esa-sceva/satcom-chunk-collection huggingface dataset:
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 3 Results and Discussion
### Endication of Established Biofilms of Pathogenic Bacteria by ZnO-NPs  
Although most of the nanoparticles have been proven to be effective against the planktonic bacterial growth, among them, few are known to inhibit the biofilms of bacterial pathogens. However, a limited spectrum of antibiotics has been found that are successful in eradicating the established biofilms. Here, the effect of ZnO-NPs on the preformed biofilms of the bacteria was also tested, and the results are presented in Figure 7B. In the presence of 1/16 \(\times\) MIC, 1/8 \(\times\) MIC, 1/4 \(\times\) MIC, and 1/2 \(\times\) MIC of ZnO-NPs, the established biofilm of _E. coli_ was reduced by 10.26%, 19.71%, 41.05%, and 52.69%, respectively. Similarly, the preformed biofilms of _S. aureus_ were decreased by 19.31%, 28.46%, 48.29%, and 59.79% by the treatment of 1/16 \(\times\) MIC, 1/8 \(\times\) MIC, 1/4 \(\times\) MIC, and 1/2 \(\times\) MIC of ZnO-NPs, respectively. The maximum eradication was recorded in _P. aeruginosa_, in which more than 65% eradication of the established biofilms was found. The major constituents of the biofilms are polypeptides, polysaccharides, nucleic acids, etc., that not only provide framework to the biofilms but also restrict the entry of antibiotics into biofilms [45]. ZnO-NPs were also found to inhibit the EPS production of the test bacteria. The reduction in EPS secretion exposed the bacterial cells residing in biofilms that could have ultimately resulted in eradication of the biofilms. Pancreatic-doped zinc oxide nanoparticles have been found to eradicate the established biofilms of _S. aureus_ (methicillin resistant) [46]. Moreover, silver nanoparticles prepared using _Withania somifera_ have also been documented to mitigate the performed biofilms of _S. aureus_, _S. mutans_, _P. aeruginosa_, and _S. typhimurium_[47]. Silver nanoparticles have been found to diffuse and penetrate the matrix of bacterial biofilms [35]. The diffusion of ZnO-NPs inside the matrix of the biofilms may also be one of the possible mechanisms of the eradication of biofilms. Based on the findings of this study, it is obvious that ZnO-NPs were potent both in inhibiting and eradicating the biofilms of pathogenic bacteria.
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 2 Methods and Materials
### The Development of Training Monitoring System
#### 2.2.2 Post-Session Measures  
Within 12 h of training or match completion, players recorded the type of session it was, the duration of the session, and the rating of perceived exertion (RPE) measured on a chromatic coloured 0-10 scale [42]. The options for session type included gym, fitness only, skills only, mixed session (fitness and skills), rehab/prehab, and match. Session duration was measured on a scale from 10-180 min in increments of 5 min. RPE is both a very common and valid method of measuring internal training intensity [7; 43]. When session duration in minutes is multiplied by the RPE the result is known as the session rate of perceived exertion (sRPE) [44]. This method of calculating TL has been shown to be valid and reliable across a wide variety of exercise modalities [44]. A screenshot of the post-session measures of the TMS can be seen in Figure 3.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Experimental Setup  
To evaluate the efficiency of the developed model, the IHEPC was used in the model's training, testing, and validation. The hybrid DNN multilayered LSTM model was trained on a MacBook Pro with an Intel Core i7 processor and 64 GB RAM running on macOS Ventura 13.2. The model was developed using Python coupled with the TensorFlow and Keras frameworks. Among the datasets, 70% were used for training, and 30% were used for testing and model validation. The model settings were a batch size of 50 and an epoch number of 50.
# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 2 Related Work
### Emotion-Based Filtering  
As explained above, music and human emotions are closely intertwined, so we have a recommended approach that considers human emotions, namely emotion-based filtering. Different audio features of the music tracks are used to understand emotions that they may trigger or induce. Then, music streaming sites build playlists based on human emotions and moods tailored to a feeling that a user might experience while listening to those songs. In this field, the research on affective computing has produced a series of interesting solutions (see [32] for a recent survey on the topic related to music). We have identified various works [33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. Some studies identify emotions through facial expressions. Others analyzed EEG [40], physiological, and video signals. These works show that musical recommendation is generally carried out by combining physiological signals, heart and respiratory rates, and facial expressions, and in general, AI methods (generally deep learning techniques) were used to analyze such information. Among the works on this kind of filtering, we found [43], where the authors propose an emotion-based music recommendation framework that learns the emotion of a user from the signals obtained via wearable sensors. In particular, a user's emotion is classified by a wearable computing device integrated with a galvanic skin response and photoplethysmography sensors. The experimental results were obtained from 32 subjects' data. The authors evaluated several machine learning methods, such as decision tree, support vector machines, and k-nearest neighbors. The results of experiments on real data confirmed the accuracy of the proposed recommender system. With respect to [43], we deploy an RL-based recommender system for music to induce emotions in a prototype Web application, and we perform a real-world experiment with end-users to get their perceptions about _Moodify_.  
_Modify_ belongs to this class. However, some novelties need to be highlighted. First, the equipment needed for the recommendation. Such solutions require EEG or ECG, facial expression, or physiological information to recommend adequate songs. However, the devices need to capture those traits for the mood analysis are not common and quite expensive in some cases. Our idea is that _Moodify_ can recommend music without requiring further devices or equipment. Though, _Moodify_ can be extended with appropriate modules to consider traits like facial expression, and EEG for recognizing the mood while in use. Furthermore, _Moodify_ adopts a \"look-forward-recommendation\", i.e., it recommends music with the aim of inducing (in the future) a specific target emotion. All the methods described, instead, adopt a \"look-back-recommendation\", i.e., to recommend music only by using previously collected or observed information.
# Broadcast Ephemeris with Centimetric Accuracy: Test Results for GPS, Galileo, Beidou and Glonass
###### Abstract  
Here we test the capability of the Broadcast Ephemeris Message, in both its GPS-like (Keplerian ellipse with secular and periodic perturbations) and Glonass-like (numerical integration of a 9D state vector) formats, to reproduce a corresponding precise ephemeris. We start from a daily Rinnex 3.04 navigation file for multiple GNSS and the corresponding SP3 precise orbits computed by CNES (Centre National d'Etudes Spatiales) for GPS, Glonass, Galileo and CODE (Center for Orbit Determination in Europe) for Beidou, and compute broadcast ECEF coordinates and clocks. The pre-fit discrepancies are converted by least squares to corrections to the broadcast ephemeris parameters in two-hour consecutive arcs (for GPS, Galileo and Beidou) and to a set of seven Helmert parameters for the entire day, to align in origin, orientation and scale to the common GNSS IGS14 Reference Frame. The test cases suggest that the Broadcast Ephemeris Message, complemented with Reference Frame information, can reproduce the precise ephemeris and clocks with centimetric accuracy for intervals at least equal to the respective validity times, typically 2 h. The broadcast ephemeris of Glonass consists of three initial positions and velocities at epoch, three constant Lunisolar accelerations for the satellite position, and of three polynomial coefficients for the satellite clock. The 9D vector of state is numerically integrated to generate position and velocity data within the validity time (0.5 h) of the message. To test the capability of this model to reproduce the corresponding values of a precise ephemeris, the 9D vector of state and clock polynomials are adjusted until the rms (root mean squared spread) of the post-fit residuals relative to a precise orbit (CNES's in our case) is minimum. We show in one example (one satellite for one day) that the Glonass type of message can reproduce a precise ephemeris and clock with a rms spread of 0.025 m over one-hour arcs. Volume computations on one month of data with all available satellites confirm the test results. For GPS, Glonass, Galileo and Beidou, the best fitting clock values predicted by our second order polynomials, based on a 15 min sampling, are shown to fit the corresponding high rate clocks (30 s sampling) of MGEX with zero bias and a rms spread of 0.062 ns (GPS G01), 0.023 ns (Galileo E01), 0.43 ns (Glonass R01), 0.086 ns (Beidou C07) and 0.086 ns (Beidou C12). Modifications to the GPS-like message structure and Glonass algorithm are proposed to increase the validity time by including the effect of the 3 rd zonal harmonic of the Earth's gravity field. The potential of the RTCM messages for broadcasting the improved navigation message is reviewed.  
navigation message; precise ephemeris; orbital model; clock model; reference frame; multi GNSS +
Footnote †: journal: _remote sensing_
## 4 Finite Element Simulations
### Finite Element Simulation for Pitch Stiffness  
In another finite element simulation, since the deformation process of a bistable mechanism is an instantaneous behavior under the same boundary conditions, we applied the 50 N-mm of torque at different displacement positions of the shuttle using the CBBM model to obtain the corresponding pitch stiffness, which allows us to observe the variation trend of the pitch stiffness of the bistable mechanism in the process of motion; a finite element model can be used to better express the results. The results are shown in Figure 11.  
Figure 10: (**a**) Finite element model of CBBM; (**b**) Finite element model of SBBM1; (**c**) Finite element model of SBBM2; (**d**) force–displacement curves of CBBM, SBBM1, and SBBM2 using finite element analysis.  
The pitch stiffness of the CBBM gradually decreases with increasing bistable displacement. Its pitch stiffness slightly improves when the bistable mechanism is close to the second stable position. Compared with the initial position, a 31% reduction in pitch stiffness occurs at the second stable position. When the mechanism moves, the bistable limbs buckle and deform and the beam state becomes unstable. Consequently, the beam exhibits low pitch stiffness. In the second steady state, the buckling deformation of the bistable beam reaches an equilibrium position and the pitching stiffness is improved. To prove that the CBBM is superior to SBBMs 1 and 2 regarding their resistance to out-of-plane deformation, we performed finite element simulations to calculate the pitch stiffness of the three bistable mechanisms at different displacements and obtained the results shown in Figure 12.  
The pitch stiffness of the CBBM is 698.3 (N \(\cdot\) mm)/Rad and that of SBBMs 1 and 2 is 607 and 634.2 (N \(\cdot\) mm)/Rad, respectively. The pitch stiffness of the CBBM increases by 15.0% and 10.1% compared with that of SBBMs 1 and 2, respectively. At the second stable position, the pitch stiffness of the CBBM is 481.1 (N \(\cdot\) mm)/Rad, while that of SBBM 1 is 363 (N \(\cdot\) mm)/Rad, and that of SBBM 2 is 426.3 (N \(\cdot\) mm)/Rad. Thus, the pitch stiffness of the CBBM increases by 32.5% and 12.8% compared with that of SBBMs 1 and 2, respectively.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 2 Motivations
### System Complexity  
Optical networks are becoming more complex in both the physical and network layer. The physical architecture of the optical network can be reconfigured, and additional electronic components can be attached at various points to collect and monitor signal data. An example is optical-time-domain reflectometry (ODTR)-based monitoring. ODTR equipment measures traces of backscattered light by first injecting short light pulses into optical fibers. The response measured by the receiver can be used to monitor fiber performance [12]. Programmable software layered on top of physical hardware further increases system complexity. The programmability of the network provides the basis for a Software-Defined Optical Network (SDON) from which ML intelligence can be introduced to the system [13]. As systems become more complex and larger in scale, it becomes more challenging for humans to make performance-affecting decisions. This is important since optical networks often must meet a certain quality of transmission. Automatic and accurate changes and adjustments to the network would be ideal for achieving this. Thus, a well-trained machine could be supplemented with an existing human-operated network to increase its performance.
###### Abstract  
This study proposes and presents a new central office (CO) for the optical metro access network (OMAN) with an affordable and distinctive switching system. The CO's foundation is built upon a novel optical multicarrier (OMC) generation technique. This technique provides numerous frequency carriers that are characterized by a high tone-to-noise ratio (TNR) of 40 dB and minimal amplitude excursions. The purpose is to accommodate multiple users at the optical network unit aside in the optical metropolitan area network (OMAN). The OMC generation is achieved through a cascaded configuration involving a single phase and two Mach Zehnder modulators without incorporating optical or electrical amplifiers or filters. The proposed OMC is installed in the CO of the OMAN to support the 1.2 Tbps downlink and 600 Gbps uplink transmission, with practical bit error rate (BER) ranges from \(10^{-3}\) to \(10^{-13}\) for the downlink and \(10^{-6}\) to \(10^{-14}\) for the uplink transmission. Furthermore, in the OMAN's context, optical fiber failure is a main issue. Therefore, we have proposed a possible solution for ensuring uninterrupted communication without any disturbance in various scenarios of main optical fiber failures. This demonstrates how this novel CO can rapidly recover transmission failures through robust switching a and centralized OLT. The proposed system is intended to provide users with a reliable and affordable service while maintaining high-quality transmission rates.  
optical communication; optical metro access network; wavelength division multiplexing; optical ring network; optical multicarrier generation 20242410742388052024241074  
2024241074
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 2 Multi-Wavelength QKD with a PUF
### QKD with PUF
#### 2.2.2 Ternary Protocol with a PUF  
We use a resistive random-access memory (ReRAM) PUF, in combination with random bits, to generate the input stream of incomplete trits for the QKD system, which [PERSON] encodes with polarized photons [34]. Figure 2 shows a protocol diagram for ternary input and output streams using a ReRAM PUF. First, [PERSON] (server) will generate a random stream of bits and convert this stream to incomplete trits (e.g., zero becomes \(-\) and one becomes \(+\), and no zero trits are present, hence the name \"incomplete trits\"). Next, she obtains a ternary message from the image of the PUF. The image of the PUF is the stored PUF responses on the server, while the PUF is the client's real-time response readings. Ideally, these two response values should be identical, but in practice, differences in the server and client PUF responses may occur and are considered to be \"errors\". Next, [PERSON] will use addition modulo 3 (ADD\({}_{3}\)) to combine the incomplete trits with the PUF ternary message. The function of this step is to obscure the incomplete trit input. [PERSON] will use the result of the ADD\({}_{3}\) as the input for the QKD system, where it will then be encoded in the photons. She will use the chart in Figure 1a to encode her input in photon polarization states, then [PERSON] (client) will choose a random basis for each photon and use the same chart to decode the ternary information. The second half of the diagram, after the QKD output, essentially undoes the first half, and should result in the same input bits. Because we start with incomplete trits, we expect, theoretically, only two of the three states to be present after unobscuring the QKD output. The unobscured QKD output is represented by the arrow directly after the right most ADD\({}_{3}\) in Figure 2. This unobscuring process is described in Section 2.2.3. If we observe three of the three states are present, we can determine eavesdropper presence (ignoring typical error sources such as noise, and assuming no error from the PUF). See Table 1 for a summary of what was described in this section.
## 3 Materials and Methods
### Saponins  
Similarly, as in our previous UHPLC-HRMS and -MS/MS analyses, the saponin profiles in aqueous methanol extracts of leaf samples were compared using the described hardware settings [45], and some of the recorded compounds were identified as saponins according to the protocol already defined [46]. For analytical purposes, the samples were prepared as in the study by [PERSON] and [PERSON] [46]. To obtain larger amounts of SAP fractions, powdered _I. aquifolium_ Silver Queen leaves weighing 60 g were macerated twice for 24 h at room temperature using 600 mL of 70% methanol. After filtration, the extracts were combined and 30 g of lead (II) acetate thhydrate in 70% methanol was added to precipitate ballast substances. The extract was then centrifuged for 15 min at 3000 rpm (MPW). Residual lead ions in the saponin fraction were precipitated with dilute sulfuric acid. The resulting supernatant was neutralised and diluted with distilled water to a concentration of 40% methanol and centrifuged again. The supernatant was then applied, in two cycles, to a 20 g octadecyl SPE column. The saponin-rich fraction was eluted with pure methanol. The saponin fraction was concentrated to dryness using a vacuum evaporator at 40 \({}^{\circ}\)C and the glassy residue was then lyophilised. The fraction prepared in this way (SAP) was then used for biological assays by getting the extract to the appropriate concentration using dedicated solvents (see Sections 3.6 and 3.7 for information). A UHPLC Ultimate 3000 instrument (Thermo Fisher Scientific, Waltham, MA, USA) coupled to an ESI-qTOF Compact detector (Bruker Daltonics, Billerica, MA, USA) was used to analyse the saponin fraction. Saponin separation was achieved using a Kinetex C-18 column (150 \(\times\) 2.1 mm, 2.6 \(\upmu\)m; Phenomenex, Torrance, CA, USA). The flow rate through
# Learning Class-Specific Features with Class Regularization for Videos
## 3 Regularization for Convolutional Blocks
### Layer Fusion with Class Predictions  
We first discuss the main process for finding class estimates based on extracted features from the convolutional block at depth \(i\) in the network. We start by creating a vector representation of the features as distributed between the activation's channels for a spatiotemporal activation map input of size (\(FxHxW\)). Considering the produced activation map of the \(i\)th block (denoted as \(a_{i}\)), a global feature representation of the activations is created through a spatiotemporal sampling operation: \(pool(a_{i})\) (Equation (1)). The produced volume can be interpreted as a single vector descriptor containing a combination of the feature intensity values in the form of their average activations in a significantly lower dimensional space.  
\[pool(a_{i})=\frac{1}{F\times H\times W}\sum_{f=1}^{F}\sum_{h=1}^{H}\sum_{w=1}^ {W}a_{(f,h,w,i)}. \tag{1}\]  
To include class-based information in intermediate feature layers, we use the weights of the network's final prediction layer \(W_{fc}\). This allows to establish a relationship between previous and current iterations in a recurrent fashion, by taking class-specific features into account. To obtain feature alignment given the channel sizes for the current activation and the prediction weights,  
Figure 1: **Class Regularization**. An additional pathway between class weights and intermediate features is added (**a**) connecting different parts of the network across iterations. In _Class Regularization_ blocks, activation maps are pooled to vector volumes (pool(\(a_{i}\))) and multiplied by the class weights in order to select the resulting highest class activation (pool(\(\overline{a_{i}^{*}}\))). A computational overview of the in-block operations (**b**) appears in Algorithm 1.  
a one-dimensional convolutional operation (\(conv=W_{fc}*\mathcal{W}\)) is applied to the class weights with a kernel (\(\mathcal{W}\)) size of 1 followed by a \(relu\) activation function.  
Next, we use a standard matrix-to-matrix multiplication to create an association between the channel descriptor and each of the classes. The volume \(Z_{i}\) is of size \(\{Z_{i}^{[1]},...,Z_{i}^{[CL]}\}\), for \(CL\) classes, with each unit \(Z_{i}^{[j]}\) having the same channel dimensionality as \(a_{i}\). This operation allows an early estimate for the indexes of the most relevant features for each class.  
One could use the outputs of the multiplication as a separate loss function but we have two main reasons for refraining from doing so. First, due to the limited feature complexity, it is significantly more difficult to make educated class predictions in early layers of the network. This also corresponds to the notion of hierarchical feature extraction in CNNs. Second, through these multiple output points, multiple error derivatives are to be calculated which will slow down the training process significantly.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband
### Performance Figure Derivation
#### 3.2.3 Symbol Demapper
After removing the GIs, the parallel decision threshold demapper manages to surpass the data symbol rate by a small margin. The remaining two parallel demappers--exact and approximative--rely on more complex processing, reflected in their lower throughput.