esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 4 Discussion
### Mechanism of Amplification Effect of Mining-Induced Earthquakes on Surface Damage  
#### 4.2.1 Validation of Heavy Layer Fracture Mechanism and Seismic Energy Characteristics  
The observed abrupt subsidence and heavy layer fracturing (Figure 9) align well with the key stratum theory proposed by [PERSON] et al. [11; 12; 13], who identified elastic strain energy release during the \"O-X\" fracturing stage as the primary source of high-energy mining-induced earthquakes. This study further confirms, through microseismic monitoring, that the largest seismic energy reached 2.1 \(\times\) 10\({}^{5}\) J during the fracturing of the 7202 working face's heavy layer (Table 1). This value is consistent with the energy magnitude (10\({}^{5}\)-10\({}^{7}\) J) reported by [PERSON] et al. [13]. However, beyond their static analysis of fracture patterns, this research reveals the dynamic accelerating effect of seismic waves on heavy layer movement via UDEC simulations. The simulations show that applying seismic waves increases heavy layer movement (Table 5), which correlates well with the observed postseismic subsidence spikes (Figure 14), confirming the mechanism by which seismic activity promotes strata movement.  
Figure 16: Comparison of separation space before and after blasting.  
Figure 17: Land subsidence strike monitoring line after blasting.
## 3 End Effector-Based Devices for Gait and Balance Rehabilitation
### Review of End effectors
#### 3.2.1 Commercially Available End Effectors  
In total, 55 end effectors were found in the literature. Among them, only 16 are commercially available, whereas the rest are designs or prototypes used for research purposes. The different commercially available end effectors, together with some of their most important features, are presented in Table 1.  
Most of the commercialized end effectors for gait rehabilitation, such as the Gait Trainer (Reha-Stim, Berlin, Germany) and Lyra (THERA-Trainer, Hochdorf, Germany), consist of two 1-degree of freedom (DOF) footplates that move each foot of the patient according to a fixed trajectory. However, devices such as the G-EO system (REHA Technology, Olten, Switzerland) and Morning Walk (CUREXO Inc., Seoul, South Korea) can simulate more complex movements (e.g., stair climbing) due to the use of two 3-DOF footplates. In general, hybrid training mode is used for gait function recovery, and depending on patient condition, the device supports gait movements completely or partially.  
Regarding direct balance rehabilitation, a great majority of the commercialized end effectors consist of a unique footplate with two rotational DOFs around the mediolateral and anterior-posterior directions (e.g., Balance SystemTM SD). However, there are also end effectors such as DynSTABLE (Motek Medical, Houten, The Netherlands) and EquiTest(r) (NeuroCom International Inc., Clackamas, OR, USA), which have the capacity to introduce translational perturbations. In contrast to gait rehabilitation devices, passive rehabilitation is the predominant training approach provided by balance rehabilitation end effectors.  
There also exist other commercialized end effectors, such as the CAREN (computer-assisted rehabilitation environment) developed by Motek Medical, which are suitable for both gait and balance rehabilitation. The CAREN consists of a 6-DOF platform (Gough-Stewart platform) with a dual-belt treadmill mounted on top [91]. Therefore, patients can walk on the treadmill while ground perturbations introduced by the Stewart platform challenge their stability during gait.  
Although the well-known LokoHelp(r) (Woodway Inc., Waukesha, WI, USA), which is an electromechanical gait device designed to be placed on a treadmill, can be considered an end effector, its motion generation depends mainly on a treadmill. Hence, unlike the CAREN, where the motion of the treadmill and the end effector are decoupled, we are not considering it as an end effector. Likewise with the MRG-P100 (HIWIN Technologies Corp., Taichung, Taiwan), where the movement transmitted to the lower limbs is generated by an end effector, but with the help of an exoskeleton. According to our classification, these devices can be considered hybrid designs.
# Speech Enhancement Algorithms: A Systematic Literature Review
## 4 Results and Discussion
Domain mismatch decreases model performance in testing environments that differ from the training data [51, 76, 80, 92]. Many recent works aim to effectively mitigate this problem [107]. In addition, the high computational complexity of deep learning models means that algorithms based on these models often require significant computational resources [51, 76, 80, 92]. Although DL-based SEAs have made significant strides, challenges such as speech distortion and artifacts persist. These problems diminish perceived auditory quality as well as the accuracy of speech enhancement systems, particularly when employing lightweight models [108]. Other issues include phonetic distortion [61, 83, 92], musical noise [69], and colored noise [69, 96]. Therefore, it is important to address the balance between speech distortion and noise reduction, deal with speech and noise modeling in real situations, and increase quality and intelligibility simultaneously. The main challenges of SEA are shown in Figure 9. It should be noted that this research focused on papers published between 2015 and 2024, providing an overview of recent challenges and trends in SEA research.
## 2 Materials and Methods
### Characterizations  
X-ray diffraction (XRD) was utilized to comprehensively characterize the crystal structure of the samples using a Rigaku DMax 2500 PC diffractometer (Rigaku Corporation, Akishima, Japan) with Cu-K\(\alpha\) radiation. The measurements were conducted over a 20 range of 20 to 90 degrees with a step size of 0.02 degrees and a scanning rate of 5 s for each measurement. The morphology of the samples was then analyzed using SEM-FEG (Zeiss Supra35 (Zeiss, Oberkochen, Germany)), operating at 5 kV. The FT-Raman spectra were recorded using a Bruker MultiRam spectrometer equipped with a liquid nitrogen-cooled Ge diode detector. The excitation light source was an air-cooled Nd:YAG laser operating at a wavelength of 1064 nm. The signal was collected and recorded over a range of 100 to 1700 cm\({}^{-1}\), utilizing a spectral resolution of 1.0 cm\({}^{-1}\) and a laser power output of 100 mW. The FT-IR measurements were performed with KBr using Shimadzu equipment, model IR Prestige 21. It operated in the range of 700 to 3600 cm\({}^{-1}\) with 50 scans and a resolution of 4 cm\({}^{-1}\), utilizing the DRS-800\({}^{a}\) accessory. Photoluminescence (PL) measurements were conducted using a Mini PL/Raman system from Photon Systems (USA), which utilized a 5.0 eV (248.6 nm) laser for excitation, operating at approximately 50 mW with a pulse width of 20 ms. The setup included a 1/8 M monochromator and a PMT detector, resulting in a high-resolution system with a resolution of 0.2 nm. UV-Vis measurements were performed using a UV-Vis-NIR double beam spectrophotometer (Cary-Varian 7000 (Agilent, Santa Clara, CA, USA)) by employing diffuse reflectance, covering the wavelength range from 300 to 1500 nm. The photovoltaic current-voltage (J-V) response of the solar cells was measured at 100 mW/cm\({}^{2}\) (AM 1.5 G) at air temperature (25 \({}^{\circ}\)C) using a solar simulator (SLB-300A, (Sciencetech Inc., London, ON, Canada)) in conjunction with a potentiostat (VersaSTAT3, AMETEK Scientific Instruments, Oak Ridge, TN, USA). Electrochemical impedance spectroscopy (EIS) was conducted in the dark, applying a signal amplitude of 10 mV across a frequency range from 100 kHz to 0.1 Hz, with a potential bias of \(-\)0.75 V.
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
Such pests and pathogens can compromise plant survival and/or fruit quality in the absence of adequate control measures [15; 16]. In organic systems, control inputs, besides being characterized by high prices, are few and specialized literature regarding appropriate control methods is lacking [12]. On the other hand, growers profitability for organic food products is generally higher than IPM ones [12] due to the application of premium prices consumers are willing to pay [17]. Indeed, in developed countries, organic food is considered safer and healthier [18; 19], as well as better for the climate and the environment [20] when compared to IPM grown one. Several studies observed higher contents of polyphenols, vitamins, carotenoids and ellagic acid, as well as fruit dry matter, sucrose, malic acid and minerals, in organic berries [21]. However, other studies came to contrasting conclusions in establishing causalities between improved nutritional properties and organic cultivation system which might be due to the numerous variables interacting with the final fruit quality. Plants are known to host a great diversity of microbes, whose composition and functionalities can shape plant phenotype, performances and productivity [22; 23]. The increasing access to genomic data has allowed a more in-depth investigation of the microbial communities (microbimes) associated to the plant [24] and of their interaction with the host, pointing to a role of crop-associated microbes in agricultural performance and crop quality [25; 26]. Although in the last few years several studies focused either on the microbial diversity present on various fruit species [27] or on the impact of root or foliar microbial applications on nutrient, flavonoid, organic acid contents and volatile compounds levels of fruits [28], a deep understanding of the influence of native fruit epiphytic microbiomes on fruit quality and on fruit volatilome building is still lacking. Cultivation methods can influence the composition and structure of the plant microbiome [29] and differences observed in the nutritional quality of differently grown fruit [30] might be due to variations in the microbiome structure. Although dynamics of the soil microbiota under different cultivation methods have been extensively investigated [31; 32; 33], only a few studies focus on the influence of cultivation practices on fruit microbiomes [29]. Thus, the aim of this work was to comprehensively dissect the effect of organic or IPM cultivation on fruit microbiome, fruit quality characteristics and aroma. Contextually, differences of organic and IPM raspberry fruit-associated microbiomes were examined by next-generation sequencing (NGS) and bacterial isolates characterization in order to highlight the potential contribution of the resident microflora to fruit quality and aroma.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods
### Measurement-Level Hybridization Method
#### 2.2.3 Hybrid Solution Using GNSS and 4G Mobile Network Pseudoranges
These constraints limit the direct application of multipath mitigation techniques at this stage. In contrast, the measurement-level hybridization method does utilize GNSS pseudorange measurements and applies the corresponding corrections for satellite geometry, clock biases, atmospheric delays, and residual multipath effects, as shown in Equation (14). For the cellular pseudorange values, however, no raw time-based measurements are available; instead, the distances are indirectly computed based on UE coordinates, which are presumed to have been derived from internal network-side corrections. This user-level approach ensures compatibility with commercial devices, but imposes certain limitations on low-level error modeling.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## 6 Log Ratio of Entropy Powers
The key points are first that the entropy power is the smallest variance that can be associated with a given differential entropy, so the entropy power is some fraction of the mean squared error for a given differential entropy. Second, Equation (11) utilizes the ratio of two entropy powers, and thus, if the distributions corresponding to the entropy powers in the ratio are the same, the scaling constant (fraction) multiplying the two variances will cancel out. Therefore, we are not saying that the mean squared errors equal the entropy powers in any case but for Gaussian distributions. It is the new quantity, the log ratio of entropy powers, that enables the use of the mean squared error to calculate the loss in mutual information at each stage.
###### Abstract  
A series of novel p-type conjugated copolymers, PTTVBDT, PTTVBDT-TPD, and PTTVBDT-DPP, cooperating benzo[1,2-b-4,5-b\({}^{\prime}\)][dithiophene (BDT) and terthiophene-vinylene (TTV) units with/without thieno[3,4-c]pyrrole-4,6-dione (TPD) or pyrrolo[3,4-c]pyrrole-1,4-dione (DPP) via Stille polymerization were synthesized and characterized. Copolymer PTTVBDT shows a low-lying HOMO energy level and ordered molecular-packing behavior. Furthermore, two terpolymers, PTTVBDT-TPD and PTTVBDT-DPP, display stronger absorption ability, alower-lying HOMO energy level, and preferred molecular orientation, due to the replacement TTV-monomer units with electron-deficient groups. Furthermore, bulk-heterojunction organic solar cells were fabricated using blends of the PTTVBDT-TPD, and PC\({}_{61}\)BM gave the best power conversion efficiency of 5.01% under the illumination of AM 1.5G, 100 mW\(\cdot\)cm\({}^{-2}\); the short circuit current (\(J_{\mathrm{sc}}\)) was 11.65 mA\(\cdot\)cm\({}^{-2}\) which displayed a 43.8% improvement in comparison with the PTTVBDT/PC\({}_{61}\)BM device. These results demonstrate a valid strategy combining the two-dimensional molecular structure with random copolymerization strikes promising conjugated polymers to achieve highly efficient organic photovoltaics.  
two-dimensional conjugated polymer; polymer solar cell; benzo[1,2-b-4,5-b\({}^{\prime}\)]dithiophene; thieno[3,4-c]pyrrole-4,6-dione; pyrrolo[3,4-c]pyrrole-1,4-dione; conjugated side chain.
## 5 Discussion
In regard to the sample with two diagonal openings, the SCFs on the roofs of the lower-left and upper-right openings were \(-0.88\) and \(-0.93\), respectively, whilst those on the floors were \(-0.97\) and \(-1.03\), respectively. Thus, the elementary tensile crack occurred first on the floor of the upper-right opening, followed by that on the bottom of the lower-left opening. No elementary tensile crack occurred on the roofs of the openings because of the small size of the opening. The corresponding maximum SCFs on the sides of the two openings were \(2.59\), \(2.64\), \(2.58\) and \(2.49\), respectively, whose order was consistent with the initiation sequence of the slabbing cracks. With respect to the sample containing two vertical openings, the maximum SCFs on the roofs (\(-0.78\) and \(-0.66\)) and floors (\(-0.76\) and \(-0.86\)) of the upper and lower openings were relatively small because the opening falls in the unloading areas of each other. This also proved that the crack initiation stresses of the samples V-3 and D-2 were the largest and lowest, respectively. Besides, the maximum SCFs on the sides of the upper and lower openings were all approximately \(2.30\), which were smaller than those of the samples with two horizontal and diagonal openings. Consequently, the slabbing cracks of the sample containing two vertical openings appeared after the posterior tensile cracks. In summary, the elastic stress distributions round the openings effectively illustrated the development mechanism of the cracks. However, to get close to the true stress state of tunnel in rock engineering, further investigations on the samples with openings under biaxial and triaxial compression would be conducted in the future work. In addition, more inclination angles and lengths of rock bridge would be considered.
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 7 Result Discussion
### Quantitative Comparison
Most publicly accessible datasets were not collected from log files, making them unable to properly represent HTTP-GET flood attack behaviors. The proposed dataset is different from the existing dataset in the following ways:  
* Real-World Implementation in CCEs: Unlike many datasets generated in simulated or virtual environments, the proposed dataset is implemented in a real CCE environment. This real-world implementation ensures that the dataset reflects the complexities and nuances present in actual CCE scenarios, providing a more accurate representation of the challenges faced by modern Intrusion Detection Systems. * Focus on Specific Attack Type: The dataset is specifically tailored to focus on HTTP-GET flooding distributed denial-of-service (DDoS) attacks. While some existing datasets may cover a wide range of attack types, the proposed dataset's dedicated focus allows for in-depth analysis and specialized detection techniques for this prevalent and critical threat. * CCE-Specific Features from Multiple Sources: The proposed dataset is enriched with CCE-specific features collected from three distinct sources: access logs, CCE logs, and VM resources. Our dataset of diverse data sources provides our data with unique advantage over existing datasets that lack CCE-specific features. Incorporating information from various sources within the CCE environment makes the dataset more comprehensive and representative of real-world network scenarios. This distinction is crucial because the availability of CCE-specific features allows IDSs to leverage context-aware information and tailor their detection mechanisms to the specific characteristics of CCEs. As a result, the proposed dataset empowers researchers to design IDSs that are better equipped to detect and mitigate threats in CCEs effectively. Finally, we suggest the following mitigation plan to prevent HTTP-GET flooding attacks on CCEs:  
* Integrating a trustworthy DDoS protection service that specializes in mitigating HTTP GET deluge attacks is crucial. This service can detect and block malicious traffic before it reaches the target system, ensuring it is accessible to legitimate users. * Implementing rate-limiting and throttling mechanisms on the targeted system controls the number of GET requests from specified IP addresses or users within a given time. This mitigates the effects of deluge attacks by preventing a single source from saturating the system with excessive requests. * It is essential to deploy a web application firewall (WAF) specifically configured to detect and thwart malicious HTTP GET inundation attempts. The WAF can analyze incoming traffic, recognize suspicious patterns, and implement predefined rules to differentiate between legitimate requests and attack traffic, thereby increasing the system's resistance to deluge attacks.
## 3 Dataset and Network Training
### Scene and Weather Adaptation Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & **Number of Actual** & **Number from Statistics** & **Accuracy rate (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline Sunny & 6823 & 6480 & 94.97 & 97.27 & 92.37 \\ Cloudy & 5922 & 5895 & 99.54 & 94.66 & 94.23 \\ Rainy & 8517 & 7106 & 83.53 & 96.36 & 80.40 \\ Night & 5731 & 5727 & 99.90 & 90.05 & 90.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: YOLO-UA multi-environment traffic statistics. Figure 5: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & **Number of Actual** & **Number from Statistics** & **Accuracy Rate (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline Sunny & 6823 & 4590 & 67.27 & 97.62 & 65.87 \\ Cloudy & 5922 & 4853 & 81.95 & 98.29 & 80.50 \\ Rainy & 8517 & 5461 & 64.11 & 99.44 & 63.05 \\ Night & 5731 & 4259 & 74.31 & 99.02 & 63.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: YOLO multi-environment traffic statistics. The YOLO model cannot predict multi-scale anchor boxes. Each grid only predicts two bounding boxes, which limits the target positioning with different sizes. This can easily cause missed statistics in vehicle monitoring, indirectly affecting the accuracy of the results. Optimization ameliorated the false detections and missed detections, and the resulting network generalizations were better.
## 4 Results and Discussion
### SDL Model Implementation on Type-1 Datasets: Forward Wave Propagation Simulation
#### 4.1.2 Ultrasonic Wave Propagation on Different Geometrical Domain Modelling
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Datasets of Testing**} & \multicolumn{3}{c}{**MAE**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**MAPE (\%)**} \\ \cline{3-14}
**Line Scan** & \multicolumn{3}{c}{**and Different**} & \multicolumn{3}{c}{**Sequence of Images at**} & \multicolumn{3}{c}{**Sequence of Images at**} & \multicolumn{3}{c}{**Sequence of Images at**} & \multicolumn{3}{c}{**Sequence of Images at**} \\ \cline{3-14}  & \multicolumn{3}{c}{**Geometrical Domain**} & **T-1** & **T-2** & **T-3** & **T-4** & **T-5** & **T-1** & **T-2** & **T-3** & **T-4** & **T-5** & **T-1** & **T-2** & **T-3** & **T-4** & **T-5** \\ \hline \multirow{2}{*}{Amplitude} & Geometry-1 & 0.00 & 0.02 & 0.05 & 0.04 & 0.04 & 0.05 & 0.12 & 0.14 & 0.20 & 0.24 & 9.50 & 25.2 & 30.6 & 75.6 & 92.7 \\  & Geometry-2 & 0.00 & 0.01 & 0.06 & 0.07 & 0.06 & 0.07 & 0.14 & 0.20 & 0.21 & 0.19 & 115.9 & 16.4 & 22.5 & 34.8 & 39.6 \\ \hline \multirow{2}{*}{Time of Flight} & Geometry-1 & 0.00 & 0.17 & 0.17 & 0.83 & 1.42 & 0.00 & 0.41 & 0.41 & 1.63 & 3.15 & 0.00 & 0.28 & 0.24 & 1.62 & 3.34 \\  & Geometry-2 & 0.29 & 0.21 & 0.21 & 0.21 & 0.21 & 0.53 & 0.46 & 0.46 & 0.46 & 0.46 & 0.46 & 0.39 & 0.46 & 0.48 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forward wave propagation on different geometrical domains datasets: the line scan amplitude and time of flight (TOF) differences between SDL and FE are computed using MAE, RMSE, and MAPE methods on the sequence of images at different time instances in Figure 5.  
Figure 5: Line scans of different geometrical domains: the displacement values are extracted at x = 15 mm and y = 0 to 30 mm in each frame from the FE, and SDL-generated simulation of the different geometrical domains for different time instances in Figure 4. **(a)** The line scans were extracted from Figure 4a for geometry-1, and **(b)** the line scans were extracted from Figure 4b for geometry-2.
## 2 Development of SH Model
### Modeling the FC  
FCs have various types depending on their fuel types and construction. Figure 2 shows the main components of a typical FC. It consists of a fuel processing unit, a stack, and a power conditioning unit. A proton exchange membrane FC is used in this work whose input is natural gas and outputs are electrical power and heat.  
Figure 1: A smart house overview.  
The efficiency of an FC depends on its part load ratio (PLR). The PLR is the ratio of electrical power generated by the FC at the \(i\)-th interval to its power capacity rating as given in Equation (1):  
\[PLR_{i}=P_{FC,i}/P_{FC_{max}} \tag{1}\]  
where \(PLR_{i}\) is the PLR at \(i\)-th interval for \(P_{FC,i}\) output in this interval.  
Mathematically, FC's efficiency \(\eta_{FC}\) and its thermal to electrical power ratio (\(r_{TE,i}\)) are represented for the \(i\)-th interval as follows:  
When \(PLR_{i}<0.05\):  
\[\eta_{FC,i}=0.2716 \tag{2}\]  
\[r_{TE,i}=0.6816 \tag{3}\]  
When \(PLR_{i}>0.05\):  
\[\eta_{FC,i}= 0.9033 PLR_{i}^{5}-2.9996 PLR_{i}^{4}+3.6503 PLR_{i}^{3}-2.0704 PLR_{i} ^{2}+0.4623 PLR_{i}+0.3747 \tag{4}\] \[r_{TE,i}= 1.0785 PLR_{i}^{4}-1.9739 PLR_{i}^{3}+1.5005 PLR_{i}^{2}-0.2817 PLR_{ i}+0.6838 \tag{5}\]  
Next, the thermal power \((H_{FC,i})\) generated by the FC at interval \(i\) is determined as:  
\[H_{FC,i}=r_{TE,i}P_{FC,i} \tag{6}\]  
Figure 3 shows the relationship between PLR and the efficiency of FC. At low PLR (<5%), the efficiency of FC is low due to high parasitic losses [34]. Beyond this low PLR region, the FC works at 30-40% efficiency. Looking at the efficiency curve, it is evident that \(\eta_{FC}\) is comparatively higher at lower PLR regions in contrast to the regions of FC's peak power operation.  
Figure 2: Typical Model of an FC.