esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Context
### Variety Management  
Product variety represents the complete range of products offered by companies to meet the diverse needs of different customers [25]. The size of the product range affects the company's competitiveness in the market. However, an increase in product variety incurs significant additional costs. Variety management seeks to optimize product diversification in order to reduce production costs while meeting market demand [32]. To distinguish between the variety to be maintained and the variety to be minimized, the literature identifies two groups of two types of varieties.  
The literature defines internal variety as the diversity of parts, assemblies, and modules required for producing the product range [19; 42]. External variety, on the other hand, relates to the size of the complete product offering as perceived by the customer [43]. The management of internal and external variety aims to reduce internal variety while maintaining or increasing external variety [23; 32]. In other words, this is a balance between diversification and standardization [34]. Reducing internal variety leads to decreased complexity in both design and manufacturing [32].  
Other strategies aim to reduce technical variety while maintaining or increasing functional variety [6; 13]. This represents a balance between customer satisfaction and ease of manufacturability [42]. Functional variety is described in the literature as the components that provide differentiation in product functions, offering benefits to the customer. Technical variety encompasses the diversity in technologies, design methods, manufacturing processes, components, and assemblies required to achieve functional variety [11]. Technical variety is targeted for minimization through various variety-reduction programs [13]. Techniques for reducing technical variety aim to lower costs and enhance manufacturability [11].  
Reducing internal variety requires examining the type of variety for each module, whether functional or technical. Functional and technical varieties apply at the module level, whereas internal and external varieties concern the entire product. Functional and technical varieties provide granularity that allows for a more nuanced management of variety.
# Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints
## 3 The Proposed Approach
### Identification of the Optimal Cutpoint  
Once the split variable is determined, the optimal cutpoint \(c^{*}\) is calculated to divide the observations at the current node \(R\). This cutpoint is used to fit two isotonic regression models that offer a better fit than the regression in the parent node \(R\). The optimal cutpoint \(c^{*}\) is chosen from all possible values of the selected split variable \(Z^{*}\) by locally minimizing the sum of errors at two separate nodes:  
\[\sum_{q}^{n_{q}}(y_{q}-\hat{y}_{q})^{2}+\sum_{w}^{n_{w}}(y_{w}-\hat{y}_{w})^{2}\]  
where \(n_{q}\) and \(n_{w}\) are the number of samples at nodes \(R_{1}=\{\mathbf{Z}|Z^{*}\leq c\}\) and \(R_{2}=\{\mathbf{Z}|Z^{*}>c\}\), respectively. Consequently, the optimal cutpoint \(c^{*}\) achieves a great decrease in error, evaluated before and after the split. The cutpoint can be found by an exhaustive search of all conceivable cupoints of the selected split variable \(Z^{*}\) as most decision trees use this. For more efficient cutpoint exploration, a histogram-based approach used in LightGBM could be another choice, which partitions continuous feature values into individual bins, and these bins are subsequently employed to build feature histograms [50; 51; 52].
# New Taipei City Smart Pavement Management Center and Road Maintenance Analysis
## 4 Pavement System Modular Work Order and Budget Analysis  
### Pavement Management Center Work Order Process  
The complete work order process for road damage cases comprises case reception, on-site work, and report submission. Typically, road damage is reported by patrol personnel or detected and uploaded by AI inspection equipment into the management system.  
After the system categorizes the case, internal staff log the damage details, including images and descriptions, before creating a work order. The contractor can review specific information (e.g., damage location, type, and severity) and prepare the necessary equipment and materials accordingly.  
Upon receiving the work order, the contractor will follow the internal staff's instructions and the priority order to carry out the repair work. The team is required to deploy promptly to the designated location, where they recheck the damage. Depending on the severity, they will choose an appropriate repair method, such as filling potholes, sealing cracks, or resurfacing, while adhering to standard procedures to ensure quality.  
During the repair, the team takes photos and documents the before-and-after conditions, creating a complete construction record. Once the work is completed, the contractor uploads the photos and records to the management system to confirm completion and back up the data for internal historical documentation.  
Through these steps, contractors effectively complete the work order, ensuring that road damage is repaired promptly. They also keep track of key information such as the  
Figure 9: Road Management Center Information Platform Road Inspection Indicators.  
damage location, cause, discovery date, maintenance date, and repair method, providing a comprehensive record for management verification and quality control (Figure 10).
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 3 Results  
The experiment regarding the Ti L-edge absorption spectra of the standard sample TiO\({}_{2}\) and the sample containing Ti element was carried out in BL07U using TEY method. This experiment adopted the normal energy scan module. The range of the scan energy was  
from 452 to 470 eV. The scan step was 0.1 eV. The dwell time at each energy point was 0.2 s. These preceding parameters were set through the GUI. The normal energy scan module can measure 32 channels of data simultaneously. In this experiment, the channel-2 was selected to measure the photon flux of standard sample TiO\({}_{2}\) in semi-cut light state. The channel-4 was selected to measure the incident photon flux detected by a clean gold mesh. The channel-5 was selected to measure the photon flux of the sample. The selection of channels was achieved through the interface. After the samples were placed and the above parameters were set through the interface, the energy scan was carried out automatically by clicking the Start button. In the process of energy scan, the Python script realized live visualization by calling the _LivePlot_ function. When the energy scan was completed, the experimental data was automatically saved to a _.csv_ file.  
The above experimental data were processed, and the spectra of channel-2 and channel-5 were normalized to the incident photon flux of channel 4. The TEY spectrum of standard sample TiO\({}_{2}\) is shown in Figure 6. The TEY spectrum of sample is shown in Figure 7. The incident photon flux is detected by a clean gold mesh and shown in Figure 8.  
Figure 6: The Ti L-edge absorption spectra of TiO\({}_{2}\) standard sample measured with semi-cut incident X-ray.  
Figure 7: The Ti L-edge absorption spectra of sample.
## 3 Results
### Relationship Relating Climatic Factors, LOS and NPP
#### 3.5.2 Structural Equation Models  
Our structural equation model (SEM) did not detect a relationship between temperature, precipitation and solar radiation, which indicates that there was a strong relationship among the climatic factors, LOS and NPP. In the SEM (\(\chi^{2}=1.0138\), d.f. = 5, \(p=0.9614\), AIC = 21.0138, BIC = \(-1815\)), climatic factors explained a total of 23% of the variation in LOS, and temperature and solar radiation had a positive effect on LOS, while precipitation had the opposite effect (Figure 8). Moreover, the effect of climatic factors and LOS on NPP was 0.63, and explained a total of 56% of the variation in NPP. Among the climatic factors, the direct effect of solar radiation on NPP was the largest (0.31), followed by precipitation (0.25), and temperature (0.07).
# Comparing the Diagnostic Accuracy of CBCT
## 3 Results
### Test Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Group** & **GS Ant Man** & **GS Post Man** & **GS Ant Max** & **GS Post Max** & **T Value** \\ \hline \multirow{2}{*}{Mean} & Osteoporosis & 519.90 & 341.00 & 371.86 & 133.33 & \(-\)3.1024 \\ \cline{2-6}  & Controls & 897.67 & 739.87 & 753.62 & 548.03 & 0.2410 \\ \hline \multirow{2}{*}{Standard Deviation} & Osteoporosis & 76.611 & 37.928 & 37.052 & 61.621 & 0.36458 \\ \cline{2-6}  & Controls & 143.161 & 178.623 & 186.895 & 110.584 & 0.85647 \\ \hline \multirow{2}{*}{Minimum Score} & Osteoporosis & 400 & 290 & 310 & 50 & \(-\)4.00 \\ \cline{2-6}  & Controls & 700 & 495 & 450 & 360 & \(-\)1.40 \\ \hline \multirow{2}{*}{Maximum Score} & Osteoporosis & 630 & 420 & 422 & 233 & \(-\)2.70 \\ \cline{2-6}  & Controls & 1112 & 1000 & 1010 & 700 & 1.50 \\ \hline \hline \end{tabular} GS = Geyscale values; Ant = Anterior; Post = Posterior; Man = Mandible; Max = Maxilla.  
\end{table} 
Table 2: Distribution of means, standard deviations, maximum and minimum scores of T value and GS values at different jaw regions for osteoporosis group and controls. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**GS Values**} & \multirow{2}{*}{**Pearson Correlation**} & \multicolumn{2}{c}{**T Value**} \\ \cline{3-4}  & & **Osteoporosis Group** & **Control Group** \\ \hline \multirow{2}{*}{GS Ant Man} & R & 0.572 & 0.751 \\ \cline{2-4}  & \(P\) & **0.000 *** & **0.000 *** \\ \hline \multirow{2}{*}{GS Post Man} & R & 0.470 & 0.774 \\ \cline{2-4}  & \(P\) & **0.002 *** & **0.000 *** \\ \hline \multirow{2}{*}{GS Ant Max} & R & 0.509 & 0.715 \\ \cline{2-4}  & \(P\) & **0.001 *** & **0.000 *** \\ \hline \multirow{2}{*}{GS Post Max} & R & 0.168 & 0.787 \\ \cline{2-4}  & \(P\) & 0.288 & **0.000 *** \\ \hline \hline \end{tabular} GSV = Grayscale value; Ant = Anterior; Post = Posterior; Man = Mandible; Max = Maxilla; R = Pearson’s correlation coefficient; P = Probability levels. \({}^{**}\)\(P<0.01\), \({}^{***}\)\(P<0.001\).  
\end{table} 
Table 3: Correlations between T value and GS values at different jaw regions for osteoporosis group and controls. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multirow{2}{*}{**t**} & \multicolumn{2}{c}{**Significance**} \\ \cline{2-2} \cline{5-6}  & **B** & **Std. Error** & **Beta** & & **(P Value)** \\ \hline \multirow{2}{*}{1} & (Constant) & 3.400 & 0.078 & & 43.503 & **0.000*** \\ \cline{2-6}  & GS Post Max & \(-\)0.004 & 0.000 & \(-\)0.921 & \(-\)21.044 & **0.000*** \\ \hline \hline GS: Grayscale values; Ant = Anterior; Post = Posterior; Max = Maxilla; Man = Mandible; t = t-statistics. R square = 0.849. Excluded variables from the model were GS values at Ant Max, CS values at Ant Man and GS values at Post Man, *** \(P<0.001\). & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Stepwise regression analysis for prediction of osteoporosis presence via GS values at different jaw regions.
# Environmental Behavior and Remediation Methods of Roxarsone
## 3 Transformation of ROX
### Abiotic Degradation  
#### 3.2.1 Photolysis  
Light can cause ROX to be transformed through photodegradation or photocatalytic oxidation. At pH 4-8, ROX can be converted into As(III) through photolysis, and the degradation rate increases as the pH increases. As(III) can then be photochemically transformed into As(V), which is less mobile and toxic than As(III). With the strengthening of light intensity, the degradation efficiency of ROX gradually increased [51]. Nitrate and organic matter increase the photolysis rate, so storage of livestock waste and composting can affect ROX degradation through photolysis [52].  
Advanced oxidation processes involving ultraviolet light (UV), such as UV/chlorine treatment [53], UV/ZnO treatment [54], UV/TiO\({}_{2}\) treatment [55], UV/H\({}_{2}\)O\({}_{2}\) treatment [56], and UV/photosensitizer treatment [57], can effectively degrade ROX. The UV/H\({}_{2}\)O\({}_{2}\) treatment process requires high chemical doses and large amounts of energy because radicals are not efficiently produced because of poor UV absorbance [58]. ROX can be degraded to As(III) and then to As(V) using a derivative of riboflavin as a photosensitizer [57].
## 1 Introduction  
Landside dam formation and stability represent a globally significant research theme in geological hazard mitigation, involving complex interactions between tectonic, geomorphic, and climatic processes [1,2]. This phenomenon is exceptionally prevalent in activerogenic zones. Since the Cenozoic, the collision between the Indian and Eurasian Plates has driven lateral extrusion of the eastern Qinghai-Tibet Plateau (QTP) margin toward the Yangtze Block. The resultant compressional transition zone exhibits complex fault systems, frequent strong earthquakes [3, 4, 5], and intense fluvial dissection that forms steep alpine valleys [6,7]. Under these coupled endogenic and exogenic dynamics, river blockages by landslides frequently generate numerous landslide dams.  
Historical research has predominantly focused on unstable dams due to catastrophic consequences, exemplified by events like the 1786 Mogangling dam failure [8,9], 2000 Yigong Tsangpo breach [10,11], and 2018 Jinsha River blockage [12, 13, 14, 15]. Contrastingly, stable landslide dams--which persist long-term without failure--remain under-studied despite their geomorphic and ecological significance [16,17]. While stable dams are widespread across the QTP, their formation mechanisms remain poorly understood, with current research primarily addressing spatial distributions rather than causative processes [16,17].  
To address this knowledge gap, we investigate the Cuona Xuecuo landslide dam--a representative stable dam on the eastern QTP margin--through integrated field investigations, numerical simulations, and geotechnical analyses. This study aims to (1) decipher structural controls enabling long-term stability, (2) establish a mechanistic framework for stable dam formation, and (3) provide practical insights for regional dam management.
## 4 Case Studies
### Case Study of COTS System: TEMPO
However, careful consideration must be given to ensure form factor is kept relatively small even when modifications are done to fully make an attractive BSN node, which is exemplified in [30, 31, 32]. The Mica mote platform presented in [30] sits atop two AA batteries side by side resulting in a form-factor difficult to place on the human body. It contains an 802.15.4 radio with a maximum data rate of 250 kbps and a small 8-bit Atmel processor. No sensors exist on the main circuit board, but a 51-pin expansion connector allows for easy expandability at the expense of wearability. The Telos mote platform presented in [31] provides similar functionality to the Mica mote, comes with a commercial Texas Instrument microcontroller, 16-pin expansion, and optional light, temperature, and humidity sensors on the main board. However, the Telos platform still sits atop two AA batteries with a similar form factor which makes it undesirable for many BSN applications. The BSN node presented in [32] contains the same processor and radio as Telos, but focuses on form factor more extensively. Sensors must be added via a daughter board and the expansion connector with 6 analog channels and two serial ports which adds size. The main board however, is only 26 mm \(\times\) 26 mm which promotes better wearability.
# Construction Industrialization in China: Current Profile and the Prediction
## 3 Construction Industrialization Profiles in China
### Prediction for the Next Five Years
#### 3.4.1 Conventional Residential Buildings  
\[\overset{\wedge}{Y}_{12+T}=1066.51+32.49T \tag{4}\]  
where \(\overset{\wedge}{Y}_{12+T}\) is the predicted value for 2016 onwards; 1066.51 is the estimated level of the series for the year 2015; 32.49 is the estimated trend (slope) of the series for the year 2015; and \(T\) is the elapsed time from 2015. The smoothing parameter for level (\(\alpha\)), the smoothing parameter for trend (\(\beta\)), and the RMSE are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Alpha** & 1.00 \\  & **Beta** & 0.15 \\ \hline \multirow{2}{*}{**End of Period Levels**} & **Mean** & 1066.51 \\  & **Trend** & 32.49 \\ \hline \multirow{2}{*}{**Sum of Squared Residuals**} & 301,020.70 \\ \hline \multirow{2}{*}{**Root Mean Squared Error**} & 158.38 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction function parameters: conventional residential buildings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Year**} & \begin{tabular}{c} **Conventional Residential** \\ **Buildings (million m2)** \\ \end{tabular} & \
\begin{tabular}{c} **Advanced Residential** \\ **Buildings (million m2)** \\ \end{tabular} &
\begin{tabular}{c} **Office Buildings** \\ **(million m2)** \\ \end{tabular} \\ \hline
1 & 2004 & 449.73 & 29.76 & 17.04 \\
2 & 2005 & 523.50 & 28.35 & 16.71 \\
3 & 2006 & 603.45 & 40.58 & 21.35 \\
4 & 2007 & 738.81 & 49.14 & 21.41 \\
5 & 2008 & 793.05 & 43.37 & 2
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 3 Engineered Water Flooding (EWF)
### Wettability Modification
#### 3.2.2 Fluid-Fluid Interactions (Microdispersion)  
Not all crude oils facilitate incremental oil recovery using EWF. It is necessary to consider the oil composition and interaction with the injected LSW in the screening criteria for carbonate reservoirs and to assess the suitability of the reservoir for EWF. Recently, [PERSON] et al. [84] carried out a comprehensive study of almost 30 offshore and onshore carbonate formations in Abu Dhabi to develop robust screening criteria for low-salinity water flooding (LSWF) based on oil-brine interactions and analysis of the resulting microdispersions. Some oils formed microdispersions with LSW (positive crude oils), which also resulted in incremental oil recovery, while others did not form microdispersions(negative crude oils) and provided negligible additional recovery upon LSWF. Similar studies by have also confirmed the role of fluid-fluid interactions in incremental recovery using LSWF [103; 104; 83].
# Indoor Vehicles Geolocalization Using LoRaWAN
## 4 The Proposed Architecture  
Our proposal (see Figure 2) is based on the use of the multiple associations that a LoRaWAN node establishes with surrounding Gateways. As defined in the standard, all gateways receiving a packet coming from a LoRaWAN node forward it to the controlling network server. These multiple copies of the packet are then filtered and a unique copy is given to the accessing application. Data from the network server can be obtained using various APIs. In our case, we used Message Queuing Telemetry Transport (MQTT) to obtain the information. The network server provides in the form of _metadata_ the various details of the data channels and the gateways where the packet got through, see Figure 3.  
The only critical condition for our solution to work is that in any position inside the area under consideration a client has to have connectivity with a minimum of three gateways. The locations where the gateways are to be installed and the total number of required gateways is strongly dependent on the context in which the localisation process has to take place. The set-up and the fine-tuning of the different parameters required by the system has to be done taking into consideration factors like the actual structure of the building or the location, the size of the area that we want to cover, the number of devices to be tracked, and so on. The proper characteristics of the hardware used for the gateways, for example, can vary depending on the model used and even supposing the same model on some intrinsic details, like the proper connection of the antenna.  
Figure 2: The architecture of the proposed system.
## 2 Materials and Methods
### Quantification of Free MC by ELISA  
#### 2.4.1 Quantification of Free MC in Cyanobacterial Cultures  
MC production by cyanobacteria was evaluated using enzyme-linked immunosorbent assay (ELISA) (Abraxics LLC, Warminist, PA, USA). In the cyanobacteria matrix, free MC measured by ELISA can account up to 99% of total MC measured by an analysis of MMPB [53]. After homogenization of cultures, 20 mL were sampled and filtered (1 \(\upmu\)m Cyclopore track etched membrane, Whatman, Maidstone, United Kingdom). Free MC were extracted with 80% MeOH, and samples were first placed in an ultrasound water bath for 15 min at 35 kHz (Bioblock scientific, Strasbourg, France) and then subjected to three cycles of centrifugation-probe sonication (UP200S, Hielscher Ultrasonics GmbH, Germany). After centrifugation, ELISA tests were performed on the supernatant according to the manufacturer's protocols. Analyses were performed in duplicate for each sample at various dilution factors (10, 50, 100). Fluorescence was measured at 450 nm using an absorbance microplate reader (Tecan, Mannedorf, Switzerland).