esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation  
### Numerical Model and Boundary Conditions  
The modeling of the foundation pit area encompasses the entire 700 m length and 110 m width, with an excavation depth of 6.05 m on the left side and 3.8 m on the right side, and an enclosure pile length of 14.5-21 m. The model extends to more than 100 m around and two-three times the pile length in the depth direction. As such, the model's plane dimension is 950 m \(\times\) 347 m \(\times\) 60 m, as shown in Figure 8.  
To conduct a numerical simulation of the foundation pit, the survey report and design drawing of the main support structure were used as inputs. The software Midas GTS NX 2022 R1 is widely used in geotechnical finite element analysis such as tunnel and foundation pit calculations, municipal subway stations, mining roadway excavation, dam stress analysis, blasting analysis, train movement load analysis, geotechnical seepage calculation, geotechnical dynamic analysis, load structure calculation, etc.; therefore, the software Midas GTS NX was utilized for the simulation, and the soil layer was simplified into four layers. The modified Mohr-Coulomb model was selected as the constitutive model of the rock and soil mass, with the unloading elastic modulus value of the soil taken as three times the compressive modulus value. The Poisson's ratios of clay, muddy clay, and silty clay were 0.38, 0.45, and 0.42, respectively. The unit weight of concrete was 23 kN/m\({}^{3}\), Poisson's ratio was 0.22, and the elastic modulus was 34.5 GPa. Other parameters are shown in Table 2.  
The internal support and underground diaphragm wall were modeled using the elastic constitutive model. The model was established using 2D plate elements for the underground diaphragm wall and 1D beam elements for the reinforced concrete horizontal and vertical supports and top beams. The model included 55,622 elements and 32,301 nodes. The boundary conditions of the model were automatically constrained foundation boundaries, i.e., four side normal constraints and fixed constraints at the bottom. The load was self-weight, and it was assumed that the foundation soil was all in a saturated state; the underground diaphragm wall and the bottom of the foundation were considered impermeable boundaries.  
Figure 8: Meshing of the computational model.  
Before the excavation of the foundation pit, the initial ground stress balance and site displacement clearing of the working conditions were set. Then, the excavation was conducted according to the construction steps, as shown in Table 3.
## 3 Results and Discussion
### Comparison of Volatile Components of Rice Grains Stored under Different Conditions  
To better investigate the variances among rice grains stored under the three storage conditions, we conducted an analysis specifically targeting the volatile components. These volatile components are crucial contributors to the aromatic characteristics of rice grains [36]. The degradation and mold growth in rice grains can result in the process of unpleasant odors. One of the significant volatile substances produced by fungi in this process is 1-octen-3-ol, along with 3-octanone. These components contribute to the unpleasant odor associated with fungal contamination in rice grains [37; 38]. It is worth noting that no mold-related components were detected in rice grains stored under the three storage conditions. The volatile components detected in the rice grains were mainly divided into seven categories (Figure 2A). The results showed that the largest composition of volatile components in the three storage scenarios was hydrocarbons whose distribution rate was more than 50%. A total of 39 volatile components were identified in rice grains stored under the condition of normal temperature, horizontal ventilation, and nitrogen gas atmosphere, including 1 alcohol, 8 esters, 1 ketone, 1 aldehyde, 3 alkenes, 23 hydrocarbons, and 2 aromatic and heterocyclic compounds (Table 2). A total of 49 volatile components were detected in rice grains stored under normal temperature, vertical ventilation, and nitrogen gas atmosphere, including 3 alcohols, 9 esters, 1 ketone, 1 aldehyde, 2 alkenes, 30 hydrocarbons, and 3 aromatic and heterocyclic compounds (Table 2). A total of 45 volatile components were detected in rice grains stored under quasi-low temperature, horizontal ventilation, and nitrogen gas atmosphere, including 3 alcohols, 9 esters, 1 ketone, 1 aldehyde, 1 alkene, 30 hydrocarbons, and 0 aromatic and heterocyclic compounds (Table 2). Among these volatile  
Figure 1: Changes in the chemical substance and mycotoxin contents of rice grains stored under three different storage conditions. (**A**) Moisture. (**B**) Fatty acid value. (**C**) Starch content. (**D**) Albumin. (**E**) Globulin. (**F**) Glaidin. (**G**) Clutenin. (**H**) OTA. (**I**) AFB1. RA: room temperature, horizontal ventilation, nitrogen gas atmosphere; RB: room temperature, vertical ventilation, nitrogen gas atmosphere; RC: quasi-low temperature, horizontal ventilation, nitrogen gas atmosphere. Different letters indicate significant difference (\(p<0.05\)) among contents of rice grain in three storage environments. components, fatty acids in rice grains are oxidized to peroxides, mainly generating aldehydes and ketones [39]. The rice grains stored under the conditions of normal temperature, vertical ventilation and quasi-low temperature, horizontal ventilation contain higher levels of ketones (RB: 6.17 \(\upmu\)g/kg, RC: 8.67 \(\upmu\)g/kg) and aldehydes (RB: 108.45 \(\upmu\)g/kg, RC: 61.27 \(\upmu\)g/kg). The results also showed the reasons for the higher fatty acid value of rice grains stored under these two storage conditions.
## 3 Results
### Plant Growth Properties  
According to root depth measurements, the deepest root was recorded in the CT system where FYM was applied at a high level. Based on the obtained values of mean emergence time (MET), the application of FYM lowered the MET significantly in both tillage systems, such that minimum MET was recoded for plots receiving 40 t\(\cdot\)ha\({}^{-1}\) FYM (Figure 2). Comparing mean values of MET between the two tillage systems revealed that there were similar MET values in both systems. A further comparison of MET between FMY-treated and fertilized subplots indicated that F2, F3, F4, and F5 had MET values of 32%, 44%, 41%, and 19%, respectively. The effects of fertilizers on MET were much more prominent in RT systems than in CT systems. Given the soil compaction in RT systems, the application of FYM could largely address this problem and enhance the seedling emergence rate, thereby improving seedling establishment even further. Further confirming this fact, a positive association was identified between BD and MET (Table 3). Moreover, MET was found to be inversely related to OM, as well as nutrient concentration (Table 3). According to the results, a further improvement in soil physical properties can raise seedling emergence rate. Table 4 reports the impacts of the two tillage systems and fertilized treatments on growth parameters and achene yield. The results showed that chlorophyll index was originally not affected by the tillage system; however, an increase by 43% was observed in the chlorophyll index upon the combined application of FYM and 50% of the recommended dose of chemical fertilizer.  
The largest leaf area was recorded with F3 and F4 in the RT systems. A positive relationship was observed between leaf area and OM, moisture content, root depth, and P, K, and N contents (Table 3). An investigation on the obtained values of achene yield indicated that, although the two tillage systems had no significant difference in the first year, the highest achene yield in the second year occurred in the RT system (Figure 2). Maximum yield in either year was obtained across F3 and F4 subplots. Surprisingly, a positive significant correlation was detected between achene yield as Ksat, OM content, soil moisture, and nutrient concentration (Table 3).
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 3 Fuzzy Approaches for Energy Performance of Buildings Forecasting  
### Data Collection  
The data used for this study stems from the UCI machine learning repository [18] under the name: _Energy efficiency_ data set. [PERSON] and [PERSON] [17] created the data by generating 768 simulated buildings using Ecotet. Ecotet is a sustainable building design software tool that allows the design of buildings by performing a complete analysis on building energy, thermal performance, and water usage, among other functionalities [29].  
All buildings in the simulation have a volume of 771.75 m\({}^{3}\), but different surface areas and dimensions. All of them are designed with the newest and most common building materials in the construction industry and with building elements achieving the lowest heat loss through the wall, floor or roof (U-value). The simulation assumes that the buildings are located in Athens, Greece, and are residential buildings.  
Three types of glazing areas were used, expressed as percentages of the floor area: 10%, 25%, and 40%. Furthermore, five different distribution scenarios for each glazing area were simulated: (1) uniform: 25% glazing on each side, (2) north: 55% on the north side and 15% on each of the other sides, (3) east: 55% on the east side and 15% on each of the other sides, (4) south: 55% on the south side and 15% on each of the other sides, and (5) west: 55% on the west side and 15% on each of the other sides. In addition, they obtained samples with no glazing areas. Finally, all shapes were rotated to face the four cardinal points.  
Each of the 768 simulated buildings can be characterized by eight building parameters, which are summarized in Table 1. These parameters correspond to the input variables.  
They also recorded the heating load (HL) and the cooling load (CL), which correspond to the output variables. Basic statistical analysis of the data was also performed, showing that linear modelling techniques are not appropriate for this data, since the scatter and density plots do not follow a Gaussian  
\begin{table}
\begin{tabular}{c c c c} \hline RC & Relative Compactness & OH & Overall Height \\ SA & Surface Area & O & Orientation \\ WA & Wall Area & GA & Glazing Area \\ RA & Roof Area & GAD & Glazing Area Distribution \\ \hline \end{tabular}
\end{table}
Table 1: Building parameters used as input variables for the models.  
Figure 6: Adaptive neuro-fuzzy inference system (ANFIS) architecture [28].  
distribution. As mentioned earlier, the data generated represent with high probability actual real data, enabling energy comparisons between buildings [17]. Moreover, the use of simulated building data is useful and justified when dealing with energy prediction challenges, since it allows us to reduce considerably the time needed to obtain the data, design easily different building configurations and work with a lower level of detail.
## 4 Experiments
### Implementation Details  
In the subsequent experiments, we choose Faster R-CNN with ResNet-50 and a Feature Pyramid Network (FPN) as the baseline, in which Greedy NMS is used for post-processing. In order to obtain more accurate features, RoI Align [45] is employed. Given the varying shapes of images in CrowdHuman, the input images are uniformly resized to 800 pixels on the shorter side and kept below 1400 pixels on the longer side. The aspect ratios of the anchors are resized to \(\frac{H}{W}=\{1,2,3\}\). Following [8, 23], the height and width of the images in CityPesons are enlarged by a factor of 1.3, and the aspect ratios of the anchors are adjusted to \(\frac{H}{W}=\{2.44\}\) to accommodate pedestrian scale.  
During the training phrase, ResNet-50 is pretrained on ImageNet [46]. The remaining parameters of our detector are initialized using Kaiming initialization [47]. Our detector is trained on 2 NVIDIA Ampere A40 GPUs, with a minimum batch size of 16 images. We employ Stochastic Gradient Descent (SGD) with a momentum of 0.9 and weight decay of 0.0001 as the optimizer. For CrowdHuman, our detector is trained for 39,979 iterations, while for the CityPesons dataset, our detector is trained for 5580 iterations. In the case of CrowdHuman, the initial learning rate is set to 0.04, and it is decreased by a factor of 10 after 18,750 and 28,125 iterations. For CityPersons, the initial learning rate is set to 0.02, and it is decreased by a factor of 10 after 3720 and 4650 iterations.  
During the inference phase, each image is subjected to a maximum of 100 detections. The same resizing operation, as mentioned during training, is applied. To ensure a fair comparison with other NMS methods, the value of the rigid threshold \(N_{t}\) is set to 0.5, unless stated otherwise.
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 2 Literature Review and Study Overview  
Research on soccer player salary prediction has advanced from traditional econometric methods to modern machine learning approaches. Table 2 summarizes key studies, followed by a discussion of major developments in the field.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{3}{c}{**Traditional Econometric and Statistical Approaches**} \\ \hline [PERSON] (07) [13] & Salary depends on productivity, position, and career longevity. \\ [PERSON] et al. (09) [14] & International experience and seniority strongly impact wages. \\ [PERSON] (07) [15] & League prestige and financial strength shape salary levels. \\ [PERSON] et al. (14) [16] & Assesses returns to education and experience using a human capital model. \\ [PERSON] et al. (07) [17] & Considers brand value, off-field visibility, and player reputation. \\ [PERSON] et al. (17) [18] & Market dynamics and negotiation power influence pay beyond performance. \\ \hline \multicolumn{3}{c}{**Machine Learning and Data-Driven Approaches**} \\ \hline [PERSON] et al. (24) [4] & Uses xG and aG metrics to model salary fairness and performance impact. \\ [PERSON] et al. (24) [19] & Employay player stats like goals, assists, and appearances for salary prediction. \\ [PERSON] et al. (22) [20] & Applies ML to identify key predictors using detailed match data. \\ [PERSON] et al. (16) [21] & Focuses on prediction using performance ratings and time on field. \\ [PERSON] et al. (14) [22] & Investigates salary using in-game activity and player involvement. \\ [PERSON] et al. (22) [23] & Combines market value, salary, age, and performance for ML modeling. \\ [PERSON] et al. (23) [15] & Proposes feature engineering to improve salary prediction accuracy. \\ [PERSON] et al. (22) [11] & Uses ensemble models across positions and leagues. \\ [PERSON] et al. (22) [9] & Applies boosting methods for player value estimation. \\ [PERSON] et al. (23) [10] & Integrates FIFA and real performance metrics for hybrid prediction. \\ [PERSON] et al. (17) [24] & ML-based salary prediction using FIFA video game data. \\ [PERSON] (23) [25] & Uses FIFA 20 attributes (e.g., potential, reputation) for salary modeling. \\ [PERSON] et al. (22) [6] & Focuses on on-field metrics but lacks contextual features. \\ [PERSON] et al. (24) [7] & Highlights the need for position-aware salary models. \\ [PERSON] et al. (25) [8] & Predicts transfer values as proxy indicators of wage. \\ [PERSON] et al. (23) [10] & Emphasizes explainable ML but not directly tied to salary. \\ Bayesian Sports Analytics (22) [11] & Applies Bayesian learning for general soccer predictions. \\ [PERSON] and [PERSON] (23) [26] & Explore goalkeeper-specific performance metrics in salary modeling. \\ [PERSON] and [PERSON] (24) [27] & Investigate behavioral and institutional factors influencing salaries. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key studies on soccer player salary prediction using traditional and machine learning approaches.
## 2 Materials and Methods
### Target Test Platforms
#### 2.5.2 ARM-Based Microcontroller Board (STM32F7)  
Our STM32F7 board is based on the STM32F769 IDISCOVERY kit, which is a complete development platform for the STMicroelectronics Arm(r) Cortex(r)-M7 core-based STM32F769 NI microcontroller with 2 MB of flash memory, 512 kB of Synchronous Random-Access Memory (SRAM), and 16 MB of SDRAM. The microcontroller has a Floating-Point Unit module and a Flexible Memory Controller for SDRAM with a data bus width of 32-bits and maximum CPU Clock of 216 MHz [80]. STM32F7 is able to run C++ LiteRT DNN models using the Lite RT library for microcontrollers [29] embedded in the firmware. The firmware is developed, compiled, and stored into the flash memory by means of C/C++ IDE for microcontrollers (STM32 CubeIDE) [81]--Figure 1.  
The test interface to STM32F7 embedded C++ LiteRT DNN models is shown in Figure 4. It uses the same communication with the master test application as describedfor the Raspberry Pi 4 above. The data are received in the slave STM32F7 via Universal Synchronous Asynchronous Receiver Transmitter (USART) port and are stored in the SDRAM by Direct Memory Access (DMA). The firmware feeds the data to the input of the preloaded instance of the C++ DNN model. Then, the model is run and returns its result after a certain processing time. The result is sent back to the master application for comparison with the ground truth.  
By specification, SRAM is faster than SDRAM; therefore, our firmware prioritizes SRAM for storing critical components, such as the instance of the class \"MicroInterpreter\" from the LiteRT library for microcontrollers, as well as the C++ LiteRT DNN model parameters. However, as SRAM has limited capacity and is also shared with other modules (e.g., USART, flexible memory controller, etc.), the efficient use of SDRAM is essential. In this implementation, a significant portion of SDRAM is allocated to support the \"MicroInterpreter\" operations with the DNN model parameters, as well as to store the input, output, and temporal data. An additional section of SDRAM is reserved for the operation of the DMA module, ensuring efficient data handling across the system.
## 3 Coded Cooperation with Hard Information Relaying and Relay Selection  
In this section, we propose a coded cooperative scheme with hard information relaying and relay selection (CC-HIR-RS). In order to mitigate error-propagation from faulty relays and increase the communication performance, correct decoding of the messages should be ensured. Let \(R_{r}\) be the selected relay to forward the information to the destination node. We assume that the received messages are decoded correctly at relay \(R_{r}\), then the hard decoded information is re-encoded with a channel encoder of rate \(R_{R}=K/N_{R}\) and transmitted to the destination node, where \(N_{R}\) is the code-word length at the relay. The received signal at the destination node can be expressed as  
\[y_{R_{r}D} = h_{R_{r}D}\sqrt{\frac{G_{SR_{r}}}{d_{R_{r}D}^{\alpha}}\frac{R_{ R}}{R_{S}}\rho_{r}\upsilon|h_{SR_{r}}|^{2}P_{SD}\mathbf{x}+n_{R_{r}D}}, \tag{4}\]  
where \(h_{R_{r}D}\) is the channel fading coefficient at the \(R_{r}-D\) link, which is modeled as a Rayleigh distributed random variable with normalized second order moment, \(n_{R,D}\) is the additive white Gaussian noise at the destination with variance \(\sigma_{n}^{2}\), and \(d_{R_{r}D}\) is the normalized distance of the \(R_{r}-D\) link. The received SNR at the destination node from the signal \(\mathbf{y}_{R_{r}D}\) is expressed as  
\[\Gamma_{R,D} = |h_{SR_{r}}|^{2}|h_{R,D}|^{2}\frac{G_{SR_{r}}}{d_{R_{r}D}^{\alpha }}\frac{R_{R}}{R_{S}}\rho_{r}\upsilon\frac{P_{SD}}{\sigma_{n}^{2}} \tag{5}\] \[= |h_{SR_{r}}|^{2}|h_{R,D}|^{2}\tilde{\Gamma}_{R_{r}D},\]  
where \(\tilde{\Gamma}_{R_{r}D}=(G_{SR_{r}}/d_{R_{r}D}^{\alpha})(R_{R}/R_{S})\rho_{r} \upsilon P_{SD}/\sigma_{n}^{2}\) denotes the average received signal power at the destination from the relay \(R_{r}\). The outage probability analyses of the proposed CC-HIR-RS scheme is performed in the next subsection.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix
### T-Type Transmission Line Fault Branch Judgment Method
#### 4.1.2 T-Node Failure  
When the fault occurs in the T-node, the distance from the fault point to any endpoint is the distance from the T-node to each endpoint, and all elements in the fault branch judgment matrix are 0. Therefore, the principle for determining a fault at the T-node of a T-type transmission line is as follows: when all elements in the fault branch judgment matrix are 0, the T-node is determined to be faulty.
## 3 Post-Transcriptional and Translational Regulation in Integrons
### Other Aspects Influencing Translation of Downstream Integon-Associated Gene Cassettes  
It was discussed above the role of ORF-11/17/18 in increasing translation of gene cassettes inserted in _att11_ site. However, other regulatory signals may enhance the expression of gene cassettes independent of their position in the integron array. A recent study revealed that expression of the plasmid-borne efflux pump _qepA1_, involved with quinolone resistance, was induced by antibiotic-driven cellular stresses and by regulatory elements found in _qepA1_\(5^{\prime}\) end [128]. It was observed that a 250-bp region was found between the class 1 integron _att11_ site and _qepA1_. This region corresponded to the 5'UTR of _dfR4_ gene cassette fused to an unknown _orf_, and this configuration was found to be universal among all _qepA_ alleles. [PERSON] et al. [128] experimentally demonstrated that this region was essential for the efficient expression of _qepA1_, although they were not able to identify which regulatory signals were involved with this regulation. Since efflux pump expression is often tightly regulated to avoid large fitness costs to bacteria, the regulation at the translational level observed for _qepA1_ could mitigate such negative effects, restricting high-level expression to environmental conditions in which QepA1 is beneficial.  
However, besides _attC_ stem-loop structures, other genetic events may negatively interfere with the efficient translation of downstream gene cassettes. A previous study demonstrated how the genetic events that permeate the integron dynamics, such as integration, may decrease gene cassette translation. [PERSON] et al. [104] revealed that an _orf_ (_gcuF1_) had been inserted immediately upstream _bld\({}_{\text{OXA-28}}\)_, which was placed in the second position in a class 1 integroSeveral studies have proposed that the aminoglycoside-inducible translation of _aac_ and _aad_ genes inserted in class 1 integrins involved the participation of regulatory RNAs, such as riboswitches and small RNAs (SRNAs) (for revision, see references [130; 131; 132; 133; 134]). However, it was verified that the DNA sequence coding for the proposed riboswitch RNA actually corresponded to the _att11_ site of class 1 integrins. Moreover, the expression regulation of _aac/aad_ gene cassettes did not involve a riboswitch, but the ORF-11 peptide production and a mechanism of aminoglycoside-inducible translation attenuation similar to that described above [135].