esa-sceva/satcom-chunk-collection huggingface dataset:
### L2 Speech Acquisition  
Two influential models of speech learning that may provide insights into the issues involved in L2 speech acquisition have been proposed in the literature. The Speech Learning Model (SLM) was originally proposed by [9; 10] to account for the differences in the learnability of L2 phonetic segments, and was recently updated as the Revised Speech Learning Model (SLM-r [11]). According to the SLM/SLM-r, the processes and mechanisms that guide L1 speech acquisition (including the ability to form new phonetic categories) remain intact and accessible for L2 speech learning across the lifespan. Another model, the Perceptual Assimilation Model (PAM), suggests that infants attune their perceptions of speech to the properties of the sounds in their ambient language environments during the early stage of language acquisition; consequently, they become less sensitive to the sounds of a new language and may have difficulty perceiving unfamiliar sound contrasts [12]. Similar to the SLM, the PAM also assumes that the ability to perceive speech continues to be refined throughout one's life in its extended version, the Perceptual Assimilation Model of Second Language Speech Learning (PAM-L2 [13]). Both models postulate a common phonetic space in the learners' minds in which the phonetic categories of both the L1 and the L2 are stored.  
One hypothesis from the SLM/SLM-r is the category assimilation hypothesis (CAH), which claims that, in the common space, an L2 sound that is perceived as being similar to an L1 sound does not form a new category, and is understood as a variant of the L1 sound at an allophonic level; in other words, the cross-linguistic equivalence between the two sounds has been established. In this case, the phonemic variants for interdialectal contact are called diaphones [14], and the CAH posits that only one single phonetic category is used to process the two linked diaphones. This mapping of diaphones will eventually give rise to a new merged category in bilingual speakers' mental representations, and will be realised differently from either the L1 sound or the L2 sound in production; this phenomenon has been documented in several studies [15; 16].  
Another hypothesis from the SLM/SLM-r, the category dissimilation hypothesis (CDH), suggests that a new category will be established if an L2 sound is absent in the L1 system, which will make the combined phonetic space more crowded; as a result, the phonemes tend to disperse in compensation to maintain the phonetic contrast. When category dissimilation takes place, neither the newly established L2 category nor the closest L1 category will be identical to the categories in monolinguals; consequently, both categories may shift away from their original phonetic spaces. Ref. [17] provided recent support for CDH, and found that Spanish-Catalan bilinguals had developed two categories to accommodate the mid-back vowels in the two languages.  
However, the majority of research working with SLM/SLM-r focused on segmental features. Whether the CAH and CDH can be generalised to suprasegmentals remains to be explored.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 3 Bloch Sphere Quantum Bee Colony Algorithm
### Multi-Robots Path Planning Based on QABC  
A two-level planning method is adopted for multi-robots based on QABC: the first level is for each robot to plan its global trajectory with QABC, and the second level is to solve the trajectory conflict problem between multi-robots and the external circular collision method to detect other robots in the nearby environment.
## 1 Introduction  
Grazinglands, natural and managed systems used primarily for livestock production, make up approximately 40% of the global ice-free land mass [1, 2, 3]. The application of geospatial sciences and technologies to the art and science of grazingland management has progressed immensely since the use of black and white aerial photography in the 1930s [4, 5]. Collectively, geospatial sciences and technologies encompass the spatial techniques and principles that used through geographic information systems (GIS), remote sensing, and global navigation satellite positioning systems (GINSS) and their applications (such as spatial data science analytical techniques). In this context, instead of being content with merely knowing where something happens, where and why are equally important [6].  
The launch of the Landsat-1 satellite in 1972 marked a new era in the ability and applicability of spatial science applications to increase our understanding of grazing-land dynamics through mapping, modeling, managing, and developing decision support tools [4, 7, 8, 9]. Recent proliferation of publicly and privately owned satellites, the use of unmanned aerial systems (drones), advances in high-resolution imaging sensor technology, and computational abilities of desktop and cloud computing have all contributed enormously in recent years to the rapid growth and expansion of geospatial sciences and technologies. In this vein, capabilities have advanced in recent years to evaluate questions that were unthinkable a decade ago.  
In this special issue, we (1) highlight the role of remote sensing in livestock and grazingland management; (2) provide a brief overview of historical applications and challenges over the past 50 years; and (3) introduce the reader to the current work featured in this Special Issue of _Remote Sensing_.
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.3 Fairness  
We use Jain's Fairness Index (JFI) to evaluate the fairness of the algorithm. JFI was proposed in [40], and its expression is:  
\[F(x)=\frac{\left(\sum x_{i}\right)^{2}}{n(\sum x_{i}^{2})} \tag{6}\]  
where \(x_{i}\) is the transmission rate of user \(i\). \(n\) is the number of users. The larger the \(F(x)\) value, the better the fairness.  
Figure 10 shows the variation in JFI over time in two experimental scenarios. TCTE always has the best fairness performance, while IDRS and NR have poor fairness performance. NR and IDRS perform similarly in Experimental Scenario 1, and NR outperforms IDRS in Experimental Scenario 2. IDRS and NR have high fairness at the beginning of the experiment, and then decline and stabilize at a small value. Unlike IDRS and NR, TCTE has a low JFI for a period of time at the beginning of the experiment, and then stabilizes at a value close to 1. As there were fewer data at the beginning of the experiment, TCTE could not perceive the network condition well.  
In addition, in order to illustrate the reliability of the experimental results, we repeated the experiment 10 times, and took the JFI for analysis. Figure 11 shows the mean and confidence interval (95% confidence level) for JFI.  
Figure 9: Mean and confidence intervals for edge node throughput.
## 3 Preliminaries
### Gradient-Weighted Class Activation Mapping (GradCAM)  
GradCAM [43] is an enhancement to the conventional class activation map (CAM) and is used to generate visual explanations by highlighting the regions in the input most emphasized when generating the final result. GradCAM uses a gradient score to generate CAMs, as illustrated in the equations below.  
The CAM for a specific class \(c\) is the weighted sum of activation maps (\(A^{1}\), \(A^{2}\),..., \(A^{k}\)) generated by \(n\) convolution filters given by:  
\[CAM^{c}=w_{1}A^{1}+w_{2}A^{2}+\cdots+w_{k}A^{k} \tag{9}\]  
In addition, global average pooling is applied to each activation map, which is illustrated as follows:  
\[GAPA^{k}=\frac{1}{Z}\sum_{i=1}^{h}\sum_{j=1}^{w}A_{ij}^{k} \tag{10}\]The following represents a general vector form for CAM:  
\[Y^{c}=\sum_{k}w_{k}^{c}\frac{1}{Z}\sum_{i}\sum_{j}A_{ij}^{k} \tag{11}\]  
where \(A_{ij}^{k}\) is the features map, \(\frac{1}{Z}\sum_{i}\sum_{j}\) is the global average pooling, and \(w_{k}^{c}\) is the class feature weights.  
By substituting the global average pooling output by \(F^{k}\), the CAM computes the final score as \(Y^{c}=\sum_{k}w_{k}^{c}\cdot F^{k}\)  
According to GradCAM, the weights \(w_{k}^{c}\) of the classes are determined by the gradients of the last layer in the NN model. By taking the gradient score of the class \(Y^{c}\) to feature map \(F^{k}\), we obtain  
\[\frac{\partial Y^{c}}{\partial F^{k}}=\frac{\frac{\partial Y^{c}}{\partial A_ {ij}^{k}}}{\frac{\partial F^{k}}{\partial A_{ij}^{k}}} \tag{12}\]  
From Equation (8), \(\frac{\partial Y^{c}}{\partial F^{k}}=w_{k}^{c}\). Hence, Equation (9) can be summarized as follows:  
\[w_{k}^{c}=\frac{\partial Y^{c}}{\partial A_{ij}^{k}}\cdot Z. \tag{13}\]  
Summing both sides of the equation over all pixels \((i,j)\),  
\[\sum_{i}\sum_{j}w_{k}^{c}=\sum_{i}\sum_{j}\frac{\partial Y^{c}}{\partial A_{ ij}^{k}}\cdot Z \tag{14}\]  
We can rearrange the above equation as follows:  
\[w_{k}^{c}=\sum_{i}\sum_{j}\frac{\partial Y^{c}}{\partial A_{ij}^{k}} \tag{15}\]  
Using the gradient flowing from the output class into the activation maps of the final convolutional layer as weight (\(w_{c}^{k}\)) to class \(c\) eliminates the need for retraining the networking [44].
## 2 Materials and Methods
### CT Acquisition  
All chest CT examinations were performed using a 128-slice MDCT scanner (Somatom Definition Flash; Siemens, Germany) with the following parameters: collimation, 128 \(\times\) 0.6 mm; beam pitch, 1.2; tube voltage, 120 kVp; and tube current-time product, 110 mAs with automatic tube current modulation. The acquisition of the thorax was obtained in inspiratory apnea. All CT data were reconstructed in the axial plane as thin-section (1-mm-thick) images by applying a high-spatial-frequency reconstruction kernel and a lung window setting.
# Review of Automatic Estimation of Emotions in Speech
## 2 Emotions
### Perception of Emotion from Speech  
Human listeners can estimate aspects of emotion based on short speech portions, but details about one's listening strategy are hard to find. Perceptual tests with synthetic speech show that aspects of intonation are major cues to emotion [34]. Synthetic utterances were heard as fearful when they had a high F0, a broad F0 range, falsetto voice, or a fast speech rate. Sadness was heard for speech with a narrow F0 range, slow speaking rate, and breathiness. Faster speech rate and tense phonation were judged as angry speech. Joy correlated with a broader pitch range and a faster rate. Bored speech had a lowered mean F0 and a F0 pitch range, and breathy or creaky voice. While some of these cues relate tobroad details of spectral envelope, most relate to intonation. Thus, intonational features are likely more useful to SER than the Mel spectra that are more often used.  
When asked to select from among five common emotions, listeners achieve 60-70% accuracy in general, except for \"disgust\" (which likely has a more varied set of acoustic cues than the standard set (fear, anger, happiness, sadness)) [8,23]. While this performance is well above chance (20%), lack of more definitive results is evidence of the level of difficulty of the SER task.
## 1 Introduction  
Additive manufacturing (AM) is an important technology for building parts from scratch, which has the advantages of high design freedom, short build cycle time, and high material utilization over traditional manufacturing techniques [1]. When combined with metal materials, it has a wide range of industrial applications and mature material manufacturing processes. Laser metal deposition (LMD) is subordinate to the branch of metal AM, which is based on the principle of using high-energy lasers to melt a metallic material and then causing it to cool and solidify at a specified location. The process builds up the material layer by layer until the part is shaped [2]. Thin-walled structures have the advantages of being lightweight and compact in size and are increasingly used as critical parts in equipment and engineering for aerospace, marine navigation, and medicine [3]. The LMD process incorporating a multi-axis strategy does not require additional support structures for the fabrication of such structural parts compared to other processes, resulting in greater cost and time savings. Regardless of which AM technology is used, most require a similar planning process to be completed with the assistance of a computer. The process includes computer-aided design (CAD) modeling, slicing, path planning, and post-processing, which are usually integrated into a computer-aided manufacturing (CAM) system. The path data generated by this procedure in the computerized digital space are very critical; they not only determine the geometry of the forming but also affect the mechanical or metallurgical properties [4].
## 4 Discussion
Although this result demonstrates the potential error associated with telemetry-based location estimates, the small number of outliers with a movement rate of \(>\)2 BLs\({}^{-1}\) (0.45 ms\({}^{-1}\)) suggest that this approach is reasonable even with occasional error. The largest errors likely occur when a relatively linear synthetic aperture produces a bimodal set of solutions with equally good _Resid_ and _DOP_ values for location estimates on both sides of the AUV's route, making it difficult to determine the \"correct\" estimate. If the wrong estimate is selected, the error would be approximately twice the distance from the actual position of the fish. With additional work, it would be possible to automatically screen out or devalue estimates made from such linear synthetic apertures. Marine movement rates undoubtedly depend on a number of factors, including the size and migratory behavior of the fish, as well as local conditions (e.g., currents and topography). Consequently, it is uncertain whether our results are analogous to the movements of wild Chinook salmon entering marine waters. During this study the fish were transported to a release site distant from their natal stream. The fish were also substantially larger in size (averaging 230 mm in length) than similarly aged fish from wild stocks. Average length of Chinook salmon smolds from rivers along the northwestern coast of North America range from 68 to 133 mm [59]. Likewise, the average length of juvenile Chinook salmon released at LPW since 2011 averaged 125 mm ([PERSON], National Marine Fisheries Service, unpublished data). However, acoustically tagged juveniles released at LPW (average length of 133 mm) exhibited localized movements near the mouth of Port Walter for over 7 days after entering the marine environment, and general observations of untagged fish suggest that juvenile salmonids utilize Port Walter as a nursery and rearing area ([PERSON], National Marine Fisheries Service, personal communication).
## 4 Discussion
[23] later described a new OCT-A feature called suspended scattering particles in motion (SSPiM), appearing as an extravascular signal of flow on OCT angiograms and thought to be related to the motion of particles within intra-retinal fluid, being considered a marker of cysts. Recently, [PERSON] et al. [24] compared the appearance of cysts and non-perfused areas in images obtained with two different OCT-A devices, a Spectral Domain device and a Swept Source device. In their study, the authors integrated data of OCT-A angiograms and corresponding B-scan OCT with flow, in order to differentiate non-perfused areas and cysts, and for the detection of SSPiM. While they confirmed that dark regions could correspond either to non-perfused areas or cysts with both instruments, they reported that both features could have some internal signal, and interestingly this internal signal (SSPiM) was more frequently associated with cysts than with non-perfused areas. All the previously mentioned studies have as their main focus the identification of intra-retinal cysts and non-perfused areas based on clinical expertise, with the objective of finding specific qualitative characteristics that could help in their differentiation. In the present paper, the proposed algorithm for cyst detection has the advantage of defining, precisely and automatically, the borders of the cysts, making this method suitable for quantitative analyses. To the best of our knowledge, this is the first work using enface structural SCP and DCP images to detect intra-retinal cysts and motion artifacts, building a sort of \"map\" to be used as a mask on enface flow corresponding images in order to remove these elements from the computation of perfusion parameters. In the past, a method aimed at compensating for signal attenuation in the choriocapillaris OCT-A slab, caused by defects of the retinal pigmented epithelium and Bruch's membrane, by using the corresponding enface structural images, was developed and validated by [PERSON] et al. [25]. However, this method does not take into account the presence of cysts and/or motion artifacts.
# Detection of Intelligent Intruders in Wireless Sensor Networks
###### Abstract  
Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders' models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (_i.e._, the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder's intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder's intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder's intrusion algorithms; and (3) it is imperative to integrate intruder's intelligence in the WSN research for intruder detection problems under various application circumstances.  
artificial intelligence; intrusion detection; mobile intruder; performance evaluation; simulation; wireless sensor networks +
Footnote †: journal: Future Internet
# Method for Improving the Reliability of SRAM-Based PUF Using Convolution Operation
## 2 Related Work  
Convolution filtering is used to process random noise in digital-image processing. Ambient noise is received during image acquisition. The noise forms fuzzy points in a two-dimensional matrix. Convolution can attenuate the effect of random noise, such as salt and pepper noise and Gaussian white noise. Convolution uses the convolution kernel to extract information. As shown in Figure 1, initial images can be filtered by convolution kernels in two main ways. Convolution kernels and strides adjust the size of the final results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**SRAM** & **Technology** & **Devices** & \(-\)**40 \({}^{\circ}\)C** & \(+\)**20 \({}^{\circ}\)C** & \(+\)**80 \({}^{\circ}\)C** \\ \hline Cypress CY7C15632 KV18 & 65 nm & 10 & 7.80\% & 3.80\% & 6.60\% \\ Virage HP ASAP SP ULP 32-bit & 90 nm & 34 & 14.80\% & 2.90\% & 6.50\% \\ Faraday SHGD130-1760X8X1 BM1 & 130 nm & 40 & 10.30\% & 4.50\% & 9.00\% \\ Virage asdsrsfnglp1750x8 cml6 swO & 130 nm & 40 & 12.00\% & 4.80\% & 10.50\% \\ Cypress cY7C1041 CV33-20 Zsx & 150 nm & 8 & 6.70\% & 3.50\% & 8.00\% \\ IDT 71V416S15 PHI & 180 nm & 8 & 6.00\% & 2.80\% & 8.40\% \\ \hline \end{tabular}
\end{table}
Table 1: Instability rate of SRAM devices for different temperatures.  
The signal consists of the true value and noise. In a 3 \(\times\) 3 flat area, the true values are f0 and the noise satisfies a Gaussian distribution. The signal can be expressed as:  
\[\mathrm{g=f+n} \tag{1}\]  
where f is the actual value and n is the additive Gaussian noise. After mean filtering:  
\[\mathrm{g^{\prime}=f0+(n0+n1+\dots+n8)/9=f0+n^{\prime}} \tag{2}\]  
The average of the true value is f0, and the average of the noise is n\({}^{\prime}\). The influence of the noise is attenuated by mean filtering. Mean filtering entails a linear filter that does not protect image details. Median filtering selects the middle value of the numbers in the convolution kernel. Median filtering is a non-linear filter that protects the sharp edges of the image.  
This paper assumes that the initial power-on value of the SRAM is a two-dimensional matrix composed of 0 and 1. The unstable SRAM cell is like a noise element in the digital image. The unstable cells in SRAM PUF are caused by both internal noise and external noise. The position affected by the noise is random.
# An Interactive Transient Model Correction Active Sonar Target Tracking Method
## 2 Description of the TMC Tracking Method
### System Model  
In general, within a single measurement period of active sonar, only the radial distance \(R(t)\) and azimuth \(\theta(t)\) of underwater targets can be obtained. With sonar as the observation origin O, a two-dimensional coordinate system is establish ed, as shown in Figure 2 (for convenience of expression, let the Y-axis point to the north). The following Equations (1) and (2) are proposed to describe the target motion model shown in Figure 2: at time \(t\), the target is located at Pt point, with a distance of \(D(t)\) and an azimuth of \(\theta(t)\); at time \((t+\mathrm{T})\) (T is the observation period of active sonar), the target is located at \(P_{t+\mathrm{T}}\) point with a distance of \(D(t+\mathrm{T})\) and an azimuth of \(\theta(t+\mathrm{T})\). The displacement of the target on the X-axis and Y-axis from time \(t\) to time \((t+\mathrm{T})\) is:  
\[\begin{cases}r_{x}=d_{x}(t+\mathrm{T})-d_{x}(t),\\ r_{y}=d_{y}(t+\mathrm{T})-d_{y}(t).\end{cases} \tag{1}\]  
in which \(d_{x}(t)\) and \(d_{y}(t)\) are the projections of the radial distance \(D(t)\) of the target at time t on the X- and Y-axis, respectively, \(d_{x}(t+\mathrm{T})\) and \(d_{y}(t+\mathrm{T})\) are the projections of the radial distance \(D(t+\mathrm{T})\) of the target at time \((t+\mathrm{T})\) on the X- and Y-axis, respectively.  
\[\begin{cases}d_{x}(t+\mathrm{T})=D(t+\mathrm{T})\cdot\sin(\theta(t+\mathrm{T} )),\\ d_{x}(t)=D(t)\cdot\sin(\theta(t)),\\ d_{y}(t+\mathrm{T})=D(t+\mathrm{T})\cdot\cos(\theta(t+\mathrm{T})),\\ d_{y}(t)=D(t)\cdot\cos(\theta(t))\.\end{cases} \tag{2}\]  
Figure 1: Schematic diagram of the general idea of the TMC tracking method.