esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation and Optimization of Available Local Feed Resources for Dairy Cattle in Burkina Faso
## 2 Materials and Methods
### Diet Formulation and Rationing Methodology Based on Survey Data
#### 2.6.4 Methods for Determining the Nutritional Value of Ingredients  
In addition to the bromatological values of the four dominant feed ingredients, nutrient values such as PDIE, PDIN, PDIA, and UFL were calculated using the formulas in the dairy cow diet table based on the formulas below [15]. These results enabled us to set our milk production target in the menu bar of the Op-Ration version V3.4.5.0. PDIA = crude protein \(\times\) [1.11 (\(1-\) DT)] \(\times\) dr with PDIA = digestible protein in the small intestine from feed in g/kg DM; MAT = crude protein in g/kg DM; DT = theoretical degradability of feed crude protein in the rumen in %; dr = actual digestibility of dietary amino acids in the small intestine in %. PDIN = PDIA + PDIMN where PDIN = digestible protein in the intestine from available nitrogen in g/kg DM; PDIA = protein digestible in the intestine from feed in g/kg DM, equation given above. PDIMN = MAT \(\times\) [1-1, 11 (\(1-\) DT)] \(\times\) 0.9 \(\times\) 0.8 \(\times\) 0.8 with PDIMN = digestible proteins in the gut of microbial origin, limited by degradable nitrogen in g/kg DM; MAT = crude protein in g/kg DM; DT: theoretical degradability in % of meat and q = EM/EB = EM concentration of feed. PDIE = PDIA + PDIME with PDIE = digestible protein in the intestine from available energy in g/kg DM; PDIA = protein digestible in the intestine from food in g/kg DM. PDIME = MOF \(\times\) 0.145 \(\times\) 0.8 \(\times\) 0.8 with PDIME = protein digestible in the intestine thanks to available energy (g/kg DM); MOF = fermentable organic matter = MOD \(-\) [MAT \(\times\) (\(1-\) DT)] where MOD = digestible organic matter = MO \(\times\) dMO; MO = organic matter in g/kg DM; dMO = digestibility of organic matter in %; MAT = total nitrogenous matter in g/kg DM, DT = theoretical degradability in %. UFL/kg DM = ENL/1700. With: ENL = net energy for lactation = 1700 Kcal. ENL = EM \(\times\) KI in kcal/kg DM. With EM = metabolizable energy in kcal/kg DM; KI = yield of EM in EN for milk production; KI = 0.60 + 0.24 (q \(-\) 0.57) where q = EM/EB = EM concentration of feed. UFV/kg DM = ENEV/1820 with ENEV = net energy for maintenance and meat production = 1820 Kcal; ENEV = EM \(\times\) Kmf in kcal/kg DM, where Kmf = (Km \(\times\) Kf \(\times\) 1.5)/(Kf + 0.5 Km), Km = 0.287 q + 0.554 = efficiency of EM in EN for maintenance, Kf = 0.78 q + 0.006 = yield of EM in EN for meat production and q = EM/EB = EM concentration of feed.
## 3 Results and Discussion  
### Slump, Slump Flow, and Tube Flow  
In order to investigate the workability of this MSWI, the experiment of Slump, slump flow, and tube flow was carried out at the same time after the sample was mixed, and the results are shown in Figure 2, when the water-cement ratio was 1.6, the slump measured 26.8-26.5 cm, slump flow was 65.3-56.9 cm, and tube flow registered values of 23.5-21.7 cm, all of which conform to the requirements for ordinary fluidity levels specified in ASTM C143 and ASTM D6103. The replacement of MSWI bottom ash did not cause an increase in slump flow. When the replacement of WFS and MSWI bottom ash was increased, the slump, slump flow, and fluidity diminished gradually, thereby increasing construction difficulty. Two possible causes of this phenomenon were determined.  
(1) The workability of the CLSM decreased with increase in MSWI bottom ash content because the bottom ash possessed a high water absorption rate. The increase in the replacement of MSWI bottom ash increased the water absorption during the mixing. The loss of mixing water decreased the fluidity,  
Figure 2: Effect of MSWI content on flowability.  
thereby causing the slurry to thicken and become sticky when the CLSM was mixed. Under mixing conditions that used the same water amount, increasing the replacement of MSWI bottom ash likely incurs a high loss of workability. Because WFS is a type of clay soil, which represents high water absorption, an increase in the WFS replacement reduces the workability of the CLSM, the result is similar to that of [PERSON] et al. [21] using high water absorption oyster shell (WOS).  
(2) The fineness modulus (FM) of the MSWI bottom ash was 2.86, lower than that (2.98) of fine aggregates. Smaller particles represent a higher specific surface area. After the MSWI bottom ash was used to replace cement in the CLSM, the surface water absorption increased, reducing the fluidity of the CLSM and severely decreasing the self-consolidating property. The increase in the proportion of the MSWI bottom ash considerably affected the workability of the CLSM.
## 3 Results
### Climate-Driving Effects on Cropland Ecosystem NPP across the Entire Study Area  
Table 1 shows the linear regression results between averaged cropland ecosystem NPP and climate factors across the entire study area during 1981-2014 and between cropland ecosystem NPP trends and climate factors' trends of all elevation bins, respectively. Interannual change trend in cropland ecosystem NPP of the entire study area was positively driven by those in precipitation (\(p\) = 0.03) and solar radiation (\(p\) < 0.001) but had a nonsignificant negative relationship with temperature. Solar radiation was the main driving factor on interannual change trend in cropland ecosystem NPP across the entire study area, and its SRC was 8.31 times than that of precipitation. However, linear regression between cropland ecosystem NPP trends and climate factors' trends of all elevation bins showed different results. NPP trend was positively driven by temperature trend (\(p\) < 0.001) and solar radiation trend (\(p\) < 0.001) but was negatively driven by precipitation trend (\(p\) < 0.001). Temperature trend is the main driving factor on NPP trend. The negative effect of precipitation trend on NPP trend was insufficient because most of the precipitation trends were nonsignificant at all elevation bins (Figure 5f).  
Figure 5: Spatial patterns and elevational gradients of interannual change trends in cropland ecosystem NPP, temperature, precipitation, and solar radiation, respectively. Subfigure (**a–d**) show spatial patterns of interannual change trends in temperature, precipitation, solar radiation, and NPP respectively, while subfigure (**e–h**) show elevational gradients of interannual change trends in temperature, precipitation, solar radiation, and NPP respectively. One asterisk next to column bar means the correlation reached a statistically significant level (\(p\) < 0.05). Three asterisks next to column bar refer to a \(p\)-value of the correlation of less than 0.001.
## 3 Results
### Acoustic Properties  
Sound absorption and insulation measurements were carried out, taking into account several test specimens with similar thicknesses and densities; only the most significant measurements were considered.  
The average normal incidence absorption coefficient trends of each sample are reported in Figure 2 in the 100-5000 Hz frequency range (combination of the large and small tube measurements), depending on the additive type in the mixture. The addition of additives improves the sound absorption properties compared to pure Safi plaster (PS 100, dotted line in Figure 2 [35]) for all three groups of additives. This behavior is hardly emphasized with synthetic additives, especially glass wool (GW), paraffin (Pa), or polyester fibers (PF) (Figure 2a). A maximum improvement of 0.02 of the sound absorption average (SAA) [42] value (from 0.06 to 0.08) is observed for Polyester Fiber.  
-planet PS 100 (TL values are in the 32-41 dB range for PS 100 with respect to 30-40 dB for PS 80-Sw 20), but they are also characterized by a lower density. Even with cement (PS 80-Ce 20) and Sisal fiber (PS 80-SF 20) the properties are lower despite having higher densities, probably due to an imperfect mixing of the material during the sample preparation. Both synthetic and mineral additives, in general, involve a slight improvement in the sound insulation properties with respect to pure plaster (up to 4-5 dB).  
Figure 2: Absorption coefficient at normal incidence vs. frequency: gypsum plasters with synthetic (**a**), natural (**b**), and mineral (**c**) additives.  
Figure 3: Transmission loss at normal incidence vs. frequency: gypsum plasters with synthetic (**a**), natural (**b**), and mineral (**c**) additives.  
Compared to polystyrene [18], the sound absorption performance of natural additives is improved; 30 wt.% and 10% of the smallest EPS balls (d = 3 mm, \(\rho\) = 700-840 kg/m\({}^{3}\)) have peak values of \(\alpha\) equal to 0.19 and 0.17 at high frequencies (about 4200-4400 Hz), whereas the natural additive peaks are in the 0.25-0.30 and in the 0.30-0.40 range at mid and high frequencies, respectively. TL values were similar with synthetic and mineral additives (TL = 30-45 dB, depending on the percentage and size of the EPS balls). Bio-additives have values even 12-13 dB higher.
## 2 Experiments
### Preparation of Doped Propellants  
Previous research has suggested that PTFE filled with carbon additives can augment the performance of laser propulsion [33; 34]. To elucidate the implications of carbon additives on plume dynamics, we employed PTFE powder as the substrate, and several carbon particles were chosen as additives to prepare composite propellants. Table 1 provides basic information about the substrate and additives.  
The composite propellant samples in the experiments were prepared under the same conditions via the sintering molding method as shown in Figure 3[29]. The first step in the process was to incorporate the prepared carbon particles into the PTFE powder at a mass ratio of 5:95 and blend to homogeneity using a high-speed agitator (JYL-C50T) at 200 rpm. The mixture was placed in a vacuum drying oven at a constant temperature of 85 \({}^{\circ}\)C for an hour to remove moisture. Subsequently, the mixture was loaded into a specific mold, and a steady pressure of 20 MPa was applied for 20 min to obtain the initial samples, which were left to stand for 24 h to relieve any residual stress. In the next step, the samples were sintered using an SK2 tube furnace with a heating rate set to 50 \({}^{\circ}\)C/h, and argon was used as a shielding gas to isolate them from the air. Once the temperature reached 380 \({}^{\circ}\)C, it was maintained for 6 h to ensure an even mixing of the matrix and dopant. Following this, the tube furnace was gradually cooled to 327 \({}^{\circ}\)C at a rate of 50 \({}^{\circ}\)C/h. when the temperature reached 327 \({}^{\circ}\)C, it was held for 2 h to enhance the crystallinity of the sample. Finally, the tube furnace was shut down, and the argon gas was maintained until the temperature dropped to room temperature. From this sintering process, we produced a series of composite propellants doped with various carbon particles for the experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Size** & **Company** & **Color** & **Melting Point** \\ \hline Nano-carbon powder (C1) & 30 nm & Beijing Dknano & Black & 3830 K \\ Graphite powder (C2) & 30 nm & Beijing Dknano & Black & 3925 K \\ Graphene powder (C3) & 1.2 nm & Beijing Dknano & Black & 4125 K \\ PTFE powder & 25 \(\upmu\)m & American 3M & White & 600 K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Carbon additives and PTFE substrate information.  
Figure 2: Schematic of the experimental system.
###### Abstract  
With the development of civil aviation in China, airspace congestion has become more and more serious and has gradually spread from airport terminal areas to en route networks. Traditionally, most prediction methods that obtain traffic flow data are based on the number of aircraft passing through an en route sector and require flight data to meet strict assumptions and conditions. While these methods are normally used in the actual operation of air traffic flow management departments in China, the results are not satisfactory due to the nonlinearity of traffic demand along en route sectors and the change in high-frequency noise. In order to refine aircraft control in airspace, it is necessary to predict traffic flow accurately. Thus, this paper proposes the quantile regression neural network and kernel density estimation method to obtain some quantiles of continuous traffic demand data in the future, which combines the strong nonlinear adaptive ability of neural networks with the ability of quantile regression to describe explanatory variables. By using these continuous conditional quantiles, we obtain the probability density function and probability density curve of the continuous traffic demand in the future using the kernel density estimation method. In this way, we can obtain not only a specific point prediction value and its change interval but also the probability of each value in the prediction change interval of traffic demand in the en route sector as well as a more accurate point prediction value for a specific day.  
air traffic demand prediction; en route sector; neural network quantile regression; kernel density estimation; probabilistic density +
Footnote †: journal: Applied sciences
## 2 Threat Model  
This work considers an outside attacker aiming to compromise a target machine within a local network protected by a typical standard firewall (e.g., the default MS Windows firewall, characterized by stateful packet filtering and allowing outbound traffic) and common antivirus software. The attacker's goal is to exfiltrate data and execute commands on the target system, thereby violating the confidentiality and integrity properties of the Confidentiality, Integrity and Availability (CIA) triad. While availability may also be a target through credential or access manipulation, this is not the primary focus of this threat model.  
In more detail, the below assumptions are considered:  
* Outside attacker: The attacker operates from outside the target network and does not have prior authorized access.
* Target environment: The target machine resides within a local network protected by a standard firewall and antivirus.
* Initial access: We assume the attacker has already gained initial access to the target network. The specific methods used for network penetration (e.g., phishing, social engineering, exploiting vulnerabilities in network services) are outside the scope of this threat model, which rather focuses on the post-exploitation phase. It is acknowledged that initial access is crucial, and different methods can impact the post-exploitation phase.
* Client-side attack: The attack vector targets the client machine within the network.
* Attack objective: The attacker aims to exfiltrate data, execute arbitrary commands on the compromised machine, and perform lateral movement.  
Communication channel: The attacker leverages the generally open outbound HTTP/3 (QUIC) [8] traffic from the target machine's web browser as a covert communication channel. This protocol is leveraged due to the challenges associated with inspecting its encrypted traffic.
* Tools and techniques: The attacker utilizes a malicious browser extension and a corresponding native application installed on the target machine. Communication between the attacker's C2 server and the target is facilitated through the browser extension and native application via the native messaging API. Reflective DLL loading is used to execute malicious code within the native application's memory space.  
Building upon the assumptions outlined, this threat model does not consider insider threats, attacks directed at network infrastructure (such as firewall compromise or router exploits), the specific methodologies employed for initial network penetration, or Denial-of-Service (DoS) attacks designed to disrupt availability. Specifically, the model addresses the post-exploitation phase, focusing on the techniques used for data exfiltration and command execution after the attacker has gained initial access to the internal network.
## 4 Experiments and Results
### Results
#### 4.1.1 Analysis of GUI  
A GUI in computer science allows seamless interaction between an application and a user. The GUI for a digital twin allows two functionalities: (1) selecting and loading the appropriate interval cycling training session, and (2) controlling the implementation of the loaded session.  
The GUI of the proposed digital twin is designed according to the following guidelines [30]:  
* minimalist and aesthetic design;
* operating system compatibility;
* data integrity;
* easy navigation and usability;
* security.  
The minimalist design means that unimportant and not relevant information is kept to a minimum. That is, the display must be reduced for displaying only those elements that are necessary for the implementation of the interval cycling training session. The central point of the digital twin GUI presents an implementation of the interval cycling training session (Figure 3), where only five icons are presented, denoting five training load indicators. The icon in the center of the screen denotes the difference between the proposed and average heart rate. In the perception process of the cyclist, the image of the icon gets relayed back to his/her brain. Obviously, a motivated athlete wants to minimize the negative value of the difference as fast as possible. These icons are designed in an aesthetic way that attracts the athlete to look at the screen. In the sense of virtual reality, the real situation is additionally emphasized by using sound besides the visual display elements. For instance, when either the end of the current interval or the start of the next one has occurred, the event is announced by a sound signal to the athlete. Although a more detailed heartrate GUI could be displayed in Figure 3, the minimalist design guideline prevents us to made it more complex.  
Operating system compatibility demands that the GUI design should ensure portability on similar platforms. The digital twin GUI is implemented in the Python programming language with the use of the PyQt5 framework. Although it is intended to be used on a Raspberry Pi device, PyQt5 ensures broad portability to various computer platforms.  
Data arising in a digital twin do not remain on the origin platform for a long time, because they are copied after finishing each implementation onto the AST central system. Obviously, this is equipped with data integrity mechanisms as well.  
Navigation of this GUI is simple due to the limited number of screens. Moreover, this is not even desired for the implementation screen, which is devoted to monitoring performance and prediction data only, where even navigation is excessive.  
The security of the mobile device is ensured using an encrypted communication channel and allowing access to the application to the registered users only.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 5 Experimental Results and Discussion
### Experimental Results  
Packet delivery ratio \((PDR)\), received signal strength indicator \((RSSI)\) and signal-to-noise ratio \((SNR)\) are the metrics selected to evaluate the performance of the LoRa transmission link. \((PDR)\) is calculated as the number of packets received by the gateway to the total number of packets sent, with a value of 1 implying 100% success and a value of 0 implying no success at all. Figure 7 and Table 4 show the approximate straight line distance of the positions with respect to the gateway. We investigate below the effects of the payload length, the \(SF\) and \(CR\) on the packet reception.  
Figure 8: Image showing the environment in the green and red subsets.  
Figure 7: The Prototype devices (LoRa end-node) placed at different positions (POS A–POS F, POS 1–POS 4), and the Gateway indicated as POSRX.
## 4 Results and Analysis  
Table 6 shows the debris flow velocity, flow depth, Froude number, total travel distance, and mobility ratio observed through the flume experiments, where \(N\) is the experiment number. In the case of the straight channel test, the development of debris flow was insufficient to observe the flow depth when \(\alpha\) was \(10^{\circ}\) and \(C_{V}\) was 0.60; however, debris flow was sufficiently developed when \(\alpha\) was \(15^{\circ}\) or greater. In the single-berm channel test, debris flow stopped in the channel when \(C_{V}\) was 0.60, regardless of \(\alpha\), so these values are not reported in the analysis below. In addition, when \(\alpha\) was \(20^{\circ}\) or less and \(C_{V}\) was between 0.50 and 0.55, the development of debris flow was insufficient to observe the flow depth in some experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Test Type** & \(N\) & \(\alpha\) (\({}^{\circ}\)) & \(C_{V}\) & \(\begin{array}{c}v\\ (\mathbf{m}\cdot\mathbf{s}^{-1})\end{array}\) & \(h\) (\(\mathbf{m}\)) & \(F_{r}\) & \(H\) (\(\mathbf{m}\)) & \(L\) (\(\mathbf{m}\)) & \(H\)/\(L\) \\ \hline \multirow{8}{*}{Straight channel} & 1 & 10 & 0.40 & 1.826 & 0.0198 & 4.14 & 0.391 & 2.459 & 0.159 \\  & 2 & 10 & 0.45 & 1.697 & 0.0160 & 4.28 & 0.391 & 2.340 & 0.167 \\  & 3 & 10 & 0.50 & 1.559 & 0.0108 & 4.79 & 0.391 & 2.194 & 0.178 \\  & 4 & 10 & 0.55 & 1.324 & 0.0058 & 5.55 & 0.391 & 2.105 & 0.186 \\  & 5 & 10 & 0.60 & 1.169 & - & - & 0.391 & 1.780 & 0.220 \\ \cline{1-1}  & 6 & 15 & 0.40 & 1.929 & 0.0184 & 4.54 & 0.507 & 2.630 & 0.193 \\ \cline{1-1}  & 7 & 15 & 0.45 & 1.811 & 0.0150 & 4.72 & 0.507 & 2.541 & 0.200 \\ \cline{1-1}  & 8 & 15 & 0.50 & 1.697 & 0.0104 & 5.31 & 0.507 & 2.339 & 0.217 \\ \cline{1-1}  & 9 & 15 & 0.55 & 1.621 & 0.0050 & 7.32 & 0.507 & 2.098 & 0.242 \\ \cline{1-1}  & 10 & 15 & 0.60 & 1.231 & 0.0038 & 6.38 & 0.507 & 1.763 & 0.288 \\ \cline{1-1}  & 11 & 20 & 0.40 & 2.265 & 0.0168 & 5.58 & 0.620 & 2.661 & 0.233 \\ \cline{1-1}  & 12 & 20 & 0.45 & 2.083 & 0.0142 & 5.58 & 0.620 & 2.560 & 0.242 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Observed data from flume experiments.  
Figure 5: Flow chart of flume experiments.
# Experimental Study on the Mechanical Properties of Deep Granite under Gradient-Confining Pressure
## 1 Introduction
[PERSON] et al. conducted uniaxial compression tests on granite specimens under high confining pressure cyclic loads. Through nuclear magnetic resonance (NMR) and acoustic emission (AE) analyses, the porosity, secondary uniaxial peak strength, and dynamic fracture characteristics were obtained throughout the pre-damage test [24]. [PERSON] et al. investigated the type I fracture characteristics of granite specimens under diverse temperatures and burial depths via the semicircular bending (SCB) test. The test results demonstrated that there were significant differences in the mineral composition and crack characteristics of granite at different burial depths. The effects of the high-temperature notably weakened the influence of burial depth on the fracture characteristics of granite [25]. Through triaxial compression creep tests under the action of temperature and disturbance loads, [PERSON] et al. reported that the triaxial compression creep curve of granite under the action of temperature and disturbance loads encompasses attenuation, stability, acceleration, and disturbance phases. Alongside the increase in the loading rate, both the shear strength and the residual strength decreased [26]. [PERSON] et al., based on the nanoindentation test, explored the microcrack evolution and crack characteristics of different minerals within granite. The results indicated that intracrystalline cracks play a crucial role in the failure process of brittle rocks and largely dominate the macroscopic properties of the materials [27]. In summary, despite the numerous existing studies on deep granite, the changes in the mechanical properties and characteristic stresses under the effects of different confining pressures are still not well understood. Moreover, the stress and deformation evolution processes during triaxial compression under high confining pressure and low confining pressure have yet to be investigated thoroughly. This paper will attempt to address these issues.