esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 AOG Downtime Reduction and Implementation Results
### Optimizing Inventory and Market Strategies with Holt-Winters
* Fleet Utilization (FU)  
\[FU=\frac{Total\ Flight\ Hours}{Total\ Available\ Hours}\times 100\]  
Percentage increase after implementing Holt-Winters:  
\[\%\Delta FU=\frac{(FUpost-FUpre)}{FUpre}\times 100\]  
The formula for AOG Reduction is as follows:  
\[\%\Delta AOG=\frac{(AOGpre-AOGpost)}{AOGpre}\times 100\]  
where AOGpre represents either the number of AOG events or the total AOG hours. By increasing their MTBF thresholds, each air operator in the table has achieved quantifiable financial and operational gains akin to SmartLynx. For example, KLM's move from 1190 to 1235 h could generate up to EUR 400,000 in yearly savings, 10-12% higher fleet utilization, and a 20% drop in AOG incidents. Lufthansa's shift from 1220 to 1250 h might eliminate at least EUR 500,000 in rush fees, boosting revenues by EUR 2 million. Meanwhile, Delta's target of 1240 h potentially can save EUR 450,000, uplift fleet usage by 10-13%, and cut AOG rates by over 15%. These hours again were typically calculated using the same framework tested within SmartLynx by analyzing detailed flight hour logs and historical reliability data indicating that components reliably last longer. Improving MTBF levels assessed from actual operational records translates directly into lower costs, better service reliability, and fewer last-minute interruptions. In the following section, we will see how fleet utilization and AOG reduction gains connect to inventory control through a safety stock framework, ensuring airlines maintain the right balance between availability and cost.
###### Abstract  
Gamma-decalactone (GDL) is a fragrance compound obtained in the process of \(\beta\)-oxidation of ricinoleic acid, which is derived from the hydrolysis of castor oil. The biotechnological method of the synthesis of this lactone has been improved for over two decades, but the vast majority of research results have been based only on determining the concentration of the lactone by chromatographic methods without separating it from the biotransformation medium. In this study, we attempted to separate GDL from the medium in which the lactone was synthesized by _Yarrowia lipolytica_ from castor oil. The effectiveness of liquid-liquid extraction, hydrodistillation, and adsorption on the porous materials (zeolite, vermiculite and resin Amberlite XAD-4) was compared. The influence of the solvent on the efficiency of GDL extraction, the influence of the acidity of the medium on the amount of GDL in the distillate, and the level of lactone adsorption on the above-mentioned adsorbents were compared by calculating the initial adsorption rate. The adsorption isotherm was determined for the most effective adsorbent. Among the five solvents tested, the most effective was diethyl ether, used at the ratio of 1:1. The extraction was characterized by higher efficiency than hydrodistillation; the difference in GDL determinations by these two methods ranged from 12.8 to 22%. The purity of the distillates was much higher than that of the extracts at 88.0 \(\pm\) 3.4% compared to 53.0 \(\pm\) 1.8%. The acidification of the biotransformation medium increased the concentration of the lactone in both the reaction mixture and the distillate. GDL was most efficiently adsorbed on Amberlite XAD-4 resin, for which the lactone isotherm adsorption was linear. The amount of lactone adsorbed on Amberlite XAD-4 within 1 h was approx. 80% (2.45 g), of which 1.96 g was then desorbed with ethanol. In the context of industrial applications, adsorption of GDL on Amberlite XAD-4 seems to be the most appropriate method due to material costs, the ease of the process, and low environmental burden.  
gamma-decalactone; separation; solvent extraction; hydrodistillation; adsorption; Amberlite XAD-4 +
Footnote †: journal: Applied Sciences
# Effect of Boundary Conditions on the Mechanical Behavior of the Geogrid-Soil Interface
## 2 Materials and Methods
### Tested Materials  
The geosynthetic used in the current research was a uniaxial geogrid composed of HDPE, as shown in Figure 3. The distance between two transverse ribs was 25 cm. The inner hole size of A, B was 235 and 14 mm, respectively. The thickness of the transverse rib was 3 mm. The tensile strength at failure, 2% strain, and 5% strain was 98.38, 31.15, and 58.23 kN/m, respectively.  
Figure 2: **(a)** The schematic representation of pullout apparatus; **(b)** the view and technical parameters of pullout apparatus used in the current study.  
In this investigation, coarse sand was employed as infill material. According to ASTM D6913/D6913M-17, the curve of the grain size distribution of coarse sand was obtained using sieve analysis, as shown in Figure 4. From Figure 4, it can be observed that the particle size distribution of the coarse sand was varying from 0.1 to 10 mm, and the d10, d30, d50, d60 were 0.16, 0.49, 0.79, and 1.1 mm, respectively. Therefore, the coefficient of uniformity (Cu) and the coefficient of curvature (Cc) of the infill material used in this study were 6.88 and 1.36, respectively. Because Cu was larger than six and Cc was between one and three, it was determined that the filling material utilized in the experiment was judged to be well-graded sand. Hence, the coarse sand was classed as well graded soil (GW), in accordance with the Unified Soil Classification System (USCS) (ASTM D2487-11). Direct shear tests were also performed on specimens having a height of 20 mm and an interior size of 61.8 mm. The shear strength index of the coarse sand used in this research was obtained. The internal friction angle at Dr of 45% was 26.5 degrees.  
The flexible boundary condition was created by flexible rubber [32] in the current study. It had the characteristics of low hardness, easy deformation, and can withstand high pressure that up to 8 MPa. Moreover, the Poisson's ratio of the flexible rubber material was closed to 0.49, namely the volume was almost incompressible. In the pullout test process, the flexible rubber can transfer the upper load to the soil sample uniformly and can be deformed with the deformation of the soil sample surface. In this current study, the flexible rubber was attached on the positive of the inside test box and the lower side of the loading plate to simulate the flexible positive boundary condition and flexible top boundary condition, respectively.
# Firewall Best Practices for Securing Smart Healthcare Environment: A Review
## 3 Firewalls Types for Smart Healthcare Environment
### Stateful Inspection  
Stateful inspection firewalls operate at the session layer of the OSI model (Level 5). Unlike packet filtering, a stateful inspection can preserve all packet information previously processed and inspect information of ingoing and outgoing packets, managing threats from packet-to-packet simultaneously. For this reason, this firewall is often called a dynamic packet filtering firewall [49]. A potential benefit from the use of this firewall is the simple customization of the internal network, allowing network administrators to set specific ports (and traffic) they want to keep open. This prevents spoofing risks, and some hacking techniques, such as port scanning (but does not covering the application layer from SQL injections or buffer overflow risks). However, even though stateful inspection firewalls reach efficient levels of network security, some potential issues are the low level of network performance when not periodically supervised, maintained, and cleared [49; 50]. This raises the important question between security and performance in every network architecture: the more we secure an environment, the more we must take care of network performances. Nonetheless, firewalls using stateful inspection rules are still applicable to a very large number of traditional and non-traditional security systems.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 3 System Overview and Method
### Principle of Binocular Positioning  
The geometric relationship between the point \(P\) and its corresponding projection point in the left and right images is illustrated in Figure 4. The baseline distance between the left and right lenses, denoted as \(b\), and the focal length of the camera is represented by \(f\). By applying the trigonometric similarity formula, we derive the following equation:  
\[u1=\frac{f\cdot x}{z},\ u2=\frac{f\cdot(x-\mathrm{b})}{z},\ v1=v2=\frac{f\cdot y }{z} \tag{8}\]  
The parallax of the \(P\) point between the two images is signified by \(d\)  
\[d=u1-u2=\frac{f\cdot b}{z} \tag{9}\]  
Therefore, the position of point \(P\) in the camera's coordinate system can be computed.  
\[x=\frac{b\cdot u1}{d},\ y=\frac{b\cdot v}{d},\ z=\frac{b\cdot f}{d} \tag{10}\]
## 4 Energy Management Systems
### Learning-Based Energy Management Systems  
Learning-based EMSs can be categorized primarily into two domains: neural network (NN) [113] and reinforcement learning (RL) [50]. The inputs of NN-based EMS necessitate the selection of characteristic values corresponding to various operating conditions, while the model's outputs represent the power distribution [114]. This methodology typically demands a substantial volume of data, and as such, NN is frequently utilized for operating condition classification [115] and also speed prediction [116; 117; 118]. In contrast, RL-based EMS has the capacity to directly learn optimal EMS strategies from data, even in situations where data availability is limited. As illustrated in Figure 22, the RL comprises components like environment, agent, action, reward function, and state [32]. The fundamental concept involves the agent's perpetual environmental interaction, exchanging information regarding action, state, and rewards [119]. This iterative process enables the agent to gradually formulate control rules, eventually converging on an optimal control strategy [120]. The RL techniques commonly applied in EM includes Q-learning and deep Q-network (DQN).  
Figure 21: Predictive control schematic diagram.
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 4 Experiment
### Datasets and Evaluation Indicators
#### 4.1.2 Evaluation Indicators  
Evaluation indicators include _mIOU_, Precision, and Recall. The evaluation indicator that needs to be focused on is _mIOU_, which is a commonly used indicator for evaluating image segmentation quality. It combines pixel-level accuracy and recall to provide a comprehensive evaluation of the overall performance of the segmentation results. The expression of the _mIOU_ indicator is shown in Equation (3).  
\[mIOU=\frac{1}{N}\sum_{i=1}^{N}IOU_{i} \tag{3}\]  
In the equation, N represents the total number of classes, and _IOU\({}_{i}\)_ represents the _IOU_ for the i-th class, where _IOU_ is an indicator for calculating the accuracy of the segmentation result of a single category, which represents the ratio between the intersection and union of the predicted area and the actual area. Its expression is shown in Equation (4). The _mIOU_ metric should fall within the range of 0 to 1. A higher _mIOU_ value closer to 1 indicates that the overlapping regions between the ground truth and the predicted values are more aligned, signifying a greater overlap between the predicted segmentation by the model and the true labels, thus indicating higher accuracy. When the model accurately predicts the segmentation regions for each class, the _mIOU_ reaches its maximum value of 1.  
\[IOU=\frac{TP}{TP+FP+FN} \tag{4}\]  
In the equation, _TP_ represents the true positive example of the category, _FP_ represents the false positive example of the category, and _FN_ represents the false negative example of the category.  
In addition to the aforementioned metrics, Precision and Recall are widely employed evaluation measures in the field of segmentation. Precision quantifies the model's ability to accurately identify positive samples among its positive predictions, reflecting the proportion of true positives within the predicted positive set. Conversely, Recall assesses the model's capacity to correctly identify all actual positive samples, indicating the fraction of true positives captured from the total positive set. These complementary metrics provide valuable insights into the model's performance from different perspectives. Their respective mathematical formulations are presented in Equations (5) and (6).  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]
## 4 Discussion  
In recent years SIF has been proposed as a means to estimate terrestrial vegetation photosynthetic rates at multiple spatial scales, ranging from the leaf and canopy [5; 6; 7; 8] to global scales [9; 10; 11; 12]. However, changes in SIF fluctuate with the relative proportions of APAR used by plants for photochemistry or NPQ and as such, information about these parameters is needed to interpret SIF [14]. Although direct measurements of YPSII and YNPQ cannot be made over large spatial scales using passive remote sensing, they can be measured at leaf and small canopy levels with active fluorescence approaches and combined with passive observations. A few studies have investigated changes in SIF at small spatial scales in relation to active fluorescence measurements. However, they have either  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment** & **Pre-Dawn** & **Sunlight +1** & **Sunlight +2** & **After Sunset** \\ \hline
**Pigment/spectral Indices** & & & & \\ \hline PRI & \(-\)0.0022 \(\pm\) 0.0028 & 0.011 \(\pm\) 0.0035 & 0.00038 \(\pm\) 0.0062 & 0.0018 \(\pm\) 0.0078 \\ NDVI & 0.73 \(\pm\) 0.035 & 0.73 \(\pm\) 0.034 & 0.73 \(\pm\) 0.051 & 0.75 \(\pm\) 0.035 \\ DEPS *** & 0.21 \(\pm\) 0.021 A & 0.49 \(\pm\) 0.046 B & 0.53 \(\pm\) 0.048 B & 0.27 \(\pm\) 0.039 A \\ Total Chl & 58.88 \(\pm\) 3.95 & 69.86 \(\pm\) 5.27 & 60.94 \(\pm\) 4.45 & 64.34 \(\pm\) 3.86 \\ Chl a/b & 2.41 \(\pm\) 0.043 & 2.55 \(\pm\) 0.057 & 2.56 \(\pm\) 0.062 & 2.59 \(\pm\) 0.056 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Leaf pigment composition and spectral indices measured from avocado leaf discs collected at one of four different time points: before sunrise (pre-dawn), 1 h (sunlight +1) and 2 h (sunlight +2) after direct sunlight exposure and after sunset. Leaf spectral indices (PRI and NDVI) were taken from diurnal measurement with a QE Pro spectroradiometer. Violaxanthin de-epoxidation status (DEPS), Chl a/b and total Chl were determined via HPLC, where total Chl is given in \(\mu\)g\(\cdot\)cm\({}^{-2}\). All data represents the mean \(\pm\) SE (\(n\) = 30) of avocado leaf discs collected at SIF for each treatment. Significant differences between treatments are marked by ***. Treatments not connected by the same letter are significantly different.  
involved broad-band SIF measurements, with a full width half maximum (FWHM) \(>\)1 nm, that are not comparable to the narrow-band SIF (FWHM \(<\) 1 nm) retrieved with spectral infilling approaches (i.e., FluorPen) [37], or have compared small canopy scale measurements with PAM measurements on single leaves [8; 21]. Our approach goes in the same direction as these experiments; however, we focus exclusively on the leaf-level in order to avoid spatial mismatches between SIF and active fluorescence measurements. Using this approach, our data shows differences in the relationships between SIF and LIFT photosynthetic measurements in seasonal as well as daily regimes, which may be of importance for the interpretation of large spatial and temporal scale SIF measurements.
## 2 Related Work
### K-Means Algorithm  
The K-means algorithm is a partitional clustering algorithm that iteratively groups a given dataset \(X\) in \(\mathbb{R}^{d}\) into \(k\) number of clusters \(C_{1},\ C_{2},C_{3},\ldots,C_{k}\) such that:  
\[C_{i}\
eq\varnothing; \tag{1}\]  
\[\cup_{1}^{k}C_{i}=X \tag{2}\]  
\[C_{i}\cap C_{j}\
eq\varnothing\ \forall i,\ j\in 1,2,\ldots,k\ and\ i\ \
eq j \tag{3}\]  
based on a specified fitness function. The K-means algorithm handles the partitioning process as an optimization problem to minimize the within-cluster variance \(\sigma\):  
\[\sigma=\sum_{x\in X}\min_{cc\in\mathbb{C}}||x-cc||^{2} \tag{4}\]  
with cluster center \(cc_{i}\) uniquely defining each cluster as:  
\[cc_{i}=\frac{1}{|C_{i}|}\sum_{x\in C_{i}}x \tag{5}\]  
with the set of \(k\) centres  
\[CC=\{cc_{1},\ldots,cc_{k}\} \tag{6}\]representing the solution to the K-means algorithm [54].  
The standard K-means algorithm involves three major phases: the initialization phase, the assignment phase, and the centroid update phase. During the initialization phase, the initial cluster centers are randomly selected to represent each cluster. This is followed by the data object assignment phase, where each data point in the dataset is then assigned to the nearest cluster, based on the shortest centroid-point distance. Each cluster centroid is then re-evaluated during the centroid update phase. The last two phases are repeated until the centroid value remains constant in consecutive iterations [44].  
The K-means clustering is an NP-hard optimization problem [55], with the algorithm having a time complexity of \(O(nkt)\), where \(n\) represents the number of data points in the dataset, \(k\) represents the number of clusters, and \(t\) denotes the number of iterations required for convergence. The computational complexity is a function of the size of the dataset, hence, clustering large real-world, or dynamic, datasets using the K-means algorithm incurs a sizeable computational time overhead. Moreover, the number of clusters \(k\) is required to be a user-specified parameter for the K-means algorithm. In most real-world datasets, the number of clusters is not known a priori; therefore, specifying the correct number of clusters in such a dataset is arduous. Furthermore, the random selection of initial cluster centroids incurs the possibility of the algorithm getting stuck in the local optimum [54]. Based on these challenges, many variants of K-means have been proposed in the literature [2,4] to improve the performance of the K-means algorithm. One of the areas being exploited for improving the standard K-means algorithm is hybridizing K-means with metaheuristic algorithms [2].
# Digital Health Technologies to Support At-Home Recovery of People with Stroke: A Scoping Review
## 4 Discussion
### Methodological Considerations  
The methodological robustness across the included studies reveals several recurring challenges that influence the strength and generalizability of their findings.  
Firstly, small sample sizes were a pervasive limitation across almost all studies [26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. Many investigations involved fewer than ten participants [26; 28; 29; 31; 32; 33; 34], which restricts statistical power and undermines the generalizability of results to broader clinical populations. For instance, some studies evaluated devices only in individuals without impairments [28] or solely at a specific functional level (e.g., Tinetti Level 2 stroke patients [29]), thus narrowing applicability.  
Secondly, restricted participant diversity further affected external validity. Several studies included only participants with mild impairments [31] or excluded those with particular sensory challenges (e.g., pain or touch sensitivity) [28]. This selective sampling limits the ability to apply findings across the full spectrum of stroke or rehabilitation populations [26; 28; 32].  
Third, short-term and highly controlled study designs were common [27; 29; 33]. Many systems were tested only under supervised, short-duration sessions in laboratory or rehabilitation center settings, rather than through prolonged independent home use. This lack of real-world evaluation introduces uncertainty about device performance in naturalistic environments where user behavior and environmental factors vary greatly [30; 34].  
Additionally, the assessment focus was often narrow. Several studies validated only single technical aspects of devices (e.g., concurrent validity or step counting accuracy) [28; 29], while omitting broader evaluations like discriminant validity, sensitivity to change, or usability over time. Moreover, few studies examined how these technologies influence clinical outcomes or rehabilitation progress, leaving important translational questions unanswered [30; 32].  
Overall, while the included studies provided valuable insights into emerging technologies for stroke rehabilitation and home-based monitoring, the evidence base remains preliminary. Larger, longer-term, and more ecologically valid studies are needed to strengthen confidence in the clinical applicability and real-world effectiveness of these digital health interventions.
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 5 Results and Discussion
### Poisson Distribution Reward
While the Poisson mechanism provides an effective method to reduce the problems caused by randomness, it clearly shows that the choice of transportation mode should change depending on the traffic density. Table 4 shows the statistics obtained for vehicles and pedestrians under different traffic densities using the Poisson distribution reward model. The table includes statistical measurements such as mean, maximum, minimum, standard deviation, median, and mode values for four different transportation modes (car, bicycle, motorcycle, and pedestrian) in each dataset size (100, 200, 300, and 400 vehicles and pedestrians). The Poisson distribution reveals that the bicycle is the most efficient transportation mode at low traffic densities, while the motorcycle becomes a better option at high traffic densities. According to the table, while the reward values of both bicycle and motorcycle are distributed more steadily at low traffic densities, it is seen that motorcycle has a higher mean and a lower standard deviation at high traffic densities. This situation occurs because the Poisson reward mechanism rewards on-time arrivals more and penalizes late arrivals more severely. In addition, it is observed that the median and mode values for vehicles and pedestrians are generally low, indicating that the reward distributions are usually concentrated in a narrow range. As traffic density increases, a wider distribution is observed in other modes of transportation such as cars and pedestrians, which shows that the increase in traffic  
Figure 10: Arrival time distribution by transport mode according to Poisson distribution. Each subplot shows simulation results with different numbers of vehicles and pedestrians: **(A)** 100 vehicles and pedestrians (400 in total), **(B)** 200 vehicles and pedestrians (800 in total), **(C)** 300 vehicles and pedestrians (1200 in total), and **(D)** 400 vehicles and pedestrians (1600 in total). Median and range of arrival times are shown for each transport mode. density has a significant effect on transportation time. The table shows that the Poisson distribution, with its sharp and fair reward mechanism, is a powerful tool for evaluating the performance of different modes of transportation depending on traffic density. In this context, the Poisson model clearly demonstrates that the choice of transportation mode should be based not only on average performance but also on the performance stability depending on traffic density.