esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study proposes a new mathematical formalism for well-being evaluation based on monitored parameters acquired by a wearable device and the sentiment analysis of employee messages. The findings indicate that the _WBI_ is a valuable metric for evaluating employee well-being, integrating various physiological and psychological parameters. Implementing ML through the WBM and utilizing Azure Cognitive Services for sentiment analysis further demonstrates a new method of understanding employee experiences in the Industry 5.0 environment.  
The research included a quantitative analysis of _WBI_ components for 605 monitoring days from 1 January 2023 to 18 October 2024. The study revealed the importance of combining objective health metrics with sentiment analysis to capture a holistic view of employee experiences. The WBM uses a classification algorithm (LbfgsMaximumEntropy) and exhibits a _MacroAccuracy_ of 91.81% and a _MicroAccuracy_ of 95.95%. The _EGI_ includes the _WBI_ calculated using the WBM and the _DSC_. The accuracy of the _DSC_ was identified as 99.7% with a precision of 99.59%. We propose an EKA intervention protocol based on an employee's last 10 _EGI_ values.  
This research contributes new knowledge to the field by demonstrating the practical application of the _WBI_ as a comprehensive measure of employee well-being. It emphasizes the importance of the employee as a human-centered entity in the organizational context of the Industry 5.0 environment. Moreover, the study highlights the synergy between quantitative health metrics and qualitative sentiment analysis, offering organizations a framework to address employee needs proactively.  
Integrating the _WBI_ and _DSC_ provides organizations a powerful tool to monitor and enhance employee well-being through the _EGI_. Organizations that foster a productive and positive work environment prioritize employee satisfaction and health.  
Conceptualization, C.-M.R.; methodology, C.-M.R. and A.S.; software, C.-M.R.; validation, C.-M.R. and A.S.; formal analysis, C.-M.R. and A.S.; investigation, A.S.; resources, A.S.; data curation, C.-M.R.; writing--original draft preparation, C.-M.R. and A.S.; writing--review and editing, A.S.; visualization, C.-M.R. and A.S.; supervision, C.-M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Petroleum-Gas University of Ploiesti, Romania.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 3 Proposed Sentiment Analysis Method
### Methods Used for Text Normalization
#### 3.1.4 Word-to-Phrase Substitution (WtPS)  
The list of standard abbreviations is part of the dictionary in a language. In SM it is very frequent to generate abbreviations from very commonly occurring phrases or words pairs, such as \"Good Morning \(\rightarrow\) GM\". These abbreviations are user-generated, which is not standard. We collected the 1365 most commonly used abbreviations from multiple web sources to make a list of new abbreviations which are not included in the lexicon, to resolve the problem of SM user-generated abbreviations. These abbreviations include tbh \(\rightarrow\) to be honest, tldr \(\rightarrow\) too long didn't read, dyk \(\rightarrow\) did you know, etc. These abbreviations are very difficult to identify/correct using existing normalization methods.  
Dialectal/Informal Usage Problem  
Dialectal usage is replacement of multiple words with one or more OOV words where each word is a combination of words such as \"are not \(\rightarrow\) aint\". Such errors cannot be corrected by the above word-level normalization methods. This problem can be resolved by a word-to-sequence approach.  
Word-Deletion Problem  
The problem of word deletion is similar to that of character deletion-based errors. This can be a word-to-sequence transformation or a sequence-to-sequence translation. In SM text to reduce the message length, some words, which are not needed for other users to understand the message, are omitted such as \"Where are you? \(\rightarrow\) Where?\". However, for machine understanding, these words may be necessary. The LSTM technique is well suited for both word-to-sequence and sequence-to-sequence normalization problems.  
We used the LSTM-based word-to-sequence model for converting the abbreviated word to its intended sequence. We created an LSTM model in MATLAB with the same settings as discussed in Section 3.2.1.
## 3 Proposed RBAVO-DE for GS  
An improved variant of AVO known as RBAVO-DE is proposed in this paper to discover the smallest relevant gene subsets from the classification process and to disregard irrelevant genes. RBAVO-DE utilizes a Relief-based binary AVO algorithm combined with the DE technique. RBAVO-DE's primary feature is its ability to maximize accuracy while utilizing the fewest features possible. The proposed RBAVO-DE consists of two primary steps. First, there is a pre-processing step in which the Relief algorithm is used to determine which features are significant by assigning each feature a weight and then removing the features that are irrelevant and have the lowest weights. In the second step, the binary AVO algorithm and the DE technique are applied to identify the more pertinent and unique features. The AVO algorithm is prone to the local optimum trap when handling large-scale problems. The AVO algorithm incorporates the DE technique to prevent this.  
The proposed RBAVO-DE algorithm for tackling the GS strategy requires applying the Relief algorithm, initializing, position boosting using the AVO algorithm, binary conversion, fitness appraisal, and integration with DE. The following subsections explain these steps.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 3 Results
### Survey B  
A 2D survey was conducted on an 80 cm by 80 cm area on the Type I wall specimen, as shown in Figure 1a. The measurement step was 5 mm in both x and y directions. Figure 7 shows the reconstructed SAR images at a depth of 3.2 cm in three different polarization channels. After SAR processing, the reconstructed images provide much higher lateral resolution than the raw radar profile in Figure 6. Besides, different polarizations show different images of the same wood structure. The reflection from the vertical post (in y direction) is stronger than that from the horizontal girt in the VV polarization channel, while weaker in the HH polarization channel. In the HV polarization channel, we can also notice two small inclined linear reflections at the joint of the vertical post and the horizontal girt. These two reflections come from two connecting steel bolts inside the wooden members, which cannot be discerned even if we remove the decorating gypsum wall. On the wooden post and sill beam, especially in the reconstructed slices at a shallow depth, we can see some evenly aligned small reflections. These reflections come from the small nails fixing the gypsum wall on the wooden structure, and are clearer on the shallower reconstructed images (data not shown). The size of these nails is about 3 mm in diameter and less than 2 cm in length.  
Figure 7: Reconstructed SAR image of the 2D survey on the Type I wall specimen (Figure 1a) in **(a)** HH; **(b)** HV; and **(c)** VV polarization channel.
## 3 Reliability Calculation of a Single Slip Pathway
### Reliability Calculation of Elements
#### 3.2.2 Stepwise Equivalent Linearization of the Joint Failure Boundary of Multiple Elements
The element reliability indices and design verification points obtained using the improved first-order second-moment method are shown in Table 1.  
The element correlation coefficient matrix is as follows:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element Number**} & \multirow{2}{*}{**Reliability Index**} & \multicolumn{4}{c}{**Design Verification Point**} \\ \cline{3-6}  & & \(x_{1}\)* & \(x_{2}\)* & \(x_{3}\)* & \(x_{4}\)* \\ \hline
1 & 2.5 & \(-2.5\) & \(0\) & \(0\) & \(0\) \\
2 & 2.5 & \(0\) & \(-2.5\) & \(0\) & \(0\) \\
3 & 3.25 & \(-0.65\) & \(0\) & \(-3.18\) & \(0\) \\
4 & 3.25 & \(-1.3\) & \(-2\[\left[\rho_{ij}\right]= \begin{bmatrix}1&0&0.2&0.4\\ 0&1&0&0.9\\ 0.2&0&1&0\\ 0.4&0.9&0&1\end{bmatrix}  \tag{22}\]  
Using the dimension reduction method, the reliability index of the parallel system composed of the above four elements is obtained as 5.23 [22]. The reliability index obtained using the stepwise equivalent linearization method is 5.47. The corresponding equivalent linear limit state equation is \(Z=0.64X_{1}+0.57X_{2}+0.51X_{3}+0.01X_{4}+5.47\).
# Design and Implementation of a Mixed IoT LPWAN Network Architecture
## 2 Related Technologies
### LoRa
LoRaWAN includes security and authentication based on the AES128 encryption scheme (Advanced Encryption Standard) and other security standards described in IEEE 802.15.4/2006. Unlike other systems that depend on a single key for both functions, LoRaWAN separates between authentication and encryption. The AppSKey key is used to encrypt the message payload. Authentication and message integrity control uses the network session key NwkSKey. There are two device association methods to the network [33]:  
* ABP (Activation By Personalization): With this method it is necessary to program the security keys in the device. Based on the DevEui, a DevAddr, NwkSKey and AppSKey are generated for each client to be used during communication. * OTAA (Over The Air Activation): In this case, the device already contains the information necessary to join the network and it is only necessary to register its DevEui and AppKey in the server. In each association request the device sends the DevEui and AppEui identifiers. The server generates the AppSKey, NwkSKey keys and sends them to the client next to the DevAddr to be used during the rest of the communication. The network topology is a star of stars. Multiple devices can access through the same gateway and all the gateways are linked via IP to the network server. The gateway performs bridge functions by adding extra information such as the level of the received signal, but no association is required with it. Several gateways can receive and forward the information of the same object and it is the network  
Figure 5: Uplink LoRaWAN frame and fields. Figure 6: Downlink LoRaWAN frame and fields. server responsible for filtering duplicates, authenticating the devices and selecting the most suitable gateway for the downlink [34].
## 2 Methods
### Phase 1  
#### 2.1.1 Procedure to Locate the Extreme Points  
At Phase 1, we construct the girding polygon for the points of set \(V\) and construct an initial yet infeasible (partial) tour that includes the points of that polygon. The construction of this polygon employs four _extreme_ points \(v^{1}\), \(v^{2}\), \(v^{3}\) and \(v^{4}\); the _uppermost_, _leftmost_, _lowermost_, and _rightmost_, respectively [14], with ones from set \(V\) defined as follows. First, we define the sets of points \(T^{\prime},L^{\prime},B^{\prime}\) and \(R^{\prime}\) with \(T^{\prime}=\{i\mid y_{i}\text{ {is maximum}},\ i\in V\}\), \(L^{\prime}=\{i\mid x_{i}\text{ {is minimum}},i\in V\}\), \(B^{\prime}=\{i\mid y_{i}\text{ {is minimum}},\ i\in V\}\), and \(R^{\prime}=\{i\mid x_{i}\text{ {is maximum}},\ i\in V\}\). Then,  
\[v^{1}=j\mid x_{j}\text{ {is maximum}};\ j\in T^{\prime}, \tag{4}\]  
\[v^{2}=j\mid y_{j}\text{ {is maximum}};\ j\in L^{\prime}, \tag{5}\]  
\[v^{3}=j\mid x_{j}\text{ {is minimum}};\ j\in B^{\prime}, \tag{6}\]  
and  
\[v^{4}=j\mid y_{j}\text{ {is minimum}};\ j\in R^{\prime}. \tag{7}\]  
See the next procedure for the extreme points in Table 1.  
Figure 1: Block diagram of the _CII_-algorithm: **(a)** Phase 1 delivers a partial (yet infeasible) solution, **(b)** Phase 2 extends the partial solution of Phase 1 to a complete feasible solution, and, **(c)** at Phase 3, the latter solution is further improved.  
**Lemma 1**: _The time complexity of Procedure extreme_points is \(O(n)\)._  
Proof of Lemma 1.: In this and in the following proofs, we only consider those lines in the formal descriptions in which the number of elementary operations, denote it by \(f(n)\), depends on \(n\) (ignoring the lines yielding a constant number of operations). In lines 5-9, there is a loop with \(n-1\) cycles, hence \(\{f(n)=n-1\}\). In lines 11-15, there is a loop with \(n\) cycles, hence \(\{f(n)=n\}\) In lines 20-21, 22-23, 24-25 and 26-27; there are four loops, each one with at most has \(n\) cycles, so \(\{f(n)=4n\}\). Hence, the total cost is \(O(n)\).
## 3 Experimental Setup and Result Analysis
### Result and Analysis
#### 3.2.1 Accuracy Performance
Shown in Figure 10, it can be seen from the curves that _error of x, error of y,_ and _total tracking error_ were 0.44 cm, 1.68 cm, and 1.68 cm respectively when the probability was 95%. In other words, 95% of _total tracking error_ was less than 1.68 cm. Therefore, it can be concluded that the proposed algorithm had a good accuracy for tracking the target signal source LED.
# One-Part Alkali-Activated Pastes and Mortars Prepared with Metakaolin and Biomass Ash
## 2 Experimental Program
### Preparation and Characterization of Pastes  
#### 2.2.1 Mix Proportion of Pastes  
One-part alkali-activated pastes were manufactured at the same water/solid (w/s) ratio equal to 0.56 by weight, by varying the quantity of biomass ash and metakaolin in order to obtain different K/Al molar ratios ranging from 0.50 to 3.00 with a step of 0.50. These pastes were labeled as \"ASH_MK_XX\", where \"X.XX\" represents the K/Al molar ratio, thus a paste with K/Al = 0.50 is referred to as ASH_MK_0.50.  
Figure 3: Thermogravimetric analysis (TGA) of metakaolin and biomass ash.  
As reference, two additional mixtures with the same w/s ratio of 0.56 were manufactured. The former was prepared with biomass ash and water, to investigate the rate of biomass ash dissolution, and was labeled as \"ASH_H2O\"; the latter was a two-part reference alkali-activated paste labeled as \"MK_KOH_1.00\" and prepared with metakaolin and KOH solution (prepared by dissolving KOH pellets in water) at a K/Al molar ratio of 1.00. Paste compositions are reported in Table 3.
## 4 4-Chain Based Routing
### Protocol Operations
#### 4.2.1 Formation of Chains  
Once the network is converged, and nodes become fully aware of coordinates of all nodes in the network, the protocol starts its first phase. In the first phase, chain creation starts with the farthest node from the sink. Every node identifies its nearest node and connects with it. Sink transmits a token towards the farthest node of the chain. The chain is created as the token is passed towards sink through the intermediate nodes. In this way, four chains are created as the nodes identify their regions and continue to insert other nodes in their respective chains by using shortest path selection.  
We also formulate the problem of chain creation by using mixed integer linear programming. In this model, our objective function is to minimize the total transmission distance, \(D\), of all the chains in a round. A round is a time in which all the nodes transmit their data towards the sink. Total energy consumption of all the nodes in a single round is directly proportional to \(D\).  
\[Minimize\;\;D \tag{30}\]  
subject to  
\[Constraint\;1. D\geq\sum_{j=1}^{4}D^{j} \tag{31}\] \[Constraint\;2. D^{j}\geq\sum_{i=1}^{N^{j}-1}(n_{i+1}^{j}-n_{i}^{j})\] (32) \[Constraint\;3. D\geq\sum_{j=1}^{4}\sum_{i=1}^{N^{j}-1}(n_{i+1}^{j}-n_{i}^{j})\] (33) \[Constraint\;4. d_{i}^{j}=(n_{i+1}^{j}-n_{i}^{j}) \tag{34}\]  
The objective function, \(D\) could be achieved fulfilling the following constraints in the above equations:  
* Constraint 1 shows that the total transmission distance; \(D\) is greater than or equal to the sum of distances \(D^{j}\) of all the interconnected chains,
* Constraint 2 shows that the total transmission distance of a \(j\)_th_ chain is larger than or equal to the sum of the distances between the nodes of the chain, and
* Constraint 3 explains the total transmission distance in a detailed way. The last constraint defines the transmission distance between any parent and child nodes in the chain.
## 5 Simulation Results and Discussion
### Proposed Model Throughput, Packet Delivery Ratio, and End-To-End Delay  
As we mentioned above in detail, that proposed model routing patterns are centralized and driven by the SDN controller. Sensor nodes are capable of sensing multi-application environmental parameters. So the final reports contain the information about different sensed data as shown in Figure 14. Figure 14 depicts the results in a scenario of 100 m \(\times\) 100 m, in which both SACFIR and SAMCFIR produce results according to threshold value defined by the central controller. Figures 15 and 16 show the results for 300 m \(\times\) 300 m and 450 m \(\times\) 450 m networks respectively. As we can observe in every scenario, the amount of different types of information is different. This is because of the application preference reset of the SDN controller. Thus, reconfigurations bring flexibility to receive the required application data for a particular time interval from a single sensor node.  
Figure 12: Energy consumption rate as network operation progress in network of 300 m \(\times\) 300 m with 200 nodes.  
Figure 13: Energy consumption rate as network operation progress in network of 450 m \(\times\) 450 m with 300 nodes.  
In conventional settings, the ideal assumptions define the optimistic transmission model. This optimistic model knowingly ignores signal distortion effects such as: interference, fading  
Figure 16: Multi-type data received at BS on network operation in network of 450 m \(\times\) 450 m with 300 nodes.  
Figure 14: Multi-type data received at BS on network operation in a network of 100 m \(\times\) 100 m with 100 nodes.  
Figure 15: Multi-type data received at BS on network operation in network of 300 m \(\times\) 300 m with 200 nodes.
## 2 Materials and Methods
### Low Versus High Mass Spectral Pattern Analysis
#### 2.2.1 Low Mass (2000 to 20,000 \(m/z\)) Spectra Range Data Generation  
Mass spectra were generated using \(\alpha\)-Cyano-4-hydroxycinnamic acid (CHCA) matrix. The MALDI-8020 (Shimadzu-Kratos, Manchester, UK), MALDI-ToF mass spectrometry instrument was externally calibrated using Bradykinin Fragment 1-7 (756.4 Da) (ProteoMassTM, Supelco, Sigma-Aldrich, Bellefonte, PA, USA). Samples were diluted to 1:80 with mass-spectrometry-grade water (Romil, Cambridgeshire, UK) and 1 \(\mu\)L plated on a 48 well stainless-steel target plate using a sandwich technique [23]. Mass spectral data were generated in positive linear mode with a 100 Hz solid-state laser (355 nm) at a mass range of 200 to 2000 \(m/z\); blanking at 150 \(m/z\), and pulse extraction set at 700. A square raster pattern consisting of 500 position and 17 penetration shots was employed to sample across the entire spot. Thus, 8500 spectra per sample were collected in order to achieve a statistically consistent average spectra per patient sample. Averaged data were exported in mzML format. For each patient's sample, we generated three replicates and compared the obtained averaged mass spectra to ensure reproducibility and high-quality assessment.
## 4 Numerical Simulation  
In order to physically visualize the flow conditions around the impeller blades of the microbubble pump and to analyze pump performance at the design and off-design conditions, three-dimensional numerical simulation is introduced. ANSYS CFX 18.2, a commercial software, was used to solve Reynolds-averaged Navier-Stokes equations (RANS) and continuity equation for incompressible fluid using the finite volume method. Diffusion and convection terms of all the governing equations were handled by adopting the central difference method and upwind differential scheme, respectively. A second-order upwind spatial discretization algorithm was introduced to solve all the equations related to turbulence, pressure, and momentum. Second-order algorithm is known for minimizing interpolation errors in numerical diffusions. As the convergence criteria of the numerical simulation, the residuals of the flow variables are set to \(10^{-4}\). A shear stress transport (SST) model is used to address turbulence. The unique features of the SST model make it a good choice for modeling high turbulence, as it combines the k-\(\epsilon\) and k-\(\omega\) models. The first grid is put near the walls at y\({}^{+}\) < 5.  
Figure 6 shows the computational domain and boundary conditions used in the present study. Considering the wake flow formed downstream of the pump impeller, the distance between the pump outlet and the impeller is set at 10 times the duct diameter (D in Figure 6). The length of the inlet duct is set to be 5 times the duct diameter.  
Figure 7 shows the computational grid system for the overall system, including the pump impeller and casing. The blade region operating in rotating frame of reference and casing region in stationary frame of reference are introduced separately. For the impeller and volute casing, tetrahedral elements are used, whereas a three-layer prism mesh is imposed near the walls. In order to decrease the overall number of mesh elements, hexahedral elements are used at the inlet, outlet, and connecting ducts.  
Figure 8 shows the grid dependency test results, which is conducted to determine the optimum number of grid elements for the overall domain. After 6.2 million elements, there is a very small change in the pressure coefficient value, so around 7 million elements are used as the number of elements in the present numerical simulation.  
As boundary conditions, atmospheric pressure is specified at the inlet, flowrate at the outlet, and all walls (casing, impeller and hub surfaces) are declared no-slip and adiabatic.  
Figure 6: Computational domain.  
Figure 7: Computational grid.