esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sources of Lipases  
Most lipases originated from microorganisms and are produced in fermenters under controlled conditions (see Table 1). Lipases are produced by a variety of gram-positive and gram-negative bacterial strains, especially from the genera of _Pseudomonas_[42; 43], also by filamentous fungus that are commercially important such as those belonging to the genera of _Rhizopus_ sp. [44], _Aspergillus_ sp. [45], _Penicillium_ sp. [46], _Geotrichum_ sp. [33], _Mucor_ sp. [47] and _Thermomyces_ sp. [48]. Lipases produced from yeasts are also relevant such as those from _Candida_ sp. [49; 50].  
Extracellular lipases are secreted into the production medium and recovered from the microorganism broth. Then, lipases are further separated and purified but downstream processing is costly. Intracellular lipases imply the use of whole cell microorganisms and this fact reduces the costs of enzyme extraction and purification but the efficiency and biodiesel yield is low when catalyzing an oily substrate due to mass transfer limitations for substrate penetration and product release [28; 51].  
Figure 1: Transesterification of animal fat to biodiesel. TGL: triacylglycerolglycerol lipase; nsTGL: non specific triacylglycerolglycerol lipase; MGL: monoacylglycerolglycerol lipase.  
Some whole cell biocatalysts used to produce biodiesel are filamentous fungi like _Aspergillus_ and _Rhizopus_[49].
# Reducing Forecast Errors of a Regional Climate Model Using Adaptive Filters
## 4 Proposed Framework for Climate Forecast Error Reduction
### Performance Evaluation  
The reduction in climate forecasts errors using adaptive filters can be evaluated by comparing the error between the Eta forecast and the NCEP reanalysis before and after it passes through the filter. Note that this applies only to the adjusted forecast; in Figure 4, the data for the year \(\YYYY+\L\).  
To evaluate the performance, we employ some error indices: the maximum error, its normalized maximum error, and the NMSE (normalized mean squared error). They are given by  
\[e_{\max} =\max_{n}\lvert e(n)\rvert, \tag{11}\] \[e_{\mathrm{NR}} =\max_{n}\left\lvert\frac{e(n)}{d(n)}\right\rvert,\] (12) \[\mathrm{NMSE} =\frac{\mathrm{E}[e(n)^{2}]}{\mathrm{E}[d(n)^{2}]}. \tag{13}\]  
Above, \(e(n)\) is the error between the series \(x(n)\) from the Eta-40-km forecasts, original and filtered, and the reference series \(d(n)\), from the NCEP reanalysis. Each time series corresponds to the temporal change of a climate variable of each volumetric cell in the climate model.  
Effectiveness in Reducing RCM Forecast Deviations  
The maximum error value as given in Equation (11) and its normalized counterpart (Equation (12)) analyze the largest error at the cells over time; while \(e_{\max}\) evaluates the extreme cases, \(e_{\mathrm{NR}}\) evaluates their relative impacts. They depend on the error at a given sample (time) in a given cell; thus, they analyze local errors in the volume-time/cell-sample space (at a given cell and time). On the other hand, the NMSE in Equation (13) compares the energy/sample of the error against the energy/sample of the reference time series. Hence, it evaluates the error in the entire time series. The smaller the error is, the better is the adjusted time series in a mean squared sense, meaning that the distance between the adjusted forecast and the observed climate is reduced. However, none of them consider the entire climate model domain.  
In order to evaluate the performance of the forecast adjustment in the entire climate model domain, we define the Effectiveness Rate of RCM forecast deviation reduction as  
\[\text{ER}=\frac{N_{RD}}{N_{S}}\times 100\qquad(\%) \tag{14}\]  
where \(N_{S}\) is the total number of time series and \(N_{RD}\) denotes the number of time series for which a reduction in RCM forecast deviation is observed. This can be computed for each one of the error metrics in Equations (12) and (13).  
Since we have one climate numerical simulation per year, we also compute the Mean Effectiveness Rate, defined as  
\[\text{MER}=\frac{1}{N_{\text{Y}}}\sum_{i=1}^{N_{\text{Y}}}\text{ER}(i)\qquad( \%), \tag{15}\]  
where \(N_{\text{Y}}\) is the quantity of years and \(\text{ER}(i)\) is the deviation reduction effectiveness rate of the \(i\)-th year.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data
### Principles of Kriging Interpolation Algorithm
#### 2.1.2 Variogram  
The variogram is the core of the Kriging interpolation algorithm and is defined as half of the variance of the difference of the regionalized variable \(Z(x)\) at two points \(x,x+l\) points, i.e.,  
\[\begin{array}{ll}\gamma(x,l)&=\frac{1}{2}Var[Z(x)-Z(x+l)],\\ &=\frac{1}{2}\Big{\{}E[Z(x)-Z(x+l)]^{2}-(E[Z(x)-Z(x+l)])^{2}\Big{\}},\\ &=\frac{1}{2}E[Z(x)-Z(x+l)]^{2},\end{array} \tag{2}\]  
where \(\gamma(x,l)\) is the variogram and \(l\) is called the lag distance.  
There are more variogram models, among which the exponential variogram, Gaussian variogram, and spherical variogram are more commonly used, and their general forms are in the following order:  
\[\gamma(l)=C_{0}+C(1-e^{-\frac{l}{a}}), \tag{3}\]  
\[\gamma(l)=C_{0}+C(1-e^{-(\frac{l}{a})^{2}}), \tag{4}\]  
\[\gamma(l)=\begin{cases}0,&l=0\\ C_{0}+C(\frac{3l}{2a}-\frac{l^{3}}{2a^{3}}),&0<l\leq a,\\ C_{0}+C&l>a\end{cases} \tag{5}\]  
where \(C_{0}\) is the nugget, \(C_{0}+C\) is the sill, \(C\) is the arch height, and \(a\) is the range.
# Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study
## 4 Implementation on Embedded Equipment
### Test Bed and Dataset Description  
The case study is conducted using the Aruba dataset, which captures human activities recorded in a smart apartment managed by the Center for Advanced Studies in Adaptive Systems (CASAS) [25]. This apartment is inhabited by an adult woman and experiences regular visits from her children and grandchildren throughout the year. The period of the test is two years, from 4 November 2010 to 30 June 2011. The layout of the home and the placement of sensors are illustrated in Figure 3.  
The sensor network setup comprises 31 motion detection sensors, 5 temperature sensors, and 3 door contact sensors, each identified by prefixes \"M\", \"T\", and \"D\", respectively. Among many constructed features, some of them are not relevant depending on the considered estimation. The objective of selecting a minimum sensor configuration is not to exclude the other sensors from the house. The objective is to filter the sensors so that the algorithm goes faster with an acceptable compromise on recognition performance but with a performance gain in terms of real-time processing time. In this study, we focus on a minimal sensor configuration, selecting 11 sensors with the following identifiers: M003, M004, M009, M013, M014, M015, M017, M020, M026, M030, and D004. The data are sampled with a sampling interval of 1 s and a sampling frequency of 1 Hz. This choice is motivated by  
Figure 2: Real-time dynamic segmentation.  
the consideration that increasing the sampling rate results in information loss. Therefore, a smaller sampling time enriches the segment with more events, enhancing the accuracy of the segmentation process. An extract from the dataset is shown in Figure 4.  
The final two columns in the dataset provide details regarding the activity name and its corresponding segment, defined by both its start and end times.  
Figure 4: An extract from the Aruba dataset.  
Figure 3: Aruba test bed.  
The dataset contains 11 distinct daily activities, namely: Sleeping (401 occurrences), Bed to Toilet (157 occurrences), Wash Dishes (65 occurrences), Relax (2910 occurrences), Work (171 occurrences), Housekeeping (33 occurrences), Meal Preparation (1606 occurrences), Eating (257 occurrences), Leave Home (431 occurrences), and Enter Home (431 occurrences). The numerical values in parentheses signify the frequency of each activity in the dataset. The activities were manually annotated by the occupant.
# Intelligent Traffic Management in Next-Generation Networks
###### Abstract  
The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These methods can model and learn network traffic behavior using training data/environments. The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection. In this paper, we survey the state of the art on these topics. We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques. Afterward, we present the study of ML/DL applications in softwarized environments. Finally, we highlight the issues and challenges that should be considered.  
machine learning; deep learning; networking; software-defined networking; resource management; feature selection; feature extraction; deep neural network; network traffic +
Footnote †:
which is called knowledge-defined networking (KDN). This architecture consists of knowledge, controller, and data planes in order to produce an automatic and intelligent process of network control. The adoption of ML with SDN for network management is an interesting area that requires further exploration (i.e., network slicing, traffic prediction). In addition, the availability of low-cost storage from the Cloud, edge computing, and the computer's evolution provide the high processing capabilities needed for training/testing ML and DL algorithms. Therefore, the combination of machine learning with SDN creates high-quality evidence in network traffic management. In this context, we provide a thorough overview of the research works in this area to gain deep insight into the significant role of ML/DL algorithms in the SDN environment.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Formal Security Proof
If \(A\) performs a hash query on \((ID_{i}||CID_{i}||E_{i}||E_{i}^{new}||C_{2}||T_{uv}(x))\), the probability that \(A\) can obtain \(SK\) does not exceed \(q_{h}^{2}/2^{l+1}\). If \(A\) executes the _Corrupt_ query, it can be inferred from the definition of freshness that the probability of \(A\) obtaining \(K\) does not exceed \((q_{s}+q_{e})^{2}/2p\). Therefore,  
\[|Pr[GM_{5}]-Pr[GM_{4}]|\leq\frac{q_{h}^{2}}{2^{l+1}}+\frac{(q_{s}+q_{e})^{2}}{ 2p} \tag{40}\]  
Due to \(A\)'s inability to distinguish between a real and random session key without using the correct input to perform \(h\) queries,  
\[Pr[GM_{5}]=1/2 \tag{41}\]  
According to the game \(GM_{0}\)-\(GM_{5}\) and Lemma 1, Theorem 1 can be verified. \(\square\)  
The above proof process means that the probability of attacker \(A\) winning in the game is negligible, so the proposed scheme is safe under the random oracle model. This protocol can guarantee the security of the session key in the process of authenticated key exchange when communicating between entities in AMI.
## 8 Future Work  
There are several perspectives that our design can be further advanced to cater for heterogeneous IoT systems. Firstly, in this paper, the IoT devices in the network are assumed with the same level of energy storage. This is normally not true in most real systems. The CH election process in BEEM has counted node energy level as one factor. When the nodes selecting CHs, their own energy levels should also be considered. Some of the nodes in the IoT systems are even rechargeable or pre-deployed with unlimited power supply, which should affect the design of the algorithm.  
Secondly, in our current study, all the IoT devices have equipped with the same 5 communication interfaces. In the future work, each sensor is assumed to have a random ability for network access interfaces. The combination can be {NAI\({}_{1}\), NAI\({}_{5}\)}, {NAI\({}_{2}\), NAI\({}_{4}\), NAI\({}_{5}\)} or {NAI\({}_{5}\)}. NAI\({}_{5}\) is a default one for every node. Furthermore, other communication interfaces with characteristics that are different from the above 5 ones can also be considered. With the fast development for radio technologies, new interfaces are highly expected in the future. For example, an interface that allows low rate and high communication range can also be considered in our solution.  
Thirdly, the price policy for different communication technologies can be another concern. Data transmission on licensed bands normally more expensive than that on unlicensed bands. User requirements on financial cost can also have influence on the interface selection.  
Figure 10: Comparison between BEEM, Self-Concerned and Smart-BEEM. (**a**) Number of sensors still alive; (**b**) network coverage.  
Fourthly, in this paper, user utility has the highest priority when selecting CHs and communication interfaces. However, in real systems, power consumption and user utility should be balanced well. In some specific circumstance, user utility may be sacrificed to gain more energy efficiency.
## 2 Related Works
### Physical Layer Security  
Recent studies have explored various PLS techniques to secure data transmissions in wireless networks. [PERSON] et al. developed a hierarchical theoretical framework for energy and secure-efficient design for precoding in a two-way MIMO system with an untrusted relay, demonstrating significant improvements in secrecy energy efficiency through extensive numerical results [19]. [PERSON] et al. introduced a novel layered physical layer security model with hierarchical information security and proposed optimal and robust beamforming schemes to minimize transmitted power while meeting secrecy rate requirements, demonstrating effectiveness through simulations [20]. [PERSON] et al. proposed a zero-forcing beam-forming scheme to optimize the secrecy rate in the presence of both active and passive eavesdroppers, considering perfect and imperfect channel state information, and highlighted that imperfect CSI between the jammer and the legitimate receiver significantly impacts the achievable secrecy rate [21]. [PERSON] et al. introduced a game-theoretic power allocation strategy for a friendly interferer to enhance wireless network secrecy against strategic eavesdroppers, demonstrating that this approach outperforms conservative power allocation strategies [22]. A novel physical-layer secure transmission scheme, decomposed and distributed modulation, was introduced that leverages decomposed and distributed modulation to prevent eavesdropping using two cooperative transmitters, effectively enhancing data transmission security through theoretical analysis and simulation [23].
## 3 Methodology and Data Collection
### Data Collection
#### 3.1.2 Traffic Measurement  
Real data on traffic measurement in individual road segments in the Czech Republic were available for analysis. The given traffic data were created by the merger of various data which are processed by the Road and Motorway Directorate of the Czech Republic. For the merger of data, the organization uses, in particular, FCD, as well as data from traffic detectors, electronic toll systems, CCTVs, and installed induction loops [47]. The resultant data, which are further distributed as real-time traffic information, contain values of the currently reached degree of traffic saturation, the average speed, and the time necessary for passing the given _TMC segment_. Values are supplied in minute-resolution in irregular intervals. There is no further publicly available information about gathering and preprocessing of data. This activity is carried out by the Road and Motorway Directorate of the Czech Republic as a state-funded organization established by the Ministry of Transport of the Czech Republic, and it provides no further information with respect to this process, see [47].  
Figure 3 illustrates the average number of total traffic measurement per 1 segment and 1 h (thick dark blue line) and according to the individual types of road segments (motorway--black, first class road--violet, residential area segments--green, and the remaining less important types--grey).  
The number of measurement sessions varies during the course of hours, between the individual days of the week, and between the individual segments. Generally, the maximum frequency is 10 records per hour (for certain motorway segments). On rare occasions, up to 12 measurement sessions are taken at a certain hour and a certain segment. During the night, on weekends, and on holidays, the frequency of measurement sessions is lower. In certain segments, during the night or on weekends, there were no measurement sessions available at all. For more information, see Sections 3.2 and 4.  
Figure 2: Frequency of _TMC segments_ according to _RoadType_.
# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 6 Conclusions  
The large gap in the research related to voice conversion in general between Arabic and other languages such as English and Chinese motivated our study. Our purpose is to answer whether it is possible to convert Arabic voice emotion using a model that has already been trained. This study conducted emotional voice conversion using StarGANv2-VC, i.e., StarGANv2-VC-Arabic Emotional, where the F0 and ASR models were trained in the English language. The evaluation process of the emotional voice conversion was performed through a prosody and spectrum conversion in addition to the state-of-the-art deep learning classifier CRNN model. The results show that StarGANv2-VC, with a pre-trained model in English, can be used to perform an Arabic emotional voice conversion mainly through spectral envelope conversion. The evaluation results revealed that male voices were rated higher than female voices and that the conversion from neutral to other emotions received a higher evaluation score than the conversion of other emotions. However, a different tendency was observed in the evaluation results depending on the source and target emotional states. The experiment's performance is promising when compared with state-of-the-art emotional voice conversion techniques. We believe that this finding is a good starting point but that it can be improved and that better results are possible. Ongoing work consists of taking advantage of the obtained results to improve the EVC of the Arabic language by integrating the F0 and ASR models that are completely trained in the Arabic language.  
Figure 13: Speaker identification results.  
**Author Contributions:** Conceptualization, A.H.M., Y.A.A. and S.-A.S.; methodology A.H.M., Y.A.A. and S.-A.S.; software, A.H.M.; validation, A.H.M.; formal analysis, Y.A.A.; investigation, S.-A.S.; resources, A.H.M.; data curation, A.H.M.; writing--original draft preparation, A.H.M.; writing--review and editing, Y.A.A. and S.-A.S.; visualization, A.H.M.; supervision, Y.A.A. and S.-A.S.; project administration, A.H.M.; funding acquisition, Y.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Researchers Supporting Project number (RSP-2021/322), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors declare no conflict of interest.
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 5 Conclusions  
In this paper, an IVC and SVA combination method is proposed to reduce NBTI and leakage simultaneously. First, the dependence of NBTI and leakage on input vectors is analyzed and the procedure for a delay degradation and leakage power reduction technique based on the ILP approach is described. Based on this, a minimum NBTI vector selection method is proposed, which considers the power effect. Our proposed ILP formulation can be generated automatically and the parameters in the objective function can be adjusted adaptively for different circuits, so that the circuit designers can find the optimal input vector conveniently. In addition, we combine our proposed IVC method with the supply voltage assignment technique, and compare the average power dissipation of SVA when using different input vectors in standby mode. The experimental results on ten benchmark circuits show that, compared with MC simulation and the other kinds of ILP formulations, our proposed method can balance the tradeoff between NBTI and leakage, and it can help SVA reduce more power dissipation and guarantee the circuit's performance simultaneously.  
Figure 9: Leakage and dynamic power of SVA for c880 and c3540 circuits when using the input vector obtained by MC simulation and our proposed IVC #3 method. (**a**) c880 circuit; (**b**) c3540 circuit.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (Nos. 61201015, 61102036, 61571161).  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; and [PERSON], [PERSON], and [PERSON] wrote the paper. This work was supported by the National Natural Science Foundation of China (Nos. 61201015, 61102036, 61571161).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Conclusions  
Our study presents an active, semi-supervised algorithm called ASEM-CAD, which utilizes expectation maximization to classify lung cancer. We evaluated the performance of the algorithm on three publicly available datasets using two distinct neural network architectures. Using active learning strategies and label acquisition policies, we showed that ASEM-CAD using 52% to 59% of labels achieved a performance comparable to that of a fully supervised model (using 100% of labels). The ASEM-CAD model achieved high positive rates of 0.9, 0.88, and 0.87 (sensitivity) for the Kaggle, NLST, and LIDC datasets, respectively.  
The active learning component of ASEM-CAD acquires additional ground truth labels during the EM training phase based on two label acquisition policies: high classification entropy and average classification entropy. The overall outcome of these two approaches proved to be better in terms of performance when compared to the semi-supervised model.  
ASEM-CAD will be a useful tool for the enhancement of medical imaging research. This training algorithm can be integrated with annotation and evaluation tools. ASEM-CAD intelligently asks experts (human in the loop) for ground truth labels during training, which results in better prediction outcomes. It can also be used to continuously retrain the model over time to learn features from more data and identify changes in datasets. The changes may be the outcome of new CT scanners, adjustments to radiation doses, or changes in scanning procedures.  
Moreover, commercial vendors can use the proposed methods to train and evaluate AI-based virtual radiology assistants that can accurately predict oncology imaging outcomes within the context of lung cancer screening and other diagnostic radiology examinations. Semi-supervised and active learning approaches have the potential to significantly enhance CAD algorithms by facilitating knowledge acquisition from large clinical PACS (picture archiving and communication system) datasets, thereby reducing the need for manual annotation by radiologists.  
Figure 10: ROC curve using LIDC 3D images and image characteristics as input. The red dotted diagonal represents ROC curve for a random classifier.