esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling and Optimization Concerns
### Design Optimization Concerns and Components of the Multi-Objective Function  
After the BPNN model was developed, the next step was to define the cost function for optimization. Here, the genetic algorithm (GA) was applied to search for the optimum solution based on unconstrained functions. The GA, relying on biologically inspired operators such as selection, crossover, and mutation to generate high-quality solutions, is commonly used in nonlinear optimization problems. The GA provided the multi-objective optimization solution with the given constraints on the interpolation parameters. Here, a weighted sum approach 16 was adopted, and the objective function was given as  
\[L=w_{1}\times f_{1}+w_{2}\times f_{2}+w_{3}\times f_{3} \tag{2}\]  
where \(f_{1}\), \(f_{2}\), and \(f_{3}\) represent the BPNN outputs, and \(w_{1}\), \(w_{2}\), and \(w_{3}\) are the weighting functions, respectively. The objective function with constraints is listed in Table 4. Depending on the machining process, different weightings were selected; the results are listed in Table 5. For example, if \(w_{1}\), \(w_{2}\), and \(w_{3}\) were set to (1, 0, 0), accuracy was the major priority. The GA algorithm determined the optimization interpolation parameters with the lowest acceleration \(A_{max}\), feedrate \(f_{max}\), and corner velocity \(V_{c}\), and the largest time constants \(T_{a}\) and \(T2\) within the given constraints. For the weighting selected as (0, 1, 0), the major priority was MT, and the corresponding results are given in Table 6. The machine time was approximately 5 s, which was the shortest among the four cases. Generating noteworthy results that had not been reported in the literature was the priority; thus, the vibration for Case 3 was selected. In Case 3, the CVib was the lowest among the four cases, but the MT was longer than that of Case 2. Neither the MCoE or MT, nor the CVib of Case 4  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MCoE** & **MT** & **CVib** \\ \hline \multirow{2}{*}{**RMSE of training**} & 0.0587 & 0.0006 & 0.38 \\  & (\(\upmu\)m) & (s) & (mm/s\({}^{2}\)) \\ \hline \multirow{2}{*}{**RMSE of validation**} & 1.0976 & 0.1305 & 3.14 \\  & (\(\upmu\)m) & (s) & (mm/s\({}^{2}\)) \\ \hline \multirow{2}{*}{**ME of training**} & 4.9264 & 0.2883 & 4.48 \\  & (\(\upmu\)m) & (s) & mm/s\({}^{2}\)) \\ \hline \multirow{2}{*}{**ME of validation**} & 5.5356 & 0.5847 & 8.28 \\  & (\(\upmu\)m) & (s) & (mm/s\({}^{2}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and validation results for the artificial neural network (ANN) model: the maximum contour error (MCoE) and machining time (MT) and corner vibration (CVib).  
achieved the highest performance among the four cases. Without the model, determining the optimization parameters was impossible.  
The predicted and experimental results are listed in Table 6; these data were used to validate the accuracy of the BPNN model. Notably, the MT accuracy for all the cases was less than 3%, which is challenging to achieve without knowing the CNC kernel for the FANUC controller.
## 4 Experimental Results
### Dataset  
The proposed method is validated by using Librispeech ASR corpus [35] ([[http://www.openslr.org/12/](http://www.openslr.org/12/)]([http://www.openslr.org/12/](http://www.openslr.org/12/)) accessed on 6 August 2015), in which 2429 utterances coming from different speakers over 8.5 h are used as the training set, and 1238 utterances over 2.1 h are selected for the test set. The sampling rate of speech signal is 16 kHz. In the experiment, the packet loss model introduced in the Section 2 is used to simulate packet loss. Five packet loss rates with 5%, 10%, 15%, 20%, and 30% are set, respectively. At the same time, it is assumed that each packet consists of a frame speech signal.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 1 Introduction
However, the relevant algorithms for the detection of old landslides in the southwest river basin of the Sichuan-Tibet area are extremely lacking in this field and need to be improved [25]. In the existing research, the landslide detection model is relatively complex, with limited accuracy and generalization ability for the location and range of the old landslides in the southwest mountainous area. To solve the above problems, this paper proposes a detection method based on the improved YOLOv8 [26; 27] (YOLOv8-CW). This method improves the detection accuracy of old landslides in various geomorphic environments and exhibits robust performance in detecting complex old landslides. The primary contributions of this paper can be summarized as follows: Firstly, an old landslides dataset is compiled using Google Earth images, with a focus on the southwest river basin of the Sichuan-Tibet area in China as the designated research area [23]. Then, the boundary box loss function Complete-IoU (CIoU) loss function is replaced by the Wise-IoU (WIoU) loss function [28; 29; 30], which solves the problem of model training caused by low-quality samples and introduces a CBAM attention mechanism to improve model detection ability [31; 32; 33; 34; 35]. Finally, we compare the WIoU loss functions of different versions and different attention mechanisms. The experimental results clearly demonstrate that the improved method has superior detection ability and generalization and has great application value to the detection of old landslides in the Sichuan-Tibet area of China.
# Latency-Aware Accelerator of SIMECK Lightweight Block Cipher
## 3 Proposed 2-Stage Pipelined Architecture for SIMECK32/64
### 2-Stage Pipelined Data Expansion (or) Round Function Block  
The pipelined design of the proposed data expansion block is shown in Figure 2. As we described earlier in Section 2, a plaintext to be encrypted is split into two words of \(m_{i}\) and \(l_{i}\), as shown in Equation (1). Therefore, in Figure 2, we split input data into two-word sizes of \(d\_in0\) and \(d\_in1\). Moreover, our data expansion block contains two 2\(\times\)1 multiplexers (we denote with MUX) and two buffers (i.e., Rdata1 and Rdata2). The multiplexers select \(n\)-bit input data or feedback data from the combinational circuit. On the other hand, Rdata1 and Rdata2 buffers are needed to load input data simultaneously. Two \(data\_round\) blocks, highlighted with orange color in Figure 2, are responsible to process the data in terms of encryption and decryption operations. The architecture of each \(data\_round\) block implements Equation (2). It takes two key segments as inputs and results in singular data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Name** & **Word Size (\(n\))** & **Block Size (\(2n\))** & **Key Length (\(mn\))** & **\# of Rounds** \\ \hline SIMECK32/64 & 16 & 32 & 64 & 32 \\ SIMECK48/96 & 24 & 48 & 96 & 36 \\ SIMECK64/128 & 32 & 64 & 128 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SIMECK family of a block cipher. We refer readers to follow Table 1 of [31] for more detailed parameters.  
Figure 1: Block diagram of our SIMECK latency-aware CryptoCore.  
as output. For instance, \(data\_round0\) takes \(key\_out0\) and \(key\_out1\) as input and produces \(d\_out0\) as output. Similarly, \(data\_round1\) takes \(key\_out2\) and \(key\_out3\) as input and produces \(d\_out1\) as output. These blocks operate serially (one after another) to generate the resultant data. Moreover, the design of \(data\_round\) blocks consists of SHIFT (i.e., \(\lll 5\), and \(\lll 1\)), Exclusive(OR) (i.e., \(\oplus\)), and AND (i.e., \(\odot\)) operations. The \(\lll 5\) and \(\lll 1\) in Figure 2 means the 5-bit and 1-bit cyclic shift towards left. The # of 0's determines the register value with all zeros. Furthermore, the multiplexers in the orange portion of Figure 3 with \(2\times 1\) size are used for selecting the right signals either before or after shifting. Finally, the concatenation of the \(d\_out0\) and \(d\_out1\) provides the encrypted/decrypted data.  
Figure 3: Proposed key expansion architecture for SIMECK32/64.  
Figure 2: Proposed data expansion architecture for SIMECK32/64.
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 2 Related Work
Moreover, the user information held in the current Twitter API is only related to the actual user retweeting the message and the original tweet author, thereby ignoring intermediate accounts [11]. More reliable retweet information cascades are the ones that merge temporal and social network information [18]. Scholars widely investigated factors affecting user retweet behavior (RB), finding that accounts with higher numbers of followers tend to be retweeted more often. However, there is not agreement on the minimum number of followers needed to be regarded as an \"influencer\" [6; 16]. A relevant study in this field is [11], which analyzed four different options based on followed accounts to derive the possible retweet graph. Since there is no ground truth to compare the possible cascade options, the authors evaluated the options computing two metrics: the connectivity-rate and the root-fragmented-rate. Other works proposed the use of additional features, such as users' metadata (e.g., number of followers, number of friends, status count, etc.) [19; 20], text and topic similarity features [13], location information [23; 31] and tweeting behavior (e.g., incidence of tweets or retweets in the user's activity) [7]. Only two studies [24; 31], as far as we know, integrated in the retweeting dynamic analysis the impacts of social relationships measured as the intensity of the interactions between two users. However, in both works [24; 31], the authors measured the interactions in terms of retweets, which are, accordingly to the actual Twitter API information, not completed and miss intermediate steps introducing a bias. Thus, in this paper, we further refer to this bias as the retweet bias. Nonetheless, as argued by [32], trust between users is an important factor for information dissemination on distributed online social networks. Therefore, starting from the findings in [32; 33] and considering the limitations of the Twitter API service, in this paper we propose a novel approach to generated retweet cascade graphs based on a user's interaction strength (IS), which is measured by taking into account not only retweets, but also quotes and replies.
## 3 Results and Discussion
### Photophysical Properties of DHSAN
TPE occurs when two lower energy photons of NIR light, whose energy spans the gap between the ground state and excited electronic states, are absorbed by the photosensitizer. Such an approach was suggested to extend the excitation wavelength allowing for deeper light penetration and lower photo-bleaching of the drug in tissues. It is well established that the size of the chromophore, in terms of length of the \(\pi\)system and then the number of \(\pi\)electrons, has a strong effect on two-photon absorption (TPA). Indeed, most of the photosensitizers suggested for TPE-based action are characterized by extended \(\pi\)systems. However, as recently reviewed by [PERSON] et al., relatively small organic chromophores can also exhibit large values of TPA cross-sections [55]. DTP-based  
Figure 1: Jablonski-like diagram of DHSAN for \({}^{1}\)O\({}_{2}\) formation and its frontier orbital contour plots. SOC values, in cm\({}^{-1}\), and \(\Delta\)E(S\({}_{1}\)–T\({}_{m}\)), in eV, are also reported. All the calculations were carried out in implicit water solvent. dyes (DTP = 2,5-dithienylpyrrole), characterized by D-\(\pi\)-A structure, exhibit cross section values comprised in the range 400-8000 GM. The TD-DFT outcomes of DHSAN are collected in Table 1, where for each obtained excited state, the photon energy (\(\Delta\)E) and wavelength (\(\lambda\)), TPA strength \(\langle\delta^{TPA}\rangle\), and cross section \(\sigma^{TPA}\) are reported. The computed TPA parameters suggest that the TPE most likely occurs near the red end of the visible light. Indeed, the highest cross-section values were found for excited states comprised between 575 and 720 nm. In particular, the excited state lying at 3.47 eV is characterized by the highest \(\sigma^{TPA}\). From the analysis of the Natural Frontier Orbitals (NTOs), whose Highest Occupied Transition Orbital (HOTO) and Lowest Unoccupied Transition Orbitals (LUTO) are shown in Figure S1, emerged that such a state originated from a charge transfer involving the whole \(\pi\) system of the DHSAN molecule, included the peripheral oxygen atoms. Therefore, the computed photon energies and the corresponding cross-section values highlight that DHSAN can be activated by two-photon laser irradiation at a wavelength falling in the phototherapeutic window as efficiently as under violet-light irradiation. This means that, under TPE, singlet oxygen, the key reactant in oxidation of DHSAN into SAN, can be efficiently generated.
# The Role of Transliterated Words in Linking Bilingual News Articles in an Archive
## 2 Background
### Digital News Stories Archive (DNSA)  
In this section, we are briefly introducing the Digital News Stories Archive (DNSA). The principal idea of the Digital News Stories Preservation (DNSP) framework is presented at the International Conference on Asian Digital Libraries 2015 (ICADL-2015) [12]. The following are the major contributions to the framework:  
* A generic systematic approach was proposed as a web preservation model, i.e., a step-wise model for web preservation projects after analyzing 120 news archives worldwide [14; 15].
* A multiple source web archive for online news articles, Digital News Stories Archives (DNSA), was created to preserve news articles from multiple sources [1].
* A tool \"Digital News Stories Extractor (DNSE)\" was developed to extract news articles from multiple sources to create the DNSA [13].
* Content-based techniques were introduced for linking news articles during the preservation process in the DNSA. These text processing techniques are based on text features, such as common ratio, terms frequency [16], named entities [17], term position, information credibility, headline terms, similar terms distance, etc. [1].
* The news recommendation techniques were studied comprehensively for similarity measures. The study helped identify various dimensions and enhanced the DNSP framework, and a few were identified for future research in the framework [18].
* The Common Ratio Measure for Stories (CRMS) technique was modified for linking English news articles during preservation and limited to news headings to reduce extra computation for the terms appearing in the news body [16].
* The CRMS technique was modified for linking dual languages, i.e., linking Urdu-language news articles with English-language news articles during preservation in the DNSA [19].
* A heading-based linking mechanism was introduced for the archived news articles during the preservation process in the framework [20].
* Recently, the framework has been enriched with news articles from the Arabic language. The challenges were identified for including low-resource languages, such as Urdu and Arabic languages, and a set of metadata was introduced to best serve the DNSP framework, which was adapted for multi-lingual news archives.  
The digital news stories archive (DNSA) was created locally from multiple sources that preserve news articles published in English, Urdu, and Arabic, due to a lack of funds and support from institutes and funding bodies.  
A news archive without efficient retrieval mechanisms will just be a collection of digital news objects, rather than a helpful information repository. Implementing an efficient search requires using indexing approaches, metadata, and linking mechanisms so that they help news readers retrieve relevant articles easily and effectively.
## 2 Preliminaries
### Sigmoid Function  
This is a mathematical function that maps any input value between 0 and 1, mostly as the probability of a binary number. The sigmoid function is expressed in different ways, but the most used is defined as in Equation 4, known as the logistic function.  
\[f(x)=\frac{1}{1+e^{-x}} \tag{4}\]  
where \(x\) is the input value, \(e\) is the mathematical constant \(e\), and \(f(x)\) is the output value. The sigmoid function has been used in many applications, including machine learning, neural networks, and logistic regression. As explained in depth in [23], the shape of the sigmoid function is characterized by the steep slope around the origin and the shallow slope away from the origin. Any changes in the input value near the origin result in larger changes in the output values. The disadvantage of the sigmoid function is that it is prone to saturation, which means the slope of the function becomes small as input values become very large or very small. To overcome this limitation, this study integrates the sigmoid function and the logistic map, which means the logistic map is the alpha, and the sigmoid function is the omega, as seen in Equation (7).  
In the proposed system, the chaotic behavior begins when the control parameter \"\(r\)\" is set to 7.0158 and persists throughout the process. In order to achieve a good encryption result, we used a value of \"\(r\)\" equal to 20.1245 for \(K_{2}\) and 20.1254 for \(K_{3}\). Its corresponding bifurcation diagram is shown in Figure 1. To obtain good and complex chaotic results, it is important that the proposed map has a Lyapunov exponent with a positive value greater than one. This is because the Lyapunov exponent reflects the rate of separation between initially close trajectories in the phase space of the system, and a positive value indicates that the system is chaotic. In this study, we have analyzed the Lyapunov exponent of both the basic logistic map and the modified sigmoid logistic map, as shown in Figure 2. The results demonstrate that both maps have Lyapunov exponents with positive values, indicating that they are suitable for use in image encryption.
## 2 System Definition
### Wildfire Detection Methods
#### 2.1.1 Ground-Based Remote Detection Methods  
Traditionally, wildfire monitoring is based on continuous human surveillance from selected vantage points in the field attempting to recognize smoke patterns during the day or the light from fire flames during the night using visible light [16; 17]. The authors argue in [18] that observers' reports are often insufficient to define an effective firefighting strategy and that the field video surveillance monitored in the operational center can provide richer, actionable information and increase the surveyed area by the operator. However, since remote field monitoring increasingly subjects the operators to fatigue, boredom and loss of concentration, several semiautomated wildfire recognition systems are proposed [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. These systems reduce the effort of the analysts, which basically need to validate the positives triggered by the automated detection methods [22].  
A class of detection methods look for combustion-generated heat using infrared (IR) imaging. The equipment is rather expensive, has reduced spatial resolution and is prone to false positives [21; 23]. Accuracy can be improved by automated cross-matching of IR images with visible or ultraviolet (UV) ones or with meteorological and geographical data using various processing and decision making techniques, but at a higher equipment and maintenance cost [24].  
Optical spectrometry can detect combustion-specific products in smoke [25; 26] by analyzing its sunlight absorption patterns. It has good reliability, but is limited to scans above the horizon (thus, for smoke that clears the tree line), and at night, it switches to the less effective flame detection [27]. Strong winds may lower or disperse the smoke, delaying detection. However, wind significantly increases the speed of fire spreading; thus, early alerting of firefighters becomes crucial for blaze containment.  
Light detection and ranging systems (LiDAR) analyze the laser light backscattered by smoke [28, 29]. High-power laser beams may be expensive to produce, although, recently, researchers proposed lower cost techniques [30], and they can also be a public health hazard. Its effectiveness may be limited by obstacles in the line-of-sight, fog or low altitude clouds, which are a likely occurrence in mountain climate.  
Radio-acoustic sounding detection (RASS) can detect crown and surface wildfires from thermal maps built by inferring air temperature from shifts in the propagation time of acoustic waves [31]. The system can account for atmospheric events, like wind and some static echoes from vegetation or landscape, but it requires a clear line-of-sight right above the tree tops for proper operation. Moreover, its deployment and calibration can be complex.  
Remote detection systems tend to generate false positives (especially those based on visible light image analysis) and need the support of human analysts. Those that are more accurate are also more expensive due to higher equipment and maintenance cost [35].
## Appendix  
### _The Unscented Kalman Filter_  
Consider an \(n\) dimensional random variable \(\mathbf{x}\), having the mean \(\mathbf{\hat{x}}\) and covariance \(\mathbf{P}\), and suppose that it propagates through an arbitrary nonlinear function \(f\). The unscented transform creates \(2n+1\) sigma vectors \(\mathbf{X}\) (a capital letter) and weighted points \(W\), given by:  
\[\mathbf{X}_{(0)}=\mathbf{\hat{x}}\] \[\mathbf{X}_{(i)}=\mathbf{\hat{x}}+(\sqrt{(n+\lambda)\mathbf{P}})^{ T}_{i},\;\;i=1,...,n \tag{39}\] \[\mathbf{X}_{(i+n)}=\mathbf{\hat{x}}-(\sqrt{(n+\lambda)\mathbf{P}}) ^{T}_{i},\;\;i=1,...,n\]\(W_{i}^{(m)}\) and \(W_{i}^{(c)}\) are the weighs for the mean and covariance, respectively, associated with the i _th_ point:  
\[W_{0}^{(m)}=\frac{\lambda}{(n+\lambda)} \tag{40a}\] \[W_{0}^{(c)}=W_{0}^{(m)}+(1-\alpha^{2}+\beta)\] (40b) \[W_{i}^{(m)}=W_{i}^{(c)}=\frac{1}{2(n+\lambda)},\;\;i=1,...,2n \tag{40c}\]  
The sigma vectors are propagated through the nonlinear function to yield a set of transformed sigma points, \(\textbf{y}_{i}=f(\textbf{X}_{i})\), \(i=0,\ldots,\,2n\).  
The implementation algorithm of UKF is summarized as follows:  
1. The transformed set is given by instantiating each point through the process model:  
\[\zeta_{i,k|k-1}=f(\textbf{X}_{i,k|k-1})\,,\;i=0,...,2n \tag{41}\]
2. Predicted mean:  
\[\hat{\textbf{x}}_{k|k-1}=\overset{2n}{\underset{i=0}{\underset{i=0}{\sum}}}W_{ i}^{(m)}\zeta_{i,k|k-1}\]
3. Predicted covariance:  
\[\textbf{P}_{k|k-1}=\overset{2n}{\underset{i=0}{\sum}}W_{i}^{(c)}[\zeta_{i,k| k-1}-\hat{\textbf{x}}_{k|k-1}][\zeta_{i,k|k-1}-\hat{\textbf{x}}_{k|k-1}]^{T}+ \textbf{Q}_{k-1} \tag{42}\]
4. Instantiate each of the prediction points through observation model:  
\[\textbf{Z}_{i,k|k-1}=\textbf{h}(\zeta_{i,k|k-1})\]
5. Predicted observation:  
\[\hat{\textbf{z}}_{k|k-1}=\overset{2n}{\underset{i=0}{\sum}}W_{i}^{(m)}\textbf {Z}_{i,k|k-1}\]
6. Innovation covariance:  
\[\textbf{P}_{zz}=\overset{2n}{\underset{i=0}{\sum}}W_{i}^{(c)}[\textbf{Z}_{i,k| k-1}-\hat{\textbf{z}}_{k|k-1}][\textbf{Z}_{i,k|k-1}-\hat{\textbf{z}}_{k|k-1}]^{T}+ \textbf{R}_{k} \tag{43}\]
7. Cross covariance:  
\[\textbf{P}_{\kappa_{\kappa_{2}}}=\overset{2n}{\underset{i=0}{\sum}}W_{i}^{(c) }[\zeta_{i,k|k-1}-\hat{\textbf{x}}_{k|k-1}][\textbf{Z}_{i,k|k-1}-\hat{\textbf{ z}}_{k|k-1}]^{T} \tag{44}\]
8. Performing update:  
\[\begin{split}\textbf{K}_{k}=\textbf{P}_{\kappa_{2}}\textbf{P}_{ zz}^{-1}\\ \hat{\textbf{x}}_{k}=\hat{\textbf{x}}_{k|k-1}+\textbf{K}_{k}( \textbf{z}_{k}-\hat{\textbf{z}}_{k|k-1})\\ \textbf{P}_{k}=\textbf{P}_{k}^{-}-\textbf{K}_{k}\textbf{P}_{zz} \textbf{K}_{k}^{T}\end{split} \tag{45}\]