esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Observed LULC and Transitions from 1984 to 2018  
Post-classification change detection means image classification is performed for two periods and the results are compared. Table 5 displays quantitative LULC dynamics that were observed between 1984 and 2018 as the city was expanding. Built-up areas increased with the decrease in vegetation and cropland areas from 1984 to 2018 in Harare. The highest  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & \multicolumn{2}{c}{**1984**} & \multicolumn{2}{c}{**2018**} \\ \hline  & **Producer’s** & **User’s** & **Producer’s** & **User’s** \\ \hline CBD Industrial & 89.51 & 97.10 & 83.77 & 88.89 \\ Croplands & 68.30 & 82.30 & 84.63 & 92.17 \\ High density & 92.41 & 86.44 & 84.81 & 88.58 \\ Low-Medium density & 90.72 & 86.10 & 94.59 & 83.16 \\ Vegetation & 92.96 & 86.78 & 74.10 & 88.78 \\ Water & 69.56 & 93.19 & 82.65 & 88.54 \\ \hline Overall Accuracy & 87.03 & & 86.67 & \\ Kappa Coefficient & 0.8220 & & 0.8236 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Land use/cover classification accuracies (%) for 1984 and 2018.  
Figure 5: LULC maps obtained from the SVM algorithm: (**a**) 1984 and (**b**) 2018.  
increase is in high density residential areas, which increased in coverage from 27,650 ha in 1984 to 49,741 ha in 2018, while Low-Medium density residential areas increased from 23,733 ha to 24,246 ha. Vegetation and croplands decreased from 24,470 ha and 11,120 ha to 8230 ha and 2631 ha, respectively. LULC maps obtained after SVM classification and post-classification provided quantitative data to show that cropland area has dropped from 1984 to 2018. Most of the cropland area has been lost to HDR. This is a result of urban sprawl due to normal urban growth and illegal settlements. The results agree with growth patterns obtained from similar studies by [PERSON] et al. [25] and [PERSON] et al. [24] in Harare.
## 3 System Architecture  
The proposed architecture of a HEMS consists of an energy management controller (EMC), smart meter, display system, smart scheduler unit (SMSU), PV system, ESS, power electronic converter, and a set of appliances. A power electronic converter serves the purpose of a rectifier and inverter. The smart appliances, PV system, and ESS are connected to the EMC and SMSU through a wireless system in this research. The smart meter is connected to the SMSU and receives RTP and CPP tariff signals through an advanced metering infrastructure (AMI). Based on the received tariff signals and the operating preferences set by the user regarding appliance operations, the SMSU employs heuristic optimization algorithms to find the optimal scheduling of appliances that results in reduction of energy cost as well as the peak demand. These scheduling results are transmitted to an EMC, which schedules the home appliances without compromising the consumer's comfort. The architecture of the proposed model is shown in Figure 2. It is assumed that the H2G energy exchange through a smart meter is allowed. These types of tariffs are normally termed as net-metering or net-billing.  
Figure 2: Proposed system model. AMI: advanced metering infrastructure, EMC: energy management controller, ESS: energy storage system, PV: photovoltaic, SMSU: smart scheduler unit.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 2 Databases and Model Evaluation Metrics
### Performance Evaluation Methodology  
Among the performance evaluation methods of offline HCCR, the model's performance is mainly measured based on the label accuracy of the model on the test set. In the testing process, the accuracy of the samples is calculated as shown in Equation (1), where \(N_{\text{C}}\) represents the number of correctly classified samples, and \(N_{T}\) represents the sum of all the numbers to be classified:  
\[\text{Accuracy }=N_{\text{C}}/N_{T} \tag{1}\]  
In the HCTR experiment, the recognition results of text lines can be evaluated by three metrics: Accurate Rate (\(AR\)), Correct Rate (\(CR\)), and Character Error Rate (CER). The three metrics are defined as:  
\[AR=(N_{t}-N_{d}-N_{s}-N_{i})/N_{t} \tag{2}\]  
\[CR=(N_{t}-N_{d}-N_{s})/N_{t} \tag{3}\]  
\[\text{CER }=(N_{s}+N_{i}+N_{d})/N_{t} \tag{4}\]  
where \(N_{t}\) is the total number of character samples in the evaluation set. \(N_{s}\), \(N_{i}\), and \(N_{d}\) denote the total number of substitution, insertion, and deletion errors, respectively.
# CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data
## 5 Case Study and Experimental Results
### Performance Evaluation
#### 5.3.3 Model Switching Overhead  
After the newly generated model passed the offline and online validation, the _orchestrator_ will replace the old model which is serves as its corresponding _modelet_. Figure 6 presents one _modelet_ container load changes and model prediction delay during model switching (one point/3 s). From the proposed master-slave design, when the master port is restarted, the newly generated model is loaded, causing the CPU to rise quickly and form the first peak. Due to the existence of the hot backup model, two models are stored in the memory of the _modelet_. With the release of the memory of the master port model, the _modelet_ memory usage drops rapidly and generates a trough. In order to ensure that the master port serving the new model can be predicted normally, we set the interval, restarting the hot backup model to 4s. The reason why the CPU generates the second peak and the corresponding memory trough is due to the restarting of the slave port and the updating and loading of the hot backup model. Although the occupancy rate of the CPU increase, the isolation feature allows the impacts to be limited to within only one core during the entire switching process. The model used for experiment is about 176 KB, which caused Disk to generate 158.6 KB/s extra overhead when the model was switched. However, since the newly generated model already has a cache in memory, the model update of the salve _modelet_ does not need to read it again. That is why the disk i/o displays a single peak.  
Furthermore, as seen from Figure 6, the prediction delay only observes a slight increase, about 24.2 ms in the whole switching process. Based on master-slave design, the model upgrade process has little effect on the prediction delay, which is the overhead of the service status checking. This service query is rather fast. This design allows for the smoothness and high efficiency of the model switching process.
## 3 _Apergillus_ Is a Microbial Chassis for Natural Product Synthesis
### A. oryzae Is a Chassis for Natural Product Production
#### 3.1.1 Transformation Methods in _A. oryzae_  
Currently, the heterologous expression of biosynthetic genes encoding natural products in _A. oryzae_ primarily relies on the PEG/CaCl2-mediated protoplast transformation method [61], and the process is shown in Figure 1. Using molecular biology, the exogenous genes were loaded onto the vectors of nutrient-deficient screening markers and resistance markers, and the target gene vectors were transformed into protoplasts with the help of the high-sugar and -salt osmotic pressure of the transformation reagents. The target gene can be inserted into the chromosome of _A. oryzae_ after homologous recombination between the vector and the host genome [62]. Transformed single genes or gene clusters can be inserted individually or as a whole. For example, [PERSON] et al. [58] successfully cloned the entire gene clusters (40 kb for monacolin K biosynthesis in _Monascus ruber_ and 12 kb forterequinone A biosynthesis in _A. nidulans_) into vectors via cosmid library construction, achieving heterologous expression of both gene clusters in the _A. oryzae_ host. Single-gene insertion is a process in which the 5\({}^{\prime}\) and 3\({}^{\prime}\) ends of the target gene are added to an amylase (_amyB_) [63] or amylase-enhanced [64] promoter and terminator, respectively, and then induced by starch or dextrin in the culture medium, which enables the efficient expression of the target gene [21; 65].  
Although protoplast transformation enables efficient selection of transformants with successful target gene integration and facilitates isolation of high-expression strains, trans-generational instability in fungal transformants may result in either physical loss of the transgene or its transcriptional silencing [66]. Therefore, optimizing the _A. oryzae_ expression system is significant for achieving high-efficiency transformation. For instance, [PERSON] et al. and [PERSON] et al. successively achieved targeted gene knock-in in _A. oryzae_ using optimized CRISPR-Cas9 genome editing systems [67; 68]. This approach enables precise genomic integration of foreign genes with high efficiency, significantly reducing screening time. Notably, the selectable marker can be automatically excised after integration, eliminating marker-associated limitations and facilitating sequential multigene insertions [67]. Recently, [PERSON] et al. improved the transformation and editing efficiency of the CRISPR/Cas9 system by optimizing the preparation conditions of protoplasts in _A. oryzae_ [69].  
Figure 1: The process of protoplast-mediated transformation in _A. oryzae_. (Figure 1 was created with BioRender.com).
## 5 The Controller Based on Fuzzy Logic
### Select the Input, Output Signals and Physical Value Domains  
#### 5.1.1 Select Input and Output Signals  
Inputs are joint position errors \(\mathrm{e}(\mathrm{t})\) and joint velocity errors \(\dot{\mathrm{e}}(\mathrm{t})\) over time of the robot system. Outputs are the adjustment amounts of the joint driving forces/torques \(\mathrm{u}(\mathrm{t})\) at each machining instant to ensure that the errors \(\mathrm{e}(\mathrm{t})\), \(\dot{\mathrm{e}}(\mathrm{t})\) are as small as the requirement of machining accuracy.  
Figure 3: Scheme of the inverse dynamics method combined with the law of derivative proportional control (Inverse Dynamics + PD) controller in joint space  
u is the vector of the adjustment amounts of joint forces/torques of the robot and platform, which is calculated according to Formula (34),  
\[\text{u}=\tau-\tau^{\text{d}}=[\text{u}_{1},\text{u}_{2},\dots,\text{u}_{8}]^{ \text{T}} \tag{34}\]  
where \(\tau^{\text{d}}\) is the vector of desired driving forces/torques at the joints in accordance with theoretical computations and requirements of machining engineering, then the joint position error \(\text{e}(\text{t})\to 0\) and the joint velocity error \(\dot{\text{e}}(\text{t})\to 0\).  
\(\tau\) is the vector of the actual forces/torques at the joints in accordance with the requirements of machining engineering so that the joint position errors \(\text{e}(\text{t})\) and the joint velocity errors \(\dot{\text{e}}(\text{t})\) approach the zero asymptotic point.  
\(\text{u}_{1},\,\text{u}_{2},\,\dots,\,\text{u}_{8}\) are the adjustment amounts of forces/torques at each joint 1, 2, \(\dots,\,6\), B and D of the robot system, respectively.  
At each machining instant corresponding to a pair of input signal values, which are the joint position error \(\text{e}(\text{t})\) and the joint velocity error \(\dot{\text{e}}(\text{t})\), the controller calculates the adjustment amount of the joint forces/torques u(t), and gives a signal u(t) to adjust the errors \(\text{e}(\text{t})\), \(\dot{\text{e}}(\text{t})\) approaching and converging at the zero asymptotic point:
# A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
###### Abstract  
Analyzing people's opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users' real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users' real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users' online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users' real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users' real mood states and could be applied in further psychological measurements and predictions.  
Keywords:mood states; cyber psychometrics; Profile of Mood State (POMS); sentiment analysis; microblog +
Footnote †: journal: Future Internet
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Iterative Training Algorithm
#### 3.5.2 Dynamic Loss Adjustment  
To balance the independent optimization of each model with the consistency of cross-model predictions in collaborative learning, we devise a dynamic loss adjustment strategy founded on IoU. Building on top of the supervision and transfer losses, we incorporate a dynamic weighting factor \(\omega\) (refer to Equation (21)) to regulate the influence of transfer loss on the total loss. The definition of the dynamic factor \(\omega\) is as follows:  
\[\omega=\begin{cases}1-\text{IoU}(A,B),&\text{if IoU}\leq\lambda,\\ \gamma\cdot\left(1-\text{IoU}(A,B)\right),&\text{if IoU}>\lambda\end{cases} \tag{21}\]Here, \(\gamma\in(0,1)\) is a parameter that governs the attenuation of the transfer loss under high consistency conditions. The total loss function after dynamic adjustment can be expressed as follows:  
\[\text{LOSS}=L_{\text{sup}}^{A}+L_{\text{sup}}^{B}+\alpha\cdot\omega\cdot L_{ \text{trans}}^{A}+\beta\cdot\omega\cdot L_{\text{trans}}^{B} \tag{22}\]  
In this expression, \(\alpha\) and \(\beta\) are the intrinsic weights for the transfer loss, and the factor \(\omega\) modulates the strength of cross-model transfer in accordance with IoU. Under conditions of strong predictive alignment, the framework grants each model greater latitude for autonomous optimization, whereas lower alignment triggers a heavier emphasis on cross-model knowledge exchange, thereby bolstering model concordance. Leveraging this strategy, the MMCL framework deftly orchestrates the magnitudes of both supervision and transfer losses, securing an optimal trade-off between inter-model knowledge sharing and standalone optimization.
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Rain Attenuation Model  
Rain introduces significant attenuation, especially in high-frequency bands (Ka and V bands), often used in satellite communications. Rain attenuation \(A_{\text{rain}}\) is modelled as  
\[A_{\text{rain}}(dB)=\gamma_{r}\cdot d \tag{11}\]  
where \(\gamma_{r}\) is the specific attenuation coefficient in \(\mathrm{dB/km}\), dependent on rainfall intensity \(R\) (mm/h). The coefficient \(\gamma_{r}\) is calculated using the ITU-R P.838 model:\[\gamma_{r}=kR^{\alpha} \tag{12}\]  
where \(k\) and \(\alpha\) are empirically derived parameters, varying with frequency and polarization. Thus, the FSPL with both atmospheric and rain attenuation is  
\[\text{FSPL}_{\text{rain}}(dB)=20\log_{10}\left(\frac{4\pi df}{c}\right)+A_{\text{ atm}}\cdot d+kR^{\alpha}\cdot d \tag{13}\]  
This model comprehensively accounts for atmospheric and rain effects, making it suitable for 5G-satellite IoT link assessment.
## Materials and Methods
### Volatile Characterization  
The separation, identification, and quantification of volatile organic compounds (VOCs) present in the UHT whole milk and fermented samples (aliquot B) were conducted using a gas chromatograph GC 7820 A (Agilent Technologies, Santa Clara, CA, USA) coupled to a mass spectrometry detector Agilent MSD series 5975. The GC-MS system was equipped with a capillary column ZB (Phenomenex; 60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness).  
For the quantification of VOCs in milk-fermented samples, the headspace-solid-phase microextraction, gas chromatography-mass spectrometry (HS-SPME GC-MS) method was used [12]. Thus, 20 uL of an internal standard solution of 3-octanol (48 mg/L in absolute ethanol) and 5 mL of sample (UHT whole milk or fermented beverages) were added to a 20 mL vial containing a 1.0 g of NaCl (to enhance the activity and concentration of VOCs in the headspace) along with a magnetic stir bar. The vial was sealed with a screw cap and silicone septum, and then immersed in a water bath at 60 \({}^{\circ}\)C for 3 min, at 500 rpm.  
For the microextraction of VOCs present in the unfermented and fermented milk samples, a 2 cm, 50/30 \(\upmu\)m, DVB/Carboxen/PDMS Stableflex fiber (Agilent Technologies Ltd., Ballytrasna, Ireland) was used. This type of fiber was used considering its suitability for the extraction of VOCs in dairy foods [12]. The fiber was inserted into the vial and remained in the headspace for 60 min at 60 \({}^{\circ}\)C and 500 rpm. The elution of the volatile compounds retained in the fiber was performed by inserting it into the injection port of the chromatograph in splitless mode for 5 min at 250 \({}^{\circ}\)C.  
For the separation of VOCs, the column temperature was held at 30 \({}^{\circ}\)C for 5 min, then increased at a rate of 2 \({}^{\circ}\)C/min up to 100 \({}^{\circ}\)C, and finally a ramp of 3 \({}^{\circ}\)C/min was set up to 150 \({}^{\circ}\)C and held constant for 10 min. The flow rate of the column was maintained constant at 1.2 mL/min, using hydrogen as the carrier gas. Mass spectra were scanned at 70 eV in a mass range of \(m/z\) 10 to 1000.  
All VOCs were identified by comparing their mass spectra with those existing in the Wiley spectral library collection. When the pure volatile organic compound was available, its identification was confirmed by comparing the corresponding retention times [10].  
The quantification of VOCs was performed using the internal standard quantification method, utilizing 3-octanol. The concentration of VOCs in the samples was determined based on the concentration of 3-octanol and the ratio between the area of the volatile compound and that of the internal standard.
## 3 Research Method for Wind Characteristics  
An anemometer can simultaneously measure the \(x\) and \(y\) axial wind speeds at a certain point, which are denoted as \(u_{x}(t)\) and \(u_{y}(t)\), respectively. The wind speed statistical process in this study uses the vector decomposition method [16] to obtain the longitudinal and transverse horizontal components, as shown in Figure 4. Ten min is selected as the basic time interval in the statistical analysis, the mean horizontal wind speed \(U\) and mean horizontal wind direction angle \(\phi\) are as follows:  
\[U=\sqrt{\overline{u}_{x}(t)^{2}+\overline{u}_{y}(t)^{2}} \tag{1}\]  
\[\cos(\phi)=\overline{u}_{x}(t)/U \tag{2}\]  
where \(\overline{u}_{x}(t)\) and \(\overline{u}_{x}(t)\) are the mean values of the \(u_{x}(t)\) and \(u_{y}(t)\) samples at the basic time interval, respectively.  
In the basic time interval, the horizontal longitudinal pulsating wind speed \(u(t)\) and horizontal longitudinal pulsating wind speed \(v(t)\) can be calculated using Formulas (3) and (4):  
\[u(t)=u_{x}(t)\cos\phi+u_{y}(t)\sin\phi-U \tag{3}\]  
\[v(t)=-u_{x}(t)\sin\phi+u_{y}(t)\cos\phi \tag{4}\]  
Turbulence intensity describes the degree to which wind speed changes with time and space, indicating the relative strength of pulsating wind. Turbulence intensity is often defined as the ratio of the standard deviation of pulsating wind speed to the mean horizontal wind speed \(u\) in the 10 min time interval.  
\[I_{i}=\frac{\sigma_{i}}{U}(i=u,v) \tag{5}\]  
In the formula, \(\sigma_{i}\) (\(I=u,v\)) is the standard deviation of pulsating wind speeds \(u(t)\) and \(v(t)\) in the analysis time interval.  
The gust factor reflects the ratio of gust wind speed to the mean wind speed and is often defined as the \(t_{g}\) ratio of the maximum mean wind speed in the gust duration (generally 3 s) to the mean horizontal wind speed \(u\) in the analysis time interval (10 min), that is,  
\[G_{u}(t_{g})=1+\frac{\max(\overline{u}(t_{g}))}{U} \tag{6a}\]  
\[G_{v}(t_{g})=\frac{\max(\overline{v}(t_{g}))}{U} \tag{6b}\]  
Figure 4: Sketch map of wind speeds and directions.  
where \(\max(\overline{u}(t_{g}))\) and \(\max(\overline{v}(t_{g}))\) represent the mean maximum wind speeds in the along-wind and cross-wind directions at time \(t_{g}\), respectively.  
\[\overline{u}(t_{g})=\frac{1}{N}\sum_{i=1}^{N}u(t) \tag{7a}\] \[\overline{v}(t_{g})=\frac{1}{N}\sum_{i=1}^{N}v(t) \tag{7b}\]  
where \(N\) is the number of samples at time \(t_{g}\).  
The turbulence integral scale is a measure of the mean size of the vortex in the air stream. On the basis of the [PERSON] hypothesis [17], this scale is expressed as  
\[L_{i}=\frac{U}{\sigma_{i}^{2}}\int_{0}^{\mu}R(\tau)d\tau \tag{8}\]  
where \(L_{i}\) is the turbulent integral scale at the \(i(i=u,v)\) direction, \(\mu\) denotes the upper limit of the integral obtained from the point where the correlation coefficient drops to 0.05 and \(R(\tau)\) represents the autocorrelation functions of the pulsating wind speed.
## 4 Conclusions and Outlook  
In this work, the damage processes of structures with fissures are analyzed with the cracking elements method (CEM). Uniaxial compression tests are considered, and the tensile damage model is used in this model. For such structures and loading conditions, cracks may propagate from the tips or other positions of the fissures. The cracks will either connect several fissures or propagate independently. Sometimes, new cracks may begin from unexpected positions. The results demonstrate the advantages of the CEM, which is capable of capturing both the initiation and propagation of cracks. On the other hand, some drawbacks of the present model are also revealed, indicating our future work with the CEM, including the implicit modeling of fissures and the implementation of shearing damage models. Furthermore, CEM can capture initiations and propagations of multiple cracks efficiently. Hence, it is supposed to be useful for predicting the damage behavior of the matrix-inclusion system such as composite materials. The corresponding studies are currently underway.  
Conceptualization, Y.Z.; Methodology, Y.Z.; Software, Y.Z. and X.Y.; Validation, Q.D., J.W., and Z.S.; Formal Analysis, Q.D. and J.W.; Investigation, Q.D., J.W., and Z.S.; Data Curation, Q.D., J.W.; Writing--Original Draft Preparation, Y.Z. and X.Y.; Writing--Review and Editing, Y.Z.; Visualization, Q.D., Z.S., and X.Y.; Supervision, Y.Z.; Project Administration, Y.Z.; Funding Acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
The authors wish to acknowledge the financial support from the National Natural Science Foundation of China (Project No. 51808185, 41807277, 51809069) and China Postdoctoral Science Foundation (Project No. 2020T130377).  
The authors declare no conflict of interest.  
Figure 19: Rock-like materials with nine parallel fissures: the experimentally obtained crack paths (the shadows indicate broken surfaces).