esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### Restructuring a Traditional AMHS Using ARB  
#### 3.1.1 Introduction to the ARB  
Figure 1 shows the configuration of an ARB. One essential part of the ARB is polyurethane rollers whose core are made of acetal. The diameter of these rollers is 22.3 mm and rollers are skewed either 45\({}^{\circ}\) from travel direction of belt. When these belt rollers are activated, products will move faster than the belt's speed; otherwise, the product travels at the belt's speed. The product behavior will vary according to its shape, weight, conveyor design, and belt speed. Custom belts consisting of any combination of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), or 60\({}^{\circ}\) are available. Custom belts can also include rollers oriented in different directions.  
Figure 2 shows the example of an AMHS with ARB conveyor. On the ARB conveyor, products are resting on the free-turning angled rollers instead of on the belt surface. These rollers stretch above and below the belt surface and are situated at an angle that correlates with the moving direction of belt travel. Rollers that are activated by the carry way surface beneath move products across the belt in the moving direction of roller orientation instead of the moving direction of belt travel. The ARB can move items specifically and selectively, and it can alter the direction, arrangement, location, and speed of a product independently.  
Figure 1: Intralox conveyor Belling Series with 45\({}^{\circ}\) Angled Roller.  
Figure 2: The example of an AMHS (Automated Material Handling System) with ARB (Activated Roller Belt) conveyor.
## 4 Experiments and Results
### Results and Analysis
#### 4.4.1 Cleanup  
Figure 4 shows the performance of A3C and PPO algorithms with SR and AEN in the _Cleanup_ environment. The experimental curve clearly shows the improvement effect of our proposed algorithm on the baseline algorithm.  
The shaded part in the figure represents the fluctuation of the experimental results of multiple groups. The baseline algorithm in the figures indicates that the collective reward of basic agents can quickly converge to 25 by A3C and 50 by PPO. However, there is no performance improvement after convergence. Without the help of external forces, the agent in _Cleanup_ is caught in a social dilemma: no agent will give up some individual interests for the long-term collective interests. However, after applying SR, the collective reward did not stop at the baseline level, but continued to improve with the passage of training time. After training six million steps, the collective reward of SR-PPO reached an average of about 400, and AEN-PPO reached 800. After training 9 million steps, the collective reward of SR-A3C reached an average of about 125, and AEN-A3C reached about 300.  
We know that in the _Cleanup_ environment, if we want to get more resources, there must be agents in the environment to clean up the river. This is not profitable for the individual as they need to lose profit. Therefore, agents that use baselines rarely choose to clean up the river, and are greedy to collect resources. However, after applying SR, agents will choose to clean the river out of consideration for collective interests. The loss of individual interests will be compensated by contributions, thus breaking the social dilemma. The calculation of the contribution in SR is based only on the current trace, which is too limited. The evaluation network in the AEN is constantly updated during the training process.  
After referring to more data, the AEN can get a more accurate contribution calculation. Therefore, compared with SR, the performance of the collective reward using the AEN will be superior. Experimental results show that our proposed algorithm breaks the social dilemma by encouraging agents to contribute to the collective.  
Figure 4: Experiment results in _Cleanup_. The top two figures are the results of our method applied to A3C. The bottom two figures are the experiment results based on PPO.
# Research on Tracking Technique Based on BPSK-CSK Signals
## 2 Materials and Methods
### BPSK-CSK Signal Model  
In terms of signal structure, the CSK-modulated part and the BPSK-modulated part are inserted by synchronised time division multiplexing, i.e., both signals have the same time period. In the BPSK cycle, the navigation information is multiplied with the spread spectrum code and then transmitted to the carrier using BPSK modulation. In the CSK cycle, the spread spectrum code is shifted by CSK and then transmitted by BPSK modulation to the carrier. Figure 3 shows the schematic of time division multiplexing with BPSK modulation and CSK modulation.  
Figure 1: CSK modulation process.  
Figure 2: FFT-based CSK demodulation schematic.  
The digital IF signal in the input tracking loop is:  
\[r(t_{1})=\sqrt{C}\cdot d(t_{1})\cdot c(t_{1})\sin[2\pi f_{i}t_{1}+\theta_{i}]+n_{w }(t_{1}) \tag{1}\]  
\[r(t_{2})=\sqrt{C}\cdot c(t_{2})\sin[2\pi f_{i}t_{2}+\theta_{i}]+n_{w}(t_{2}) \tag{2}\]  
where \(r(t_{1})\) is the received BPSK periodic signal at moment \(t_{1}\); \(r(t_{2})\) is the received CSK periodic signal at moment \(t_{2}\); \(C\) is the total signal power; \(d(t_{1})\) is the navigational message symbol; \(c(t_{1})\) is the periodic spreading code; \(c(t_{2})\) is the spreading code after modulation by CSK; \(f_{1}\) is the received signal frequency in Hz; \(\theta_{i}\) is the carrier phase of the signal; \(n_{w}(t_{1})\) and \(n_{w}(t_{2})\) are additive Gaussian white noises; and the power spectral density is \(N_{0}\).
# Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture
## 4 Smart Agriculture, Sensing Technology and Security Attacks
In this work, we try to address the following attacks using IoT status and anomaly data monitoring solutions. Denial of service (DoS): An adversary may send malicious network traffic to the victim farmer network to shut down the services, including the sensor devices and routers connected to the network. This may interrupt the operations if we consider that these devices are used for food supply-chain applications. The attack might also originate from diverse source IP addresses, making it difficult to detect and block the attack traffic. DoS attack scenarios in IoT include consuming the IoT device resources, IoT device and gateway communication bottlenecks, or flooded the gateway with malicious attack traffic. Physical security attacks: Intruders trespass on the agriculture fields and farm premises to destroy the property or with other bad intents such as theft, arson, etc. The camera sensors installed on the farm premises send the data to monitor and alert the farm owners when physical attacks happen in smart agriculture. An adversary may also visit the farm site to install or compromise the farm network. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor Type** & **Smart Agriculture Application** & **Sensor Input** \\ \hline Temperature & Plants, air, water & Heat energy \\ \hline Humidity & Air, water, crop, plant & Humidity of the surrounding air \\ \hline Camera & Storage area or farm monitor & Photon detection \\ \hline Light & Fields, crop & Light or radiant energy \\ \hline Accelerometer & Smart-agriculture equipment maintenance & Electromechanical \\ \hline pH & Soil nutrient deficiencies, presence of unwanted chemicals & Electrochemical \\ \hline GPS & Keeping track of flocks, highly precise agriculture vehicle guidance & Measuring the distance using satellite technology \\ \hline Dielectric soil moisture & Water moisture in soil & Dielectric constant measurements \\ \hline Pressure & Atmospheric pressure in air & Physical pressure \\ \hline Passive Infrared & Track individuals’ movement & Motion detection \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Smart-agriculture sensors and their applications. Sensing-data-manipulation attack: The IoT sensor-data manipulation with malintent prior to going to the destination is another kind of attack seen in IoT. The adversary may perform a man-in-the-middle attack to read the data passing through the communication channel and embed malicious data to perform the attacks. A zero-day vulnerability in the IoT devices can also be exploited to compromise the sensor device and fake the sensor data to hide the malicious activity. There are various ways to get into the network and manipulate the data unless we have good security controls covering the data-link-layer to application-layer protocols.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
## 3 GBSM Non-Stationary Channel Statistical Properties  
In this section, we present the distribution of scatterers and derive formulas for temporal ACF, spatial CCF, and FCF. The cylinder model based on GBSM is shown in Figure 3, we use different distributions to describe the azimuth and elevation angles of scatterers, respectively. We assume that the azimuth angles \(\varphi_{2}\) and elevation angles \(\theta_{z}\) of the received signal are independent, \(\varphi_{2}\) follows von-Mises distribution, and \(\theta_{z}\) follows Laplacian distribution [31; 40]. Specifically, the von-Mises distribution can simulate some typical channel characteristics well, such as multipath scattering in the HAP channel. Moreover, the parameters of the von-Mises distribution can reflect the correlation and spatial coherence characteristics of the channel, which more comprehensively describes thechannel state transition process. Similarly, the Laplacian distribution has similar advantages in describing elevation angle distributions. It can well describe phenomena such as high correlation and vertical signal attenuation in the HAP channel. The scatterers distribution map can be outlined as follows  
\[f_{1}(\varphi_{z})=\frac{exp(k_{1}(\varphi_{z}-\overline{\varphi_{z}}))}{2\pi I_ {0}(k_{1})},-\pi\leq\varphi_{z},\overline{\varphi_{z}}\leq\pi, \tag{23}\]  
\[f_{2}(\theta_{z})=\frac{\pi}{4\theta_{m}}cos(\frac{\pi(\theta_{z}-\overline{ \theta_{z}})}{2\theta_{z}}),\overline{\theta_{z}}-\theta_{m}\leq\theta_{z} \leq\overline{\theta_{z}}+\theta_{m}\leq\pi/2, \tag{24}\]  
where \(I_{0}(.)\) is the zero-order Bessel function and \(k_{1}\) controls the density of \(f_{1}(\varphi_{z})\), setting \(k_{1}=0\) incurs isotropic scattering, i.e., \(f_{1}(\varphi_{z})=1/2\pi\). As \(k_{1}\) increases, the scattering becomes increasingly non-isotropic. \(\overline{\varphi_{z}}\), and \(\overline{\theta_{z}}\) are the mean values of \(\varphi_{z}\) and \(\theta_{z}\), respectively, and \(\theta_{m}\) denotes the maximum elevation angle in the communication environment.  
The non-stationary nature of the channel can be reflected through the correlation function. This chapter models the channel model for non-stationary characteristics in the time, space, and frequency domains. By calculating and comparing the consistency of the changes in the two parameters of the correlation function, the non-stationary nature of the channel with the changes in the above parameters can be verified separately [41].  
\[\begin{array}{l}R_{qm,pn}(t,f;\Delta r,\Delta t,\Delta f)=E[h_{qm,pn}^{*}(t, f)h_{\Delta r}(t+\Delta t,f+\Delta f)]\\ =\frac{K}{K+1}R_{qm,pn}^{\text{LoS}}(t,f,\Delta r,\Delta t,\Delta f)+\frac{1}{K +1}\sum\limits_{m=1}^{M}R_{qm,pn}^{\text{NLoS}}(t,f,\Delta r,\Delta t,\Delta f )\end{array}, \tag{25}\]  
where (\(J\))* denotes complex conjugate operation, \(E\)[\(\,\)] denotes the statistical expectation operation.
## 2 Results and Discussion
### Discussion
The absence of the male-specific Imp induces the female-specific splicing of _Bmdsx_ pre-mRNA (Figure 6B). In this model, both the proximal and distal poly(A) sites are exposed to the polyadenylation factors at the same time in male cells. How then is the distal poly(A) site exclusively selected? While the distal poly(A) signal sequence is perfectly matched to the most canonical poly(A) signal hexamer AAUAAA, the proximal poly(A) signal sequence is AAGAAA, which is consistent with a single-nucleotide variant of the canonical hexamer. The motifs that are functional in vertebrates are AAUAAA and its highly conserved variants (e.g., AUUAAA, UAUAAA, AGUAAA, AAGAAA) [54]. Among those hexamers, the canonical AAUAAA was reported to be present in 53% of the mRNAs; in contrast, a single-nucleotide variant AAGAAA was found in only 3% of the mRNA [55]. Therefore, one can reasonably suppose that the polyadenylation machinery prefers the distal poly(A) site rather than the proximal poly(A) site when both poly(A) sites are present on the nascent transcript simultaneously. This scenario is consistent with the kinetic coupling model for the regulation of alternative splicing by RNAP II elongation [56]. In this model, when a proximal suboptimal (weak) 3' splice site and a downstream canonical (strong) 3' splice site are presented simultaneously to the splicing machinery, the strong 3' splice site could easily outcompete the weak site, resulting in alternative exon skipping. Our results indicate that both alternative polyadenylation and alternative splicing depend on RNAP II kinetics. In the present study, we were unable to assess precisely whether DOT1L plays a crucial role in regulating sex determination or sexual differentiation of _B. mori_ because nearly all the embryos injected with _DOT1L_ siRNA died before hatching. Consistent with our results, _Dot1L_-deficient embryos died between 9.5-10.5 days _post coitum_ due to developmental abnormalities, including growth impairment, angiogenesis defects in the yolk sac, and cardiac dilation [57]. In _D. melanogaster_, _gappa_ (_gpp_) is an ortholog of _Dot1L_[58]. _gpp_ is an essential gene identified in a genetic screen for dominant suppressors of pairing-dependent silencing where a _Polycomb-group_ (_Pc-G_)-mediated silencing mechanism necessary for the maintenance of parasegment identity during embryo development [59]. As is the case in _D. melanogaster_, _DOT1L_ in _B. mori_ may be required to maintain developmental gene expression through _Pc-G_-mediated mechanism. Assuming that our model presented above is valid, an ELL family protein--such as ELL2--might be a key factor in facilitating male differentiation as a result of inducing the male-specific splicing of _Imp_ pre-mRNA. Recently, we found that an _ELL2_ homolog is expressed in embryos in early developmental stages. Ongoing investigations are aimed at determining whether the _ELL2_ homolog is involved in regulating the sex-specific splicing of _Imp_ pre-mRNAs.
# Implications of Metamaterial on Ultra-Wide Band Microstrip Antenna Performance
## 5 Conclusions  
UWB circular microstrip antenna with elliptical rings was designed covering bandwidth between 3.5 GHz to 9 GHz. In order to enhance the antenna performance, multiple techniques used based on implementing metamaterials SRR. Arrays with different structure and position with respect to the UWB antenna were investigated. The study covered two types of double-ring SRR, these are C-SRR and S-SRR. The C-SRR array effect is noticed on antenna's behavior, such as increasing the frequency bandwidth to be 2.2 GHz to 9.8 GHz with rejection bands and frequencies. Additionally, C-SRR ring rotation was investigated; rotation resulted in a larger bandwidth, exhibited frequency band rejection, increased antenna gain and could obtain multiple rejection frequencies. A similar performance was observed with the implementation of the S-SRR array structure. Other designs of SRR can be analyzed and implemented; different designs have different resonant frequencies, which will affect the antenna's performance. This study will open the door for future investigation of tunable array configuration to suite desired metamaterial applications. Furthermore, antenna's width and length, patch shape, the radius of a circular patch, patch slots and ground size are parameters that affect the whole performance of the antenna. In this case, the antenna can be selective and operates at other frequency bands rather than UWB.  
Conceptualization, M.I.H. and E.A.S.; methodology, M.I.H. and E.A.S.; software, E.A.S.; validation, M.I.H. and E.A.S.; formal analysis, E.A.S.; investigation, E.A.S.; resources, M.I.H.; data curation, E.A.S.; writing--original draft preparation, E.A.S.; writing--review and editing, M.I.H.; visualization, E.A.S.; supervision, M.I.H.; project administration, M.I.H.; funding acquisition, M.I.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 4 Stratigraphic Characteristics
### Marker Beds  
Marker beds are strata with distinct physical characteristics that are critical for stratigraphic division and correlation. The sedimentary environment within the Yan'an Formation exhibits considerable variation, leading to the development of abundant marker beds. These primarily consist of the Baotashan sandstone, Peizhuang sandstone, Zhenwudong sandstone, and coal seams.  
Baotashan Sandstone: Located at the base of the Yan'an Formation, this is a massive, gray, thick-bedded sandstone. It generally displays a fining-upward sequence (Figure 2a), primarily medium- to coarse-grained. Large planar and trough cross-bedding are prominent (Figure 2b), distinguishing it clearly from the underlying strata.  
Peizhuang Sandstone: This is a yellow-green, massive, fine-grained sandstone, with a thickness ranging from 20 to 40 m. It is overlain by gray-black muddy siltstone (Figure 2c) and underlain by interbedded mudstone and siltstone. The sandstone contains iron nodules (Figure 2d), shows high lithologic maturity, and has good sorting, making it a key marker for identifying the Yan 6 reservoir.  
Zhenwudong Sandstone: This is a reddish-brown transitioning to gray-white (Figure 2e), fine- to medium-grained feldspathic sandstone with gravel content. It features parallel bedding and cross-bedding (Figure 2f), with distinct scour marks at the base. The Zhenwudong sandstone occurs within the Yan 3 member and serves as a key marker for identifying Yan 3.
## 3 Results
### Regional Heterogeneity Analysis
#### 3.3.1 Eastern Region  
As shown in Table 8, both the DE and GD levels in the eastern region passed the unit root tests, allowing for PVAR estimation with a lag order of 1. After constructing the model using GMM estimation, we conducted impulse response analysis to explore the dynamic relationship between the two variables, as illustrated in Figure 5.  
The DE exhibited a positive feedback response to its own shocks, reaching its peak impact in the current period, which gradually diminished over time. This suggests that the DE relies on its own inertia for development, although this influence weakened as time progressed. In terms of GD, the initial impact of the DE was zero, but it quickly shifted to a negative effect. By the second period, this negative impact diminished and began to evolve toward a positive influence. This indicates that GD initially experiences a  
Figure 5: Impulse response function in the eastern region. (**a**) Impulse response function of DE to DE. (**b**) Impulse response function of DE to GD. (**c**) Impulse response function of GD to DE. (**d**) Impulse response function of GD to GD. (The black solid line is an impulse response curve that reflects the response effect of the variable to an impact, and the upper and lower black dashed lines represent confidence intervals of two standard deviations, estimates of 95% and 5% quantiles, respectively. The red dashed line represents the position where the response level is 0. The same is true for other impulse response graphs).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Test Methods**} & \\ \cline{2-4}  & **LLC** & **IPS** & **Result** \\ \hline DE Levels (East) & 0.00 & 0.08 & Smooth \\ GD Levels (East) & 0.00 & 0.00 & Smooth \\ DE Levels (Central) & 0.00 & 0.10 & Smooth \\ GD Levels (Central) & 0.00 & 0.08 & Smooth \\ DE Levels (West) & 0.00 & 0.36 & Unsmooth \\ GD Levels (West) & 0.00 & 0.10 & Smooth \\ \hline \hline \end{tabular}
\end{table}
Table 8: Unit root test for the DE and GD levels in the eastern, central, and western regions (LLC and IPS).  
suppression due to the shocks from the digital economy, but over time, the negative impact gradually weakens. Simultaneously, the response of GD to its own shocks is initially the most pronounced, but subsequently shifts from positive to negative. This change suggests that while green development can effectively enhance itself in the early stages, it may encounter internal challenges as time progresses. Notably, the impact of GD on the DE displayed an opposite trend to that of its own shocks: it began with a negative influence but shifted to a positive impact from the first period onward. This reflects the complex interplay between the two variables.
###### Abstract
To validate the proposed monitoring system, this study was designed in two phases: first, a controlled laboratory evaluation using cylindrical concrete specimens; and second, a field validation in an industrial environment with precast New Jersey barriers. This sequential approach allowed progressive assessment of sensor performance under increasing complexity. To evaluate the system's performance and sensor accuracy, preliminary tests were conducted both in a controlled laboratory setting and in an industrial production environment using cylindrical concrete specimens. Following these initial validations, the system was deployed to monitor New Jersey barrier specimens under varying environmental conditions, one stored indoors in a warehouse and another exposed to outdoor conditions. The collected data were analyzed to assess the feasibility of the monitoring system in optimizing the production chain of precast concrete elements and improving final product quality. This paper is structured into three main sections. Section 2 details the materials used, sensor system design, experimental procedures, and data analysis methodology. Section 3 presents and interprets the findings, emphasizing correlations between temperature and humidity variations and the early-age behavior of concrete, as well as the influence of embedded sensors on mechanical performance. Finally, Section 4 summarizes the key outcomes of this study, highlights system limitations, and outlines potential future research directions for further optimization.