esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Hypothesis Testing Results and Discussion
The following is a summary of the final model:  
* Workplace happiness decreases as workplace anger increases. * Workplace happiness decreases as workplace anxiety increases. * As the level of dejection in the workplace increases, so does the level of workplace anxiety. * As the level of anxiety in the workplace increases, so does the level of workplace anger. * Workplace anxiety has a role in the relationship between dejection and happiness. * Workplace anxiety has a role in the association between anger and happiness. The findings of this study are supported by prior experiments using a dimensional approach, which have shown that three different types of work-related sentiments are intertwined [25, 40, 41]. The results of this study support the use of WORAF as a measurement instrument in further studies. Other work-related emotions including guilt, humiliation, jealousy, optimism, and sympathy should also be included in future studies, as suggested by other researchers [29]. The participants in this study were all Saudis, and hence the findings cannot be generalized to workers with different ethnic or cultural backgrounds because the constructs studied are affected by culture and society. For this reason, further experiments using the WORAF scale and workers with different cultural and social backgrounds should be performed. Regulating the emotions of workers during work and in the workplace is in the interest of companies, because regulation of one's feelings leads to several positive outcomes, such as a lower risk of depression [42], greater control over the expression of violence [43], a more developed sense of morality [44], and improved psychological development [45]. The importance of this study stems from the need for an understanding of the relationships between WORAF to provide full understanding of emotional regulation in the workplace. Because this study used self-reported data collection through survey distribution, the research participants might possibly have been influenced to report the generally accepted psychological functioning or conduct in work environments rather than stating their actual feelings regarding each question in the survey. In addition, this study did not evaluate relationships with objective indicators, such as other affective states, in relation to particular conditions in the workplace. Because the study design was cross-sectional, causal relationships between variables are uncertain. Figure 5: Measurement and structural model results (SmartPLS output).
# MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float
## 4 Experiments
### Detection of Earthquakes
#### 4.1.3 Implementation with MeLa  
The implementation with MeLa requires functions, like STA/LTA, triggers, wavelets transforms, and cumulative distributions. Implementing a specialized algorithm may require the involvement of an embedded software expert to write specific functions in C language in order to add them in the MeLa library. The MeLa language does not offer the full flexibility of a generic programming language in order to ensure the reliability and efficiency of the applications, and to permit the analysis of applications. However, the current library of functions (Table 10) is already generic enough to be of use in many different applications. The MeLa code of the seismic application is accessible on Github, see the Supplementary Materials section.  
Once the seismic application has been implemented with the MeLa language, the analysis tool allows for verifying that the limits of the instrument are not exceeded. Figure 8 shows that the processor is used only 0.1 pct of the time. Indeed the time between each packet of data is long compared to the time required to process them. The autonomy of the float depends on the frequency of the ascent requests; the estimated autonomy was found to be five years if the algorithm records four earthquakes and triggers one ascent per week (2.9 years if it records 10 earthquakes and triggers 10 ascents per week). The estimated amount of data transmitted per month was found to be, respectively, 708 kB and 915 kB. The 708 kB compares well with the Mermaids floats currently operating in the Pacific which transmit 400 kB per month with a compression algorithm that divides the size of data by 2.  
The processor usage of 0.1 pct is compared to the maximum allowed processor usage that is not necessarily 100 pct if several applications must be executed at the same time (as defined by the Liu and Layland theorem).  
We have tested a version of the algorithm that uses floating point numbers instead of integers to filter the micro seismic noise, process the STA/LTA, and compute the wavelet transform. With floating point numbers the processor is used 0.17 pct of the time, higher than the integer implementation but still very low. It shows that choosing a floating point implementation is possible and may be preferable, because it gives flexibility to design the high pass filter that removes micro seismic noise. We have also tried another version of the algorithm that is based on a Fourier transform instead of the numeric filter. It was found early that using a Fourier transform (1024 samples processed every 512 samples) increases the processor usage to 0.7 pct. This demonstrates that using Fourier transforms in real time for seismic monitoring is possible, but it will use more processor time than the original algorithm.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
###### Abstract  
For the secure deployment of network platforms tailored for IoT devices, the encryption of data transmission is equally as crucial as the process of authentication. In this context, we introduce the Secure and Scalable IoT network (SSI) network platform, designed to accommodate a diverse range of IoT devices. It provides scalability and implements effective many-to-many and end-to-end encryption across extensive regions. With the emergence of quantum computing, secure public key exchange mechanisms have become important. Among the various post-quantum cryptography (PQC) algorithms assessed, Nth Degree Truncated Polynomial Ring Units (NTRUs) have emerged as an optimally suited PQC algorithm for IoT devices constrained by limited computational capabilities. We have integrated NTRUs with SSI as a lightweight PQC solution. Moreover, SSI-PQM (Post-Quantum MACsec) enhances the SSI's initial authentication structure to minimize PQC-TLS session attempts and protect the SSI's important configuration information. When applying TLS with PQC for secret key exchange purposes, it was verified that this approach ensures stable performance in IoT environments. Upon the implementation of our proposed SSI-PQM on Raspberry Pi 3B+ based IoT devices, SSI-PQM exhibited acceptable performance at security levels from 80 to 128 and achieved a minimum speed improvement of 161% over RSA at security levels above 160. It can be concluded that SSI-PQM stands out as an effective Zero Trust-based IoT network platform, demonstrating its viability and efficiency in safeguarding data transmission against potential quantum computing threats.  
IoT; L2 TP; MACsec; network overlay; Nth Degree Truncated Polynomial Ring Units; post-quantum cryptography; TLS; VXLAN +
Footnote †: journal: Applied Sciences
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 2 System Model and Routing Evaluation Parameter
### Routing Evaluation Parameter
#### 2.2.3 Effective Forward Rate  
Due to the limited coverage of sensor nodes, they rarely transfer data directly to the sink. Most nodes need forward data. Therefore, in the routing process, the geometry relationship in the current node, the forward neighbor, and the sink should also be considered.  
For selecting the next hop, this paper introduces the effective forward rate \(R_{f}^{ij}\). As shown in Figure 1, \(\Theta sinkj\) represents a circle with sink as the center and \(d_{j,sink}\) as the radius. A line _isink_ represents a link between node \(i\) and sink. Node \(j^{\prime}\) is the intersection of circle \(\Theta sinkj\) and line _isink_. Here, the forward distance \(d_{f}(i,sink)\) between node \(i\) and the sink is defined as follows:  
\[d_{f}(i,sink)=d_{i,sink} \tag{15}\]  
Then, for a node \(j\in FN(i)\), the forward distance from node \(i\) to \(j\) can be expressed as follows:  
\[d_{f}(i,j)=d_{i,i^{\prime}}=d_{i,sink}-d_{j,sink},j\in FN(i) \tag{16}\]  
Finally, based on \(d_{f}(i,j)\) and \(d_{f}(i,sink)\), the effective forward rate \(R_{f}^{ij}\) of the node \(j\) can be expressed as follows:  
\[R_{f}^{ij}=\frac{d_{f}(i,j)}{d_{f}(i,sink)} \tag{17}\]  
The effective advancing rate considers the geometric distance relationship between nodes \(i,j,sink\); hence, it directs the packet flow directly to the sink. Thus, energy consumption can be cut down. In the free space model, sensor nodes tend to consider select neighbors with long single-hop transmission distance as the next hop. In this way, after the packets are forwarded to the next hop, they will be closer to the sink. Thus, the energy consumption of data transmission in a single-hop range is more productive and reduces the number of hops in the transmission path.
# Recent Advancements in Anaerobic Digestion and Gasification Technology
## 3 Anaerobic Digestion Technology  
The term \"anaerobic digestion\" (AD) refers to the degradation of biomass under anoxic conditions in both natural and engineered systems [5]. [PERSON] et al. [6] defined anaerobic digestion as a process wherein microorganisms degrade organic matter in the absence of oxygen to produce biogas. This technology is regarded as the earliest approach toward achieving a circular bioeconomy. Moreover, due to increasing industrial interest, studies on anaerobic microbial processes have greatly improved in identifying the important process parameter and factors that can promote methane production from all types of organic matter, such as manure and slurry [5]. Methane is said to substitute natural gas, especially as it tends to build up opportunities with regard to traditional petrochemistry [7]. Furthermore, anaerobic digestion has proven to be an ideal end-of-pipe technology for the treatment of waste. It serves as a process in the recovery of various biotechnological applications because of the wide substrate spectrum [5]. Such a biotechnological application, which includes the microbial electrochemical process and production of medium-chain fatty acid by anaerobic fermentation, can be coupled with anaerobic digestion. Anaerobic digestion has received immense interest in recent times because it addresses the major challenges faced by developing countries regarding environmental pollution resulting from the use of fossil fuels [8].
_Applied_  
_sciences_  
_Article_  
**Optimal Measurement Speed and Its Determination Method in the Transmission Precision Evaluation of Precision Reducers**  
**[PERSON], [PERSON], [PERSON] \({}^{*}\) and [PERSON]**  
Beijing Engineering Research Center of Precision Measurement Technology and Instruments,  
College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology,  
Beijing 100124, China; [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (Z.S.);  
[EMAIL_ADDRESS] (H.W.)  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +86-158-1049-5293  
Received: 22 April 2019; Accepted: 22 May 2019; Published: 26 May 2019  
**Featured Application: The method that is proposed in this paper provides a scientific basis for the speed determination in the transmission error measurement. The optimal measurement speed and its determination method are suitable for the transmission error measurement of various precision reducers.**  
**Abstract:** Transmission error is the key index for characterizing the transmission precision of precision reducers, and its accurate measurement is significant for the precision evaluation of precision reducers. Transmission error is generally measured under the conditions of zero-load and low speed. However, low speed is a general concept and there is no general standard of measurement speed or solid scientific basis. Therefore, it is difficult to obtain consistent transmission precision evaluation results for the same precision reducer. The concept of optimal measurement speed in the transmission precision evaluation of precision reducers was put forward in order to reduce the influence of measurement speed. The determination method of optimal measurement speed was proposed and the calculation model of the optimal measurement speed was established, according to the Stribeck friction model of precision reducers. Taking a certain type of RV reducer as an example, the transmission error measurement experiments were carried out under different speeds. The friction torque of the RV reducer and the peak-to-peak value of the measured transmission error were the least under the optimal measurement speed. The influence of speed on the measurement results can be effectively reduced. The determination of optimal measurement speed of transmission errors could improve the measurement precision of the transmission errors for the objective evaluation of transmission precision of precision reducers.  
**Keywords:** precision reducer; transmission error; Stribeck curve; optimal measurement speed
# From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
## 5 Experiments and Results
### Real Datasets  
#### 5.2.1 Datasets  
To answer the above question, we performed experiments on two medium-sized multi-label real datasets: Eurlex dataset (EURLex4K) [51] and Simple English Wikipedia Dataset (SimpleWiki2K) [49]. The statistics of these datasets are given in Table 1.  
We used two classic classifiers (scores) to obtain score datasets: a simple \(k\)-Nearest Neighbors (\(k\)-NN) with the multi-label scoring system described in [52] and LEML [53], as a classic representative of a modern, embedding-based, extreme, multi-label classifier. After training both scorers on both train datasets, we obtained four distinct _thresholding training datasets_ (see Section 2.3): SimpleWiki2K\({}^{\text{KNN}}\), SimpleWiki2K\({}^{\text{LEML}}\), EURLex4K\({}^{\text{KNN}}\), and EURLex4K\({}^{\text{LEML}}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Feature Dims** & **Label Dims** & **Training** & **Testing** & **Avg. Examples** & **Avg. Labels** \\  & & **Examples** & **Examples** & **per Label** & **per Example** \\ \hline SimpleWiki2K & 97,179 & 1849 & 60,744 & 6761 & 52.60 & 1.60 \\ EURLex4K & 5000 & 3956 & 15,539 & 3809 & 20.86 & 5.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EURLex4K and SimpleWiki2K—statistics of datasets used in the experiments.  
Figure 4: Test macro F1 score performance of traditional thresholding methods and ThresNets for synthetic data for various FPR and FNR: (**a**) low score noise level \(\zeta=0.02\) and (**b**) high score noise level \(\zeta=0.4\). Number of labels \(L=1000\), size of \(\mathbf{h}=50\).
###### Abstract
However, compared to other languages such as English, very few research investigations have concentrated on Roman Urdu/Hindi, due to resource constraints such as a lack of lexical resources and morphological issues. The challenge of Roman Urdu/Hindi sentiment analysis has not yet been fully investigated, despite the widespread use of these languages. Hence, the main subject of this research study is Roman Urdu sentiment analysis. The primary contribution of this research is to introduce a novel deep learning method designed on CNN-LSTM for Roman Urdu and English sentiment analysis based on user-generated textual data on social media, using various word embedding models, and to compare the performances of Word2 Vec (CBOW and skip-gram), GloVe, FastText, and TF-IDF words-to-vectors models on Roman Urdu text classification. The remainder of this study is divided into five sections. Section 2 discusses related work on Roman Urdu sentiment analysis. Section 3 outlines our recommended methodology. The experimental setup is described in Section 4 of this paper. The experimental results and assessments of the proposed methodology are explained in Section 5. The final section brings the study to a conclusion and discusses future research.
# Nitrogen-Related Defects in Crystalline Silicon
## 3 Conclusions  
In the present review, we aim to offer a comprehensive survey of nitrogen-related defects in Si. We considered both experimental and theoretical studies in order to gain a better understanding of these defects and their impact on the properties of Si. N is always present in Si, and understanding it is of fundamental importance, as the Si-related devices have reduced dimensions. In particular, it is expected that a better understanding of dopant-defect interactions involving nitrogen and/or oxygen and/or carbon and/or intrinsic defects will guide scientists/engineers to further improve the optical, electrical, and electronic properties of Si.  
The manuscript was written and edited by all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Defect** & **LVM/cm\({}^{-1}\)** & **Levels/eV** & **Refs.** \\ \hline N\({}_{\mathrm{s}}\) & 653 & E\({}_{\mathrm{c}}\)\(-\)0.08, E\({}_{\mathrm{c}}\)\(-\)0.31, E\({}_{\mathrm{c}}\)\(-\)0.64 & [(33; 61; 62)] \\ N\({}_{\mathrm{i}}\) & 550, 773, 885 & E\({}_{\mathrm{v}}\)\(+\)0.5, E\({}_{\mathrm{c}}\)\(-\)0.2 & [(50; 51; 63)] \\ N\({}_{\mathrm{i}}\)N\({}_{\mathrm{i}}\) & 766, 963 & & [(9; 67)] \\ N\({}_{\mathrm{s}}\)V & 663 & E\({}_{\mathrm{c}}\)\(-\)0.5, E\({}_{\mathrm{c}}\)\(-\)0.7 & [(50; 54)] \\ N\({}_{\mathrm{i}}\)N\({}_{\mathrm{s}}\) & 573.4, 774.1 & & [(63)] \\ N\({}_{\mathrm{s}}\)N\({}_{\mathrm{s}}\) & & E\({}_{\mathrm{c}}\)\(-\)0.42 & [(70; 71; 72)] \\ N\({}_{\mathrm{i}}\)N\({}_{\mathrm{i}}\)Si\({}_{\mathrm{i}}\) & 930, 953 & E\({}_{\mathrm{v}}\)\(+\)0.2 & [(39; 50; 79)] \\ NO & 722, 801, 1001 & E\({}_{\mathrm{c}}\)\(-\)0.06 & [(14)] \\ NO\({}_{2}\) & 855, 973, 1002 & & [(81; 87)] \\ N\({}_{2}\)O & 801, 996, 1026 & & [(9; 43; 88; 89; 90; 91)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the properties of the most significant nitrogen-related defects considered.  
Figure 10: (**a**) The DFT relaxed structure of C\({}_{\mathrm{i}}\)NO\({}_{\mathrm{i}}\) defect in Si, (**b**) the Bader charges on C, N, O, and their nearest neighbor Si atoms, and (**c**) the charge density plot representing the electron distribution around the C, N, and O atoms [(57)].
## 6 Simulation Environment and Results
### Simulations Results
#### 6.2.2 Service Discovery Delay  
Now, we compare service discovery delay in OLSRv1 and OLSRv2 networks. We measure the service discovery delay according to the node speed in the network and the number of nodes offering the same type of service. By default, there are only five nodes announcing services. We do the comparison in simulation scenarios with 50 nodes. The parameters for sending periodic HELLO and TC messages are the ones established by OLSRv1 and OLSRv2 RFCs. SDM INTERVAL_TIME to announce services is set to 5 s.  
In Figure 13, we can see that in OLSRv2 networks, the delay to discover a service is lower than in OLSRv1 networks. This result is as expected, taking into account that the average end-to-end delay is less in OLSRv2 than OLSRv1 [52].  
If we analyze the delay according to node speed, as the network changes more, the service discovery delay increases. When the network is more static, there is less probability of broken links and packet losses, so it is faster to find a service.  
If we analyze the delay according to the number of servers, when the number of servers increases, the service discovery delay decreases, because there are more nodes that offer the same service.  
On the other hand, in SD-AODV, the average time to access a service decreases as the server redundancy increases; Figure 14. However, whereas in SD-OLSRv2, services are available almost immediately, it takes hundredths of seconds in SD-AODV. This can be explained as follows. Proactive protocols produce higher routing efficiency than reactive protocols. OLSR maintains an up-to-date routing table at all times. When a node in a network running the OLSR protocol wants to find a route to a node, it only has to look in its routing table, whereas in an AODV network, a route discovery process has to be started. It goes without saying that looking in the routing table takes less time than flooding the network for a route discovery.  
We also note that for SD-AODV, the average time to access a service increases in conditions of high mobility. In conditions of high mobility, a big number of routes will break, discovered routes may be invalid after a short period of time and, hence, a new route discovery process is needed. In SD-OLSRv2 services are available almost immediately, and it takes hundredths of seconds in SD-AODV.  
Figure 13: Service discovery delay _vs._ average speed (**left**) _vs._ service redundancy (**right**).
## 2 Materials and Methods  
### Reagents and Materials  
The Hg(II) stock solution employed in this study was prepared using a single standard element solution containing Hg(II) at a concentration of 1000 mg/L, specifically produced for atomic absorption and inductively coupled plasma-optical emission spectrometry in a 10% HNO\({}_{3}\) environment (Perkin Elmer Single Standard Solution, 125 mL, (Waltham, MA, USA)). Both standard and sample solutions used in the method were derived by diluting the stock solution in appropriate proportions. The extraction solvent, 1-dodecanol (CH\({}_{3}\)(CH\({}_{2}\))\({}_{10}\)CH\({}_{2}\)OH), was sourced from Merck, while 1-Hexyl-3-methylimidazolium hexafluorophosphate (HMIMPF6), an ionic liquid (IL) forming ion pairs with mercury ions, was obtained from Sigma-Aldrich, St. Louis, MO, USA). For pH adjustment, a phthalate buffer solution was employed, prepared by combining 0.1 mol/L potassium hydrogen phthalate (Merck, Darmstadt, Germany) and 0.1 mol/L hydrochloric acid (Merck) in suitable proportions. Ethanol (Merck) was used to dilute the organic phase. Matrix modifier chemicals: palladium nitrate (Pd(NO\({}_{3}\))\({}_{2}\)) and magnesium nitrate hexahydrate (Mg(NO\({}_{3}\))\({}_{2}\)\(\cdot\)6H\({}_{2}\)O) were obtained from Merck. The 0.05% Pd solution was prepared by its nitrate salt, and 10 \(\mu\)L of this solution was injected during the analysis. Similarly, the 0.03% Mg(NO\({}_{3}\))\({}_{2}\) solution was prepared by its nitrate salt, and 10 \(\mu\)L of this solution was injected during the analysis. The concentrations of matrix modifiers were calculated through the recommended conditions obtained from Perkin Elmer. To assess the accuracy of the method, ERM-CA713 certified reference wastewater material from LGC Standards GmbH (Wesel, Germany) was utilized. In the interest of maintaining the integrity of laboratory materials, all equipment used during the experiments underwent an overnight soak in 10% HCl solution and were subsequently rinsed three times with deionized water.