esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Segmentation Tasks
#### 3.1.3 Clustering  
Clustering is the grouping of similar instances, objects, or pixels in this specific case. In order to group pixels together, there must be some sort of measure that can determine whether they are similar or dissimilar. The two main types of measures used to estimate this relation are distance measures and similarity measures [62].  
In the case of OCTA image segmentation, the majority of the analyzed studies used pixel intensity as a way to group together objects, using common methods such as k-means clustering [63; 64; 65], or other clustering algorithms such as fuzzy c-means clustering [66] and a modified CLIQUE clustering technique [67]. An interesting study that used local features for clustering and not pixel intensity is a method by [PERSON] et al. [68] which was based on building a dictionary using pre-annotated data and then processing the unseen images by computing the similarity/dissimilarity.  
Clustering methods were employed in two clinical applications: general eye vasculature segmentation and choroidal neovascularization (CNV)/Choriocapillaris segmentation. The study by [PERSON] et al. [68] was a rare study that provided a quantitative validation of general eye vessel segmentation, even though only one image was used for validation. On this image, the DSC was equal to 0.82 for larger vessels and 0.71 for capillaries. For the CNV/Choriocapillaris application, the study by [PERSON] et al. [67] had a final DSC equal to 0.84.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Hold Power Comparison  
Since SRAM cells spend most of their time in hold mode, the power consumed in hold mode, known as hold power (\(H_{PWR}\)), constitutes a significant portion of the total power consumption of SRAM cells [28]. As shown in Figure 9, \(H_{PWR}\) has been calculated based on the average power consumption while the SRAM cell is in hold mode for various supply voltages. \(H_{PWR}\) is primarily consumed due to bit line leakage and the leakage of inverters used in the SRAM cell. Generally, \(H_{PWR}\) is proportional to the number of transistors used in the cell, and the excessive use of access transistors tends to increase \(H_{PWR}\)[6]. Cells like RHHS16T, RHBD14T, and RHPD14T consume higher \(H_{PWR}\) compared to RSP14T, which uses a single access transistor due to the presence of excessive access transistors. Among these, RHHS16T consumes higher \(H_{PWR}\) than RHBD14T and RHPD14T because it uses a larger pull-down transistor. Moreover, the proposed RHHS16T consumes the highest \(H_{PWR}\) among the comparison cells due to the use of the most transistors (Figure 9).  
Figure 8: WWTV (**a**) measuring method and (**b**) simulation result for different comparison cells with supply voltage variation.  
Figure 7: Write access time (**a**) measuring method and (**b**) simulation result for different comparison cells with supply voltage variation.
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 2 Data Overview  
The Jiudingshan Tunnel on the Chuxiong-Dali highway in Yunnan, China, is 7575 m long. The depth is 570 m, and the maximum excavation width is 17.34 m. The tunnel is located on composite site with various rock structures. Moreover, the tunnel has poor surrounding rock stability and passes through geology of poor structural competence, such as karst, a fault fracture zone, and a contact zone between limestone and granite porphyry. A geological survey was conducted; the geologic map in Figure 1 shows the boreholes. Since the tunnel is so long and the number of boreholes is limited, it was difficult to obtain a complete picture of the actual geological conditions prior to tunnel excavation. It was necessary to design the tunnel dynamically according to the geology prediction results, so advanced horizontal drilling testing was conducted.  
An RPD-180 CBR multifunctional drill rig (KOKEN, Japan) was used for the horizontal advanced drilling testing. The drilling speed, torque, rotation speed, propulsion, blow energy, blow number, water supply rate, water supply pressure, and EV energy (i.e., the energy required to break out a unit volume of rock, is a function of blow energy, blow number, drilling speed, and bit diameter) data were recorded every 2 cm during the horizontal drilling. Actual geological conditions were recorded as tunnel excavation proceeded.  
Horizontal advanced drilling testing was conducted for 10 boreholes, and the cumulative depth of the boreholes was 567.56 m. The first nine borehole drilling data sets  
Figure 1: Geological cross-section of the tunnel’s route (ZK57—64: the location of eight boreholes).  
were used to perform the neural network training, while the tenth data set was used for the testing. In total, there were 33,113 groups of training data and 3611 groups of testing data. Fractured limestone, limestone, clay, and soft interlayer were the four rock types encountered in this part of the tunnel. Figure 2 shows one set of drilling data and the actual geological profile for a borehole 69.86 m deep. Inconspicuous relationships were found between the geological profile and the drilling parameters. For example, the drilling speed increased when soft interlayers were encountered. However, an accurate quantitative relationship between the rocks and the drilling data could not be obtained through observation. To determine the complex nonlinear relationships between the drilling test data and geological conditions, the neural network method with machine learning was introduced for tunnel geology prediction, and the deep neural network method with the GA algorithm was introduced to improve the accuracy of the tunnel geology prediction.
## 3 Results and Discussion
### Model Results, Comparison and Discussion  
#### 3.5.1 Analytical Model Analysis  
To compare the results of the physical testing program to analytical models, comparison plots at 10, 50 and 100 kN were prepared for all 24 specimens. An example of this can be seen below, in Figure 15. Both selected models were generally exponential, matching what was largely seen across the data. It was seen that [PERSON]'s model [11] tended to provide  
Figure 14: Effects of grout annulus on the shear stress and displacement profiles: (**A**) specimen A-1, (**B**) specimen B-1-ii, (**C**) specimen D-1.  
a better fit to the data but was underpredicted in the attenuation rate resulting in higher strain compared to laboratory data at the same length along the specimens. Opposite to this was [PERSON]'s [16] model, which was over-predictive in the attenuation rate of the strain. When examining the model's fits to laboratory data across changes of the independent variables, [PERSON]'s model appeared to have improved fit with increased rib spacing.
# A Wideband Digital Pre-Distortion Algorithm Based on Edge Signal Correction
## 4 Experimental Validation and Analysis
### Experimental Parameter Setup  
To evaluate the effectiveness of the algorithm in linearizing wideband signals, the detailed parameter settings of the experimental platform are listed in Table 2.
### Scope and Contributions  
Readers interested in reading DL-based NOMA should read this review. This paper offers an in-depth analysis of existing research works on DL-based NOMA. The purpose of this review is to assist readers with a thorough understanding of the DL methods in NOMA systems from a variety of perspectives. It provides a comprehensive discussion of key performance indicators for DL-based NOMA. It also explains the integration of emerging technologies, such as MEC, IRS, OFDM, MIMO, SWIPT etc., into DL-based NOMA. Finally, it explains a number of potential challenges and future research areas for further breakthrough novelties.
# Practical Use of Starlink Downlink Tones for Positioning
## 4 Multi-Epoch Positioning
### A Simplified Frequency Error Model  
The frequency model (18) has eight unknowns (3D position, 3D velocity, user clock bias, and user clock drift) plus as many unknowns as satellite clock drifts. The system is therefore undetermined. Moreover, the small number of satellites captured simultaneously is not very helpful. Therefore, we cannot perform point-solution batch positioning for a dynamic user. However, in the case of a static user, its position coordinates do not change over time and typically, a multi-epoch positioning is more practical in this situation. This positioning consists in using--for each tracked satellite--the measurements of several epochs simultaneously to make a state estimation. This is relevant because the fast motion of the LEO satellite makes successive measurements spaced a few seconds apart, sufficiently different for the observation of the unknowns.  
We consider the multi-epoch positioning in which Doppler shifts of \(m\) epochs (numbered from \(1\) to \(m\)) of \(K\) tracked Satellites (\(k=1,..,K\)) are used to make an estimation of the unknowns. We assume that the user clock drift error is constant over a short time interval of \(m\) epochs, then \(c\delta t_{i}\approx c\delta t_{r}\), for \(i=1,..,m\). In addition, the receiver clock bias error at epoch \(i\), \(\delta t_{i}\) can be deduced from its value at the first epoch, \(\delta t_{r}\), as  
\[c\delta t_{i}=c\delta t_{r}+\Delta t_{i,1}\times c\delta t_{r} \tag{19}\]  
where \(\Delta t_{i1}=t_{i}-t_{1},i=1,..,m\). Based on these assumptions, the range rate error of the \(k\)th-satellite at epoch \(i\) given by (18) becomes  
\[\Delta\widetilde{\rho}^{\widetilde{k}}_{i}=h^{k}_{r,i}\times\delta r+h^{k}_{ c\delta t,i}\times c\delta t_{r}+\left(1+h^{k}_{c\delta t,i}\Delta t_{i,1} \right)\times c\delta t_{r}-\lambda\delta f^{k}_{syi}+\eta_{\rho,i} \tag{20}\]  
For a static user, at each update based on \(m\times K\) measurements (of \(m\) epochs and \(K\) satellites), the unknowns are: 3D user coordinates, \(r\), user clock bias and user clock drift as \(\delta t_{\rm r}\) and \(\delta t_{r}\), respectively, both at the first epoch, and \(m\times K\) satellite clock drift range rates \(\lambda f^{k}_{syi}\), \(k=1...K\), \(i=1...m\). There are a total of \(5+m\times K\) unknowns for \(m\times K\) measurements. The system is therefore underdetermined regarding the number \(m\) of epochs that can be stacked. If we assume, in addition, that satellite clock errors are slightly varying over a short time interval and then considered constant i.e., \(\delta f^{k}_{syi}=\delta f^{k}_{sy}\) for, \(i=1,...,m\), the number of unknowns reduces to \(5+K\). By gathering measurements from several epochs, one should be able to estimate the user's position. In this condition, the range-rate error model (20) becomes:  
\[\Delta\widetilde{\rho}^{\widetilde{k}}_{i}=h^{k}_{r,i}\times\delta r+h^{k}_{ c\delta t,i}\times c\delta t_{r}+\left(1+h^{k}_{c\delta t,i}\Delta t_{i,1} \right)\times c\delta t_{r}-\lambda\delta f^{k}_{sy}+\eta_{\rho,i} \tag{21}\]
## 3 Results and Discussion
### Screening Experiments  
Variation limits of the three analysed factors were defined by conducting 'one-factor-at-a-time' experiments for a constant applied voltage (V) of 3 kV, corresponding to a constant electric field level of 6 kV/cm.  
Experiment 1.1.: Variable pulse width T (2-150 \(\upmu\)s) at constant values of the number of pulses n = 200 and ethanol concentration [C] = 50%.  
Experiment 1.2.: Variable pulse number n (100-400) at constant values of pulse width T = 100 \(\upmu\)s and ethanol concentration [C] = 50%.  
Experiment 1.3.: Variable ethanol concentration [C] (0-60%) at constant values of the number of pulses n = 200 and pulse width T = 100 \(\upmu\)s.  
The results obtained in the aforementioned experiments are presented in Figures 2-4. The extraction yields of total phenol content, total flavonoid content, and condensed tannin content were selected for the evaluation of PEF pre-treatment and represented as a function of the three control factors.  
Figure 2: Variation of the phenolic compound extraction yield as a function of the pulse width. (**a**) Total phenolic content; (**b**) Total flavonoid content; (**c**) Condensed tannin content. Means not sharing any letters are significantly different.  
Figure 4: Variation of the phenolic compound extraction yield as a function of the ethanol concentration. (**a**) Total phenolic content; (**b**) Total flavonoid content; (**c**) Condensed tannin content. Means not sharing any letters are significantly different.  
Figure 3: Variation of the phenolic compound extraction yield as a function of the pulse number. (**a**)Total phenolic content; (**b**)Total flavonoid content; (**c**) Condensed tannin content. Means not sharing any letters are significantly different.
## 4 Method
### Nash-Sutcliffe Efficiency (NSE)  
In the hydrology, the Nash-Sutcliffe coefficient [51] was widely used to evaluate the performance of simulated time series against observations. The formula for calculating this coefficient is as follows [52]:  
\[NSE=1-\frac{\sum\limits_{i=1}^{n}\left(X_{i}^{obs}-X_{i}^{sim}\right)^{2}}{ \sum\limits_{i=1}^{n}\left(X_{i}^{obs}-X^{mean}\right)^{2}}(i=1,2,\cdots,n) \tag{7}\]  
where \(X_{i}^{obs}\) is the \(i\)th value in the observation series, \(X_{i}^{sim}\) is the \(i\)th value in the simulated series, \(X^{mean}\) is the mean value of observation series and \(n\) is the number of observations.  
\(NSE\) ranges between \(-\infty\) and \(1\). \(NSE\) = 1 means the perfect fit. \(0<NSE\leq 1\) means that the performance of simulated series is acceptable. If \(NSE\geq 0\), it explains that the simulated series is worse [53]. In our study, NSE was used as an indicator to compare GRACE/Swarm-DSI (observation series) with the traditional drought indices (simulated series).
# Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews
## 3 The Proposed Approaches
### Automatic Identification of Domain-Optimized Priors and Number of True Topics  
To address the problem of automatically identifying initial priors suitable to a given domain, we focus on an empirical approach instead of mathematical optimization solutions. Dirichlet priors (\(\alpha\) and \(\beta\)) and the number of topics are the priors for a given topic model. \(\alpha\) and \(\beta\) adjust the distribution of topics per document and the probability distribution of words per topic, respectively. If \(\alpha\) increases, the number of topics per document increases and if \(\beta\) increases, the probability values of a few words within the topic increase. In existing topic models, the prior probabilities such as \(\alpha\) and \(\beta\) are determined either by default (\(\alpha=\frac{50}{\#\text{ of topics}}\) and \(\beta=0.1\)) or arbitrarily decided by domain experts. However, we propose Algorithm 1, where the average topic coherence is measured while increasing the values of \(\alpha\) and \(\beta\) by 0.05, and the values of \(\alpha\) and \(\beta\) which maximize the average topic coherence value are returned. To measure the topic coherence value (corresponding to Function \(MeasureTopicCoherence()\) in Line 11), to the best of our knowledge, we propose a new topic coherence measure based on word embedding for the first time. Given a topic \(\theta_{i}=\{w_{1},w_{2},w_{3}\}\), where \(w_{i}\) is a word, \(MeasureTopicCoherence()\) (1) converts each word \(w_{i}\in\theta_{i}\) to \(p\)-dimensional word embedding vector \(v_{w_{i}}\) through a domain-customized word embedding model (\(f_{we}\)) which is one of word2 vec, GloVe, fastText, ELMo, and BERT; (2) creates all possible combinations of vector pairs--e.g., \(S=\{(w_{w_{1}},v_{w_{2}}),(v_{w_{1}},v_{w_{3}}),(v_{w_{2}},v_{w_{3}})\}\); (3) computes \(sim(v_{w_{i}},v_{w_{j}})\), the similarity of each pair through \(\frac{v_{w_{i}},v_{w_{j}}}{||v_{w_{j}}||||v_{w_{j}}||}\); and (4) returns the topic coherence value \(\frac{\sum_{(v_{w_{i}},v_{w_{j}})\in sim(v_{w_{i}},v_{w_{j}})}}{|S|}\), where \(|S|\) is the number of pairs in \(S\).  
Similarly, the optimal number of topics is computed using the above \(MeasureTopicCoherence()\) function. A topic model is individually executed with each different number of topics. Next, the proposed method computes the average topic coherence value from the topic results and then finds the number of topics with the maximum topic coherence value.
## 2 Concepts and Theory
### Principles of FCS
For long time intervals, the fluctuations will become uncorrelated, i.e., \(\phi(\tau)\to 0\) As stated in [13], \"The rates at which \(\mathrm{\phi}\)[sic] decays to zero with increasing \(\tau\) give a measure of how quickly diffusion and chemical reaction change the configuration of the system.\" By a theoretical argument in the context of freely diffusing fluorophores excited and detected in a Gaussian laser beam sample volume, it is argued that the autocorrelation of recorded fluorescence fluctuations, \(\mathrm{G}(\tau)\), takes the form:  
\[(\tau)=\frac{\mathrm{G}(0)}{1+\frac{\tau}{\tau_{0}}} \tag{2}\]  
Figure 1: Schematic illustrations of the fundamental principles for a fluorescence correlation spectroscopy (FCS) experiment, (**a**) as adopted from the original paper by [PERSON] et al. 1974 [13], and (**b**) when employing a confocal microscope setting. where \(\tau_{\rm D}\) corresponds to the characteristic diffusion time of the fluorophores. In the [PERSON] and [PERSON] paper, the more general term \(\tau_{l}\) is used instead of \(\tau_{\rm D}\), but since we here focus on diffusion the term \(\tau_{\rm D}\) is here used for clarity. Thus, by determining the shape of the correlation function information about the diffusion (or chemical reaction) can be acquired by finding \(\tau_{\rm D}\). In addition, the equilibrium state G(0) will be inversely proportional to the ensemble average of molecules detected in the sample volume N so that  
\[\rm G(0)\propto\frac{1}{N} \tag{3}\]  
It should be noted that N is related to the concentration and detection volume. In the literature, several different more or less complex fitting models have been proposed [7; 9; 25]. However, to our experience neither of these can directly be applied to the experiments in order to yield reliable and consistent results. Thus, in this practical guide, we propose a strategy to return to the most simple and conceptual model to validate the experimental system and confirm the theoretical agreement in the performance in each specific case. Based on the parameters extracted, the model can then be expanded to fit the purposes for each specific experiment. It can further be concluded from the last two mathematical implications above that the shape of the correlation curve will depend on the time scale by which the data points are sampled. If the time scale is shorter than the diffusion time, a correlation around equilibrium should be observed, while for data sampling at a too long timescale the correlation will approach zero. This effect will be of importance when interpreting data from TCSPC. These theories [11; 13] are based on three assumptions that should be kept in mind:  
1. The measurements are carried out with very dilute, close to ideal solutions so that statistics of solute molecules are independent. 2. Concentration fluctuations of different species must be uncorrelated. 3. The mean-square fluctuation in a unit volume follows Poisson statistics. This means that the variance of the fluctuation will increase as mean number of recorded signal increases.
###### Abstract  
The real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational capacity of edge devices, a new automatic crack detection algorithm is proposed: BT-YOLO. We combined Bottleneck Transformer with You Only Look Once (YOLO), which is more conducive to extracting the features of small cracks than YOLOv5s. The introduction of DWConv to the feature extraction network reduced the number of parameters and improved the inference speed of the network. We embedded the SimAM (Simple, Parameter-Free Attention Module) non-parametric attention mechanism to make the crack features more prominent. The experimental results showed that the accuracy of BT-YOLO in crack detection was increased by 4.5%, the mapped value was increased by 8%, and the parameter amount was decreased by 24.9%. Eventually, we deployed edge devices for testing. The frame rate reached 89, which satisfied the requirements of real-time crack detection.  
automatic crack detection; transformer; BT-YOLO; attention mechanism; edge device deployment +
Footnote †: journal: Applied sciences
# Supporting Privacy of Computations in Mobile Big Data Systems +
## 2 A High-Level Overview of Our System
### Our Adversarial Model  
We assume that there exists a secure communication channel between the client and each of the \((n+2)\) servers, \(\{p_{1},p_{2},\ldots,p_{n},p_{c},p_{e}\}\), to send the unique seed values for pseudorandom bit generation, identity of the other servers, _etc._ We assume that every pair of communicating servers authenticate one another (Note that without authentication, an external adversary can masquerade any of the servers to disrupt the construction of the garbled circuit in an unpredictable manner. Additionally, if the communication between the client and the \((n+2)\) servers is compromised by an external adversary, then it can determine the semantics of garbled inputs/outputs based on the seed values. Therefore, both authenticity and confidentiality of communication are essential for the construction of garbled circuits). We assume a very capable adversary, where the evaluator \(p_{e}\) will remain unable to determine the plain text values from the garbled values that it can observe during evaluation even if it individually colludes with any proper subset of the \(n\) servers, \(\{p_{1},p_{2},\ldots,p_{n}\}\). Our adversary model captures a very realistic scenario -- where the client may be certain that some (however, not all) of the parties are corrupt, however, it is uncertain which of the parties are corrupt. If any adversarial party eavesdrops and analyzes all the messages between different parties, and also analyze all the messages that it has legitimately received from the other parties, it still cannot determine the shares of the other parties, or the plain text values of the garbled value pairs that are assigned to each wire in the circuit. Furthermore, the client can efficiently detect a cheating evaluator \(p_{e}\) which returns arbitrary numbers as output to the client without performing any computation. A new garbled circuit is created for every evaluation in our model. Thus, the set of inputs and outputs that have changed or remained the same between different evaluations remains unknown to the evaluator.  
Figure 1: Our secure cloud computing model with \((n+2)=5\) servers. (1) Client sends unique seed value, \(s_{i}\), to each \(p_{i}\)\((1\leq i\leq 3)\); (2) \(p_{1},\ p_{2},\ p_{3}\) interact \(\{l_{ij},1\leq i<j\leq 3\}\) to construct shares of the garbled circuit \(GC\); (3) Each \(p_{i}\) sends its share \((GC_{i})\) to \(p_{c}\); (4) \(p_{c}\) computes \(GC=GC_{1}\oplus GC_{2}\oplus GC_{3}\), and sends it to \(p_{e}\); (5) Client generates garbled inputs, and sends them to \(p_{e}\) for evaluation; (6) \(p_{e}\) evaluates \(GC\), and returns the garbled outputs to the client.