esa-sceva/satcom-chunk-collection huggingface dataset:
# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 3 Motivation  
Among the various approaches used to develop the recommendation systems, the memory-based Collaborative Filtering technique is most popular and widely used, but it has some limitations as discussed below.  
In the CF approach, the efficiency of the RS mainly depends upon finding the effective neighbors which are based on the similarity calculated by a similarity measure. There are various traditional and recently developed similarity measures, but they mainly suffer from the sparsity, cold- start and scalability problems. Some traditional and popular similarity measures in the CF technique compute incorrect similarity in some cases. For instance, the cosine measure [48; 49] computes the maximum similarity between users when they have only rated only one item. Pearson Correlation Coefficient (PCC) calculates low similarity regardless of the similar ratings of two users. The Jaccard measure [49] does not count the absolute values of ratings, while the Mean Squared Difference (MSD) measure ignores the proportion of common ratings. The combination of these two is JMSD, which does not utilize all the ratings provided by both users. Some recently developed similarity measures, i.e., Relevant Jaccard (RJaccard) and Relevant Jaccard MSD (RJMSD), compute inaccurate similarity when the user rates the items with equal ratings. In addition, the accuracy of the CF techniques is also affected when some inconsistency/noisy ratings [50] exist in the dataset. In this case, similarity measures compute inaccurate similarity. These existing CF algorithms only consider the historical ratings when computing similarity, but as time goes by, users' tastes may change; therefore, the generated recommendation might not be relevant.  
To address the aforementioned problems and improve the performance of recommendation systems, a Time Decay-based Deep Neural Network (TD-DNN) has been proposed in this paper. In this, firstly, we do some preprocessing on data to remove noise and give weightage to the most recent ratings. For correcting the noisy ratings, the Matrix Factorization approach is used, while the power decay function is used to give weightage to the most recent ratings. The resultant data matrix is inserted as the input into the DNN model for training purposes, and it produces the predicted ratings as output.
## 6 Trusted Routing Operation in AODV
### Trust Judging Rules  
For trust judgment, we have set the threshold 0.5, as can be seen in Table 1. This threshold value can be changed depending upon one's system design and security level.  
* If node \(A\) want to communicate with node \(B\) and if belief of \(A\) in \(B\) is \(\geq\)0.5, then \(A\) will trust \(B\) and start to route packet to node \(B\).
* If disbelief opinion of \(A\) in node \(B\) i.e., \(>\)0.5, then \(A\) will not trust node \(B\) and will not route packets unless to verify it by certification or destroy it.
* If uncertainty of node \(A\) in node \(B\) is \(>\)0.5, then \(A\) will ask for digital signature for node \(B\) and waits for the verifying. If \(A\) successfully verifies \(B\)'s signature then \(A\) will start communication with \(B\) and reverse is also true.  
Figure 4: Extended routing table for AODV routing protocol having trust fields i.e., positive and negative sensing.
# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 3 Proposed Approach
### Two-Stage Classification Process  
#### 3.3.1 Customer Profiling (Stage 1)  
The objective of stage 1 is associating each consumer with features that are typical of the shape of the consumption profile (Figure 2), e.g., the time of morning and evening peaks, the deepness of the valleys in the curve, and the maximum difference between peak and off-peak consumption. Classical features such as customer's annual and daily consumption commonly are less important than the shape of the consumption.  
As mentioned above, the consumption patterns are split into four quarters; in each quarter, data analysis identifies the consumption curves for workdays, Saturdays, and holidays. Year quarters reflect with a good approximation the seasonality (QI--winter, Q2--spring, Q3--summer, Q4--autumn) [1].  
Customer's consumption is different in all days of the same category. Differences are particularly significant for the residential customers that may exhibit very different load curves one day from another, even of the same type. In Figure 3a, given residential end-users' consumption patterns in the winter weekdays are reported (the black curve is the average profile). For industrial, commercial, and tertiary customers, these differences are less significant since the consumption is prevalently linked to job activities (Figure 3b).  
The methodology needs to associate a single profile to each customer for any quarter of the year and for any category of the day. Several methods have been tested to minimize the risk of losing significant information. In this paper, the most frequent average profiles assignment was implemented--for each customer, the consumption patterns during a specific typical day are clustered with [PERSON]'s linkage hierarchical clustering algorithm [12; 35]. The optimal number of clusters is identified according to the Davies-Bouldin index (DBI) minimum value, calculated for a limited number of the dendrogram's resulting levels [12; 35]. These clustering process results are classes of daily profiles with different shapes for the same typical day and for the same customer. The profile associated with customer behavior is the centroid of the most populous class, which is the average of the most frequent profiles.  
The representative consumption pattern is constituted by several samples dependent on the intervals on which is discretized the day (i.e., 96 samples for a sampling rate of 15 min) for each of the 12 typical ones associated with each customer  
Figure 3: Example of the consumption patterns during the winter weekdays of (**a**) a residential (main residence) customer and (**b**) an industrial customer.  
Figure 2: Customer profiling (step 1).
# Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality
## 5 Discussions
### Awareness, Intention to Use and Satisfaction in Government SNS Applications  
For traditional e-government services, the three tested determinants of e-government adoption are e-government awareness, intention to use, and satisfaction. These have been widely examined as synthesized in the literature review. Not surprisingly, the result of the regressions for government website portals and the government official app justified that the influence of these three factors are persistent to this day. As one of the goals of this research, these three determinants were tested as well in the new government SNS platforms to observe their applicability. Their effects should be examined not only in traditional e-government services, but also in emerging e-services platforms: Weibo government official account and WeChat public services. Upon completion of this statistical analysis, we can state that higher awareness of new e-services, higher behavioral intention to use, and higher satisfaction significantly increase citizens' adoption rates. Therefore, this result supports that relevant determinants as put forth in various technology acceptance models are affecting people in choosing and practically using local government SNS applications in a positive way.
## 3 Results
### Single and Multi-Objective Optimization
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Weight (WOB)** & **Weight (MSE)** & **WOB (klb)** & **RPM (rev/min)** & **TOR (klb-ft)** & **FLOW (gpm)** & **ROP (ft/h)** & **MSE (kpsi)** \\ \hline
1 & 9 & 1 & 13.88 & 109.36 & 19.00 & 803.32 & 23.99 & 156.43 \\
2 & 8 & 2 & 13.45 & 109.36 & 19.00 & 803.32 & 23.99 & 152.60 \\
3 & 7 & 3 & 13.45 & 109.36 & 19.00 & 803.32 & 23.99 & 152.60 \\
4 & 6 & 4 & 1 possible to achieve concomitantly an ROP of 21.58 ft/h and MSE of 11.36 kpsi through the following values of drilling parameters: WOB of 11.75 klb, RPM of 109.36 rev/min, FLOW of 803.32 gpm, and TOR of 3.34 klb-ft. These results demonstrate the importance of developing and implementing new methods and processes applied for the drilling engineering discipline, focusing on statistical and numerical analysis and also on efficiency in the process (which does not always reflect operational speed). Response surface method and multi-objective analyses represent significant advancements over traditional analyses (2D analysis), which are widely used in the O&G industry. While 2D analyses provide simplified and limited insights by considering only two parameters simultaneously (mechanical parameter versus depth), 3D approaches offer a more comprehensive evaluation, integrating complex variables in a three-dimensional space that better reflects the drilling operations. Furthermore, multi-objective analyses go even further by simultaneously optimizing multiple response variables, such as ROP and MSE. These advanced approaches enable more precise decision making, operational cost reduction, and greater efficiency in resource exploration, making them indispensable in today's technologically and economically challenging scenarios.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 4 Reliability and Lifespan Improvement Methods for EMD Components
### Active Thermal Control
Increase in the switching frequency can lead to higher switching losses, and limiting the current may reduce the power delivered to the motor. Similarly, paper [99] presents an ATC method that also adjusts the switching frequency to control switching losses. The proposed solution was applied to a wind turbine and extended the lifespan of the frequency converter from 3.35 to 9.77 years. Additionally, the cost of replacing the IGBT modules was significantly reduced, which previous ATC methods had not taken into account. A new method based on ATC for drive systems of electric motors, applied to stopping conditions, is proposed in [100]. The proposed method uses the optimal phase angle to redistribute thermal stress between the phases in the VSI, thereby reducing the maximum junction temperature of the semiconductor components. Figure 7: Flowchart for ATC power routing. A drawback of the given solution is that it was tested under specific conditions and on a limited number of applications, which may restrict its universal applicability. An alternative ATC approach is described in [101], where the DC-link voltage is controlled to reduce thermal stress in PMSM traction drive systems. The speed mission profile used is the US06 driving cycle. The control strategy is based on adapting the DC-link voltage to the demands of torque-speed trajectories. The solution demonstrates good results in low-speed regions, but it is necessary to test its effectiveness in higher speed areas to ensure its performance across the full operating range. In [102], the impact of semiconductor chip size on the reliability of traction inverters is investigated. The proposed algorithm for reducing inverter size is based on thermal management and significantly reduces stress caused by rapid start-ups. A model predictive control (MPC) method for reducing thermal stress is given in [103]. The selection of the optimal space vector in the VSI is achieved through multiparameter optimization. A drawback of the proposed method is that it could introduce additional current and torque ripples. Another MPC method is presented in [104]. A cost function is composed of the current reference error, efficiency, and junction temperature. According to the authors, implementing ATC could negatively affect reliability due to an increase in THD compared to the insignificant reduction in thermal cycling.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 2 State of the Art
### The Advent of Smart Homes
This has been the motivation for the development of smart homes, in which almost all the appliances are locally manageable and controlled in real-time [17]. Several studies discuss the effect of providing feedback on energy usage to users employing different technologies [20]. It was found that, displaying real-time information on electricity usage to users, they effectively modify their behavior achieving energy saving of up to 30% [20]. In that regard, smart grids enable communication among buildings, as well as among energy generation systems. Although the traditional power grid has been serving humanity for about the last 100 years, it is now inadequate to face the increased demand for electricity and the deployment of sensors, active automation tools and bidirectional communication [21]. This last aspect is the most relevant feature that promotes the advent of smart grids. The traditional vision of energy production and consumption is changing because users are starting to generate and use their own energy locally, even if they are not always the only or final consumers of their generated energy. The surpluses are exported to the grid, which means that electricity transfer has to be bidirectional, _i.e._, energy has to flow in the two directions. In this sense, a smart grid differs from a traditional power grid through its ability to predict, monitor and manage the bidirectional energy flows. It collects information on users' behavior and actions of the various connected items in order to ensure that energy demand and supply are well balanced as well as energy is efficiently used. Smart homes hence represent the main element of the smart grid where the monitoring of real time energy and environmental data allows energy control and electricity price forecasting at transmission and distribution levels [22].
# An Experimental Study of Glycerol Carbonate Synthesis over g-C\({}_{3}\)N\({}_{4}\) Catalysts
## 2 Experimental
### Catalyst Characterization Equipment  
The catalyst was analyzed using various techniques, such as FTIR spectroscopy, X-ray diffraction (XRD), textural analysis (N\({}_{2}\) adsorption/desorption), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA-DTG). XRD analysis was carried out at ambient temperature using a Bruker X-ray diffractometer (Karlsruhe, Germany; \(\theta\)-\(\theta\) type) equipped with a Cu-K\(\alpha\) radiation source (\(\lambda\) = 1.5418 A) operating at 40 kV and 5 mA. The scans covered a 2\(\theta\) range of 5-80\({}^{\circ}\) at a rate of 10\({}^{\circ}\)/min. Microstructural morphology was examined using a SEM instrument from FEI Company (Hillsboro, OR, USA). Thermogravimetric and derivative thermogravimetric analyses (TGA-DTG) were performed on a METTLER TOLEDO TGA/IST Thermal Analysis System (Greifensee, Switzerland) within the temperature range of 25-600 \({}^{\circ}\)C, under a nitrogen atmosphere, and at a heating rate of 10 \({}^{\circ}\)C/min.  
The textural properties of the samples were evaluated using a Quantachrome NOVA 2200 e Gas Sorption Analyzer (Boynton Beach, FL, USA). The nitrogen adsorption/desorption isotherms were recorded at 77.35 K across a relative pressure (p/p\({}_{0}\)) range of 0.005 to 1.0. Data processing was carried out using NovaWin version 11.03 software. The specific surface area was calculated using the BET (Brunauer-Emmett-Teller) method, while the total pore volume was estimated from the desorbed volume at a relative pressure near unity, utilizing the BJH (Barrett--Joyner--Halenda).  
FTIR spectroscopy was performed using a Shimadzu IRAffinity-1S spectrophotometer (Kyoto, Japan) equipped with a GladiATR-10 accessory, allowing spectra acquisition through the ATR (attenuated total reflectance) technique. FTIR spectra were captured across a wavenumber range of 400-4000 cm\({}^{-1}\), with a spectral resolution of 4 cm\({}^{-1}\).
## 2 Related Works
### Uploading Pattern of Source Nodes  
In the uploading pattern of source nodes, all algorithms can be further classified into passive uploading and active uploading. In the passive uploading pattern, source nodes do not upload the data immediately. Sensory data is buffered in some appointed nodes, which keep the buffered data until a mobile sink is reached [3; 4; 16; 17; 18; 19; 20; 21; 22; 23]. In contrast, in the active uploading pattern, source nodes upload sensory data to the sink by multi-hop as soon as they sense the data [24; 25; 26; 27; 28; 14].
## Appendix D Performance Measures  
A brief explanation of the four measures is provided below [36]:  
* Accuracy refers to the closeness of a measured value to a standard or known value. In other words, it is the number of true predictions made divided by the total number of predictions made. \[Accuracy=\frac{TP+FN}{TP+FN+TP+TN}=\frac{\text{correct predictions}}{\text{all predictions}}\] (1)
* Precision refers to the closeness of two or more measurements to each other. In other words, it is the fraction of relevant instances among the retrieved instances. However, precision is independent of accuracy since a network can be very precise, but inaccurate. \[Precision=\frac{TP}{TP+FP}=\frac{\text{positive predicted correctly}}{\text{all positive predictions}}\] (2)
* Recall (known also as sensitivity) is the fraction of relevant instances that have been retrieved over the total relevant instances. \[Recall=TPR=\frac{TP}{TP+FN}=\frac{TP}{P}=\frac{\text{predicted to be positive}}{\text{all positive observations}}\] (3)
* The F1 score is the weighted average of precision and recall. In other words, it is the harmonic mean of precision and recall. F1 score is an 'average' of both precision and recall. We use the harmonic mean because it is the appropriate way to average ratios (while the arithmetic mean is appropriate when it conceptually makes sense to add things up). \[F1=2\frac{Precision\times Recall}{Precision+Recall}\] (4)
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 5 Results and Discussion
### End-to-End Delay  
The end-to-end delay is the time taken by a data stream to be transmitted from the source node \(fc_{s}\) to the destination node \(fc_{d}\). The three D2D routes have different numbers of intermediate nodes (or hops). Route \(k_{2}=fc_{s}-fc_{3}-fc_{6}-fc_{d}\) has four nodes with two intermediate nodes and routes \(k_{3}=fc_{s}-fc_{1}-fc_{4}-fc_{7}-fc_{d}\) and \(k_{4}=fc_{s}-fc_{2}-fc_{5}-fc_{8}-fc_{d}\) have five nodes with three intermediate nodes. Route \(k_{2}\) has the lowest end-to-end delay among the D2D routes since it has the lowest number of intermediate nodes. Since routes \(k_{3}\) and \(k_{4}\) have the same number of intermediate nodes, the source node selects one over the other through the learning mechanism, explained in Section 4.2.3.  
Figure 10 shows a comparison of the end-to-end delay incurred between the traditional reinforcement learning (TRL) mechanism with \(\alpha=0.5\) and the dynamic reinforcement learning (DRL) mechanism. In the traditional RL mechanism, the learning rate is constant at \(\alpha=0.5\) for the entire experiment. The end-to-end delay reduces with increasing PU OFF time. Compared to traditional reinforcement learning, dynamic reinforcement learning shows a lower end-to-end delay for routes \(k_{2}\), \(k_{3}\), and \(k_{4}\) when the PU OFF time increases from 50 to 250 s.  
Figure 9: Average packet delivery ratio comparison among D2D routes with dynamic \(\alpha\) at different PU OFF time.
## 3 Results and Discussion
### Comparison of the Metabolomic Profiles for AAO and ABA
Shikimic acid has previously been detected in koji fermentation with _B. amyloliquefaciens_[22]. \begin{table}
\begin{tabular}{c c c c} \hline
**Amino Acids** & **AAO** & **ABA** & _p_**-Value** \\ \hline Glutamate & \(2\times 10^{10}\) & \(4.42\times 10^{9}\) & \(7.39\times 10^{-4}\) \\ \hline Glutamine & \(1\times 10^{10}\) & \(1.36\times 10^{10}\) & \(8.92\times 10^{-2}\) \\ \hline Histidine & \(1\times 10^{10}\) & \(1.64\times 10^{10}\) & \(1.88\times 10^{-4}\) \\ \hline Isoleucine & \(2\times 10^{11}\) & \(1.40\times 10^{11}\) & \(7.86\times 10^{-3}\) \\ \hline Leucine & \(1\times 10^{11}\) & \(6.79\times 10^{10}\) & \(1.11\times 10^{-1}\) \\ \hline Lysine & \(4\times 10^{10}\) & \(1.64\times 10^{10}\) & \(5.72\times 10^{-6}\) \\ \hline Methionine & \(4\times 10^{10}\) & \(2.88\times 10^{10}\) & \(6.87\times 10^{-2}\) \\ \hline Phenylalanine & \(2\times 10^{11}\) & \(8.36\times 10^{10}\) & \(7.22\times 10^{-3}\) \\ \hline Proline & \(2\times 10^{11}\) & \(2.92\times 10^{10}\) & \(2.02\times 10^{-5}\) \\ \hline Serine & \(3\times 10^{9}\) & \(1.70\times 10^{9}\) & \(2.86\times 10^{-2}\) \\ \hline Threonine & \(6\times 10^{9}\) & \(5.76\times 10^{9}\) & \(9.17\times 10^{-1}\) \\ \hline Typtophan & \(3\times 10^{10}\) & \(9.64\times 10^{9}\) & \(5.52\times 10^{-5}\) \\ \hline Tyrosine & \(4\times 10^{10}\) & \(2.14\times 10^{10}\) & \(2.05\times 10^{-3}\) \\ \hline Valine & \(2\times 10^{11}\) & \(9.37\times 10^{10}\) & \(4.00\times 10^{-3}\) \\ \hline GABA & \(1\times 10^{10}\) & \(4\times 10^{8}\) & \(0.00808\) \\ \hline \end{tabular} The values are the means of triplicate experiments; \(p\) < 0.01. N/F means not found. Results are reported by area.  
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline
**Organic Acids** & **AAO** & **ABA** & _p_**-Value** \\ \hline Citrate & \(8\times 10^{10}\) & \(2\times 10^{9}\) & \(0.00067\) \\ \hline Coumaric acid p or o & \(4\times 10^{9}\) & \(2\times 10^{8}\) & \(0.00382\) \\ \hline Fumarate & \(1\times 10^{9}\) & \(5\times 10^{7}\) & \(1\times 10^{-5}\) \\ \hline Malate & \(1\times 10^{10}\) & \(1\times 10^{9}\) & \(0.00054\) \\ \hline Shikimic acid & N/F & \(2\times 10^{7}\) & - \\ \hline Succinate & \(1.33\times 10^{9}\) & \(9.96\times 10^{8}\) & \(0.00283\) \\ \hline \end{tabular} The values are the means of triplicate experiments; \(p\) < 0.01. N/F means not found. Results are reported by area.  
\end{table} 
Table 2: Organic acid compositions in _A. oryzae_ and _B. amyloliquefaciens_ amazakes after 16 h of fermentation at 55 \({}^{\circ}\)C.  
Overall, AAO fermentation results in a richer metabolite profile than ABA, showcasing higher concentrations of both amino acids and organic acids. These differences reflect the distinct fermentation pathways utilized by _A. oryzae_ and _B. amyloliquefaciens_.