esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Antibiotic Resistance in Healthcare: Challenges in Treating Common Infections
### Impact of Resistant Infections on Hospital Stay Duration  
Resistant infections are frequently associated with prolonged hospital stays, which are often used as a proxy for morbidity. Infections caused by resistant pathogens, such as Methicillin-resistant _S. aureus_ (MRSA), ESBL-producing _Klebsiella_ spp., _E. coli_, and penicillin-resistant _pneumococci_, are well-documented to extend the length of hospital admissions [83]. MRSA, for example, is a major cause of surgical site infections and can significantly prolong hospital stays, with studies indicating that patients with MRSA infections may experience hospitalizations lasting several days longer than those with susceptible strains. Similarly, patients infected with penicillin-resistant _pneumococci_ tend to have extended hospital stays compared to those with penicillin-susceptible strains, due to the need for alternative, more intensive treatments. The duration of hospitalization is also markedly increased for individuals infected with ESBL-producing organisms such as _Klebsiella_ spp. and _E. coli_, as these infections are more difficult to treat with conventional antibiotics, often necessitating the use of broader-spectrum or combination antibiotics. This extended stay not only increases the burden on hospital resources, but also exacerbates patient morbidity, as prolonged exposure to the hospital environment increases the risk of additional healthcare-associated infections and complications. Moreover, the delayed recovery resulting from resistant infections can further compound the length of stay, with patients requiring additional surgeries, prolonged antibiotic courses, and intensive care [81; 83].
## 4 Experimental Results
### Classification Results
#### 4.3.2 Traditional Machine Learning Baselines  
In this part of our study, we undertook a comparative analysis of the LSTM-Attention-BiTCN model against a range of conventional machine learning algorithms, including K-Nearest Neighbor [21], Random Forest [22], Decision Tree [23], Gradient Boost [24], XGBoost [25], and Logistic Regression [26]. The primary objective was to assess the efficacy of these models in differentiating between suicide-related text and regular posts on Reddit.  
For this comparison, all models utilized the GloVe word embedding technique, processing inputs as embedded vectors corresponding to the words in the dataset. However, when training traditional machine learning algorithms, the embedded vectors were generated by taking the average of the GloVe embedding vectors of all words contained within the document. This approach ensures compatibility with the traditional machine learning models. The training of these models was conducted using their default hyperparameters. The results, as detailed in Table 2, provide a comparative overview of the performance on the testing subset of six traditional machine learning models against our LSTM-Attention-BiTCN model.  
Among the traditional models, XGBoost exhibited superior performance, achieving an accuracy of 0.8396, a precision of 0.8401, a recall of 0.8396, and an F1-score of 0.8396. Following closely, the Random Forest and Gradient Boost models demonstrated competitive results, with all performance metrics' scores of 0.8037 and 0.8005, respectively. The Decision Tree, Logistic Regression, and K-Nearest Neighbor models were ranked in that order.  
However, when compared to these traditional machine learning models, our LSTM-Attention-BiTCN model showed a significantly higher performance in accurately identifying suicide in social media text. The LSTM-Attention-BiTCN model achieved an accuracy of 0.9405, a precision of 0.9385, a recall of 0.9424, and an F1-score of 0.9405. These results underline the effectiveness and robustness of the LSTM-Attention-BiTCN model in the context of suicide ideation detection on social media platforms, surpassing the capabilities of the various traditional classification models examined. This comparison highlights the advanced potential of LSTM-Attention-BiTCN in addressing complex classification challenges within the realm of social media content analysis.
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 3 Background of BIM and Digital Twin  
In the construction field, the application of digital twin modeling can be categorized into three stages: The first stage involves the widespread adoption of conventional Building Information Modelling (BIM) methods, which primarily facilitate information exchange among stakeholders to manage the lifecycle of structures effectively. The second stage entails the integration of digital twins with real-time monitoring and simulation based on 3D BIM models. The third stage, digital twin technology, combines more advanced technologies such as deep learning and AI to drive digitalization further and enable real-time decision-making in building management.  
This section will provide an overview of the evolution of BIM and digital twin technologies, as well as an exploration of their current interactions.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 4 Shallow Underwater Acoustic Channel Model  
An underwater channel is doubly selective in nature as the signals undergo both frequency and time selectivity [1; 29]. Over longer distances, the bandwidth is limited (a few kilohertz), restricting intercarrier spacing and thus making the system sensitive to even smaller Doppler shifts [5; 30]. Motion induced attenuation is significant because the speed of sound is much less compared with RF waves [3]. For time shifted \(L\) multipath components [1], the response can mathematically be represented as:  
\[H(t,\tau)=\sum_{p=1}^{L}A_{p}(t)\delta\big{(}\tau-\tau_{p}(t)\big{)} \tag{12}\]  
where \(A_{p}\) represents the amplitude of the \(pth\) multipath component, \(\tau_{p}\) denotes the delay coefficient, and \(\delta\) indicates the Dirac delta function. The effect of Doppler frequency shifts differ for different subcarriers because the bandwidth and center frequency are comparable. The envelop channel response consists of random multipath fading and deterministic responses.  
Figure 2 features the modulated time domain signal \(s(t)\) from Equation (3) passing through several channel blocks. The elaborated system enabled the tuning of various parameters including the channel taps, absorption-related path loss from [PERSON]'s formula [31], and the SNR. For a shallow underwater acoustic channel, the delay spread is usually between 10 to 20 ms and can be up to 100 ms [32].
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 2 Tracking Error Estimation  
This section first reviews some of the relevant baseband signal models that can be used in the tracking error estimation phase. In fact, the tracking error is extracted from these baseband signals. After that, three methods to estimate tracking error are introduced. They are ATAN/ATAN2/NC-EMLE discriminator, coherent pre-filter, and non-coherent pre-filter. This section is the basis of tracking error estimation.
## 4 Simulation and Analysis
### System Performance of CPCE-UACE under Different Numbers of UEs  
In this subsection, we evaluated the proposed CPCE-UACE algorithm jointly in terms of throughput and backhaul load under different network scales with \(\mu=0.1\). The number of UEs in the network is set to be from 50 to 200 with an interval 50.  
Figure 8: Network throughput under different \(\mu\).  
Figure 9: Backhaul load under different \(\mu\).  
As shown in Figure 10, the average data rate of each UE decreases as the number of UEs in the network grows. It is obvious that the proposed CPCE-UACE algorithm can always achieve an outstanding performance compared to the No-CoMP and N-Best scheme, and 40% performance gain is obtained if \(|M^{\Omega}|\leq 150\). Despite the threshold scheme being comparable to the CPCE-UACE algorithm in terms of the average data rate, the threshold scheme has the heaviest backhaul load as shown in Figure 11. It is observed that the proposed CPCE-UACE algorithm has the better performance of the backhaul load compared to the \(N\)-Best scheme and threshold scheme, as shown in Figure 11. Furthermore, the proposed CPCE-UACE algorithm is comparable with the No-CoMP scheme under a small number of UEs (\(|M_{\Omega}|\leq 50\)) and outperforms the other schemes in large scale networks (\(|M_{\Omega}|\geq 100\)).  
The performance of No-CoMP, \(N\)-best, and Threshold compared with the proposed CPCE-UACE in terms of data rate and backhaul load is listed in Table 3.  
Figure 11: Backhaul load under different numbers of UEs.  
Figure 10: Network throughput under different numbers of UEs.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
###### Abstract  
This review aims to address the current data on edible packaging systems used in food production. The growing global population, changes in the climate and dietary patterns, and the increasing need for environmental protection, have created an increasing demand for waste-free food production. The need for durable and sustainable packaging materials has become significant in order to avoid food waste and environmental pollution. Edible packaging has emerged as a promising solution to extend the shelf life of food products and reduce dependence on petroleum-based resources. This review analyzes the history, production methods, barrier properties, types, and additives of edible packaging systems. The review highlights the advantages and importance of edible packaging materials and describes how they can improve sustainability measures. The market value of edible packaging materials is expanding. Further research on and developments in edible food packaging materials are needed to increase sustainable, eco-friendly packaging practices that are significant for environmental protection and food safety.  
edible packaging; edible coatings; environmental pollution; food safety; sustainability +
Footnote †: journal: Applied Sciences
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 3 Current Signal Feature Extraction  
### Time-Frequency Analysis Characteristics on the Basis of Wavelet Packet Decomposition  
The three-layer wavelet packet decomposition relationship is shown in Figure 7:  
\[\begin{array}{c}S=AAA_{3}+DAA_{3}+ADA_{3}+DDA_{3}\\ +AAD_{3}+DAD_{3}+DDD_{3}\end{array} \tag{8}\]  
where \(S\) is the original signal, \(A\) and \(D\) represent the low and high frequency, respectively, and number three indicates the decomposition layers.  
Figure 5: Drive end current detection system: (**a**) Schematic diagram of the drive end current detection system; (**b**) The physical picture of the current detection system at the drive end.  
Figure 6: Electromagnetic directional valve drive end current.  
Information entropy is an information theory that measures the uncertainty of a random variable and quantifies the value of the information contained in the expected message [24]. Thus, the research group calculated the energy statistics on the signal after applying wavelet packet decomposition, normalized the statistic energy of each frequency band and used the normalized energy value as the characteristic value of the fault condition (Equations (9)-(12)):  
\[E^{\prime}_{3j}=\sum_{k=1}^{n}\left|x_{jk}\right|^{2} \tag{9}\]  
\[E^{\prime}=\sqrt{\sum_{j=0}^{7}\left|E^{\prime}_{3j}\right|^{2}} \tag{10}\]  
\[E_{3j}=\ E^{\prime}_{3j}/E^{\prime} \tag{11}\]  
\[\mathrm{T_{1}}=\left[\begin{array}{ccc}E_{30}&E_{31}&E_{32}&E_{33}\\ E_{34}&E_{35}&E_{36}&E_{37}\end{array}\right] \tag{12}\]  
where \(x_{jk}\) (\(j=0\):\(7\), \(k=1\):\(n\)) is the amplitude of the discrete point of the reconstructed signal S3j, \(E^{\prime}\) is the energy sum of each frequency band of the signal, \(E_{3j}\) is the statistical value of each frequency band of the signal, \(E_{3j}\) is the t. Normalized energy of each frequency band of the signal and \(\mathrm{T_{1}}\) is the time-frequency analysis feature vector per fault type. The wavelet packet decomposition process to extract the signal energy value is presented in Figure 8.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 4 View Factors for Complex Shapes and Obstructions
### Incorporating Obstructions  
Unlike radiosity-based methods, where including obstructions in view factor calculations necessitates the use of additional algorithms [7], such geometry can be included in a straightforward manner in Radiance [28]. As shown through the equations in Figure 16, incorporating geometry in the calculation only requires adding the surfaces into the contextual geometry. By pre-identifying surfaces whose view factors are being calculated through ray-origins and surfacledentifier inputs, the remaining geometry automatically behaves as obstruction.  
Figure 15: An assortment of curved shapes whose view factors can be calculated by the planar sub-division and ray-arrangement for each shape. As explained previously, once the view factors between rays and finite planar shapes have been calculated, further results can be derived through superposition rule. (**a**,**c**) Show open and closed curved surfaces respectively while (**b**) shows two open flat surfaces.
## 7 Factors Affecting Loss for Electrical Steel
### Grain Size  
Grain size plays a pivotal role in magnetic materials and can be influenced by factors like cold rolling, strengthening temperature, and time. A larger grain size tends to enhance magnetic permeability since it results in uniformly oriented domains, whose alignment requires only a small magnetic field. However, larger grain sizes also lead to wider domain wall spacing, which improves overall loss. At higher frequencies, wide domain spacing necessitates rapid movement of the domain walls to return to their initial state, causing energy dissipation when new walls are created or when bowing occurs due to the larger distance between walls. Oppositely, a smaller grain size helps mitigate anomalous losses because the velocity of domain walls is reduced, but it may result in a higher proportion of grain boundaries, which can hinder domain wall motion through pinning [62]. Striking the right balance in grain size is crucial for minimizing losses. Refining or narrowing the domains can be employed as a technique to reduce losses, and finding the optimal balance is essential to achieve loss minimization. The mechanical characteristics of steel are greatly influenced by the size of grains and precipitates. Being able to assess these features without causing damage is important. In the study, a group of researchers altered the sizes and distributions of grains and precipitates in both low-carbon steel and lab model steel [63]. They then compared different magnetic properties obtained from major and minor magnetization loops [64].
###### Abstract  
User-in-the-loop (UiL) content delivery is a recently proposed scheme for personalized content retrieval over mobile communication networks. It is a promising scheme that can better manage the overall user quality-of-experience (QoE) throughout the entire content retrieval process. The performance of the scheme, however, has only been investigated in a simplified system model that assumed synchronized user behavior, inflexible delay constraint, and identical quality-of-service (QoS) requirement among users. This paper studies the performance of UiL content delivery scheme in a generalized and realistic system model with asynchronous user behavior, flexible outage delay constraint, and customized user QoS. Heuristic algorithms and theoretical bounds are investigated for the UiL content delivery problem. The proposed system is shown to be effective in managing the user QoE in generalized practical scenarios.  
Keywords:content recommendation; content delivery; mobile communication; QoS; QoE +
Footnote †: journal: Applied Sciences
# ADLBiLSTM: A Semantic Generation Algorithm for Multi-Grammar Network Access Control Policies
## 3 The Semantic Generation Algorithm
### CRF Layer  
After the output of the double-layer BiLSTM, we obtained the prediction score matrix, which, however, disregards the connections between labels. Therefore, we used CRF to impose certain constraints on the final prediction results to ensure the accuracy of label generation. CRF is a probabilistic graph-based model for modeling sequence labeling problems, which achieves specification of labels by learning the joint distribution between the input sequence and the output sequence, taking full account of the relationship between individual markers, and dealing with the dependencies between the markers. For a given input sequence \(X\), its \(Y=[y_{1},y_{2},\dots,\ y_{n}]\). When inputting \(x\), calculate its score to obtain the score matrix, as in Equation (6).  
\[score(x,y)=\sum_{i}h_{i}[y_{i}]+P_{y_{i-1},y_{i}} \tag{6}\]  
Figure 2: The inner workings of the DL-BiLSTM.  
where \(h_{i}[y_{i}]\) represents the emission score, which is the score when predicting the result to be a particular label, and it comes from the output of DL-BiLSTM, and \(P_{y_{i-1},y_{i}}\) represents the transition score, which is the probability of the previous character's labeling result to the current character's labeling result. Considering the labeling process as a path, the true path (the correct prediction result) is the highest scoring of all the paths, and thus the formula is expressed as the sum of the emission score and the transition score over the paths. We want the correct scores to have a greater weight in the total score, and we typically apply softmax to the scores, so we defined the loss as in Equation (7):  
\[loss=-\log(\frac{e^{score(x,y)}}{\sum\limits_{y\in Y}e^{score(x,y^{\prime})}}) \tag{7}\]  
By training, we obtained the final prediction and in the process normalized the prediction labels.  
As the CRF layer needs to calculate the scores for the entire sequence during training, this can lead to increased computational complexity.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 3 The Noise-Tolerant Method  
The traditional Diff-4 th CPR methods necessitate substantial estimation block sizes and are particularly susceptible to burst errors. As illustrated in Figure 5, the Diff-4 th method consists of two estimation stages: differential frequency offset estimation (Diff-FOE) and 4 th-order phase estimation (4 th-PE). The smoothing applied to the data in these stages helps mitigate noise interference. The smoothing lengths for Diff-FOE and 4 th-PE are \(N_{1}\) and \(N_{2}\), respectively. Both stages employ feedforward estimation, which results in significant performance degradation under low-SNR conditions.  
To eliminate the burst errors while maintaining low complexity, a noise-tolerant method has been introduced, which utilizes a two-stage framework for the joint estimation of residual Doppler FO and PN. The first stage involves a coarse estimation facilitated by a feedback loop, while the second stage employs a feedforward structure for fine estimation. The joint estimation of feedback and feedforward has advantages in improving noise tolerance and addressing phase slips, offering a powerful solution for more reliable and accurate results.  
The block diagram illustrating the noise-tolerant method is shown in Figure 6. The feedback loop comprises a phase detector, a loop filter, and a numerically controlled oscillator (NCO). Herein, \(\theta_{\text{in}}\) represents the angle of the signal affected by noise, \(\theta_{d}=\theta_{\text{in}}-\hat{\theta}_{1}\) denotes the phase difference, \(K_{d}\) signifies the phase detector gain, and \(K_{1}\), \(K_{2}\) are parameters related to loop gain, while \(\theta_{\text{loop}}\) indicates the output of the loop. The variable \(\hat{\theta}_{1}\) represents the outcome of the first-stage phase estimation. The feedforward stage executes a phase non-linear transformation and smoothing on estimation windows of block length \(N\), followed by phase detection. The result of the second-stage phase detection is denoted as \(\hat{\theta}_{2}\). Moreover, \(\hat{\theta}\) denotes the estimated value of the carrier phase \(\theta\), expressed as \(\hat{\theta}=\hat{\theta}_{1}+\hat{\theta}_{2}\).  
Figure 5: Block diagram of the traditional Diff-4 th CPR methods. The symbol (\(\cdot\))\({}^{\star}\) represents the conjugate operation.
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 3 Results and Discussion
### Reaction Time  
To study the effect of reaction time on the dechlorination efficiency of ascorbic acid, we conducted experiments with reaction times ranging from 0.5 to 40 min under the following conditions: an initial pH of 3.6, a molar ratio of n(Cu\({}^{2+}\))/n(Cl\({}^{-}\))/n(H\({}_{2}\)A) = 1.25:1:0.75, and a reaction temperature of 20 \({}^{\circ}\)C.  
From Figure 12, it can be observed that as the reaction time increases, the concentration of remaining chloride ions in the solution initially decreases and then increases. At a reaction time of 3 min, the removal rate of chloride ions is 70.0%, and the concentration of chloride ions decreases from 1 g/L to 0.3002 g/L, with a rapid decrease in chloride ion content. At the 10 min reaction time, the removal rate reaches 78.8%, and the chloride ion concentration drops from 1 g/L to 0.2116 g/L. When the reaction time is extended to 20 min, the removal rate of chloride ions reaches its highest value of 90.8%, and the remaining chloride ion concentration is only 0.0917 g/L. This is mainly due to the unique end structure of ascorbic acid, which has a high electron density. The enediol group (-C(OH)=C(OH)-) of ascorbic acid drives spontaneous charge transfer through the proton-coupled electron transfer (PCET) mechanism, significantly enhancing the electron transfer efficiency, which, in turn, rapidly reduces Cu\({}^{2+}\) to Cu\({}^{+}\), accelerating the chlorination removal process.  
When the reaction time exceeds 20 min, the chloride ion removal rate starts to decline. This is because after the reaction is complete, as the reaction progresses, the concentration of ascorbic acid gradually decreases, weakening the reducing environment of the entire system. The dissolved oxygen in the solution promotes the oxidation of the generated cuprous chloride (as shown in reaction Equation (19)), and as some cuprous chloride is oxidized and re-dissolved, chloride ions are released back into the solution, leading to an increase in the remaining chloride ion concentration and ultimately reducing the chloride ion removal efficiency.  
\[2\mathrm{CuCl}+\frac{1}{2}\mathrm{O}_{2}+2\mathrm{H}^{+}\to 2\mathrm{Cu}^{2+}+2 \mathrm{Cl}^{-}+\mathrm{H}_{2}\mathrm{O} \tag{19}\]