esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 2 Materials and Methods
### Population Estimation at Fine-Scale
#### 2.3.3 Model Estimation  
In order to evaluate the performance of the fine-scale population model, the training set and test set are independent of each other. The performance of the model is evaluated, by comparing the differences between the predicted population data and the actual population data. In the experiment, the estimation error rate is used as the evaluation index, where the estimation obtained by the model is better when the error rate is lower. The error rate is defined as follows, where \(\epsilon\) represents the error of the population estimation, \(\mathit{POP_{e}}\) represents the estimated subdistrict population, and \(\mathit{POP_{r}}\) is the result obtained by the population census.  
\[\epsilon=\frac{|\mathit{POP_{r}}-\mathit{POP_{e}}|}{\mathit{POP_{r}}}\times 100\% \tag{5}\]  
In summary, the population estimation method proposed in this study has the following advantages. The characteristics of the population at a fine scale in a city are closely related to the distribution of the city's residents. Thus, residential houses are selected from the subdistrict buildings, by considering the differences in the per capita occupancies of buildings with different heights, where the residential buildings are divided into low-rise, middle-rise, and high-rise buildings. A multiple linear regression model is established based on the number of buildings and their geometric features to estimate the population.
## 2 Materials and Methods
### Analysis of VOCs  
Lyophilized culture filtrate (200 mg) was mixed with 2 mL of water and placed in a 10 mL glass vial. VOCs were identified by gas chromatography with a single quadrupole mass spectrometer detector (GC/MS) analysis. Prior to analysis, samples were incubated in a water bath at 55 \({}^{\circ}\)C for 45 min, while being gently stirred with a rod. VOCs were extracted from the vial headspace by solid-phase microextraction (SPME). An SPME holder (Supelco, Bellefonte, PA, USA) containing a fused-silica fiber coated with a 50/30 \(\upmu\)m layer of divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) was used to trap VOCs in the vial headspace. The fiber was introduced into the splitless inlet of an Agilent 6890N GC system (Agilent Technologies, Palo Alto, Santa Clara, CA, USA), and thermal desorption of the analytes was performed at 250 \({}^{\circ}\)C for 5 min. The GC system was equipped with an HP-5 MS (30 m \(\times\) 0.25 mm and 0.25 \(\upmu\)m 5% diphenyl/95% dimethylpolysiloxane) capillary column (J&W Scientific, Folsom, CA, USA). The oven was programmed to start at 40 \({}^{\circ}\)C (held for 2 min) and to ramp up to 160 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, and then, it was increased to 260 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min (held for 4 min). Helium (99.999%) was used as the carrier gas, and the flow rate was 1 mL/min. The flow was transferred from the column into an Agilent 5973 MS detector (Agilent Technologies, Palo Alto). The ion source temperature was set at 230 \({}^{\circ}\)C, the ionizing electron energy was 70 eV, and the mass range was 40-450 Da in full scan acquisition mode. Compounds were identified using the NIST Atomic Spectra Database version 1.6 (Gaithersburg, MD, USA), considering spectra with 95% similarity. Results were expressed as a percentage of the VOC by dividing the area of each peak by the total area of the chromatogram peaks [18]. The analysis was carried out in triplicate.
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
## 4 Proposed Methodology: Convolved Feature Vector Based Fuzzy Filter
### Noise Detection Mechanism
#### 4.1.2 Fuzzy Based Contextual Model
DROAD values \(d_{k},\ k=1,2,3,4\), in four direction can be calculated as,  
\[d_{k}=\left|x^{\prime}{}_{i,j}-x_{i,j}\right|,k\in Dir_{k} \tag{8}\]  
Figure 3:0  
\(D^{2}\) gives a measure of deviation of central pixel in a certain direction. Large values of \(D^{1}\) and \(D^{2}\) indicate that the pixel under consideration is more likely to be a noisy pixel and vice versa. Any crisp decision at this stage may lead to false prediction and ultimately degradation of the restored image. To avoid this, fuzzy membership degrees can be used to approximate the noise added to the pixels. We have utilized two fuzzy membership functions Small and Large [22] as shown in Figure 3 and simplified rule base for two inputs of contextual model. Let \(Rule_{1},\ n=1,2,3\), be reduced set of rule numbers defined as,  
Figure 3:1  
Figure 3:2  
Figure 3:3  
The two fuzzy membership functions Small and Large are as follows,  
\[\text{Small}(D^{*},\lambda_{1},\lambda_{2})=\left\{ \begin{array}{cc}1,&D^{*} <\lambda_{1}\\ \left(\frac{D^{*}-\lambda_{2}}{\lambda_{1}-\lambda_{2}}\right),&\lambda_{1} \leq D^{*}<\lambda_{2}\\ 0,&D^{*}\geq\ \lambda_{2}\end{array} \right. \tag{10}\]  
\[\text{Large}(D^{*},\lambda_{1},\lambda_{2})=\left\{ \begin{array}{cc}0,&D^{*} <\lambda_{1}\\ \left(\frac{D^{*}-\lambda_{1}}{\lambda_{2}-\lambda_{1}}\right),&\lambda_{1} \leq D^{*}<\lambda_{2}\\ 1,&D^{*}\geq\lambda_{2}\end{array} \right. \tag{11}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are threshold parameters which are set adaptively based on neighboring pixels and are discussed in upcoming section. Fuzzy membership degrees \(\mu_{Degree}\) for simplified set of rules is calculated as,  
\[\mu_{Degree}=Max\ [Rule_{1},Rule_{2},Rule_{3}] \tag{12}\]
## 3 The Proposed Method
### Feature Extraction Module  
After characterizing the view weights, feature extraction is performed on them. Different from the traditional fault diagnosis method based on the CNN model, the first convolution layer of the CAM_MCFCNN model first extracts the features within the channel, and then extracts the features between the channels. The process of extracting features from multi-channel input by the first convolution layer can be expressed as:  
\[\begin{array}{ll}fea_{k}^{L1}=Conv(CAM(x^{1},x^{2},\ldots,x^{v}))\\ &=f(\sum\limits_{v=1}^{M}\sum\limits_{j=1}^{N}\widetilde{x}^{v}_{i,j}*w^{L1}_ {k}+b^{L1})\end{array} \tag{3}\]  
where \(j\) represents the length of the input feature map; \(M\) represents the number of input channels, that is, the total number of views; \(\widetilde{x}^{v}_{i,j}\) represents the \(i\)th sample of the input of the \(v\)th channel of the first input layer; \(w^{L1}_{k}\) represents the weight of the \(k\)th convolution kernel of the first convolution layer L1; and the corresponding generated feature map is represented by \(fea_{k}^{L1}\).  
After the convolution operation, the PReLU function [22] is used as the activation function to non-linearly transform the output value of each convolution. Different from the commonly used ReLU function, the PReLU function adds an adaptive slope a on the negative half axis. When the input value is negative, the non zero activation value is output, which improves the zero-gradient problem of the negative half of ReLU and makes the network respond to both positive and negative directions. The PReLU function is well adapted to the reciprocal fluctuation nature of vibration signals and reduces information loss [23]. The expression of the PReLU activation function is as follows:  
\[\begin{array}{ll}PReLU(fea_{i})&=\left\{\begin{array}{ll}fea_{i}&iffea_{i }>0\\ a_{i}fea_{i}&iffea_{i}<0\end{array}\right.\end{array} \tag{4}\]  
where \(fea_{i}\) represents the feature of the \(i\)th channel, and \(a_{i}\) is learnable parameter.
## 3 Experimental Results and Analysis
### Results of Automatic Feature Region Searching for Subregion Image Registration  
A printed image is usually divided into many detection areas to overcome the influence of paper deformation during printing defect inspection. Moreover, a registration feature region is determined for each detection area. If more than one feature region is searched in a detection area, then the feature region near the center of the detection area is generally taken as the feature region for the registration of the area, and the others are discarded. If no feature region is searched in one detection area, then the neighboring feature region is taken as the registration feature region to align with the detection area. This approach is called the subregion-associated feature region searching method. The steps of the detection zone partition and extraction of registration feature region are as follows.  
1. The detection zone is divided automatically according to the size of the printed image, which is mainly based on the appropriate number of partition lines and columns. One registration feature shape region is selected in each subregion of the large printed image.
2. As described above, the setting of the subregion detection is no longer done by manually drawing a rectangular box. It is instead performed by an automatic division method, which can conveniently increase the number of detection areas on the detected sample image and greatly improve the automation of the modeling of the standard template reference image. As shown in Figure 13, the printed images adopt a \(3\times 2\) partition mode.
3. The index of the detection of subregions and feature shape regions adopts the method of automatic nearby index, that is, each detected subregion selects the feature region closest to its own center point as its own index of the registration shape feature region.
4. If the feature region is not searched in the current detected subregion, then the method of searching for the feature region associated with the adjacent subregion is adopted. That is, the feature region closest to the current subregion among all neighboring regions is selected as the registration feature region.  
Figure 13 shows the subregion processing result of Figure 10. The printed image size is \(1604\times 888\), which is divided into six detection areas. As shown in Figure 13, each detection area is automatically searched for a suitable feature region, i.e., the green region in Figure 13.
# The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits
## 3 Hypotheses and Literature Review
In addition, implementing a TPM approach leads to increased efficiency and work quality; minimizes customer complaints, accidents, and internal waste [49]; and improves delivery performance, stock turnover and employee morale, productivity, and performance [50]. Therefore, the fifth hypothesis in this research is proposed as follows:  
**Hypothesis 5**. : _TPM implementation has a positive direct effect on productivity benefits._  
PM is a scheduled activity usually initiated under certain statistical parameters (e.g., average time, usage) that determine when maintenance actions are necessary before entering the risk zone (where the probability of random equipment component failure increases) [51]. In addition, PM must help prevent unplanned equipment downtime and maximize system availability by keeping equipment in proper working condition and improving its availability. PM includes scheduled tasks, such as supervision of hardware replacement and control before the equipment ends its life cycle. Due to these PM activities, there is a direct impact on the production system [52]; the sixth hypothesis in this research can be presented as follows:  
**Hypothesis 6**. : _PM implementation has a positive direct effect on productivity benefits._  
Figure 1 illustrates the established relationships between the studied variables relating CSFs for TPM and gained benefits.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 9 Conclusions  
In this paper, we presented an NFV architecture for SFC isolation and encryption in inter-domain NFV topologies. The architecture suggests using a tiered control plane protocol to distribute the SFC routing information as a control plane protocol standard between NFV domains. This is accomplished by using three tiers of BGP that both scale the route distribution and enable network redundancy. The tiers are reflected as Transport Links and two layers of SFC headers. The layered network model enables the possibility of encrypting and isolating the traffic in an SFC and ensures that encryption services must be co-located with the VNF. Furthermore, the use of a KMS server is suggested, in order to automate the setup of the tunnels between the encryption services. The architecture has been presented in four abstraction layers for completeness, namely: (i) Model; (ii) Service; (iii) Protocol and Interface; and (iv) Implementation. The main contribution of this article is the introduction of a new NSH extension header that enables such an architecture. A simplified proof of concept demonstration verified that extended NSH headers can be classified and forwarded in order to support an architecture that can isolate and encrypt SFCs. Our immediate future plans are focusing on the implementation details for the control plane components in the architecture. This includes a full-scale test-case based validation and verification of the proposed architecture.  
The main author of this paper is [PERSON]; [PERSON] and [PERSON] have contributed with respect to the paper structure, quality assurance and editing.  
This research was funded by Eidsiva, the Norwegian Research Council and the Norwegian University of Science and Technology (NTNU).  
The authors declare no conflict of interest.
## 1 Introduction
### Paper Organization  
The rest sections of this article are presented as follows: In Section 2, the system model is given and channel capacity are derived by examining four different scenarios. In Section 3, the optimization problem to maximize the average secrecy rate is discussed. In Section 4, simulation results are given and the findings are interpreted in detail. Finally, the results are briefly presented in Section 5.
In the following, we list the main contributions of this work:  
* In Section 2, a review of the technological background is given by resuming the most important technical reports, projects, and papers addressing scenarios similar to the one we consider. * In Section 3, we first describe the main enabling technologies that can be crucial for shaping the next generation VNs. In particular, we focus on ML, VEC, and network softwarization technologies, their characteristics, corresponding benefits, and challenges while used in vehicular environments. * In Section 4, for efficient utilization of the previously introduced key technologies over the vehicular environment, we provide a joint air-ground network-based VN architecture (for better clarity, in the following the term air-ground refers to the architecture while T-NTN to the terrestrial and non-terrestrial network issues and deployment). We highlight the important characteristics of individual layers of the proposed architecture, including capacity, coverage, and applications. With multiple EC platforms, the proposed architecture can be useful for implementing various intelligent vehicular services and applications that can ease the vehicular experience. * In Section 5, we further describe three main VN scenarios including the implementation of an efficient ML platform for VN services and applications, enabling the computation offloading based EC services in VN, and a network function placement for the network slicing-based vehicular services. Important challenges, and the solution methodologies that can be used while considering the proposed network architecture over these important vehicular scenarios, are analyzed. * In Section 6, we summarize the important characteristics, challenges, and proposed solutions for the scenarios discussed in the previous section. * In Section 7, we conclude our work by highlighting the proposed solutions and possible future research directions that can be followed.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Data Inputs
#### 3.5.1 Demand Data  
Demand data was used to inform methods employed by the authors 44 times among the studies reviewed in this paper [1; 8; 10; 11; 12; 13; 14; 15; 16; 18; 19; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 34; 35; 37; 39; 40; 44; 45; 46; 47; 49; 51; 52; 53; 54; 55; 58; 59; 61; 63; 65; 66; 68; 69]. Notably, [10; 23; 54; 63] used demand data at the individual appliance or plug-level. However, this level of granularity may not always be accessible due to the need for more granular metering and data collection behind the meter. Privacy concerns are a major obstacle when considering this type of measurement.  
Data illustrating energy use was used in a variety of applications. Naturally, the data can be employed to develop load profiles and discern what end-uses are present. This allows researchers to understand where opportunities may exist for demand shifting or shedding. Data can also be used to understand occupant energy-use characteristics to inform flexibility potential estimations.  
Demand data was frequently used in parallel with other data sources to inform model development and case studies. For example, in [35], demand measurements were paired with electricity prices, building stock data, weather data, and technology characteristics to project residential flexibility potential under various future scenarios and assess retrofit efforts in Italy.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 4 Multi-Application Scheduling Algorithm (MASA) Based on DRL
### Network Training
#### 4.2.2 Early Termination of the Initial Episode Method  
During the initial phase of neural network training, network parameters are randomly set, resulting in a weak initial scheduling decision for SCHEDULER. As jobs randomly and successively arrive during the training process, the initial job DAG may not receive a reasonable scheduling decision. This may result in a negative impact on the final job execution time, and this process is irreversible. Furthermore, in most of the initial episodes, the outstanding queue may accumulate a large number of waiting tasks. If too much empirical data are sampled during these episodes, it will lead to increased duration of network training. To prevent such a situation, this paper limits the initial episode length and increases it gradually during the training process. The memoryless termination process is employed for achieving incrementally longer episodes. Timestep T is randomly sampled from an exponential distribution, causing the episode length T to obey an exponential distribution as well \(\mathrm{T}\sim\mathrm{exponential1}/\mathrm{T_{mean}}\).
## 2 Materials and Methods
### Preparation of Metallic Nanoparticle-Coated Biochar
#### 2.2.1 Materials Characterization  
SEM images and EDX spectra were acquired using a Zeiss Merlin Field Emission Scanning Electron Microscope operating at 5 kV (Oberkochen, Germany) coupled with a SDD X-Max from Oxford Instruments. In order to avoid static charge on the surface, all samples were coated with a 3 nm-thin layer of palladium using a Cressington 208 HR sputter-coater coupled with a Cressington MTM-20 thickness controller.  
TGA measurements were performed with a Setaram apparatus (Setsys Evolution model). The ramp was from RT to 800 \({}^{\circ}\)C at a 10 \({}^{\circ}\)C/min heat rate.  
The XRD patterns of all of the prepared samples were recorded using X'Pert-Pro Panalytical diffractometer equipped with a Cobalt X-ray source (\(\lambda\) = 1.7889 A) and operating in the reflexion Bragg-Brentano geometry.  
Magnetic measurements were conducted at room temperature on a Vibrating Sample Magnetometer (VSM) from QUANTUM DESIGN.  
The biochar sample was characterized using a Horiba HR 800 apparatus fitted with a He\(-\)Ne laser beam, the wavelength of which was set at 514 nm.
## 5 Results and Discussion
### Optimization of Thermocouple Numbers
Conducting this research is of utmost importance to enhance the design of TTEGs, specifically in establishing the optimal quantity of thermocouples and the length of the module. The objective is to achieve equilibrium between the expanded surface area that absorbs heat (and thus leads to a potential rise in voltage) and the rise in internal resistance, affecting the current and power output. Attaining this equilibrium is crucial for optimizing the effectiveness of the TEG, particularly in scenarios that involve the transformation of wasted heat into electrical power. Figures 15 and 16 demonstrate that the open-circuit voltage of the TEG rises in correlation with the quantity of thermocouples. Simultaneously, there is a rise in the average temperature at the cold end and a decline at the hot end. As the number of thermocouples rises, the temperature difference between the hot and cold ends decreases. The TEG reaches its maximum output power of 121.63 W with 625 thermocouple pairs. Nevertheless, exceeding this threshold by adding more thermocouples results in a decline in output power. Figure 14: This diagram illustrates the temperature distribution at the hot ends of 125 thermocouple pairs. It effectively visualizes the temperature gradients and variations present at these points, providing insights into the heat distribution of thermocouples. Figure 13: This diagram presents the temperature distribution observed at the cold ends of 125 thermocouple pairs. It provides a comprehensive visualization of temperature variations across these points, highlighting the differential cooling patterns within the thermocouple array. The observed phenomenon, in which the output power of the TEG reaches its maximum at a specific number of thermocouples and then falls as more pairs are added, is consistent with the results reported by [PERSON] and colleagues [47]. The study emphasizes a crucial element of thermoelectric module design: an optimum quantity of thermocouples exists that maximizes the output power. Once the ideal number is exceeded, the overall power production is negatively affected by the diminishing temperature differential and higher internal resistance, despite the possible increase in voltage generation resulting from a bigger heat-absorbing surface. These insights are vital for implementing TTEGs, particularly in systems where optimizing energy recuperation from waste heat is imperative. By adjusting the number of thermocouples, designers may guarantee that TTEGs function at their maximum efficiency, offering a more efficient alternative for turning waste heat into usable electrical energy. Achieving this equilibrium is crucial for improving the efficiency and usefulness of thermoelectric generators in a wide range of industrial and automotive applications.