esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 2 Materials and Methods
### Variables and Data  
#### 2.2.1 Forest Ecological Function  
Forest ecological function (FEF) is the core variable we would like to investigate in this study, which is commonly quantified by forest ecological function index (FEFI) [32]. We list the eight factors and their weights to calculate FEFI in Table 1, including forest volume, naturalness, community structure, tree species structure, total vegetation coverage, canopy density, mean tree height, and litter thickness.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multicolumn{4}{c}{**Classification Standard**} & \multirow{2}{*}{**Weight**} \\ \cline{2-3}  & **I** & **II** & **III** & \\ \hline Forest volume & \(\geq\)150 t/hm\({}^{2}\) & 50 t/hm\({}^{2}\)\(\sim\)149 t/hm\({}^{2}\) & \(<\)50 t/hm\({}^{2}\) & 0.20 \\ Forest naturalness & 1,2 & 3,4 & 5 & 0.15 \\ Forest community structure & 1 & 2 & 3 & 0.15 \\ Tree species structure & 6,7 & 3,4,5 & 1,2 & 0.15 \\ Total vegetation coverage & \(\geq\)70\% & 50\%\(\sim\)69\% & \(<\)50\% & 0.10 \\ Canopy density & \(\geq\)0.70 & 0.40\(\sim\)0.69 & 0.20\(\sim\)0.39 & 0.10 \\ Mean tree height & \(\geq\)15.0 m & 5.0 m\(\sim\)14.9 m & \(<\)5.0 m & 0.10 \\ Litter thickness grade & 1 & 2 & 3 & 0.05 \\ \hline \hline \end{tabular} Note: I, II and III refer to the categories in which each indicator is classified in the raw data. They are assigned with values 3, 2, and 1 when calculating the FEFI of sample plots for ease.  
\end{table}
Table 1: Evaluation factors and classification standards of forest ecological function.  
Figure 1: (**a**) Location of Yellow River basin in China; (**b**) land-use types in different physical geographic regions. Note: Figure 1b is mapped on the basis of the Globe Land30 land cover remote sensing data for 2020, where land uses are classified into10 types: cropland, forest, shrub, grassland, tundra, artificial surfaces, wetland, water bodies, barren, glacier and perennial snowfield. In order to be more relevant to the topic of this study, the last four types are combined into “others”.  
The calculating equation for county-level FEFI can be expressed as [32]:  
\[FEFI=\sum_{k}^{n}\frac{s_{k}}{S\sum\limits_{i=1}^{8}\omega_{i}x_{ki}} \tag{1}\]  
where the county-level FEFI is obtained by the weighted average method. Suppose there is a total number of \(n\) sample plots surveyed in a certain county, \(s_{k}\) represents the area of sample plot \(k\) and \(S\) is the sum area of \(n\) sample plots; \(x_{ki}\) is the value of factor \(i\) of sample plot \(k\) and \(\omega_{i}\) is the weight shown in Table 1. Within the range of [0, 1], a higher FEFI means a better forest ecological function.
## 7 Results
### Fake News Detection Performance
To answer the first question raised in the introduction, based on the experiments, we observed that the interplay between the news text and the user posting behavioral features improves the detection performance, achieving state-of-the-art results. This seems to confirm our hypothesis--stated in Section 2--that user posting behavior attributes and news content contain complementary information that must be encoded and captured simultaneously in order to detect fake news. 2. To answer the second question raised in the introduction, the experimental results demonstrate that by excluding context-based features, such as user-posted behavioral cues, the performance deteriorated in the two datasets, showing the power of harnessing such clues in the modeling process. Mining user interactions with news articles on social media can aid in the identification of false information [39]. As a result, adding pertinent information from user-news interactions would provide additional knowledge and improve detection performance. The experimental findings showed that, to a certain extent, user features could be used  
Figure 19: Performance comparisons (%) of models in GossipCop w/o user posting behavior features. Figure 17: Confusion matrix of BERT\({}_{base}\)-CNN-BiGRU in GossipCop w/o user posting behavior features. Figure 18: Performance comparisons (%) of models in PolitiFact w/o user posting behavior features. to efficiently quantify detection performance. Experimental findings of two real-world datasets show how the proposed models can improve detection performance significantly. Additionally, we found that simplified versions of our models that utilized specific components only exhibited degraded performance, although still superior to baseline models, but were outperformed by the full proposed models that integrate the semantically rich information of news with user posting behavior features. The proposed model structures, on the other hand, are relatively complex. We anticipate that by using DistilBert, which has fewer parameters than BERT\({}_{base}\), our model will be trained efficiently in a large volume of real-world data. It has been demonstrated that combining content and context features provides discriminatory power for distinguishing fake from real content. This study emphasizes the existence of a strong and consistent correlation between news content and user behavior information when semantic contextualized relations derived from content and context features are combined for improved detection quality.
## 3 Performance Evaluations
### Fiber Channel
#### 3.2.1 Communication System  
A binary phase-shift keying (BPSK) waveform consisting of 20.088 Kbits obtained from the text image shown in Figure 10a is generated and spread with a PN sequence by a factor of 7. The resulting signal after scrambling is upconverted to 28 GHz with a bandpass bandwidth of 0.7 GHz. The spread BPSK signal, whose spectrum is depicted in Figure 10b, modulates the optical carrier, after being scrambled, using an MZM. The optical signal at the output of the MZM propagates through a fiber of different lengths and then is injected into a PD. The output of the PD undergoes amplification before being wirelessly transmitted by a horn antenna. Another horn antenna captures the received signal, and an OSC is used to process it further.  
Figure 9: The proposed ISAC model using photonics technology.  
Figure 11 shows the reconstructed images received as the fiber length varies at 10, 25, 50, and 75 km. To better understand the impact of fiber length on system performance, we computed the BER for each fiber length. The BER values for fiber lengths of 10, 25, 50, and 75 are 0, 0.0793, 0, and 0.4132, respectively. Note that the performance at 50 km is better than that of length 25 km and 75 km. This is due to the nature of optical double-sideband (ODSB) transmission, where spectral nulls arise from chromatic dispersion. The number and position of these nulls depend on the fiber length, as shown in Figure 12. At a fiber length of 25 km, a spectral null occurs near the 28 GHz carrier frequency, whereas at 50 km, there is no null at 28 GHz. This explains why the performance at 50 km is better than at 25 km.  
Figure 11: The reconstrued image for fiber lengths 10, 25, 50, and 75 km.  
Figure 10: (**a**) Transmitted image (box is not included). (**b**) Spectrum of transmitted bandpass DSSS BPSK before scrambling.
# Muscular and Molecular Pathology Associated with SPATA5 Deficiency in a Child with EHLMRS
); Helmoltz-Zentrum Munchen-German Research Center for Environmental Health, Institute of Neurogenomics, 85764 Neuroberg, Germany; (U.S.-S.S.); [PERSON]-S.S.S. ); [PERSON]S.S.S.-S.S.); [PERSON]S.S.S.-S.S.S. ); [PERSON]S.S.S.-S.S. The role of SPATA5 in mitochondrial integrity and function has been discussed in accordance with its involvement in synaptic development and plasticity, as well as developmental expression throughout the brain [2; 3; 4]. In functional studies, [PERSON] et al. demonstrated its role in mitochondrial dynamics, causing an imbalance in mitochondrial fusion-fission rates, impaired energy production and short axons [5]. In contrast, there is incomplete knowledge on SPATA5-associated myopathy. Although morphological studies reveal that muscle biopsies have shown enlarged and abnormally shaped mitochondria, intermyofibrillar linear mitochondrial aggregates, a mild increase in subsarcolemmal and intermyofibrillar mitochondria, biochemical studies are limited and, thus far, have shown only reduced electron transport chain activity [2]. The epilepsy, hearing loss and mental retardation syndrome (EHLMRS, OMIM #616577) is caused by pathogenic biallelic variants in the SPATA5 gene and recent descriptions of cases (38 total) have led to the delineation of a coherent phenotype [6; 7; 8]. The EHLMRS was first described by [PERSON] et al. and is associated with microcephaly, developmental delay, intellectual disability, hypotonia, spasticity, seizures, sensorineural hearing loss and cortical visual impairment. Most affected individuals are unable to sit or have no head control and have no, or minor, speech development, with their speech limited to just a few words. Brain affection is present, including cerebral atrophy, hypomyelination or a thin corpus callosum [2; 6; 8; 9; 10; 11; 12]. Dysmorphic facies with a depressed nasal ridge, broad eyebrows and retrognathia was described by [PERSON] et al., but does not seem to be shared with other cases in the literature [9]. We report on a comprehensive workup of muscular pathology, including proteomic profiling and microscopic studies, in an 8-year-old girl with typical clinical presentation of EHLMRS, where exome analysis revealed two pathogenic, compound-heterozygous variants in SPATA5.
## Appendix A Proof of Theorems 1 and 2
### Proof of Theorem 1
#### a.1.2. \(H(A|z)\leq I(U;y)-\max\{I(U;z),I(U;s|A)\}\)  
In this case, we need to prove that any rate-equivocation triple \((R_{1},R_{2},R_{e})\) satisfying the following constraints are achievable.  
\[R_{1} \leq H(B)\] \[R_{1}+R_{2}\leq I(U;Y)-I(U;S|A)\] \[R_{e} \leq R_{1}+R_{2}\] \[R_{e} \leq H(A|Z)\]  
It is sufficient to show that the rate triples \((R_{1},R_{2},R_{e}=H(A|Z))\) are achievable. The coding scheme is as follows.  
_Codebook generation and encoding_: Let \(R_{1}=H(B)-\tau_{1}^{\prime}\) and \(R_{1}+R_{2}=I(U;Y)-I(U;S|A)-\tau_{2}^{\prime}\), where \(\tau_{1}^{\prime}\), \(\tau_{2}^{\prime}\) are fixed positive numbers. For each \(m_{1}\in\{1,2,...,2^{NR_{1}}\}\), an independent and identically distributed (i.i.d) codeword, \(b^{N}(m_{1})\), is generated according to \(p(b^{N})=\prod\limits_{i=1}^{N}p(b_{i})\). Then, \(2^{NR_{2}}\) action sequences \(a^{N}(m_{1},m_{2})\) are i.i.d generated for each \(b^{N}(m_{1})\) according to \(p(a^{N}(m_{1},m_{2})|b^{N}(m_{1}))=\prod\limits_{i=1}^{N}p(a_{i}|b_{i})\), where \(m_{2}\in\{1,2,...,2^{NR_{2}}\}\). For each \(a^{N}(m_{1},m_{2})\), we generate \(\|\mathcal{T}\|=2^{N(I(U;Y)-R_{1}-R_{2}-\epsilon)}\) i.i.d codewords \(u^{N}(m_{1},m_{2},t_{u})\) according to \(p(u^{N}(m_{1},m_{2},t_{u})|a^{N}(m_{1},m_{2}))=\prod\limits_{i=1}^{N}p(u_{i}| a_{i})\). To send the message \((m_{1},m_{2})\) with the action sequence, \(a^{N}(m_{1},m_{2})\), and corresponding state sequence \(s^{N}\), the encoder chooses a \(u^{N}(m_{1},m_{2},t_{u})\) from the \(\|\mathcal{T}\|\) sequences, such that \((u^{N}(m_{1},m_{2},t_{u}),a^{N}(m_{1},m_{2}),s^{N})\in T_{US|A}^{N}\). If no such sequence exists, it picks \(t_{u}=1\). Then, the input sequence of the channel is generated by \(p(x^{N}|u^{N},s^{N})=\prod\limits_{i=1}^{N}p(x_{i}|u_{i},s_{i})\).  
_Decoding and error probability analysis_: Decoder 1 can decode the message, \(m_{1}\), correctly, since \(R_{1}\leq H(B)\). For the receiver, he tries to find a unique sequence \(u^{N}(\hat{\hat{m}}_{1},\hat{\hat{m}}_{2},\hat{\hat{\hat{t}}}_{u})\), such that \((u^{N}(\hat{\hat{m}}_{1},\hat{\hat{m}}_{2},\hat{\hat{t}}_{u}),a^{N}(\hat{\hat {m}}_{1},\hat{\hat{m}}_{2}),y^{N})\in T_{UAV}^{N}\). It is easy to show the decoding error probabilities \(P_{e1}\leq\epsilon\) and \(P_{e2}\leq\epsilon\) by similar arguments in [11; 12].  
_Equivocation analysis_: We need to prove \(\lim\limits_{N\rightarrow\infty}\triangle=\lim\limits_{N\rightarrow\infty} \frac{H(M_{1},M_{2}|Z^{N})}{N}\geq R_{e}\). The methods in [18] are utilized.  
\[\lim\limits_{N\rightarrow\infty}\frac{H(M_{1},M_{2}|Z^{N})}{N} =\lim\limits_{N\rightarrow\infty}\frac{H(A^{N}(M_{1},M_{2})|Z^{N })}{N} \tag{56}\] \[=\lim\limits_{N\rightarrow\infty}\frac{NH(A|Z)}{N}\] (57) \[=H(A|Z)\] \[\geq R_{e}\]  
where Equation (56) is from that \(A^{N}\) is a function of \((M_{1},M_{2})\), and Equation (57) is from that the sequences \(A^{N}\) and \(X^{N}\) are i.i.d generated and the channels are discrete memoryless.  
We complete the proof of Theorem 1.
## 3 Methodology and Model
### IPSA-DV-Hop Algorithm
#### 3.1.1 First Hop Distance Refinement and Weighting  
In the algorithm, the first hop distance is refined using the RSSI, with the reference signal strength taken as the RSSI value at unit distance. The log distance path loss model can be simplified as shown in Equation (10).  
\[P(d)=P(d_{0})-10\eta\cdot lg\frac{d}{d_{0}}+X_{\sigma} \tag{10}\]  
where the \(P(d)\) represents the RSSI value when two nodes are \(d\) meters apart. The reference distance used in the experiments in this paper is \(d_{0}=1\) m. \(\eta\) is the wireless channel attenuation coefficient, which is often set to 2-4. \(X_{\sigma}\) represents a Gaussian random noise.  
The relationship between the distance of two nodes and the communication radius is represented by classifying the first hop into \(n\) levels. The refinement grade in this paper is 3. The hierarchical classification is shown in Equation (11).  
\[\mathit{hops}=\left\{\begin{array}{ll}\frac{1}{n},&0<d\leq\frac{R}{n}\\ \frac{i}{n},&\frac{(i-1)R}{n}<d\leq\frac{iR}{n}\\ 1,&\frac{(n-1)R}{n}<d\leq R\end{array}\right. \tag{11}\]  
The relationship between distance and the RSSI can be expressed by Equation (12).  
\[P(d_{0})-10\eta\cdot lg\left(\frac{iR}{n}\right)+X_{\sigma}\leq P(d)<P(d_{0})- 10\eta\cdot lg\left[\frac{(i-1)R}{n}\right]+X_{\sigma} \tag{12}\]  
The estimated distance error is used as a weighting coefficient to calculate the distance error for each beacon node, as expressed by Equation (13). The two algorithms complement each other, effectively addressing the limitations of using RSSI technology alone, such as low accuracy and susceptibility to interference.  
\[e_{i}=\frac{1}{N}{\sum_{i\
eq j}\frac{\left|d^{\prime}_{ij}-d_{ij}\right|}{h_{ ij}}},\mathit{aveHops}_{i}={\sum_{i=1}^{n}w_{i}\cdot hops}_{i},w_{i}=\frac{e_{i}}{ \sum_{j=1}^{N}e_{j}} \tag{13}\]  
Figure 1: Radiation at different DOIs. (**a**) DOI = 0; (**b**) DOI = 0.05; (**c**) DOI = 0.1.
## 3 Results and Discussion
### Experimental Results of Thermo-Physical Properties
#### 3.4.2 Thermal Conductivity  
Thermal conductivity characterizes the ability of a material to conduct heat. The more thermal conductivity is low the more thermal performance of a material is significant.  
Figure 15 displays the thermal conductivity of composite at dry and saturated states as function of RWG volume content with an average measurement error for all samples respectively of 5% and 7%. One can obviously observe that the thermal conductivity decreased with the increase of the RWG volume content in concrete at both dry and saturated states. At dry state, the thermal conductivity dropped from 1.48 W.m\({}^{-1}\)K\({}^{-1}\) for RC to 0.95 W.m\({}^{-1}\)K\({}^{-1}\) for RWG50, representing a reduction of about 36%. This result is due to the thermal conductivity of recycled waste glass which is 38% lower compared to the one of natural sand 0.463 W.m\({}^{-1}\)K\({}^{-1}\)[56]. The same behavior was reported in [57] as the thermal conductivity of mortar-based composite decreased with the natural fibers  
Figure 13: Flexural strength influence of concrete reinforced with different volume percentages of RWG.  
weight content increase; this is due to the fact that the natural fibers are known to have lower thermal conductivities than the ones of mortar solid matrixes.  
At saturated state, the thermal conductivity also decreased by around 44% as RWG is further incorporated into concrete. For each volume percentage, the thermal conductivity value at saturated state is greater than the one at dry state. This difference diminishes slightly from 22.37% to 22.32% respectively for RC and RWC50. These findings can be explained by the fact that the air in pores is replaced by water and thermal conductivity of water (0.601 W.m\({}^{-1}\)K\({}^{-1}\)) is higher than that of air (0.026 W.m\({}^{-1}\)K\({}^{-1}\)) [58].
## 1 Introduction
This divided structure of the generator and decoder that utilize different latent vectors to assign scale-specific roles in image generation improves the visual quality of the image. We also adopt a training technique that differs from that used in the conventional BEGAN model. The instability of GANs usually occurs when generating high-resolution images; thus, we adopt the progressive growth concept for the generator and discriminator introduced in [17]. The size of a generated image at the beginning of the training process is small, but it becomes twice the size after several epochs. This training scheme reduces instability and consequently improves the visual quality of the output images. In addition to generating images using random vectors, we also propose a method to synthesize two existing images by exploiting the auto-encoder structure of our discriminator. The encoder of discriminator learns to encode both real and fake samples during the training process; thus, it does not need to train an additional model. However, in order for the decoder or generator to combine real images, the encoded latent space of the real images should be similar to that of the fake images. To guarantee this, we propose the novel double-constraint loss function, which constrains the latent vectors of encoded real images. Therefore, the images are combined when the decoder decodes an image using the latent vectors obtained from the different images in an unsupervised manner. This paper is structured as follows. Section 2 presents the theoretical background and provides a detailed description of the proposed model. We then demonstrate the superiority of our model by qualitatively and quantitatively comparing it to conventional models [12; 18] in Section 3. Concluding remarks are presented in Section 4.
# Compensation of Limited Bandwidth and Nonlinearity for Coherent Transponder
## 2 Principle
Adding nonlinear response, \(\varphi_{mzm}\) is expressed as  
\[\phi_{mzm}=0.5\pi\alpha_{\omega}\Big{(}\gamma_{\omega}x^{2}+x\Big{)}\sin( \omega t), \tag{4}\]  
here \(\gamma\) is the coefficient for a nonlinear response. The subscript of \(\alpha\) and \(\gamma\) indicates that they are dependent on the frequency. Using the Jacobi-Anger expansion [17], one can show that Equation (4) can be written as the following  
\[ \begin{array}{l}P_{out}(\omega,x)=0.5P_{stdy}+0.5P_{stdy}\cos(\pi\beta)J_{0 }\Big{(}\pi\alpha_{\omega}\Big{(}\gamma_{\omega}x^{2}+x\Big{)}\Big{)}\\ +P_{stdy}\cos(\pi\beta)\sum\limits_{m=1}^{\infty}J_{2m}\Big{(}\pi\alpha_{\omega }\Big{(}\gamma_{\omega}x^{2}+x\Big{)}\Big{)}\cos(2m\omega t)\\ -P_{stdy}\sin(\pi\beta)\sum\limits_{m=1}^{\infty}J_{2m-1}\Big{(}\pi\alpha_{ \omega}\Big{(}\gamma_{\omega}x^{2}+x\Big{)}\Big{)}\sin((2m-1)\omega t)\end{array} , \tag{5}\]  
here, \(J_{m}(\,)\) is the first-kind \(m\)-th Bessel function. The average output power over the time \(P_{avg}(\omega,x)\), detected by a low-speed PD, can be expressed as  
\[P_{avg}(\omega,x)=0.5P_{stdy}\Big{[}1+\cos(\pi\beta)J_{0}\Big{(}\pi\alpha_{ \omega}\Big{(}\gamma_{\omega}x^{2}+x\Big{)}\Big{)}\Big{]}, \tag{6}\]  
We fix \(\omega\) and scan the amplitude of the sinusoidal stimulus between 0 and 2\({}^{*}(Bit_{ADC}\)-1). We get a curve of \(P_{avg}(\omega,x)\) versus \(x\). The underlying fitting parameter [\(P_{stdy}\), \(\alpha_{\omega}\), \(\beta\), \(\gamma_{\omega}\)] can be extracted using a curve fitting method like sequential quadratic programming (SQP). The SQP minimizes the relative root-mean-square (RMS) error  
\[Err_{rms}=\frac{\sum\limits_{k=1}^{K}\Big{[}P_{avg}^{Mass}(x_{k})-P_{avg}^{Fit} (x_{k})\Big{]}^{2}}{K\Big{[}P_{avg}^{Mass}(x_{k})\Big{]}^{2}}, \tag{7}\]  
where superscript \"_Meas_\" and \"_Fit_\" indicate the measurement results and the fitting results. Here \(K\) and \(k\) are the total measurements and the index of measurement. Next, we scan the frequency from a value close to DC (for example, a few GHz) to the value of the baud rate. We record the optical power from each tributary when the amplitude of the sinusoidal stimulus is varied from zero to full swing. We perform the curve fitting according to Equations (6) and (7). Then, we determine the \(\alpha_{\omega}\) and \(\gamma_{\omega}\) over the different frequencies. Accordingly, we find the coefficients of the FIR filter and Volterra filter to mitigate the limited bandwidth and the nonlinearity. Furthermore, the power imbalance between tributaries and the bias point of the MZM can be decided by \(P_{stdy}\) and \(\beta\). The DAC's input returns to the regular data path for normal operation after the impairments are calibrated.
# Named Entity Recognition Networks Based on Syntactically Constrained Attention
## 1 Introduction
If the label information of the entity type does not exist, the query is constructed in the form of template filling, as shown in the queries of the cell_line, DNA and protein types in the GENIA dataset, where the entity types are colored blue. Figure 1: The process of recognizing entities, using a named entity recognition model, based on a machine reading comprehension framework. syntactic constraint-based neural attention network that takes the dependency of each word in a sentence as a constraint. We establish associations between related words under the constraint of syntactic structure clues, and guide the model to select strong words based on these associations. This provides more accurate attention signals, and ultimately alleviates the distraction problem that is caused by lengthy sentences. We analyze sentences using a syntactic dependency tree, which describes the dependencies between words, and use these dependencies as constraint information, in order to enhance the attention mechanism, and to capture the syntactically relevant parts of each word of interest. Specifically, we used the natural language-processing toolkit Spacy ([[https://spacy.io/](https://spacy.io/)]([https://spacy.io/](https://spacy.io/)) (accessed on 8 April 2021)) to generate a syntactic dependency tree of the context, obtaining the relevant nodes for each word in the context, as shown in Figure 2. Unlike [PERSON] et al. [2], we focus on contextual information through two pruning strategies. They used syntax to guide the modeling of query and context, which is not applicable to the NER task, as our queries contain task-relevant, prior knowledge that is useful for identifying ambiguous and nested entities. In summary, our contribution is as follows:  
* For NER tasks, we propose a new **S**yntactic **C**onstrained Dual Context **A**ggregation **N**etwork (SCAN-Net) model. SCAN-Net is the first model that uses a syntactic dependency tree to apply the dependency of a single word in a sentence, as a constraint, to the NER task. * To make relatively better use of syntactic information for the model to understand the text, we propose two syntactic constraint strategies. * Numerous experiments have been conducted on the ACE2004, ACE2005, and GENIA datasets, and the results have demonstrated the effectiveness of SCAN-Net model.