esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of AI in Food Integrity
### AI and ML in Food Pathogen Detection, Classification, and Virulence/Resistance Prediction
With average validation errors of 3.1% and 2.2% (n = 144), the SVM and random forest models properly classified the data. The burden of atypical mutations in protein-coding genes across independently evolved _Salmonella enterica_ lineages was measured and the data was used to train an ML classifier (i.e., random forest) in order to identify strains associated with extraintestal disease [93]. The classifier was able to accurately distinguish between invasive and long-established gastrointestinal _Salmonella serovars_. Furthermore, in immunocompromised individuals in sub-Saharan Africa, the model demonstrated the ability to distinguish between the recently identified _Salmonella enteritidis_ and _Typhimuriumium_ lineages linked to invasive illness, as well as within-host adaptation to invasive infection. In their study, [PERSON] et al. [94] presented an AI-biosensing framework intended for automated and accelerated pathogen detection in a variety of water samples, including liquid food and agricultural water. Target bacteria were identified and quantified by the framework using a deep learning model that was trained on the bacteria's microscopic patterns generated by unique interactions with bacteriophages. The model was then applied to real-world water samples with environmental noises that were not observed during training. The AI model demonstrated its capacity to generalize to unseen data by making quick predictions in less than 5.5 h on real-world water samples, even though it was only trained on lab-cultured bacteria. The model's accuracy ranged from 80% to 100% and it can be effectively utilized to evaluate the microbiological quality of water during culinary and agricultural processes. Elsewhere, a pan-genome-based ML technique was used in the [PERSON] and [PERSON] [95] study to forecast the antimicrobial resistance behaviors of _E. coli_ strains. Findings from the study revealed that the group of AMR genes located in the accessory region of the pan-genome had the highest prediction accuracy, indicating the significant contribution of these gene clusters to AMR activities in _E. coli_, a well-known persistent food pathogen. Additionally, the researchers were able to select subsets of AMR genes for various antibiotic medications using a genetic algorithm (GA). Compared to the gene sets reported in the literature, the subsets chosen by the GA performed better in predictions.
# Sustainable Cross-Laminated Timber Structures in a Seismic Area: Overview and Future Trends
## 3 Traditional CLT Buildings  
### Technology  
Low and mid-rise traditional CLT buildings are made with CLT panels used for both vertical shear-walls and horizontal floors. The panels are prefabricated off-site and connected on the building site using metal connectors such as hold - downs, angle brackets, screws and nails.  
Depending on their arrangement, the shear-walls can be divided in two groups: (i) single (or monolithic) and (ii) segmented (or coupled) walls. In the case (i) the wall has high width-to-height ratio, with width equal to plant dimension of the building and length corresponding to its inter-story height. In the case (ii) the wall is composed by narrow panels joined to each other by means of vertical step joints [21; 37], and width-to-height ratios greater than 0.20 are advisable [31].  
CLT traditional buildings are constructed using the \"platform\" technique, i.e., the vertical continuity of the walls is interrupted at each story by horizontal panels (floors) included, as a \"sandwich\", between two consecutive vertical panels (Figure 4). At the ground floors the vertical panels are directly placed on a reinforced concrete foundation or a horizontal timber beam interposed between foundation and vertical panels. Low-rise buildings can be also built-up adopting the \"balloon\" technique (Figure 5), where the vertical continuity of the walls is not interrupted by the floors at each story, but they are connected on the internal side of the vertical walls by means of specific connection devices [38].  
Panel to panel and panel to foundation connections consist of mechanical steel to timber connections, made with punched cold-formed thin steel plates-typically hold-downs (HD) and angle brackets (AB)--fastened to the timber panels with ring shanks nails or screws and bolted to the foundations (Figure 4).  
Being characterized by a high level of prefabrication, several connection-types are needed to connect the panels among them (in addition to HDs and ABs), such as metal fasteners between the orthogonal walls, metal fasteners to join the floor panels or to connect segmented walls against sliding (Figure 4). Connections between floor panels and between segmented walls can be realized with different types of fasteners arranged differently, typically overlapped joints with inclined screws running at 45\({}^{\circ}\), half-lap joints with vertical screws or other solutions which can be found in [2; 11].  
Door and window openings are obtained as a function of the type of the vertical walls arrangement: by cutting the zone interested by the opening in the case of monolithic walls, or by assembling multiple elements in the case of segmented walls. In the first case the lintels, i.e., horizontal CLT panels above the openings, have the same cross layout of the vertical panels, while in the second case the layout can be also different.
###### Abstract  
This study develops an integrated modeling approach to analyze the effects of different scenarios compared to a baseline (2019) scenario on energy demand and supply in Zambia. Aiming at minimizing costs, the model combines available resources and technologies, adhering to technonomic and environmental limits. On the demand side, the study uses time series analysis to forecast future electricity demand in Zambia. The supply-side energy model identifies the optimal combination of resources and technologies needed to satisfy exogenously specified electricity demand levels at the least cost. The baseline scenario results revealed that the power outages in Zambia, especially at peak times, have been attributed to reliance on imported fuel for power generation, the slow-paced integration of renewable resources, and not fully utilizing resource potential for the supply side. To overcome the power outages and meet the electricity demand in 2035, a comprehensive scenario analysis was conducted, including the main scenarios of 30% integration of renewable energy, no coal power generation, and low emission targets (10%, 20%, 30%, 40%, and 50% reduction from the baseline). The expected CO\({}_{2}\) reductions from the renewable energy scenario, no coal scenario, and 50% low emission target are estimated at 5222 kilotons in 2035, respectively.  
Zambia; integrated energy system modeling; cost-minimization approach; greenhouse gas; seasonal autoregressive integrated moving average 2020
## 1 Introduction  
The demands for increasing the amount of data that can be transmitted within a limited bandwidth is continuing to grow rapidly, especially with developments, where users are now being attracted by multimedia data and video streaming, as well as the Internet of Things technology revolution. Hence, the 5G mobile generation will include several technologies that can help to achieve its promised goals. Some of these technologies are: beamforming, carrier aggregation, massive multiple input multiple output (MIMO), and more complex modulation schemes, which produce a high peak to average power ratio (PAPR). The high PAPR requires the power amplifier to be backed off from the most efficient point into a region where the efficiency drops sharply to keep the linearity requirements of any communications standard [1; 2; 3]. Working in the back-off region of the power amplifier means that less efficiency will be obtained, where a large amount of supplied power will be converted into heat [1]. With the simple amplifier, there is inevitably a trade-off between high efficiency and linearity. Modern power amplifiers should be designed to produce high efficiency at a large output power back-off (OBO); known efficiency enhancements techniques include: Doherty power amplifier (DPA), envelope elimination and restoration (EER), envelope tracking (ET), and linear amplification using nonlinear components (LINC), and Chireix out-phasing. However, the simplest technique is the Doherty amplifier, where neither signal processing blocks nor additional controlling circuits are required [4; 5; 6; 7]. Also, new terms have entered the communications systems which are multi-band and multi-mode, where the term \"multi-band\" refers to a transmitter which works on two different frequency bands simultaneously; in this case, the number of devices, size, and cost will be reduced. On the other hand, the term \"multi-mode\" refers to a transmitter who can support and combine different access technologies such as 2G, 3G, 4G and 5G on a single platform [8; 9; 10; 11; 12; 13]. To design a dual-band power amplifier, a hybrid configuration can be used but occupies a large area. Ideally, the power amplifier should be designed to handle multi-band and multi-mode systems concurrently.  
This paper, following a brief introduction to the Doherty power amplifier mechanism, reviews the techniques that have been used in designing dual-band Doherty power amplifiers, and finally draws conclusions from this.
# Use of Social Media Data in Disaster Management: A Survey
## 6 Information Dimension
### Spatial  
Although spatial representations of social media data, such as geo-location, play an essential part in social-media-based event detection or event analysis, there are few social media data that provide information about users' locations [34]. Furthermore, there is a variety of location information represented in social media, ranging from a very precise location using geographic coordinates (e.g., longitude and latitude) to a very fuzzy location using descriptive language (e.g., city name). Geographical information is represented in several ways on social media, as shown in Figure 4.  
* Geo-tagging: The social media systems attach geographical information automatically or manually (by users) when the users post a message. * User-defined: The user mentions the location in the post, either as the place name or as geographic coordinates. * Spatial coverage: Many posts only mention the geographical extent, such as the town/village/locality, a district, a country/province, the continent, or similar information. Figure 4: Spatial representation of geographical information on social media. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{5}{c}{**Language**} \\ \cline{2-5}  & **Global** & **Local** & **Mixed Language** & **Mixed Script** \\ \hline [22] & & - & - & - \\ [23] & & Arabic & English, Arabic & - \\ [30] & & - & - & - \\ [31] & & Filipino & English, Filipino & - \\ [32] & & Hindi & English, Hindi & Hindi \\ [33] & & Spanish, German & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Language used on social media for disaster management. Several approaches have been proposed in the literature to address the issues of spatial representation in social media. Here, Table 4 presents the state-of-the-art methods for identifying and analysing the spatial information of social media data. Geo-location identification: The research of [34] shows that 0.42% of all tweets use the latitude and longitude function to tag their geo-location, and out of 1 million Twitter users, only 26% have listed a city name. In most cases, general expressions (such as California) or nonsensical expressions (such as wonderland) are used. This research aimed to detect the locations of tweets that do not clearly mention geographic information. To this end, the authors proposed a method of computing the probability that a word is linked to a city. In order to improve the accuracy, the authors introduced a model of spatial variation for
## 4 Result
### Monitoring Results
The end of the sedimentation rate funnel in subsidence zone A, subsidence zone B, and subsidence zone C is separated and is refined into multiple funnel mouths. The sedimentation rate funnel in subsidence zone D has the characteristic of the funnel mouth being too large, which can easily cause geological disasters. subsidence zone E is the most typical subsidence funnel, and there is a greater probability of overall expansion of the subsidence funnel at a later period. Compared with other settling areas, the settling rate of zone F is lower, and the settling tends to stop. The maximum sedimentation rate of subsidence zone A, zone B, and zone C is 47.564 mm\(\cdot\)a\({}^{-1}\), 38.829 mm\(\cdot\)a\({}^{-1}\), and 50.468 mm\(\cdot\)a\({}^{-1}\), respectively, all of which are located at the tiny funnel mouth of the subsidence funnel. The small proportion of the distribution area of the maximum sedimentation rate should focus on whether each small funnel mouth has a trend of contiguous pieces. The maximum sedimentation rates of zone D, zone E, and zone F are 25.086 mm\(\cdot\)a\({}^{-1}\), 28.184 mm\(\cdot\)a\({}^{-1}\), and 26.804 mm\(\cdot\)a\({}^{-1}\), respectively, which are lower than those of zone A, zone B, and zone C. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subsidence Zone**} & **Cumulative Settlement** & **The Average Settlement in** & **Maximum Settling** & **Subsidence Area** \\  & **over Three Years (mm)** & **the Settling Zone (mm)** & **Rate (mm\(\cdot\)a\({}^{-1}\))** & **(km\({}^{2}\))** \\ \hline \hline \multirow{2}{*}{Subsidence zone A} & 0–99.4 & 19.0 & 47.564 & 19.104 \\  & 0–83.9 & 11.6 & 38.829 & 42.554 \\  & 0–105.5 & 16.4 & 50.468 & 49.334 \\  & 0–66.7 & 15.1 & 25.086 & 13.485 \\  & 0–67 & 8.3 & 28.184 & 6.796 \\  & 0–86.9 & 15.9 & 26.804 & 3.121 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: In-SAR monitoring results in time series. Figure 13: Settlement rate diagram of the subsidence area of Kunming City (**(A–F)** are the subsidence zones of A–F, respectively). From the time series sedimentation data of 41 stages, 8 stages were selected according to a certain time step (Figure 14). Using the radar data to obtain the reference plane at the earliest time, serious surface subsidence occurred in subsidence zone A to zone F during the study period. The subsidence funnel in subsidence zone A and zone E developed rapidly and the subsidence rate was faster, followed by zone C, zone D, and zone F. According to the results, scattered and irregular settling funnels began to develop in zone B from January 2020 to April 2021 and gradually accelerated after 2021.
## 2 Materials and Methods
### Extraction Process Using Dispersive Liquid-Liquid Microextraction (DLLME) Methodology  
The study of this analytical approach (i.e., the DLLME procedure) can be divided into several phases. The choice of the best extraction solvent among different solvents, such as _n_-hexane (density 0.66 g cm\({}^{-3}\)), _n_-heptane (0.68 g cm\({}^{-3}\)), _iso_-octane (0.69 g cm\({}^{-3}\)), and benzene (0.88 g cm\({}^{-3}\)), is the first step. All the solvents tested had a lower density than water. Several volumes of each solvent were tested to evaluate the best extraction solvent. Taking into account the absence of the dispersive solvent (e.g., acetone), it was necessary to use other methods to achieve efficient emulsification. Among the different possibilities, ultrasounds were tested; 6 min in the ultrasonic bath (100 W power) allowed us to reach a stable and homogeneous emulsion. Second, NaCl addition was necessary to break the emulsion; different NaCl concentrations were compared in terms of percentage recoveries. Using this approach, 1 L of water sample (pH 5) with the addition of 100 \(\mu\)L of PAE/BPA mixture solution (50 pg \(\mu\)L\({}^{-1}\) of each analyte) and 50 \(\mu\)L of I.S. (1 \(\mu\)g \(\mu\)L\({}^{-1}\)) was treated with 200 \(\mu\)L of _n_-hexane identified as the best extraction solvent. Subsequently, the sample was subjected to 5 min stirring and ultrasounds for 6 min and NaCl 15 g L\({}^{-1}\) was added. The solution was vortexed for 10 min to break the emulsion, then 1 \(\mu\)L withdrawn by syringe was injected into the GC-MS instrument. All experimental conditions were applied to study the analytical parameters of the PAE and BPA extraction.
## 3 Sample Preparation and Velocity Measurement
Figure 2 shows the SEM image of the artificial rock, pore structure, and parallelly distributed fractures with controlled geometry. P- and S-wave velocity measurements were conducted on an ultrasonic bench-top pulse transmission system at room temperature and atmospheric pressure for dry and water-saturated conditions. The measurements were based on the test standard DZ/T0276.24-2015. The central frequency of P and S transducers was 0.5 MHz and the time sampling interval was 0.04 us for both P- and S-wave signals. The measurement error was about 0.5% for P-wave velocity and 0.8% for S-wave velocity. Water saturation was obtained by immersing the samples in a water-filled container, which was placed in a sealed bin to extract the air. To ensure full water saturation, the saturation rate was calculated as follows:  
\[Sw(water)=\frac{m_{sat}-m_{dry}}{V\varphi\varphi_{f}}\times 100\% \tag{2}\]  
where \(m_{sat}\) and \(m_{dry}\) are the wet and dry masses of the sample, respectively, \(V\) and \(\varphi\) are the volume and helium-porosimetry-measured porosity of the sample, respectively, and \(\rho_{f}\) is the bulk density of water. P- and S-wave velocities were measured in three directions at 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\) to the fracture normal. In each direction, both S\({}_{1}\) and S\({}_{2}\) velocities were measured for polarization parallel (S\({}_{1}\)) and perpendicular (S\({}_{2}\)) to the fractures by rotating the transducers. Through a numerical modeling experiment, [PERSON] and [PERSON] (1994) indicated that, for wave propagation parallel or perpendicular to the layering (or in this case fractures) [37], a true phase velocity is measured in laboratory ultrasonic experiments. However, a wavefront propagating at 45\({}^{\circ}\) to the fracture normal can suffer a lateral translation. If the lateral translation suffered by the wave is greater than the radius of the receiving transducer, then a group velocity is measured instead of a phase velocity. Equations in [PERSON] & [PERSON] (1994) were used to calculate the lateral translations at 45\({}^{\circ}\), and the results show that the value was within 8.03 mm for all measurements, smaller than our transducer radius (12.5 mm). Therefore, we concluded that we measure the phase velocity for both P- and S-waves in three directions. A similar conclusion was reached by [PERSON] (2018) using the same experimental setup as that used in this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Fracture** & \multicolumn{4}{c}{**Length (mm)**} \\ \cline{3-6}
**Number** & **Density** & **X** & **Y** & **Z** & **45\({}^{\circ}\)** \\ \hline
1\# & 0 & 68.54 & 48.75 & 68.46 & 67.03 \\ \hline
2\# & 0.0312 & 68.75 & 48.44 & 67.71 & 67.55 \\ \hline
3\# & 0.0624 & 68.78 & 49.42 & 68.44 & 67.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The scale of the three samples.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods  
### Study Area  
Danzhou city is in northwestern Hainan Province, China, within coordinates ranging from 19\({}^{\circ}\)11\({}^{\prime}\)-19\({}^{\circ}\)52\({}^{\prime}\)N and from 108\({}^{\circ}\)56\({}^{\prime}\)-109\({}^{\circ}\)46\({}^{\prime}\)E. This city has the largest land area (3398 km\({}^{2}\)) and the longest coastline (307 km) of any city in Hainan Province. The region has a tropical monsoon marine climate, with an average annual temperature of 23.1 \({}^{\circ}\)C and an average annual rainfall total of 1823 mm. The terrain is mainly hilly, and from southeast to northwest, the ground surface gradually inclines downwards; the elevations are high in the southeast and low in the northwest, and the south and southeast regions contain low, hilly areas. As one of the major rubber plantation areas in China, the rubber plantation area in Danzhou city was 848.20 km\({}^{2}\) in 2019, and the total yield was 60,969,000 kg according to statistics [33] (p. 286). With the development of rubber plantations in China over the past 100 years, Danzhou city has developed into one of the dominant regions in terms of its rubber plantation area, rubber latex quantity and yield among all rubber plantation regions in China. The research region considered in our study is located in eastern Danzhou city and includes Xiqing Farm and some regions of Dongcheng town, Nada town, Dacheng town, and other areas (see Figure 1). Rubber trees display obvious phenological change characteristics. According to the growth changes of rubber buds and leaves, the phenological stages can be divided into the red leaf stage, deciduous stage, fresh leaf stage (sprout stage, elongating stage, bronze stage, color-changing stage, light green stage, and stable stage) and mature leaf stage within a year, and these cyclic changes are shown in Figure 2. The workflow by which rubber plantations were mapped in this study is shown in Figure 3.  
Figure 1: The location of the research region.  
Figure 3: Workflow by which rubber plantations were mapped in this study.  
Figure 2: The life cycle and different phenological stages of rubber plantations. Note: 1, 2, 3, 3, 4, and 5 represent the sprout stage, elongating stage, bronze stage, color-changing stage, light green stage, and stable stage, respectively.
## 4 Simulation and Experimental Results
### Simulation Analysis
Nyquist frequency characteristic of the open-loop system with a parallel co-frequency ANF and six cascaded multi-frequency improved DTNFs is shown in Figure 15a. By the normalized optimization of the depth factor \(\zeta_{i}\) and width factor \(k_{i}\) based on the multi-objective constraint algorithm, as the number of DTNFs increases, the phase stability margin of the AMB-rotor system can still be maintained, although the phase margin is inevitably reduced. The pole-zero map of the characteristic polynomial is shown in Figure 15b. Zeros of the characteristic polynomial represent closed-loop poles, and poles of the characteristic polynomial represent open-loop poles. The right half S-plane has an open-loop pole, that is, although the open-loop system is unstable, the closed-loop system is stable.