esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Role of Ap4A: Functions in Normal and Cancer Cells
### Apoptosis Evasion  
Ap4A plays a crucial role in regulating apoptosis, the process of programmed cell death vital for maintaining cellular balance and development [24]. Apoptosis eliminates damaged, dysfunctional, or unnecessary cells through tightly controlled mechanisms, and Ap4A is a significant regulator in this context.  
Under certain conditions, Ap4A promotes apoptosis, often through activating caspases [25], proteases critical for the execution of cell death. Ap4A enhances caspase activity, leading to the dismantling of cellular components and cell death, which is essential for removing cells with irreparable DNA damage and preventing malignancies. Moreover, Ap4A influences apoptosis by modulating various apoptotic pathways through its interaction with purinergic receptors, particularly P2X and P2Y families. By binding to these receptors, Ap4A triggers intracellular signals that can either promote or inhibit apoptosis, depending on the cellular context and receptor subtype. Conversely, Ap4A can also exhibit anti-apoptotic effects in other scenarios. For instance, under moderate stress, Ap4A may activate survival pathways like the PI3K/AKT signaling pathway [26], promoting cell survival and protecting against premature apoptosis. This ability to balance pro-apoptotic and anti-apoptotic effects allows Ap4A to fine-tune cellular responses to environmental conditions and internal signals, maintaining cellular integrity and function (Figure 2). Tumor cells may exploit Ap4A's anti-apoptotic properties to evade destruction, highlighting the potential for targeting Ap4A-mediated pathways in cancer therapy.  
Figure 2: Ap4A as a central regulator of stress response, metabolism, and cell fate decisions. This scheme illustrates the multifaceted roles of diadenosine tetraphosphate (Ap4A) in cellular stress adaptation and signaling. Upon exposure to different stressors intracellular levels of Ap4A increase, triggering various downstream pathways. Altogether, this figure underscores Ap4A’s central role in orchestrating diverse signaling networks that determine cell fate between survival and apoptosis.
# Switched Observer Design for a Class of Non-Linear Systems
## 2 System and Observer Description
### Minimum-Type Switching Rule  
Based on the results of [29], the minimum-type switching strategy is proposed  
\[\sigma(t)=\operatorname*{arg\,min}_{i\in\{1_{\ldots,n_{p}}\}}\hat{y}^{T}(t)Q_ {i}\hat{y}(t), \tag{13}\]with \(\tilde{y}(t)=y(t)-\tilde{y}(t)\) and \(Q_{i}\in\mathbb{S}^{n_{y}}\) as a stabilizing rule for the switched observation scheme. Furthermore, the set of optimal indexes  
\[\mathcal{I}(\tilde{y}(t))=\{i\in 1,...,n_{p}:\min_{i\in\{1,...,n_{p}\}}\tilde{y}^{T}Q _{i}\tilde{y}=\tilde{y}^{T}Q_{i}\tilde{y}\} \tag{14}\]  
and at \(t\in\mathbb{R}^{+}\), the switching rule is given by \(\sigma(\tilde{y}(t))=i\) for some \(i\in\mathcal{I}(\tilde{y}(t))\).  
Considering a matrix dependent on the unknown parameter \(\alpha\) defined in (12) [33], that is, \(\Pi(\alpha):\Lambda\rightarrow\mathbb{R}^{n_{p}\times n_{p}}\) whose entries are defined by  
\[\pi_{ji}(\alpha)=\begin{cases}\mu\alpha_{j},&j\
eq i\\ \mu(\alpha_{i}-1),&j=i\end{cases} \tag{15}\]  
with \(\mu\geq 0\), we can verify that they constitute entries of a Metzler matrix \(\Pi(\alpha)\in\mathcal{M}\) for all \(\alpha\) defined in (12). Indeed, based on definition (15), all elements outside the main diagonal are non-negative and the identities  
\[\begin{array}{rcl}\sum_{j=1}^{n_{p}}\pi_{ji}(\alpha)&=&\sum_{j\
eq i=1}^{n_{ p}}u\alpha_{j}+\mu(\alpha_{i}-1)\\ &=&\mu(\sum_{j=1}^{n_{p}}\alpha_{j}-1)\\ &=&0\end{array} \tag{16}\]  
are verified for all \(i\in\{1,...,n_{p}\}\). Furthermore, using \(\Pi(\alpha)\in\mathcal{M}\) defined in (15), the following statements are verified  
\[\begin{array}{rcl}\sum_{j=1}^{n_{p}}\pi_{ji}(\alpha)Q_{j}&=&\mu\sum_{j\
eq i =1}^{n_{p}}\alpha_{j}Q_{j}+\mu(\alpha_{i}-1)Q_{i}\\ &=&\mu\sum_{j=1}^{n_{p}}\alpha_{j}Q_{j}-\mu Q_{i}\\ &=&\mu\sum_{j=1}^{n_{p}}\alpha_{j}(Q_{j}-Q_{i}).\end{array} \tag{17}\]  
Furthermore, for \(i\in\mathcal{I}(\tilde{y})\) and \(\Pi(\alpha)\in\mathcal{M}\)  
\[\begin{array}{rcl}\tilde{y}^{T}(\sum_{j=1}^{n_{p}}\pi_{ji}(\alpha)Q_{j}) \tilde{y}&=&\pi_{ii}(\alpha)\tilde{y}^{T}Q_{i}\tilde{y}+\sum_{j\
eq i}^{n_{p} }\pi_{ji}(\alpha)\tilde{y}^{T}Q_{j}\tilde{x}\\ &\geq&\pi_{ii}(\alpha)\tilde{y}^{T}Q_{i}\tilde{y}+\sum_{j\
eq i}^{n_{p}}\pi_{ ji}(\alpha)\tilde{y}^{T}Q_{i}\tilde{y}\\ &\geq&\left(\sum_{j=1}^{n_{p}}\pi_{ji}(\alpha)\right)\tilde{y}^{T}Q_{i}\tilde{y }\\ &\geq&0,\ \ \forall\tilde{y}\in\mathbb{R}^{n_{y}}.\end{array} \tag{18}\]  
These instrumental statements provide the foundation of the switched observer design that follows.
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 2 Preliminaries and Problem Formulation  
### Notations  
The following notations are used throughout the paper. Let \(\mathbb{N}\) be the set of nonnegative integers. \(\|\cdot\|\) represents Euclidean norm. \(\mathbb{R}^{n}\) refers to the set of all n-dimensional real column vectors. \(I_{N}\) stands for a \(N\times N\) identity matrix. The Kronecker product is denoted by \(\otimes\), and the operation \(\otimes\) on matrix \(A\in\mathbb{R}^{m\times n}\) and matrix \(B\in\mathbb{R}^{p\times q}\) results in a block matrix \(A\otimes B\in\mathbb{R}^{mp\times nq}\). The floor and ceiling functions are defined as \(\lfloor x\rfloor=max\{p\in\mathbb{Z}\mid p\leq x\}(x\in\mathbb{R})\) and \(\lceil x\rceil=min\{q\in\mathbb{Z}\mid q\geq x\}(x\in\mathbb{R})\) respectively. \(T(\zeta,t),(\zeta\leq t\in\mathbb{R})\) refers to the time interval \([\zeta,t)\). \(T^{-}(\zeta,t)\) and \(T^{+}(\zeta,t)\) stands for the total time length of stable and unstable subsystems from initial time \(\zeta\) till the present time \(t\), respectively. And the main parameters of the model in this paper are introduced in Table 1.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 5 Experiments
### Compared Methods  
To demonstrate the effectiveness of our proposed approach, we conducted experiments to compare it with eight other state-of-the-art self-supervised graph learning methods, namely, DGI [17], MVGRL [15], GMI [39], GRACE [4], SUGRL [40], GraphMAE [12], and MaskGAE [13]. As our paper focuses on the node classification downstream task, we included five representative supervised node classification methods as baselines for evaluation as well, namely, MLP, GCN, and GAT.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
###### Abstract  
Mobile ad hoc networks (MANETs) offer a decentralized communication solution ideal for infrastructure-less environments like disaster relief zones. However, their inherent lack of central control and dynamic topology make them vulnerable to attacks. This paper examines the impact of various attacks on mobile nodes within two network types: randomly and uniformly distributed stationary networks. Four types of attacks are investigated: delay, dropping, sinkhole (alone), and a combined black hole attack (dropping + sinkhole). The effects of these attacks are compared using the packet delivery ratio, throughput, and end-to-end delay. The evaluation results show that all single attacks negatively impacted network performance, with the random network experiencing the most significant degradation. Interestingly, the combined black hole attack, while more disruptive than any single attack, affected the uniformly distributed network more severely than the random network.  
MANET; delay attack; dropping attack; sinkhole attack +
Footnote †: journal: Electronics Electronics
## 3 Results and Discussion
### Thermal Conductivity  
The thermal conductivity (\(\lambda\)) of TPU/GE compact and porous structures was examined and compared with the thermal conductivity of compact TPU, to gain insight on the effect of GE addition, porosity, and geometry on their thermal behavior. Compact TPU showed a thermal conductivity of 0.24 \(\pm\) 0.012 W(m \(\times\) K) that is approximately half of the value for compact TPU/GE composites. This means that with the addition of 1% of GE, the thermal conductivity doubles its value for pristine TPU compact structures. Moreover, a porous TPU/GE structure with 40% of porosity showed values of thermal conductivity comparable with that of pristine compact TPU. Figure 10 shows that \(\lambda\) is strongly affected by the porosity of the structure, making it possible to tune the thermal conductivity of the TPU/GE composites by modifying the overall porosity.  
Figure 10: Effect of porosity and geometry on the effective thermal conductivity of TPU/GE porous structures.  
Figure 9: (**a**) Piezoresistive behavior of TPU/GE S40 over 50-cycle compression test, and (**b**) resistance values at 8% strain as a function of time for all TPU/GE composite structures.  
Obviously, \(\lambda\) decreases as the porosity increases, going from 0.451 \(\pm\) 0.023 W/m\(\cdot\)K for the bulk material down to 0.086 \(\pm\) 0.004 W/m\(\cdot\)K for the Gyroid structure with 80% porosity. It was ascertained that an increase in porosity is linked to a decrease in the trabeculae size and an increase of pore dimension for a given unit cell [48]. On the other hand, results obtained for the three different architectures (D, G, and S) indicate that the effect of geometry on the thermal conductivity is small if compared with the effect on the electrical properties and piezoresistivity. This confirms that the transfer of heat phonons and electrons are subject to different physical laws. However, the thermal conductivity remained similar for the different geometries until 40% of porosity. Above this value, the Schwarz geometry showed the highest \(\lambda\) values, while the Gyroid displayed the lowest values, both at 60 and 80% porosity. This effect is again ascribed to the different size of trabeculae generated by the D, G, and S unit cell architectures.
## 3 Results and Discussion
### USM Processing of P3 HB/C20 and P3 HB/C116 Nanocomposites: Molecular Weight Distribution  
The two nanocomposites, P3 HB/C20 and P3 HB/C116, were successfully processed using USM with clay contents of 3 wt%, 5 wt%, and 8 wt%. Representative GPC curves are shown in Figure S1, and detailed molecular weight data for all compositions are summarized in Table S2.  
GPC analysis revealed that P3 HB/C20 nanocomposites exhibited minimal molecular weight degradation across all clay concentrations, with \(M_{w}\) values decreasing by 5.4%, 6.3%, and 11.5% for clay contents of 3 wt%, 5 wt%, and 8 wt%, respectively. These results highlighted the stability of the polymer matrix when processed with C20 clay, attributed to its organic surface modification, which minimizes catalytic degradation.  
In contrast, nanocomposites containing C116 clay experienced significant molecular weight reductions, particularly at higher clay concentrations. For example, \(M_{w}\) decreased by 35.7% at 8 wt% clay, with the majority of degradation (28%) occurring during the initial extrusion step and only an additional 10% reduction attributable to the USM process. The absence of organic surface modification in C116 likely catalyzed polymer degradation during processing, emphasizing the sensitivity of P3 HB to untreated clays.  
A crucial observation was the effectiveness of USM in producing P3 HB/C116 nanocomposites across all compositions, which was not feasible with conventional microinjection. Conventional microinjection resulted in excessive molecular weight reduction (\(M_{w}\) as low as 33,000 g/mol). By contrast, the shorter residence times and rapid cooling associated with USM allowed for the successful processing of P3 HB/C116 nanocomposites, overcoming the limitations of conventional methods.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### The Effect of Re-Queuing Scheme  
We evaluated the effect of re-queuing scheme by comparing the performance of DRBF (ADRBF) and DRBF\({}_{\text{RQ}}\) (ADRBF\({}_{\text{RQ}}\)).  
Figure 13a compares \(\overline{F}_{val}\) and \(\overline{T}_{dis}\) of DRBF and DRBF\({}_{\text{RQ}}\). In the case of DRBF, \(\overline{F}_{val}\) increased from 943 to 977 until \(N_{node}\) increased from 60 to 120 but slightly decreased if \(N_{node}\) exceeded 120. However, when the re-queuing scheme was applied, \(\overline{F}_{val}\) of DRBF\({}_{\text{RQ}}\) was hardly affected by \(N_{node}\), it was maintained between 994 and 999. These results mean that the re-queuing scheme is more effective when the density of node is low. By comparing the results of \(\overline{F}_{val}\) and \(\overline{T}_{dis}\), we found the trade-off between transmission reliability and traffic dissemination time, The values of \(\overline{T}_{dis}\) in DRBF and DRBF\({}_{\text{RQ}}\) increased almost linearly as \(N_{node}\) increased, but \(\overline{T}_{dis}\) of DRBF\({}_{\text{RQ}}\) was larger than that of DRBF by 28%-42% for the entire range of \(N_{node}\) between 60 and 140. Similarly, we can observe the effect of re-queuing scheme applied to ADRBF from Figure 13b. Due to the approximation error of duplication ratio, \(\overline{F}_{val}\) of ADRBF was somewhat smaller than that of DRBF; it ranged between 901 and 955 when \(N_{node}\) is between 60 and 140. Compared to DRBF\({}_{\text{RQ}}\), the gain of re-queuing in terms of \(\overline{F}_{val}\) was magnified in ADRBF\({}_{\text{RQ}}\); \(\overline{F}_{val}\) of DRBF\({}_{\text{RQ}}\) was greater than that of DRBF by 22-51, but the difference between \(\overline{F}_{val}\)s in ADRBF\({}_{\text{RQ}}\) and ADRBF was increased up to 75. On the other hand, there was little difference between \(\overline{T}_{dis}\)s in DRBF and ADRBF, implying that the approximation of duplication ratio has a marginal effect on \(\overline{T}_{dis}\) when the re-queuing scheme was not used. However, if the re-queuing scheme was applied, ADRBF\({}_{\text{RQ}}\) had lower \(\overline{T}_{dis}\) than DRBF\({}_{\text{RQ}}\) by 0.6 s when \(N_{node}\) was 60, but \(\overline{T}_{dis}\) of ADRBF\({}_{\text{RQ}}\) was greater than that of DRBF\({}_{\text{RQ}}\) by 0.3 s when \(N_{node}\) was 140.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 6 Related Works
### Other Related Studies
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref** & **Year** & **Study Focus** & **Proposed Method** & **Solution Target** & **Environment** \\ \hline
[59] & 2019 & Experimental work on handover & Performance evaluation based on experimental data & Study the effect of cell selection on handover & LTE-A network \\ \hline
[106] & 2012 & Mobility & Performance evaluation & Seamless horizontal and vertical mobility & VANET \\ \hline
[107] & 2011 & Mobility/handoff & Survey study & State of the art on mobility & Vehicular networks \\ \hline
[108] & 2015 & Coverage and handover control & Algorithm based on RSS regulates the coverage of each drone. & Optimal coverage control and efficient handover & Drone networks \\ \hline
[109] & 2019 & Handover & Survey study & State of the art on handover & Vehicular ad hoc in 5G mobile networks \\ \hline \end{tabular}
\end{table} 
Table 3: A summary list of related works on drone mobility management and connectivity.
## 2 Materials and Methods  
Many physiological variables can be valuable to classify the occurrence of PA from APS [17; 18; 19; 20; 21; 22; 23], such as hormonal changes of lactate and cortisol levels, eye-tracking [24], and speech wave analysis. However, currently, these variables cannot be measured noninvasively and frequently in free living. In this work, we used data collected noninvasively by the Empatica E4 wristband. Empatica E4 has a 3-axis ACC that captures motion-based activity, a photoplethysmography (PPG) sensor that measures BVP from which HR and HR variability is derived by an internal algorithm of E4, an infrared thermopile to read peripheral ST, and EDA, also known as GSR, to measure the electrical activity conducted through sweat glands in the skin. A Cosmed K5 wearable metabolic system is used to measure energy expenditure (EE) to determine the intensity of the PA (the ground-truth) [25] to compare with the EE estimated from E4 signals. A limited number of experiments are conducted using the Bioplux finger-tip PPG sensor device that provides a higher accuracy PPG and electrocardiogram (ECG) signal as the ground-truth measurement [26]. The characteristics of physiological variables recorded by Empatica E4, Cosmed K5, and Bioplux are summarized in Table 1.  
The signals collected from the Empatica E4 wristband are preprocessed to remove noise and artifacts. Random convolutional kernel transformation (ROCKET) is utilized to extract a large number of feature maps. Features with the most predictive power are selected using partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) for classification and regression tasks, respectively. The selected features are used to train the machine learning (ML) algorithms including multi-task RNN with an LSTM layer for simultaneously classifying the type and intensity of PA and the type of APS. To deal with imbalanced class sizes and avoid bias in model training, we used adaptive synthetic sampling (ADASYN) [27; 28] and weighted training.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 3 Materials and Methods
### Scaling Auto-Period Seasonal Length Detection Algorithm
#### 3.1.1 Seasonal Components Scaling  
The specific operation of _ASAD_ using down-sampling for seasonal component scaling is as follows. For the seasonal component of KPI time series data \(S=\{s_{1},s_{2},s_{3},\ldots,s_{n}\}\), the down-sampling seasonal components can be denoted as \(S^{\prime}\), where \(s^{\prime}_{i}=\frac{sum_{i}}{m}\). The formal expression is  
\[sum_{i}=s_{m\times(i-1)+1}+s_{m\times(i-1)+2}+\cdots+s_{m\times(i-1)+m} \tag{1}\]  
where \(m\) is the number of sample points for \(i\)th sample sequence.  
The seasonal components after scaling are shown in Figure 3, and we can see a more clearer seasonality than before. The horizontal axis in Figure 3 represents the number of scaling sample points, and the vertical axis represents the seasonal value of the KPI time series data. Meanwhile, the up and down oscillations have almost the same range. Through a series of evaluations, it can be seen that a scaling ratio of roughly 1% can help to clarify the seasonality of seasonal components in long KPI time series data. If the seasonal component's scaling ratio is too large or too little, the scaled profile will struggle to indicate seasonality of the seasonal component.
## 4 The Deployment of WDM-PON Protection Schemes  
Nowadays, an expansion of WDM-PON networks is arising. Despite their high capital investments, a network resiliency must be increased at a sufficiently high level. Various network architectures are prospective for future WDM-PON networks depending on a way of origin--a greenfield deployment from TDM-PON networks or a brownfield deployment from HPON networks. Considering a moderate upgrade from TDM-PON networks to hybrid passive optical networks, traffic protection schemes (Table 1) simultaneously play an increasing role in the development of modern WDM-PON networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**P2 MP Architectures** & **RING Architectures** \\ \hline unprotected P2 MP network & unprotected RING access network \\ Type B protection & protected RING access network \\ Dual-parented Type B protection & unprotected RING metro-access network \\ Type C protection & protected RING metro-access network \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of considered presumptive scenarios for WDM-PON protection schemes.
## 5 Methodology for Anomaly Detection and Mitigation
### Mitigation
#### 5.2.2 Global Reconfiguration
Each of the collected reconfiguration candidates can itself consist of a sequence of reconfigurations. For example, if a configuration for the next three hours is requested, a reconfiguration candidate could consist of one configuration for each hour, hence being a sequence of three configurations. In the third step, the prediction of transport task emergence (step 1) is combined with a Digital Twin of the fleet. The Digital Twin is used for calculating predictions of the fleet performance for each of the chosen reconfiguration candidates (step 2). As starting point, the Digital Twin uses the current AGV situation, including the current AGV status and detected anomalies. The result of this step is a set of fleet key performance indicator (KPI) evolutions as predictions of the effect of different configurations. In the fourth step, those predictions are used in a decision making process. The purpose of the decision making is to pick one of the reconfiguration candidates as a result of the whole reconfiguration process. Due to the multiple objectives of the fleet and potential conflicts between them (e.g., fast task fulfillment can conflict with low driven distances for avoidance of wear), deciding which fleet performance is better than another is already a challenge. Additionally, the evolution of performance indicators has to be taken into account; hence, a decision cannot simply be made on a single snapshot of the indicator valuations. Decision criteria taken from the theory of multi-objective optimization problems (MOOP, [60]) can be used to decide which fleet behavior is best for a single point in time. A modification of well-known MOOP solutions for dynamic system behavior can be applied here to choose one of the performance predictions (step 3) and hence one of the configurations. In the running example, it is assumed to be desired to have one recommended configuration for each of the next three hours after identifying the anomaly and to choose one of the simplest MOOP solutions as a decision criterion: the weighted sum method, in which a weighted sum of all KPIs should be minimized. To be able to apply this criterion, the fleet performance predictions are processed, such that for each pair of prediction and KPI, one value representing the KPI valuation in this prediction is calculated. The weighted sum is then determined using all values of the same prediction. The sequence of configurations that corresponds to the prediction with the lowest weighted sum is then chosen as the recommended reconfiguration. The reconfiguration process is repeated at the latest two hours after the last one, i.e., before the last element of the latest proposed sequence of configurations is applied. Thereby, the risk of running in a local fleet optimum that might be followed by highly undesired fleet performance is reduced.