esa-sceva/satcom-chunk-collection huggingface dataset:
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology
### Mask-Enhanced Multi-Object Tracking and Segmentation Framework
#### 3.3.2 Mask-Enhanced Multi-Hypothesis Tracking Framework  
The proposed framework integrates three core components to achieve MOTS in occluded scenarios. The proposed framework also establishes an integrated pipeline that performs MOTS with five interlinked processing stages.  
The framework workflow follows the steps below:  
* Short tracklet generation. Match background tracklets with foreground tracklets and generate short tracklet pairs. The tracklets to be matched come from detections generated by object detection and instance segmentation. These tracklet pairs have one-to-one correspondence with targets and are used for subsequent feature extraction and track association.
* Short tracklet representation. Based on the Mask-Conditioned Feature Fusion Network, extract the spatial-temporal features of target tracklet pairs to generate more robust features suitable for occluded scenarios. They are used for subsequent data association.
* Track association with multi-hypothesis tracking algorithm. For target \(A\) in frame \(t\), if \(\sigma_{t}^{A}=0\) or \(\sigma_{t}^{A}=2\), update the tracker's internal template using the MHT algorithm. Compute similarity between adjacent nodes through mask association evaluation and perform track association.
* Occlusion state perception. Use the crowded perception model to detect target occlusion states. If \(\sigma_{t}^{A}=0\) or \(\sigma_{t}^{A}=2\), perform data association as described in step (3). If \(\sigma_{t}^{A}=1\) or \(\sigma_{t}^{A}=3\), suspend template updates in the multi-hypothesis tracker and proceed to step (5) for occluded track completion.
* Mask propagation. Generate target masks in occluded regions with the mask propagation network from Section 3.2 to complete missing track segments. When the target disappears or \(\sigma_{t}^{A}=0\), return to step (3) for tracking with the MHT algorithm and complete the association of all track segments.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Workflow  
In summary, the new method is a combination of the traditional process and our novel improvements. Figure 7 shows its entire workflow: (1) inputting the image data of spaceborne ScanSAR; (2) geolocation; (3) image preprocessing by the improved Wallis filter; (4) overlapping area coarse extraction based on the longitude and latitude of the pixels; (5) overlapping area downsampling, (6) equal division of the downsampled overlapping areas; (7) keypoint detection by the SIFT algorithm, keypoint matching and coordinate offset compensation of matched keypoints (all run in parallel); (8) homography matrix calculation, RANSAC filtering and scaling compensation; (9) preprocessed image (in original size) projection based on the compensated homography matrix; (10) image blending by the weighted-average algorithm; (11) outputting the smooth, seamless and large-area image of spaceborne ScanSAR.  
Figure 6: Equal division and parallel registration.
soil properties. Lime [14], cement [15], fly ash [16], and gypsum [17] have been demonstrated to improve the strength properties, capacity, and durability of soils. However, the presence of sulfates in the treated soils can induce the formation of expansive minerals, resulting in volume swelling, strength loss, and numerous heaves in earthworks [18], further ruining the efficiency of these treatments [19; 20]. Moreover, these approaches generally have disadvantages such as high energy consumption and high cost and maintenance, sometimes leading to the secondary pollution of the environment [21]. Therefore, there is an increasing demand for an effective and eco-friendly measure to ameliorate soil salinity.  
Polyacrylamide utilization in saline soils can inhibit the accumulation of soil salinity and increase soil fertility [22]. Polyacrylamide addition increased water retention and reduced the cracking degree of saline soils [23; 24; 25]. A study indicated that polyacrylamide addition improved plant growth in saline soils by restricting the transport of salt ions [26]. Biochar can effectively modify the physical, chemical, and biological characteristics of saline soils [27]. Biochar addition provides carbon [28; 29] and other essential soil mineral nutrients, thereby significantly improving vegetation productivity [30] and soil micro-biological properties [31]. Lignin, a kind of byproduct collected from paper mills, performs efficiently in improving unconfined compressive strength [32], shear strength [33], durability [34], and erosion resistance [35]. In addition, the application of lignin-based hydrogels to saline soils increases the water retention capability of the soils [36]. [PERSON] et al. [37] indicated that the strength of a saline soil increased significantly after an SH agent treatment. Previous reviews have indicated that arbuscular mycorrhizal fungi can protect plants from salt stress by regulating the soil macroaggregates [38; 39]. Arbuscular mycorrhizal fungi and earthworms can cooperatively promote the remediation of saline soils by changing soil macroaggregates and microbial biomass nitrogen [40].  
Most research has focused on the effects of the hydraulic properties, soil quality, and plant productivity of treated saline soils, but the information regarding the effects of amelioration methodologies on the mechanical properties of saline soils is limited. This study aimed to characterize the effect of CLI on the modification of sulfate saline soils. A series of triaxial tests and a test on the soluble salt content of a treated soil were performed on the modified saline soils with various contents of CLI and curing times to explore the effect of CLI on the mechanical properties and salinity variation. The results of this study provide insights for the application of polymer technology to the improvement of sulfate saline soil.
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 3 Results and Discussion
### Prediction Results by SSR Model  
Figure 7 shows the prediction results by the SSR model for the input data of the experimental compression strength of the GBFS. A simulation was conducted for the sets of input data for 28 days, 180 days, 1 year, and 5 years, respectively, and a prediction was made for the 10-year time period in each simulation. In this model, the results were outputted in the form of maximum and minimum values and when compared with the actual strength, the actual strength was found between the predicted values for all cases, which verified the correctness of the prediction. The 5-year input data produced the most accurate results, suggesting a correlation between the amount of input data and accuracy. However, it can be observed that the prediction results for the 28-day input data were more accurate than those of the 180-day and 1-year input data. The reason for this is the detailed settings of \"iter\" and \"chains\". In the model, \"iter\" specifies the number of iterations in the given data, and \"chains\" specifies the number of times the simulation is performed using \"iter\". The predictions are made by estimating the data change in which the state changes and observation errors are distinguished. This can also be observed in the results when the prediction results become more accurate for more input data where the data change pattern has been stabilized. The state is obtained after adding the process error to the predicted value used in the previous time. Then, the observed value is obtained by adding the observation error to the state. These observation and process errors control the \"iter\" and \"chains\". The actual experimental data pattern changed remarkably compared to the initial trend of the 28-day data, causing the prediction results to deviate from the actual results.  
Figure 6: Fitting results of the ARIMA model.
# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 4 Principles of Context Reasoning
### Context Reasoning Methods
#### 4.3.1 Similarity Based on Euclidean Distance  
When the distance is within a fuzzy threshold, \(\theta_{d}^{C_{\tau}}\), which specifies a range for the condition of \(C_{\tau}\), signifies no difference between \(S_{c}\) and \(C_{\tau}\); hence, \(C_{\tau}\) becomes the next context. Note that \(\theta_{d}^{C_{\tau}}\) is context-specific; thus, each context has its own value. This is obtained by averaging the distances from each state space to context during the initialization step [4]. Some conditions may be defined with a range of values, instead of one value. In this case, to apply Euclidean distance, the range is represented by a median of the range. For instance, if the range of sensor's values is from 0 to 5 in an integer, its median, which is 3, is used to compute the Euclidean distance. The Euclidean distance is calculated by the following Equation (11):  
\[\mathbf{dist}(S_{c},C_{\tau})=\|S_{c}-C_{\tau}\|=\sqrt{\sum_{i=1}^{\omega}(s_{i}^{ S_{c}}-s_{i}^{C_{\tau}})^{2}} \tag{11}\]  
In Equation (12), \(s_{i}^{S_{c}}\) stands for the status of sensor \(s_{i}\) in the current state space \(S_{c}\), and \(s_{i}^{C_{\tau}}\) stands for the status of sensor \(s_{i}\) in the possible next context, \(C_{\tau}\).  
If the distance is below \(\theta_{d}^{C_{\tau}}\), the \(C_{\tau}\) is considered to possibly be the next context. On the other hand, when the distance is greater than \(\theta_{d}^{C_{\tau}}\), there is no chance for \(S_{c}\) to become \(C_{\tau}\). The similarity is calculated and compared for all \(C_{\tau}\) which satisfy the distance condition, that is, \(\mathbf{dist}(S_{c},C_{\tau})\leq\theta_{d}^{C_{\tau}}\):  
\[\mathbf{sim}(S_{c},C_{\tau})=1-\frac{\mathbf{dist}(S_{c},C_{\tau})}{\|\mathbf{c}_{c}\|}=1 -\frac{\mathbf{dist}(S_{c},C_{\tau})}{\sqrt{\sum_{i=1}^{\omega}\left(s_{i}^{S_{c} }\right)^{2}}} \tag{12}\]  
If none of \(C_{\tau}\) verifies the condition, the current state space cannot not be any of the contexts, therefore no change in context occurs.
# A Mathematical Model for Reduction of Trim Loss in Cutting Reels at a Make-to-Order Paper Mill
## 5 Results and Discussion
The model selects minimum possible patterns of master reels for cutting and also chooses stocked reels to fill an order. Additionally, considerable savings have been achieved in terms of energy as well. While direct cost figure are not known, if we compare our results with the past practices (shared by the concerned industry), this study helped in using minimum number of master reels and selected stocked reels to fill an order. The model results in minimum trim loss and saves paper. By adopting the proposed method, the waste paper produced in the mill was decreased by at least 1.25% which corresponds to several hundred tons of paper a year. Hence, minimizing the loss in paper trimming, the reprocessing of waste paper is decreased and the discharge of wastewater containing toxic pollutants to the environment is reduced. Although there are a number of studies on trim loss analysis in steel [1,2] wood, leather, and plastic industries [3], this is a first attempt towards the analysis of trim loss in paper mill in this region. The findings will help practitioners in the selection on appropriate parameters of width and length of master reels, number of blades at winder etc. to minimize the trim loss in production and cutting of reels. The results have been shared with the relevant department in the paper mill and the implementation of our findings resulted in reduction of total cost, trim losses, reprocessing of paper, and discharge of effluents to the environment. The proposed algorithm is generic in nature and it can be applied to multiple similar contexts. Hence, this study contributes theoretically as well as practically towards the analysis of trim loss at a paper mill. The proposed mathematical model results in minimum or no paper trim loss in the paper mill and hence the reprocessing of paper trimming is reduced. Bleaching in the reprocessing of paper trimming affects the environment badly as it discharges chlorine-based toxic organic compounds to the environment. The discharge of chlorinated compounds due to bleaching is one of the important environmental issues. The paper mill highly focuses on the reduction of wastewater discharge containing chlorine-based compounds and other toxic organics. Additionally, because of industrial development, total chlorine-free bleaching is feasible for paper products but cannot produce certain paper grades. However, it is encouraged to adopt these modern process developments in the paper mill wherever feasible. To protect the environment, the paper mill gives stress on the minimization of the generation of effluents, reduction of bleaching, minimization of unplanned wastewater discharges, and on the minimization of sulphur emission to the atmosphere. Figure 3: Comparison of the outcomes of adjustable and fixed widths of master reels.
## 3 Results  
### Classification Performances  
In Figure 3, the classification performances in discriminating subjects with ASD from controls are illustrated. The results are reported for each classifier analyzed (TabNet, MLP, XGBoost, L-SVM, and SVM-RBF), and for different amounts of retained PCs. The best classification results were obtained by the SVM-RBF classifier, which achieved an AUC of 0.75 \(\pm\) 0.03 (with 100 PCs), followed by L-SVM with an AUC of 0.74 \(\pm\) 0.02 (with 50 and 100 PCs). As for the DL classifiers, the classification results fall behind for MLP, with AUC = 0.71 \(\pm\) 0.02 (with 200 PCs and without PCA), and for TabNet, with AUC = 0.65 \(\pm\) 0.02 (no PCA). These results are shown in Table 2.
## 3 Results and Discussions
### Optical Characterization
#### 3.2.2 Incidence Angle and Refractive Index  
The optical characteristics of the PV panel are mainly dependent on the incident angle of light. The influence of different incident angles on the reflectance and transmittance of the CaF\({}_{2}\)-CG computed using Essential Macleod is clearly shown in Figure 3a. The graph shows that the reflectance increases and the transmittance decreases as we go from a low incidence angle to a high incidence angle, and the results are in good agreement with the analysis presented in [30].  
Figure 1: (a) Cross sectional FE-SEM image of CaF\({}_{2}\) thin film; (b) XRD pattern for the CaF\({}_{2}\) film.  
The refractive index (\(n\)) provides knowledge regarding the local fields, electronic polarization and the phase velocity of light spreading inside a material [25]. The refractive index depends on the deposition technique of coating because the mass density and crystal orientation of the material influences \(n\). The Lorentz-Lorenz equation provides information on the increase in the value of \(n\) with the increase in the film density [31]. The dependence of \(n\) for the CaF\({}_{2}\)-coated film on BSG over a broad range of wavelength is shown in Figure 3b. We used the Cauchy model for fitting the data from the VB-250 VASE ellipsometer for determining the \(n\) value of the produced CaF\({}_{2}\) thin film. The measured refractive index of \(\sim\)1.39 at 600 nm indicated the excellent transparency of \(\sim\)95% of CaF\({}_{2}\)-CG. There was good improvement in the refractive index, as a result of the vacuum thermal evaporation process, when compared to that presented in the literature and that of bulk material [24].
## 3 Proposed Methodology
### Sentence Representation
#### 3.1.2 Un-Normalized Representation of Sentence Vectors  
In this method, vector representations of sentences are obtained by omitting the normalization step, and this is known as un-normalized representation of a sentence vector. It is a much simpler way of representing sentence features using word vectors.  
Difference of Word Vectors.  
In this method, sentence vectors are obtained by subtracting all the word vectors of a sentence. Then the resultant vector is used as a feature vector to illustrate the semantics of a sentence:  
\[DOW=\overrightarrow{w_{1}}-\sum_{i=2}^{n}\overrightarrow{w_{i}} \tag{9}\]  
Concatenation of Sum and Difference of Word Vectors.  
In this method both feature vectors \(SOW\) (sum of word vectors in a sentence) and \(DOW\) are concatenated. The joined form is used as a feature vector for representing any given sentence:  
\[SOW\oplus DOW \tag{10}\]  
The motivation behind this research was to find a natural and fast method to represent a sentence vector by combining word vectors. The performance of the proposed sentence representations were tested on real-world problems.
# Down the Rabbit Hole: Fostering Active Learning through Guided Exploration of a SCADA Cyber Range
## 3 Development of the Cyber Range Environment
### Complete CANVAS Scenario  
The complete hybrid environment for the cyber range, contains a mix of virtualized and physical equipment instances, integrated within a layered IEC 62443-compliant [22] structure (see Figure 5).  
Figure 4: Testbed topology.  
Figure 5: Cyber range environment (green VMs used for students; yellow VMs support services and testbed components for the specific scenario).  
This scenario was entirely built by tapping into existing CANVAS assets and resources, including (but not limited to) physical equipment, communications infrastructure, computing resources, Virtual Machine (VM) templates and template service sets (as it is the case for the HMI + SCADA Honeypot VMs, depicted below). It is structured as follows.  
* Level 0 contains the sensors and actuators used by the the SCADA process that provides the main use case for the environment. Note that as the Arduino RTU also emulates a virtual temperature probe, its context crosses both the Level 0 and 1 domains;
* Level 1 contains the process control equipment, both the PLC and Arduino-based RTUs used by the aforementioned cyber-physical process, but also a set of additions, namely a virtual PLC (based on the OpenPLC platform [23], and hosted on a VM) and a low-interaction SCADA honeypot configured to resemble a real PLC device;
* Level 2 contains the operational SCADA nodes: two production HMI VMs, one for the cyber-physical process and one for the emulated process that runs on the virtual PLC (vPLC) instance; one SCADA station VM with an OPC UA (Unified Architecture) server and PLC programming and provisioning software; and a honeypot HMI VM which communicates with the SCADA honeypot, providing a complete setup designed to increase the engagement of a potential attacker;
* Level 3 contains a remote access station VM, providing managed and authenticated access to the lower levels of the environment, as well as a router/firewall VM, configured to let the telemetry and log feeds reach the VM instances deployed on the topmost layer;
* Level 4 contains a set of VM appliances for data integration, log collection and storage and analytic purposes, used to support advanced training and research activities. Furthermore, note that student VMs (used to support learning activities) are depicted as intersecting the Level 4 and 2 domains because each VM has one network interface directly connected to each zone.  
This ICS cyber range environment provides support for course activities, being accessible from the campus network or by means of Virtual Private Network (VPN) connections, supporting in situ, distance learning and hybrid models on the same environment.
# Terpenes as Potential Anti-Alzheimer's Disease Agents
## 2 Activities of Terpenes on the Mechanisms Associated with AD
### Inhibition of AB Production
#### 2.1.1 Monoterpenes and Diterpenes  
From the essential oil of _Lavandula luisieri_ (Rozeira[PERSON] collected at Serrado-Acor, Portugal, the monoterpenes linalool (**1**) and 2,3,4,4-tetramethyl-5-methylene-cyclopent-2-enone (**2**) were obtained. While linalool was inactive, the 2,3,4,4-tetramethyl-5-methylene-cyclopent-2-enone, an uncommon necrodane monoterepenic ketone, inhibited by 31.8 \(\pm\) 0.9% recombinant proBACE1 produced in _Escherichia coli_ (at a concentration of 45 uM). The same compound (**2**) inhibited a cell culture of Chinese hamster ovary cells (CHO) stably transfected with the APPwt coding sequence (CHO-APPwt) by 71.4 \(\pm\) 2.5% in the same concentration (45 uM) [63]. These results suggest the importance of planarity of a molecule for the inhibition of BACE1.  
Gracilins are C19 diterpenes (nor-diterpenes) which were extracted from _Sprongionella gracilis_ [PERSON] [64; 65]. BACE1 inhibition capacity of these compounds was evaluated at 1 and 0.1 uM with the BACE1 FRET assay kit [65]. Gracilin L (**3**) ([(15,3\(R\),3\(R\),4\(Z\),75,7_aS_)-3-acetyloxy-4-ethylidene-7-hydroxy-5-[(15)-1,3,3-trimethylcyclohexyl]-3,3,4,7,7a-tetrahydro-1H-2-benzofuran-1-yl] acetate) at 1 uM produced inhibition of BACE1, decreasing its activity by 24.6 \(\pm\) 4.2%. Gracilin H (**4**) and tetrahydroaplysulphurin-1 (**5**) also presented activity but lower than the one presented by compound **3**, while gracilin A (**6**), the (15,3\(R\),3\(R\),4\(Z\),7_aR_)-4-ethylidene-5-[(15)-1,3,3-trimethylcyclohexyl]-1,3,3\(a\),4,7,7,4-hexahydro-2-benzofuran-1,3-diyl diacetate, was inactive. It is interesting to note that gracillin L (**3**) is the only compound of this series that presents a polar hydroxyl group at C7 and can present an almost planar conformation.
## 6 Swift Heavy Ion Irradiation-Induced Shape and Structural Changes in Cobalt Nanoparticles
### Summary  
In summary, various \(S_{e}\)-dependent shape and structural changes in Co NPs embedded in SiO\({}_{2}\) were measured. XTEM and SAXS analysis revealed NPs below a threshold size remained spherical in shape and progressively dissolved in the matrix with increasing fluence. NPs above the threshold size transformed into nanorods with their major dimension parallel to the incident ion beam direction. The nanorod width saturates at a value that increases with \(S_{e}\) and was directly correlated to the molten ion track in SiO\({}_{2}\). The elongation mechanism was consistent with the elongation of a once-spherical molten NP confined to the diameter of the molten ion track in SiO\({}_{2}\). The results demonstrate the usefulness of SHII as a technique for the fabrication of aligned, elongated NPs with specific dimensions.  
The atomic-scale structure of the NPs as a function of SHII energy and fluence were determined by XAS. Prior to SHII, analysis of the XANES region demonstrated that all Co atoms were in a metallic environment. After SHII, however, two distinct environments gave rise to the observed changes in the XANES and EXAFS signals: Co atoms in a metallic environment attributed to the elongated and spherical NPs; and a non-crystalline fraction from oxidised Co atoms and clusters (dimers/trimers) dispersed in the matrix. Structural changes in the NP ensemble, including a decreasing \(BL\) and \(C_{3}\) and increasing \(\sigma^{2}\) with ion fluence, were attributed to the increasing SBR for the elongated and small spherical NPs while the reduction in the measured \(CN\) was attributed to the increase in the fraction of small Co clusters.