esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As animal husband advances, the demand for premium feed has seen substantial growth, while the availability of natural forage resources remains limited. Corn stover, characterized by its high yield and rich nutritional content, has become a vital source of roughage. The application of silage technology to corn stover enhances its palatability, improves its nutritional value, and exerts positive effects on livestock production performance. This study aims to evaluate the impact of different additives and their proportions on the nutrient composition, fermentation quality, and microbial community structure of corn stover silage. Specifically, _Lactobacillus plantarum_, cellulase preparations (commercial cellulase enzyme preparations), and xylanase preparations (commercial xylanase enzyme preparations) were employed as additives to investigate their synergistic effects and underlying mechanisms during the silage process. Furthermore, dosage gradients were established to determine the optimal dosage range, providing a robust scientific basis for the optimization of additive applications. In this experiment, corn stover was used as the substrate, with the moisture content maintained at 60%. Treatments with _Lactobacillus plantarum_, cellulase, and xylanase were applied, and silage samples were analyzed after 30 and 60 days of fermentation to assess fermentation quality, nutritional quality, and microbial community structure. The findings revealed that the nutritional quality of corn stover silage improved progressively with fermentation time. Compared to the control group, the addition of _Lactobacillus plantarum_, cellulase, and xylanase significantly increased the abundance of lactic acid bacteria, reduced the pH value, and effectively suppressed the proliferation of spoilage microorganisms. Among the treatments, xylanase demonstrated the most pronounced effects, substantially increasing lactic acid and soluble carbohydrate content while reducing levels of neutral detergent fiber (NDF) and acid detergent fiber (ADF). Notably, the incorporation of 20 U/g xylanase into the silage process facilitated the breakdown of xylan in corn stover into soluble carbohydrates, thereby providing essential substrates for lactic acid bacteria and other beneficial microorganisms. This, in turn, inhibited the growth of harmful microorganisms, ultimately improving the nutritional quality, fermentation quality, and microbial community structure of the silage. These findings provide a theoretical framework and practical guidance for optimizing the production of corn stover silage, promoting efficient resource utilization, and supporting the sustainable development of animal husbandry.  
**Keywords:** cellulase; xylanase; silage execution; corn stalks; straw feedstuff
## 3 Experimental Results
### Ultrasonic Pulse Velocity  
The ultrasonic wave velocity measurement results are shown in Figure 5a. At each age, we can see that the ultrasonic velocity also decreases as the amount of quartz powder substitution increases. In the early period (1 day), the ultrasonic velocity measurements are relatively close. Later (28 days), the differences in ultrasound velocity measurements are larger. In other words, the development trend of ultrasonic velocity is similar to that of compressive strength. During the hydration process, solid hydration products can fill capillary pores. As the solid hydration products increase, the UPV and strength increase. Hence, strength is closely related to UPV. Figure 5b shows the relationship between the UPV and compressive strength measurement results at 1, 3, 7, and 28 days. Overall, there is an exponential function relationship between the two [37]. In this test, the compressive strength unit is MPa and the UPV unit is m/s.  
Figure 3: Hydration heat. (**a**) Heat flows of Qu0, Qu7.5, and Qu15; (**b**) cumulative hydration heat of Qu0, Qu7.5, and Qu15; (**c**) heat flows of cement mass of Qu0, Qu7.5, and Qu15; (**d**) cumulative hydration heat of cement mass of Qu0, Qu7.5, and Qu15.
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 2 Accessibility  
Accessibility, which is mainly measured by travel distance or travel time, can be calculated using various methods. First, buffer analysis is a method of evaluating an area within which access is good based on the boundaries created by a set of points within a specific distance based on a facility. [PERSON] et al. (2010). analyzed the accessibility of natural green spaces and urban parks according to the income-class factors in South Korea. GIS buffer analysis was used to measure the accessibility of local green spaces and urban parks. [PERSON] et al. (2011) measured the accessibility of healthcare facilities in Ashanti, Ghana. They identified the accessible areas using buffer analysis around the healthcare facilities. [PERSON] et al. (2012) used buffer analysis to measure the accessibility score of care facilities by residents in residential care facilities in Guangzhou, China.  
Second, network analysis in GIS has been used to measure accessibility. For example, [PERSON] and [PERSON] (2013) proposed measuring the accessibility to health services in Khulna, Bangladesh. The travel time-based accessibility measurement method was proposed by computing the average travel time to the nearest hospital using various modes of transportation. [PERSON] et al. (2014) and [PERSON] et al. (2015) used the two-step floating catchment area to measure the accessibility of healthcare services and urban parks. The distance was measured using road network analysis to calculate the accessibility.  
Finally, the navigation API has been recently used for accessibility calculation. Several studies have been performed using navigation API, such as using Google Maps to calculate the travel time. [PERSON] and [PERSON] (2016) compared the travel time provided by Google Map Navigation API, which was calculated using the road network provided by the GIS software. Their study proved that the navigation API of Google Maps provided more accurate results than the road network analysis using the GIS software. [PERSON] et al. (2017) used the Google Maps navigation API to develop a method of measuring the Beijing public transportation accessibility.  
The travel time calculated using buffer analysis or network analysis in GIS may not be accurate because it does not reflect the actual resistance, such as the crosswalks and slopes of walkways. However, the travel time provided by a navigation API is more accurate because it can reflect various resistances on the roads. Therefore, navigation API was employed to calculate the travel time to social infrastructure facilities in this study. The navigation API has not been used for the accessibility calculation of social infrastructure planning in South Korea.
# FEAROL: Aging Flow Entries Based on Local Staircase Randomized Response for Secure SDN Flow Tables
## 2 Background and Related Work
### Flow Table Management  
As TCAM has constraints of capacity, power consumption, heat generation and cost, flow tables may not have the enough space for the fine-grained flow entries in high performance networks [4], which is a major concern for SDN systems. The current research typically involves three types of approaches to alleviate this concern: dynamic flow-entry timeouts and eviction, flow entry aggregation and compression as well as flow entry distribution and caching.  
* Dynamic flow-entry timeouts adjust the type and number of timeouts [5; 19]. Timed out flow entries should be evicted efficiently [20; 21] to mitigate the impacts of lack of space in the flow table. Mechanisms for adjusting flow-entry timeouts and evicting flow entries are often based on predicting the flow type, flow duration and flow grouping interval using statistical data of the flows in a given time period [22; 23]. Since flow-entry timeouts affect the flow table space utilization and network-forwarding latency, and different types of flows in the network have different quality of service requirements, dynamic flow-entry timeouts and eviction mechanisms can be very complicated in practice.
* Flow entry aggregation and compression schemes directly reduce the number of flow entries required by forwarding flow packets. Such schemes typically rely on the observation that each flow can have multiple flow entries based on the prefix classifiers using wildcards [4; 24]. Source or/and destination addresses of flows are often included by the wildcard to significantly reduce the flow table occupancy [25; 26; 27]. However, such approaches may result in a loss of visibility and control flexibility over the aggregated flows.  
Figure 2: Major functions of SDN switches.  
By deciding which rules to place on which switch while respecting memory constraints and rule dependencies, flow entries can be split up and distributed across the network to reduce the amount of flow table space used by a single switch [28; 29]. Caching the timed out flow entries in memory instead of discarding them allows for an efficient use of the flow table while reducing the network latency [30; 31].  
All of these approaches can mitigate the flow table overflow attacks reactively but cannot identify attacks, as listed in Table 1; however, our proposed approach can proactively protect against flow table overflow attacks and mitigate the flow table space shortage.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Satellite Data  
#### 2.2.1 Sentinel-1  
The Sentinel-1 constellation acquires C-band SAR (Synthetic Aperture Radar) data, at the spatial resolution of 10 m \(\times\) 10 m. The synergic acquisitions of Sentinel-1A (launched on 3 April 2014) and Sentinel-1B (launched on 25 April 2016) provide co-polarized (Vertical-Vertical, VV) and cross-polarized (Vertical-Horizontal, VH) images with a 6-days frequency and an incident angle of approximately 39\({}^{\circ}\) at the study site.  
The Sentinel-1 data are available at different processing levels, depending on the corrections applied to the raw acquisitions. The Level-1 of Sentinel-1 data provides GDR (Ground Range Detected) images, available as gridded backscattering coefficients derived from raw signals through a processing procedure: thermal noise removal, radiometric calibration, terrain correction and speckle filtering [31].  
The Sentinel-1 data used in this study were downloaded from the Google Earth Engine platform [32] as multi-band rasters in decibel units, which were pre-processed to meet the Level-1 GDR requirements [33]. The mask of maize fields shown in Figure 2a (whose characterization is discussed in Section 2.3) was used to select just the maize pixels from the SAR images. The VV and VH polarization bands were extracted from the multi-band rasters and converted from decibel (dB) to linear (lin) units:  
\[VV_{lin}=10^{(\frac{VV_{lin}}{10})}\;\;VH_{lin}=10^{(\frac{VH_{lin}}{10})} \tag{1}\]  
where \(VV_{dB}\) and \(VH_{dB}\) are the backscatter signals provided in decibels as Level-1 SAR product; \(VV_{lin}\) and \(VH_{lin}\) are the backscatter signals converted into linear units, which are consistent with the units of raw acquisitions.  
The SAR instrument from Sentinel-1 is an active microwave sensor, which has been shown to be sensitive to several ground variables, such as soil moisture [17; 34; 35] and surface geometry [36]. Both VH and VV channels can be used for crop growth monitoring, since the phenological cycle impacts the ground geometry of the fields, both in terms of roughness and biomass volume. The co-polarized signal has been shown to be more sensitive to combined surface-volume scattering, also having a high signal-to-noise ratio,while the cross-polarized channel is more suitable for detecting volumetric changes of vegetation [37].  
Figure 2: (**a**) Spatial distribution of maize fields over the studied region and frequency distribution of field areas. (**b**) Example of VH/VV 10 \(\times\) 10 m data over the maize fields. (**c**) Example of NDVI 10 m \(\times\) 10 m data over the maize fields. (**d**) Example of soil moisture 10 m \(\times\) 10 m data over the maize fields. (**e**) NDVI series for 1154 fields classified as “maize” in 2019. Comparison with NDVI from same fields in 2018 (**f**) and 2020 (**g**).
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 2 Problem Statement, Notations, and Assumptions
### Toll Station Queuing System  
A toll station is a discrete flow facility. At the entrance lanes of the toll station, vehicles proceed in sequence to either obtain a ticket or record electronically; at the exit lanes, vehicles sequentially make payments to complete the transaction. This creates a queuing process that allows for further analysis based on queueing theory. Due to the physical separation between lanes, vehicles cannot freely switch lanes during queuing, enabling each lane to be considered an independent single-server queuing system. Since this study focuses on solving lane allocation issues at highway toll stations rather than analyzing traffic flow characteristics, traffic volume prediction, or queuing system modeling, and to reduce computational burdens in subsequent model iterations, this study assumes an \(M/M/1\) queuing system to describe vehicle arrival and service processes at the toll station.  
Let \(\mu_{di}\) denote the service rate of a single lane of type \(i\) in direction \(d\), \(\lambda_{d}\) denote the arrival rate of vehicles in direction \(d\) and \(\lambda_{di}\) denote the vehicle arrival rate for lane type \(i\) in direction \(d\). We assume that the service rate of each type of toll lane is independent of the category of vehicles allowed to pass. On the MTC lanes, ETC-HVs and MTC-HVs are treated equivalently; on the ETC lanes, ETC-HVs and CAVs are considered equivalent. Thus, the average passage time \(Ws_{di}\) of vehicles in a specific category of lanes in a given direction at a toll station can be expressed as follows:  
\[Ws_{di}=\frac{1}{\mu_{di}-\frac{\lambda_{di}}{\mu_{di}}}, \tag{1}\]  
where \(Ws_{di}\) includes both service time and queueing time.  
Figure 3: Liulin toll station toll plaza.  
Furthermore, the lane allocation scheme at the toll station must ensure the stability of the queuing system, which requires that the service intensity for each type of lane is less than 1. This condition can be mathematically expressed as  
\[\frac{\lambda_{di}}{n_{di}n_{di}}<1,\quad\forall d\in\mathbf{D},i\in\mathbf{I}. \tag{2}\]  
It ensures that the system can manage incoming traffic without resulting in congestion, thereby maintaining efficiency.
## 1 Introduction
Robust H\({}_{2}\) control is an effective control technique that has greatly contributed to practical industry applications minimizing the effect of disturbances [21; 22; 23]. The robust H\({}_{2}\) control is based on minimizing the quadratic norm of the transfer function between the input disturbance signal and the plant output signal or H\({}_{2}\) norm [24; 25; 26]. The purpose of this paper is to design, for the first time, a robust H\({}_{2}\) state feedback controller (H\({}_{2}\)-SFC) for the effective temperature control in the heating zone of a steel slab reheating furnace. The robust H\({}_{2}\)-SFC controller uses a mathematical model of the industrial process to be controlled to calculate an optimal sequence of actions that are projected to design the dynamics of the system through state feedback based on the minimization of the quadratic norm of the transfer function between the input disturbance signal and the plant output signal. Among the most outstanding features of the robust H\({}_{2}\)-SFC controller is the possibility of taking advantage of the representation in states space of the transfer function taking advantage of the continuous feedback of the information on the states and the output of the system in order to stabilize the said function minimizing its H\({}_{2}\) norm [26]. An important advantage of this type of advanced controller is the possibility of incorporating a significant performance measure that guarantees the stability and performance of the system allowing efficient design criteria under the influence of external disturbances [26]. An optimal sequence of controller actions that are calculated taking into account the system behavior and uncertainties from the design stage is estimated, this introduces two important improvements: Good performance and greater robustness. The controller sequence converges complying with the restriction of the H\({}_{2}\) norm. Unlike a PI/PID controller, the robust H\({}_{2}\)-SFC controller has a greater tolerance or insensitivity to changes or disturbances that may occur in the operation conditions of the industrial process [26]. This paper presents the following contributions: (1) A mathematical model that characterized the dynamic behavior of the heating zone of a steel slab reheating furnace based on the available field data and system identification procedures is obtained. (2) For the first time, a robust H\({}_{2}\) state feedback controller (H\({}_{2}\)-SFC) for the effective temperature control in the heating zone of a steel slab reheating furnace is designed. This paper is organized as follows. In Section 2, a mathematical model of the heating furnace zone under study is obtained using systems identification techniques. In Section 3, the design of the robust H\({}_{2}\)-SFC controller is developed. Section 4 shows the discussions of the obtained results. Finally, Section 5 gives some conclusions.
###### Abstract  
Although the light fields and apparent optical properties (AOPs) within the ocean euphotic layer have been studied for many decades through extensive measurements and theoretical modeling, there is virtually a lack of simultaneous high spectral resolution measurements of plane and scalar downwelling and upwelling irradiances (the so-called irradiance quartet). We describe a unique dataset of hyperspectral irradiance quartet, which was acquired under a broad range of environmental conditions within the water column from the near-surface depths to about 80 m in the Gulf of California. This dataset enabled the characterization of a comprehensive suite of AOPs for realistic non-uniform vertical distributions of seawater inherent optical properties (IOPs) and chlorophyll-\(a\) concentration (_Chl_) in the common presence of inelastic radiative processes within the water column, in particular Raman scattering by water molecules and chlorophyll-\(a\) fluorescence. In the blue and green spectral regions, the vertical patterns of AOPs are driven primarily by IOPs of seawater with weak or no discernible effects of inelastic processes. In the red, the light field and AOPs are strongly affected or totally dominated by inelastic processes of Raman scattering by water molecules, and additionally by chlorophyll-\(a\) fluorescence within the fluorescence emission band. The strongest effects occur in the chlorophyll-\(a\) fluorescence band within the chlorophyll-\(a\) maximum layer, where the average cosines of the light field approach the values of uniform light field, irradiance reflectance is exceptionally high approaching 1, and the diffuse attenuation coefficients for various irradiances are exceptionally low, including the negative values for the attenuation of upwelling plane and scalar irradiances. We established the empirical relationships describing the vertical patterns of some AOPs in the red spectral region as well as the relationships between some AOPs which can be useful in common experimental situations when only the downwelling plane irradiance measurements are available. We also demonstrated the applicability of irradiance quartet data in conjunction with Gershun's equation for estimating the absorption coefficient of seawater in the blue-green spectral region, in which the effects of inelastic processes are weak or negligible.  
oceanic light field; irradiance quartet; apparent optical properties; inelastic processes; Gershun equation; ocean euphotic zone
## 2 Channel Statistics and System Structure  
### Channel Statistics  
In FSO systems, one of the remaining impediments is atmospheric scintillation, which, resulting from the index of refraction fluctuations, can cause fading. The statistics of the strength of scintillation events are generally regarded as following the log-normal distribution. The Rytov variance, \(\sigma_{\text{R}}\)\({}^{2}\), is often used to describe the irradiance fluctuations due to scintillation for the case of weak fluctuations when using the Kolmogorov spectrum for a plane wave [16; 17; 18].  
\[\sigma_{\text{R}}^{2}=1.23C_{\text{n}}^{2}\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\text{\text{\text{\  
where \(D\) is the aperture diameter, and \(r_{0}\) is the Fried parameter, which describes the spatial correlation of phase fluctuations in the receiver plane. For plane waves and Kolmogorov turbulence, \(r_{0}\) can be expressed in terms of the wavenumber \(k\), \(\text{C}_{\text{n}}{}^{2}\), and \(L\) as \(r_{0}=1.68(\text{C}_{\text{n}}{}^{2}Lk^{2})^{-3/5}\)([PERSON], 2016; [PERSON], 2016). The coefficient \(C_{\text{J}}=1.0299\) in the phase variance assumes that no terms are corrected by a receiver employing modal compensation ([PERSON], 2016).
## 3 Methodology
### Data Collection  
The field measurement was conducted to acquire live signal data around three Long Term Evolution (LTE) transceiver base station antennas for one year (i.e., 12 months). The measurement took one year to cater to the study locations' seasonal variations and three LTE transceiver base station antennas operating at 2600 MHz with 10 MHz bandwidth [43]. The transceiver base station antennas (called NodeBs) are sectorized with 17.5 dBi gain and 43 dBm transmit power. The LTE network belongs to one of the major GSM/WCDMA/HSPA/LTE telecom service providers operating across major towns, villages and cities in Nigeria. The measurements were performed with field test tools with TEMS application tools for radio spectrum analysis. The test tools, some of which are displayed in Figure 1, include a Rover car, scanner, two Samsung mobile phones, and an HP lap, were explored to assess the performance of eNode B over the LTE radio air interface by connecting mobile phones directly to the Node B transmitters. To obtain the eNode B locations and delineate measurement data locations/information, the Global Positioning System (GPS) equipment was employed. The path loss data to be predicted are related to the acquired radio signal data by the measured path loss data where \(PL_{\textit{mean}}(\text{dB})\), values have been obtained from the measured signal, RSRP (dBm) by Equation (1):  
\[PL_{\textit{mean}}(\text{dB})=EIRP+G_{A}-RSRP_{\textit{meas}} \tag{1}\]With _EIRP_ calculated as Equation (2):  
\[EIRP=P_{TX}+G_{TX}-CL_{TX} \tag{2}\]  
where \(G_{TX}\) and \(G_{A}\) are the base station (BS) transmit antenna gain and receiver (MS) antenna gain, respectively, \(P_{TX}\) is the transmitted power, and \(CL_{TX}\), denotes transmission cable loss, all in dB. Table 1 reveals some of the key BS antenna site parameters acquired during the field drive test for calculation.