esa-sceva/satcom-chunk-collection huggingface dataset:
# Acute Otitis Media in Children--Challenges of Antibiotic Resistance in the Post-Vaccination Era
## 3 Results
### Antimicrobial Resistance Profile
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Resistance Pattern** & **Classes of Resistance** & **Number of Strains (\(n\) = 70)** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} MDR \\ (\(n\) = 58) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Penicillin, TMP/SMX, macrolides, macrolides \\ \end{tabular} } & 4 \\ \cline{2-3}  & TMP/SMX, macrolides, limosamides, cephalosporors & 4 \\ \cline{2-3}  & TMP/SMX, macrolides, limosamides, cloraphenicol & 4 \\ \cline{2-3}  & & Susceptible to all the tested antibiotics & 3 \\ \cline{2-3}  & TMP/SMX & 6 \\ \cline{2-3}  & Macrolides & 3 \\ \hline \hline \end{tabular}  
* MDR, multidrug resistance; non-MDR, non-multidrug resistance; TMP/SMX, trimethoprim/sulfamethoxazole.  
\end{table} 
Table 3: Multidrug resistance patterns of _Streptococcus pneumoniae_ strains. For the two strains of _Monxella atarthalis_, the nitrocefin test was positive. _Escherichia coli_, found in one sample, was sensitive to all antibiotics (ampicillin, amoxicillin/d clavulanic acid, cefuroxime, ceftaxone, gentamicin, amilacin, ciprofloxacin and trimethoprim/sulfamethoxazole). _Pseudomonas aeruginosa_ (two samples) was also sensitive to all antibiotics (piperacillin/tazobactam, ceftazidine, imipenem, meropenem, gentamicin, ciprofloxacin). The _Enterobacter aerogene_ strain, also found in only one sample, was a multidrug-resistant strain, sensitive only to piperacillin/tazobactam, carbapenemems (imipenem, meropenem, arctapenem), amilacin and tetracycline, but resistant to a wide range of antibiotics: cephalosporors (cefurozime, ceftazidine, ceftaxone), aminoglycosoides (gentamicin, tobramycin), fluoroquinolones (ciprofloxacinacin), ampicillin and amoxicillin/clavulanic acid. Out of 147 children enrolled in our study, 79 encountered treatment failure. _Streptococcus pneumoniae_ was the most common microorganism involved in these cases (41 cases), followed by culture-negative cases (27 cases) and sporadic cases of other microorganisms (Table 5). Due to the lack of a national guideline regarding the treatment of AOM, the clinicians' first choice in AOM treatment was characterized by a wide diversity: monotherapy (amoxicillin/clavulanic acid, ceftaxone, ceftaxone or even clindamycinin cases of suspected mastoiditis) or associations of two antibiotics (ceftriaxone and ciprofloxacin, ceftaxime and clindamycinin, ceftaxime and levofloxacin, clarithromycin and amoxicillin/clavulanic acid), indicated after 24 to 48 h of persistent symptomatology.
## 2 Mathematical Formulation
### Model Constraints
#### 2.2.1 Active and Reactive Power Constraints  
**Nodal active power balance:**  
The first constraint presented in (3) establishes the active power balance in the electrical network, ensuring power equilibrium at each node of the system during every time period.  
\[P_{i,h}^{cg}+P_{i,h}^{dg}\pm P_{i,h}^{B}-P_{i,h}^{d}=v_{i,h}\sum_{j\in \mathcal{N}}Y_{ij}v_{j,h}\cos\left(\theta_{i,h}-\theta_{j,h}-\varphi_{ij} \right), \tag{3}\]  
\[\{\forall h\in\Omega_{\mathcal{H}},\forall i\in\Omega_{\mathcal{N}}\}\]  
In this constraint, \(P_{i,h}^{B}\) is the active power supplied by a BESS at node \(i\) over period \(h\), and \(P_{i,h}^{d}\) is the power demand at node \(i\) during hour \(h\). \(v_{i,h}\) and \(v_{j,h}\) are the voltage magnitudes at nodes \(i\) and \(j\), and \(Y_{ij}\) is the magnitude of the admittance between them. \(\theta_{i,h}\) and \(\theta_{j,h}\) represent the voltage angles at nodes \(i\) and \(j\), while \(\varphi_{ij}\) is the admittance angle of the line connecting the two nodes.  
**Nodal reactive power balance:**  
Constraint (4) performs a similar role to the previous one but focuses on the reactive power balance of the system.  
\[Q_{i,h}^{cg}-Q_{i,h}^{d}=v_{i,h}\sum_{j\in\Omega_{\mathcal{N}}}Y_{ij}v_{j,h} \sin\left(\theta_{i,h}-\theta_{j,h}-\varphi_{ij}\right), \tag{4}\]  
\[\{\forall h\in\Omega_{\mathcal{H}},\forall i\in\Omega_{\mathcal{N}}\}\]  
The new terms, \(Q_{i,h}^{cg}\) and \(Q_{i,h}^{d}\), represent the reactive power supplied by conventional generators and the reactive power demand at node \(i\) during hour \(h\), respectively.  
A unit power factor is assumed for generation in this initial analysis, focusing only on active power. Reactive power control is left for future work.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 3 Transmitter Design  
In this section, we describe the proposed transmitter; the receiver design will be discussed in the next section. In Figure 1, we present the block diagram of the \(u\)th user terminal. We consider M-QAM constellations, where the data symbols \(s_{u,t}\), have \(\mathbb{E}[\left|S_{u,t}\right|_{2}]=\sigma_{u}^{2}\). The sequence \(\left\{s_{u,t}\right\}_{t=1}^{N_{c}}\) is divided into \(R\) data blocks of size \(S=N_{c}/R\), where \(\left\{s_{u,t}\right\}_{t=(r-1)S+1}^{rS}\) denotes the \(r\)th data block, and the sequence \(\left\{c_{u,t}\right\}_{k=(r-1)S+1}^{rS}\) is the DFT of \(\left\{c_{u,t}\right\}_{t=(r-1)S+1}^{rS}\) a transformation from the time domain to the frequency domain. After the time-frequency transformation the frequency domain data is interleaved and mapped to the OFDM symbols. Finally, the cyclic prefix (CP) is inserted in the blocks associated with each RF chain. Since the proposed hybrid equalizers detect each \(S\)-length data block independently, let us focus on a single \(S\)-length data block in order to simplify the math formulation [10]. Therefore, hereinafter the index \(r\) is not considered and the formulation is done for the frequency domain \(S\)-length block \(\left\{c_{u,k}\right\}_{k=1}^{S}\), which is the DFT of the time domain sequence \(\left\{s_{u,t}\right\}_{t=1}^{S}\).  
The analog precoder is modeled mathematically by the vector \(\mathbf{f}_{a,u}\in\mathbb{C}^{N_{tx}}\). Because of hardware constraints, only analog phase shifters are employed. This forces all elements of the vector \(\mathbf{f}_{a,u}\) to have equal norm \(\big{(}\big{|}\mathbf{f}_{a,u}(n)\big{|}_{2}=N_{tx}^{-1}\big{)}\) The analog precoder is independent of the subcarrier index \(k\), i.e., it is the same for all subcarriers. These assumptions are followed by most of the recently works on hybrid massive MIMO mmWave based systems [23; 24; 25].  
The discrete transmit complex baseband signal \(\mathbf{x}_{u,k}\in\mathbb{C}^{N_{tx}}\) of the \(u\)th user at subcarrier \(k\) may be mathematically expressed by  
\[\mathbf{x}_{u,k}=\mathbf{f}_{a,u}c_{u,k}, \tag{7}\]  
where \(c_{u,k}\in\mathbb{C}\).  
Since the aim is to design a multi-user equalizer, in this paper, we consider the pure analog precoders discussed in [29; 31], which are briefly discussed here. In the first case, presented in [31], we consider that the UT has no access to CSI, which simplifies the overall design, and in the second case, designed in [29], we consider an analog precoder based on the knowledge of partial CSI at the transmitter, i.e., only the average AoD is assumed to be known at the transmitters.
# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 4 Experiments and Results
### Personality Trait Analysis Based on Multimodal Feature Fusion  
After the first phase of the experiments, it was found that the use of deep learning models is effective in predicting the scores of the Big Five personality traits; however, we found that the margin of error in the text and speech experiments was significant, so we extracted the features learned from the deep learning model, which used a flatten layer to convert the arrays into one dimensional features. The facial expression features, acoustic features, and text features were fused and mapped to the Big Five personality traits by nonlinear changes in the fully connected layer. Table 6 presents the multimodal assessment of the five personality trait scores based on the feature fusion approach. It is noticeable that for each personality trait, each assessment outperforms the results predicted by a single modality, which supports our belief that different aspects of personality need to be considered in determining personality. From Figure 8, we can observe that the performance of the multimodal approach based on personality trait prediction is significantly better than that of the unimodal approach in terms of loss value.  
Figure 6: Comparison of loss values for speech features using CNN model.
## 6 Experimental Results
### Performance Evaluation  
Unfortunately, we could not compare our results with other approaches, as the executions of our TSP model are _richer_, capturing more time dependencies than the timed models mentioned in Section 2. An honest comparison with the existing approaches would not be possible. First, the synchronisation in the network of timed automata and the interpreted systems is entirely different (Section 3.1). The possible comparison is to compare the modelling of protocol execution by a network of timed automata and interleaved interpreted systems.  
Our experiments were meticulously conducted on a high-performance computer featuring an I7-5500U processor (Intel Corporation, Santa Clara, CA, USA), 12 GB of RAM,and a Linux operating system running kernel 5.2.9. This robust setup ensured the reliability and accuracy of our results.  
In our experiments, we examined the standard authentication and secrecy properties of TSP. In addition, we also detected and prevented situations where a passive intruder participates in the protocol execution by positioning themselves between the users and merely relaying all transmitted data. This scenario represents a Man-in-the-Middle (MitM) attack [53]. Although the intruder may not gain access to secret data or compromise the authentication process, the mere presence of the intruder in the network is undesirable. Such situations can be detected and prevented by selecting appropriate lifetime values.  
We will discuss the experimental results and draw our conclusions based on nine timed versions of protocols well-known from the literature. We tested the following: the Needham-Schroeder Public Key Protocol [46] and [PERSON]'s modification of NSPKT (NSPKT\({}_{L}\)) [1]; the Denning-Sacco Protocol (DS) [54]; the Woo-Lam Pi Protocol (WLP) [34; 55]; other versions of the Woo-Lam Pi protocol, WLP 1 and 3 [55]; the Andrew Protocol (AP) [56]; [PERSON]'s modification of the Andrew Protocol (A\({}_{L}\)P) [57]; and MobInfoSec (MIS) [58].  
Table 1 presents short security protocols' characteristics, advantages, and disadvantages.
# A Historical Survey of Advances in Transformer Architectures
## 1 Introduction
### Position Encodings  
As the self-attention process of the transformer discards with the sequential way in which RNNs or LSTMs handle input embeddings and instead treats all inputs simultaneously and identically, it means that the self-attention layer is not able to account for the position of words in a sentence. However, since the words are sequential, a mechanism is needed which maintains the positions of the words within the encoded information and, therefore, the transformer model makes use of position encodings which are added to the input embedding. In the context of the example, this would mean the position encoding helps maintain the sequential context that the word \"bank\" is related to \"river\" and \"slippery\". The formulation for the added embeddings is seen in Equation (5).  
\[\begin{split} PE(pos,2i)=sin\text{sin}\Bigg{(}\frac{pos}{10000^{ \frac{2}{d^{2}}}}\Bigg{)}\\ PE(pos,2i+1)=cos\Bigg{(}\frac{pos}{10000^{\frac{2}{d^{2}}}} \Bigg{)}\end{split} \tag{5}\]Wherein _pos_ is the position of a word within a sentence, \(d_{model}\) is the dimension and \(i\) is the current dimension of the position encoding. Using this, each element of the positional encoding corresponds to a sinusoid, thereby allowing the transformer model to learn to pay attention based on relative positions as well, consequently allowing it to extrapolate to longer sequences. These encodings have indeed been a focal point of the consequent research aiming to optimize the learning process. Indeed numerous works have proposed modifications such as a learning process for the encodings [17; 18] or a relative form of position encoding [19].  
Having discussed the importance and the working of transformer architecture, and given the rapid advances in the field of deep learning brought forth due to this model, it might be noteworthy to examine the historical progression since its introduction in 2017 leading up to transformers taking over many of the state-of-the-art techniques. While there exist surveys on the various types of transformer architectures that have been proposed, there seems to be a gap in the analysis from a historical viewpoint. Therefore, the rest of the paper examines a historical perspective on the progression of notable transformer architectures in addition to discussing the state-of-the-art techniques and architectures for data of different types.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 2 Grid-Forming Inverter Modelling and Overload Mechanisms
### Overload Mechanisms in GFM Inverters  
Under a grid frequency excursion, the GFM inverter modifies the exchanged active power, providing frequency support while synchronizing with the grid. The active power contribution of a GFM inverter can be classified into two different terms: (1) frequency regulation power and (2) inertial power. As described in the following subsections, both terms can lead to overloading the inverter.
# Fast Path Planning for Autonomous Ships in Restricted Waters
## 2 Materials and Methods  
### Environment Modeling  
At present, the environmental information of ship navigation, especially static data such as the depth of water, obstacles, and land areas, is mainly presented by ECDIS. Therefore, an automatic CA system should adapt to the data structure of the electronic navigational chart (ENC) as much as possible. The ENC, made in a vector format, geometrically is an ordered set of elements on a plane: point, curve (line) or surface (face), which represent objects in the chart. The elements are described by their coordinates and object attributes [17]. These objects may correspond to different types of obstacles (as shown in Table 2). By referring to the attribute information of point, line and surface elements, we can know the type of an object and its degree of danger and then select different safety distances in the path planning algorithm.
## 2 Control and Estimation under Information Constraints  
### Problem Description  
Let us consider control and observation (estimation) systems containing a digital communication channel. For these systems, plant output measured by the sensor at discrete instants \(t_{k}=kT_{0}\), where \(T_{0}\) denotes the _sampling interval_, \(k=0,1,\ldots\), is converted by the coder into the characters of the _coding_alphabet_\(\mathbb{S}\). The sequence of characters is transmitted over a digital communication channel to the _decoder_. The decoder transforms messages from the transmitted form in a form adequate to subsequent calculations and transformations by the controller. In [37; 38; 39] the communication channel was considered of limited capacity, but was otherwise ideal. The cases of packet erasure channel and 'blinking' channel widely appear in various real-world systems, see, e.g., [40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Therefore, for a more realistic analysis, the properties of the communication channel, such as distortion, erasure, and data loss, should be taken into account. In the present study, the effect of data erasure is considered.  
Signal quantization introduces essentially non-linear properties into the system, characterized by the presence of the _dead zone_, _discontinuities_, and _saturation_ (associated with bit grid overflow). Additionally, the signal sampling on time involves the _hybrid_ (continuous-discrete) system description. A rigorous examination of the influence of time sampling and the level quantization is a complex nonlinear analysis problem, that usually does not have an exact analytical solution. In the early studies, the level quantization in digital control systems was usually considered a source of independent additive random noise affecting the system. This assumption makes it possible to significantly simplify the study of level quantized systems, especially for LTI plants. However, if the quantization level is relatively high (for example, in the case of binary quantization), this can lead to the emergence of self-oscillations and even the system divergence, see [50; 51; 52; 53]. Therefore, to analyze the system, its nonlinear model is required. Besides, the possibility of the bit grid overflow can also affect the quantizer, as a result of which the saturation is introduced to the control loop [54; 55].
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 4 Skin and Diffusion through the Skin
### Mechanism of Drug Release through Hydrogels
#### 4.4.2 Swelling-Controlled Release  
In the mechanism of swelling-controlled release, drug release takes place when the diffusion of the drug surpasses the rate of swelling. With an escalation in the rate of hydrogel swelling, the release of the drug also rises. The key factors governing this type of drug release mechanism are the capacity for water absorption and the thickness of the polymeric gel [54]. The major drawback of the swelling-controlled drug release mechanism is its slow response due to slow diffusion. One solution to this shortcoming is reducing the diffusion length by reducing the hydrogel's size or designing a system of interconnected macropores.
## 3 Results and Discussion  
### Physical Properties  
#### 3.1.1 Mass  
The mass change proportion following freeze-thaw cycles serves as an indicator for assessing the frost resistance and stability of the grouting reinforcement body, providing valuable insights for engineering design and material selection. This proportion is typically represented by the freeze-thaw mass loss rate, which can be calculated using the following equation:  
\[K_{m}=\frac{m_{0}-m_{n}}{m_{0}}\times 100\% \tag{3}\]  
where \(K_{m}\) is the freeze-thaw mass loss rate (%); \(m_{0}\) denotes the mass before freeze-thaw cycling (g), and \(m_{n}\) refers to the mass after \(n\) freeze-thaw cycles (g). Meanwhile, \(n\) is 5, 10, 15, 20, 25, or 30.  
Figure 13 illustrates the mass and mass loss rate of grouting reinforcement bodies with varying crack dip angles as a function of the number of freeze-thaw cycles. The figure shows that the mass loss trend for grouting reinforcement bodies with different crack dip angles generally follows a consistent pattern. As the number of freeze-thaw cycles increases, the mass of the grouting reinforcement body decreases, while the mass loss rate progressively rises. Similarly, as the crack dip angle increases, the mass loss rate exhibits a corresponding gradual increase. For example, with a crack inclination angle of 60\({}^{\circ}\), after 5 and 10 freeze-thaw cycles, the mass loss of the grouting reinforcement body  
Figure 11: NIUMAG MesoMR12-060H-1 CORE MRI system.  
Figure 12: FRTX-1000 low-temperature high-pressure servo-controlled rock triaxial test system.  
remains minimal, with maximum mass loss rates of 0.07% and 0.12%, respectively. After 30 freeze-thaw cycles, the mass loss rate increased to 1.25%. This phenomenon occurs due to frost heaving forces during the freeze-thaw process, which causes crystals and particles to detach from the specimen surface and along the edges of prefabricated cracks. This effect is most pronounced at the specimen edges and crack tips.
## 1 Introduction  
Furan (C\({}_{4}\)H\({}_{4}\)O) and its methyl derivatives are known to be present in a wide range of foods especially in those undergoing heat treatment [1, 2]. These compounds exhibit some harmful effects, and the parent furan itself is classified by the IARC as a possible human carcinogen (group 2B) [3, 4]. Since the report published by the US Food and Drug Administration on the occurrence of furan in foods [5], it has become an increasing matter of concern and thus different aspects related to this compound have been studied. These aspects include mainly the toxicity [6, 7, 8], the formation pathways [9, 10, 11], the occurrence in foods [12, 13, 14], the risk assessment [15, 16, 17, 18], and the analytics [19, 20]. However, these data mainly refer to furan, while limited data are available on its alkyl derivatives. Hence, the European commission asked Member States to provide more information on the contamination levels of these compounds in food [21, 22].  
Two main sampling strategies can be implemented to conduct a food survey on a food contaminant. The first one focuses on sampling specific food group(s) [23; 24], while the second one is an exhaustive sampling of different foods groups [25]. The second one was used in the current work in order to give an overall estimate of food contamination with furan compounds to be used in a further work in the risk assessment.  
A preliminary characterization of the hazard related to the co-occurrence of these compounds in food was introduced in this work as a starting point for further risk assessment.  
The hazard characterisation was done firstly using the Hazard Ratio per sample (HRs) approach, which consists in comparing the intake determined in this work with the product of the available effective toxicological values and the standard body weight of human. Subsequently, the individual HRs of all furan compounds are combined into a global Hazard Index per sample analysed (HIs) [26; 27; 28].  
In this work, two objectives are addressed. First, we aim to create a representative sampling plan for food samples collected in Belgium. Second, we aim to preliminary characterize the hazard associated with the co-occurrence of furan compounds (i.e., furan, 2-methylfuran (2-MeF), 3-methylfuran (3-MeF), 2-ethylfuran (2-EtF), 2,5-dimethylfuran (2,5-dMeF), and 2,3-dimethylfuran (2,3-dMeF)) in food.  
This work is limited to preliminary hazard characterization, while a full risk assessment will be explored in detail in subsequent work based on EFSA approaches (EFSA, 2017).
# Signal Separation Based on Knowledge Representation
## 2 Theory
### Knowledge Representation  
Three scenarios for knowledge representation are as follows:  
(1) Observed knowledge representation: People can utilize experiential knowledge to represent the target signal based on the observed mixed signals.  
(2) Physical knowledge representation: The target signal has specific underlying physical mechanisms, satisfying certain physical partial differential equations and enabling corresponding knowledge representation.  
(3) Data-driven knowledge representation: Abundant data samples related to the target signals are available, allowing for knowledge representation through a data-driven approach.  
In these three scenarios, the framework of knowledge representation effectively captures the distinctive features of the target signal. The illustration in Figure 1 showcases how knowledge representation varies across different scenarios using the framework.