esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitations  
Several limitations should be considered when interpreting the findings of this review. One key limitation is the heterogeneity among studies in terms of intervention protocols, outcome measures, and follow-up durations. The included studies varied significantly in their approach to rehabilitation, with some focusing on strengthening and motor control exercises, while others incorporated manual therapy or neuromuscular training. This variability makes direct comparisons challenging and limits the ability to establish standardized rehabilitation guidelines for FAIS. Additionally, follow-up periods ranged from a few months to two years, making it difficult to determine the long-term sustainability of the observed improvements, particularly in pain relief and functional recovery.  
Another limitation is the inconsistency in diagnostic criteria across studies. While some trials used the imaging-based confirmation of FAIS, others relied on clinical examination alone, potentially leading to variability in patient selection. This heterogeneity raises concerns about the generalizability of findings, as patients with different structural presentations or symptom severity may respond differently to treatment. Furthermore, most studies included relatively small sample sizes, which may have limited their statistical power to detect subtle but clinically relevant differences between treatment approaches. The reliance on patient-reported outcomes also introduces potential bias, as the subjective assessments of pain and function may be influenced by individual expectations and placebo effects.  
Lastly, the lack of standardized rehabilitation protocols across studies limits the ability to determine the most effective combination of conservative treatments. While some studies demonstrated strong outcomes with exercise therapy alone, others included multimodal interventions, making it difficult to isolate the effects of specific rehabilitation components. Future research should aim to develop more standardized physiotherapy protocols and identify patient subgroups that may benefit most from conservative management.
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 4 Discussion  
### Estimating GPP and Drought's Effects on GPP  
In this study, special attention was given to determining water stress (including atmosphere vapor pressure deficit, soil moisture content and the humidity deficit) in the LUE model to estimate the global GPP. We found that different LUE models have a good fit-in estimating the LUE GPP (R\({}^{2}\) exceed 0.7). [PERSON] et al. [42] showed that the range of global GPP estimated by different light energy utilization efficiency models was 95-140 Pg C yr\({}^{-1}\), which is consistent with this study's estimation results. Generally, because of the lack of water during drought periods, vegetation's stomatal conductance tends to decrease to minimize water loss and prevent hydraulic conductivity loss [43; 44], which reduces GPP. Many studies have shown that drought reduces terrestrial ecosystem carbon sinks significantly, and may even transform them into carbon sources [13; 45]. However, our results showed that approximately 30% of the global GPP values increased after being disturbed by drought (Figure 2b), a phenomenon attributable to climatic factors' influence. Our results showed that GPP is correlated negatively with temperature and solar radiation in arid and semiarid areas, while the converse is true in humid and semi-humid areas. Further, GPP is correlated positively with soil moisture globally. When a drought episode occurs, because of lower radiation and temperature, and the replenishment of soil moisture in the high latitudes of the Northern Hemisphere, Eastern Asia, the Amazon River basin and Central Africa, it causes an increase in GPP in these regions relative to those in non-dry periods (as shown in Figure 11). This result can be confirmed by [PERSON] and [PERSON] [46]. In contrast, in most parts of the world, GPP decrease during drought periods because of the increase in radiation and temperature and the decrease in soil moisture.  
Figure 10: Conditional probabilities of vegetation productivity losses under different drought scenarios in different land cover types when the GPP \(\leq\) 40 th percentile in March–May. (**a–c**) represent conditional probabilities of vegetation productivity losses under moderate drought, severe drought and extreme drought, respectively. Boxes represent interquartile ranges of the values of 25 th and 75 th percentiles (Q\({}_{25}\), Q\({}_{75}\)), whiskers cover Q\({}_{25}-1.5\times\) (Q\({}_{75}-\) Q\({}_{25}\)) to Q\({}_{75}+1.5\times\) (Q\({}_{75}-\) Q\({}_{25}\)), horizontal lines represent the median, black empty squares represent the mean, and black solid diamonds indicate outliers.
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 4 The Proposed Approach
### ACOSIMv2--Ant Colony Optimization for Sensor Selection Based on Similarity  
This section explains how ACOSIMv2 finds the optimal set of sensor nodes which will form the user's virtual sensors. The first subsection shows a brief review of Ant Colony Optimization (ACO). The latter subsections detail how ACOSIMv2 implements the two main steps of ACO system: (i) build the solution; and (ii) pheromone update. Figure 7 gives an overview of the whole process.
## 4 Role of sRNA in Antibiotic Resistance
### Modification of Cell Envelope
#### 4.2.1 SprX  
_Staphylococcus aureus_ is a major cause of bacteremia, infective endocarditis, and osteomyelitis colitis, and frequently colonizes medical implants [192]. The emergence of MRSA strains has severely limited treatment options, often leaving glycopeptide antimicrobials, which target peptidoglycan cell wall synthesis, as the primary therapeutic option [193]. The sRNA SprX encoded within a pathogenicity island is highly conserved among _Staphylococcus aureus_ strains and is present in several _S. aureus_ phages [194; 195; 196; 197]. SprX controls virulence factors such as delta-haemolysin and clumping factor B by interacting with _lld_ and _clfB_ transcripts, while also influencing IsaA expression and biofilm formation [195]. In _S. aureus_ HG001, SprX plays a role in modulating glycopeptide resistance and complement-binding protein [196; 197]. Most strains carry a single copy, while the Newman strain has three copies with minor nucleotide differences. SprX plays a pivotal role in modulating antibiotic resistance mechanisms by directly base pairing with the translation initiation site of SpoVG mRNA, a key regulator of capsule production, virulence, and cell wall metabolism. This interaction prevents ribosome loading, thereby suppressing SpoVG protein expression [198]. SpoVG regulates critical pathways associated with methicillin and glycopeptide resistance, including the activation of the _lytSR_ operon, the glycine glycyltransferases (_femAB_), which mediate peptidoglycan crosslinking and repression of the murein hydrolase _lytN_. By inhibiting SpoVG, SprX increases MRSA susceptibility to glycoptides.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 3 Positioning Algorithm
### Extended Kalman Filter
#### 3.2.2 EKF Initialization  
\[\mathbf{x}(\mathbf{0})=\Big{[}x(0),y(0),v_{x}(0),v_{y}(0),N_{ru}^{1j}(0), \ldots,N_{ru}^{mj}(0)\Big{]}^{T}. \tag{43}\]  
For the first step of the EKF (\(t=0\)), the estimated initial UE position \((x(0),y(0))\) is obtained from the time difference of arrival (TDOA) or other approaches [33]. The initial estimates of \((v_{x}(0),v_{y}(0))\) can be set to \(\mathbf{0}\). The initial ambiguities \(N_{ru}^{1j}(0),N_{ru}^{2j}(0),\ldots,N_{r}^{mj}(0)\) can be simply determined based on the initial UE position and known positions of cell, i.e.,  
\[\begin{split}& N_{r}^{i}(0)=\frac{\sqrt{\left(x(0)-x_{i}\right)^{2}+ \left(y(0)-y_{i}\right)^{2}}}{\lambda}-\phi_{r}^{i}(0);\\ & N_{r}^{ij}=N_{r}^{i}-N_{r}^{j};\\ & N_{ru}^{ij}=N_{r}^{ij}-N_{u}^{ij};\end{split} \tag{44}\]  
here, \(N_{u}^{ij}\) is the SD integer ambiguity of the reference device. The initial covariance matrix \(\mathbf{P}_{0}\) can be set as the diagonal matrix as follows:  
\[\begin{split}&\mathbf{P}(\mathbf{0})=\text{diag}\Big{\{}P_{x}(0),P_{y} (0),P_{\text{P}_{y}}(0),P_{\text{N}}(\mathbf{0})\Big{\}};\\ &\mathbf{P}_{\mathbf{N}}(\mathbf{0})=\Big{\{}P_{N_{1j}}(0), \ldots,P_{N_{(j-1)}}(0),P_{N_{(j+1)j}}(0),\ldots,P_{N_{ru}}(0)\Big{\}};\end{split} \tag{45}\]  
where \(P_{x}(0)\), \(P_{y}(0)\) can be set based on the assumed maximum positioning error of the TDOA. \(P_{\text{P}_{x}}(0),P_{\text{P}_{y}}(0)\) can be set based on the expected maximum velocity of the UE; \(P_{N_{1j}}(0),\ldots,P_{N_{mj}}(0)\) are set based on the maximum assumed DD measurement error.
## 4 Graphene Nanoplatelets for Strain Sensors and Stretchable Electronics
### Graphene Nanoplatelets Based Structural Health Monitoring  
Structural health monitoring (SHM) is a specific strain-sensing application which needs extremely high sensitivity to minute structural changes [134]. It was thoroughly investigated to enhance the safety of buildings and monitor the stability of structures in real-time, enhancing public security. Due to the high gauge factor, GnP-based nanocomposites are adequate for this purpose. Indeed, recently, researchers have focused on such devices. For example, [PERSON] and collaborators [107] added GnPs to an epoxy resin used for aeronautic applications. Nanocomposites with nanofiller contents of around the electrical percolation threshold displayed high gauge factors (\(\approx\)750) at strains inferior to 1%. They discovered that the dominant strain sensing mechanism was based on changes on the distance between nanoflakes. The resultant exponential electrical change was explained with a diminished tunnel effect. Increasing the amount of GnPs, the direct contact mechanism dominated, and the electrical response with stretch became linear. They were able to discriminate between structural changes due to tensile or flexural stress. They also discovered that such modified epoxy resin-GnPs present no hysteresis during 50 cycles of loading-unloading in flexural test conditions, demonstrating the reversibility of the SHM mechanism in the plastic deformation regime [108]. Recently, the same group [115] performed a study on SHM materials obtained by combining NH\({}_{2}\) functionalized GnPs, epoxy resins, and glass fiber. The experiments performed in this section were performed with commercially available GnPs provided by XGScience (nominal thickness 6-8 nm and lateral size 25 microns).
## 3 Model Interpretation for Signal Classifier
### Data Description  
Our target model, which is to be explained, is trained using SleepEDF [37]. The research consists of the cassette study and the telemetry study, which are able to discover the effect of nothing from healthy patients and an insomnia medication temazepam from patients who have mild difficulty falling asleep, respectively. Because our purpose focuses on model interpretation, not the effect of a medicine, we consider only the dataset from the cassette study. The dataset is obtained from around 40 healthy patients of varied ages and sexes. The original dataset includes various channels, but we use only one channel of EEG  
Figure 2: The process of generating perturbed samples consists of two sub processes: (1) As shown in the left diagram, given one-dimensional EEG signal \(x\), the input is transformed using STFT. After that, it is divided using image segmentation such as Felzenszwalb [34]. (2) The divided samples are randomly permuted to generate perturbed samples as depicted at the upper side in the right diagram. Then, the perturbed samples are converted into one-dimensional signal again using ISTFT.  
in this work. Each record is around 8 h. We split the record for each epoch, which is 30 s. Moreover, the dataset is annotated into six sleep stages, which are wakefulness, non-REM 1-4 and REM according to R&K rule [38], but we merge non-REM 3 and 4 as stated in the American Academy of Sleep Medicine (AASM) standard [26]. Therefore, the target model classifies five sleep stages given a 30 s EEG signal. Finally, we obtain 6955, 2581, 15951, 5051 and 7042 records of wakefulness, non-REM 1-3 and REM, respectively. We split the data into 75% and 25% to train and interpret the target model, respectively.
# Systems Engineering: Availability and Reliability
## 4 Production Systems Scheduling, Management and Maintenance
Although the decentralization of the supply chain has led to rapid advancements in manufacturing systems, finding an efficient supplier simultaneously from the pool of available ones as per customer requirements and enhancing the process planning and scheduling functions are the predominant approaches that still need to be addressed. Therefore, they have decided [10] to address this important issue by considering a set of gear manufacturing industries located across India as a case study. An integrated classifier-assisted evolutionary multi-objective evolutionary approach is proposed for solving the objectives of makespan, energy consumption and increased service utilization rate, interoperability and reliability. To execute the approach initially,text-mining-based supervised machine-learning models were adopted for the classification of suppliers into task-specific suppliers. Following this, with the identified suppliers as input, the problem was formulated as a multi-objective Mixed-Integer Linear Programming model, and finally, a Hybrid Multi-Objective Moth Flame Optimization algorithm was proposed to optimize process planning and scheduling functions. Numerical experiments have been carried out with the formulated problem as well.
## 3 Results and Discussion
### Detection of Protein Overexpression in Gels Using the IPBBIS Algorithm
#### 3.3.5 Choice of Threshold for the Elimination of Multiple Maximums
IPBBIS was applied separately to obtain one graph per sample, as shown in Figure 15. The average of the multiple maxima obtained was calculated to produce a single maximum value per sample (Figure 15A-I), which was related to the amount of protein concentrated in each of the different samples analyzed; these values were aggregated in Table 4 for future reference (row ROI-GPN Table 4). Image profiling based on binarized image segmentation was applied to each of the nine samples containing GPN protein at different concentrations in the ROI region of interest gel in Figure 14D. As shown in the Table 4, we obtained additional data by analyzing the different samples at different concentrations of recombinant GPN protein distributed on the polyacrylamide gel in Figure 14D. Figure 14: (**A**) Random sample selection within the gel (red box in the image). (**B**) Automatic band detection using image profiling based on binarized image segmentation. (**C**) Molecular weight detection using IPBBIS for GPN protein. (**D**) Selection of GPN protein bands from different samples for ROI. (**E**) The application of the image profile is carried out in the binarized image segmentation of (**D**). The array data obtained (ROI-GPN row in Table 4) revealed that the lane 2 sample (with a maximum value of 10.38 and a concentration of 2 mg/mL) shows the lowest overexpression, followed in order of overexpression by lane 5 (18.67, 9.5 mg/mL), lane 11 (20.11, 14 mg/mL), lane 4 (20.55, 14.5 mg/mL), lane 7 (22.87, 18 mg/mL), lane 9 (23.08, 18.5 mg/mL), lane 10 (27.26, 26 mg/mL), lane 6 (27.34, 27 mg/mL), and lane 8 (28.82, 30 mg/mL). These results show that the array data obtained for the new image profile calculated by the IPBBIS method for each sample are related to the concentration of each protein and can be used to calculate the level of GPN overexpression by comparing every sample present in the gel image.
## 3 Results and Discussion
### Evaluation of Memory Effect
#### 3.2.2 Delayed Memory Effect Evaluation  
From the overall questionnaire test results, the average scores of the control group and the experimental group after 2 days of teaching were lower than the immediate test after teaching, and the difference between the two groups was similar to the immediate test. The average score of the control group was higher than that of the experimental group by 14%. In the four grades of achievement, due to the forgotten knowledge points, the experimental group with no advantage in test results was at the \"qualified\" level and below after 2 days, while the control group hierarchy was slightly better than the experimental group, as shown in Figure 5a. Therefore, preliminary analysis shows that VR teaching does not have much impact on the retention of newly acquired knowledge after a period of time.  
In delayed memory, the scoring rate of each type of students in the two groups is basically the same as the immediate test, as shown in Figure 5b. In the first six questions concerning the construction principle of the coiled waterproof roofing, the control group scored twice as much as the experimental group. In the identification questions involving specific process operations, the control group has forgotten the teaching content, but the experimental group is still more than 90% accuracy in the whole group. In the seventh question of construction safety details and the eighth and ninth questions on construction structure, there is no significant difference in the scores between the two groups. Therefore, compared with the traditional teaching methods, VR teaching has no obvious advantages for the delayed mastery of theoretical knowledge, but it is more beneficial to the delayed mastery of specific-process practice memory.  
Figure 4: Immediate-memory-test results: (**a**) immediate-memory-test overall effect; (**b**) immediate-memory-effect score.
# Theoretical Study: High Harmonic Generation by Light Transients
A part of the output of the Yb:YAG amplifier is used for generating a phase-stable multi-octave super-continuum seed, which is then split into three channels, centered at 550 nm, 1 μm and 2 μm, respectively. The different channels are pumped by different harmonics of the multi-mj level kHz, Yb:YAG regenerative amplifier output. Each channel supports few-cycle pulses. By coherently combining the three few-cycle channels, light transients spanning from 0.45 to 2.7 μm can be generated (1). The sub-cycle waveforms can be furthermore extended to far-infrared by difference frequency generation (DFG) of the seed pulses (2). The figure is adopted from [17]. FIR: far-infrared; MIR: mid-infrared; NIR: near-infrared; VIS: visible; SHG: second harmonic generation; SFG: sum frequency generation.
## 3 Proposed Method
### Dual-Branch Attention Feature Fusion Mechanism
#### 3.2.1 Channel-Level Attention Module  
A pixel-level self-attention mechanism can enhance the correlation between distant pixels well, but the effect of channel information on the model is ignored. Reference [26] proposed the SE network module, which enhances or suppresses the channel features by calculating the importance of each channel itself, but does not calculate the degree of correlation between channels. The channel attention designed in this paper pays more attention to the correlation between channels; this method calculates the proportion of each channel in all channels by using the softmax function, obtains the channel attention weight matrix of the sonar feature image, enhances the feature expression ability of the model, and improves the fidelity of the generated image. Our channel-level attention module is shown in Figure 2, and the operation process is as follows.  
Firstly, the input feature map \(\text{X}\in\mathbb{R}^{H\times W\times C}\) is reshaped into \(\text{X}\in\mathbb{R}^{N\times C}\), and the transposition of \(\text{X}\) is multiplied by \(\text{X}\); then the channel attention weight matrix \(w_{ij}\) is obtained by the softmax function, and \(w_{ij}\) is multiplied by \(\text{X}\) to obtain the output result \(O_{j}\) of the channel-level attention mechanism layer:  
\[w_{ij}=\frac{\exp\left({X_{i}}^{T}\cdot{X_{j}}\right)}{\sum_{j=1}^{C}\exp\left( {X_{i}}^{T}\cdot{X_{j}}\right)} \tag{1}\]\[O_{j}=\sum_{j=1}^{C}w_{ij}\cdot X_{i} \tag{2}\]  
We multiply the result by the scale parameter \(\alpha\), which is initialized as 0. The final output value of the channel-level attention layer is obtained by adding the input feature map:  
\[Z_{j}=X_{j}+\alpha O_{j} \tag{3}\]
## 3 Results  
The intraclass correlation coefficients were all \"excellent\" [10]. The ICC values for linear measurements ranged from a minimum of 0.902 for Co-Go to a maximum of 0.992 for Co-Gn. The ICC values for angular measurements ranged from a minimum of 0.954 for the ANB angle to a maximum of 0.995 for the Co-Go-Me angle. The random error for the linear measurements ranged from a minimum of 0.3 mm for OVB to a maximum of 1.4 mm for Co-Go, while, for angular measurements, it ranged from a minimum of 0.3 degrees for SNB to a maximum of 1.5 degrees for the lower incisor to mandibular plane. The mean ages, stages of maturation of the cervical vertebrae at T1 and T2, and mean durations of the T1-T2 intervals for both the treated and control groups are reported in Table 1. No statistically significant differences between the two groups were observed. Descriptive data and statistical comparisons between the treated and control groups for the starting forms and cephalometric changes from T1 to T2 are reported in Tables 2 and 3, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**PK Group**} & \multicolumn{3}{c}{**Control Group**} & \multirow{2}{*}{**Diff.**} & \multirow{2}{*}{\(p\)} \\  & & **(\(n\) = 25)** & & & **(\(n\) = 23)** & \\ \cline{2-6}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline Age at T1 (years) & 9.6 & 1.6 & 9.6 & 1.6 & 0.0 & 0.885 \\ Age at T2 (years) & 13.0 & 1.5 & 12.9 & 1.4 & 0.1 & 0.722 \\ T1-T2 interval (years) & 3.4 & 1.3 & 3.2 & 1.3 & 0.2 & 0.580 \\ CVM at T1 & CS1 = 15; CS2 = 8; CS3 = 2 & CS1 = 12; CS2 = 10; CS3 = 1 & & 0.668 \\ CVM at T2 & CS1 = 2; CS2 = 2; CS3 = 13 & CS1 = 1; CS2 = 2; CS3 = 11 & & 0.905 \\ CS4 = 3; CS5 = 5 & CS4 = 5; CS5 = 4 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographics with statistical comparisons (independent-samples \(t\)-tests, Mann–Whitney U tests, or Chi-square tests). The comparison of starting forms (Table 2) showed no significant differences between the treated group and the control group, except for the inclination of the palatal plane to SN (SN-Pal. Pl), which was significantly greater in the treated group compared with that in the control group (8.5\({}^{\circ}\) vs. 6.7\({}^{\circ}\), \(p=0.014\)). \begin{table}