantly the accuracy on top of all these factors, or whether it is less important, is something that we cannot assess in advance, and it is not realistic to assume that we have an analytical expression (i.e., function) for its effect.  
Figure 6: Transmission delay for different batch sizes with good channel conditions.
# Enhancing Human-Agent Interaction via Artificial Agents That Speculate About the Future
## 4 Results
### Utterence Counts  
First, we wanted to examine the amount of speech (i.e., utterance counts) to see if there were any effects due to adding anticipatory speech capabilities to an artificial agent. Before doing so, we checked the speech output of the agent in the H1 condition to confirm if it was producing a reasonable number of anticipatory speech utterances while still producing its normal self-generated speech (on current/past game events) and ASR responses (to human queries). As shown in Figure 2, that appeared to be the case, with anticipatory speech comprising about 30% of the agent's total speech (the original design intention).  
After validating that the system was working as designed, we analyzed the average amount of speech utterances for both the human and agent across game sessions, by both language and condition. The results can be seen in Table 1. The main takeaway was that introducing anticipatory speech in the artificial agent (H1 condition) caused both the human and the agent to talk more than without such speech (Control condition), including the agent producing more self-generated speech based on game events. However, those differences depended on the language being spoken, with the significant differences mainly being on the Korean side. In the Control condition, Korean speakers spoke much less with the agent than English speakers (or bilingual speakers, for that matter), which we have discussed in previous publications [57; 61]. However, with anticipatory speech added, Korean speakers were much more similar to English speakers. We do note there was an  
Figure 2: Artificial agent speech patterns (H1 condition).  
increase in English human speakers' utterances on average, though that did not obtain statistical significance here, but might if the study was re-run with a larger sample size.  
Curiously, the increase in the artificial agent's self-generated speech here also suggests there may have been a change in the human playing the game, perhaps more adventurously, leading to more game events that the artificial agent could comment about. In other words, anticipatory speech might possibly affect human non-verbal behavior during HRI/HAI. This is an intriguing possibility, though our attempts to confirm this hypothesis by analyzing the gameplay data produced only mixed results. More research (and/or possibly a different study design) is needed to tease that apart. As such, it remains an intriguing but speculative idea for now.
# The Computability of the Channel Reliability Function and Related Bounds
## 3 Results for the Rate Function \(R_{\infty}\) and Applications on the Sphere Packing Bound
Then, from Theorem 7 and the assumptions from this theorem, it can be concluded that \(R_{\infty}\) is a Banach-Mazur computable function. This has created a contradiction. With this, we immediately get the following:  
**Corollary 1**. : _Consider finite alphabets \(\mathcal{X},\mathcal{Y}\) with \(|\mathcal{X}|\geq 2,|\mathcal{Y}|\geq 2\), and let \(\{F_{N}\}_{N\in\mathbb{N}}\) be a sequence of Banach-Mazur computable functions that satisfies the following:_  
1. \(F_{N}(W)\leq F_{N+1}(W)\) _with_ \(W\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\) _and_ \(N\in\mathbb{N}\)_,_
2. \(\lim_{N\to\infty}F_{N}(W)=R_{\infty}(W)\) _for all_ \(W\in\mathcal{C}H(\mathcal{X},\mathcal{Y})\)_._  
_Then, there exists \(\hat{W}\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\) such that \(\lim_{N\to\infty}F_{N}(\hat{W})<R_{\infty}(\hat{W})\) holds true._  
We now want to apply the results for \(R_{\infty}\) to the sphere packing bound as an application. With the results via the rate function, we immediately get  
**Theorem 10**. : _Let \(\mathcal{X},\mathcal{Y}\) be finite alphabets with \(|\mathcal{X}|\geq 2\) and \(|\mathcal{Y}|\geq 2\). The sphere packing bound \(E_{SP}(\cdot,\cdot)\) is not a Turing computable performance function for \(\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\times\mathbb{R}_{c}^{+}\)._  
Proof. : Assuming that the statement of the theorem is incorrect, then \(R_{\infty}\) is a Turing computable performance function on \(\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\times\mathbb{R}_{c}^{+}\). But then the channel functions \(\underline{f}(W)=R_{\infty}(W)\) for \(W\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\) and \(\overline{f}(W)=C(W)\) for \(W\in\mathcal{C}H_{c}(\mathcal{X},\mathcal{Y})\) must be Turing computable channel functions. As was already shown, however, \(R_{\infty}\) is not Banach-Mazur computable. We have thus created a contradiction.
###### Abstract  
China's Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using W10U-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model's complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science.  
agricultural science; deep learning; tea garden pests; object detection; YOLOv8n
###### Abstract  
Vibration signal analysis, which aims to monitor and assess the operational status of mechanical systems in real time, has proven to be an effective method for fault diagnosis. As a crucial component of hosting equipment, the identification of fault signals in rolling bearings is of paramount importance. However, in practical industrial applications, fault diagnosis often fails to achieve satisfactory results. The challenges stem from the fact that vibration signals generated during the operation of rolling bearings are often accompanied by complex noise, which significantly impairs the accurate identification of fault characteristics. To address this issue, a multi-path information fusion fault diagnosis network (MPIFNet) has been proposed for rolling bearings. Specifically, a time series two-dimensional transformation module is introduced to extract key periodic features from the time series signals, thereby extending the original one-dimensional signal into two-dimensional space. Additionally, the multi-path time series extractor is designed to represent multi-scale features. Experimental results demonstrate the superiority of the proposed method, achieving state-of-the-art performance on a public dataset. The potential extends beyond academic applications, offering significant benefits for industrial settings, including cost savings, improved operational efficiency, and enhanced safety by minimizing the risk of unexpected failures in critical machinery.  
multi-path; information fusion; fault diagnosis; time series; two-dimensional transformation Article
## 4 Discussion
### Limitations and Recommendations
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(B\) & \(SE\)\(B\) & \(\beta\) & \(p\) \\ \hline Positive emotions & & & & \\ SA (R\({}^{2}\) = 0.33) & & & & \\ Constant & 4.16 & 0.34 & & 0.00 ** \\ \multicolumn{1}{l}{Constant} & 0.27 & 0.06 & 0.38 & 0.00 ** \\ \multicolumn{1}{l}{Crammel taste} & \(-\)0.37 & 0.07 & \(-\)0.45 & 0.00 ** \\ \multicolumn{1}{l}{Attitude: guilt} & \(-\)0.57 & 0.16 & \(-\)0.31 & 0.00 ** \\ \multicolumn{1}{l}{Occupational status} & \(-\)0.59 & 0.28 & \(-\)0.17 & 0.04 * \\ \multicolumn{1}{l}{Swiss (R\({}^{2}\) = 0.10)} & & & \\ \multicolumn{1}{l}{Constant} & 3.16 & 0.21 & & 0.00 ** \\ \multicolumn{1}{l}{Attitude: guilt} & \(-\)0.19 & 0.08 & \(-\)0.26 & 0.01 * \\ \multicolumn{1}{l}{People \textgreater{}16 years in household} & 0.14 & 0.07 & 0.21 & 0.05 \\ \multicolumn{1}{l}{Negative emotions} & & & & \\ SA (R\({}^{2}\) = 0.21) & & & & \\ \multicolumn{1}{l}{Aggressive} & & & & \\ \multicolumn{1}{l}{Constant} & 2.01 & 0.43 & & 0.00 ** \\ \multicolumn{1}{l}{Attitude: guilt} & 0.29 & 0.09 & 0.28 & 0.00 ** \\ \multicolumn{1}{l}{Single} & \(-\)0.84 & 0.22 & \(-\)0.41 & 0.00 ** \\ \multicolumn{1}{l}{Income} & \(-\)0.54 & 0.21 & \(-\)0.26 & 0.01 * \\ \multicolumn{1}{l}{People \textgreater{}16 years in household} & \(-\)0.21 & 0.10 & \(-\)0.21 & 0.03 * \\ \multicolumn{1}{l}{Bored (R\({}^{2}\) = 0.22)} & & & & \\ \multicolumn{1}{l}{Constant} & 1.98 & 0.33 & & 0.00 ** \\ \multicolumn{1}{l}{Attitude: guilt} & 0.28 & 0.10 & 0.26 & 0.00 ** \\ \multicolumn{1}{l}{Age} & \(-\)0.57 & 0.19 & \(-\)0.28 & 0.00 ** \\ \multicolumn{1}{l}{Income} & \(-\)0.68 & 0.22 & \(-\)0.32 & 0.00 ** \\ \multicolumn{1}{l}{Single} & \(-\)0.50 & 0.22 & \(-\)0.24 & 0.02 * \\ \multicolumn{1}{l}{Guilty (R\({}^{2}\) = 0.10)} & & & & \\ \multicolumn{1}{l}{Constant} & 0.52 & 0.37 & & 0.16 \\ \multicolumn{1}{l}{Attitude: craving} & 0.35 & 0.12 & 0.27 & 0.01 * \\ \multicolumn{1}{l}{Worried (R\({}^{2}\) = 0.21)} & & & & \\ \multicolumn{1}{l}{Constant} & 1.03 & 0.51 & & 0.05 \\ \multicolumn{1}{l}{Nutty taste} & 0.50 & 0.12 & 0.40 & 0.00 ** \\ \multicolumn{1}{l}{Vanilla taste} & \(-\)0.33 & 0.12 & \(-\)0.28 & 0.01 * \\ \multicolumn{1}{l}{Attitude: craving} &.36 & 0.15 &.21 & 0.02 * \\ \multicolumn{1}{l}{Swiss} & & & & \\ \multicolumn{1}{l}{Aggressive (R\({}^{2}\) = 0.19)} & & & & \\ \multicolumn{1}{l}{Constant} & 0.61 & 0.15 & & 0.00 ** \\ \multicolumn{1}{l}{Vanilla taste} & 0.21 & 0.07 & 0.30 & 0.00 ** \\ \multicolumn{1}{l}{Residence area} & 0.27 & 0.11 & 0.24 & 0.02 * \\ \multicolumn{1}{l}{Gender} & 0.23 & 0.11 & 0.20 & 0.04 * \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of regression analysis for variables predicting consumers’ emotions before biscuit consumption.
## 4 Results and Discussion  
### Experimental Setup  
The experiments are carried out on a system with Core (TM) i7-7820 HQ, 64-bit OS, CPU 2.90 GHz, and RAM 32 GB specifications. The results are compiled for the different test video sequences. The performance is analyzed by running the experiments on full video sequences with a frame rate (fps) of 15 Hz and a group of pictures (GOP) of size 2. The experiments were performed using six different values of the quantization metric, \(\mathbf{Q_{m}}\), for quantizing the WZF to achieve different output qualities. The definitions and values of \(\mathbf{Q_{m}}\) used in the proposed codecs are the same as the ones used by DIS in [10]. A list of abbreviations for the different codecs evaluated is given in Table 1. Different parameters are involved in phase-based interpolation for SI generation. Parameter setting is required to obtain the best SI quality. Therefore, the following parameter settings are adopted for SI generation; the number of pyramid levels is 17, the phase shift is 0.4 radians, the number of orientations is 12, and the pyramid scale is \((0.4)^{\frac{1}{2}}\).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**INPUTS**. & Transformed Coefficients of Band- \(X_{DCT}(j)\), \(Y_{DCT}(j)\) \\
**INITIALIZATION**. & Mean of coefficient bands of WZF \\
**OUTPUTS**. & \(\alpha_{f}\) \\ Step 1. & **for all**\(b=1:16\) **do** \\  & Calculate \(\mu_{b}^{WZ}\) & Equation (23) \\  & **end for** \\ Step 2. & **for all**\(b=1:16\) **do** \\  & Calculate \(\mu_{b}^{SI}\) & Equation (24) \\  & **end for** \\ Step 3. & Calculate Residual of corresponding \(\mu_{b}^{WZ}\) and \(\mu^{SI}\) \\  & **for all**\(b=1:16\) **do** \\  & \(\mu_{b}^{R}\) \(\leftarrow\)\(\mu_{b}^{WZ}\)\(-\mu_{b}^{SI}\) & Equation (22) \\  & **end for** \\ Step 4. & Calculate \(\sigma_{f}^{2}\) \\  & \(\sigma_{f}^{2}\) \\ Step 5. & Calculate \(\alpha_{f}\) \\  & \(\mu^{Resum}\) \(\leftarrow\)\(\sum_{b=1}^{16}\left|\mu_{b}^{R}\right|\)\(\forall\)\(b\) \\  & **if**\(0<\sigma_{f}^{2}\leq 1\) **then** \\  & \(\alpha_{f}\) \(\leftarrow\)\(\frac{1}{2\times 10^{2}\times\left(\sigma_{f}^{2}\right)}\) \\  & **else if**\(1<\sigma_{f}^{2}<100\) and \(\mu^{Resum}\leq 50\) **then** \\  & \(\alpha_{f}\) \(\leftarrow\)\(\frac{1}{10\times\mu^{Resum}}\) \\  & **else if**\(1<\sigma_{f}^{2}<100\) and \(\mu^{Resum}>50\) **then** \\  & \(\alpha_{f}\)\(\leftarrow\)\(\frac{1}{20\sqrt{\mu^{Resum}}}\) \\  & **else**\(\sigma_{f}^{2}>100\) and \(\mu^{Resum}\)\(R\) **then** \\  & \(\alpha_{f}\)\(\leftarrow\)\(\frac{1}{\mu^{Resum}}\) \\  & **end if** \\ Step 6. & **Repeat STEPS 1-5 FOR THE UPComing FRAME** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations of different video codecs.
## 2 Modular Multifunctional Approach Advantages
### The Volumetric Efficiency
If the single efficiencies \(\gamma_{i}\) are all equal to \(\gamma=1+\delta\), the expression of the CubeSat efficiency (Equation (2)) can be simplified:  
\[\gamma_{CS}=1+\delta_{CS}=\frac{\gamma}{s+\gamma\left(1-s\right)}=\frac{1+ \delta}{1+\delta(1-s)}\simeq(1+\delta)[1-\delta(1-s)]\simeq 1+s\delta \tag{5}\]  
The volumetric efficiency can be evaluated for the proposed embedded battery tile, whose CAD model is shown in Figure 2.  
In the proposed design, the batteries do not occupy the whole cross-section of the internal volume of the tile (Figure 2a,b); however, the remaining volume among the batteries could hardly be occupied by other bus subsystems or payload components. For this reason, for the systems volume \(V_{sys,i}\) at Equation (1), the entire tile volume is considered. As a first approximation, the thickness of the PCB that accompanies the batteries is neglected. Thus, the systems volume is:  
\[V_{sys,i}=V_{batt}=l_{cube}^{2}\,t_{batt} \tag{6}\]  
where \(l_{cube}=100\,\mathrm{mm}\) is the side length of the internal CubeSat volume and \(t_{batt}=4.5\,\mathrm{mm}\) is the battery thickness. The tile volumetric efficiency \(\gamma_{batt}\) can thus be evaluated:  
Figure 2: (**a**) Sectional view of the embedded battery tile; (**b**) front view of the tile with hidden external components; (**c**) two embedded battery tiles mounted on the CubeSat commercial skeleton. The tile in the foreground is complete, while for the lateral tile, the external components were hidden to show the LiPo batteries colored in grey. Tile side length scale: 100 mm, tile thickness scale: 5 mm. \[\gamma_{batt}=\frac{V_{int}}{V_{int}-V_{batt}}=\frac{l_{cube}^{3}}{l_{cube}^{3}-l _{cube}^{2}\,t_{batt}}=\frac{1}{1-t_{batt}/l_{cube}}=1.0471 \tag{7}\]  
Thus, the volumetric efficiency can be approximated as \(\gamma_{batt}=1+\delta_{batt}\), with \(\delta_{batt}=\delta=4.71\%\). If the same volumetric efficiency is kept for other \(s\) subsystems, the total volumetric efficiency is \(\gamma_{CS}=1+s\cdot\delta\) (Equation (5)). Table 1 lists the possible CubeSat volumetric efficiencies with an increasing number of smart tiles \(s\). The total volumetric efficiency is given as a total volumetric increment \(\delta_{CS}=\gamma_{CS}-1\). If a constant volumetric increment of \(\delta=4.71\%\) is kept for all subsystems, a remarkable total volumetric increment of \(36.97\%\) can be reached. This value of \(\delta\) seems feasible, at least for a battery pack, since a prototype has been produced and tested, as discussed in Sections 3 and 4, respectively.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 6 Simulation Results  
Simulation tools, setting, and numerical results are presented in this section to demonstrate the performance of the SROR algorithm in terms of attack success ratio, EED, and PDR. In addition, we discuss the convergence of SROR by analyzing the average reward and cumulative rewards of agent in different learning rate and exploratory factor policy.