esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 3 Improved Spatiotemporal Multi-Channel Network (IQGMCL)
### Multiplicative Gaussian Noise  
Multiplicative Gaussian noise is also called GuassianDropout in TensorFlow.Keras framework. Although there is Dropout, it is not Dropout in essence. The effect is to add multiplicative noise with a mean of 1 and a standard deviation of \(\sigma\) to the results. It is often used as a stochastic regularization technique in deterministic neural network training [32; 33; 34]. Multiplicative Gaussian noise follows a normal distribution, and its probability density function expression is shown below.  
\[f(x)=\frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-1)^{2}}{2\sigma^{2}}},\;x\sim N (1,\sigma) \tag{10}\]  
where \(x\) is the noise level, 1 is the mean of the noise level, \(\sigma\) is the standard deviation of the noise level, and \(\sigma^{2}\) is the variance.  
Suppose the input variable is \(\widetilde{in}\), the formula is as follows:  
\[\widetilde{in}=\begin{bmatrix}I(t_{1}),I(t_{2}),I(t_{3}),&\dots&,I(t_{i}),& \dots&,I(t_{n})\\ Q(t_{1}),Q(t_{2}),Q(t_{3}),&\dots&,Q(t_{i}),&\dots&,Q(t_{n})\end{bmatrix} \tag{11}\]  
The output variable is: \(\widetilde{out}=\widetilde{in}\cdot x\)  
The output expression with multiplicative Gaussian noise is obtained:  
\[\widetilde{out}=\begin{bmatrix}I(t_{1})x_{1I},I(t_{2})x_{I2},I(t_{3})x_{I3},& \dots&,I(t_{i})x_{li},&\dots&,I(t_{n})x_{ln}\\ Q(t_{1})x_{Q1},Q(t_{2})x_{Q2},Q(t_{3})x_{Q3},&\dots&,Q(t_{i})x_{Qi},&\dots&,Q(t_ {n})x_{Qn}\end{bmatrix} \tag{12}\]  
Noise is random [35]; in the neural network training process, adding Multiplicative Gaussian noise to corrode features randomly will effectively alleviate the training overfitting problem. It can eliminate the influence of isolated features on the training results during model training and improve the robustness of the model. To some extent, multiplicative Gaussian noise can improve the test accuracy of the model and improve the generalization ability of the network.
## 4 Comparative Analysis
#### 4.2.2 When Top-1 Nearest Neighbor Is Used  
The Figure 14 represents the comparison between the proposed recommendation approach (MSMPPA) and CF algorithms using items' categorical attributes (CF_UIMC, CF_UIWA and CF_UIZS) based on MAE value at Top-1 nearest neighbor. As shown in the graph, the proposed recommendation approach has a comparatively low MAE value than other CF algorithms. Therefore, the comparative results of the above graph clarify that MSMPPA is better to approach than CF_UIMC, CF_UIWA and CF_UIZS.  
Figure 13: RMSE Value at Dataset2.  
The Figure 15 shows the comparison between the proposed recommendation approach and CF algorithms using items' categorical attributes, based on MAE value at various datasets ML\({}_{3}\), ML\({}_{4}\) and ML\({}_{5}\). The proposed recommendation approach has a comparatively low prediction error than CF algorithms using items' categorical attributes for all traditional prediction approaches (i.e., MC, WA, and ZS). Therefore, on the basis of the accuracy of recommendation, MSMPPA is more preferable approach than CF_UIMC, CF_UIWA, and CF_UIZS.  
Figure 14: MAE Value at Dataset1.  
The Figure 16 shows the comparison between the proposed recommendation approach and CF algorithms using items' categorical attributes, based on RMSE value at various datasets ML\({}_{1}\) and ML\({}_{2}\). In the graph, CF algorithms using items' categorical attributes attain high prediction errors than the proposed recommendation approach. Therefore,  
Figure 15: MAE Value at Dataset2.  
CF_UIMC, CF_UIMA, and CF_UIZS provide less accurate recommendation results than MSMPPA.  
The Figure 17 depicts the comparison between the proposed prediction approach and CF algorithms using items' categorical attributes, based on RMSE value at various datasets ML\({}_{3}\), ML\({}_{4}\) and ML\({}_{5}\). We can notice that the proposed prediction approach provides less prediction error. Therefore, MSMPPA outperforms CF_UIMC, CF_U  
Figure 16: RMSE Value at Dataset1.  
From aforementioned Figures 2-17, we can conclude that ICA in SeM and '_most_' similar neighbor in prediction approach surpass recommendation results from the other state-of-the-art in CF. The justification of the aforementioned statement is clarified by the comparative outcomes of the stated recommendation approach.
# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Evaluation Using Real-World Mobility Traces
#### 3.3.2 Single Technique  
In Figure 6, we observe that Fuzzy-Spray can be roughly described as Epidemic implementing also a QM technique. For this reason, its behavior is similar to the one observed in Epidemic, but the effectiveness of the QM allows Fuzzy-Spray to obtain significantly better delivery ratios; this is particularly evident in the small buffer case. The effectiveness of the QM with small buffers is confirmed comparing Fuzzy-Spray to PRoPHET and recalling that PRoPHET implements only the FW technique. Spray-and-Wait generates a limited number of copies (four in our experiments). When the meeting opportunities are relatively high, as in real traces, this number of copies allows Spray-and-Wait to provide a fairly good delivery ratio. On the contrary, in synthetic traces, the meeting probability is lower, and thus, such a number of copies cannot provide the good performance observed in real traces.  
The high number of copies generated by Fuzzy-Spray increases the probability of a fast delivery; indeed, the delivery delay achieved by this protocol is the lowest achieved among the 10 protocols considered, but at the same time increases the overhead. Similarly, the limited number of copies generated by Spray-and-Wait results in a higher delay and in a lower overhead. The effectiveness of the QM is more evident when the meeting opportunities are lower, as in the synthetic traces; while for real traces, the performance in terms of delay is pretty similar and mainly depends on the buffer size.  
In terms of the outdoor real-world mobility trace, the performance of the single technique protocols is roughly the same. As with the case of the simple protocols, the nature of the mobility of taxis in the metropolitan area heavily affects the overall performance of the protocols, thus keeping the delivery ratios much lower than in the case of the indoor real-world mobility trace. Therefore, since the meeting opportunities remain very low, Spray-and-Wait achieves the lowest delivery ratio, while the performance of the other two protocols is almost identical.
## 3 The FQS Algorithm
### Preprocessing Phase
#### 3.1.1 Computing the \(Es\) array  
In the naive computation, \(ES_{j}=\sum_{c}\left(j-prepos(c)\right)\) for each symbol \(c,c\in\Sigma\). The total time complexity for computing all \(ES_{j}\), where \(0\leq j\leq m-1\), is \(O(m|\Sigma|)\). Needless to say, it can be improved. The \(ES_{j}\) can be calculated from \(ES_{j-1}\), when \(j>0\). That is, the expected skip value at the current position can be calculated by utilizing the known expected skip value at the previous position. The difference between \(ES_{j}\) and \(ES_{j-1}\) is:  
\[\begin{array}{l}ES_{j}-ES_{j-1}=\sum_{c}(j-prepos_{j}(c))-\sum_{c}(j-1-prepos _{j-1}(c))\\ =\sum_{c}(j-prepos_{j}(c)-(j-1-prepos_{j-1}(c)))\\ =\sum_{c}\left(1-(prepos_{j}(c)-prepos_{j-1}(c))\right)\\ =\sum_{c}\left(1\right)-\sum_{c}(prepos_{j}(c)-prepos_{j-1}(c))\end{array} \tag{5}\]  
Since \(\sum_{c}\left(1\right)=|\Sigma|\), then \(ES_{j}-ES_{j-1}=|\Sigma|-\sum_{c}(prepos_{j}(c)-prepos_{j-1}(c))\).  
For each \(c\in\Sigma\) and \(c\
eq P[j],prepos_{j-1}(c)=prepos_{j}(c)\). That is, all symbols in pattern \(P\) have the property that \(prepos_{j-1}(c)=prepos_{j}(c)\), except for the symbol at the current position, \(j\). Put another way, except the current symbol in pattern \(P\), for all of the other symbols in \(\Sigma\), their current \(prepos_{j}(c)\) is equal to \(prepos_{j-1}(c)\). The difference between \(ES_{j}\) and \(ES_{j-1}\) can be further analyzed:  
\[\begin{split}& ES_{j}-ES_{j-1}=|\Sigma|-\sum_{c}(prepos_{j}(c)- prepos_{j-1}(c))\\ &=|\Sigma|-(prepos_{j}(P[j])-prepos_{j-1}(P[j]))\\ &=|\Sigma|-(j-prepos_{j-1}(P[j]))\end{split} \tag{6}\]  
Finally, we get:  
\[ES_{j}=ES_{j-1}+|\Sigma|-(j-prepos_{j-1}(P[j])) \tag{7}\]
# An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems
## 2 Research Methods
### Improved Deviation Coupling Control Strategy  
The deviation coupling control strategy exhibits escalated computational demands with an increasing number of motors, resulting in prolonged control durations and diminished synchronization accuracy of the speed compensator. In pursuit of enhancing synchronization, this study introduces an improved deviation coupling control strategy. The schematic diagram of the improved deviation coupling control structure is presented in Figure 2. The conceptual framework of this control methodology draws primarily from the principles of average deviation coupling control [25]. Initially, it computes the average speeds of all motors and generates the compensation signal 1 by computing the deviation between the speed of the \(i\)th motor and the overall average speed. Subsequently, it calculates the average speeds of all motors, excluding the \(i\)th motor, generating the compensation signal 2 by assessing the deviation between the speed of the \(i\)th motor and the sub-average speed. Both signals, 1 and 2, collectively formulate a speed compensator structure, where \(\omega_{avg}\) signifies the total average speed and \(\omega_{avgi}\) represents the sub-average speed of the multi-motor synchronous control systems. These values can be mathematically expressed as  
\[\left\{\begin{aligned} &\omega_{avg}=\sum_{i=1}^{n}\left(\omega_{i}/n \right)\\ &\omega_{avgi}=\sum_{j=1,j\
eq i}^{n}\left(\omega_{j}/(n-1)\right) \end{aligned}\right. \tag{5}\]  
Figure 1: Deviation coupling control structure.  
The compensation speed error \(e_{i}\) can be expressed as  
\[e_{i}=\sum_{j=1,f\
eq i}^{n}K_{ij}(\omega_{i}-\omega_{avg})+\sum_{j=1,f\
eq i}^{n} K_{ij}(\omega_{i}-\omega_{avgi}) \tag{6}\]  
Correspondingly, the control conditions are  
\[\left\{\begin{array}{l}\lim_{t\to\infty}[\omega_{i}(t)-\frac{\omega_{1}(t)+ \cdots+\omega_{i}(t)+\cdots+\omega_{n}(t)}{n}]=0\\ \lim_{t\to\infty}[\omega_{i}(t)-\frac{\omega_{1}(t)+\cdots+\omega_{i-1}(t)+ \omega_{i+1}(t)\cdots+\omega_{n}(t)}{n-1}]=0\end{array}\right. \tag{7}\]  
In comparison with the conventional speed compensator, the average speed compensator structure proposed in this study notably diminishes system arithmetic complexity and abbreviates system response time. Furthermore, contrasting with mean deviation coupling control, the incorporation of sub-average speed compensation mitigates the impact of residual motor disturbances on the system, consequently enhancing the synchronous performance of the multi-motor system.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### Native Integral Ratio (NIR) Method  
Typically, global thresholding is done in one stage where the image pixels are separated into foreground and background. Global threshold methods target to achieve a specific threshold value to do the separation [8]. NIR thresholding aims to add a third category of pixels called fuzzy class. In the fuzzy class it is not clear whether these pixels fall in the foreground or background [8, 15]. Two critical values, \(M\) and \(N\), are, therefore,determined. The foreground and fuzzy class boundary, and the background and fuzzy class boundary, exist respectively. The technique first determines these two boundaries then later determines the threshold within the fuzzy class region. In the first stage of thresholding, a range of threshold values lying between \(M\) and \(N\) are obtained. In the subsequent stages, a final threshold level is picked between \(M\) and \(N\)[8]. The integral function can be represented as a function, \(y:J\to K\), where \(J\) is a subset of \(K\). Hence:  
\[y(t)=\frac{\int_{s_{1}(t)}^{s_{2}(t)}p(u)du}{\int_{s_{3}(t)}^{s_{4}(t)}p(u)du} \tag{12}\]  
where \(s_{1},s_{2},\ s_{3},\ s_{4}\) are all real functions with variable \(u\) and parameters \(u_{o}\ p\) is also a real function.  
\[\begin{array}{c}s_{i}:\ J_{i}\to K,\ J_{i}\subset K,\ and\ i=1,2,3,\ldots\\ p:\ J_{p}\to K,\ J_{p}\subset K\end{array}\]  
For similarly spaced adjacent intervals, the integral ratio function can be expressed as:  
\[y(t)=\frac{\int_{t}^{t_{o}+dt}p(u)du}{\int_{t_{o}+dt}^{t_{o}+2 dt}p(u)du} \tag{13}\]  
where \(t_{o}\) is the pivot of \(s_{o}\), and \(i=1,2,3,\ldots\)  
The NIR technique determines the lower and upper bounds of the fuzzy region using estimators [8]. These estimators use the intensity histogram of the image as the threshold. Assume for example that \(q_{f}\) represents the intensity value of local maximum of the pixels in the foreground. Let \(q_{o}\) denote the background peak if it has a single background peak. Let \(q_{b}\) represent the background peak with the highest intensity of the background that has more than one peak.  
For the lower bound of the fuzzy region, \(J\):  
\[J=q_{f}+\underset{t=1\ldots(q_{b}-q_{f})/2}{argmax}y(t)-1\]  
where \(y(t)\) represents an NIR estimator of the form:  
\[y(t)=\frac{\sum_{z_{i}=q_{f}}^{z_{i}=q_{f}+t-1}p(u_{i})}{\sum_{z_{i}=p_{f}+t-1 }^{z_{i}=2p_{f}+t-1}p(u_{i})} \tag{14}\]  
Additionally, for the upper bound of the fuzzy region, \(K\):  
\[K=q_{f}-\underset{t=1\ldots(q_{b}-q_{f})/2}{argmax}y(t)+1\]  
where \(y(t)\) represents an NIR estimator of the form:  
\[y(t)=\frac{\sum_{z_{i}=q_{b}-t+1}^{z_{i}=q_{b}}p(u_{i})}{\sum_{z_{i}=p_{b}-2t }^{z_{i}=p_{b}-t}p(u_{i})} \tag{15}\]  
In addition, \(u_{i}\) represents gray-scale intensity values ranging from 0 to 255.
# Improved Brain Storm Optimization Algorithm Based on Flock Decision Mutation Strategy
## 5 Discussion  
Drawing upon the experimental outcomes detailed in Sections 4.2 and 4.3, it is evident that the FDIBSO algorithm possesses several advantages. First, the FDIBSO algorithm demonstrates superior optimization precision compared to other variations of the BSO algorithm, showcasing its ideal advantage. The FDIBSO algorithm can achieve optimal optimization search results on more than half of the functions featured in the CEC2018. This assertion is supported by the data in Tables 1, 2, 5 and 6. Second, the optimization precision of the FDIBSO algorithm holds a distinct edge over other contemporary competitive intelligent algorithms. The results show that although the FDIBSO algorithm achieve the same level of effectiveness as the MFO-SFR algorithm, it performs better at some functions and significantly outperforms other intelligent algorithms. This assertion is substantiated by Tables 3 and 4. Third, the rate of convergence for the FDIBSO algorithm does not match the velocity of some BSO variants in the early stage, but with advancement of iterations, the convergence speed can be realized to overtake that of the other algorithms. This assertion is supported by Figures 3 and 4. Fourth, although the FDIBSO algorithm is slightly inferior in time complexity to some BSO variant algorithms when calculating the MMCW model, its convergence performance is superior to all algorithms. It can converge to the optimal solution with the fewest iterations for problems with various dimensions, thus improving the accuracy of weight calculations. The conclusion can be confirmed by Figures 5 and 6 and Tables 7 and 8.  
Numerous experiments have confirmed FDIBSO's advanced efficacy over prior enhanced BSO algorithms. As an innovative approach influenced by human behavioral  
Figure 6: Convergence curves for BSO variants based on the MMCW model in four dimensions.  
patterns, FDIBSO has shown significant promise for tackling intricate optimization issues. Additionally, incorporating the flock decision mutation strategy, good point set, and spectral clustering method within FDIBSO encourages the development of more innovative approaches for increasingly complex challenges. Moving forward, FDIBSO is set to tackle high-dimensional and large-scale applications.  
Future studies may explore several avenues: further improving the optimization accuracy of the FDIBSO algorithm on all tested functions, improving the algorithm's convergence speed in the early stage, and utilizing the BSO algorithm for more real-world engineering optimization challenges.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
###### Abstract  
The bandwidth requirement of wireline communications has increased exponentially because of the ever-increasing demand for data centers and high-performance computing systems. However, it becomes difficult to satisfy the requirement with legacy electrical links which suffer from frequency-dependent losses due to skin effects, dielectric losses, channel reflections, and crosstalk, resulting in a severe bandwidth limitation. In order to overcome this challenge, it is necessary to introduce optical communication technology, which has been mainly used for long-reach communications, such as long-haul networks and metropolitan area networks, to the medium- and short-reach communication systems. However, there still remain important issues to be resolved to facilitate the adoption of the optical technologies. The most critical challenges are the energy efficiency and the cost competitiveness as compared to the legacy copper-based electrical communications. One possible solution is silicon photonics which has long been investigated by a number of research groups. Despite inherent incompatibility of silicon with the photonic world, silicon photonics is promising and is the only solution that can leverage the mature complementary metal-oxide-semiconductor (CMOS) technologies. Silicon photonics can be utilized in not only wireline communications but also countless sensor applications. This paper introduces a brief review of silicon photonics first and subsequently describes the history, overview, and categorization of the CMOS IC technology for high-speed photo-detection without enumerating the complex circuital expressions and terminologies.  
Keywords:CMOS; integrated circuit; photodetector; silicon photonics; transimpedance amplifier +
Footnote †: journal: Sensors
# Decentralized Storage with Access Control and Data Persistence for e-Book Stores
###### Abstract
However, to the best of the authors' knowledge, there is currently no existing distributed storage solution that meets these requirements simultaneously. This paper presents a decentralized storage system for e-books, referred to as \"d-book-repository\", that effectively addresses the aforementioned requirements. The proposed system employs a smart contract to manage access rights, ensuring that data are only transferred to authorized clients and preventing unauthorized downloads. To bolster data security, we divided the nodes in the system into multiple groups, each responsible for storing a distinct data shard. This approach prevents any single storage-providing node from accessing the entirety of the stored content, enhancing data security. To improve data persistence, we utilized Reed-Solomon codes [9] to generate shards and implement group-based replication. Furthermore, we introduce an economic incentive mechanism that regularly audits storage content, promoting reliable storage practices and supporting the goal of data integrity. In summary, our designed decentralized storage system, d-book-repository, simultaneously fulfills the requirements of robust access control and data persistence, providing enhanced security and encouraging reliable data storage practices. We constructed a prototype of the proposed system on AWS and conducted an evaluation of its basic performance. The results demonstrated that both downloading and uploading 100 MB of e-book data (equivalent to one comic book) were accomplished within 10 s. Notably, when using the m5.xlarge instance type, the time required for the aforementioned tasks was only 1.2 s longer for downloading and 2.2 s longer for uploading than a simple download/upload process without access control. This finding confirms that the overhead associated with the proposed method is negligible, indicating the efficiency of our approach. We also conducted simulations on the increase and decrease in node numbers for various group settings and evaluated the data retention capability of the proposed system. Furthermore, we carried out simulations under the same conditions for Arweave's architecture and compared the results. As a result, it was shown that when the total amount of stored data is large (more than 10 TB), even with the same number of nodes, the proposed system has a lower risk of data loss. The remainder of this paper is organized as follows. Section 2 provides an overview of blockchain and smart contracts. In Section 3, we review the decentralized distributed storage systems that have been built so far and point out that all of them are insufficient for our purpose. Section 4 describes the details of our proposed method. The details of our prototype system and the results of experiments conducted on it are presented in Section 5. Section 6 discusses several important considerations for the practical application of the d-book-repository. Finally, Section 7 concludes the paper with concluding remarks and future work.
## 3 Materials and Methods
### Optimisation of SPME Extraction Conditions  
Initially, the effect of 3 experimental conditions for the HS-SPME method was studied by triplicate analysis following the well-established one-factor-at-a-time approach (OFAT). Sample mass was studied in the range of (1.000-4.000 g) in order to investigate any fiber saturation effects. Additionally, a range of settings was investigated for stirring of the sample (100-300 rpm). The latter two were optimised using the total peak area. The effects of extraction temperature for VOCs that are both desirable and undesirable were screened in the range of 30 to 85 \({}^{\circ}\)C so that an initial assessment was made for this crucial experimental parameter. Regarding the optimisation of the rest SPME conditions, a modelling approach was performed using the DOE and RSM approaches for the total peak area of each chromatogram representing all the ionisable and detectable volatile components of the sample. The optimisation was performed using a base fractional factorial (FF) 2\({}^{5-1}\) design with 5 factors (resolution V) that was augmented to a circumscribed central composite (CCC) with an alpha value of 2. A total of 32 runs included 16 base runs for FF (cube portion), 6 centre points to measure the experimental error, and 10 runs for CCC (star portion). Runs were randomised for each portion and were run in a single analytical sequence. The SPME factors selected for optimisation were extraction temperature, conditioning time, extraction time, desorption temperature, and desorption time. Although a preliminary screened factor, extraction temperature was included in the model as a significant factor to have a more complete picture of the SPME condition effects and to increase the model's applicability. The experimental area for the corresponding factors is given in Table 3, with the corresponding design provided in Table S3. Factor and level selection was made based upon instrumental limitations, fit-for-purpose needs, and by referring to relevant literature [18; 52].  
The response selected for optimisation was the total chromatogram area, as measured by the sum of the areas of each ion, after the removal of the ions that were attributed mainly to siloxane contamination, as described in Section 3.6.