esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## Efficiency Analysis  
### Complexity Analysis  
When L represents the number of bits in the prime used in the ElGamal scheme, efficiency is analyzed by representing the time and space complexity for each construction as a Big-O notation.  
System Setup  
* _SysPrm_ Construction:. Time Complexity: \(O(g(s))\), where g is the complexity of the parameter generation algorithm.. Space Complexity: \(O(L)\). Registration and Encryption
* _KeyGen_ Construction:. Time Complexity: \(O(L)\) for selecting random values.. Space Complexity: \(O(L)\).
* _Enc_ Construction:. Time Complexity: \(O(L^{2})\) for modular exponentiation.. Space Complexity: \(O(L)\). Accident Occurrence
* _Ord_P(_R_) Construction:. Time Complexity: \(O(1)\). Space Complexity: \(O(L)\).  
* _Rpt_CC_ Construction:
* Time Complexity: \(O(1)\).
* Space Complexity: \(O(L)\).
* _Ord_F_ Construction:
* Time Complexity: \(O(1)\).
* Space Complexity: \(O(L)\). Proxy Re-Encryption
* _RKeyGen_ Construction:
* Time Complexity: \(O(L)\).
* Space Complexity: \(O(L)\).
* _PrxyRE_ Construction:
* Time Complexity: \(O(L^{2})\) (involving exponentiation and multiplication of keys and messages).
* Space Complexity: \(O(L)\). Treatment
* _Dec_ Construction:
* Time Complexity: \(O(L^{2})\) (involving exponentiation and decryption).
* Space Complexity: \(O(L)\).
* _Trt_Str_ Construction:
* Time Complexity: \(O(1)\) (assuming treatment and storage are basic operations).
* Space Complexity: \(O(L)\). Simplifying, we can approximate the overall complexity as: \(O_{\text{total}}(L)\approx\ O(L^{2})\).
# Energy and Spectral Efficiency Balancing Algorithm for Energy Saving in LTE Downlinks
## 4 Performance Evaluation of the Proposed SET algorithm
### Impact of Varying Traffic Loads in Congested Network  
In order to carry out this experiment, we implemented two scenarios: (1) varying network loads on a congested network using an omni-directional antenna. (2) varying network loads on a congested network using directional antennas. Thus, the impact of using different types of antennas, such as omni and tilted directional antennas are presented in Sections 4.3.1 and 4.3.2, respectively.  
Figure 11: Effect of SE-EE trade-off in non-congested network.  
Figure 10: Energy consumption vs. packet arrival rate.
# Study of an Atmospheric Refractivity Estimation from a Clutter Using Genetic Algorithm
## 4 Conclusions  
In this paper, we have investigated the method for estimating atmospheric refractivity using both sea and land clutters. To estimate the atmospheric refractivity, the artificial tri-linear model that can express all atmospheric conditions was applied in a GA process. In addition, the clutter power spectrum was calculated using an AREPS EM propagation simulator. At the same time, the clutter power spectrums were again obtained based on the measured atmospheric refractivity data. The cost function for the GA was then defined based on the difference between the two clutter power spectrums. From the estimation result, the estimation errors for the duct slopes and duct thickness were maintained at below 46.1 and 36, respectively. The results demonstrated that atmospheric refractivity can be predicted using the proposed method from the clutter powers.  
Conceptualization, D.J., J.K., Y.B.P. and H.C.; formal analysis, D.J. and Y.B.P.; funding acquisition, H.C.; investigation, D.J. and J.K.; methodology, D.J. and J.K.; project administration, H.C.; software, D.J. and Y.B.P.; supervision, H.C.; validation, D.J., J.K., Y.B.P. and H.C.;  
Figure 8: Comparison of the estimated and real atmospheric refractivity. (**a**) Using the measured data of Case 2; (**b**) using the measured data of Case 3; (**c**) using the measured data of Case 4; (**d**) using the measured data of Case 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Option** & [14] & [17] & **Our Best** \\ \hline Duct slopes error & 4 & 240 & 29 \\ Duct thickness error & 0.14 & 47 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results.  
visualization, D.J.; writing--original draft, D.J.; writing--review and editing, D.J., J.K., Y.B.P. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work has been supported by the Agency for Defense Development (ADD) of Republic of Korea under Project No. UD210013 YD.  
The authors declare no conflict of interest.
# Feasibility Study on Color Grading of Blue Iolite Based on GemDialogue Color Comparison Charts
## 3 Result and Discussion  
### Rules of Color Charts  
The color parameters of the five color charts B, P2B, B2P, B3P*, B4P*, and their overlapping blackgray (transparent) and brown (transparent) color masks were tested under a D65 light source. A total of 7250 data were collected. The changes in lightness, \(L^{*}\), chroma, C*, and hue angle, \(h^{\circ}\), were observed to study the change pattern of the color charts and the effect of color masks on color.  
Without superimposed masks, the range of the lightness, \(L^{*}\), of B, P2B, B2P, B3P*, and B4P* are, in order, \(L^{*}{}_{B}\in(43.51,\,77.38)\), \(L^{*}{}_{P2B}\in(31.43,\,81.33)\), \(L^{*}{}_{B2P}\in(34.1,\,79.18)\), \(L^{*}{}_{B3P^{*}}\in(20.48,\,65.43)\), and \(L^{*}{}_{B4P^{*}}\in(17.44,\,69.18)\); the range of chroma, C*, are successively \(C^{*}{}_{B}\in(26.29,\,66.58)\), \(C^{*}{}_{P2B}\in(15.76,\,74.2)\), \(C^{*}{}_{B2P}\in(21.18,\,103.2)\), \(C^{*}{}_{B3P^{*}}\in(28.54,\,93.61)\), and \(C^{*}{}_{B4P^{*}}\in(21.93,\,90.28)\); the range of hue angle, \(h^{\circ}\), are \(h^{\circ}{}_{B}\in(237,\,266.4)\), \(h^{\circ}{}_{P2B}\in(258.6,\,286.8)\), \(h^{\circ}{}_{B2P}\in(307,\,312.9)\), \(h^{\circ}{}_{B3P^{*}}\in(259.1,\,302.5)\), and \(h^{\circ}{}_{B4P^{*}}\in(285.6,\,303.6)\), in order. With the color band value from 100 to 10, the lightness, \(L^{*}\), of the five color charts increased successively, the chroma, C*, decreased successively, and the hue angle, \(h^{\circ}\), showed an overall decreasing trend (Figure 2). Very few samples did not follow this pattern, which may be related to the stability of the instrument. As can be seen from the figure, there are many overlapping parts of lightness, \(L^{*}\), and chroma, C*, in the five color charts, but the difference in hue angle, \(h^{\circ}\), is obvious, which belongs to five different hue intervals, and the size relationship is: \(h^{\circ}{}_{B2P}>h^{\circ}{}_{B4P^{*}}\geq h^{\circ}{}_{B3P^{*}}\geq h^{\circ} {}_{P2B}\geq h^{\circ}{}_{B}\). The hue of B2P tends to be purple, the hue of B4P* lies in blue-purple, and the hue of B3P* partly lies in bluepurple and partly tends to blue. Color chart B and P2B are both located in blue, but the blue of B is lighter. Secondly, although the change trend of color parameters in each color chart is the same, the change speed is different, the chroma, C*, and hue angle, \(h^{\circ}\), are the most obvious.
# Energy-Efficient Wireless Communication Strategy for Precision Agriculture Irrigation Control
## 4 Results and Discussion
### Discussion  
Self-triggered is a model-based control approach, and thus its implementation requires estimating the evolution of the process state variables to predict the next relevant event to communicate data. This capability allows the self-triggered strategies to perform better than the periodical implementations due to adjusting the sampling period according to the irrigation process conditions. Moreover, if the sampling period is set to save water, the device power consumption increases due to a significant amount of messages.  
On the other hand, if the periodical strategy is tuned to reduce the frequency of communications messages, this negatively affects the control performance regarding the water consumption and, to a lesser degree, the irrigation error. Self-triggered algorithms provide a balanced solution where the control objectives are fully accomplished and energy efficiency is achieved due to communication messages being transmitted only when needed.  
No significant performance difference between both self-triggered algorithms was detected. Therefore, it is difficult to identify which one is more suitable for agriculture applications and what advantages one may provide over the other. The tuning parameters of each algorithm were selected empirically through simulations; however, an analytical solution should be explored. Thus, a more profound analysis must be conducted on self-triggered strategies optimizations for precision agriculture applications.  
Another critical aspect that makes viable the implementation of self-triggered algorithms in precision agriculture applications is that the inherent low dynamics of agriculture allows real-time execution regardless of the slight increase of the computation time required to calculate the next sampling instant. This additional processor load is considerable lower--practically insignificant--when compared with the time spent in the communication events. Smart farming relies on the use of WSN to communicate data from sensor devices. The results obtained in this work encourage the implementation of self-triggered algorithms to provide an energy-efficient wireless communications strategy to precision agriculture and other environmental monitoring applications where a state-space model can appropriately describe the process dynamics.
# Variable Tap-Length Algorithm with Mixed Parameter
## 2 Variable Tap-Length Algorithm
### Fractional Variable Tap-Length Algorithm  
Fractional variable tap-length means that the tap-length varies in the form of fraction. The sectional error refers to \(M\) length in front of the filter:  
\[e_{M}^{(L)}=d(n)-\mathbf{W}_{L,1:M}^{T}\mathbf{X}_{L,1:M} \tag{8}\]  
where \(1\leq M\leq L\); \(\mathbf{W}_{L}\) and \(\mathbf{X}_{L}\) are the weight coefficients and the input in the algorithm, respectively. \(\mathbf{W}_{L,1:M}\) and \(\mathbf{X}_{L,1:M}\) represent the first \(M\) elements of the weight coefficients and the input, respectively. The cost function of the tap-length is the square of the fragment error \(\xi_{M}^{(L)}=E\bigg{\{}\left(e_{M}^{(L)}(n)\right)^{2}\bigg{\}}\). Then, the algorithm outputs the most compatible \(L\) to meet the requirements.  
\[\xi_{L-\Delta}^{(L)}-\xi_{L}^{(L)}\leq\varepsilon \tag{9}\]  
where \(\Delta\) is error width. It is a positive integer less than \(L\). \(\varepsilon\) is a small positive integer required by the system. The minimum \(L\) satisfying Equation (9) is chosen as the optimal filter tap-length.  
Tap-length is defined as an FT \(L_{f}(n)\) and the iterative procedure of FT is:  
\[L_{f}(n+1)=\left(L_{f}(n)-\alpha\right)-\gamma(n)*\left[\left(e_{L(n)}^{(L(n)) }(n)\right)^{2}-\left(e_{L(n)-\Delta}^{(L(n))}(n)\right)^{2}\right] \tag{10}\]  
where \(\alpha\) is a positive leakage parameter. Its function is to avoid the problem that the tap-length is much larger than the optimal value. \(\gamma(n)\) is the step-size of tap-length (ST). It is fixed in FT algorithm. When the cumulative change of the fractional tap-length exceeds a certain value, the tap-length updates as:  
\[L(n+1)=\begin{cases}\left\lfloor L_{f}(n)\right\rfloor&if\Big{|}L(n)-L_{f}(n )\Big{|}>\delta\\ L(n)&others\end{cases} \tag{11}\]  
where \(\left\lfloor\cdot\right\rfloor\) represents the nearest integer to \(L_{f}(n)\), and \(\delta\) is a small integer threshold. The weight vector values in the FT algorithm are updated as follows:  
\[\mathbf{W}_{L(n)}(n+1)=\mathbf{W}_{L(n)}(n)+\mu^{\prime}e_{L(n)}^{(L(n))}(n) \mathbf{X}_{L(n)} \tag{12}\]  
where \(\mathbf{W}_{L(n)^{\prime}}e_{L(n)}^{(L(n))}\), and \(\mathbf{X}_{L(n)}\) represent the weight vector, error, and the input vector, respectively. \(\mu^{\prime}\) is the normalized step-size. It can be calculated from \(\frac{\mu}{\mathbf{X}_{L(n)}^{T}\mathbf{X}_{L(n)}+\beta}\); \(\mu\) is step-size. \(\beta\) is the regularization factor. It is a small positive number multiplied by the unit matrix.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.6 Low-Cost Communication Methods  
In [17; 87; 88; 89; 90; 100; 101], the existing techniques for solving the high-cost issues in UIoT are discussed, and some methods are indicated herewith. In [89], [PERSON] et al. designed a low-cost acoustic modem to reduce underwater acoustic sensor network cost and power consumption. In [99], [PERSON] et al. designed a low-cost application to monitor water quality using underwater wireless communication. In [100], [PERSON] et al. designed and developed a low-cost glider to perform in shallow water, around 3-m depth, 3-m radius and a minimum of 60 h durability. Finally, in [101], [PERSON] designed and developed a low-cost coral reef monitoring application for shallow water.
# An FPGA Implementation of a Convolutional Auto-Encoder
## 2 FPGA Implementation of the CAE
### Design of Convolutional Encoding Module  
Convolutional encoding is the most important module in the framework; therefore, its structure is further discussed below. To effectively enhance the compatibility of our framework and control the utilization of hardware resources reasonably, the number of input channels is fixed to eight rather than the maximum number of neurons in a certain layer. To realize the simultaneous convolution calculation of eight channels, the parallel channels method is developed by caching the input data.  
The operational process of the method is shown in Figure 3. Four identical processing elements (PEs) are used to process the input data, and the ones with identical sequence number are the same. These PEs are independent of each other so they can operate simultaneously. Therefore, all the convolutional results of the eight channels can be calculated simultaneously by inputting the data in sequence. The RAM1 and RAM2 are used to cache the last two-channel data of every eight inputs, which can avoid loading repetition of the input and improve the efficiency of data utilization. In Figure 3, \(8\times i+j\) (\(i=1\),..., \(\lceil\text{M}/8\rceil-1\) and \(j=1\), \(2\),..., \(8\)) represents the index of channel. Meanwhile, M represents the total number of rows of zero-padding matrix, and the symbol \(\lceil\rceil\) represents a rounding-up operation.  
At stage 1 (the black box in Figure 3), the first two inputs are transmitted to the first two channels of PE1, while the next two inputs are transmitted simultaneously to the other channels of PE1 and the first two channels of PE2. The next two inputs are provided to both the last two channels of PE2 and the first two channels of PE3. The last two inputs are sent to the last two channels of PE3 and RAM1 at the same time. The RAM1 is employed to buffer data, so that these data can be recycled without re-entering from outside. As shown in Figure 3, once the first set of eight-channel data from the zero-padding matrix has been processed at stage 1, the subsequent set of eight-channel data is sent to PEs at stage 2 (red box in Figure 3). At stage 2, the data in RAM1 and the subsequent inputs are sent to PEs, and a similar operation is undertaken as that at stage 1.
## 4 Experiments and Analysis
### Analysis
#### 4.3.2 Impact of Pretrained Datasets  
The choice of pretrained datasets may influence the performance of prompt-based continual learning methods. As shown in Table 5, the performance difference between the iBOT models pretrained on ImageNet-21k and ImageNet-1k datasets is approximately 4% on the CUB-200 dataset. Similarly, using three pretrained CLIP models with different datasets (see Appendix A.1) on the CUB-200 dataset, the performance gap between FAA and CAA is close to 2%. Overall, the experimental results reveal that different pretrained datasets indeed have a significant impact on the performance of the prompt-based method for continual learning. Therefore, when comparing different models, it is imperative to use the same pretraining datasets to ensure a fair and accurate assessment of their capabilities.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Characterization of Optical Networks  
Statistical characterization of optical networks involves analyzing their performance, reliability, and efficiency using probabilistic models and statistical techniques. Statistical distribution models, stochastic models, and Gaussian noise models, help predict performance under varying traffic loads and environmental conditions. Monte Carlo simulations and machine learning techniques further refine network optimization. Statistical methods enable the assessment of link failures, congestion patterns, and quality of signal, aiding in the design of resilient, high-capacity optical communication systems. By leveraging statistical analysis, network operators can enhance fault tolerance, optimize resource allocation, and improve overall network efficiency, making optical networks more adaptive to dynamic data demands.
## 3 VaNetChain: Architecture
Thus, for any new transaction started by a vehicle (i.e., for any action to keep track of in the blockchain), the virtual node covering its current location can check its reputation and discard the transaction straight away if the value is too low or the vehicle has been blacklisted, thus helping to avoid waste of resources. The behavior of the virtual nodes in VaNetChain has been extended with regard to the VaNetLayer in order to support the transient states while some transactions have not been added to the blockchain yet. This is summarized in the state diagram of Figure 3.  
* In an INITIAL state, the VN waits for a physical node to initiate a transaction, which takes it to REQUEST state and sets to 0 the counters used thereafter: _requestCount_ and _validationCount_. * Within REQUEST, the _t_RequestValidators_ timer is activated and an _m_RequestValidators_ message is sent, incrementing the _requestCount_ by 1. The objective is to request the presence of at least 3 local nodes in the region to pre-validate the transaction. If _t_RequestValidators_ expires without reaching the minimum number of validators, the REQUEST is reactivated once, and if it fails again the VN evolves to CHANGE state. Otherwise, the state becomes VALIDATION. * The CHANGE state denotes that the region does not have enough vehicles to pre-validate the transaction. In response to that, a neighboring VN is asked to take over by transmitting an _m_changeCVN_ message. * In VALIDATION state, the VN confirms the participation of the validators and responds with an _m_InitValidation_ message, which activates the _t_waitValidation_ timer and increases the _validationCount_. This process is repeated up to 3 times, remaining in VALIDATION until it receives the requested verifications through _m_ValidatedTransactions_ messages. If _validationCount_ rises to 4, it automatically moves to CHANGE, otherwise it can fall into DROP or PROPAGATE. Figure 3: State machine of virtual nodes in relation to blockchain operations. * The DROP state means that positive validations were not sufficient, so the transaction is discarded. Then, an _m_DropTransaction_ message is broadcast and the state becomes VIRTUAL NODE. * Having got the minimum number of positive validations, the PROPAGATE state is reached, where the VN sends the validated transaction to the nearest mRSUS accompanied by the message _m_confirmMRSU_, which activates the _t_WaitMRSU_ timer. While the timer is active, it waits for the confirmation message _m_StatusMRSU_==true. If this is not received, the process is repeated indefinitely in order to guarantee that the information has reached the mRSU.
## 4 Numerical Results and Discussions
Figure 5: BER performance of GCI-DCSK SWIPT changes along with \(d_{tr}\) over a multipath Rayleigh fading channel, where \(P_{s}/N_{0}=15\) dB and \(P_{s}/N_{0}=20\) dB. and labeled as \(n_{c}=0\). Obviously, both cases of \(n=16\), \(p_{2}=15\) and \(n=64\), \(p_{2}=1\) degrades by about 2 dB compared with the corresponding cases with \(n_{c}=0\) over both AWGN and Rayleigh fading channels. The deterioration of 2 dB is caused by conversion noise. In particular, when the number of active carriers is 1 or \(n-1\) GCI-DCSK SWIPT becomes conversion noise-aware CI-DCSK SWIPT. It can be clearly observed in the two figures that when \(n\) selects 16, both cases of \(p_{2}=7\) and \(p_{2}=11\) outperform the case of \(p_{2}=15\) not only in BER performance but also in SE. In addition, with \(n=64\), the case of \(p_{2}=4\) outperforms the case of \(p_{2}=1\) in BER performances and EE. Thus, some cases of GCI-DCSK SWIPT own better BER performance than conversion noise-aware CI-DCSK SWIPT, and a higher SE or EE is obtained at the same time. In addition, taking both path loss and conversion noise into consideration, GCI-DCSK SWIPT is still self-sustainable with good BER performance. Figure 6: Simulations and analytical BER performance of GCI-DCSK SWIPT over AWGN channel with \(d_{tr}=1\). Figure 7: Simulations and analytical BER performance of GCI-DCSK SWIPT over multipath Rayleigh fading channel with \(d_{tr}=1\). Figure 8 shows the comparison between GCI-DCSK SWIPT and GCI-DCSK with the same conversion noise over Rayleigh fading channel. GCI-DCSK achieves nearly 2 dB performance gain over GCI-DCSK SWIPT with the same configurations of \(p_{1}\) and \(n\) because only the first part of chips (\(\beta_{1}\)) are used for decoding in GCI-DCSK SWIPT. However, GCI-DCSK SWIPT can provide the function of energy harvest.
## 3 Analytic Hierarchy Process and Initial Conditions  
### Analytic Hierarchy Process  
The analytic hierarchy process (AHP) is a quantification method for processing qualitative problems that can be used to analyze the weight index of complex problems. The basic steps are as follows:  
(1) Analyze the primary and secondary relations among various factors of the system and construct a hierarchical structure, which is generally divided into the highest level target layer, the middle level criterion layer, and the lowest level scheme layer. The number of elements in the criterion and scheme layers should not exceed nine.  
(2) For each element at the same level, make pairwise comparisons for the importance of a certain criterion at the previous criterion level, and construct judgment matrix \(A=\left(a_{ij}\right)_{t\times n}\) for pairwise comparisons, as shown in Table 2.  
(3) Calculate the weight vector \(w_{i}\) and consistency index (\(CI\)) of each element of a certain criterion according to the judgment matrix, and check the consistency ratio (\(CR\)). \(CR<0.1\) is required, and the next step can be performed only if the consistency check is satisfied; otherwise go back to step 2 to modify the judgment matrix.  
Weight vector \(w_{i}\) calculation methods include geometric average (square root), arithmetic average (summation), eigenvector, and least square methods. Since the value of each column in the judgment matrix approximately reflects the weight, the arithmetic average of all column vectors can be used to calculate the weight vector. Therefore, this paper adopted the arithmetic average method. The calculation steps are as follows. Calculate \(a_{ij}/\sum_{k=1}^{n}a_{kj}\)  
Figure 4: Schematic diagram of grid and boundary.  
Figure 3: Grid independence verification. (**a**) Nondimensional velocity profile. (**b**) Nondimensional velocity ratio.  
and normalize the judgment matrix elements by column, and then add and divide the normalized columns to obtain the weight vector, as shown in Equation (2):  
\[w_{i}=\frac{1}{n}\sum_{j=1}^{n}\frac{a_{ij}}{\sum_{k=1}^{n}a_{kj}} \tag{2}\]  
\[CI=\frac{\lambda_{\max}}{n-1} \tag{3}\]  
where \(\lambda_{\max}\) is the maximum eigenvalue of the judgment matrix \(A=\left(a_{ij}\right)_{n\times n}\), and \(n\) is the order of the judgment matrix.  
\[CR=\frac{CI}{RI} \tag{4}\]  
where \(RI\) is the random consistency index, as shown in Table 3.  
(4) Calculate the score according to the weight matrix, sort the weight of the factors, and provide evaluation suggestions.