esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Low-Temperature NH\({}_{3}\)-SCR Reaction Mechanism
### Langmuir-Hinshelwood (L-H) Mechanism  
The Langmuir-Hinshelwood (L-H) mechanism (Figure 17) mainly refers to the reaction process between the NH\({}_{3}\) (ad) component and the adsorbed NOx component [21]. The L-H reaction pathways in the NH\({}_{3}\)-SCR reaction on MnO\({}_{\text{x}}\)-based catalysts can be broadly categorized into two types. First, NH\({}_{3}\) is adsorbed at the Lewis acid site to form coordinated NH\({}_{3}\), while the adsorbed NO is oxidized by high-valent Mn\({}^{4+}\) ions to form adsorbed monodentate nitrite (Mn\({}^{3+}\)-O-NO), reducing Mn\({}^{4+}\) ions to Mn\({}^{3+}\). Subsequently, Mn\({}^{3+}\)-O-NO reacts with coordinated NH\({}_{3}\) to produce Mn\({}^{3+}\)-O-NO-NH\({}_{3}\), and NH\({}_{3}\)+O-NO-NH\({}_{3}\) decomposes into N\({}_{2}\) and H\({}_{2}\)O. Concurrently, Mn\({}^{3+}\) is oxidized to Mn\({}^{4+}\) by O\({}_{2}\).  
\[\text{NH}_{36}\xrightarrow{\text{Lewis acid sites}}\text{NH}_{3 \text{ad}} \tag{6}\] \[\text{NO}_{\text{g}}\rightarrow\text{NO}_{\text{ad}}\] (7) \[\equiv\text{Mn}^{4+}=\text{O}+\text{NO}_{\text{ad}}\rightarrow \equiv\text{Mn}^{3+}-\text{O}-\text{NO}\] (8) \[\text{Mn}^{3+}-\text{O}-\text{NO}+\text{NH}_{3\text{ad}}\rightarrow \equiv\text{Mn}^{3+}-\text{O}-\text{NO}-\text{NH}_{3}\rightarrow\equiv \text{Mn}^{3+}-\text{OH}+\text{N}_{2\text{g}}+\text{H}_{2}\text{O}\] (9) \[\equiv\text{Mn}^{3+}-\text{OH}+\frac{1}{4}\text{O}_{2}\rightarrow \equiv\text{Mn}^{4+}=\text{O}+\frac{1}{2}\text{H}_{2}\text{O} \tag{10}\]  
Second, NH\({}_{3}\) is adsorbed on the Bronsted acid site of the MnO\({}_{\text{x}}\)-based catalyst, forming NH\({}_{4}\)\({}^{+}\). NO is oxidized by surface active oxygen on the MnO\({}_{\text{x}}\)-based catalyst and then adsorbed on the active site to form NO\({}_{2}\) (ad). Subsequently, NO\({}_{2}\) reacts with NH\({}_{4}\)\({}^{+}\) (ad) to generate NH\({}_{4}\)NO\({}_{2}\), which eventually decomposes into N\({}_{2}\) and H\({}_{2}\)O.  
\[\text{NH}_{36}\xrightarrow{\text{Bernsted acid sites}}\text{NH}_{4}{}^{+} {}_{\text{ad}} \tag{11}\] \[\text{O}_{2\text{g}}\rightarrow\text{2O}_{\text{ad}}\] (12) \[\text{NO}_{\text{g}}+\text{O}_{\text{ad}}\rightarrow\text{NO}_{ \text{2 ad}}\] (13) \[\text{NH}_{4}{}^{+}{}_{\text{ad}}+\text{e}^{-}+\text{NO}_{\text{2 ad}}\rightarrow\text{NH}_{4}\text{NO}_{2\text{a}}\rightarrow\text{N}_{2}+2\text{H}_{2} \text{O} \tag{14}\]
# Usefulness of OSM and BDOT10k Data for Developing Tactile Maps of Historic Parks
## 3 Results
### BDOT10k and OSM Database Assessment for Park Styles
At the second level, the results were slightly lower, but they were still above 50% in three categories (Figure 8d). The Oriental/Japanese park style (Figure 9) is characterised by several typical objects, such as a formed tree, formed shrub, stream, bridge, waterfall, stone lantern, Torii gate, tea house, Karesansui--dry park, hill, an old element, or stones (including the Triad). For this style, a map should cover three levels of detail, with the numbers of objects at each level being 11, 16, and 3. Figure 7: Percentage of points for Baroque-style park in 5 categories: (**a**) level 1; (**b**) level 2; (**c**) level 3. Percentage of points for Renaissance-style park in 5 categories: (**d**) level 1; (**e**) level 2; (**f**) level 3. Figure 8: Percentage of points for Landscape/English-style park: (**a**) level 1; (**b**) level 2. Percentage of points for Romantic/Sentimental-style park: (**c**) level 1; (**d**) level 2. Figure 9: Percentage of points for Oriental/Japanese-style park: (**a**) level 1; (**b**) level 2; (**c**) level 3. For the first level of detail, the best results were obtained for correctness of geometry, with the BDOT10k and OSM databases receiving ratings of 58% (Figure 9a). The other categories obtained scores below 50%. For existence of geometry, both databases were rated at 45%. For the second level of detail, only for correctness of geometry were both databases rated similarly, i.e., BDOT10k at 55% and OSM at 50% (Figure 9b). The databases were rated the lowest in the first category: BDOT10k had a separate feature class for 11% of objects and OSM for 23%. The lowest scores were obtained for the third level of detail (Figure 9c). The OSM database received 0% in all categories (it did not contain any data for the selected objects), while BDOT10k scored 33%, 16%, and 33% in categories 2, 4, and 5, respectively, with 0% in other categories (Figure 9c). The DUI ratings of BDOT10k and OSM databases were the highest for the Romantic/Sentimental park style, at both the first and second levels of map detail. For the Landscape/English park, the score was lower, and the usefulness of the databases for the Oriental/Japanese park was rated the lowest. As the level of detail increased, the usefulness decreased (Table 4). It is worth noting that both databases showed similar usefulness, differing from each other to a small extent.
## 3 Problem Description and Mathematical Modeling  
### Problem Description  
The problem in our study based on graph theory is described as follows. In the directed graph \(G=\left(V^{\prime}_{DD,AD},A\right)\), \(V^{\prime}_{DD,AD}\) is the vertex set containing all customer nodes, HERI with their copies, and the departure and arrival ends of depot with their copies. \(A\) is the set of arcs, with \(A=\{\,(i,j)|i\in V^{\prime}_{DD},j\in V_{AD},i\
eq j\}\). Each arc corresponds to a traveling distance of \(d_{ij}\) and a travel time of \(t_{ij}\). As the vehicle passes through the arc \((i,j)\), the on-board battery discharges at the rate of \(h\), with power consumption equal to \(h*d_{ij}\). Each customer \(i\in V\) is associated with cargo demand \(q_{i}\) and service time \(s_{i}\), and the delivery route must cover all customers. The fleet is equipped with homogeneous electric logistics vehicles integrated with both charging and battery swapping capabilities, which have the same maximum load of \(q_{v}\), and all vehicles start with fully charged batteries from the depot. For all \(i\in V^{\prime}_{DD,AD}\), continuous decision variables \(\tau_{i}\), \(u_{i}\), and \(y_{i}\) represent, respectively, the time the vehicle arrives, the remaining cargo volume, and the state of charge (SOC) of the vehicle at node \(i\), while \(z_{i}\) represents the SOC of the vehicle when leaving node \(i\). The binary decision variable is \(x_{ij}=1\) if the arc \((i,j)\) is accessed; otherwise, it is \(x_{ij}=0\). A set of vertices \(V_{t}\in V^{\prime}_{DD,AD}\) is determined to minimize the total fleet distribution cost without exceeding the vehicle load \(q_{v}\), the CI capacity \(q_{c}\), and the driver shift time \(t_{max}\).
## 1 Introduction
Finally, although in some countries, such as The Netherlands, no energy recovery takes place during the incineration of regulated medical waste [29], the use of thermo-chemical processing for the energy valorization of PPE has been also explored [30; 31; 32]. Despite the existence of studies that have addressed the environmental problems derived from the production of waste from PPE as a consequence of the COVID-19 pandemic, none of them used a science mapping approach, a tool that gives objective criteria for evaluating research [33], as well as a macroscopic overview of the scientific literature [34]. Therefore, the main objective of this paper was to analyze this research field's development since the outbreak of SARS-CoV-2 to quantify the production of personal protective equipment (PPE) waste by the general population, as well as to evaluate the environmental impact produced by PPE waste, providing an overview of the management of used COVID-19 PPE through a literature review supported by a bibliographic analysis. As result, this study will contribute to the existing body of knowledge of the management of used COVID-19 PPE by highlighting the trends and patterns in this field, establishing the most-significant research themes, mapping networks of researchers from various countries, and defining areas for future study. Understanding how the knowledge base of this strategic field is disseminated in scientific sources, countries, and the communities of cooperating countries, in addition to discerning the most-relevant themes in the research field are fundamental to assist in designing strategies to prevent the environmental impacts related to the use of PPE as a means to mitigate disease transmission in possible future pandemics. Consequently, we sought to answer the following research questions:  
RQ1: What are the most-relevant sources in the research field? RQ2: What are the most-prolific countries in the research field? RQ3: Who are the most-prolific authors in the research field? RQ4: What are the major alliances between authors and countries in the research field? RQ5: What are the most-influential works in the research field?RQ6: What are the major themes in the research field? RQ7: What is the status of the research field? To answer these research questions, we retrieved papers in the field published from 2020 to June 2022 from the Scopus database and analyzed the resulting corpus by means of science mapping analysis, bibliographical networks, and themes' evolution to include a final overview that highlights the status of the research field. The remainder of this paper is organized as follows: First, the methodology used to carry out the study is explained; second, our most-considerable results are presented and described; next, we use a literature review of the most-relevant documents to show the status of the research field; finally, the conclusions and proposal for future research are described.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 4 Experimental Results
### Experimental Results and Analysis
#### 4.3.4 Effectiveness of Fine-Tuning  
This section discusses the performance improvements achieved when pretrained models were configured using the VeRi-776 dataset and then fine-tuned. Table 4 shows the differences between models that conducted training only with the KITTI MOT dataset and models for which pretrained models were constructed and fine-tuning was performed. The experimental results also indicated that fine-tuned models have better performance.
## 3 Datasets and Methodology
### Methods
#### 3.2.3 NPP Calculation Based on CASA Model  
The CASA model is the most extensively applied model for estimating light energy utilization. [PERSON] [56] was the first to use CASA to estimate the North American vegetation NPP. Two main parameters, the vegetation absorbed photosynthetic active radiation (APAR) and the light energy utilization ratio (\(\varepsilon\)), are primarily used to estimate the NPP [57]. The formula for calculating this is listed as follows:  
\[\text{NPP}(\text{x},\text{t})=\text{APAR}(\text{x},\text{t})\times\varepsilon (\text{x},\text{t}) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Descriptive Name** & **ID** & **Definition** & **Unit** \\ \hline \multirow{4}{*}{Extreme temperature indices} & \multirow{2}{*}{Min T\({}_{\text{min}}\)} & \multirow{2}{*}{TNn} & The minimum daily minimum temperature per month & \({}^{\circ}\)C \\  & & & & The maximum daily maximum & \({}^{\circ}\)C \\  & & Max T\({}_{\text{max}}\) & TXx & temperature per month & \({}^{\circ}\)C \\ \cline{2-6}  & \multirow{2}{*}{Cold indices} & Cold daytimes & TX10P & Count of days where TX \(<\) 10 th percentile & \% of days \\  & & Cold nights & TN10P & Count of days where TN \(<\) 10 th percentile & \% of days \\ \cline{2-6}  & \multirow{2}{*}{Warm indices} & Warm daytimes & TX90P & Count of days where TX \(<\) 10 th percentile & \% of days \\  & & Warm nights & TN90P & Count of days where TN \(>\) 90 th percentile & \% of days \\ \cline{2-6}  & \multirow{2}{*}{Absolute index} & Summer days & SU25 & Annual count when TX (daily maximum) & \multirow{2}{*}{days} \\  & & & & \(>\) 25 \({}^{\circ}\)C & \\ \cline{2-6}  & \multirow{2}{*}{Other indices} & Diurnal temperature range & \multirow{2}{*}{DTR} & Mean value of the difference between TX & \multirow{2}{*}{\({}^{\circ}\)C} \\  & & Max. one-day & & & \\  & & precipitation & RX1d & Maximum daily precipitation per month & mm \\  & & Max. five-day & & Maximum precipitation for five & \\  & & precipitation & & consecutive days per month & mm \\ \cline{2-6}  & \multirow{2}{*}{Intensity indices} & Simple daily & & Annual total precipitation divided by the & \\  & & intensity index & SDII & number of wet days (defined as PRCP & mm/day \\  & & & & \(\geq\) 1.0 mm) in the year & \\ \cline{2-6}  & \multirow{2}{*}{Continuous indices} & Consecutive dry & \multirow{2}{*}{CDD} & Maximum length of dry spells & days \\ \cline{2-6}  & & days & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The names and meanings of extreme climate indices.  
In Equation (2), the APRP denotes the favorable radiation for plant photosynthesis (MJ\(\cdot\)m\({}^{-2}\)) on a specific pixel (x) during the t month. The symbol \(\varepsilon\) represents the efficiency of light energy utilization (x), also known as the photogenic utilization rate (gC\(\cdot\)M)\({}^{-1}\)). Regarding the calculation methods for APAR and \(\varepsilon\), please refer to the literature [58].
## 3 Results  
In this section, detailed performance evaluation results are presented. Section 3.1 provides our results from P2P and P2 MP topologies using dedicated fiber links for classical and QKD links, while Section 3.2 addresses the performance of P2P and P2 MP topologies using shared fiber links for the propagation of both classical and quantum channels. In Section 3.3, performance evaluation results are presented for the P2 MP topology based on a mesh radio networking through wireless nodes.
## 4 Results  
### Analysis of Land Use Dynamic Changes in Xiong'an  
#### 4.1.1 Land Use Classification  
This study systematically monitored the transformation of Xiong'an New Area across four temporal nodes (2014, 2016, 2019, and 2022; Figure 2). The land use classification scheme incorporated seven distinct categories: grassland, cultivated land, construction land, wetland, water bodies, coniferous forest, and broad-leaved forest. This classification was achieved through an integrated approach combining a random forest algorithm processing with manual visual interpretation to ensure accuracy. Validation results demonstrated consistently high classification accuracy exceeding 80% across all study periods (2014: 83.38%; 2016: 84.93%; 2019: 89.16%; 2022: 88.22%), confirming the robustness of the adopted methodology.
## 6 Proposed Model
### Shared-Private Scheme  
The central idea of multi-task learning is the sharing scheme in latent feature space. Latent features can be regarded as the hidden states of Bi-LSTM encoders in the multi-task NMT model. So that, sharing schemes are different in how to group the shared features. Here, a shared-private scheme is used. The shared-private scheme has two feature spaces for each translation task: Bi-LSTM encoder is used to capture task-dependent features and a private Bi-LSTM layer is used to capture  
Figure 3: Long short-term memory (LSTM) Structure.  
the task-invariant features. As shown in Figure 4, each translation task for each Arabic dialect is assigned a private Bi-LSTM layer (LA encoder, MA encoder) and a shared Bi-LSTM layer (shared layer). Formally, for any Arabic dialect sentence in task \(k\), its shared representation \(s_{t}^{k}\) can be captured along with task-specific representation \(h_{t}^{k}\):  
\[s_{t}^{k} = BiLSTM\Big{(}x_{t},s_{t-1}^{k},\theta_{s}\Big{)} \tag{7}\]  
\[h_{t}^{k} = BiLSTM(x_{t},h_{t-1}^{m},\theta_{k}\Big{)} \tag{8}\]  
Consequently, the features from the private space and the shared space are concatenated. By exploiting the shared-private scheme, multitask NMT model can learn task-specific and task-invariant features non-redundantly, therefore capturing the shared-private separation on different translation tasks. Moreover, the model will improve the representation of the Arabic dialect sentence, capture more features and produce better translation quality for the Arabic dialects.
## 3 Tidal Characteristics of DS0 and DW2
### Altitude-Latitude Variations
During winter, DW2 shows negligible amplitudes over the tropical latitudes and high amplitudes in the high latitudes of the Northern Hemisphere. The amplitude is small, reaching values of 0.5 K at 5\({}^{\circ}\) S at 1 hPa in CMAM30, and much larger in ERA, reaching values of \(\sim\)2 K at 1 hPa over the equator and an amplitude of 1 K at 10 hPa over 10\({}^{\circ}\) N during winter. There appear to be two bands emanating and going upward around the equator, with a stronger Southern Hemisphere band in the both CMAM30 and ERA. The phase of DW2 indicates that two bands over the tropical latitudes are out of phase and anti-symmetric in nature within a range of \(\pm\)25\({}^{\circ}\) latitudes, and the vertical wavelength is approximately \(\sim\)20 km. This clearly indicates that CMAM30 and ERA results for mean DS0 and DW2 variability are highly consistent witheach other in the stratosphere. COSMIC compares well in terms of vertical wavelengths over the tropical altitudes, but not in terms of amplitudes. Above 40-45 km, the data quality poses problems, and in the high latitude regions, aliasing poses problems. Figure 5: Altitude versus latitude cross-section of mean amplitude and phase of D50 tide during August 2008 and February 2009 from COSMIC, CMAM30, and ERA-interim data.
## 4 Fast Automatic Adjustment Algorithm of Imaging Waveform
### Automatic Judgment of Measurement Mode
#### 4.2.2 Peak Intensity Adjustment Interval  
It was difficult to adjust the peak intensity to the ideal value at a time. Therefore, according to the static and dynamic working modes, different adjustment convergence intervals were determined to improve the peak intensity adjustment efficiency.  
On the basis of the result of Equation (6), the \"centroid position-peak intensity\" fitting curve of different objects are subtracted to obtain the static centroid difference \(Xc(p)\) caused by the change of the peak intensity of different measured objects, as shown in Figure 7a, which was different from the dynamic centroid difference of two adjacent waveforms. We define the absolute deviation evaluation function \(\Phi(p)\), as shown in Equation (7), where \(\varphi\) is the deviation threshold. The peak intensity adjustment intervals under different working modes are determined according to \(\varphi\):  
\[\Phi(p)=\left|X_{c}(p)\right|=\left|X_{b}(p)-X_{w}(p)\right|\leq\varphi \tag{7}\]  
When \(\varphi\) = 0.125 pixel, the corresponding dynamic peak intensity interval is [400,800], as shown in Figure 7b. When \(\varphi\) = 0.025 pixel, the corresponding static peak intensity interval is [500,550], as shown in Figure 7c. The pixel coordinates are converted to the world coordinates, the theoretical errors of dynamic and static measurement caused by \(\varphi\) are 2.7 um and 0.5 um respectively. Therefore, the selection of the above peak intervals satisfied the needs of high-precision measurement.
# Intelligent End-Edge Computation Offloading Based on Lyapunov-Guided Deep Reinforcement Learning
## 3 System Model
### Edge Computing Model  
When \(n\)-th industrial terminal decides to offload data on the end layer, we define transmit power as \(P_{n}\) with an upper limit \(P_{n}^{\max}\), namely \(P_{n}\leq P_{n}^{\max}\). Note that \(n\)-th industrial terminal evaluates the computation resources of each candidate industrial base station, and only chooses one of the multiple industrial base stations for computation offloading at one time.  
The energy consumption for computation offloading is calculated as \(e_{n}=P_{n}\tau_{n}T\), where \(P_{n}\) is the transmit power. At this time, we neglect the delay on edge computing and result downloading. Thus, according to [PERSON]'s theorem, the computation size of the offloaded task on the end layer is given by  
\[\begin{split} D_{n,m}^{edge}&=\mathsf{Wlog}_{2} \Big{(}1+\frac{P_{n}h_{n,m}}{N_{0}}\Big{)}\tau_{n}T\\ &=\mathsf{Wlog}_{2}\Big{(}1+\frac{e_{n}h_{n,m}}{\tau_{n}T\overline {N}_{0}}\Big{)}\tau_{n}T,\forall d_{n,m}=1,\end{split} \tag{4}\]  
where \(W\) is bandwidth, \(h_{n,m}\) is the channel gain between \(m\)-th industrial base station and \(n\)-th industrial terminal, and \(N_{0}\) denotes the noise power.  
In this way, the total computation task by end computing or edge computing is calculated as \(D_{n,m}=(1-d_{n,m})D_{n}^{end}+d_{n,m}D_{n,m}^{edge}\), namely  
\[D_{n,m}=\frac{(1-d_{n,m})f_{n}\tau_{n}T}{\phi}+d_{n,m}W\text{log}_{2}\bigg{(}1+ \frac{e_{n}h_{n,m}}{\tau_{n}N_{0}}\bigg{)}\tau_{n}T \tag{5}\]  
Correspondingly, the network computation rate given by \(z_{n,m}=D_{n,m}/T\) is calculated as  
\[z_{n,m}=\frac{(1-d_{n,m})f_{n}\tau_{n}}{\phi}+d_{n,m}W\text{log}_{2}\bigg{(}1+ \frac{e_{n}h_{n,m}}{\tau_{n}TN_{0}}\bigg{)}\tau_{n}, \tag{6}\]  
As the industrial base stations are powered by constant energy, we ignore the computing energy consumption and only consider the offloading energy consumption. Thus, the energy consumption for edge computing is calculated as \(E_{n}^{edge}=e_{n}\). Furthermore, the total energy consumption is calculated as \(E_{n,m}=(1-d_{n,m})E_{n}^{end}+d_{n,m}E_{n}^{edge}\), namely  
\[e_{n,m}=(1-d_{n,m})\kappa(f_{n})^{3}\tau_{n}T+d_{n,m}e_{n}, \tag{7}\]  
where \(\kappa\) denotes the computing energy efficiency.
# Selection of Grid Road Networks Based on Raster Data
## 4 Experiments and Evaluations
### Experimental Process  
Using the proposed ARWS road selection method, we generated the visualization results at two different levels of detail. The parameter values used for the detection of the main road direction in the area based on the Hough detection at the lower level are \(h=15\), \(c=0.3\), \(fg=30\), and \(ml=10\), where \(h\) represents the maximum positive integer number of peaks that can be identified, \(c\) represents the minimum nonnegative value to be considered a peak, \(f_{\mathcal{S}}\) represents the distance between two line segments that have the same Hough transform bin, and \(ml\) represents the minimum length of the line segments that should be preserved. The superpixel sizes used for the multilevel division of raster road networks based on the LSC method at the two levels are 30 and 60 pixels. The given angle threshold (\(AT\)) and ratio threshold (\(RT\)) in the ARWS method at the two levels are \(AT=20^{\circ}\), \(RT=0.70\) and \(T=10^{\circ}\), \(RT=0.50\).  
Since the closed areas generated from the roads at the higher level may contain severely non-connected parts, each part should separately perform road intersection superpixel matching. For example, Figure 6a shows the original segmented road superpixels at a lower level. According to the connectedness of the roads, these segmented superpixels can be divided into three parts, as shown in the light-blue, orange, and green superpixels in Figure 6b. Building three different graph structures, the results of the road intersection superpixel matching can be seen in Figure 6c, in which the dark-blue lines connect the matching intersection superpixels in the three graph structures. For some short road line segments, when the corresponding road intersection superpixels cannot be matched, these segments should be removed (for instance, the road segments highlighted in light blue depicted in Figure 6c).  
Figure 5: Experimental road networks in Beijing, China.