esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Improved Network Module
#### 2.2.3 Domain Fusion Module (DFM)  
Features in the frequency domain and spatial domain capture different aspects and attributes of an image. It is necessary to enhance the information transfer between cross-domain feature maps and selectively fuse spatial and frequency features to improve the model's representation capability.  
Figure 4 illustrates the structure of the Domain Fusion Module (DFM). First, after performing dimensionality reduction using \(1\times 1\) convolutions in both the spatial and frequency domains, the corresponding pixel vectors \(\vec{v}_{s}\) and \(\vec{v}_{f}\) are subjected to a dot product operation. The result is then upscaled to the input dimension, and a Sigmoid activation is applied to generate the weight information for the corresponding pixels:  
\[\sigma(i,j)=\text{Sig}\Big{(}\text{Upsample}\Big{(}\vec{v}_{s}(i,j)\cdot\vec{v }_{f}(i,j),C_{\text{in}}\Big{)}\Big{)} \tag{5}\]  
Here, \(C_{\text{in}}\) represents the input dimension, Sig(0) represents the Sigmoid activation function, and \(\sigma(i,j)\) denotes the similarity between the corresponding pixels in the spatial and frequency domains. If \(\sigma(i,j)\) is high, the spatial domain feature weight becomes larger. The feature selection is performed using \(\sigma(i,j)\), and the final fused feature is generated as follows:  
\[Out_{DFM}=\sigma\vec{v}_{s}+(1-\sigma)\vec{v}_{f}. \tag{6}\]  
Figure 4: Domain Fusion Module.
# The Removal of Crystal Violet from Textile Wastewater Using Palm Kernel Shell-Derived Biochar
## 3 Results and Discussion
### Effects of the pH of the Solution  
The adsorption efficiency under different solution pH was investigated because variations in pH affect the degree of ionization of the adsorptive molecule and the BC-PKS surface properties [92]. Thus, adsorption of CV onto BC-PKS at 25 \({}^{\circ}\)C with initial dye concentration of 400 mg/L and a BC-PKS dose of 0.5 g was examined by adjusting the solution pH from 2 to 10. The adsorption of CV on BC-PKS increased from 12.4 to 16.4 mg/g when the pH increased from 2 to 4 (Figure 3). Additionally, the percentage removal of CV on BC-PKS increased from 51.8% at pH 2.0 to 68.7% at pH 6.0. CV adsorption on BC-PKS showed a rapid increase in the adsorption capacity and high percentage removal from pH 4 to pH 6, primarily due to the change of surface charge of BC-PKS. The zero point charge of BC-PKS was reported to be 5.50 [93] and 3.94 [94]. As the pH of the CV dye solution increases, the surface of BC-PKS become negatively charged, enhancing the adsorption of the positively charged cationic dye through electrostatic attraction [92; 95]. Similar observations were reported in the use of rice husk for the removal of basic dyes from wastewater [96].
## 3 Method
### Data Pre-Processing  
Gaode POIs include real estate, corporate enterprises (companies, factories), shopping, transportation facilities (coach stations, wharf ports, subway stations, etc.), education and training, finance, hotels, beauty salons, tourist attractions, gourmet food, automobile services, life services (logistics, photo studios, intermediaries, telecommunications business halls, etc.), culture and media (cultural palaces, art museums, news publishers, etc.), leisure and entertainment, medical care, sports and fitness (stadiums, fitness centres, etc.), and  
Figure 2: Process used to identify building functional types.  
government agencies. The first-level classification contains 22 categories, while the second-level classification contains 263. Some transportation facilities (parking lots, bus stations, etc.), tourist attractions, public toilets, can easily interfere with the functional identification of buildings. Data for these places have a low information content and unclear functional indicators, so were excluded from the POI dataset. In addition, duplicate POI points in the POI dataset were deleted to improve the data quality.  
Against the Standard for Classification of Land Use Status Quo (GB/T 21010-2017) [37], this paper classifies building function types into four categories: residential (R), office (O), commercial (C), and public services (S). The existing POI categories were reclassified into these four categories. A building containing more than one type of function is a mixed-function building. Mixed-function building types include mixed commercial and office (CO), mixed commercial and public service (CS), mixed office and public service (OS), mixed residential and public service (RS), mixed residential and commercial (RC), mixed residential and office (RO), and complex functional (F). The POIs corresponding to the functional categories of urban buildings are shown in Table 2.
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 3 Data and Methods
### Accuracy Assessment  
We performed an accuracy assessment with each reference dataset available, resulting in three assessment groups: comparison to our own reference data based on very high resolution satellite images, comparison with agricultural statistics at the province level and comparison with the AMS field measurements. For the first group, we used 25% of our reference _rice_ polygons and 50% of the reference _no_rice_ polygons. We extracted all OCSVM\({}_{2014}\) pixel values where the pixel centroid is located within one of the polygons and counted the number of correctly- and incorrectly-classified pixels for each class. From this, we derived a complete confusion matrix and the accuracy metrics overall accuracy, producer's and user's accuracy, precision, recall, f1-score, kappa-coefficient and the Matthews correlation coefficient [68; 69; 70; 71].  
In the second assessment group, we compared the paddy rice areas derived from our classification to the areas published in China's Statistical Yearbooks corresponding to the same year as our classification. For each province and year, we counted the number of pixels classified as rice and derived their area based on the exact spatial resolution of our MODIS pixels. The pixel size of MODIS is commonly referred to as 250 m, but the actual pixel size in our datasets is 231.66 m. China's Statistical Yearbooks do not contain a category for \"total paddy area\"; therefore, we compared the areas from our classifier to the reported _Sown Area_ with the largest extent to minimize errors arising from multi-cropping where more than one season of rice is planted on a paddy field. Additionally, we also performed the same analysis using provinces where only one rice season is reported or the largest reported season accounts for more than 95% of the _Total Sown Area_. Provinces where the reported _Total Sown Area_ is equal to the total area of rice paddies, to within 5%, are Beijing, Chongqing, Gansu, Guizhou, Hebei, Heilongjiang, Henan, Inner Mongolia, Jiangsu, Jilin, Liaoning, Ningxia, Qinghai, Shaanxi, Shandong, Shanghai, Shanxi, Sichuan, Tianjin, Tibet, Xinjiang and Yunnan [50; 51; 52]. These assessments where performed for the years 2010, 2005 and 2002 due to the seasonal data for the year 2014 not being published yet.  
In the last assessment group, we extracted the locations of the agricultural meteorological stations as point measurements on the grounds that each AMS was located inside a rice paddy. We then extracted the pixel value of OCSVM\({}_{2002}\) in which these points are located and calculated the accuracy.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.6 Cloud Computing  
Cloud computing presents a valuable opportunity for DTs to enhance their scalability and availability, assisting their real twins anytime and anywhere. By leveraging cloud computing infrastructures, computation and control tasks can be offloaded from the local environment to remote servers, enabling DTs to handle complex processes and ensure seamless availability efficiently. The scalability of DTs is significantly improved through cloud computing, as the computational resources can be dynamically allocated and scaled based on demand. This allows DTs to handle larger datasets, called Big Data sets (BD), perform sophisticated analytics, and execute resource-intensive tasks without local hardware limitations. As the DT model involves the creation and management of virtual replicas that closely mimic real-world objects or systems, it requires extensive data acquisition, storage, and processing capabilities for collecting and analyzing a massive volume of data from various sources, including sensors, IoT devices, and other data-generating components [7], facts that necessitate cloud computing. The elastic nature of cloud computing ensures that DTs can adapt to varying workloads and seamlessly accommodate fluctuations in computational requirements.  
Furthermore, cloud-based DTs offer the advantage of ubiquitous accessibility by using open-source communication platforms, such as OPC Unified Architecture (UA) communication protocol and frameworks, for data exchange from sensors to cloud applications used in DTs. Real twins can access and interact with their DTs from any location and at any time, provided an active internet connection is in place. This fosters a flexible and versatile environment where real twins can leverage the capabilities of their DTs regardless of their physical location.
# Action of Antimicrobial Peptides against Bacterial Biofilms
## 2 Antimicrobial Peptides
### Mechanism of Action against Biofilms
Human liver-derived antimicrobial peptide hepcidin 20 can reduce the mass of extracellular matrix and alter the architecture of biofilms of _S. epidermidis_ by targeting polysaccharide intercellular adhesin (PIA) [89]. Another peptide S4(1-16) M4 Ka, a derivative of S4, has been shown to act against immature _P. aeruginosa_ biofilms by disintegration and release of membrane lipids, detachment of bacteria and inhibition of biofilm formation [90]. The fish derived AMP piscidin-3 has nucleosidase activity and can destroy extracellular DNA of _P. aeruginosa_ by coordinating with Cu\({}^{2+}\) through its N-terminus [91]. (iv) inhibition of the alarmone system to avoid the bacterial stringent response  
Anti-biofilm peptides may act by targeting an almost universal stringent stress response in both Gram-positive and Gram-negative bacteria [92]. Many bacteria produce the signaling nucleotides guanosine 5\({}^{\prime}\)-diphosphate 3\({}^{\prime}\)-diphosphate (ppGpp) and (p)ppGpp, that can regulate the expression of a plethora of genes [93; 94] and are important in biofilm formation [95]. The AMPs 1018, DJK-5, and DJK-6 can block the synthesis and trigger degradation of (p)ppGpp in both Gram-positive and Gram-negative bacteria, and this can lead to reduction in biofilm formation which in turn increases susceptibility to AMPs [15]. Some other AMPs such as DJK-5 and 1018 can act on the stringent response in _P. aeruginosa_ by suppressing spoT promoter activity [96]. DJK-5 and DJK-6 can degrade (p)ppGpp on _P. aeruginosa_ biofilms to higher extent than 1018 [14]. (v) downregulation of genes responsible for biofilm formation and transportation of binding proteins  
Biofilm formation by staphylococci is an accumulative process which crucially depends upon the synthesis of polysaccharide intercellular adhesin molecule PIA encoded by icaADBC locus in staphylococci [97]. Human \(\beta\)-defensin 3 (hBD-3) can reduce the expression of icaA, icaD and icaR genes of _Staphylococcus epidermidis_ ATCC 35,984 thereby reducing biofilm formation [98]. AMPs can inhibit genes controlling the mobility of extrachromosomal elements and transport and binding proteins [99]. A peptide Nal-P-113, can inhibit _Porphylomonas_ gingivalis biofilm formation by down-regulating genes such as PG0282 and PG1663 which encode ABC transporter and ATP-binding protein [99]. ABC transporters have been involved in cell-to-surface and cell-to-cell interactions in biofilms formation [100; 101]. Figure 2 summarizes the targets sites of representative anti-biofilm AMPs. Figure 1: Anti-biofilm activity and mechanism of action of antimicrobial peptides (AMPs). AMPs effect mainly involve prevention of bacterial attachment and inhibition of biofilm formation or disruption of pre-formed biofilms. \(\frac{\star}{\star}\) activation \(\frac{\star}{\star}\) inhibition.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access
### FBE Variants
#### 3.2.7 BITR FBE  
[PERSON] and [PERSON] [35] also design backoff and idle time reduction FBE (BITR FBE), an improvement to E-FBE. In BITR FBE, if backoff is interrupted, then the node's silence time is reduced to COT only. On the other hand, when the backoff countdown is successful, then the node follows the standard FFP. This behavior should equalize channel access opportunities and reduce the problem of single-node domination. An example of BITR FBE operation is presented in Figure 8.
## 5 Simulations  
Simulation experiments were performed to demonstrate the effectiveness of the proposed algorithm with the adaptation law compared with ASMC in two-dimensional and three-dimensional space in three scenarios.  
The dynamic equation of each robot is selected as  
\[M_{i}\ddot{q}_{i}+C_{i}\dot{q}_{i}=\tau_{i}+\tau_{i}^{d} \tag{25}\]  
The definitions of the parameters are similar to Equation (1). The noise is considered to be contained in a compact set \(\sigma\mathbb{B}^{n}=\{x\in\mathbb{R}^{n}:\|x\|\leq\sigma\}\), and the measured states are \(\overline{q}_{i}=\overline{q}+0.02\mathbb{B}^{n}\) and \(\dot{\overline{q}}_{i}=\dot{\overline{q}}+0.01\mathbb{B}^{n}\). The control forces are assumed to saturate at \(\|\tau_{i}\|\leq 10N\). All parameters of controller are adjusted with a trial and error method to achieve the best performance.  
Consider five robots in two-dimensional space. The system parameters are assumed to be \(M_{i}=1\) and \(C_{i}=0\), where \(i=1,\ldots,5\). The parameters of the proposed controller and adaptation law are \(k_{pi}=30\), \(k_{di}=50\), \(k_{i}=1\), \(\gamma=diag(20,\ldots,20)\), \(\hat{\rho}_{i,0}=[0.1,0.1]^{T}\), and \(\mu_{i}=20\), where \(i=1,\ldots,5\). The mission parameters are \(R=5\), \(d_{i}=2\), \(l_{i}=2R\cos\!\left(\frac{\pi}{2}-\frac{\pi}{5}\right)\), \(\Lambda_{2}=\Lambda_{3}=0.8I\), and \(\varrho_{i}=0.1\), where \(i=1,\ldots,5\). The external disturbance parameters are \(\tau_{i}^{d}=0.5[\sin(0.5t),\sin(0.7t)]^{T}\), where \(i=1,\ldots,5\). The initial positions of the five robots are \(q_{1}(0)=\left[5,10\right]^{T}\), \(q_{2}(0)=\left[-5,5\right]^{T}\), \(q_{3}(0)=\left[-5,-5\right]^{T}\), \(q_{4}(0)=\left[5,-10\right]^{T}\), and \(q_{5}(0)=\left[5,0\right]^{T}\).  
In three-dimensional space, six robots are considered in the simulations. The corresponding parameters and initial positions are set as \(l_{i}=\sqrt{2}R\), \(\hat{\rho}_{i,0}=\left[0.1,0.1,0.1\right]^{T}\), \(\tau_{i}^{d}=0.5[\sin(0.5t),\sin(0.7t),cos(0.5t)]^{T}\), \(i=1,\ldots,6\), \(q_{1}(0)=\left[-10,1,0\right]^{T}\), \(q_{2}(0)=\left[-1,-10,0.3\right]^{T}\), \(q_{3}(0)=\left[10,0,1\right]^{T}\), \(q_{4}(0)=\left[0,0.5,10\right]^{T}\), \(q_{5}(0)=\left[0,10,0.3\right]^{T}\), and \(q_{6}(0)=\left[0,0,-10\right]^{T}\). The other parameters are similar to those in the two-dimensional case.  
For literature completeness, an ASMC scheme can be designed as \(\tau_{i}=\hat{\rho}_{i}+k_{ni}sign(s_{i})\) with the adaptive control law (10). The controller parameter of ASMC is given as \(k_{ni}=80\).  
In three-dimensional space, the six robots reach the vertices of the regular octahedron, while the target is the centroid, as in Figure 4. The edges of the regular octahedron are the distances between the adjacent robots. Thus, the task function and pseudoinverse of the Jacobian matrix can be rewritten as  
\[\begin{array}{l}\sigma_{21}=\left[\frac{1}{2}\!\left(p_{k_{1}}-p_{k_{2}} \right)^{T}\!\left(p_{k_{1}}-p_{k_{2}}\right),\frac{1}{2}\!\left(p_{k_{2}}-p_{ k_{3}}\right)^{T}\!\
## 4 Discussion  
### Improvement of the IBIS Model  
Our results revealed that soil erosion in O-IBIS is not realistic, as it largely underestimates soil water content (Figure 3). Also, the simulated ET, LAI, and NPP in O-IBIS are significantly lower than our observations (Figures 4 and 5). We tentatively attribute this deficiency to the original IBIS, as most of the soil hydrothermal regime improvements and the soil erosion process of arid regions in IBIS require more validation and improvement. Previous works have suggested that the reduction in soil fertility and the deterioration of  
Figure 6: Climate–related carbon flux from redundancy analysis (RDA); the colored dots are the different vegetation sites. The principal variables are net primary production (NPP), net ecosystem exchange (NEE), and soil respiration (Re). The climate variables are the highest temperature (tmax), average temperature (tm), minimum temperature (tmin), precipitation (prec), wind speed (windm), cloud amount (cloud), relative moisture (em), pressure (pm), soil temperature (ST), and soil water content (SW).  
Figure 7: Pie chart of total explained variation of environmental factors in the simulated carbon flux.  
soil structure under the influence of long-term hydraulic erosion structural deterioration will further affect the growth and development of plants. In our study, we not only added the soil erosion process to the IBIS model, but we also recalibrated the physiological parameters according to the actual climate conditions in the study area. These modifications resulted in better fits with the observed data and reduced model uncertainties. We suggest further collection of such detailed ecological factor data at multiple locations to improve the arid vegetation modeling and reduce errors due to multi-site heterogeneity.  
Further development always requires field-measured data for calibration. The LP is dominated by the C3 plants. The physiological parameters of different vegetation types differ greatly between the C3 plants. The RU-IBIS model, with its more complete soil erosion process, may provide better opportunities for parameter calibration in arid regions.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 2 Related Work
### Deep Learning Models
#### 2.3.3 Vae  
The VAE is a generative model based on the concept of Bayesian variational inference theory [40]. The core concept involves the use of neural networks to model two complex conditional probability density functions to sample from the distribution \(P(z)\), obtain the latent variable \(z\), and generate data through \(P_{\theta}(x|z)\).  
The VAE consists of an inference network and generating network. The inference network learns to map the independent and identically distributed variables \(X=\left\{x^{(i)}\right\}_{i=1}^{N}\) to the latent variable \(Z=\left\{z^{(i)}\right\}_{i=1}^{M}\) following the distribution \(\mathcal{N}(0,\mathbf{I})\) in the latent space, and can be regarded as an encoder. The generating network learns to use \(Z=\left\{z^{(i)}\right\}_{i=1}^{M}\) to sample the reconstructed original input data from the learned distribution. The difference between the VAE and traditional AE is that the output of the encoder and decoder in the VAE is a distribution or a parameter of the distribution, rather than a deterministic encoding.  
Specifically, the generated data samples have the following probability density:  
\[P(x)=\int P(x,z|\theta)=\int P(x|z,\theta)P(z)dz, \tag{5}\]  
where \(P(x|z,\theta)\) is the conditional probability density function for observation of the spatial data sample when \(z\) is known. The probability of generating x from some \(z\) can be maximized by optimizing the parameter \(\theta\), where \(z\) is generated from \(P(z)\). Then, the VAE learns to optimize the decoder and encoder parameters \(\theta\) and \(\phi\), respectively, by maximizing the evidence lower bound as follows:  
\[\begin{array}{c}\underset{\theta,\phi}{max}ELBO(Q,x|\theta,\phi)=\underset{ \theta,\phi}{\max}E_{z\sim Q(z|\phi)}\left[\log\frac{P(x|z,\theta)P(z|\theta)} {Q(z|\phi)}\right]\\ =\underset{\theta,\phi}{max}E_{z\sim Q(z|x,\phi)}\left[\log P(x|z,\theta) \right]-D_{KL}\left[Q(z|x,\phi)||P(z|\theta).\right.\end{array} \tag{6}\]  
For sample \(x\), the expectation \(E_{z\sim Q}\left[\log P(x|z,\theta)\right]\) can be computed from samples taken from \(Q(z|x,\phi)\) as follows:  
\[E_{z\sim Q}\left[\log P_{\theta}(x|z)\right]\approx\frac{1}{L}\sum_{i=1}^{L} \log P_{\theta}(x|z^{(l)}). \tag{7}\]
## 2 Experimental Investigation
### Test Program  
The test program consisted of pull-out tests on SCGC specimens reinforced with BFRP and steel bars. The test variables were the bar embedment lengths (5 d\({}_{\text{b}}\), 10 d\({}_{\text{b}}\), 15 d\({}_{\text{b}}\)) and bar diameters (6 mm, 10 mm) of the basalt FRP bars, aiming to assess their effects on the bond behaviour with SCGC. Thus, three pull-out specimens were cast for each configuration, along with the specimens made of SCGC with steel bars for comparison purposes. The reinforcement bars were of 400 mm length and the SCGC cubes were of 150 mm \(\times\) 150 mm \(\times\) 150 mm size. A total of 21 specimens were designed for the experimental investigation; the parameters were selected in such a way that the desired failure would be of pull-out type, ensuring accurate depiction of the bond stress distribution and bond strength development.
# Analysis of Measured Temperature Field of Unpaved Steel Box Girder
## 2 Experimental Work  
The track beam structure mostly consists of slender members running in the longitudinal direction. Studies have shown that the temperature distribution along the longitudinal direction of the bridge can be assumed to be uniform (that is, the heat flow along the longitudinal direction of the bridge can be ignored). Therefore, the unit-length track beam segments of typical cross-sections are studied here. Figure 2a shows the test steel beam, while Figure 2b shows the specimen section's geometric dimension diagram and the sensors' positions. As shown in the figure, the test piece was welded with Q345 steel with a thickness of 6 mm, and the surface was painted gray. The sensor is a PT100 platinum resistance temperature sensor (accuracy \(\pm\)0.15 \({}^{\circ}\)C, temperature measurement range \(-\)60-200 \({}^{\circ}\)C), and the acquisition module is the JY-DAM-PTX series temperature acquisition module (temperature measurement accuracy \(\pm\)0.1 \({}^{\circ}\)C, acquisition frequency 10 Hz). The orientation and surface orientation of the track beam structure have a significant impact on the temperature distribution of the web, and the distribution of the maximum temperature difference along the thickness of the web [17]. For the most unfavorable situation, the length of the specimen was placed in an open area (latitude 37.2 \({}^{\circ}\)N) along the north-south direction to meet the requirements of the maximum sunlight exposure during the day, as shown in Figure 3. To account for the influence of the surface of the steel beam, the air convection heat exchange, and solar radiation reflection, the beam segment was placed on brick, and the bottom plate was not in contact with the ground.  
Figure 3: Orientation of the test beam segment.  
Figure 2: The experimental steel beam and the sensors. (**a**) The experimental steel beam; (**b**) the sensors.