esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Improving Effects of AAE by Electrical Conductivity Imaging  
MR-based electrical conductivity imaging of phantom, which consisted of four liver blocks, was performed for image-based analysis of hepato-protective effect. From morphological MR images (Figure 6a), significant difference in contrast was not observed between the groups. However, electrical conductivity images showed clear difference in contrast between the groups (Figure 6b). Specifically, the conductivity contrast was the highest in the normal group and the lowest in the DMN-only group. The contrasts in DMN + AAE and DMN + Lac groups were higher than that of the DMN-only group.  
For image-based analysis, conductivity values were measured in whole liver tissue from four groups (Figure 6c). The conductivity was the highest in the normal group (52 \(\pm\) 2 mS/m) and lower in the order of DMN + AAE (48 \(\pm\) 4 mS/m), DMN + Lac (46 \(\pm\) 4 mS/m), and DMN-only (41 \(\pm\) 2 mS/m) groups. Based on the value of the normal group, the difference was 7.7% for DMN + AAE, 11.5% for DMN + Lac, and 21.2% for the DMN-only group. The tissue conductivity of the DMN + AAE group was closer to that of the normal group which may also indicate the hepato-protective effect against DMN as well as the results of western blot analysis.  
Figure 5: Western blot analysis of \(\alpha\)-SMA and COX-2 protein in four experimental groups. Protein levels of \(\alpha\)-SMA (**a**) and COX-2 (**b**) in liver tissues were examined using anti-\(\alpha\)-SMA and anti-COX-2 specific antibody. Data was normalized against \(\beta\)-actin levels and expressed as a percentage compared with normal values. * \(p\) < 0.05 vs. normal liver group, # \(p\) < 0.05 vs. DMN-only group.  
From the conductivity spectra obtained from in vitro measurement using an impedance analyzer (Figure 6d), the measured conductivity was the highest in the normal group and lower in the order of DMN + AAE, DMN + Lac, and DMN-only groups. However, the absolute conductivity values were lower than that of imaging results due to the frequency dependence, but gradually increased with the measuring frequency.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 4 Experiments
### Quantitative Evaluation  
For the quantitative evaluation, we compare the temporal consistency and attribute differences among the results of ours and others. We use the stability error \(e_{stab}\) to measure the temporal consistency:  
\[e_{stab}(O_{t},O_{t-1})=M^{f}\odot||O_{t}-\mathcal{W}_{t-1}^{t}(O_{t-1})||^{2}, \tag{15}\]  
where \(e_{stab}(O_{t},O_{t-1})\) measures the coherence between two adjacent output \(O_{t}\) and \(O_{t-1}\), \(M^{f}\) is the facial area mask, \(\mathcal{W}_{t-1}^{t}(\cdot)\) is the function to warp \(O_{t-1}\) to time step \(t\) using the ground truth backward flow as defined in [28], \(O_{t}\) and \(O_{t-1}\) are the results of frame \(t\) and \(t-1\). Here, we only evaluate stability in facial regions. Lower stability error indicates more stable results. For the entire video, we use average errors instead. As shown in Table 1, Our method outperforms all mentioned methods which means that our method produces more steady results.  
We also evaluate the attribute differences, including gaze direction, pose, 2d landmark, and 3d landmark with Openface [29]. A lower difference indicates better inheritance. As shown in Table 2, our method can inherit more attribute information than previous methods.  
\begin{table}
\begin{tabular}{c|c c c c} \hline
**Methods** & **DF** & **FS** & **FSGAN** & **FShift** \\ \hline \(e_{stab}\) & 1.471 & 1.518 & 1.498 & 1.214 \\ \hline Ours-\(e_{stab}\) & 0.944 & 1.026 & 0.928 & 0.930 \\ \hline \end{tabular}
\end{table}
Table 1: Temporal coherence \(e_{stab}\) comparison of different face-swapping methods. DF denotes Deepfake, FS denotes FaceSwap, FSGAN denotes FSGAN, and Fshift denotes FaceShifter. Our framework can reduce the stability error of swapped results which represent better temporal coherence.  
\begin{table}
\begin{tabular}{c|c c c c} \hline
**Methods** & **Gaze** & **Pose** & **2D Imk** & **3D Imk** \\ \hline DF & 2.360 & 2.827 & 3.302 & 3.500 \\
**Ours-DF** & **2.038** & **2.611** & **3.055** & **3.270** \\ \hline FS & 3.555 & 0.864 & 1.639 & 1.581 \\
**Ours-FS** & **2.665** & **0.729** & **1.379** & **1.340** \\ \hline FSGAN & 2.803 & 1.469 & 1.768 & 1.801 \\
**Ours-FSGAN** & **2.226** & **1.290** & **1.560** & **1.609** \\ \hline FShift & 2.471 & 1.085 & 1.750 & 1.801 \\
**Ours-FShift** & **2.201** & **0.945** & **1.650** & **1.647** \\ \hline \end{tabular}
\end{table}
Table 2: Quantitative comparisons among different face-swapping methods of gaze direction, pose, 2D landmarks, and 3D landmarks. Our method apparently reduces the attribute differences which represents that our method can better inherit the attributes from the target video.
## 4 Ground Segment  
The setup and operation of the EnMAP ground segment is under the responsibility of the Earth Observation Center (EOC) and the German Space Operations Center (GSOC) at the DLR [52]. The ground segment completed its design phase by successfully passing the critical design review in 2010 and is now in production phase.  
The ground segment is organized into 15 systems covering all relevant aspects to assure successful mission operations. This comprises controlling and commanding the satellite using multi-mission infrastructures, as well as data reception, hyperspectral data processing ( including calibration), data archiving, data dissemination and provision of web-interfaces to the international user community. Two major topics of particular interest for the users are addressed next. These are the handling of image acquisitions based on user requests and the generation of standardized products.
## 2 Materials and Methods
### Data Sources and Preprocessing  
#### 2.2.1 Landsat Datasets  
On the Google Earth Engine (GEE) platform, a total of 570 Landsat images were selected from January 1999 to December 2023. Detailed information regarding the Landsat images used in this study is provided in Table 1. During data selection, images with cloud cover exceeding 20% were excluded. Subsequently, atmospheric correction, radiometric calibration, cloud removal, and band combination were performed on the platform to enhance accuracy and minimize topographic and atmospheric interference [37]. Image cropping was conducted using the Quality Mosaic algorithm, which further mitigates the
## 3 Results  
### The Results of Physicochemical Parameters (Water Content, pH, and Water Activity)  
During the dry-curing process, a progressive diminution of water content was observed due to the water losses in all sausages (Table 1). No significant differences were observed among batches, so the gradual losses were similar in all cases. On the last day of the study, the sausages lost half of their water content, from an average of 66% on Day 1 to 33% on Day 30. On the first day of analysis, all batches registered the highest pH values except the control, where the highest was at Day 30 (5.92) (Table 1). Moreover, the pH did not vary among the samples on the first day (5.69-5.78). The pH decreased up to 20 days of ripening, reaching the lowest values on the same day. However, on Day 30, there was a slight increase. It should be noted that the control sausages showed the highest pH on the rest of the sampling days (5.56, 5.27, and 5.92 at 10, 20, and 30 days, respectively). Conversely, the sausages with chia seeds were characterized by the lowest pH.  
As in the case of water content, the water activity in the samples decreased significantly throughout 30 days (Table 1). There were no significant differences within the different sausages regardless of the day of the study. The samples on Day 1 were characterized by water activity in the range of 0.968-0.973, while on Day 30, they were characterized by water activity in the range of 0.798-0.813.
## 3 Case Study
### Initial Finite Element Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{\
\begin{tabular}{c} **f-Simulation** \\ **(Hz)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **f-Measurement** \\ **(Hz)** \\ \end{tabular} } & \multirow{2}{*}{**Error (\%)**} & \multirow{2}{*}{**MAC**} & \multirow{2}{*}{**Type**} \\ \hline
1 & 7.102 & 7.47 & 4.92 & 0.87 & 1 st torsion \\
2 & 9.282 & 8.62 & 7.67 & 0.85 & 1 st vertical bending \\
3 & 23.472 & 24.99 & 6.07 & 0.86 & 2 nd torsion \\
4 & 38.227 & 36.16 & 5.71 & 0.63 & bending \\
5 & 46.105 & 48.81 & 5.54 & 0.66 & 3 rd torsion \\ \hline \hline \end{tabular} \(\overline{\text{Error}=1f_{simulation}-f_{measurement}\text{ }\text{ }\times 100/f_{measurement}}\).  
\end{table} 
Table 3: The results of the FE model’s dynamic analysis. uncertain parameters such as material properties, stiffness parameters, etc. For this reason, it is recommended to perform a model update procedure to reduce errors.
## 2 Materials and Methods
### Formalization of the Problem  
#### 2.1.1 Periodicity Correctness Model  
Consider data series with periodically posted samples into an archive: they may be generated not only by immovable equipment, but also wearable devices, like biomedical electrocardiogram sensors. Hardware provides data on demand of software, which usually performs periodic requests and sends data to the cloud [22; 23; 24; 25]. If periodicity of the incoming data stream is violated, it is highly likely to have an attack, if a simple loss of network connection is not the case. The following describes the proposed model of periodicity correctness for incoming data streams. It allows some variance of the periodicity to handle rare network connection issues.  
Let \(X=\left\{X_{1},X_{2},\ldots,X_{1}^{r},X_{2}^{r},\ldots,X_{n}^{r},\ldots,X_{1}^ {d},X_{2}^{d},\ldots,X_{m^{\prime}}^{d},\ldots\right\}\) be the time series of considered modality. It is divided into two parts: reference part (samples denoted as \(X_{\bullet}^{r}\)) and a part for analysis (samples denoted as \(X_{\bullet}^{d}\)).  
Let \(time(X_{\bullet}^{r})\) be the function returning timestamp of the sample \(X_{\bullet}^{r}\) passed as the argument.  
Let \(t_{i}^{r}=time(X_{i+1}^{r})-time(X_{i}^{r}),i=1,\ldots,n-1\) be the time interval of two samples in the reference part.  
Then average time interval for the reference part \(\frac{1}{n-1}\sum\limits_{i=1}^{n-1}t_{i}^{r}\) must belong to \([T^{r}-\Delta^{r};T^{r}+\Delta^{r}]\), where \(T^{r}\) is the expected period, \(\Delta^{r}\) is allowed variation of the expected period. Both are user-defined external parameters represented by positive datetime values.  
Consider \(t_{j}^{d}=time(X_{j+1}^{d})-time(X_{i}^{d}),j=1,\ldots,m-1\) as the time interval of two samples in the analyzed part. The average value \(\frac{1}{m-1}\sum\limits_{i=1}^{m-1}t_{j}^{d}\) must also belong to \([T^{r}-\Delta^{r};T^{r}+\Delta^{r}]\)and \(\forall j\in\{1,2,\ldots,m\}\left(k_{l}\min_{i=1,2,\ldots,n}\left(t_{i}^{r}\right) \leq t_{j}^{d}\leq k_{u}\max_{i=1,2,\ldots,n}\left(t_{i}^{r}\right)\right)\) for the period of the analyzed part to be considered as valid, where \(k_{l}\) and \(k_{u}\) are user-defined external parameters represented by positive real values.
## 3 Results and Discussions
### The Thermal Performance of the Drying System  
Based on the analysis above, the energy distribution of the whole drying system for the optimized drying process was concluded by a Sankey diagram and shown in Figure 9. As can be seen from the figure, the energy transmission and conversion of the system mainly occurred at the CC, HE, RA and DC. During the drying process, 1807.56 kW of energy was carried by the coal and delivered into the CC, and 1734.04 kW of thermal energy \(\overset{\bullet}{(\mathcal{Q}_{fg,in})}\) was produced by burning the coal, while 73.52 kW of thermal energy \(\overset{\bullet}{(\mathcal{Q}_{loss,CC})}\) was lost in the CC, which might have been caused by surface irradiation and convection, heat loss in the discharged ash and inadequate combustion of the coal. The \(\overset{\bullet}{Q}_{fg,in}\) into the HE was mainly divided into three parts: the thermal energy carried by the hot air \(\overset{\bullet}{(\mathcal{Q}_{air,in})}\), the thermal energy input into the RA \(\overset{\bullet}{(\mathcal{Q}_{fg,out})}\) and the thermal energy loss in HE \(\overset{\bullet}{(\mathcal{Q}_{loss,HE})}\). The values of the \(\overset{\bullet}{Q}_{air,in}\), \(\overset{\bullet}{Q}_{fg,out}\) and \(\overset{\bullet}{Q}_{loss,HE}\) were ascertained to be 1096.59, 444.26 and 193.19 kW, respectively. As for the RA, the \(\overset{\bullet}{Q}_{fg,out}\) was considered as the fuel of the RA, while the \(\overset{\bullet}{Q}_{FIR}\) was considered as the product of the RA. The average value of the \(\overset{\bullet}{Q}_{FIR}\) was ascertained to be 98.15 kW, i.e., 98.15 kJ of thermal energy was recycled to the DC per second; however, 346.11 kW of thermal energy in the outlet flue gas of the RA was still wasted, indicating that the \(\eta_{RA}\) should be further improved by using other alternative material of the radiators [43]. Accordingly, as can be seen from Figure 9, there were two fuel energy flux inputs into the DC, and the energy inputs into the DC were mainly consumed by four aspects: thermal energy used for evaporating moisture from the wet corn material \(\overset{\bullet}{(\mathcal{Q}_{eup})}\), thermal energy loss carried by the outlet corn material \(\overset{\bullet}{(\mathcal{Q}_{e,out})}\), thermal energy loss in outlet hot air \(\overset{\bullet}{(\mathcal{Q}_{air,out})}\) and the thermal energy loss in DC \(\overset{\bullet}{(\mathcal{Q}_{loss,DC})}\), and the values of \(\overset{\bullet}{Q}_{eup}\), \(\overset{\bullet}{\mathcal{Q}_{e,out}}\), \(\overset{\bullet}{Q}_{air,out}\) and \(\overset{\bullet}{Q}_{loss,DC}\) were ascertained to be 384.74, 357.72, 175.49 and 178.64 kW, respectively.  
Figure 9: The Sankey diagram for the thermal analysis of the overall drying system.
## Materials and Methods
### Contact Angle Measurement  
Contact angle was measured using self-fabricated equipment as shown in Figure 3[43]. A 5 \(\mu\)L water droplet was put onto the rubber surface using a micrometric syringe and the droplet image was captured by a digital microscope installed inside the equipment as shown in Figure 4a. The contact angle of the droplet was measured using the Image J software. The angle was measured between the tangent to the water at the touch point and the solid rubber surface as shown in Figure 4b. For each sample, the contact angle was measured for six times to ensure measurement accuracy.  
Figure 4: **(a)** Image of the water droplet and **(b)** contact angle measurement with Image J software.  
Figure 3: Contact angle measurement equipment.
# The Role of Plant Growth-Promoting Rhizobacteria (PGPR) in Mitigating Plan's Environmental Stresses
## 2 Properties and Potential of Plant Growth-Promoting Rhizobacteria (PGPR)
### Phosphate Solubilization  
Phosphorus (P) is an essential element for plant growth but is scarcely available in the soil. P occurs naturally in organic (Po, average 50%) and inorganic (Pi, average 50%) forms [59]. They are both not very soluble (generally no more than 5%) and therefore cannot be absorbed by the roots. The action that soil bacteria can carry out is fundamental for the solubilization and mineralization of P [60]. Plants can absorb monobasic (H\({}_{2}\)PO\({}^{-4}\)) and dibasic (HPO\({}^{-24}\)) ions. The solubilization of Pi occurs mainly with the decrease of pH in the soil due to the production of low molecular weight organic acids. The mineralization of organic phosphorus, on the other hand, occurs through the hydrolysis of phosphoric esters by phosphatase [49].  
The availability of sufficient quantities of P following the activity of phosphate solubilizing bacteria (PSB) could lead to a significant decrease in the use of chemical fertilizers. The beneficial effect on plants also promotes greater efficiency of phytoextraction or phytostabilization in the presence of heavy metals.
# A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow
## 4 Results
### Cloud Gaming under Mobile Networks
In detail, we perform experiments by using a 4G good network profile as, currently, it is the best quality and most widespread network, and with a 3G good network profile, as it is the lowest quality profile showing enough bandwidth to support a gaming session. To reproduce the mobile network variability, we set ERRANT to resample the network condition every 10 s; i.e., we pick and apply new constraints for the download rate, upload rate, and latency from the selected profile. Figure 6 reports an example of the frame rate (right y-axis), the packet loss (left y-axis), and the video resolution quality when a sudden change happened during a gaming session while using the 4G good profile. Interestingly, we experienced stable performance for most gaming sessions, with no packet loss, 60 FPS, and 1080p resolution. Only when ERRANT picks a download bandwidth below 10 Mbit/s do we experience, for a short time, a reduction in frame rate and resolution down to 20 FPS and 720p, respectively, with there seldom being an increase in packet loss. Interestingly, the Stadia platform can quickly react and adapt itself by reaching a frame rate of 60 FPS and resolution at 720p, rising again to 1080p as soon as a better bandwidth is available. With a 3G good network profile, instead, we could not run an entire gaming session, as the network variability introduced by the mobile network caused the game to stop suddenly. This shows how the promising benefits of these solutions currently have limitations that could be overcome with the increasing popularity of fast mobile network technologies--4G and, in particular, 5G. _Take away: Stadia cannot run in most of the cases with a bad 3G network. With proper signal strength, 3G allows a resolution of 720p. With 4G, it is possible to achieve higher quality levels, and good signal strength allows 4K video resolution in 40% of situations. Controlled experiments reveal how Stadia quickly reacts to deteriorated network conditions._
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 4 Virtual Network Function as A Service, Overview  
The scope of this section is to illustrate the operation of the NGPaaS workflow, via a demonstrable use case. It outlines a service scenario called \"VNF-as-a-Service\" (VNFaaS), which is based on the CORD platform. More specifically, this section highlights the composition of both the platform and services as RFB graphs and their subsequent deployment in their corresponding execution environment (IaaS for CORD, PaaS for the services).
## 3 Damage Identification Paradigm
### Formation of Damage Features Using AANNs  
#### 3.1.1 Wavelet Packet Node Energies (WPNEs)  
The WPNEs of structural dynamic responses are newly emerging damage indices used in intelligent damage identification methods in recent studies [29; 51]. Let \(f(t)\) be an acceleration response of a structure subjected to an excitation. The WPNEs can be defined on \(f(t)\) as:  
\[E(i,j)=\sum_{k}\left|d_{j}^{i}[k]\right|^{2} \tag{9}\]  
\[d_{j}^{i}[k]=\left\langle f,u_{j,k}^{n}(t)\right\rangle=\int f(t)u_{j,k}^{n}(t) \tag{10}\]  
where \(i\) labels the \(i\)th wavelet packet node at the \(j\)th layer, \(d_{j}^{i}[k]\) is the WPT coefficient of the \(i\)th wavelet packet node, \(k\) is the translation parameter, and \(u_{j,k}^{n}(t)\)is the wavelet packet function. The principle of using WPNEs to represent damage is that damage can modulate the structural dynamic response, and therefore it can change the WPNE distribution derived from the dynamic response. Compared to traditional dynamic features such as frequency or modal shape, WPNEs are much more sensitive to damage. Moreover, WPNEs are more robust than some newer damage features such as the WPT coefficient. In what follows, WPNEs are used to characterize damage.  
The operation of WPNEs in characterizing damage are related to the wavelet packet function and the decomposition level of the WPT. In this study, the wavelet packet function is set at Daubechies4 (db4) and the number of decomposition layers is set at six. The choice of the db4 wavelet as the mother wavelet in the WPT to decompose the acceleration for damage identification is based on the trial-and-error method. The trial-and-error method is a general method for determining the proper mother wavelet for use in the WT-based structural damage detection, typically referring to [42; 51]. Equations (9) and (10) give rise to the vector of WPNEs, \(e_{p}=\left(e_{1,p},e_{2,p},\cdots e_{64,p}\right)^{T}\). Although this vector carries sufficient damage information, its high dimensionality causes considerable difficulty in the efficient representation of damage. In particular, it is unfeasible to employ the vector of WPNEs as damage features to frame an intelligent damage pattern recognition paradigm. This instance entails a procedure of extracting damage features from WPNEs. The extracted damage features should have the characteristic of low dimensionality and also preserve the damage information of the WPNEs.
## 8 Discussion
Considering the high cost of logistic support for polar exploration, scientific drilling of subglacial lakes is often costly. Therefore, similar to the launch vehicle concept, maximizing the data obtained through a single borehole should be considered. Projects, such as SPINDLE and TRIPLE, are exploring the use of AUVs for comprehensive subglacial lake detection. Currently, the RECAS scientific load platform can only measure temperature, electroconductivity, pH, and water pressure. To acquire comprehensive subglacial lake environmental data, targeted detection instruments for methane, dissolved oxygen, and other parameters should be integrated. The deep-sea soft robot developed by Zhejiang University [29], characterized by its small structural size, could be an ideal payload customized for the RECAS sonde, enabling the large-scale observation of subglacial lakes with various sensors. It is anticipated that an optimized RECAS sonde will play a significant role in the future polar subglacial lakes detection.
## 2 Materials and Methods
### Construction of Moisture Content Prediction Model
#### 2.3.4 Model Evaluation  
Root-mean-square error (RMSE) and coefficient of determination (R\({}^{2}\)) metrics are used for assessment and comparison. A smaller RMSE indicates higher accuracy, and the R\({}^{2}\) closer to 1 indicates a better fit of the model to the data.
## 2 Materials and Methods
### Statistical Analyses  
Data are presented as means and standard deviations (Mean \(\pm\) SD). Statistical analyses were conducted using SPSS version 25.0 for Windows (IBM SPSS, Chicago, IL, USA). Statistical significance was set at \(p<0.05\), with a 95% confidence interval. The normality of the data distribution was assessed using the Shapiro-Wilk test, and homogeneity of variances between groups was evaluated using [PERSON]'s test. All variables met the assumptions of normality (\(p>0.05\)) and homogeneity of variances (\(p>0.05\)), allowing the use of parametric test for data analysis. A two-factor mixed analysis of variance (ANOVA) test was employed to assess the effects of \"time\" (within-subject factor: pre- vs. post-intervention) and \"training group\" (between-subject factor: LL-HV vs. HL-LV) on bench press MV at various relative loads (80%, 85%, 90%, and 95% 1 RM), maximal bench press absolute (1 RM) and relative (body mass, and Wilks and IPF-GL points) strength, and anthropometric variables (arm and chest circumference). The magnitude of the changes was assessed using Hedges' g effect size (ES), along with 95% confidence intervals. ES was calculated using pre-test SD for within-group and pooled pre-test SD for between-group comparisons. ES magnitudes were classified as: trivial (\(<\)0.20), small (0.20-0.59), moderate (0.60-1.19), large (1.20-2.00), and extremely large (\(>\)2.00) [44].