esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Packing Problems
#### 4.1.3. Subgraph Packing  
A special family of packing problems can be obtained by subgraph packing. Let \(H\) be a fixed \"pattern\" graph. The \(H\)-Packing problem, given the \"host\" graph \(G\), asks to find the maximum number of vertex-disjoint copies of \(H\). One can also let \(H\) be a family of graphs and ask the analogous problem. There is another choice whether each copy of \(H\) is required to be an induced subgraph or a regular subgraph. We focus on the regular subgraph case here.  
When \(H\) is a single graph with \(k\) vertices, a simple greedy algorithm that finds an arbitrary copy of \(H\) and adds it to the packing, guarantees a \(k\)-approximation in time \(f(H,n)\cdot n\). Here \(f(H,n)\) denotes the time to find a copy of \(H\) in an \(n\)-vertex graph. Following a general result for \(k\)-Set Packing, a \((k+1+\varepsilon)/3\)-approximation algorithm that runs in polynomial time for fixed \(k\), \(\varepsilon\) exists [114]. When \(H\) is 2-vertex-connected or a star graph, even for fixed \(k\), it is NP-hard to approximate the problem better than a factor \(\Omega(k/\mathrm{polylog}(k))\)[115]. There is no known connected \(H\) that admits an FPT (or even XP) algorithm achieving a \(k^{1-\delta}\)-approximation for some \(\delta>0\); in particular, the parameterized approximability of \(k\)-Path Packing is wide open. It is conceivable that \(k\)-Path Packing admits a parameterized \(o(k)\)-approximation algorithm, given an \(O(\log k)\)-approximation algorithm for \(k\)-Path Deletion[116] and an improved kernel for Induced \(P_{3}\) Packing[117].  
When \(H\) is the family of all cycles, the problem becomes the Vertex Cycle Packing problem, for which the largest number of vertex-disjoint cycles of a graph needs to be found. No polynomial time \(O(\log^{1/2-\varepsilon}n)\)-approximation is possible for this problem [118] for any \(\varepsilon>0\), unless every problem in NP can be solved in randomized quasi-polynomial time. Furthermore, despite being FPT [119] parameterized by the solution size, Vertex Cycle Packing does not admit any polynomial-sized exact kernel for this parameter [120], unless NP\(\subseteq\)coNP/poly. Nevertheless, a PSAKS can be found [18].  
**Theorem 16** ([18]).: _For the Vertex Cycle Packing problem, a \((1+\varepsilon)\)-approximate kernel of size \(k^{O(1/(\varepsilon\log\varepsilon))}\) can be computed in polynomial time, where \(k\) is the solution size._
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 4 MODY Subtypes and Their Treatments
### Kcnj11-Mody (mody 13)  
MODY 13 is caused by heterozygous mutations in the potassium inwardly rectifying channel subfamily J member 11 (_KCNJ11_) gene, which encodes for human BIR (beta cell inward rectifier) and Kir6.2, subunits of K-ATP channels [7; 98; 97]. This gene mutation causes severe conditions, such as channel inactivation due to disrupted subunit interaction. This disruption was discovered to be linked to Arg301 mutations, which typically result in hyperinsulin and possibly neonatal diabetes [95; 65]. _KCNJ11_-MODY is best treated with high-dose sulfonylureas for extended periods of time [34; 50; 100].
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 7 Introducing the System of Systems Lifecycle Management Model
### An Implemented System Integration for Arrowhead Using the Newly Developed SoSLM Methodology
#### 7.2.3 Operation  
This is the most significant lifecycle phase, which is the MoL according to the PLM model presented in Section 5, i.e., the _Sphere of activities_, where the application will exist and operate in the SoS architecture during its lifecycle. Most of the time, the newly developed applications are integrated into an existing system infrastructure. According to the Industry 4.0 expectations, the system must satisfy the flexibility and modularity requirements. As mentioned earlier, the _Sphere of activities_ is a collective term, where the concept is that the subphases should always be formed according to the type of lifecycle model used. To manage SoS, the _Sphere of activities_ is transformed into two further subcycles where the determinant lifecycle processes are the same as in the MoL stage of the introduced PLM model.  
* System management: the presented model approaches the industrial-specific PLM strategy from a holistic view, assuming that the heterogeneous products can be organized and integrated into larger groups or systems and managed as SoS. In this case, it is crucial to distinguish the management tasks on the different levels of the SoS. System management refers to SoS-related activities. On this level, the emphasis is on the tasks of the main systems and creating the connection and other inter-cloud-related events, such as network management with remote clouds and managing their subsystems or CPSs. To support proper functioning, the system must be able to handle the integration and management of a newly installed application. In our case, the new application is integrated into the Arrowhead framework, where the regarding services--orchestration, service registry, and authorization--will be provided by the Arrowhead Core Systems.
* Application management: the smallest units, i.e., the smallest operating systems or applications, are the building blocks of the SoS, which also require careful management. This is the subcycle where the developed application operates. Its main processes are identified on the third ring and assigned to the functions on the fourth ring. In the current implementation, the newly integrated application's workflow-specific features are monitored and managed by the used WSO2 infrastructure.
## 4 Results
### Regional-Scale Model Comparisons
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**AAGPP as a Function of Latitude**} & \multicolumn{2}{c}{**AAGPP as a Function of Longitude**} \\ \cline{2-7}  & **Regression Function** & \multirow{2}{*}{\(\mathbf{R^{2}}\)} & \multirow{2}{*}{\
\begin{tabular}{c} **Slope** \\ \(p\) **Value** \\ \end{tabular} } & \multirow{2}{*}{**Regression Function**} & \multirow{2}{*}{\(\mathbf{R^{2}}\)} & \multirow{2}{*}{
\begin{tabular}{c} **Slope** \\ \(p\) **Value** \\ \end{tabular} } \\ \hline MOD17A2H & \(y=2.270x+1379\) & 0.070 & 0.304 & \(y=6.797x+2031\) & 0.636 & \(<\)0.0001 \\ TL-LUE & \(y=-43.619x+3330\) & 0.980 & \(<\)0.0001 & \(y=-12.797x+541.9\) & 0.787 & \(<\)0.0001 \\ VPM & \(y=-38.550x+2632\) & 0.950 & \(<\)0.0001 & \(y=-10.381x+242.4\) & 0.627 & \(<\)0.0001 \\ GPD & \(y=-46.933x+3271\) & 0.970 & \(<\)0.0001 & \(y=-16.273x+65.21\) & 0.852 & \(<\)0.0001 \\ BESS & \(y=-21.962x+2508\) & 0.724 & \(<\)0.0001 & \(y=0.100x+1629\) & 0.0002 & 0.944 \\ EC-MOD & \(y=-40.233x+2844\) & 0.963 & \(<\)0.0001 & \(y=-15.598x-42.2\) & 0.787 & \(<\)0.0001 \\ \hline \end{tabular}
\end{table} 
Table 3: Changing rates of zonally and meridionally averaged profiles of 12-year (2001–2012) average annual gross primary productivity (AAGPP) derived from different models along latitude and longitude. Figure 2: The (**a**) zonally and (**b**) meridionally averaged profiles of 12-year (2001–2012) average annual gross primary productivity (AAGPP) derived from different models. vegetation photosynthesis. The simulated AAGPP levels of TL-LUE, VPM, GPD, and EC-MOD generally match those of average LAI and air temperature, while the MOD17A2H data show the opposite, indicating that the current version of the algorithm that generates MOD17A2H is highly sensitive to VPD. The spatial pattern of AAGPP derived from BESS is apparently not related to the distribution of those key meteorological variables. Since both MOD17A2H and BESS data are produced using downscaled coarse-resolution climate data, the uncertainties among different meteorological data could influence the GPP simulation.
###### Abstract  
Interleaved dc-dc converters in sensitive applications necessitate an enhanced reliability. An interleaved converter equipped with redundant components can fulfill the reliability requirements. Mean Time to Failure (MTTF), as a reliability index, can be used to evaluate the expected life span of the mentioned converters. The Markov model is a helpful tool to calculate the MTTF in such systems. Different scientific reports denote different failure rates with different weight for power elements. Also, in reliability reports, failure rates of active and passive components are uncertain values. In order to approximate the failure rates fuzzy-logic-based Markov models are proposed in this paper. Then it is used to evaluate the MTTF of an interleaved multi-phase dc-dc converter, which is equipped with parallel and standby switch configurations. For the first time, fuzzy curves for MTTFs of the converters and 3D reliability function are derived in this paper. The reliability analyses give an insight to find the appropriate redundant-switch configurations for interleaved dc-dc converters under different conditions. Simulation and experimental results are provided to lend credence to the viability of the studied redundant-switch configurations in interleaved dc-dc boost converter.  
reliability; interleaved dc-dc converters; Markov model; MTTF; fuzzy logic +
Footnote †: journal: Applied Sciences
## 4 Proposed Approach and Experiments
### Experiment 2: GAT as Classifier with Node2 Vec and GraphSage  
In Experiment 2, GAT is used as a standalone classifier, and the results are compared with the combinations of Node2 Vec + GAT and GraphSAGE + GAT. This experiment analyzes the performance of GAT as a standalone classifier and its contribution when used with other embedding methods. In the same manner as Experiment 1, embedding is generated for each node using Node2 Vec or GraphSAGE. Using these embeddings, GAT analyzes the relationships between nodes and creates a new representation \(h_{v}\) for each node:  
\[h^{\prime}_{v}=\sigma\left(\sum_{u\in N(v)}\alpha_{vu}Wh_{u}\right) \tag{13}\]  
In the last layer of GAT, a Sigmoid activation function is used to classify the resulting node representations. The sigmoid activation function used in the last layer of GAT makes it possible to estimate the probability (between 0 and 1) that a node belongs to a certain class in two-class malware detection. This function provides a non-linear activation, allowing the model to distinguish between classes more precisely.  
\[\hat{y}_{v}=\sigma(W^{\prime}h^{\prime}_{v}) \tag{14}\]  
Here, \(W^{\prime}\) represents the learnable weights, and the Sigmoid activation function reduces the output of the model to the range [0, 1], which is especially suitable for binary classification. The model is trained using a loss function between the model outputs \(\hat{y}_{v}\) and the true labels \(y_{v}\):  
\[L=-\sum_{v}[y_{v}\log(\hat{y}_{v})+(1-y_{v})\log(1-\hat{y}_{v})] \tag{15}\]  
This last layer and activation process is one of the necessary steps for using GAT as a classifier. This allows it to transform the node representations of the GAT model into a classification output that will decide on the presence or absence of malware. This allows GAT to be effectively used as a standalone classifier.
## 3 Results
### Trends over Time  
Between 2014 and 2016, the publication volume was limited and predominantly non-clinical. From 2017 onward, thematic diversification increased, with growing contributions from geriatrics, psychiatry, and cardiology. The COVID-19 pandemic marked a sharp inflection point, after which implementation-focused research gained prominence. Post-pandemic years have witnessed a gradual return to strategic evaluation and interoperability studies. Figure 2 presents the trend analysis of telemedicine and eHealth publications in Poland from 2014 to 2024.  
There has been a steady increase in the number of publications since 2015, which is aligned with the formal legal implementation of telemedicine. COVID-19-related publications experienced a notable surge starting in 2020 and peaking in 2021 and 2022, reflecting the pandemic's catalytic effect on research.  
The comparison of two periods: 1995-2015 and 2014-2024 shows changes in the number of publications on telemedicine and eHealth by specialization (Figure 3).  
Figure 2: Trend analysis of telemedicine and eHealth publications in Poland from 2014 to 2024.  
An analysis of progress in telemedicine and eHealth has shown that, between 2014 and 2024, these solutions have evolved from niche innovations to integral parts of national healthcare systems. The COVID-19 pandemic has significantly accelerated these changes, highlighting the shortcomings of traditional healthcare delivery methods, and accelerating the implementation of remote services. Such interdisciplinary bridges connect and integrate the expanding digital infrastructure in healthcare in Poland, ensuring the flow of data, funding, and innovation between sectors, while protecting patients' needs, rights, coverage, and accountability.
## 3 Materials and Methods
### Framework of the MSCS-CNN  
The first problem that we came across with our model was how to improve the accuracy of the CNN. Bone age assessment is generally used for humans aged 0-18 years. According to our experiment (see Section 4), we tried to fit all bone ages into one regression model. As bone age has large variation, this could have resulted in large error in the building of the regression model. In order to reduce the variation, we divide the X-ray film into several stages. A regression model will be built in each stage so that, in each regression model, only a small range of bone ages needs to be fitted.  
At present, there needs to be a classifier that can accurately determine the maturity stage. Child development is a continuous process and there is no obvious decision boundary. Therefore, in this step, we propose a cross-stage class strategy for an intermediate stage which will exist in both the previous stage and the next stage. Ref. [27] shows that bone age can be divided into seven stages: infancy, toddlers, pre-puberty, early, mid-puberty, late puberty, and post-puberty. For convenience, we denote these stages as stages A to F. In our cross-stage class strategy mentioned above, we will convert the maturity stages into AB, BC, CD, DE, and EF. To illustrate this strategy, we take the B stage as an example for the AB and BC stages. The subsequent experiments will also show that the cross-stage strategy is not only helpful for bone age assessment, but also alleviates the problem of data imbalance.  
The MSCS-CNN consists of three steps, as shown in Figure 3. Step 1 to step 3 involve coarse maturity stage classification, fine maturity stage classification and bone age regression. We will describe the details of each stage in the subsequent sections. In addition, the CNN model architectures that we used in the three steps are mostly the same. The only differences between the three-step models are the number of nodes and the activated function of the output layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Maturity Stage** & \
\begin{tabular}{c} **Number of Female X-ray** \\ **Films** \\ \end{tabular} &
\begin{tabular}{c} **Number of Male X-ray Films** \\ \end{tabular} \\ \hline A & 3 & 4 \\ B & 63 & 59 \\ C & 463 & 4 \\ D & 677 & 441 \\ E & 59 & 93 \\ F & 20 & 40 \\ Total & 1285 & 1071 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stage distribution of our dataset.
## 4 Discussion
Given that this cheese was produced from pasteurized milk and without starters, we might exclude milk lipoprotein lipase and enzymes from microbial starters acting as lipolytic agents of Pecorino cheese. A possible explanation worthy of investigation could be the esterase activity of _S. thermophilus_, which seemed to dominate the bacterial biota of Pecorino cheese. Among the strains of dairy origin and belonging to seven different LAB species, those of _S. thermophilus_ showed the highest esterase activity in alcoholysis [84]. Given that esterase from _S. thermophilus_ showed ester-synthesizing capacity [84], we may hypothesize that _S. thermophilus_ could have been the source of esterase contributing to lipolysis and catabolism of FFA in the Pecorino cheese object of study. The VOC profile of Caprino (from goats' milk) included pyrazine derivatives as a distinctive trait. Only a few studies have assessed the presence of pyrazines in goats' milk chees [77; 91]. Although pyrazines have been frequently detected in ewes' milk chees [38; 92; 93], in our study this class of compounds was not detected in Pecorino cheese (from ewes' milk). Pyrazines confer to cheese a mild to intense roast nut aroma, and originate during Maillard reaction, which depends on many factors, such as temperature and the availability of sugars [77]. Caprino, along with Vaccino cheese as the object of the current study, were characterized by high levels of FAA and peptidase activities, which may have impacted on pyrazines formation in Caprino cheese, in agreement with a previous study [94]. Although we found that Caprino and Vaccino cheeses shared several traits (e.g., high abundance of _L. helevticus_ and level of peptidase activity), the VOC profiles markedly differed between these two cheeses, Caprino cheese being richer in, besides pyrazines, aldehydes, some medium chain fatty acids and their derived ethyl esters. We may hypothesize that this could be due to differences in the type of milk and in the cheese-making protocols. 1-Butanol,3-methyl and phenylethyl alcohol were the distinctive VOC of Cacioricotta cheese. In addition, this cheese was characterized also by the highest concentration of 3-hydroxy-2-butanone (acetoin), originating from either diacetyl (2,3-butanedione) reduction or metabolism of pyruvate, lactose, or citrate [95]. 3-methyl butanoic acid was another distinctive compound of Cacioricotta cheese. The highest amount of acetoin and 3-methyl butanoic acid found in Cacioricotta could be ascribed to the presence of _S. equorum_, which has been recognized as a potential producer of acetoin and branched chain fatty acids, which are important flavor compounds [59; 96].
## 1 Introduction
The objective of this paper is to build on the SMA in [18] by adapting the algorithm to analog signals, rather than digital signals, to further increase the output dimensionality and enable the use of analog sensors to lead to more intuitive interfaces. The proof of concept consists in the development, design, and implementation of an intuitive and robust IMU-based hand gesture interface to control any assistive technology (e.g., robotic arm, mouse, and keyboard). To that effect, a classification pipeline has been developed to detect and recognize hand gestures from IMU signals. This pipeline was then embedded into the SMA. With that system, we aim to answer the previously stated issue of the dimensionality difference between the interface and the assistive technology without relying on grouping the different modes, but rather by artificially augmenting the dimensionality of the interface itself. The goal is to help people living with upper or lower body incapacities in their daily life tasks. This novel approach combines several key concepts of the multiple interface design paradigm (previously presented). First, the usage of wearable sensors, such as IMU and EMG, offers a high level of flexibility for the user, since they can be fixed on various body parts. Furthermore, in most cases, they are not cumbersome for the user. Then, the scanning menu panel strategy is intuitive and simple to use. The idea to have a display showing the modes to help the user navigate through them is effective but not necessarily fast. The SMA can then help speed up the usage. This work aims to show that combining those ideas adds value to the user experience and can definitely help some people. This paper is structured as follows. First, the methods for the development are presented in Sections 2-4. This includes the development of an offline version of the algorithm, followed by a real-time version and, finally, the integration of the latter to the SMA. The experimental methods are presented in Sections 5 and 6, with the description of the actual implementation of the interface and the experiments performed with it. Finally, the results are presented and discussed in Section 7.
## 2 Materials and Methods
### Surgical Technique  
As for the extracorporeal version previously described, the VON is constructed with 36 cm of the ileum, isolated about 15-20 cm from the ileocecal valve [34]. The neobladder configuration takes shape through the following ten steps:  
1. Identification of the ileocecal valve and subsequent selection and measurement of an intestinal tract of 36 cm, 20-25 cm from the ileocecal valve.
2. Placement of three Vicryl 3/0 stay sutures at 6 cm, 18 cm, and 30 cm on the anti-mesenteric side of the selected intestinal tract in order to shape the reservoir in three horns: left, right, and caudal (Figure 1A).
3. Isolation of the mesocolon and resection of the intestinal tract selected. A side-to-side anastomosis, for the restoration of the intestinal continuity, is performed using a 60 mm mechanical stapler (SureForm(r), Intuitive Surgical, Sunnyvale, CA, USA) (Figure 1B-D).  
4. Utilizing the opening in the intestinal tract resulting from the first anastomosis, another side-to-side anastomosis is performed in the lateral right horn, utilizing the 60 mm mechanical stapler in order to detubularize this intestinal tract (Figure 2A).
5. A 1.5 cm perpendicular incision of the intestinal tract is made at the level of the lateral left horn, in order to permit the introduction of the 60 mm mechanical stapler (SureForm(r), Intuitive Surgical, Sunnyvale, CA, USA) and perform the detubularization of the lateral left horn, similarly as done before for the right horn (Figure 2B,C).
6. To avoid the incision of the caudal horn (in order to provide a narrow neck of the neobladder for the successive anastomosis with the native urethra), utilizing the detubularized lateral left horn, the 60 mm mechanical stapler (SureForm(r), Intuitive Surgical, Sunnyvale, CA, USA) is introduced up to reach the caudal horn of the reservoir, to detubularize this tract. If an incomplete detubularization occurs, the process can be repeated in order to complete the detubularization (Figure 2D).
7. Ureters are incannulated with double J ureteral catheters, 7 mm \(\times\) 260 mm, utilizing a guidewire if needed.
8. Incannulated ureters are anastomosed with the homolateral horns, using interrupted 3/0 Vicryl sutures (Figure 3A-C). This step can also be made after the neobladder-urethral anastomosis.
9. A small perpendicular incision of 1 cm is made at the apex of the caudal horn, to create the neck of the neobladder.
10. A Vicryl 3/0 suture is used to approach the posterior periurethral tissue, the Denonvillier fascia, and the posterior margin of the neobladder neck, similarly to Rocco's stitch used in radical prostatectomy. Finally, neobladder-urethral anastomosis is performed on a Foley 18F or 20F catheter with two continuous Vicryl 3/0 sutures (Figure 3D). The neobladder is then slowly filled with 100-150 mL of saline to confirm water tightness.  
Figure 1: **(A)** Shaping the reservoir. (**B–D**) Intestinal resection.
# Investigating the Influence of Feature Sources for Malicious Website Detection
## 3 Preliminary Experiments
### Expanding [PERSON]'s Dataset
#### 3.3.2 Host-Based Features  
Host-based features comprise of the characteristics of host-name properties of a raw URL. They typically provide information regarding the host of the webpage. For instance, domain name properties, ports, named servers time to live and connection speed. This category of features has been used for the task of Phishing detection in [14].