esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiments and Methodology
### Time Series Resolution Strategy and Analysis Process  
In this study, 25 Sichuan CORS(SCCORS) stations were selected in Sichuan Province. Figure 1a shows the distribution of the stations and the baseline triangulation network constructed by the Delaunay method. The stations were observed from 2015 to 2021, with continuous observations for over three years. [PERSON] et al. verified that the velocities estimated by different geodetic software packages (GIPSY-OASIS, Bernese, and GAMIT) agree with each other [26]. The GAMIT 10.70 software was used for data processing. The baseline solution was used to obtain single-day solutions for the Sichuan reference station and its surrounding IGS stations. The coordinates and velocities of the 12 IGS stations under ITRF2014 are used as the reference. Figure 1b shows the distribution of the 12 IGS stations. The GLRED module is used to compute the single-day solutions, and the time series of both reference station and baseline are obtained from the adjustment result files.  
Figure 2 illustrates the flow chart of the spatial and temporal characterization of the reference frame. The different observation environments or the occurrence of equipment replacement at each reference station make the satellite data received by the reference station to be interrupted. There may also be random and severe coordinate errors in the coordinate time series, so the daily coordinate time series need to be pre-processed. In this study, the outliers in the coordinate time series are removed by the quartile method. For the discontinuous coordinate time series, the interpolation method is used to complete them. Next, spectral analysis is performed on the station and baseline time series to obtain the frequencies of periodic signals. Then, PCA is used to extract the CME, the velocity field is obtained with a time series model to summarize the spatial motion pattern in the study area and different fitting methods are used to quantify the covariance model among the reference stations to characterize the random influence of the regional reference frame.  
Figure 1: Distribution of the IGS and Sichuan CORS(SCCORS) stations. (**a**) Triangulation network of regional reference stations in Sichuan. (**b**) Distribution of the 12 IGS stations.
# Overexpression of _RrGGP2_ and _RrDHAR_ Increases Ascorbic Acid Content in Tomato
## 3 Results
### Analysis of Ascorbate Levels in Transgenic Lines
#### 3.2.2 Analysis of Ascorbate Levels in Transgenic Tomato Leaves  
The overexpression of _RrGGP2_ and _RrDHAR_ genes also significantly increased AsA and DHA contents of transgenic tomato leaves (Figure 3C,D). Notably, the AsA content in the leaves of the four tomato lines overexpressing _RrGGP2_ was significantly increased, ranging between 99.1% and 116.3%, while DHA content increased from 2.6% to 188.9%;  
Figure 2: Positive identification and overexpression verification of transgenic tomato lines. (**A**) confirmation of _RrGGP2_ by PCR analysis; (**B**) confirmation of _RrDHAR_ by PCR analysis; (**C**) relative expression of _RrGGP2_ in transgenic tomato plants; (**D**) relative expression of _RrDHAR_ in transgenic tomato plants. _RrGGP2_ transgenic lines are represented by C3, G4, G5, and G7, and _RrDHAR_ transgenic lines are represented by D1, D2, D3, and D6. M = marker; WT = wild-type plant; + = positive control (PCR amplification products of the GGP2 and DHAR gene of _R. rxburnglini_); bp—number of bases. The relative quantification of _RrGGP2_ and _RrDHAR_ expression was calculated using the comparative Ct (2\({}^{-\Delta\text{ACT}}\)) method; three replicate experiments were performed; error bars represent standard error, means \(\pm\) SE. The asterisks above the bar chart represent significant differences from WT (**_p_ < 0.01).  
however, only two lines (G4 and G7) of transgenic lines showed a significant increase (Figure 3C). In contrast, the AsA content in the leaves of the four overexpressing _RrDHAR_ tomato lines was significantly increased between 136.6% and 243.5%, and DHA content increased from 55.8% to 220.5% (Figure 3D). On average, the AsA and DHA levels in the leaves of tomato lines overexpressing _RrGGP2_ increased by 108.5% and 75.7%, respectively. Similarly, in the tomato plants overexpressing _RrDHAR_, both the AsA and DHA levels in the leaves increased by 183.9% and 127.3%, respectively (Figure 3C,D). Therefore, it is evident that the overexpression of _RrDHAR_ leads to a more pronounced increase in the AsA and DHA content in tomato leaves than _RrGGP2_.
## 1 Introduction
This total of 150 publications was considered sufficient to provide an informative comprehensive review of the field of recent (in the previous three years) AMP-associated biofilm research. After collating these 150 published titles, the publications were manually sorted into seven distinct categories based on a 'weighting' of publication titles (Figure 1). The highest weighting was reserved for research into the use of AMP combinations as this research requires collaboration between multiple scientific fields, including, for example, chemistry, materials science, clinical medicine, and microbiology. Investigations into AMP delivery efficiency are crucial for potentially overcoming the challenges associated with the inactivation of AMPs, including proteolytic degradation, salt sensitivity, and binding to host proteins. Strategies such as encapsulation, nanoparticle carriers, liposomes, and micelles ensure the successful transport of AMPs to specific sites within microbial cells, such as the inner membrane, where their antimicrobial activity can be exerted. Next, the genus/species of bacteria used in AMP research are important in defining the clinical relevance/importance of the AMP under investigation. Descriptions of research into AMPs (including novel AMPs) and biofilm formation are relatively common and therefore may not have as much scientific impact as the categories mentioned previously. The final two categories included review-type publications and miscellaneous articles that were not easily placed into the categories mentioned above. Finally, although alternative weightings may generate slightly different categorizations, the general description and conclusion of this comprehensive review would not be seriously affected if a few publications switched categories. Most importantly, this categorization process allows the different types of publications to be sorted into more easily describable narrative features, simplifying the narrative review for the reader. In all, 150 references included research into a total of approximately 150 different AMPs, with these AMPs being obtained from various natural and synthetic sources (Supplementary Table S1). Short descriptions and contexts for all of these publications are shown in Section 2 below. Figure 1: Hierarchy for organizing 150 ‘AMP-and–biofilm’-related publications (January 2020–September 2023) into seven categories. Top = highest weighting. * = One publication specifically mentioned _Staphylococcus aureus_ and _Pseudomonas aeruginosa_ in its title.
## 5 Discussion
### Spatial Distribution of Park Cultural Services and Factors Influencing Users' Sentiment Evaluation  
The spatial autocorrelation analysis (Z = 0.59) indicated no significant spatial clustering in the supply of cultural ecosystem services (CES) between the 12 major urban parks on the Macau Peninsula. This finding suggests that, although urban parks in Macau are geographically distributed relatively evenly, variations in cultural service quality are primarily determined by their functional positioning, landscape design, and service content rather than spatial location. Compared with the findings of [PERSON] and [PERSON] [25] regarding the distribution characteristics of cultural services in urban green spaces, cultural services provided by Macau's urban parks show a certain degree of homogeneity. This homogeneity is particularly evident in the limited provision of cultural education and heritage-related functions, leading to relatively weak individualized service characteristicsamong parks. Service homogeneity not only limits the potential of urban parks to meet diverse cultural demands but also weakens Macau's ability to express cultural diversity as a city renowned for its heritage. To address these limitations, multimedia interactive technologies could be introduced in the future. Examples include virtual tours, digital exhibitions, and immersive educational projects. Integrating these technologies within Macau's cultural heritage context would enable each park to develop a more distinctive cultural service theme.  
The sentiment analysis further revealed that esthetic appreciation and landscape experience were the primary factors influencing users' sentiment evaluations (\(\beta\) = 0.95, \(p\) < 0.01). Landscape design and high vegetation coverage in Macau's urban parks significantly enhanced users' visual satisfaction and emotional connections. This result is consistent with findings by [PERSON] [26] on how the esthetic value of green spaces influences user sentiment. However, user comments indicated that insufficient facility maintenance and overcrowding negatively affected sentiment experiences. Notably, more abstract CES dimensions, such as cultural heritage and educational services, showed relatively weak effects on sentiment evaluations [27]. This result reflected an insufficient explicit representation of these dimensions in Macau parks. Therefore, the introduction of virtual reality technologies and interactive cultural experience activities [23] is suggested to further enhance users' perceptions and emotional connections with cultural services.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 1 Introduction
### Overview of Prediction Method of Multiple Source Data

Table 1: Comparison of common prediction methods. of information and packet loss of single source data. At present, big data application technology are widely used in the traffic field, and many studies have been conducted in the field of freeway travel time prediction based on big data analytics. However, there are several deficiencies:  
* The method pays attention to machine learning algorithms and does not consider the characteristics of the traffic flow, resulting in uncoordinated and unsuitable correspondence between the data and the traffic flow. * As big data updates continuously, it provides conditions for traffic travel time prediction; however, some advantages and characteristics of these data are not captured, and a great deal of useful data are not being used and mined. * Some model parameter calibrations are too subjective, which largely depends on the researchers' experiences. * Some models are too specific examples and cannot be easily adapted to other situations. Therefore, in this study, we collected historical data from a freeway toll station. These data were categorized using the Semantic Technology (SVM) algorithm. In previous studies, [PERSON] [27] used the support vector regression method to predict time; [PERSON] [28] proposed one short-term prediction of travel time based on support vector. Using machine technology, [PERSON] [29] obtained a regression method for comparing long-term travel time prediction through intelligent data analysis. However, their analysis is merely based on machine learning algorithms and does not improve the transportation system. The SVM model is not as sensitive to dirty data as the Kalman filter, resulting in unstable calculation of the filter parameters, and is not as vulnerable to dimensional catastrophe as other machine learning algorithms. Therefore, this paper uses the SVM model based on historical data to predict the common traffic state. Also, we simplified the method used for model construction. The practicability of the prediction algorithm was enhanced to overcome assumptions and uncertainties in the existing traffic flow theory. Through semantic technology, meteorological data, traffic records, and other field data are related.
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 2 Related Works
The EU 5 GENESIS project [26] presents the design and implementation of an end-to-end experimental testbed for integrated satellite/terrestrial 5G services. The aim of the project is to test various vertical use cases such as maritime communications, IoT in rural areas, etc. The testbed builds upon five distributed experimental platforms with diverse capabilities, which makes their integration complex with respect to the prototype that we propose in this paper. Table 1 summarizes the related works while highlighting their strengths and limitations.
## 7 Implementations of Sigmoid Activation  
### Centralized Implementation  
In the centralized implementation of the sigmoid activation, all calculations are performed in one PE, which allows to minimize and optimize it efficiently. These optimizations are possible because of the features of the fixed-point number format and the applied approximation [36]. The piecewise linear approximation is described as:  
\[f(x)=ax+b \tag{1}\]  
where \(a\) and \(b\) are the constants related to the subrange in which \(x\) is located.  
One of the most difficult parts of the piecewise linear approximation is finding the range where the input value lies. Approximation nodes are the integers ranging from \(-5\) to \(5\) (Figure 12). The \(\Delta\) symbol denotes the smallest step for this number format, and it is equal to \(1/256\) for numbers with \(8\) fractional bits. Therefore, the PE must include \(11\) comparisons. At the same time, the fixed-point format puts the integer part of the number in high order bits. Our models use \(16\)-bit fixed-point numbers, and the \(8\) high order bits contain an integer part.  
Figure 11: Sigmoid activation.  
The analysis of these values shows that the four high order bits are the same for all positive and all negative values (within the approximation range):  
\[is\_neg=x_{15}\,\&\,x_{14}\,\&\,x_{13}\,\&\,x_{12} \tag{2}\]  
\[is\_pos=\overline{x_{15}}\,\&\,\overline{x_{14}}\,\&\,\overline{x_{13}}\,\&\, \overline{x_{12}} \tag{3}\]  
where \(x_{n}\) is the \(n\)-th bit of \(x\).  
The next four bits help to determine the specific range. For example, to check if the \(x\) value is in the range from \(-5\) (inclusive) to \(-4\) (excluding), the following formula can be used:  
\[is\_neg5\_to\_neg4=is\_neg\&\,x_{11}\,\&\,\overline{x_{10}}\,\&\,x_{9}\,\&\,x_{8} \tag{4}\]  
For all values above 5, use the following function:  
\[is\_pos5\_to\_inf=\overline{x_{15}}\,\&\,\overline{(is\_pos\,\&\,\overline{x _{11}})}\,\&\,\overline{(\overline{x_{10}}\,|\,(x_{10}\,\&\,\overline{x_{9}}\, \&\,\overline{x_{8}}))} \tag{5}\]  
As a result, the approximation coefficients can be found as:  
\[a=(is\_neg5\_to\_neg4\&\,a1)\,|\,\,(is\_neg4\_to\_neg3\&\,a2)\,|\,\,\cdots\, |\,\,(is\_pos4\_to\_pos5\&\,a10)\,|\,\,(is\_pos5\_to\_inf\&\,0) \tag{6}\]  
\[b=(is\_neg5\_to\_neg4\&\,b1)\,|\,\,(is\_neg4\_to\_neg3\&\,b2)\,|\,\cdots\, |\,\,(is\_pos4\_to\_pos5\&\,b10)\,|\,\,(is\_pos5\_to\_inf\&\,1) \tag{7}\]  
Thus, 11 comparisons of 16-bit numbers can be replaced by 11 comparisons of 5-bit numbers, which leads to the significant simplification of this implementation.  
The centralized implementation has two major disadvantages. Firstly, a PE with the proposed implementation of the sigmoid occupies a larger area on a chip. Secondly, the focus on the sigmoid function does not allow to reuse this implementation for another activations.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 2 Related Work
### IEEE 802.11-Based Solutions  
Wi-Fi based on (IEEE 802.11) [5] plays a vital role in industrial settings, supporting operations with high data rates and long-distance connectivity through high-gain antennas. The authors of [6] demonstrated this in their design by integrating PLCs, Input/output (I/O) modules, and sensors for efficient water level management, indicating how Wi-Fi facilitates precise control and monitoring, supporting real-time data exchange.
# An Improved Algorithm for Low-Light Image Enhancement Based on RetinexNet
## 5 Conclusions  
In this paper, a composite model for image enhancement with low illumination is proposed that avoids color distortion, preserves image details as much as possible, considers the effect of image noise, and makes full use of the rich self-similar structure in the image to denoise the image. Experimental results show that compared with HE, Retinex, MSR, MSRCP, LIME, and RetinexNet algorithms, the peak signal-to-noise ratio, structure similarity, and universal quality index of images processed by the proposed algorithm are improved by 2.7%, 23.4%, and 15.7%, on average. Through many experiments, it has been proven that the method in this paper has good improvement in both the objective evaluation index and the subjective visual effect.  
Conceptualization, H.T. ([PERSON]) and C.X.; methodology, H.T. ([PERSON]); software, H.T. ([PERSON]); validation, H.T. ([PERSON]); formal analysis, H.Z.; investigation, H.T. ([PERSON]); resources, C.X.; data curation, C.X.; writing--original draft preparation, H.T.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **MOS (Figure 3)** & **MOS (Figure 4)** \\ \hline HE & 2.84 & **3.84** \\ Retinex [10] & 2.36 & 2.88 \\ MSR [15] & 2.38 & 2.92 \\ MSRCP [17] & 3.62 & 3.44 \\ LIME [14] & 3.98 & 3.76 \\ RetinexNet [21] & 3.7 & 3.24 \\ Ours & **4.12** & **3.84** \\ \hline \hline \end{tabular}
\end{table}
Table 2: MOS score table in Figures 3 and 4. The bold numbers indicate the best performance.  
Figure 4: Contrast of outdoor image enhancement effect. (**a**) Original image; (**b**) HE; (**c**) Retinex; (**d**) MSR; (**e**) MSRCP; (**f**) LIME; (**g**) RetinexNet; (**h**) Ours.  
([PERSON]); writing--review and editing, C.X.; visualization, H.T. ([PERSON]); supervision, C.X.; project administration, C.X.; funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant 61901221, in part by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX21_0872, and in part by the National Key Research and Development Program of China under Grant 2019 YFD1100404.  
Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available at [[https://arxiv.org/abs/1808.04560](https://arxiv.org/abs/1808.04560)]([https://arxiv.org/abs/1808.04560](https://arxiv.org/abs/1808.04560)), accessed on 10 August 2018.  
The authors declare no conflict of interest.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
###### Abstract
The most significant challenge for them is what spectrum they are to utilize, considering D2D communications usually do not have particular bands assigned to them; instead, they complement the networks which use either cellular or unlicensed spectra [6]. * Cognitive Radio (CR) Networks. They increase the utilization of the spectrum allocated to other previously deployed wireless systems by accessing it opportunistically (i.e., only at locations, periods of time, and frequency channels in which no transmissions from the incumbent users, are present). CR-enabled UEs and APs (cognitive APs or CAPs) can determine the spectrum availability and perform DSA independently from, or in conjuncture with, the primary network. Each of these types of networks can be used in conjuncture with each other, and as technologies which are used as a basis for the implementation of a specific network, or its integration with already deployed networks [6]. Then, as they increase in the number of connected devices and complexity, it becomes more difficult to achieve their primary communication objectives, namely (1) interference reduction; (2) spectrum utilization; and (3) spectral efficiency. For a long time, graph theory has been used to define, as well as to relax, optimization problems in telecommunications. It is also an appropriate instrument for solving these tasks, because wireless nodes and their parameters (type, frequency resources, location, transmission power and interference limits, and computational capabilities) can apply be represented as a graph. Some general communication problems that have utilized graph methods include (but are not limited to) RA and transmission power control in cellular networks, beamforming, link scheduling, traffic prediction, channel estimation, localization, cooperation and information transfer between vehicles for autonomous driving, compression of point clouds for transfer of images, UAV trajectory control for throughput maximization, detection of unauthorized traffic and of its sources, user association (UA), cooperative caching of data between D2D wireless nodes, wired networks configuration and communication delay analysis, and encrypted traffic classification [9; 10; 11; 12].
## 5 Security Analysis of Strike
### Cryptanalysis Attacks
#### 5.3.3 Ciphertext-Only Attack  
In this kind of attack, an attacker attempts to gain access to the ciphertext in order to retrieve the plaintext after guessing the secret key. Once the attacker succeeds, all the ciphertext can be decrypted using the key. However, our stream cipher generates a new lightning strike in each iteration. Hence, two identical ciphertext segments are mapped to two completely different lightning strikes. As a result, our Strike cipher is found to be secure against this kind of attack.
## 3 Design of CP-ABE
### Overview of Ciphertext Policy Attribute-Based Encryption  
In this work, we enhanced the functionality of the CP-ABE scheme to embrace the fully hidden access policy. Therefore, four significant modules of CP-ABE were involved in constructing our hiding reinforcement approach.  
System Setup (\(1^{\lambda}\)): It takes security parameter, \(1^{\lambda}\) as an input, and produces a public parameter key \(PK\), also a master key \(MSK\) as the outputs. They are used as input for the key generation algorithm.  
Key Generation (\(PK,\ MSK,\ S\)): Attribute Authority is the main component in CP-ABE that we utilized to execute the key generation algorithm. The Attribute Authority used inputs from the system setup (i.e., public parameters \(PK\), master key \(MSK\), and users' attributes \(S\)) for generating a secret key \(SK\). Later, the secret key is employed by the data user to decipher the encrypted data.  
Encryption (\(PK,\ M,\ A\)): In this module, it captures the public parameters \(PK\), a message \(M\) and an access structure (\(A,\ \rho,\ \mathcal{T}\)) as the inputs. The encryption algorithm then produced the ciphertext \(CT\). The data owner sends out the ciphertext along with the hashed value in the Cloud environment.  
Decryption (\(PK,CT,SK\)): The decryption module took the public parameters \(PK\), a secret key \(SK\) associated with the attributes set (\(A,\ \rho\)), and a ciphertext \(CT\) as the input. All these three inputs are used to decrypt the ciphertext \(CT\) and produce message \(M\).