esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
All living cells are surrounded by the cytoplasmic membrane, a unit membrane of which overall architecture, a fluid lipid bilayer with integral and peripheral membrane proteins, is conserved among the three domains of life _Bacteria_, _Archaea_, and _Eukarya_. The importance of such a structure is underlined by the notion that everything that exists outside of the biological membrane is non-living.  
Outside of the universally conserved cytoplasmic membrane, prokaryotes have developed complex architectures, collectively named the cell wall, which provide additional strength, contribute to the cell shape determination, and allow them to face unpredictable and often hostile environmental insults [1; 2; 3]. Many _Bacteria_ are surrounded by an additional lipid bilayer, the outer membrane (OM), and are, thus, described as diderm bacteria. The OM is not present in monoderm bacteria, which possess the cytoplasmic membrane as the unique lipid membrane [4; 5; 6].  
The OM is an essential structure that has been extensively studied over the last half century in _Proteobacteria_ phylum and particularly in _Enterobacteriaceae_, it possesses a peculiar asymmetric structure due to the presence of a complex glycolipid, the lipopolysaccharideaccharide (LPS), in the outer leaflet of the lipid bilayer, whereas the inner leaflet is composed of phospholipids. OM proteins (OMPs) and lipoproteins are also embedded and anchored, respectively, in the OM [7]. The space in between IM and OM is an aqueous compartment, the periplasm, which contains a peptidoglycan layer, a large cell polymer that surrounds the bacterial cell [8; 9]. This cellular exoskeleton is the main structure responsible for the cell shape and the mechanical strength and elasticity of the bacterial envelope [10]. The presence of a highly structured OM poses several problems for its biogenesis. Both lipid and protein components must not only be synthesized in the cytoplasm and/or at the inner membrane (IM) level and translocated across the IM lipid bilayer, but must also traverse the aqueous periplasmic space and be assembled at the amphipathic final destination. The cell compartments external to the IM are devoid of ATP and other high-energy carriers [11]. As a consequence the energy to build up OM structures is either provided by exergonic reactions (thus involving substrates that have been energized before their translocation across the IM) or transduced by devices (usually protein machines) connected to the IM and capable to exploit the energy released by ATP hydrolysis in the cytoplasm or the proton motive force.  
This review, after a short overview on the OM structure and functions will focus on its peculiar component, the LPS. First LPS structure and biosynthetic pathway will be briefly discussed. Then, the machinery that ferries LPS from the IM to its final destination in the OM together with recent advances in the molecular mechanisms of LPS export will be reviewed in more details.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Evaluation Metrics
#### 3.3.5 F1 Score  
The F1 Score is an evaluation metric that takes the harmonic mean of precision and recall. The formula to compute the F1 score is given below in Equation (4):  
\[\text{F1 Score}=(2\times\text{Precision}\times\text{Recall})/(\text{Precision + Recall}) \tag{4}\]  
The F1 Score is a trade-off between precision and recall, and it is more useful when the two measures diverge. For instance, if precision is high but recall is low, the F1 Score will be lower than the precision number, indicating that recall may be improved. Conversely, if recall is high but precision is low, the F1 Score will be lower than the recall value, showing that precision can be improved by minimizing false positives.
## 6 Result and Discussion
### Result  
In this section, we compared our scheme's performance regarding the storage cost of ciphertext and encryption time with previous works [23; 33]. Figure 9 shows the ciphertext size for our PHLC-based CP-ABE scheme, [PERSON]'s work [23; 33]. As illustrated in this figure, our proposed scheme is comparable with the with [PERSON]'s work and [33]. This figure indicates that our data privacy strategy realizes effective ciphertext size even though the PHLC scheme hide both attribute names and attribute values and attached it to the ciphertext. While in Figure 10 illustrates the time taken for the Data Owner (DO) to encrypt the file before outsourcing it to the Cloud storage. Based on the figure, it shows that  
Figure 8: Packet capture using Wireshark after access policy hiding.  
Figure 7: Packet capturing using Wireshark.  
the encryption time exhibits a linear increase with the number of attributes. Further, the PHLC encryption time is less than in [PERSON]'s work [23], on average 20%. Even though the difference is slight, it greatly impacts the processing overhead and complexity of the encryption process. The data privacy approach by [33] showed the maximum encryption time, which can be improved to gain effective encryption time.  
To reveal the effectiveness of our PHLC approach in terms of ciphertext size, we enhanced the experiment by adding a pre-processing module. This module eliminates redundant data in a raw shared file using a new technique called frequent wording appearance pre-processing. Based on Figure 11, the ciphertext size in PHLC after applying the pre-processing module have decreased by approximately 17% on average. Reducing the size of the ciphertext helps reduce the storage space and leads to efficient decryption process performance.  
Figure 10: Encryption Time.  
Figure 9: Storage cost of ciphertext.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 3 Implementation
### System Initialization
#### 3.1.2 User Pseudonym Generation  
When the EV, CP, FN, and CSO first join the V2G network, they must register with the TA using real identity information, and the TA then generates unique identifiers \(RID_{i}\), \(ID_{CP_{i}}\), \(ID_{FN_{w}}\), \(ID_{CSO}\in\{0,1\}^{*}\), respectively.  
The process of generating pseudonyms is as follows: first, \(EV_{i}\) chooses a random number \(t_{i}\in Z_{q}^{*}\), computes part of the pseudonym \(PID_{i,1}=t_{i}P\), simultaneously computes \(k_{i}=t_{i}T_{pub}\oplus RID_{i}\), and sends the request \((PID_{i,1},k_{i})\) to the TA to generate the pseudonym. Then, the TA verifies the identity of \(EV_{i}\) by computing the equation \(RID_{i}=k_{i}\oplus tPID_{i,1}\); if the verification fails, the pseudonym generation request is discarded. Otherwise, the TA computes the other part of the pseudonym \(PID_{i,2}=RID_{i}\oplus H_{0}(tPID_{i,1},T_{i})\) and secretly sends the complete pseudonym \(PID_{i}=(PID_{i,1},PPID_{i,2},T_{i})\) to \(EV_{i}\), where \(T_{i}\) is the pseudonym's valid timestamp.  
When \(EV_{i}\)'s pseudonym expires, i.e., when the pseudonym's valid timestamp \(T_{i}\) is less than the current timestamp, \(EV_{i}\) must repeat the above process to request for the generation of a new pseudonym from the TA.
## 3 Results
### GPP, NPP, and EVI Trends Within the Study Sites  
In the 21 st century, gross and net primary productivity have been strongly increasing at all study sites. Similar to tree GI, maximal GPP/NPP values occurred in the last decade. Minimal GPP/NPP values correspond to wildfire events. Despite repeated fires, strong increasing GPP/NPP trends have been observed (Figure 8).  
Increasing trends of GPP and NPP were also observed throughout the surrounding bush-steppe area. Positive GPP and NPP trends occurred in 81% and 88% of the areas, respectively, whereas negative ones were observed in less than 1% (Figure 9).  
A difference between GPP/NPP during the period of increased precipitation (2010-2024) and the earlier period (2001-2009) is given in Figure 10. It is notable that an increase in the GPP and NPP has been observed throughout the entire territory. In particular, in pine stands, both GPP and NPP have increased by about 1.25 times.  
Figure 8: Averaged GPP/NPP dynamics throughout all sites. Since the warming restart, both GPP and NPP have shown a strong increase. The arrows indicate the impact of severe fires on the GPP/NPP.  
Figure 7: The tree GI positively correlated with the previous year’s (August-September) soil moisture content. That dependence was higher during the period of low precipitation (**a**) compared to the period with increased precipitation (**b**).  
Alongside vegetation productivity, vegetation \"greening\" (indicated by the EVI index) is also increasing. Positive EVI trends occurred in 23% of areas, whereas negative ones occurred only in ca. 1% (\(p<0.05\)) (Figure 11).  
Figure 10: The difference (delta) between vegetation GPP **(a)** and NPP **(b)** values during 2001–2009 and 2010–2024. The positive delta of GPP and NPP occurred in 82% and 93% of the area, respectively. Negative ones were observed in \(<\)1% of the area (\(p<0.05\)). Elevations above 1000 m are indicated by gray. Annual GPP and NPP data were extracted from the MODIS MODI7A3 HGF product [27]. Differences were estimated using the Mann–Whitney U-test. Sites: 1—Biche-Hady, 2—Ulug-Hady, 3—Balgazyn.  
Figure 9: Vegetation GPP **(a)** and NPP **(b)** trends. Both the GPP and NPP in relict pine forests (1–3) and in bush–steppe are increasing (\(p<0.05\)). Positive GPP and NPP trends were observed in 81% and 88% of the areas, respectively. Negative ones were observed in less than 1% of the area. Period: 2001–2024. Elevations above 1000 m a.s.l. are indicated by gray. Annual GPP and NPP data were extracted from the MODIS MODI7A3 HGF product [27]. Trends were estimated using the Theil–Sen algorithm [28]. Sites: 1—Biche-Hady, 2—Ulug-Hady, 3—Balgazyn.  
Finally, the growth index of trees is strongly correlated with vegetation GPP/NPP values (\(r=0.82\)) (Figure 12).
## 2 Methodology
### Wavelet Analysis Denoising  
After gross error detection, the deformation time series of the target point may still have a certain amount of noise. To reduce the influence of noise and improve the signal-to-noise ratio, a wavelet threshold denoising method was used to process the deformation time series of the target point. During the processing, the wavelet function \"sym4\" was selected, the deformation time series was decomposed into seven layers by wavelet multi-scale analysis, the threshold value was estimated by Heuresure criterion, and the threshold value was quantified by soft threshold method [40; 41; 42]. On this basis, denoising analysis of the deformation time series of the target point was carried out.
## 4 Results and Experimental Evaluation
### Ablation Studies  
To understand the contribution of each component of our stochastic trajectory model, we performed ablation studies by removing or modifying key components. Table 3 presents the results for sampling rates 1, 2, and 4, respectively.  
Switching from a stochastic model to a deterministic model (by removing the probabilistic output layer) results in performance degradation at SR = 1 and SR = 2, with the test MSE increasing by 74.19% and 55.91%, respectively. Interestingly, at SR = 4, the deterministic model performs better than the stochastic model (38.78% improvement). This suggests that for sparse trajectory data, maintaining a probabilistic model becomes more challenging, and a simpler deterministic approach might be more appropriate.  
Removing the weather attention mechanism (\"NoWeatherAttention\" variant) leads to substantial performance drops across all sampling rates, with relative performance decreasing by 611.50%, 996.09%, and 170.18% for SR = 1, SR = 2, and SR = 4, respectively. This confirms our hypothesis about the critical role of explicitly modeling weather influences in general aviation trajectory prediction.  
The variant \"NoLyapunov\", which removes the Lyapunov stability regularization term, shows significant performance degradation at all sampling rates (628.10%, 729.58%, and 149.89% for SR = 1, SR = 2, and SR = 4, respectively). This validates our approach of incorporating stability constraints to ensure physically plausible trajectories that conform to standard flight patterns.  
Reducing the hidden layer size (\"SmallHidden\" variant) or the latent space dimension (\"SmallLatent\" variant) both lead to worse performance, indicating that sufficient model capacity is necessary to capture the complex dynamics of general aviation trajectories.  
In summary, our ablation studies demonstrate the critical importance of each component in our proposed model architecture. The weather attention mechanism and Lyapunov stability regularization prove particularly essential, with their removal causing the most significant performance degradation across all sampling rates. These findings validate our integrated approach to general aviation trajectory prediction, which successfully balances model complexity with physical constraints while appropriately handling varying data sparsity conditions.
# Effect of Mechanical Damage in Green-Making Process on Aroma of Rougui Tea
## 2 Materials and Methods
### Analysis of Aroma Components  
After the samples were fully ground, 2 g of each sample was weighed and placed in a headspace vial. The aroma substances were extracted by solid-phase microextraction (SPME), and the extracted aroma substances were detected by GC-TOF-MS (Agilent 7890B/Pegasus HT TOF MS, Agilent, Palo Alto, CA, USA/LECO, St. Joseph, MI, USA). In brief, firstly, 2 g of powdered tea was placed into an 18 mL headspace vial. Additionally, 10 \(\upmu\)L 2-octanol (10 mg/L) was added as an internal standard. Subsequently, 5 mL of boiling deionized water was added into the vial to brew the sample. The vial was then sealed with a silica gel diaphragm and allowed to incubate in a water bath at 80 \({}^{\circ}\)C for 30 min. Following this, the SPME fibers (2 cm, 50/30 \(\upmu\)m, PDMS/DVB, Stableflex, Sigma-Aldrich, Shanghai, China), which had been activated in advance, were then inserted into the headspace of the vial and kept for 60 min to ensure the complete extraction of the volatiles [26; 27]. Following extraction, the fiber was manually inserted into the gas chromatograph sample inlet, which was set to 250 \({}^{\circ}\)C, to desorb the volatiles. Specifically, an Agilent 7890B gas chromatograph (Agilent, Palo Alto, CA, USA) equipped with a Rxi-Ssi1 MS column (Agilent, Palo Alto, CA, USA) was used. The GC injection method applied here was no-split injection, where the temperature of the injection port was set to 250 \({}^{\circ}\)C. The carrier gas was high-purity helium (purity \(>\) 99.999%) with a flow rate of 1 mL/min. Initially, the temperature was maintained at 50 \({}^{\circ}\)C for 5 min, followed by a gradual temperature increase to 210 \({}^{\circ}\)C (at a rate of 3 \({}^{\circ}\)C/min) and this temperature was maintained for 3 min. Subsequently, the temperature was raised further to 230 \({}^{\circ}\)C (at a rate of 15 \({}^{\circ}\)C/min). For MS detection, the EI ion source temperature was set to 250 \({}^{\circ}\)C, the scan rate was 10 spectra/s, the solvent delay time was 5 min, the EM voltage was 70 eV, and the MS scan range was 45 to 600 amu. The experiment was repeated three times. At the same time, the same amount of each sample was mixed to prepare QC. During the data acquisition process, one QC sample was collected for every three samples to test the stability of the instrument. The GC-TOF-MS data were integrated with the Chroma TOF version 4.51.6 (American LECO Company, St. Joseph, MI, USA) software. The volatile peaks were identified by matching the National Institute of Standards and Technology (NIST) mass spectral database and the retention index (RI, determined by n-alkanes C7-C40).
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Mask Recognition Approaches
#### 5.1.1 Convolutional Neural Network  
Convolutional neural networks are crucial in computer vision due to their efficient pattern recognition and spatial feature extraction capabilities. By applying convolutional filters directly to input images, CNNs efficiently isolate high-level features, enhancing both the accuracy and computational speed for tasks like image classification and object detection. [PERSON] et al. [59] exploring three deep learning techniques for face mask recognition: Max pooling, Average pooling, and MobileNetV2. MobileNetV2 achieved the highest accuracies--99.72% in training and 99.82% in validation--demonstrating a robust capability, while [PERSON] et al. [60] developed an automated face mask recognition model to enforce mask wearing in public spaces. This model, capable of processing both static images and real-time video feeds, classifies subjects as \"with mask\" or \"without mask.\" Trained and evaluated using a dataset of approximately 4000 images from Kaggle, the model achieved an accuracy rate of 98%. It demonstrated computational efficiency and precision.
## 4 Results and Discussion
### Unsupervised Pattern Recognition Results  
WPF vs. PP relationships were examined to classify different fracture mechanisms (related AE sources) in fiber-reinforced concrete (Figure 7). It is seen that the AE activities of the specimens are concentrated at different frequency values, but the common frequency range in all specimens is 50-200 kHz. While only this cluster (Cluster-1) appeared in REF and CM, a new cluster (Cluster-2) at a higher frequency range was observed in the fiber-reinforced specimens. In other words, Cluster-1 activities were recorded from damage mechanisms in cement paste. On the other hand, Cluster-2 attributes to fiber activities [35, 46, 47, 48, 49]. Particularly in samples containing 1.0% fiber by volume, much more fiber activities concentrated at 250 kHz were observed due to the higher amount of fiber.  
Evaluations made above for the mechanical findings, and the frequency distributions and damage type can also verify AE activities: Fiber activities preventing crack formation in S10, which has higher toughness due to the amount of increased steel fiber, increased the number of high-frequency activities (Cluster-2). With the increase of fiber amount in micro synthetic fiber-reinforced specimens (Mi10), toughness of the concrete increased and since these fibers were less rigid than the steel fibers, they were more damaged and the number of AE activities in Cluster-2 increased. Since macrofibers are more rigid compared to microfibers, increasing the amount of fiber (Ma10) did not change the amount of activity in Cluster-2. In general, much more AE activity describing fiber activities due to the higher fiber content and concentrated around 250 kHz were observed in specimens containing 1.0% fiber by volume.  
In addition, Figure 8 indicates the origination time of these clustered activities. To evaluate the originations of these activities regarding the mechanical behavior of fiber-reinforced concrete, graphs were divided into three phases as P1 (where the load reaches to ultimate capacity and decreases in a plain concrete), P2 (hardening phase where the load values continue increasing), and P3 (descending slope drawn after second maximum load up to the end of the test) [50]. As can be seen, all the high-frequency activities defined as fiber activity Cluster-2 occurred in phases P2 and P3 and only matrix cracking was encountered up to ultimate load capacity (P1) such as the plain concrete behavior. This proves that AE activities were clustered in accordance with the mechanical behavior of fiber-reinforced concrete.  
Figure 6: AE activities of the specimens.  
Figure 7: Clustering of AE activities as matrix cracking and fiber activities based on WPF vs. PP1, parameters (\(\bullet\) Cluster-1, \(\bullet\) Cluster-2).
###### Abstract
The authors also concluded that marginal bone loss was significantly lower at implants with PS than at implants with platform matching; this difference increased with the increasingfollow-up time and degree of mismatch between the implant platform and the abutment. Another method attempting to reduce crestal bone remodelling is the use of laser-microgrooves, otherwise known as laser micro-etching, LM, or by the commercial name \"Laserlok\" surfaced implants. This surface was developed to alter the way different cell lineages would respond to, and hence migrate across, the titanium surface. Human and animal histology has shown an alteration in the behaviour of cells to LM surfaces, possibly altering fundamental concepts such as collagen fibre orientation [23; 24; 25; 26], with perpendicularly orientated collagen fibres as opposed to parallel [27]. Clinical evidence is mounting in favour of this technique reducing marginal bone loss in a similar manner to PS [28; 29; 30; 31; 32; 33]. A systematic review, including 25 articles, was conducted to investigate clinical outcomes of implants with LM collars or LM abutments [34]. Only four of these studies were randomized controlled trials; half of the studies observed LM implants alone with no control group. Where there were control implants, these were most often machined collar implants. The implants were placed in different clinical conditions, with variable placement, timing and loading protocols, location, restoration and use of augmentation. The results consistently reported a relative reduction in peri-implant probing depths (PPD) and marginal bone levels (MBL) when using LM collars. However, those studies comparing LM collars to roughened collars of competing designs did not note differences [35; 36]. Few studies reported on marginal soft tissues height (MTH) [37; 38; 39], but in three it was stated that LM reduced recession compared to machined collar controls [40; 41; 42]. The review [34] shows that LM implants consistently have clinically acceptable outcomes in both the short and medium term, and possibly a lower biological complication rate than machined collar implants. Whether there is any additional effect of LM when compared to roughened collars and competing macro-designs is not able to be determined from the current literature. Conversely, the propensity for these roughened collar or abutment surfaces to collect plaque is yet unanswered. As with PS, there is only a small amount of evidence to suggest that the marginal soft tissues are affected by these design alterations. Whether or not a combination of these designs may be of further benefit is not reported in the literature. Hence, this study aimed to test the hypothesis that LM implants with PS (test) would result in reduced marginal tissue remodelling when compared to those without PS (control). MBL, MTH, PPD and bleeding on probing (BOP) were statistically compared between test and control implants. In addition, the abundance of perio-donto-pathogenic microbiota was compared among test, control and adjacent tooth to highlight possible differences in bacterial colonization.
# Structural Health Monitoring of a Road Tunnel Intersecting a Large and Active Landslide
###### Abstract  
Dealing with engineering structures that are not easily replaceable requires frequent assessment of the damage state of the construction in order to estimate its durability and reliability. The procedures that allow damage to be detected and identified are broadly defined as Structural Health Monitoring (SHM). In this work, a SHM network has been deployed in a road tunnel that intersects a massive landslide, whose movements are causing the formation of cracks along the tunnel lining. The monitoring system measures in real time the displacements across major cracks and the rotation of the tunnel segments; data are gathered and made easily accessible through a web-based platform. The mechanisms by which the tunnel deforms under the landslide-induced stress have been defined through the analysis of three years of monitoring data. The factors triggering an increase in deformation rates and causing damage to the structure have also been investigated. This evidence will support the design of mitigation works to extend the life-span of the tunnel.  
t 10 October 2017; Accepted: 1 December 2017