esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### LC-MS/MS: Sample Preparation and Analysis  
Samples from photocatalytic tests were centrifuged, diluted 20 times with 50% methanol, and filtered through a 0.2 \(\upmu\)m PTFE syringe filter. LC-MS/MS analysis was conducted on an UltiMate 3000 liquid chromatograph from Dionex (Sunnyvale, CA, USA) coupled with an API 4000 QTRAP tandem mass spectrometer from Applied Biosystems/MDS Sciex (Waltham, MA, USA). A Synergi Fusion RP column (50 mm \(\times\) 2.0 mm I.D.; 2.5 \(\upmu\)m) from Phenomenex (Torrance, CA, USA) was used for chromatographic separation, onto which 5 \(\upmu\)L of the sample was injected at a mobile phase flow rate of 0.3 mL/min. The chromatograph worked in a mobile phase gradient consisting of a 5 mM solution of ammonium acetate in water (A) and methanol (B). The initial phase composition was set at 70% B, then it was ramped up to 100% B over 2.5 min and held for 0.5 min. Analysis was performed under a constant temperature of 35 \({}^{\circ}\)C. The eluate from the column was directed to the mass spectrometer through an electrospray ion source operated in the negative ion mode. The source and spectrometer parameters were set as follows: curtain gas pressure 10 psi, nebulizing gas pressure 40 psi, auxiliary gas pressure 40 psi, temperature 400 \({}^{\circ}\)C, electrospray voltage \(-\)3500 V, declustering potential \(-\)35 V, and collision gas was set to medium. The mass spectrometer was operated in the multiple reaction monitoring (MRM) mode. The ion transition from 205 to 161 \(m/z\) was selected with a collision energy of \(-\)10 eV.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 6 Conclusions  
In this paper, we highlighted the significance of self-organizing networks for future network management using learning techniques. Our main objective was to deploy mobility prediction as a promising tool for mobile network management. We introduced a hybrid VAR-GRU mobility model to predict user future trajectory. The proposed mobility model was able to extract user mobility behaviour and repetitive patterns in their movement history. After predicting users' future trajectories, we effectively reduced the required number of handover signalings and optimized the HO signaling procedure based on our predictions.  
Moreover, we investigated the handover signaling costs for the predictive and non-predictive scenarios to analyze the impact of mobility awareness on handover-related costs. The simulation results showed that the HO processing cost and transmission cost are reduced when we use mobility prediction. Our experiments indicate that the accuracy of prediction is of great importance and has a leading role in the effectiveness of the proposed method. It was shown that the proposed mobility model had the lowest error in comparison with the baseline methods.  
For future work, the proposed hybrid VAR-GRU model can be exploited in many potential areas to further optimize cellular network services. Mobility-aware resource allocation, location-based services, and mobility-aware call admission control mechanisms are some of the promising applications for the proposed mobility prediction model.  
Conceptualization, methodology and analysis, N.B. and S.P.; writing--original draft preparation, N.B.; writing--review and editing, S.P.; supervision, S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data is available under the Open Database License at [[https://www.openstreetmap.org/copyright](https://www.openstreetmap.org/copyright)]([https://www.openstreetmap.org/copyright](https://www.openstreetmap.org/copyright)) (accessed on 12 October 2020).  
The authors declare no conflict of interest.
# A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
## 2 Literature Review
### Fuzzy Probabilistic Neural Network  
A fuzzy set and system was introduced by [PERSON] about five decades ago [26]. As the fuzzy theory has advantages in dealing with indeterminate and nonlinear problems, it was widely used in control engineering, state recognition, information processing, decision making, and so on [27]. With the rapid development of electronic techniques in recent decades, Artificial Neural Network (ANN) hasdeveloped as an important tool in many fields [28; 29]. PNN was proposed by [PERSON] in 1989 [30], which was a significant part of ANN. PNN has a better ability for self-adaption, self-organization and self-learning compared with a SVM and K-nearest neighbor (KNN), and it was mainly applied in classification [31].  
A Fuzzy Probabilistic Neural Network (FPNN) combines the existing expert knowledge, fuzzy control theory and probabilistic neural network organically, and it is widely used in prediction, classification and fault diagnosis. In [32], to identify partial discharge location in a power cable, the fuzzy inference system and probabilistic neural network was proposed, the peak absolute value and average power of partial discharge signals were set as input variables, and the experimental results validated the proposed approach. In [33], a novel approach based on fuzzy logic control and PNN was presented to realize direct torque control in permanent magnet synchronous motor drives. In [34], a credit risk assessment model of commercial banks through a fuzzy probabilistic neural network model was proposed, which combined the relative membership degree in fuzzy mathematics with probabilistic neural network. Ref. [35] put forward a fuzzy system to improve the performance of a probabilistic neural network for detecting MEMS analog faults, which was actually a pattern recognition task.
# Improving the Production Efficiency Based on Algorithmization of the Planning Process
## 4 Research Results
### Possibilities of Further Development of the Planning Algorithm  
The proposed solution improved production efficiency (Figure 4). The planning algorithm represents a certain initial prototype, which can be further developed. There are specific challenges and general challenges which can be further solved. Specific challenges have been identified for the specific production line where the case study was conducted, general challenges are tied to a generalized planning algorithm. Specific challenges include, for example, the lack of feedback on the production information system or the gray area. The gray zone represents temporarily unregistered products, however, this weakness will be removed with a connection to the information system. This will bring a complete overview of real-time production and additional saved production capacity. General challenges are about extending functionality to users. It can be a development of reports for various company departments, as well as advanced functions for management support in decision-making in risky production situations (failure, lack of material, etc.). For the purchasing department, there can be added functions, that will enable more accurate material planning, thus reducing material stocks. Also, there can be made a module for the maintenance department, which will enable forecasts of line stops for better planning of technical maintenance and engineering changes. The connectivity within the internal information system for faster monitoring of the current production situation seems to be very useful, mainly due to faster response in the event of crises. The proposed planning algorithm represents an increase in production efficiency, almost without financial investment. Of course, further improvements and increases in production productivity can be extended with the application of investments in interconnection with information systems and more detailed product monitoring.  
Figure 4: Chart of production time in past two weeks with manual and automatic planning.
# A Novel Hybrid Recommender System for the Tourism Domain
## 3 Content-Based Recommender Component
### Hybrid Similarity Measure-Based Recommendations
Intuitively, two items would be considered by \(WEJS\) highly similar, and also of high recommendation value to a user, if they share the exact same characteristics (i.e., share features with the exact same values), and these characteristics are deemed important (i.e., they are highly \"weighted\" by the user) [22]. As mentioned, Algorithm 1 presents the method of generating the weights vector with respect to the user preferences for the POI's _characteristics_, or, as we term it, the _weights user profile vector_. As seen in Algorithm 1, the cosine similarity distance metric [36] is employed between the twelve features of the _user profile vector_ and each actual POI's twelve _character_ features. The most similar POIs are selected according to a _cosine threshold_. This threshold was empirically set to 0.70 during in our experiments. Then, the total appearances of their last 44 features with non-empty values are counted. Finally, these features' counters are normalized into weights comprising the generated \(weightsProfile_{u}\) of the user, to be used in the \(WEJS\) calculation. ```
1:\(profile_{u}\leftarrow\) user profile vector
2:\(POIs\leftarrow\) all POIs with their 56 features
3:\(counters\leftarrow\) count vector of length 44 initialized to zero
4:for each POI in POIsdo
5:\(character_{POI}\leftarrow\) first 12 features of POI
6:\(characteristics_{POI}\leftarrow\) last 44 features of POI
7:if\(cosine_{sim}(profile_{u},character_{POI})\geq 0.70\)then
8:for each \(f\) in \(characteristics_{POI}\)do
9:if\(v_{f}\
eq\emptyset\)then
10:\(counters_{f}\getsounters_{f}+1\)
11:\(weightsProfile_{u}\gets normalize(counters)\)
```  
**Algorithm 1** Weights generation of the user profile
## 3 Boundary Positions of Rolling Bearing  
One of the most common simplifications used in the mathematical analysis of rolling bearing is the observation of the bearing in just one position, when the center of one of the rolling elements coincides with the direction of action of the external radial load (Figure 1a). Although from the point of load distribution, this is the most unfavorable position of support, analyzing only one position is insufficient and does not provide complete information about the behavior of the bearing in operation. Namely, during working, the set of rolling elements is constantly oscillating between two boundary positions, as shown in Figure 1. This phenomenon has been detailed in previous papers [25; 28; 30]. It is clear that, due to the different position of the sets of the rolling elements, the load distribution in the two boundary positions will be different. Therefore, the value of the contact forces and contact deformations between elements of the bearing will be different in these boundary positions. Differences in the level of contact force and contact deformation will inevitably cause the differences in other running characteristics of the bearing, such as the level of deflection, stiffness, vibration characteristics, rotation accuracy, and so on.  
If we want to describe as accurately as possible the behavior of the bearing in operation, then we need to introduce this second boundary support position in the analysis.  
In previous studies [24; 25; 26; 27; 28], the following terms were adopted for these positions of support:  
* The boundary position of support on an odd number of rolling elements: BSO position of support (Sl.1.a);
* The boundary position of support on an even number of rolling elements: BSE position of support (Sl.1.b).  
The first boundary position of the support occurs when the center of one of the rolling elements coincides with the direction of external radial load, and the other when the rollers are symmetrically arranged relative to the direction of action of external radial load. In the following section, the value  
Figure 1: Boundary positions of rolling bearing.  
relating to the boundary position of support on an odd number of rolling elements will have index \(o\), and the value relating to the boundary position of support on an even number rolling elements will have index \(e\).
## 4 Performance Assessment
### Parameters Setting  
Based on the channel responses measured in the real experiment mentioned above, the proposed method is compared with the method without ECC and the method based on hard decision through simulation analysis. In the simulation, the communication frequency band, the carrier modulation mode, the number of subbands, the roll-off factor of transmit filter, and the symbol rate on each subband were the same as those for the real experiment shown in Table 1. The other parameters are shown in Table 2.  
Referring to Table 2, the rate \(\mathrm{R}=1/2\) recursive systemic code (RSC) with generator polynomial [5; 7] is used for coding; and, therefore, in order to compare three methods with the same information rate, quadrature phase shift keying (QPSK) is used in the proposed method and the method based on hard decision, while BPSK is adopted in the method without ECC.
## 2 Materials and Methods
### Synthesis of Silver and Gold Colloids  
Stock solutions were prepared with the following concentrations: 100 mM silver nitrate (AgNO3), 50 mM chloroauric acid trihydrate (HAuCl4 3H2O), 1 M sodium hydroxide (NaOH), 1 M potassium carbonate (K2 CO3), and 1.1 mM beta-cyclodextrintrim (\(\beta\)-CD).  
For the microwave-assisted synthesis (MW), either Ag or Au precursors were combined with \(\beta\)-CD and NaOH or K2 CO3 in 10 mL vials (e.g., 9 mL beta-cyclodextrintrim, 0.1 mL AgNO3, 0.1 mL K2 CO3, and 0.8 mL water for final concentrations of 1 mM Ag, 1 mM \(\beta\)-CD, and 10 mM K2 CO3) positioned centrally in a commercial microwave oven, operating at a power output of 2100 W. After 1 min microwave irradiation, the solutions exhibited a color change to yellow-brown in the case of AgNPs and red in the case of AuNPs, signifying the formation of the colloidal particles.  
In the case of ultrasound-assisted synthesis (US), the same precursors were mixed in 100 mL glass bottles to yield a 50 mL solution. The tip of the sonicator Vibra-Cell(tm) Ultrasonic Processor, model VCX 500 (Sonics & Materials Inc., Newtown, CT, USA) equipped with a tapered microtip (\(\omega\) 6 mm) and integrated temperature controller was introduced 1 cm in the solution. Different exposure times and ultrasound amplitudes (20, 30, and 40%) were tested and the formation of the colloids was followed by using UV-Vis spectrophotometry.  
Synthesis at room temperature was performed in either 10 mL or 20 mL vials where the stock solutions were gently mixed in the desired concentration ratios for a few seconds and the solutions were left undisturbed for 12-24 h (usually overnight).  
In some experiments, higher concentrations of cyclodextrin stock solution were prepared to vary the ratio between the metal and the CDs.  
The Au citrate colloid was prepared according to references [41; 42]. Briefly, 50 mL of a 2.5 mM solution of chloroauric acid was heated on a hotplate while steering at 300 rpm. After the solution started to boil, 5.6 mL of 1% sodium citrate was added at once. The solution rapidly changed the color and became reddish after a few tens of seconds. The boiling continued for 15 min, and afterward, the solution was left to cool at room temperature. The colloids present a plasmonic maximum at 542 nm and sizes in the 40-50 nm range as revealed by the transmission electron microscopy images (Figure S1)
## 5 Case Study
### Test Campaigns
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detected Issue, Evaluation Criteria, and Fault Classification** & **Cause Identification and Effects on the System** & **Proposed Hierarchized Adjustments** \\ \hline Too high distance of the camera from the to-be-detected area: irregular detection of users with a continuous detection/disappearance of a moving user, resulting in wrong collected data as if there were multiple users closely entering the room one after the other. Due to the higher value of O than the real number of people in the room, when people leave, O does not return to zero, with remaining values of O > 0 even after the end of the working day & (++) Improve the deep learning algorithms for image recognition (-) Add new cameras and re-verify the system \\ \hline Elevated lighting contrast between different areas of corridors: irregular detection of users with a continuous detection/disappearance of a moving user, resulting in wrong collected data as if there were multiple users closely entering the room one after the other. Due to the higher value of O than the real number of people in the room, when people leave, O does not return to zero, with remaining values of O > 0 even after the end of the working day & (++) Review the camera settings regarding lighting and contrast (-) Add a new camera in the brighter zone and re-verify the system \\ \hline Difficulty detecting two people entering in a room close together and/or quickly. This causes wrong collected data as if there were multiple users closely entering the room one after the other. Due to the higher value of O than the real number of people in the room, when people leave, O does not return to zero, with remaining values of O > 0 even after the end of the working day & (++) Possibility to ignore the related error, which does not affect the next phase of statistical data analysis for the definition of the occupancy pattern (-) Add a new camera allowing a multiple detection of the same area and re-verify the system \\ \hline Difficulty detecting cleaning employees due to the presence of the cleaning trolley, which impedes a complete view of the operator. Therefore, often the cleaning employee is detected entering the room (O = +1) but not leaving, so the value O remains unchanged & (++) Optimization and training of the recognition algorithm to identify the cleaning trolley by excluding the cleaning service employee in the occupancy count (-) Add a new camera allowing a multiple detection of the same area and re-verify the system \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont_. Figure 9: **Preliminary analyses and two test campaigns process. **
## 5 Performance Evaluation Results and Discussion
### Evaluation Viewpoints
#### 5.1.3 Effect of Configuration Parameter \(T_{b}\) on Successful Bids  
We evaluated how the configuration parameter \(T_{b}\) affects the successful bids because this parameter can be modified to reduce D2D communication messages. \(T_{b}\) specifies the intervals between a mobile host's broadcasts of its location information via D2D communication. Because the D2D communication capacity has an upper limit, reducing D2D messages by making \(T_{b}\) larger can be useful. However, a larger \(T_{b}\) can affect a successful bid for the following reason. According to the way an NEA is calculated by REP (described in Section 4.4.2, the location information of one-hop neighbors used for calculating the NEA becomes more outdated with a larger \(T_{b}\). Older location information for one-hop neighbors makes the NEA less accurate because the hosts are mobile. As the server's selection of REPs to which it sends a bid invitation depends on the NEA, as described in Section 4.3, changes in the NEA can change the set of REPs to which the server sends a bid invitation. Therefore, the evaluation score of a successful bid can change when parameter \(T_{b}\) is increased. We measured the number of successful bids with different evaluation scores between the cases with \(T_{b}=1\) and \(T_{b}=3\) among 1970 data requests. We also measured the number of D2D messages for broadcasting mobile host locations and the number of D2D messages for  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Message**} & **Existing Method** & **Proposed Method** \\  & **[Byte (\# of Packets)]** & **[Byte (\# of Packets)]** \\ \hline Bid Invitations Query & 40 (1) & 52 (1) \\ \hline Bid Invitations & 290 (3) & 290 (3) \\ (No bid invitation) & 322 (3) & 322 (3) \\ \hline Bid Invitations & 354 (3) & 354 (3) \\ (Two bid invitations) & 386 (4) & 386 (4) \\ (Three bid invitations) & 326 (3) & 326 (3) \\ \hline Bids (Two bids) & 358 (3) & 618 (5) \\ \hline Bids (Three bids) & 390 (4) & 910 (8) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Size of messages in bytes and number of packets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{} & **5\% Misbehaving Hosts** & **30\% Misbehaving Hosts** \\ \hline No S-REP per P-REP & (a) & (c) \\ \hline One S-REP per P-REP & (b) & (d) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Four cases with misbehaving hosts.  
RevAucs. Simulations were conducted 10 times with different random number seeds for 1000 s with 500 mobile hosts in an environment without misbehaving REPs. We chose 1 s as the value of \(T_{b}\) considering the balance between the accuracy of the location information of the mobile hosts and the number of D2D messages. We chose 3 s as the maximum value of \(T_{b}\) so that a mobile host close to a REP is less likely to move out of the D2D communication range of the REP during \(T_{b}\).
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 2 Related Work
in [38] proposed a satellite-assisted maritime network architecture on edge computing, using deep \(Q\) learning to minimize the total service latency. In aforementioned both works, although cloud servers with substantial computing resources are mentioned, both network models only include the LEO satellite layer and base station layer, and the explicit incorporation of cloud servers into the model is missing. In contrast, we aim to propose the multi-tier cloud-edge-end architecture, encompassing computing-capable end users, MEC-assisted LEO satellites, and the cloud server with rich resources, thereby providing users with a wider range of offloading options and access opportunities. Furthermore, [PERSON] et al. in [39] used the deep reinforcement learning (DRL) method to learn the optimal offloading decision dynamically in an air-ground integrated edge network, meanwhile proposing heuristics to solve the mixed integer program of joint computing resource allocation and task scheduling. [PERSON] et al. in [40] respectively used the Lagrange multiplier and DRL methods to optimize the service latency, provided the resource allocation is given. [PERSON] et al. in [41] likely decomposed the joint problem into two sub-problems, using the Lagrange multiplier method for communication and computing resource allocation provided the computation offloading is preserved. The cooperative offloading problem in LEO satellite Internet of Things (IoTs) was studied in [42], where LEO satellites forward tasks to ground MEC servers, and the weighted latency and energy minimization problem is designed as a partially observable MDP (POMDP), followed by the multi-agent DRL framework. In brief, existing works have extensively explored the challenges of computation offloading and resource allocation in either ground-edge networks [26; 27; 28; 29] or UAV-assisted MEC [35; 36]. Yet, unlike previous studies on satellite edge networks [39; 40; 41; 42], we consider the more precise description of dynamic characteristics of the network, including the relative position variation between LEO satellites and ground terminals, together with time-varying channel fading. Further, in existing RL works addressing computation offloading in LEO satellite edge networks, it is common to decompose the original problem into two sub-problems [37; 38; 39; 40; 41; 42]. In contrast, we resort to the P-DQN method, thereby handling the hybrid action space and offering a more integrated solution, without the intricate problem decomposition.