esa-sceva/satcom-chunk-collection huggingface dataset:
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 6 Further Discussion
### Number of Network Rules  
It is evident that exploring multiple paths of customers prefixes can increase the demand for OpenFlow rules. For example, in the topology adopted (see Figure 5), the execution of a load balancer application increased \(n+1\) times the number of OpenFlow rules required to forward a given customer prefix, where \(n\) is the number of source network prefixes needed to be managed by the transit AS. Thus, instead of just one forward rule to the prefix 172.16.0.0/24 to the AS 400 (the default \"best\" next hop of BGP rule), 51 OpenFlow rules were required to manage the inbound traffic for the customer AS from the 50 prefixes of the _50 Networks_.  
Regarding the number of network rules, the OpenFlow protocol required multiple match fields to compose a single rule, and the OpenFlow switches often deployed a flow rule using the _Ternary Content Addressable Memory_ (TCAM). TCAM memories are a high-speed memory, but with the limitation of very high monetary costs. Moreover, TCAM requires a lot of space in its chip hardware and has a notoriously high energy consumption. Thereby, due to the fine-grain of the OpenFlow rules, these rules can be cumbersome to an inter-domain environment where the number of prefixes (or flows) is tremendously high [42]. Thus, it is worth noting that any OpenFlow based solution requires appropriate flow management. The rule placement problem inside an OpenFlow network is further detailed in the excellent and extensive survey of [PERSON] et al. [43].  
Furthermore, OpenFlow specification, and hence the OpenFlow switches, aims to make the data plane elements flexible and efficient as Application Specific Circuits Integration (ASCI) used in the BGP routers (traditional network approach). However, to increase the flexibility of the OpenFlow protocol, the number of TCP/IP protocols supported for each new version of the OpenFlow protocol stumbles into the TCAM limitations. Therefore, a trade-off between flexibility (e.g., number of match fields) and performance (for example, number of rules) must be reached. Ideas for allowing OpenFlow data plane elements to be more flexible and programmable are at the very beginning such as P4 [44] or OpenState [45] and can be an answer for the flexibility and performance of OpenFlow switches.
# Region-of-Interest Based Coding Scheme for Live Videos
## 5 Discussions  
The experimental findings reveal that when the ROI enhancement parameter \(K_{roi}\) surpasses a certain threshold, an excessive reduction in bitrate can lead to severe degradation of metrics within the ROU area. This may result in subjective quality issues that cannot be overlooked, such as pronounced blocking artifacts or texture loss. To address this issue, it is crucial to set a minimum \(\lambda_{min}\) value for the ROI region and a maximum \(\lambda_{max}\) value for the ROU region, thereby establishing upper and lower bounds on QP values in the corresponding areas and ensuring that subjective image quality remains uncompromised. These parameters, \(\lambda_{min}\) and \(\lambda_{max}\), can be derived by consulting a predefined \(\lambda\)-\(QP\) lookup table using the corresponding \(QP_{min}\) and \(QP_{max}\).  
In contrast with conventional linear or nonlinear models manually designed, leveraging Convolutional Neural Networks (CNNs) for model parameter prediction allows for a more comprehensive consideration of diverse influencing factors. Traditional manual empirical modeling techniques are prone to overlook high-order features and their intricate interactions, which can inadvertently introduce inaccuracies into the system. On the other hand, employing CNN models in encoding's mode decision-making processes, especially those involving multiple inputs and outputs, offers a superior alternative. The intrinsic capability of CNNs to discern complex relationships among these variables makes them a more reliable choice, effectively reducing potential errors associated with less advanced methodologies.  
In our CNN-based ROI rate control model, the accuracy in predicting the encoding complexity \(C_{roi}\) and \(C_{roui}\) for both ROI and ROU constitutes a paramount parameter. Currently, our prediction relies on estimating this complexity using Sum of Absolute Difference Transform (SADT) within the current frame. However, future refinements could involve enhancing the estimation of encoding complexity for ROI and ROU by incorporating inter-frame prediction using multiple reference frames.
## 3 Results and Discussion
### Effects of Treatments on the Soil Characteristics  
For the non-cultivation and non-liquid fertilizer (NCNL) group, soil pH was significantly increased in LS and RM. All treatments significantly increased soil EC and inorganic nitrogen contents. For the cultivation and non-liquid fertilizer (CNL) group, soil pH was significantly increased in LS and RM and EC was increased by all types of amendments. For the cultivation and liquid fertilizer (CL) group, the tendency of changes in soil pH and EC was the same as that of NCNL and CNL groups. Both LS and RM were well known for their great effect of raising pH when they were applied to the soil [8; 22]. In this study, the alkali characteristic of RM was very strong, and the pH of soil was increased to nearly 9. Significant increases in soil pH affect the solubility of soil organic matter [23], and DOC concentration was highest in the RM treatment in each group. Since DOC in soil affects the availability of As and copper, which have high affinity with organic adsorption colloid [24; 25], it could be supposed that these increases in DOC also affect the As bioavailability. The bioavailability of As assessed by CaCl\({}_{2}\) solution was significantly decreased by all types of treatments in all groups. There was no significant differences in stabilization efficiency between LS and RM. On the other hand, in the case of AMDS, the stabilization efficiency was highest in all groups. It is well known that the decreases in As bioavailability in alkali soil are highly dependent on the amorphous metal oxides, and similar results were found in this study [26]. The high efficiency of AMDS could be confirmed through several previous studies [27; 28; 29]. The content of inorganic nitrogen was significantly changed in some treatments, but it was difficult to find a certain trend depending on groups and the types of amendments.
## 1 Introduction  
### Wireless Sensor Networks and Network Architecture Types  
Wireless sensor networks (WSNs) are used widely as a means of monitoring and collecting data from the environment they are deployed in. The small, battery-powered network nodes are capable of sensing, processing, and transmitting data using wireless communication protocols [1]. Normally, the data collected by individual nodes are sent to a sink node or a base station connected to the WSN. From there, the data are transmitted through the Internet to a database (DB) server for further processing and analysis. In general, the communication architecture of a WSN can be flat or hierarchical [2, 3]. As shown in Figure 1, in a flat WSN, the networks nodes transmit data to the sink node via multi-hop paths. In a hierarchical (cluster-based) WSN, the network nodes are grouped into clusters; the nodes send data to the cluster head (CH), which then relays the data to the sink node (Figure 2). In addition, a WSN can deploy a software-defined network (SDN) architecture in which the network control plane is separated from the data plane (Figure 3). A software-defined wireless network (SDWSN) supports network scalability and node recycling [4].
## 5 Discussion
### Breakpoints and the RPMS  
Based on the RPMS data, a decrease in the mean annual productivity was detected over the pixels with significant breakpoints (i.e., identified by Segmented VPR method) during the 1984-2019 period (Figure 13). The mean productivity of these pixels was lower than that of the long-term of the 36 years which was about 613.2 kg/ha. Moreover, the variability in the annual productivity of these pixels ranged from 588 kg/ha in 1995 to 641 kg/ha in 2006.  
Figure 11: Timeseries of breakpoints concurrent different types of fire incidents in New Mexico during the 1984–2015 period.  
Of the sampled pixels that were evaluated for changes in productivity before and after the break years, 38% with decreasing trend showed a significant difference in mean productivity after the break years with 12% in grasslands (Table A3) and 26% in shrublands (Table A4). Similar to this finding, [84,85] indicated that there was an apparent decline in NPP in the southwestern USA that was attributed to the response of these ecosystems to the combination of a warmer temperature and a decline in precipitation. The pixels that showed significant decrease in productivity in NM's rangeland (i.e., in Chihuahua Desert and AZ/NM plateau ecoregions) coincided with those from [45] that showed a similar behavior (Table A5). The difference in the productivity before and after the break years might be substantiated by the seric nature of the southwestern USA and southeastern Great Plains in response to interannual variability of the precipitation [86]. On the other hand, 8% and 36% of sampled pixels with decreased and increased productivity (i.e., based on the Segmented VPR), respectively showed insignificant difference in mean productivity before and after the break years based on the RPMS data [45]. These findings were in agreement with those by [83] which suggested that the structure and composition of semi  
Figure 12: A summary of breakpoints and fire incidents indicated with the timing of some major incidents.  
Figure 13: Productivity anomaly based on the Range land Production Monitoring Service (RPMS) [45] averaged over the pixels that were identified with the Segmented VPR method with decreasing trend during the 1984-2019 period.  
arid and arid regions of the southwestern USA have undergone noticeable changes over the last two decades.
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 8 Behavioral Anomaly and Change Detection
### Point Anomaly Detection
#### 8.1.1 Sequence Prediction-Based Anomaly Detection  
We formulate abnormal behavior detection in a TCN-based multi-variate time-series prediction framework, where the prediction errors are used as anomaly scores. Due to the sparsity of the sensor setting and the multi-person household nature of our systems, we consider the sleep profile alone to represent the daily activity, based on which abnormal days are identified. Given an input time series \(X\) of length \(L_{M}\) (memory length), where each data point \(x_{t}\) in the sequence is a 5-D daily sleep profile representation vector, the model aims to predict sleep activities in the next \(L_{P}\) (prediction length) days. In this paper, the memory length is 20 days (\(L_{M}=20\)), and the prediction length is 1 day (\(L_{P}=1\)). The 5-D sleep profile of the coming day is the prediction target. For the TCN structure, we stacked 4 causal CNN layers with increasing dilation and width (1, 2, 4, 8). With a kernel size \(k=2\), the final receptive field is 16. The output from the TCN layer is passed to a fully connected layer for the final prediction of the target 5-D sleep profile. The Rectified Adam [55] optimizer is used.  
As any deep learning framework, TCN relies on a large amount of training data. However, at the initial stage of an AAL deployment, only a limited amount of personalized data is available. To tackle the cold-start problem, we first train a generic model using data from the other users and later personalize it with data from the specific test user. To minimize the amount of personal data required, only the fully connected layer is retrained, as typically done in transfer learning approaches. To adapt to new behaviors, the model is retrained daily with data collected from the previous \(N\) days, regardless of whether there are abnormal days. Additionally, old data is 'forgotten' (removed from the training data pool). The lower the number of previous days (\(N\)) used in the training, the faster the model adapts to new behaviors, although it could lead to a lower prediction accuracy.  
We consider \(M_{PE}+\alpha\times ST_{PE}\), where \(M_{PE}\) and \(ST_{PE}\) are the mean and standard deviation of the prediction errors of the personalized training data, as the threshold to decide whether the test day is abnormal or normal. The parameter \(\alpha\) can be adjusted to achieve higher recall or precision in detecting 'point anomalies'. Figure 14 (top) depicts the detected abnormal days (\(\alpha=2\), \(N=60\)) as red dots. For visualization purposes, we show them on the sleep duration curves. The entire sleep profile can be seen in Figure 6.
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 3 Results
### Fungal Growth Dynamics on Plastic Substrates  
The analysis of _Aspergillus flavus_ growth revealed effective colonization across all evaluated HDPE plastic substrates, both in the presence and absence of weathered diesel fuel residues. Throughout the incubation period, a steady increase in the colonized area by fungal hyphae was observed, along with peripheral halo formation, reaching over 300 mm\({}^{2}\) in radial expansion and up to 3000 mm\({}^{2}\) in halo extension, depending on the experimental conditions. These results suggest a suitable physiological adaptation of the fungus to the tested substrates.
## 2 Materials and Methods  
### Cork Waste  
A cork waste from the manufacturing process of agglomerated cork bottle caps of a local company (Italsughero, Montecchio Emilia (RE), Italy) was considered. Precisely, this powder waste is generated during the smoothing phase of agglomerated cork caps and is directly collected through a cyclonic air-filtering system. The involved cork waste has a particle size distribution of 0.063 mm \(<\) d \(<\) 1 mm and contains polyurethane glue and paraffin which are industrially used as binders and additives for cork particles, respectively.  
Simultaneous thermal analysis (STA 449 F3 Jupiter, Netzsch-Geratebau GMBH, Selb, Germany) was considered for thermal characterization and composition description of cork waste. This technique allowed to merge differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA) in the same measurement. Thus, it allowed relating each thermal transformation of the material to the corresponding mass loss. The test was performed considering a heating ramp from 0 C to 900 C at 10 C/min heating rate, a silicon furnace (heating up to 1500 C), and aluminum crucibles. Data analysis was carried out through Proteus software (Netzsch Proteus Thermal Analysis, Proteus 6.1.0, Netzsch-Geratebau GmbH, Selb, Germany), resulting in a combined plot of DSC, TGA and the first derivative of TGA (DTGA). The composition of cork waste was also detected through attenuated total reflectance Fourier transformation infrared spectroscopy (ATR-FT-IR analysis, FTIR VERTEX 70, Bruker Optics, Germany). A transmittance range between 400-4000 cm \({}^{1}\), 32 scans, and a resolution of 4 cm \({}^{1}\) was considered, while FTIR spectra were analyzed through OPUS software (OPUS 6.5, Bruker Optics GmbH, Selb, Germany).  
A laser particle size (Malvern Mastersizer 2000, Malvern Panalytical Ltd., Malvern, UK) was used to determine the particle size distribution of cork waste particles. Optical microscopy (LEICA EZ4D, Leica Microsystems) with 8, 20 and 35 magnitude was considered for the morphological description of cork waste particles.  
According to UNI EN 1079-6 2013, water absorption (WA) and apparent density (\(\rho_{\text{rd}}\)) values were evaluated by performing a 24 h water absorption test. Finally, real density was measured through a helium pyrometer (Micrometrics Accupyc 1330, Micrometrics Instruments).
Although the implementation of the Yellow River Basin's \"returning farmland to forest\" project has markedly increased vegetation greenness, concurrent observations of progressive soil desiccation and native vegetation degradation in localized zones [22] demonstrate a critical trade-off between carbon sink enhancement and hydrological resource sustainability [23]. Although the global greening trend enhances the carbon sequestration potential of vegetation [24], high-density plantation forests may induce ecosystem degradation through resource competition [25]. Existing studies have failed to effectively quantify the regulatory role of drought in the coupled climate-human-ecology system, which limits the optimization of ecological restoration strategies in semi-arid areas. Conventional drought identification methodologies predominantly rely on threshold-based approaches or run theory [26]. However, these techniques exhibit limited capacity to characterize the multidimensional spatiotemporal evolution patterns of drought episodes [27]. The 3D clustering algorithm enhances drought event identification accuracy through integrated analysis of temporal, spatial, and intensity attributes [28], establishing a novel framework for investigating drought-NPP interactions. Current drought research predominantly focuses on single temporal scales or static analytical frameworks [29]. There is a lack of a comprehensive dynamic identification mechanism that can fully capture the physical cumulative of drought events. Critical knowledge gaps persist in understanding the spatiotemporal propagation dynamics of drought events, including trajectory patterns and cumulative impacts, as well as their interaction mechanisms with vegetation responses. The Yellow River Basin, a core region for plantation forests in China's semi-arid zone, has accumulated about 1.7 million hectares of afforestation since the 1980s. However, concurrent with this large-scale afforestation, the frequency of extreme drought events has significantly increased [30; 31], highlighting a critical tension between ecological restoration and climatic challenges. By integrating the three-dimensional clustering algorithm and the improved CASA model, this study aims to (1) quantify the spatial and temporal evolution characteristics of drought events; (2) analyze the response patterns of NPP to drought-afforestation under different scenarios; and (3) reveal the regulatory mechanism of the synergistic effect of climate and human activities on the carbon sink function of ecosystems. The research results can provide theoretical support for the ecological protection and high-quality development of the Yellow River Basin, as well as a scientific basis for the paradigm shift of sustainable ecological management in semi-arid regions around the world.
# Hybrid Model-Based Traffic Network Control Using Population Games
###### Abstract
By incorporating these metrics, we provide a more holistic evaluation of traffic control strategies, capturing their impact on efficiency. * Third, we implement and compare multiple traffic control strategies based on **population dynamics*
* and payoff functions that consider queue lengths and green times of network links. These strategies include the following:
* Fictitious play, implemented from a fixed-time perspective due to its offline nature, and
* adaptive strategies such as **Smith**, **Replicator**, **Logit**, and **Brown-Von Neumann-Nash (BNN)**, which leverage their ability to be continuously updated in real time. This approach allows us to contrast the performance of **offline** and **adaptive** control methods, highlighting their respective strengths and limitations in managing traffic flow under varying conditions. The remainder of this paper is structured as follows. First, in Section 3, we provide a mathematical description of a vehicular traffic network using graph theory, modeling intersections as nodes and streets as links. In Section 3.1, we describe the mathematical details of the continuous traffic model implemented in the proposed traffic simulator. In Section 4, we show the different population dynamics implemented within the traffic simulator, along with the fitness function definitions of each case, which were obtained considering network parameters such as the queue length and number of vehicles in links. In Section 6, we test the game theory-based model and the traffic software simulator in a nine-intersection network in order to evaluate traffic parameters such as number of stops (NS), total travel time (TTT), total distance traveled (TDT), average speed (AS), total stop time (TST) and average stop time per vehicle (ASTV). Finally, we evaluate the performance of each game-theory based control strategy in Section 7.
## 1 Introduction
[PERSON], _et al._[23] used the MODIS vegetation index to conduct in-depth research on the spatial distribution of the aboveground and underground biomass of the Xilingol grassland. Using a remote sensing vegetation index to estimate aboveground biomass provides accurate and rapid results, but the limitations of the vegetation index itself may affect the obtained grassland biomass estimates. Specifically, in low-coverage grasslands, due to the significant influence of the soil background and grassland vegetation types, the estimation results exhibit high error rates. In addition, in high-coverage grasslands, the NDVI shows decreased sensitivity. A \"saturation\" phenomenon appears when the NDVI is higher, resulting in a decline in the accuracy of grassland biomass estimates. The MODIS productivity products regularly published by NASA are surface photosynthetic products obtained from model estimates. After processing these products, we established a relational model between the MODIS productivity products and field-measured aboveground biomass. We then estimated aboveground biomass by accepting the MODIS productivity data as input parameters to avoid or reduce saturation problems if the input parameters were too high. In the present study, based on this strategy, we used ground survey data from the Xilingol grassland for the years 2005-2012 and MODIS productivity data for the same time period to establish statistics-based models for estimating biomass. We further tested the accuracy of the models and selected the optimal model for estimating the aboveground biomass of the Xilingol grassland during the growing period.
###### Abstract  
To study and quantify the impact of water storage on lake slope stability after the closure of an open-pit mine, we targeted slope control measures by large-scale parallel computing methods and strength reduction theory. This was based on a three-dimensional refined numerical model to simulate the evolution of slope stability under different water storage levels and backfilling management conditions, and to quantitatively assess the risk of slope instability through the spatial distribution of stability coefficients. This study shows that during the impoundment process, the slope stability has a nonlinear decreasing trend due to the decrease in effective stress caused by the increase in pore water pressure. When the water storage was at 0 m, the instability range is the largest, and the surface range is nearly 200 m from the edge of the pit; when the water level continued to rise to 50 m, the hydrostatic pressure of the pit lake water on the slope support effect began to appear, and the stability was improved, but there is still a wide range of unstable areas at the bottom. In view of the unstable area of the steep slope with soft rock in the north slope during the process of water storage, the management scheme of backfilling the whole bottom to \(-150\) m was proposed, and the slope protection and pressure footing were formed by discharging the soil to \(-40\) m in steps to improve the anti-slip ability of the slope.  
pit closure; water storage; open-pit mine slope; numerical simulation; stability evaluation; strength discount theory; backfill management Article