esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Slip Behavior
#### 3.6.2 Interaction with the Force Distribution Relationship  
The average bond stress-slip curve before the yielding of the headed bar is shown in Figure 27. In general, before yielding, the bond stress is approximately linearly increased with the slip which indicates that the bond-slip relationship is in the elastic range before yielding. In addition, the bond stiffness is smaller than that of the straight bar without head specified in Chinese code GB 50010-2020 [37]. Concrete is under compression due to head bearing and the slip is the deformation difference between steel bar and concrete. So, the slip becomes larger because of the additional concrete compression deformation of head bearing for the headed bar when compared to the straight bar without a head and the larger slip indicates the smaller bond stiffness.  
For specimen M4-D12-6d, the head bearing functions more while the bond behavior functions less when compared to other specimens (Figure 17) and the average bond stress of specimen M4-D12-6d is smaller than that of other specimens as shown in Figure 27a. Based on the relationship between slip and force, it indicates a larger concrete compression deformation at heads and a smaller bond slip for specimen M4-D12-6d when compared to other specimens. Such a tendency is consistent with the trend in Figure 26a.  
For specimen M3-D12-7.5d, the head bearing functions less while the bond behavior functions more when compared to other specimens (Figure 21) and the average bond stress is higher than other specimens as shown in Figure 27b, indicating a larger bond slip as shown in Figure 26b. However, for specimen M3-D12-7.5d, the head-bearing force is smaller (Figure 22) while the concrete compression deformation at the head is even larger when compared with other specimens as shown in Figure 26b. The head size of specimen M3-D12-7.5d is the smallest one and moreover, due to the welding fillet effect, the bearing surface area of specimen M3-D12-7.5d is reduced significantly. It is known that the concrete stress is inversely proportional to the bearing area under the same compression force and therefore, then the concrete compression strain or deformation is also inversely proportional to the bearing area. So, even with the smallest head compression force, specimen M3-D12-7.5d with the smallest head size and head-bearing surface area shows the largest head-compression deformation. In addition, head sizes are increased incrementally across specimen M4-D12-7.5d, specimen M6-D12-7.5d and specimen M8  
Figure 27: Average bond stress–slip curve (**a**) Comparison of different lap length; (**b**) Comparison of different head size.  
D12-7.5d while these three types of specimens show a similar head-bearing force. So, the concrete compression deformation at the heads should show the decreased tendency across these three specimens, which is consistent with the trend in Figure 26b.
# A Comprehensive Analysis of Various Tokenizers for Arabic Large Language Models
## 4 Experiments
### Datasets
#### 4.3.4 Topic Classification (TC)  
Topic classification (TC) is a key task in natural language processing. In TC, texts are automatically categorized based on their content. The purpose of this task is to measure the impact of different tokenizers on model performance in analyzing a large body of text. For the TC task, we employed four datasets, all of which consist of news articles written in MSA. Descriptions of the four datasets used are provided below:  
* Single-labeled Arabic News Articles Dataset (SANAD): SANAD is a large collection of single-labeled Arabic news articles [63]. It contains 194,797 articles covering different topics such as sports, culture, finance, politics, and medicine. The articles were collected from three different news websites: AlKhaleej, AlArabiya, and Akhbarona. In this paper, we used the AlKhaleej subset labeled into seven categories, and it contains 45 K articles.
* Open Source Arabic Corpora (OSAC): OSAC is a large collection of Arabic datasets that were collected from various sources, including news articles (BBC and CNN Arabic news), cooking recipe books, and religious books [64]. The corpora are divided into three sets: BBC Arabic, CNN Arabic, and OSAC. In total, the corpus contains 22,429 articles covering different topics on economics, astronomy, law, etc. In this paper, we used the CNN Arabic corpus, which contains 5070 articles labeled into 6 different categories: business, entertainment, Middle East news, science and technology, sports, and world news.
* A Moroccan News Articles Dataset (MNAD v2): MNAD is an Arabic dataset that was collected from four Moroccan news articles websites [65]. It contains 418 K articles in total covering 19 different categories such as sports, finance, politics, and business.
* An Arabic News Text (ANTCorp v1.1): ANTCorp is a large collection of Arabic news articles that were collected from Tunisian RSS Feeds [66]. The corpora contain about 6000 articles covering different categories: economy, culture, local News, sports, etc.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 3 Improved Adaptive Federated Kalman Filter (IAFKF)  
In obstructed spaces, GNSS is prone to positioning anomalies or even satellite unavailability, resulting in a decrease in the positioning accuracy of the integrated navigation system. Additionally, visual navigation systems have poor performance in cloudy or dark environments. Furthermore, since the estimated state values of the federated Kalman sub-filters are fed back to each sub-filter after global fusion by the main filter, the other sub-filters are further contaminated, resulting in positioning errors or even divergence. Therefore, this paper introduces fault detection between the sub-filters and the main filter to detect and process faults in real-time, improving the overall navigation accuracy and fault tolerance.
## 2 Materials and Methods
### Statistical Analysis  
Three general variables (CMJ peak force, IMTP peak force and DSI) and five performance aspects (linear sprint, curvilinear sprint, CoD test, horizontal jumps and vertical jumps) were taken into statistical analysis. The data are presented as means \(\pm\) standard deviations. The normality of the data distribution for all variables was verified with the Shapiro-Wilk test, which showed normal data distribution. Inter-repetition reliability analysis was performed using a two-way random intraclass correlation coefficient (ICC) with absolute agreement and 95% confidence intervals (CI) and coefficient of variation (CV). ICC was interpreted as follows: \(>\)0.9 (excellent), 0.9-0.75 (good), 0.75-0.50 (moderate) and \(<\)0.5 (poor) [37]. Absolute reliability was assessed by calculating the coefficient of variation (CV) according to [PERSON] (2000) [38] and interpreted as poor (CV \(>\) 10%), moderate (CV = 5-10%), or good (CV \(<\) 5%). Pearson correlation coefficients were used to examine the associations between DSI and performance variables. The associations were interpreted as follows: 0.0-0.1 (no association), 0.1-0.4 (weak), 0.4-0.6 (moderate), 0.6-0.8 (strong) and \(>\)0.8 (very strong) [39]. A one-way ANOVA with a post hoc test was used to determine the differences between high, mid and low DSI groups. Furthermore, post hoc testing was performed with a Bonferroni correction to measure statistical differences between groups (high, mid, low DSI), and Hedges \(g\) was used to determine between-group effect sizes. Effect sizes were interpreted as trivial (\(<\)0.2), small (0.2-0.5), medium (0.5-0.8) and large (\(>\)0.8) [40]. The threshold for statistical significance was set at \(\alpha\)\(<\) 0.05, and all analyses were carried out in SPSS statistical software (version 25.0, CJ278 ML, IBM, USA).
## 5 Simulation Results
### Scenario 1: Simulation of Reaching Laws  
In order to verify the advantage of the reaching law proposed in this paper, the traditional sliding mode controller was adopted to track the velocity of the AHV [45]. Four different approaching laws were used for comparative analysis: traditional symbolic function reaching law (Trl), exponential reaching law (Erl), traditional double power reaching law (Tdprl), and new double power approaching law (Ndprl). The four reaching laws are expressed as follows:  
(1) \(\text{Trl: }-D_{1}sgn(s_{1})\)  
(2) \(\text{Erl: }-D_{1}sgn(s_{1})-D_{2}s_{1}\)* Tdprl: \(-D_{1}|s_{1}|^{\gamma}sgn(s_{1})-D_{2}|s_{1}|^{\beta}sgn(s_{1})\)
* Ndprl: \(-D_{1}|s_{1}|^{\gamma}sgn(s_{1})-D_{2}|s_{1}|^{\beta}sgn(s_{1})-D_{3}s_{1}\)  
The sliding mode surface in this scenario is defined as follows [45]:  
\[s_{1}=\left(\frac{d}{dt}+\lambda_{1}\right)^{3}\int_{0}^{t}e(\tau)dt \tag{81}\]  
Simulation parameters of reaching laws are presented in Table 1.  
In the simulation, the AHV is flying in the cruise phase, and the initial parameters of the AHV are set as \(V_{0}=4590\) m/s, \(h_{0}=33528\) m, \(\gamma=0^{{}^{\circ}}\), \(\alpha=2.745^{{}^{\circ}}\), and \(q=0^{{}^{\circ}}\)/s, the command signal of step velocity  
As can be seen from Figure 1, the velocity tracking under Ndprl proposed in this paper reaches a steady state at 2.5 s, while those for the other three reaching laws are as follows: 2.7 s for Tdprl, 3.5 s for Erl, and 6.5 s for Trl, respectively. Therefore, compared with the other three reaching laws, Ndprl proposed in this paper can ensure the velocity tracking reaches a steady state in a shorter time. In Figure 2, compared with the other three reaching laws, the sliding mode surface can slide to the zero equilibrium point in the shortest time. As can be seen from Figure 3, the control input exhibits chatting under Trl and Erl, but there is no chatting under Ndprl and Tdprl, because the expressions of Trl and Erl both contain \(-D_{1}sgn(S_{1})\), while the expressions of Ndprl and Tdprl do not contain \(-D_{1}sgn(S_{1})\).  
From the above analysis, it can be seen that the reaching law proposed in this paper can make the system converge to a stable equilibrium state in a shorter time without chattering.  
Figure 3: Controller output, \(\beta_{c}\), under reaching laws.  
Figure 2: Condition of s1 under reaching laws.
## 3 Applications of the CDS for the Fiber Optic Link
### Perceptor
#### 3.1.4 Selecting Maximum Probability and BER Calculation  
The block selecting maximum probability in perceptor (see Figure 7) is equivalent the entropic-state part of the preceptor in typical CDS (see Figure 5). The CDS can save the posterior probability \(P(X_{n}^{k}\mid Y_{n}^{k})\) in the perceptor library after one-time calculation and it is not necessary to calculate Equation (1) and Equation (2) for each symbol. The symbol that has the maximum probability to be transmitted is selected using  
\[\overline{X}_{n}^{k}=\underset{t=1,2,\dots,M}{\text{argmax}}\{P(X_{n}^{k}=X_{ l}\mid\hat{Y}_{n}^{k})\} \tag{3}\]  
where, \(t=1\), \(2\), \(\dots\), \(M\), and \(M\) is the constellation size. For example, for QAM-16, \(M\) is 16, and \(P(X_{n}^{k}=X_{l}\mid\hat{Y}_{n}^{k})\) represents the probability that the transmitted symbol \(X_{n}^{k}\) is \(X_{l}\) which is one of the values from the symbol alphabet \(\{\pm 3\pm 3j,\ \pm 1\pm 1j,\ \pm 1\pm 3j,\ \pm 3\pm 1j\}\) for the given \(\hat{Y}_{n}^{k}\). The symbol \(\overline{X}_{n}^{k}\) that has the highest chance to be transmitted for the given \(\hat{Y}_{n}^{k}\) is selected. Comparing \(\overline{X}_{n}^{k}\) and \(\hat{X}_{n}^{k}\), the BER is computed by error counting and the BER is sent to executive. The extrinsic information transfer (EXIT) chart is used to find the BER before FEC [35]. As top-bottom attention, this sub-system also sends the BER to the other subsystems in the perceptor. If the BER exceeds the FEC threshold, the FEC output will not converge and the post-FEC BER will be high and, hence, the system would infer that the pre-FEC BER has exceeded the FEC threshold. Therefore, the executive will take some actions such as decreasing the data rate. In addition, the local cycles in the preceptor will examine a different type of model for prediction and try to decrease the BER as much as possible.
# Advances in Engineering and Application of Optogenetic Indicators for Neuroscience
## 2 Calcium Indicators
Very recently, [PERSON] et al. suggested an alternative design of GECIs, which implicates the insertion of a calcium-binding domain into a fluorescent protein (Figure 1b). Implementing this design resulted in the generation of a new family of GECIs, named after the progenitor NTnC [38], exhibiting a set of unique features (Table 1). In comparison to GECIs with the classical GCaMP design,the NTnC-like family is characterized by a smaller molecular size, lower calcium-binding capacity, higher tolerance to fusion partners, and a non-mammalian origin of calcium-binding domains. The smaller molecular size is beneficial for packaging efficiency into viral particles and perhaps ensures better folding and targeting to subcellular compartments, such as mitochondria and the endoplasmic reticulum [39]. The lower calcium-binding capacity of the NTnC family (two or one calcium ion per molecule vs four ions per molecule for GCaMP family) reduces the deleterious confound of calcium buffering, which can corrupt the patterns of registered neuronal activity [30]. However, in spite of the different stoichiometry of calcium binding sites in GCaMP and NTnC-like indicators, they linearly respond to the increasing number of action potentials in the range determined by affinity of the respective indicator to calcium ions [38; 39]. Unlike the GCaMP-like GECIs, where different tags affect their dynamic range and affinity for calcium ions [40], the GFP-like N- and C-termini make the NTnC-like sensors tolerate fusions with other proteins by eliminating distortion of calcium-binding domain. Utilization of the truncated version of troponin C from muscles as a calcium-binding domain, which does not interact with endogenous proteins in mammalian cells, minimizes potential disturbances on neural physiology. Figure 1: Molecular design of genetically-encoded calcium and potassium indicators. (**a,b**) The structure of calcium indicators are shown as ribbon diagrams according to the crystal structures of NTnC (protein database (PDB) 5 MWC), dYTnC2 (unpublished), GAF-CaMP (combination of GAF-domain of PaBphP phytochrome PDB 3C2W and CaM/M13-peptide pair from GCaMP6m GECI PDB 3 WLD), NIR-GECO1 (combination of the PAS-GAF domain of PaBphP phytochrome PDB 3C2W and CaM/M13-peptide pair from GCaMP6m GECI PDB 3 WLD), LUCI-GECO1 (combination of NanoLuc luciferase PDB 5 IBO and GCaMP6m GECI PDB 3 WLD), FGCaMP (unpublished), and K-GECO (PDB 5 UKG) in calcium-bound state. (**c**) The organization of potassium indicators is shown schematically and according to the X-ray structures of Cerulean and mVenus fluorescent proteins (PDB 5 OXC and 1 MYW, respectively), and Kpp potassium binding protein from _E.coli_ (PDB 5 FIM). \begin{table}
## 3 Results and Discussion
### Excess Water Analysis  
As indicated in Table 2 and Figure 3, (P-Ru) is generally increasing, and dS/dt(gw) changes do not fully compensate for these trends, since change in this quantity is shown to be negligible for most hydrological units in the study region, and over the total area. Quantifying the cumulative amount of the \"excess water\" from 1901 to 2008, which we can attribute to increases in P outpacing increases in Ru, further illustrates that water storage components have not fully accounted for rising (P-Ru) trends. We estimated the total amount of water produced by the observed difference between precipitation and runoff (P-Ru) over the study area and over time. To minimize the effects of interannual variations in (P-Ru), we used a linear fit from Table 2 to estimate the baseline (P-Ru) in 1901 as 506 mm\(\cdot\)yr\({}^{-1}\). The total excess water accumulated above this baseline from 1901 to 2008 was then obtained by integrating (P-Ru) over the 108 years and was estimated to be 3.18 m.  
We consider which components of the water budget could account for this excess water associated with (P-Ru).
## 3 Results  
### Study Selection  
A total of 149 articles were generated in the database searches. Duplicates were eliminated, leaving 113 articles, which were checked by title and abstract according to the PICO model [22]. Sixteen studies were fully reviewed according to the eligibility criteria. Seven were excluded: two because they were retrospective studies, three because they did not present results of interest after reading the full text, and another two articles because they did not present an intervention of interest (Table 2). Finally, nine articles were included, as can be seen in Figure 1 (diagram).  
\begin{table}
\begin{tabular}{l l} \hline
**Articles Excluded after Title and Abstract Screening Phase** & **(n = 113)** \\ Review papers & 43 \\ Meta-analysis papers & 2 \\ Case reports & 2 \\ Pilot studies & 11 \\ Validation studies & 1 \\ Feasibility studies & 3 \\ Book chapters & 3 \\ Studies on other neurological conditions & 11 \\ Studies on balance disorders other than those caused by multiple sclerosis & 22 \\ Not intervention of interest & 14 \\ Language different than English & 1 \\
**Articles excluded after full-text screening phase** & **(n = 9)** \\ Full-text unavailability & 2 \\ Retrospective studies & 2 \\ Not intervention of interest & 2 \\ Not outcome of interest & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Reasons for article exclusion.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline
**Section/Theme** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON]** & **[PERSON] et al.** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON] et al.** & **[PERSON]** \\  & [18] & [17] & **et al.** [24] & [25] & **et al.** [26] & **et al.** [27] & **et al.** [28] & **et al.** [29] & **et al.** [30] \\ \hline
1 & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
2 & YES & YES & YES & YES & YES & YES & NO & YES & YES \\
3 & YES & - & NO & NO & YES & YES & - & YES & NO \\
4 & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
5 & NO & NO & NO & NO & NO & NO & NO & NO & YES \\
6 & NO & NO & NO & NO & YES & NO & NO & NO & NO \\
7 & YES & YES & NO & YES & YES & NO & YES & YES & YES \\
8 & YES & YES & YES & YES & YES & NO & YES & YES & NO \\
9 & YES & YES & NO & YES & - & UNCLEAR & - & YES & NO \\
10 & YES & YES & YES & YES & YES & YES & YES & YES & YES \\
11 & YES & YES & YES & YES & YES & YES & YES & YES & - \\ Total score & 8/10 & 7/10 & 5/10 & 7/10 & 8/10 & 5/10 & 6/10 & 8/10 & 5/10 \\ \hline \end{tabular}
\end{table}
Table 1: The quality of the RCT studies included in the review paper.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 3 Single Photon Counting Noise Modelling and Analysis
### Valley to Peak Ratio Method  
[PERSON] et al. proposed the Valley to Peak ratio Method (VPM) detailed in [10; 35]. This measures the peak height and the neighbouring valley height (or dip between photon peaks) in the PCH. The VPM has an upper and lower RN measurement limit. Although theoretically possible, it is difficult in practice to obtain peaks and valleys in PCHs in the region of 0.5 e\({}^{-}\) to \(\approx\)0.45 e\({}^{-}\) RN giving an upper limit to VPM. At the lower limit, below 0.15 e\({}^{-}\) RN, the VPM is inherently restricted as the valley has reached the \"floor\" of the PCH (zero counts in more than one adjacent bin), and a companion method is needed.
## 2 Methodology  
### The Framework of FLFR  
The framework of the method proposed in this paper is shown in Figure 1. Firstly, we use test cases to execute the program, which have been instrumented in advance. After executing the program under test by using the test cases, we will get the coverage information of each statement.  
We repeatedly utilize the information of software fault localization, specifically, it refers to the list of likelihood scores for each fault localization, which provides information on potential faults and is very important for generating new test cases. However, with the existing technology based on genetic algorithm, it is difficult to use the information of software fault localization. To this end, we improved the genetic algorithm by adding the position parameter of the suspected fault statement to its severity function. In this way, when generating new test cases, we can maximize the coverage of these suspected fault statements.  
The process of FLFR is as follows. Firstly, we input the test case set and the program under test. After executing the program under test, we can obtain the statement coverage information of the program under test during the execution process, which can be used to construct a coverage matrix. We use the software fault location method to obtain a list of suspected fault statements. Secondly, we need to judge whether iteration is required. The iteration condition is limited by the max number of iterations. If the current number of iterations is less than the max number of iterations, we need to use the improved genetic algorithm for generating new test cases. The new test cases will be added to the original test cases. Then, we need to do the whole process again. If the current number of iterations is greater than or equal to the max number of iterations, we need to terminate the iterative process. At last, we obtain the final list of suspected fault statements.  
We use iterative fault location because the result information of each fault location multiple times is useful. If we can increase the ranking of the reported failure statements every iteration, then we can make multiple iterations in this direction. Then the final positioning result will definitely be better than the first positioning result. The genetic algorithm is able to increase the number of times a statement is covered when execution succeeds and execution fails, based on our proposed fitness function. In this way, according to the findings in the motivation example, we will likely get good fault location results.
# Isolation and Properties of _Enterobacter_ sp. LX3
## 3 Results
### Plant Growth Promotion  
To examine potential role of strain LX3 in plant growth, isolate LX3 was evaluated with Chinese barley (_Hordeum vulgare_ L.) for its plant growth promoting capability. Inoculation of barley plants with strain LX3 increased barley seedling growth when compared to control (Table 2). After inoculation for 10 days, an increase in dry weight of whole-plant relative to uninoculated control was 20.8%. Similarly, an increase in root length by 18.2% and in plant height of 21.6% was recorded. Chlorophyll production was increased by 8.1% when compared with the control.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Fresh Weight** & **Root Weight** & **Plant Height** & **Root Length** & **Chlorophyll Content** \\  & **(g)** & **(g)** & **(cm)** & **(cm)** & **(mg/g)** \\ \hline Control & 0.34 \(\pm\) 0.013 & 0.20 \(\pm\) 0.007 & 10.83 \(\pm\) 0.17 & 12.83 \(\pm\) 0.21 & 0.74 \(\pm\) 0.011 \\ Strain LX3 & 0.41 \(\pm\) 0.012  & 0.22 \(\pm\) 0.005  & 15.16 \(\pm\) 0.17  \\ \hline \hline \end{tabular} Asterisk indicated significant differences between control and strain LX3 at \(p<0.05\) (*) and \(p<0.01\) (*).  
\end{table}
Table 2: Barley growth in the water with and without addition of culture fluid after 10 days.  
Figure 2: Phylogenetic tree constructed based on 16S rRNA gene sequence comparisons between isolate LX3 and some close relatives. A percentage of 100 replications showed on branching points represented bootstrap value. Bar length indicates 1% sequence divergence.