esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic-Based Spin-Programmable Gate Array with Emerging MTJ Device Technology
## 3 Stochastic-Based Logic Block (SLB)
### Algebraically Reinterpreting K-Map  
We now interpret the standard Boolean K-map in a different angle. For example, a 4:2 encoder shown in Figure 3a can be represented as a truth-table listed in Figure 3b. If we encode its binary input and output bits as decimal numbers, we obtain a single-input-single-output function as in Figure 3c. Furthermore, such a functional one-to-one mapping can be plotted as a simple algebraic curve shown in Figure 3d. For example, as shown in Figure 3a, when \((D_{3}\), \(D_{2}\), \(D_{1}\), \(D_{0})=(0,1,0,0)\), the output \((Q_{1},Q_{0})=(1,0)\), which can be computed either by looking up the K-map in Figure 3b, or by evaluating the curve at the point \((4,2)\). This idea can be used to implement a new kind of reconfigurable logic fabric, which has been wildly successful in modern computing due to its low NRE cost. However, the conventional Look-Up Table (LUT), although logically universal, incurs approximately O(\(2^{N}\)) of  
Figure 2: (a) 2-D Island-style FPGA architecture; (b) Spin-programmable Gate Array (SPGA) architecture with hybrid MTJ-CMOS devices.  
hardware cost for a \(N\)-input-1-output logic function due to the number of required reconfiguration memory bits. Our stochastic curve-based logic block can potentially achieve O(\(N\)) in hardware cost. This research thrust also involves developing various CAD modules of logic synthesis, LUT placement, and routing algorithms.  
Note that both representations in Figure 3a,b are logically equivalent, albeit in totally different forms. Moreover, any given \(m\)-input and \(n\)-output logic function, with a properly chosen encoding scheme, can be transformed into a well-defined algebraic curve. Therefore, we can recast the problem of logic circuit design into building hardware structure that performs functional evaluation by interpolating algebraic curves. Unfortunately, although Taylor expansion is known to be capable of approximating any form of complex algebraic function by summing power terms calculated at specified points, i.e., \(f(x)=\sum_{n=0}^{N}\frac{f^{(n)}(x_{0})}{n!}(x-x_{0})^{n}\), where \(x_{0}\) is constant, directly performing all these constituting operations are quite expensive in CMOS digital hardware. Fortunately, we exploit the quantum-induced Gaussian-like stochastic switching behavior of spin-torque-transfer devices, therefore completely bypassing the aforementioned performance bottlenecks. Specifically, \((x-x_{0})^{n}=\exp(\ln{(x-x_{0})^{n}})=\exp(n\ln(x-x_{0}))\), where \(n=0,1,2,\cdots\). Because, the stochastic switching behavior of a MTJ device exhibits a probability density function (P.D.F.) in an exponential form, as discussed in the following subsection, we can bypass both expensive operations \(\exp(\cdot)\) and \(\ln(\cdot)\).
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 4 Prediction-Based Energy Saving Mechanism
### UE Procedure  
Algorithm 2 describes the procedure to apply the PBESM in the UE side. The UE stores a prescheduling command, which is transmitted via NPDSCH, and checks the stored prescheduling command when the uplink packet occurred. If there is no pre-scheduling command, the UE follows the legacy scheduling request procedure. However, if there is stored information, the UE defers the uplink packet transmission until the prescheduled time without triggering the scheduling request procedure.  
```
1:Scheduling request procedure:
2:if scheduling request is triggered then
3:if scheduling command is already stored then
4: store uplink packet in the buffer.
5: delay uplink transmission by prescheduled time
6:else
7: process scheduling request procedure with RA
8:endif
9:endif
10:Uplink Scheduling Procedure:
11:ifTX time equals scheduling command time then
12:if buffer is not empty then
13: process NPUSCH transmission
14:else
15: ignore NPUSCH
16:endif
17:endif
```  
**Algorithm 2** Proposed UE procedure in an NB-IoT network On the other hand, if the eNB fails to predict the processing delay and the UE does not have uplink packets to send at the prescheduled time, the UE does not send empty packets to save energy. In this case, the UE does nothing at the prescheduled time and follows the legacy scheduling request procedure when uplink packets are generated.
## 3 Underwater Routing Protocols
### [PERSON] and his co-workers proposed a channel-aware routing protocol (CARP) [70] that keeps in view the successful packet transmission history of nodes to deliver packets from the source to the destination. It combines hop count with a power control strategy to successfully deliver packets and avoid void and shadow zones. However, the end-to-end delay is larger in dense networks, where constant checking of the packet transmission history introduces delay.
## 2 Materials and Methods
### Input Data  
#### 2.2.1 Geo-Spatial Data  
Hydroological modeling requires topographic data, soil characteristics, and land-use/land-cover (LULC) maps, as summarized in Table 1. The digital elevation model (DEM) is the most important data in the SWAT setup, providing topographic details. A shuttle radar topography mission digital elevation model (SRTM-DEM) with 90 m resolution (as delineated in Figure 2), obtained from the CGIAR Consortium for Spatial Information (CGIAR-CSI) ([[http://strtm.csi.cgiar.org/](http://strtm.csi.cgiar.org/)]([http://strtm.csi.cgiar.org/](http://strtm.csi.cgiar.org/))), was used. The DEM is the basic input layer in the model setup and is used to extract the slope and drainage network in the study area. The required soil map of the study area, the FAO-UNESCO global soil map (with 5 km resolution), was obtained from the Food and Agriculture Organization (FAO) ([[http://www.fao.org/nr/land/soils/digital-soil-map-of-the-world/en/](http://www.fao.org/nr/land/soils/digital-soil-map-of-the-world/en/)]([http://www.fao.org/nr/land/soils/digital-soil-map-of-the-world/en/](http://www.fao.org/nr/land/soils/digital-soil-map-of-the-world/en/))).  
Three LULC data types were obtained from various agencies to evaluate performance, presented in Table 1 and Figure 3. The Land-Cover Institute (LCI) of United States Geological Survey (USGS), based on 10 years (2001-2010) of collection, provided the 5.1 MCD12Q1 land-cover type data (with a resolution of 15 arc-seconds) ([[https://archive.usgs.gov/archive/sites/landcover.usgs.gov/global_climatology.html](https://archive.usgs.gov/archive/sites/landcover.usgs.gov/global_climatology.html)]([https://archive.usgs.gov/archive/sites/landcover.usgs.gov/global_climatology.html](https://archive.usgs.gov/archive/sites/landcover.usgs.gov/global_climatology.html))).  
Figure 3: Process of the Soil and Water Assessment Tool (SWAT) hydrological modeling.  
Figure 2: (**a**) Digital elevation model (DEM), (**b**) Land use and land cover (LULC) 2015, (**c**) Soil map, and (**d**) Slope map of Upper Xijiang Watershed.  
The LULC 2000 was obtained for the China region from the Global Land Cover 2000 map developed by Forest Resources and Carbon Emissions (IFORCE). ([[https://forobs.jrc.ec.europa.eu/products/glc2000/products.php](https://forobs.jrc.ec.europa.eu/products/glc2000/products.php)]([https://forobs.jrc.ec.europa.eu/products/glc2000/products.php](https://forobs.jrc.ec.europa.eu/products/glc2000/products.php))).  
LULC 1995, 2005, and 2015, developed by the European Space Agency (ESA) under the Climate Change Initiative CCI-LC project, were also analyzed to simulate streamflow and water yield. ([[http://maps.elie.ucl.ac.be/CCI/viewer/download.php](http://maps.elie.ucl.ac.be/CCI/viewer/download.php)]([http://maps.elie.ucl.ac.be/CCI/viewer/download.php](http://maps.elie.ucl.ac.be/CCI/viewer/download.php))).
# A Simple Unsupervised Knowledge-Free Domain Adaptation for Speaker Recognition
## 3 Methodology
### AHC Clustering  
LDA training requires speaker labels of speech from the target domain. To avoid the cost associated with manual labeling, the clustering approach is used to generate pseudo-labels. We anticipate that the clustering approach can produce reasonable pseudo-labels for two reasons: (1) As shown in Figure 1, the speaker embeddings of the target-domain speech are largely separated, so can be well clustered; (2) From the theoretical analysis, only the within-class variation is important in C-LDA, which substantially reduces the demand for the accuracy of the clustering.  
The agglomerative hierarchical clustering (AHC) algorithm [46] was adopted in this study. AHC is a bottom-up hierarchical clustering process that initially treats each sample as one cluster and iteratively merges the two most similar clusters until a predetermined stopping criterion is satisfied. Due to its simplicity and effectiveness, AHC has been widely used in unsupervised speaker adaptation [22; 34; 48]. We use AHC to cluster the embedding vectors of the target domain data and use the cluster index as the pseudo-speaker label for each embedding.  
For simplicity, we manually set the maximum number of clusters and use cosine distance as the distance metric. Specifically, when the number of clusters does not reach the predefined maximum cluster, in each iteration, we enumerate all possible cluster pairs to merge. For each pair, we compute the cosine distance between each embedding vector and the mean vector of the whole embeddings in the pair of clusters, and compute the sum of the distances. Subsequently, we select the pair with the smallest sum of distances and merge the two clusters as a single one. Although this process differs from some typical AHC implementations, it has been demonstrated to be effective in our experiments.  
Formally, in the \(p\)-th iteration, assuming \(C^{p}\) as the set of existing clusters, the two clusters \(C^{p}_{i}\) and \(C^{p}_{b}\) are merged if they satisfy the following criterion:  
\[\left\{C^{p}_{a},C^{p}_{b}\right\}=\operatorname*{arg\,min}_{C^{p}_{i},C^{p}_{ j}\in C^{p},i\
eq j}\sum_{x^{p}_{ij}|k|\in\left\{C^{p}_{i},C^{p}_{j}\right\}}\cos \left(x^{p}_{ij}|k|,\tilde{x}^{p}_{ij}\right) \tag{2}\]  
where \(C^{p}_{i}\) represents cluster \(i\) at the initial stage of the \(p\)-th iteration. \(x^{p}_{ij}|k|\) denotes the \(k\)-th embedding of the merging cluster of \(C^{p}_{i}\) and \(C^{p}_{j}\), \(\tilde{x}^{p}_{ij}\) is the mean of all the embeddings that belong to \(C^{p}_{i}\) and \(C^{p}_{j}\).
## 5 Hardware-In-The-Loop Results
The results of implementing these profiles in the HIL system are shown in Figure 6:1 The bus voltage is shown, along with the current supplied from each of the three sources--all three use the same zero point on the oscilloscope trace. As the power available from the sun and the required load change, the conventional and storage sources change their output based on traditional linear droop control. Source 1 changes its output based on optimal high dimension droop control, where the reference current is determined by (4). In order to verify that the proposed controller is operating as desired, data from the oscilloscope trace in Figure 15 was imported and plotted using MATLAB. The bus voltage and solar power available were used in (4) to calculate the reference current that Source 1 should be supplying. This reference current is plotted with the actual Source 1 current in Figure 6:2 The source output does match the desired reference value calculated using optimal high dimension droop control. Figure 6:3 (**a**) Solar power available and (**b**) load profiles for HIL implementation. Figure 6:4 HIL controller for Source 3 (storage)–traditional droop.
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 2 Related Work
### Resilience of Cloud Systems  
Cloud systems generally have resilience requirements that define their required behavior with respect to various parameters such as fault tolerance, reliability, quality of service, and availability [18].  
Resilience is defined as \"the capacity of a system...to remain reliable, dependable, failure tolerant, survivable and secure in case of any malicious or accidental malfunctions or failures that result in a temporal or permanent service disruption\" [18]. Reliability is defined as \"the probability that the system will be functioning at a given time and capable of providing service\" [6; 19].  
Various approaches exist to improve the resilience of cloud systems at different levels of the cloud infrastructure. For example, in [4], the authors propose a cloud systems architecture that automatically configures the fault detection and fault recovery methods in the context of particular service level requirements, while in [18], the authors propose to map component failures to different layers in the cloud architecture so that the component failures propagate from the lower layers to the application layer if not handled.  
Broadly speaking, the different approaches to cloud resilience can be classified into three categories (Figure 3): failure forecasting, protection, and recovery, with protection being further divided into replication-based and checkpointing-based approaches [6; 18; 20]. Checkpointing, where periodic snapshots are taken to enable rollback and recovery from errors, is inherently applicable to stateful systems; thus, the replication strategy is most appropriate for stateless microservice containers [6].  
Figure 3: Classification tree of approaches to cloud resilience.  
Replication of redundant components is the core principle upon which reliability and fault tolerance strategies are based [19]. Through replication, individual component failures can be masked and tolerated, thereby preventing a failure of the entire system [19].  
We propose that replication is particularly suitable for improving the resilience of stateless microservice containers based on the low cost of replication and user agnosticism with respect to which particular microservice container will provide them with their desired service [6]. Thus, provided that sufficient redundancy exists to cover the temporary shortfall, failed components can be seamlessly replaced with no loss of user experience.  
Given a suitable algorithm for determining the level of redundancy, microservice containers can be spawned and shut down accordingly to achieve the desired level of reliability. Various approaches exist in the literature to guide such choices of replication and redundancy levels in cloud systems, including the classic, static redundancy level approach [19], adaptations of the Mean-Time-To-Failure (MTTF) metric [21], Petri Nets [22; 23; 24], and Markov chains [25], among others.
## 5 Discussion
### Limitation and Future Research  
On the basis of the empirical results from 2003 to 2020, this study explores the dynamic characteristics of UBLGUE in the CPUA by applying the super-SBM-DEA analysis model and determines the influencing mechanism of UBLGUE via a panel regression model since Central China Rise. Exploring the influencing mechanism of UBLGUE in the CPUA not only supports the sustainable utilization of national space but also promotes green development since Central China Rise. This study proposes concrete evidence and scientific suggestions to support sustainable cities. However, some points still require further research. Because of the difficulty of continuous carbon emission data and socioeconomic data related to land, they are not taken into account in the index system of UBLGUE. The evaluation index system for UBLGUE could be improved as more data become accessible. Additionally, research may be more accurate with county-scale data. In the next stage of research, we will further explore the influence mechanism and the interactions between inputs and outputs via the relevant improved methods and from multiscale perspectives. In future research, we would attempt to connect the findings to broader global debates on sustainable growth, territorial cohesion, and resilience.
# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 5 Evaluation and Results  
The results of the evaluation of KosaNet vis-a-vis other multinomial classifiers models are presented and discussed based on the metrics discussed previously in Section 4.2.  
Figure 13 shows the confusion matrix that was obtained from the KosaNet model.  
The label encoder assigned the columns in the following order: DLE, DHE, NOF, PDH, PDL, THF, and TH3. To facilitate further discussion, the confusion matrix has been decomposed as shown in Figure 14.  
In Figure 14, an additional row and column for tabulating the total has been included to allow us to compute evaluation metrics discussed in Section 4.2. The confusion matrix enables us to calculate the classification accuracy, classification error, sensitivity, precision and the f1-score [70; 71; 72; 73; 74; 75; 76; 77]. Table 3 is an extract of the DGA data in ppm that was used to evaluate the model.  
Figure 13: Confusion Matrix.  
Table 4 shows how classical techniques compare against the proposed model. The incorrectly labelled classes are marked in red.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Grades Concentration (ppm)**} \\ \hline
**SN** & **Hydrogen** & **Methane** & **Acetylene** & **Ethylene** & **Ethane** & **CO** & **CO\({}_{2}\)** \\ \hline  
7 & 113 & 600 & 1964 \\ \hline \hline \end{tabular}
\end{table}
Table 3: DGA data extract.  
Figure 14: Labelled Confusion Matrix including Row and Column totals.  
In Table 4, a comparison of classification results using Key Gases, Duval Triangle, Nomography, and KosaNet is made. These results show that the Key Gases method correctly classified 18 out of 20 or 90%. This performance was replicated by the Duval triangle method that also correctly classified 90%. The Nomography technique had the least accuracy attaining only 85%. In comparison, KosaNet attained an accuracy of 95% of accurately classified instances. Table 5 provides the results obtained when the model is compared against other algorithms with multiclass classification ability.  
As seen in Table 5, the decision tree, Naive Bayes, gradient boosting, \(k\)-NN, random Forest, and KosaNet realized an accuracy of 68.5%, 70%, 83%, 89.7% 92.4% and 99.9% respectively. This implies that with KosaNet, less than 1 out of 10 labels are incorrectly labelled. With KosaNet, the precision value was high with an implication that more than 9 out of 10 labels are incorrectly classified. Similarly, the recall value of 99.9% means that less than 1 out of 10 DGA readings are incorrectly labelled. The f1 score, which helps preserve a balance between precision and recall, tends towards one, which is an excellent value for this metric [14].
## 4 Functional Model of the Framework
### Level 4--Support Services  
We developed a set of useful applications that exploit the generated knowledge to reduce congestion. These applications include:  
1. A parking reservation application that provides a pre-booking car-parking service and allows inquiries about the status of a parking space.
2. A driver awareness application that helps commuters to enhance their awareness about the traffic rules and the status of the streets.
3. An LBS-based application for searching points of interest.
4. An application that supports public transportation for schools, organizations, and government institutions to solve the problem of congestion.
5. An application that exploits smart signals to enables medical emergency vehicles such as ambulances to arrive their destination without any delays due to traffic congestion.
6. An application that exploits sensors to measure pollution and noise levels in cities to provide smarter services.
## Results
### Aggregating Data to 49 m\({}^{2}\)
#### 3.2.2 Combining Data Types
The hysteresis trends were similar for all canopy ages, but the beginning/end points within the diurnal cycle for the PRI were very different for young vs. older stands. Values for both PRI and F685 at the later afternoon period indicate recovery from stress. It is unlikely that diurnal TChI changes could produce this hysteresis effect. Note that it would be difficult to develop a relationship between F685 and PRI at any single time period, but that the relationship between these two small signals is evident when all four time periods are examined together, whether examining the oldest stand by itself (Figure 14A) or all canopies (Figure 14B). Figure 13: Fluxes at the NC2 eddy covariance tower in the mature LP stand (age class 4, 27–33 year): **(A)** the Fratio (F685/F740) vs. the photosynthetic light use efficiency (LUE, GPP/APARestimated, J\(\,\)umol\({}^{-1}\) PAR sr\({}^{-1}\) nm\({}^{-1}\)) for two time groups, mid-day (red triangles) and morning/afternoon combined (blue circles); the relationships are linear, but depend on time of day (\(r^{2}=0.84\), \(p\leq 0.000\), F-ratio = 32.02, \(n=31\); \(p\leq 0.000\)). Stronger results were obtained for the combined morning/afternoon set only, LUE (\(r=0.96\), SEM of 0.002), where, LUE = 0.016 + 0.027 \(\times\) Fratio. **(B)** The influence of the temperature difference (canopy-air, \({}^{\circ}\)C) on canopy LUE varied throughout the day (\(r^{2}=0.82\), \(p\leq 0.02\), \(n=47\)). **(C)** The measured GEP vs. the estimated GEP (umol CO\({}_{2}\) umol\({}^{-1}\) PAR) at three time periods. The estimated GEP was computed with three remote sensing variables: F685 yield, the PRI, and NDVI (\(r=0.75\)).
## 3 Results and Discussion
### Chromatographic Method Development
According to the stability study (Section 3.3) the solvent that keeps ACA stable, for at least 8 h, was acetonitrile or a mixture of ACN-H\({}_{2}\)O of 30:70 \(v/v\). Because 100% acetonitrile affects the quality of the chromatogram (broken and broadened DIP peak), a 60:40 \(v/v\) H\({}_{2}\)O-ACN mixture was preferred (Figure 3). An ultraviolet detector was utilized for the quantification of DIP, SAL and ACA in the pharmaceutical formulation. Based on the literature and the obtained UV spectra (Figure S2), acetylsalicylic acid exhibits a maximum absorbance at 230 nm, salicylic acid at 237 nm and dipyridamole at 285 nm. The same detector and \(\lambda_{\text{max}}\) values were used for SAL and ACA in both digestion and permeability experiments. However, for DIP, an additional peak (contamination), with a retention time similar to that of the analyte, appeared in the chromatogram of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Analytes** & **Retention Time** & **Tailing Factor** & **Capacity (k’)** & **Resolution** & **Number of** & **HETP * \(\times\) 103** \\  & **(tr)** & **(Tf)** & **(Rs)** & **Theoretical Plates (N)** & **USP** \\ \hline DIP & 5.5 & 1.05 & 1.375 & - & 2265 & 110 \\ ACA & 7.2 & 0.975 & 2.1 & 3.69 & 13,818 & 18 \\ SAL & 10.2 & 0.989 & 3.388 & 8 & 14,883 & 17 \\ \hline \end{tabular}  
* Height equivalent of a theoretical plate.  
\end{table} 
Table 2: System suitability parameters of the proposed method. Figure 3: Chromatograms used as diluent: **(a)** H\({}_{2}\)O/MeOH 50/50 **(b)** MeOH 100% **(c)** ACN/MeOH 50/50 **(d)** H\({}_{2}\)O 100% **(e)** H\({}_{2}\)O/ACN 60/40 **(f)** ACN 100%. the digestion substrate samples. In order to eliminate this interference and since it does not absorb at \(\lambda=405\) nm, the determination of DIP was conducted at this wavelength. Respectively, for the permeability study, in order to detect DIP at very low concentration levels, a fluorescence detector (set at \(\lambda_{\text{exc}}=285\) nm and \(\lambda_{\text{em}}\) 480 nm) was used. The two other analytes (ACA and SAL) were not measured by the fluorescence detector because they did not provide reliable results (%RSD > 5.5, r\({}^{2}\) < 0.840).