esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology And Implementation
### Metrics
#### 4.4.3 Adjusted Hierarchical Similarity  
The adjusted hierarchical similarity metric extends component-wise string similarity (e.g., Jaro-Winkler) by also rewarding correct positional alignment of UDC code components and penalizing any additional components in the prediction. It is designed for comparing two lists of UDC code components, namely, the ground truth \(\{C_{\text{gt},i}\}_{i=1}^{N_{\text{gt}}}\) and prediction \(\{C_{\text{pred},j}\}_{j=1}^{N_{\text{pred}}}\), where \(N_{\text{gt}}\) and \(N_{\text{pred}}\) are the number of ground truth and predicted UDC code components, respectively.  
The metric uses a component similarity term, an order factor, a component-level matching score and a penalty term. The component similarity term is defined as \(sim_{jw}(C_{\text{gt},i},C_{\text{pred},j})\) and is the Jaro-Winkler similarity between the \(i\)-th ground truth UDC code component \(C_{\text{gt},i}\) and the \(j\)-th predicted UDC code component \(C_{\text{pred},j}\). The order factor is defined to penalize misaligned positions within the UDC code. It is defined as  
\[F_{\text{order}}(i,j)=\frac{1}{1+\delta|i-j|} \tag{5}\]where \(\delta\geq 0\) is a hyperparameter controlling the penalty severity due to order differences. The component-level matching score \(S_{i}\) (\(i=1,2,...,N_{\text{gt}}\)) is then computed for each ground truth component \(C_{\text{gt},i}\) with  
\[S_{i}=\max_{1\leq j\leq N_{\text{pred}}}[sim_{j\
u}(C_{\text{gt},i},C_{\text{ pred},j})\cdot F_{\text{order}}(i,j)] \tag{6}\]  
The average similarity before penalty is then calculated with  
\[S=\frac{1}{N_{\text{gt}}}\sum_{i=1}^{N_{\text{gt}}}S_{i} \tag{7}\]  
A penalty factor \(\alpha\) is applied per extra component if there are extra predicted UDC code components (when \(N_{\text{pred}}>N_{\text{gt}}\)):  
\[P=\alpha\cdot\max(0,N_{\text{pred}}-N_{\text{gt}}) \tag{8}\]  
Finally, the adjusted hierarchical similarity score is given with  
\[\text{AHS}=\max(0,S-P) \tag{9}\]  
In the context of UDC codes, the adjusted hierarchical similarity metric's combination of component-wise edit similarity, positional weighting, and additional component penalty maps naturally onto UDC's hierarchical faceted structure. By first decomposing UDC expressions into their component lists using a parser that splits on standard delimiters (colon (:), plus (+), and slash (/)) and considers only main table numbers (ignoring auxiliaries), the metric uses Jaro-Winkler similarity to capture how closely each component matches at the string level. While rewarding components that appear in the correct sequence via an order factor, it gives full credit when the \(i\)-th ground-truth component aligns with the \(i\)-th prediction. Furthermore, if a prediction introduces additional components, the metric subtracts a penalty proportional to the count of additional components, which discourages over-specific or noisy outputs. In our experiments, \(\alpha=0.1\) and \(\delta=0.5\) were used as they provided a good balance of penalizing misaligned positions within the UDC code and additional UDC components.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 3 Setup and Measurement Campaign  
The testing area was established in July 2015 close to the village of Cmotice in Slovenia (Figure 1). The main reasons that this location was chosen was that it is a remote area with a minimal impact of jamming for users and because several different jamming experiments have already been successfully conducted there. Moreover, there are no elevated obstacles near the location of the GNSS receivers that would disturb the GNSS signal reception, there is almost no traffic, and the straight road enables the vehicle to be run at a constant speed. The most important fact is that the use of a jammer was authorised by the Agency for Communication Networks and Services of the Republic of Slovenia(AKOS). In July 2019, open field GNSS measurements were performed in order to carry out experiments of the vulnerability of different GNSS instruments under real-life jamming conditions.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 6 Bug Detection Capability of Emulator's Fuzzing Application  
Given that the main use of current firmware emulators is fuzzing, this section further compares and discusses their bug detection capabilities in fuzzing applications. We focus on emulators whose main purpose is fuzzing and which have been tested for at least 24 h over multiple targets. Emulators not primarily used for fuzzing, such _Gerbil_[38],HEAPSTER[37], and _AIM_[42], are excluded. Similarly, _Conware_[26] and _Avatar_[14], which perform short-time and single-target fuzzing without a dedicated fuzzing process adoption, are also out of scope, as are _Pretender_[25], _Laelaps_[30], and _Jetset_[32]. In our study, we evaluate emulators that satisfy our predefined criteria by comparing their relative false-positive and false-negative rates. This comparison utilizes results derived from fuzzing experiments conducted on shared datasets. Specifically, we employ real-world firmware samples from \(P^{2}\)_IM_[27], which have been tested across all the tools under consideration. For instance, the _Fuzzware_ tool research paper reports a higher false-positive rate, and subsequent studies confirm this using \(P^{2}\)_IM_ samples. In contrast, _SEmu_ tests the same dataset with no false positives, indicating a low false-positive rate. Meanwhile, _MultiFuzz_ claims to find more bugs and achieve higher coverage on the same dataset as _Fuzzware_, suggesting a lower false-negative rate. The results are summarized in Table 4. Table 4 also outlines their fuzzing strategies, including the main functions in front-end and back-end components.
## 4 Precursors to Materials
### Non-Ionic Surfactants  
Surface-active agents (surfactants) are a class of compounds that present outstanding physical properties as solubilizers, with a wide range of application in the most varied industry sectors, for example being used in the soap industry, in cosmetics, and in the food area [199]. Among the range of surfactants, non-ionic structures are used in diverse areas, such as in cleaning products, textile processing, pulp and paper processing, emulsion polymerization, and agrochemicals. An important characteristic of these materials is the absence of a hydrophilic group with filler bound to their hydrophobic chain [200,201].  
In this regard, [PERSON], [PERSON], and co-workers [202] prepared glycerol-carbonate-derived fatty esters **1q** using a hybrid Nafion-silica composite (Nafion SAC-13) as an acid catalyst in the absence of solvent [203]. In this work, the desired compounds were prepared through direct esterification of GC **1a** with carboxylic acids of different chain lengths; the protocol led to the formation of several side-products, such as monoglycerides **12** and **173**,as well as the triglyceride **174**. Two different possible reaction routes were proposed by the authors:  
* GC **1a** and the acid **61** firstly react under acid catalysis to give the GC carboxylate **1q**; this intermediate is subsequently decarbonylated to form two isomeric monoglycerides, **12** and **173**, which can further undergo stepwise esterification to provide diglycerides **173'** and **174\"** and the triglyceride **174** (Scheme 81, route I);
* Firstly, **1a** undergoes hydrolysis to glycerol, which similarly reacts with **58** to yield monoglycerides **174** and **12**, diglycerides **173'** and **173\"**, and triglyceride **174** (Scheme 81, route II).  
Several parameters were evaluated in this reaction, such as the solvent, heterogeneous catalysts, zeolites, and hybrid organic-inorganic acids. The best results for the esterification of GC **1a** were obtained by using 3.5 wt% of Nafion-silica (with respect to the total weight of the reactants) at 100 \({}^{\circ}\)C. The highest activity was exhibited by the Nafion SAC-13 nanocomposite, which proved to be an excellent esterification catalyst, while other ion exchange resins showed lower activity and selectivity. It has to be pointed out that when increasing the chain length in **61** from hexanoic to lauric and palmitic acids, the conversion rate decreased considerably from 98% to 71% and 31%, respectively.  
Being interested in the synthesis of an important non-ionic surfactant, glycerol monos-tearate **12f** (GMS), [PERSON] and [PERSON] [204] devised a solvent-free esterification approach for GC **1a** with stearic acid **61a** catalyzed by copper 4-toluenesulfonate (CPTS) to prepare the intermediate (2-oxo-1,3-dioxolan-4-yl)methyl stearate (ODOMS) **1r**. This compound presents good thermal and oxidative stability, in addition to surfactant properties. Using Et\({}_{3}\)N-catalyzed hydrolysis at 140 \({}^{\circ}\)C, ODOMS **1r** was converted into GMS **12f** with 64% yield (Scheme 82).
## 5 Conclusions  
Correct assessment of the degradation of bearing defects is urgent for reliable bearing condition monitoring and appropriate period maintenance decision in rotary machines. This paper proposed a DNN-based method to provide an effective solution for bearing fault prediction using a large number of data samples collected from bearing defects. It is necessary to precisely estimate the fault severities of bearings which make up a dataset for training the DNN. Thus, an effective GDM-based window method was used only to obtain the characteristic fault frequency components from the obtained EPS signals for the HI calculation, which served as an efficient quantitative metric for estimation of the severity of bearing defects. These obtained HI values were then used as appropriate templates for training the DNN. Moreover, the proposed DNN was trained using the [PERSON] optimization-based backpropagation algorithm and Xavier initialization method for enhancing the learning speed and accuracy of the DNN. Experimental results indicated that the proposed DNN method yielded better performance and higher average accuracy compared to state-of-the-art approaches in predicting the degradation level of primary bearing defect conditions (BCO, BCI, and BCR) under different crack sizes.  
All the authors have contributed equally to the conception and idea of the paper, implementing and analyzing the processing methods, evaluating and discussing the experimental results, and writing and revising this manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Grant No. 20181510102160). It was also supported in part by the Ministry of SMEs and Startups (MSS), Korea, under the \"Regional Specialized Industry Development Program (R&D, P006800689)\" supervised by the Korea Institute for Advancement of Technology (KIAT), and in part by the Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016H1D5A1910564).  
The authors declare no conflict of interest.
## 4 Models and Methods
### Free-Energy Analyses  
The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method [58, 59, 60, 61] in the AMBER9 package was employed to perform the free energy analyses. The binding free energy was computed through calculating the free energy differences of ligand, receptor and their complex as follows:  
\[\Delta G_{\rm binding}=G_{\rm complex}-G_{\rm ligand}-G_{\rm receptor} \tag{1}\]  
In MM-PBSA, the free energy (\(G\)) of each state is estimated from molecular mechanical energy \(E_{\rm MM,}\) solvation free energy \(G_{\rm SOLV}\) and vibrational, rotational, and translational entropies \(S\), respectively.  
\[G=E_{\rm MM}+G_{\rm SOLV}-TS \tag{2}\] \[E_{\rm MM}=E_{\rm int}+E_{\rm vdw}+E_{\rm ele}\] (3) \[G_{\rm SOLV}=G_{\rm pb/solv}+G_{\rm np/solv} \tag{4}\]  
where \(T\) is the temperature; \(E_{\rm int}\) is internal energy, _i.e._, the sum of bond, angle, and dihedral energies; \(E_{\rm vdw}\) is van der Waals energy; \(E_{\rm ele}\) is electrostatic energy; \(G_{\rm SOLV}\) is the sum of electrostatic solvation free energy, \(G_{\rm pb/solv}\), and the non-polar salvation free energy, \(G_{\rm np/solv}\). The entropy \(S\) is estimated by a normal mode analysis of the harmonic vibrational frequencies, calculated using the Nmode module in Amber9 package [62, 63]. Prior to the normal mode calculations, each structure was fully minimized using a distance dependent dielectric of \(\varepsilon=4r\) (\(r\) is the distance between two atoms) to mimic the solvent dielectric change from the solute to solvent until the root-mean-square of the elements of the gradient vector was less than \(5\,\times\,10^{-4}\) kcal\(\,\cdot\,\)mol\({}^{-1}\cdot\)A\({}^{-1}\). Then, the entropy was calculated based on standard statistical mechanics expressions [59, 64]. Computational details are available in the Supplementary Information.
## 6 Conclusions  
This article aimed to compare the learning levels of two groups: one group received traditional teaching methods, while the other group received VR support. The study also evaluated satisfaction, engagement, and attractiveness with the use of VR in the teaching-learning process. The results showed that both groups learned significantly about production sequencing, the topic studied, regardless of the teaching method. However, the group with VR support showed more significant learning after classes and retained a higher level of knowledge after three weeks. On average, the satisfaction, engagement, and attractiveness indices for the VR-supported group were close to 6.56 points (on a Likert scale of 1-7), indicating that the method generated enthusiasm and was well accepted by the students. The evaluation of qualitative questions highlighted concerns such as ease of understanding the topics covered and the appeal of virtualization, making the classes more dynamic and attractive.  
The study has limitations. First, the application of VR focused only on production sequencing, so the results may not apply to some other areas of knowledge. Additionally, the study did not account for the participants' previous experiences, curiosity about the topic, and interest in the activity, which could have affected the retention of information in the VR-supported group. Another limitation is the small number of respondents, which did not allow for the identification of underlying factors in the perceptions of the students. Further studies should focus on knowledge retention over longer periods, such as three or six months after classes. Additionally, developing a standardized method for creating teaching-learning process activities and evaluating their quality and results would be beneficial. Additionally, directing attention toward emerging themes in production and operations management education, such as the shift from Industry 4.0 to human-centricsystems (Industry 5.0) [82] and local renewable energy systems [83], may offer intriguing avenues for future research. Finally, employing a large sample of respondents could help improve the measurement model by exploring underlying factors in the students' responses.  
Conceptualization, F.E.V.d.A., D.O.d.S. and A.d.O.M.; methodology, F.E.V.d.A., D.O.d.S. and A.d.O.M.; validation, G.S.R. and M.A.S.; formal analysis, G.S.R. and M.A.S.; original draft preparation, F.E.V.d.A.; writing--review and editing, F.E.V.d.A., D
## 2 System Model
\[\underbrace{=\mathbf{h}_{d,k}^{H}\mathbf{F}^{RF}\mathbf{f}_{d}^{BB} \sqrt{p_{d,k}}x_{d,k}}_{\text{Desired signal}}\] \[+\underbrace{+\mathbf{h}_{d,k}^{H}\mathbf{F}^{RF}\mathbf{f}_{d}^{ BB}\left(\sum_{j=1}^{k-1}\sqrt{p_{d,j}}x_{d,j}+\sum_{j=k+1}^{|\Xi_{d}|}\sqrt{p_{d,j}}x_{d,j }\right)}_{\text{Intra-beam Interference}} \tag{4}\] \[+\underbrace{+\mathbf{h}_{d,k}^{H}\mathbf{F}^{RF}\sum_{i\
eq j} \sum_{j=1}^{|\Xi_{i}|}\mathbf{f}_{i}^{BB}\sqrt{p_{i,j}}x_{i,j}}_{\text{Inter- beam Interference}}\underbrace{+n_{d,k}}_{\text{Noise Signal}},\] \[=\mathbf{h}_{d,k}^{H}\mathbf{F}^{RF}\mathbf{F}^{BB}\mathbf{p} \mathbf{x}+n_{d,k},\]  
wherein \(\mathbf{F}^{RF}\in\mathbb{C}^{N_{T}\times N_{RF}}\) and \(\mathbf{F}^{BB}=[\mathbf{f}_{1}^{BB},\dots,\mathbf{f}_{D}^{BB}]\in\mathbb{C}^ {N_{RF}\times D}\) are the precoding matrix corresponding to AP and DP. Here, the channel vector on behalf of every \(k^{th}\) UE within the \(d^{th}\) beam is represented by \(\mathbf{h}_{d,k}^{H}\in\mathbb{C}^{1\times N_{T}}\). \(\mathbf{P}=diag\{\mathbf{p}_{1},\dots,\mathbf{p}_{D}\}\) symbolizes the PA matrix. The signal vector that gets transmitted has been defined via \(\mathbf{x}=\left[x_{1,1},\dots,x_{1,|\Xi_{d}|},\dots,x_{D,1},\dots,x_{D,|\Xi_{ d}|}\right]^{T}\in\mathbb{C}^{K\times 1}\); the \(p_{d,k}\) denotes the average power received for the \(k^{th}\) UE within the \(d^{th}\) beam, and \(n_{b,k}\sim\mathcal{CN}(0,\sigma_{n}^{2})\) represents the AWGN having zero mean along with \(\sigma_{n}^{2}\) variance introduced across the \(k^{th}\) UE within the \(d^{th}\) beam. As in (4), there is a high possibility that the received signal can be greatly affected by the interference. Therefore, SIC will be very effective at the UE in detecting the desired signal free from interference.
###### Abstract  
Background: Currently, it has been observed that a fast-paced lifestyle, along with factors such as work pressure and economic and political uncertainty, among others, negatively impacts cognitive functions and increases levels of anxiety and stress in the population. In this context, physical exercise has been shown to be an effective means of alleviating these effects. The aim of this study is to analyze the acute effects of a single-bout session of high-intensity functional training (HIFT) and a single-bout session of moderate-intensity functional training (MIFT) on cognitive functions and psychological responses such as anxiety and stress. Methods: The study presents an experimental design with an analytical approach, involving 57 healthy adults in a 30 min functional exercise circuit. The HIFT group, with 28 participants, performed a high-intensity exercise circuit (>85% HR max). The MIFT group, with 29 participants, performed a moderate-intensity exercise circuit (70-80% HR max). To evaluate the dependent variables of executive function, attention, memory, anxiety, and stress, the Stroop Test, d2 Test, Word-Recall Test, STAJ, and PSS were used, respectively. These tests were administered, in the order mentioned, immediately before and immediately after the training session. Results: Both groups (HIFT and MIFT) showed significant improvements in executive function, attention, and memory, as well as significant reductions in anxiety and stress after the intervention (all \(p\) values ranged between \(p<0.001\) and \(p<0.04\)). However, the HIFT group demonstrated greater improvements in cognitive function and more pronounced reductions in anxiety and stress when compared to the MIFT group. Conclusions: Both HIFT and MIFT can improve cognitive function and reduce anxiety and stress, but high-intensity exercise appears to offer greater benefits.  
high intensity; cognitive function; moderate intensity; acute effects 2025
## 4 Results
### Statistics
#### 4.1.2 Yearly Distribution  
We also determined the number of studies published per year to identify any trends. We discovered that the number of published studies gradually increased, with 2019 featuring the highest number of publications thus far.
## 3 Results and Discussion
### Electrochemical Characterization of the Biosensor  
Electrochemical impedance spectroscopy (EIS) is used to determine the electron transfer properties of bare gold electrode and modified gold electrode surface. Figure 2 shows the Nyquist plots of the bare gold electrode (curve a), the capture probe modified gold electrode (curve b), the aptamer probe modified gold electrode (curve c) and the aptamer probe modified gold electrode after reaction with _E. coli_ (curve d) in a solution containing 1.0 mM [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) and 0.1 M KCl. The Randles circuit model (inset of Figure 2) is chosen to fit the impedance data obtained, where the element Ru is the electrolyte resistance, the resistance to charge transfer (Rct) and the diffusion impedance (Zw) were both in parallel with the interfacial capacitance (Cd). The Rct at the electrode surface is equal to the semicircle diameter of EIS, which is used to describe the interface properties of the electrode [40]. For curve a, it presents an almost straight line, which demonstrates that there is a very high electron-transfer rate between the electrochemical probe [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\) and the bare gold electrode. When the capture probes are immobilized onto the gold electrode, the Rct value increases at about 4100 \(\Omega\) (curve b), which is attributed to an electrostatic repulsion force to [Fe(CN)\({}_{6}\)]\({}^{3-/4-}\)[41] produced by the combination of the negatively charged single-stranded oligonucleotides. For aptamer probes modified gold electrode (curve c), the Rct further increases to a much larger value (nearly 38,000 \(\Omega\)) after the aptamer probes hybridized with the capture probes because of the increased negative charge. Afterwards, the Rct value (curve d, nearly 11,000 \(\Omega\)) further decreased when the electrochemical biosensor is incubated with _E. coli_. This is due to the dissociation of the aptamer probes from the electrochemical biosensor,  
Figure 1: Schematic of the Electrochemical Biosensor for _E. coli_ detection.  
which causes the reduction of negative charge. These Nyquist plot proves the successful modifications and reactions as described in the principle scheme.
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
## 5 Resemble Implementation  
As described in Section 4, the main component of RESEMBLE is the Bearer Layer, which replaces the Advertising Bearer defined in the Bluetooth Mesh specifications [22]. This component is able to provide:  
* A TDMA-based medium access mechanism for the transmission of real-time messages over BLE.
* A medium access mechanism to allow for both unsynchronized and synchronized nodes to transmit/receive non-real-time messages, such as synchronization and provisioning messages, without interfering with the real-time transmissions.  
* A centralized clock synchronization protocol suitable for Mesh BLE communications.
* Support to message priority, to cope with the different temporal requirements of the messages. The details of the RESEMBLE software components are depicted in Figure 6.  
The RESEMBLE Bearer consists of three main modules:  
* **The System Interface.** This module interfaces the AdvBearer module with the BLE Host Controller Interface (HCI) and System. Moreover, it maintains the clock that provides the network timings and is synchronized with the reference clock maintained by a node (the timing master) in the network. This component is platform-dependent, i.e., its implementation depends on the platform on which it runs.
* **The AdvBearer module.** This module implements the transmission mechanism that handles both real-time and non-real-time communications.
* **The Sync module.** This module implements the clock synchronization protocol. It is strictly coupled with the AdvBearer module.