esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Day-Ahead Coordinated Preheating Control
### Grouping Coordinated Preheating Framework  
Although the above optimization model can reduce user bills, if all users schedule based on the same price curve, the marginal cost of power generation will change, which cannot guarantee the reduction in total power generation cost. In order to solve this problem, this paper proposes a grouping coordinated preheating framework with edge computing and central coordination.  
As shown in Figure 6, each household acts as an edge computing unit to solve the demand response problems (18)-(20) according to the received marginal electricity price. The central controller is the key to coordinate all groups, which contains three functional modules: user grouping, marginal cost calculation and specifying scheduling group. According to the central controller, the user adjusts the heat load planning based on the updated marginal cost. And in each round of adjustment, only a small number of users participate in scheduling so that the marginal cost will not be significantly changed.  
The interactive process of coordinated preheating is shown in Figure 7, which consists of the original load upload and several scheduling rounds. First, users of all groups upload the original load curve shown in 1. Next, Group # 1 \(\sim\) Group # G participates in each scheduling round in turn. Taking Group # 1 as an example, each round incorporates three steps: (1) The central controller sends the marginal generation cost curve to Group # 1 as process 2. (2) All local controllers in Group # 1 receive marginal cost curve based on Equations (18)-(20) to update the heat load curve. (3) The local controller uploads the new load curve, namely process 3. After the central controller updates the marginal cost curve, Group # 2 conducts the next round of adjustment. The iteration will continue until the groups balance or the set number of rounds are met. The process is summarized as Algorithm 1.  
Grouping: Group #1 \(\sim\) Group #G  
**Local controller input**: Day-ahead travel planning and temperature range of each user  
**Central controller input**: Generation cost function  
1: Each user sets the day-ahead travel plan and acceptable temperature range, then the local controller calculates and uploads the heat load curve based on the on-off control.  
2: The central controller calculates the marginal generation cost curve \(\mathbf{\lambda_{n}}\) based on the aggregated load curve and passes it to Group #1.  
3: **for** iteration = 1 to \(N\)**do**  
4: After receiving the marginal cost curve, all users within selected group get their own  
optimized heat load curve based on Equations (18)-(20). The local controller uploads the curve.  
5: The central controller updates the load curve, calculates the new marginal generation cost curve and passes it on to the next group.  
6: **end for**  
Figure 6: Edge computing architecture for coordinated preheating.
## 4 Water Security Assessment of Asian Countries
### Water Security Assessment Results by Key Areas
#### 4.1.4. Core Area 4: Environmental Sustainability  
Environmental sustainability of countries was analyzed for the health of rivers by examining pollution and ecological deterioration of rivers due to various activities, including industrial activities, in an assessment of the adequacy of the management status to keep and improve the ecosystem associated with water health. The results of the assessment are shown in Table 10. Four other countries besides Mongolia, Malaysia, and Bhutan have been analyzed as top-ranked, from 3.0 to 4.0, but all other countries are generally below 2.5, which can be found to be vulnerable to river health and environmental water safety.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nations** & **Biodiversity Threat** &
\begin{tabular}{c} **Human Water** \\ **Security Threat** \\ \end{tabular} & **Result** \\ \hline Bangladesh & 1.0 & 1.0 & 1.0 \\ Bhutan & 3.0 & 3.0 & 3.0 \\ Brunei Darussalam & 3.0 & 3.0 & 3.0 \\ Cambodia & 2.0 & 2.0 & 2.0 \\ China & 2.0 & 2.0 & 2.0 \\ Georgia & 2.0 & 1.0 & 1.5 \\ India & 1.0 & 1.0 & 1.0 \\ Indonesia & 3.0 & 3.0 & 3.0 \\ Japan & 2.0 & 1.0 & 1.5 \\ Kazakhstan & 2.0 & 2.0 & 2.0 \\ Kyrgyzstan & 2.0 & 2.0 & 2.0 \\ Laos & 3.0 & 3.0 & 3.0 \\ Malaysia & 3.0 & 3.0 & 3.0 \\ Mongolia & 4.0 & 3.0 & 3.5 \\ Myanmar & 3.0 & 3.0 & 3.0 \\ Nepal & 2.0 & 2.0 & 2.0 \\ North Korea & 2.0 & 1.0 & 1.5 \\ Pakistan & 1.0 & 1.0 & 1.0 \\ Philippines & 2.0 & 2.0 & 2.0 \\ Singapore & - & - & - \\ South Korea & 1.0 & 1.0 & 1.0 \\ Sri Lanka & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 10. Assessment result of environmental sustainability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Nations** & \begin{tabular}{c} **Drought** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Areas** \\ **Vulnerable to** \\ **Drought** \\ \end{tabular} & \begin{tabular}{c} **Flood** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Areas of** \\ **Vulnerable to** \\ **Flood** \\ \end{tabular} & \
\begin{tabular}{c} **Irrigation** \\ **Area Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Flood Control** \\ **Ability** \\ \end{tabular} & **Result** \\ \hline Malaysia & 3.0 & 3.0 & 2.0 & 2.0 & - & 5.0 & 2.5 \\ Mongolia & 5.0 & 5.0 & 4.0 & 2.0 & - & 1.0 & 2.8 \\ Myanmar & - & 4.0 & 2.0 & 4.0 & 1.0 & 1.0 & 2.0 \\ Nepal & 1.0 & 4.0 & 1.0 & 5.0 & 3.0 & 1.0 & 2.5 \\ North Korea & 5.0 & 4.0 & 3.0 & 3.0 & - & 5.0 & 3.3 \\ Pakistan & 2.0 & 2.0 & 2.0 & 4.0 & 3.0 & 1.0 & 2.3 \\ Philippines & 3.0 & 2.0 & 2.0 & 4.0 & 1.0 & 1.0 & 2.2 \\ Singapore & 3.0 & 5.0 & 3.0 & 1.0 & - & 3.0 & 2.5 \\ South Korea & 3.0 & 4.0 & 2.0 & 2.0 & 3.0 & 4.0 & 3.0 \\ Sri Lanka & 5.0 & 3.0 & 2.0 & 5.0 & 2.0 & 2.0 & 3.2 \\ Tajikistan & 2.0 & 5.0 & 2.0 & 5.0 & 5.0 & 2.0 & 3.5 \\ Thailand & 5.0 & 2.0 & 2.0 & 4.0 & 2.0 & 4.0 & 3.2 \\ Timor-Leste & 2.0 & 4.0 & 3.0 & 5.0 & 1.0 & - & 2.5 \\ Turkmenistan & 2.0 & 5.0 & 4.0 & 4.0 & 5.0 & 1.0 & 3.5 \\ Uzbekistan & 2.0 & 5.0 & 3.0 & 5.0 & 5.0 & 1.0 & 3.5 \\ Viet Nam & 2.0 & 3.0 & 2.0 & 5.0 & 3.0 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 9. _Cont._
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Particulate Matter Sensor
#### 3.2.3 Black Smoke Method [90]  
The black smoke technique collects the PM on a paper filter over 24 h period through a size selective inlet. The darkness of the paper filter is then measured by a reflectometer and converted to the PM's mass concentration. This kind of monitoring equipment is relative simple, robust and cost-efficient. However, the mass concentration is derived by measuring the darkness of the filter and the darkness of PM varies in different locations. This makes the darkness-to-mass coefficient change from time to time and location to location.
# Wavelength-Switchable Ytterbium-Doped Fiber Laser Based on All-Fiber Lyot Interferometer Filter
## 4 Conclusions  
The study introduced an all-fiber Lyot filter for wavelength-switchable YDFL generation. The Lyot fiber filter was manufactured by PMF and PC, and a comb spectrum was  
Figure 7: Multi-wavelength laser stability: (**a**) single-wavelength laser spectrum; (**b**) single-wavelength laser power fluctuation; (**c**) double-wavelength laser spectrum; (**d**) double-wavelength laser power fluctuation; (**e**) triple-wavelength laser spectrum; (**f**) triple-wavelength laser power fluctuation.  
obtained in the experiment. For the proposed YDFL, the system successfully produced switchable single-, dual-, triple-, and quadruple-wavelength laser outputs. A single-wavelength tuning range of 13.02 nm with a minimum tuning interval of 2.23 nm was achieved with power drifts under 1.959 dB. For double-wavelength outputs, tuning ranging from 1071.64 to 1081.65 nm was obtained with 3.02 nm minimum wavelength intervals. For the triple-wavelength laser generation, the tuning scope was from 1068.31 to 1080.23 nm, and three different sets of quadruple-wavelength laser emissions were realized in the experiment. The SNRs for the single-, dual-, triple-, and quadruple-wavelength lasers were 41.283, 41.157, 33.031, and 26.233 dB, respectively. Regarding the spectrum stability of the proposed YDFL, the single-, dual-, and triple-wavelength laser power fluctuations were less than 0.574, 0.631, and 1.195 dB, respectively. The proposed YDFL demonstrates wavelength tuning capability and stability, making it highly suitable for applications in fiber laser sensing and spectral analysis. The designed laser can be used as the seed source of a high-power laser to realize flexible and switchable laser wavelength outputs, and it has great application value in the laser processing field.  
Conceptualization, Y.W.; methodology, Y.W. and Y.C.; data curation, Y.W.; model validation, Y.W. and Q.L.; formal analysis, Y.W. and Q.L.; investigation, Y.W. and K.R.; visualization, Y.W. and K.R.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shandong Provincial Key Research and Development Program (2023 CXPT075).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
### Contributions  
This review provides the following main contributions:  
* A comprehensive taxonomy of the related literature in wireless positioning systems over the last 30 years classified according to (i) the localization technique and (ii) the wireless technology employed.
* All the different localization methods and wireless technologies explained from first principles, in order to serve as a self-contained source for the interested audience and facilitate the comparison between the different methods.
* A critical analysis of the different techniques and technologies, identifying existing gaps and challenges and recommending research directions for building the positioning systems of the future.
## 1 Introduction
Table compression was introduced in the work of [PERSON] _et al._[16], where it was empirically shown that partitioning the table into contiguous intervals of columns, compressing each interval separately and grouping dependent columns, can achieve significant compression improvements. [PERSON] _et al._[17] provide theoretical evidence to a generalized partitioning approach of [16], and design new algorithms for continuous partitioning. Experimental results suggest that these algorithms yield better compression than optimal contiguous partitioning without reordering. The structured files we deal with in this paper are _dictionaries_, the set of all the different words of an underlying text. Several possibilities exist to store such a file: among others, one can organize it as a hash table, or sort it according to some criterion, such as the frequency of occurrence or simply lexicographically. This latter approach has some evident advantages, as similar terms have a high chance to be requested simultaneously and can thus profit from the fact of being stored in neighboring positions. Some known Information Retrieval Systems storing their dictionaries in lexicographic order are the _Responsa Retrieval Project_[18] and the _Tresor de la Langue Franoise_[19]. We shall thus assume that dictionaries are lexicographically ordered, and more specifically that they were compressed by the _prefix omission method_ (POM); the goal is to enable pattern matching that could be done directly on these files. Prefix omission is a very simple, yet effective, dictionary compression technique, and is therefore widely used. For compressed matching, however, it raises problems that are reminiscent of the problems for a compressed match in LZ coded files: the pattern we are looking for, if it appears in the text, does not necessarily appear there contiguously [3]. One could argue that dictionaries are usually very small when compared to the text they derive from, and that they can often fit into memory even in their original form. One could thus avoid the compression altogether and use an uncompressed search, which has been studied extensively. But we also consider a scenario of weak machines with limited RAM on which we would like to run our application. Moreover, dictionaries are not always small (even if they are partitioned into independent small blocks), and in some applications, extended dictionaries are used that are an order of magnitude larger, for example to enable the processing of truncated terms [20]. The following section recalls the details of POM and presents an algorithm for searching in POM encoded files. Section 3 deals more specifically with Huffman coding and in Section 4, a new variant based on the use of Fibonacci codes is suggested. In Section 5 we mention an alternative solution to the problem, based on tries, and the final section brings some experimental results.
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 2 Proposed Facial Expression Recognition System Using the Mixture of CNN and SVM Classifier
### Preprocessing
#### 2.2.2 Motion Flow Estimation  
Let \(\tilde{I}(x,y,t)\) be an original video sequence, \(I(x,y,t)\) be a normalized facial motion video sequence, and \((x(t),y(t))\) be the trajectory of a point in the image plane; then, the brightness constancy assumption states that \(I(x(t),y(t),t)\) is constant. Thus,  
\[\frac{d}{dt}I(x(t),y(t),t)=0 \tag{1}\]  
when the trajectory of every point is defined by a vector field \(\mathbf{\mu}(x,y)=(\mu_{1}(x,y),\mu_{2}(x,y))\), the vector field \(\mathbf{u}(x,y)\) satisfies the optical constraints equation [59]:  
\[\
abla I\cdot\mathbf{u}+\frac{\partial}{\partial t}I=0 \tag{2}\]  
Several solutions have been developed and implemented to solve this under-determined system of equations with additional regularization [55; 60; 61; 62].  
We employed total variation regularization and the robust \(L_{1}\) norm in the data fidelity term proposed by [PERSON] et al. [63]. To solve the under-determined linear system, the sum of the total variation \(\mathbf{\mu}\) and \(L_{1}\) regularization terms are minimized by introducing convex relaxation and applying a duality-based algorithm [63] and point-wise threshold [62].  
The collection of \(\mathbf{\mu}(x,y,t)=(\mu_{1}(x,y),\mu_{2}(x,y),t)\) represents the motion flow of the facial expressions from the video sequences. The estimated dense motion flow can be represented by the motion flow direction and its intensity, as shown in Equation (3). This motion flow sequence is robust to variations in the appearance and skin color of the subject. In Figure 2c, the consistent motion flow estimation is shown.  
\[Intensity(I) = \sqrt{(\delta x)^{2}+(\delta y)^{2}},\] \[Angle(\theta) = tan^{-1}\left(\frac{\delta y}{\delta x}\right) \tag{3}\]  
The dense motion flow clearly characterizes the facial expression characteristics of individual expressions. Figure 3 shows the dense motion flow of seven different expressions.
## 3 Methodology
### Data Preprocessing
#### 3.2.2 Data Transformation  
During the data cleaning, we discovered inconsistencies in the year-built variable across the time-series data for bridges. These inconsistencies arose when comparing the year a bridge was built from one inspection record to another, resulting in discrepancies among the inspection records.  
To address this issue, we consulted with a subject matter expert from the Nebraska Department of Transportation. We learned that a newer year-built than previously reported in the dataset would indicate that a structure has been replaced. In order to maintain consistency in the bridge life-cycle despite changes in the year-built, we divided the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Frequency Count** & **Percentage** \\ \hline \multicolumn{3}{c}{Material} \\ \hline Steel & 4684 & 0.511801 \\ Concrete Continuous & 987 & 0.107845 \\ Wood or Timber & 975 & 0.106534 \\ Pres. Concrete & 964 & 0.105332 \\ Steel Continuous & 689 & 0.075284 \\ Concrete & 662 & 0.072334 \\ Pres. Concrete Continuous & 186 & 0.020323 \\ Other & 5 & 0.000546 \\ \hline \multicolumn{3}{c}{Membrane Type} \\ \hline None & 8744 & 0.955420 \\ Preformed Fabric & 151 & 0.016499 \\ Built-up & 135 & 0.014751 \\ Unknown & 77 & 0.008413 \\ Not Applicable & 22 & 0.002404 \\ Other & 15 & 0.001639 \\ Epoxy & 8 & 0.000874 \\ \hline \multicolumn{3}{c}{Deck Protection} \\ \hline None & 7473 & 0.816543 \\ Epoxy Coated Reinforcing & 1429 & 0.156141 \\ Unknown & 120 & 0.013112 \\ G galvanized Reinforcing & 87 & 0.009506 \\ Not Applicable & 25 & 0.002732 \\ Other & 8 & 0.000874 \\ Other Coated Reinforcing & 4 & 0.000437 \\ Cathodic Protection & 2 & 0.000219 \\ Internally Sealed & 2 & 0.000219 \\ Polymer Impregnated & 2 & 0.000219 \\ \hline \multicolumn{3}{c}{Owner} \\ \hline County Highway & 7072 & 0.772727 \\ State Highway & 1664 & 0.181818 \\ City Highway & 294 & 0.032124 \\ Other Local & 62 & 0.006774 \\ Other State & 25 & 0.002732 \\ Railroad & 12 & 0.001311 \\ Private & 10 & 0.001093 \\ Bureau of Indian Affairs & 8 & 0.000874 \\ Corps of Engineers (Civil) & 2 & 0.000219 \\ Bureau of Reclamation & 2 & 0.000219 \\ National Park Services & 1 & 0.000109 \\ \hline \multicolumn{3}{c}{Maintenance} \\ \hline Intervention & 3739 & 0.408 \\ No Intervention & 5413 & 0.591 \\ \hline \end{tabular}
\end{table}
Table 3: Frequency and percentage breakdown of categorical variables.  
existing time series of each bridge. We then introduced the remaining segments of the bridge as separate entities, each assigned a new structure number that included the original structure number with a segment suffix. This allowed us to treat each segment of a bridge as a distinct entity during the computation of bridge metrics, including performance scores, number of maintenance interventions, and deterioration. Ultimately, when calculating bridge health metrics for all bridges, the values were averaged across all segments of each bridge to ensure comprehensive representation.
## 3 Results and Analysis
### Characteristics of Desertification Land Area Change  
The changes in the total area of various types of desertification land reflect the general development trend of desertification land in the study area. The statistical results for different types of desertification land areas in the study area in the two periods of 2010 and 2020 are reported in Table 2. As can be observed, the land area of the Urat front flag expanded from 7462.5750 km\({}^{2}\) to 7462.9026 km\({}^{2}\) between 2010 and 2020, increasing the total area by 0.3276 km\({}^{2}\). Among these, the areas of extremely serious and serious desertification land were both reduced to varying degrees, from 16.6% and 30.2% of the total desertification land, respectively. The area of moderate desertification land increased from 1535.6655 km\({}^{2}\) in 2010 to 2790.1539 km\({}^{2}\) in 2020, while the area of weak desertification land increased from 13.9% to 19.7%. Additionally, the area of non-desertification land increased from 1395.3690 km\({}^{2}\) to 1704.9951 km\({}^{2}\), with the proportion increasing from 18.7% to 22.8%. As can be seen, the desertification land in the study improved overall throughout the 10-year period from 2010 to 2020, with the degree of desertification reversing from serious and extremely serious to moderate and weak, resulting in a spike in moderate desertification.  
Between 2010 and 2020, the dynamic attitudes of extremely serious and serious desertification areas were \(-9.2\%\) and \(-3.8\%\), respectively, indicating that extremely serious and serious desertification areas decreased by 9.2% and 3.8% per year, respectively, while the moderate, weak, and non-desertification areas increased by 8.2%, 4.1%, and 2.2%, respectively. Throughout the study period, the area of moderate desertification land increased the most, whereas the area of extremely serious desertification land decreased the most, demonstrating that desertification improved throughout the study period.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**2010**} & \multicolumn{2}{c}{**2020**} & \multicolumn{2}{c}{**2010–2020**} \\ \cline{2-6}
**The Type of** & & & & & **Annual Rate of** \\
**Desertification Land** & **Area** & **\%** & **Area** & **\%** & **Change (\%)** \\ \hline Extremely serious & 1236.1077 & 16.6 & 95.0265 & 1.3 & \(-9.2\) \\ \hline Serious & 2253.2328 & 30.2 & 1400.8446 & 18.8 & \(-3.8\) \\ \hline Moderate & 1535.6655 & 20.6 & 2790.1539 & 37.4 & 8.2 \\ \hline Weak & 1042.2000 & 13.9 & 1471.8825 & 19.7 & 4.1 \\ \hline Non-desertification & 1395.3690 & 18.7 & 1704.9951 & 22.8 & 2.2 \\ \hline Sum & 7462.5750 & 100.0 & 7462.9026 & 100.0 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Types and area of desertification land in the Urat front banner, km\({}^{2}\).
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Improvement of Air Conditioning and Heating Systems (HVAC)  
[PERSON] et al. (2004) studied the consumption of energy of a hospital in Taiwan, concluding that the use of Air conditioning and heating systems (HVAC) has a great influence in the consumption of energy, since HVAC represents over 50% of the total amount of energy consumed [20]. Studies performed by Natural Resources Canada pointed out that the highest energy consumption was generated by the use of the heating, lighting, and ventilation [21].  
[PERSON] et al. (2011) studied that HVAC generated the highest consumption of energy in hospitals, about 30-65% [22]. After a study of a suburban hospital, the Health Department in Australia concluded the HVAC is the highest contributor to energy consumption and CO\({}_{2}\) emissions, at 65% and 47%, respectively [23]. [PERSON] et al. (2014) show hospitals represent approximately 6% of total energy consumption in the service buildings sector [24].  
In healthcare buildings, most energy consumption is consumed by HVAC facilities. [PERSON] et al. (2014) realized a study about energy efficiency improvements in a boiler room in a 600-bed hospital complex. They monitored six gas-fired boilers of heat and hot water over a six-month period and actions were implemented in the hospital boiler room control system to increase the efficiency of the heat production facilities [25].  
To determine the positive economic and environmental impacts due to improvement of HVAC facilities, the annual average saving and investment for the whole set of buildings was quantified for a three-year period, in five hospitals and 20 healthcare centres.  
The contribution of natural ventilation as an energy-efficient technique for hospital was studied by [PERSON] et al. (2010). The author suggest that up to 70% of net floor area of small to medium sized hospital could be naturally ventilated. A high ventilation rate helps reduce cross-infection of airborne diseases [26].  
Figure 2 represents the potential mean average energy saving per built surface area (m\({}^{2}\)), by means of upgrading HVAC installations, in healthcare buildings with less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds). An annual average energy saving of 1.50 kWh and 1.80 kWh per built surface area (m\({}^{2}\)), with a payback lower than 2.5 years, was achieved, respectively.  
Figure 2: Potential of energy saving derived from improvement of air conditioning and heating systems (HVAC) systems.  
The following specific energy saving measures were applied: introduction of efficient air conditioning technologies that minimize energy consumption is proposed and replacement of low-performing machines aged more than eight years by new, high-performance models was suggested. Other feasible measures to be carried out in this area are the following: maintenance of air-conditioning systems, zoning according to air conditioning units, and installation of air-handling system adjustable thermostats.