esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 Sample Collection  
Sediment samples were taken at the end of August 2019 and 2020, respectively. Both sampling campaigns were performed during low-stage periods, preceded by summer floods reaching similar heights (Figure 3). Thus, the sampled sediments were deposited by the summer flood waves, and they represent just the pollution of a preceding flood wave.  
Sediment samples were collected from in-channel sedimentary bodies of the Tisza and its main tributaries. We aimed to sample the Tisza evenly, at approx. every 30 km, and to have sampling above and below confluences so that the impact of a tributary could be assessed. During the 2019 sampling campaign, samples were collected at 59 sites on the Tisza and its tributaries [29]; however, in 2020, we condensed the sampling points (70 sites) along the Tisza and extended the measurements to the territory of Serbia. Unfortunately, the sampling at five sites in the Upper Tisza became impossible due to the Ukrainian-Romanian border's increased control. Altogether 11 tributaries were sampled near their confluence and ca. 20 km upstream.  
The most commonly sampled in-channel fluvial form was side-bar (Tisza: \(n=27\), tributaries: \(n=14\)), which developed along sinuous sections. Point-bars along the convex banks of meanders are disappearing due to the human-induced incision of the river [43]. Therefore, they were sampled only at 10 sites along the Tisza and at 3 places in tributaries. Sediment sheets were sampled along the almost straight sections (Tisza: \(n=12\), tributary: \(n=4\)).  
During the first sampling in 2019, mainly coarse-grained samples were collected; however, at the 2020 sampling, both coarse- and fine-grained samples were collected. Based  
Figure 3: The sampling campaigns were performed in August 2019 and 2020. The sampled sediments were deposited by an early summer flood wave.  
Figure 2: Daily water stages measured in 2006 at the Upper, Middle and Lower Tisza.  
on our former field experiences, it was expected that during falling or low water stages only a thin sediment-layer is deposited. As we aimed to sample the freshly deposited sediments, only the upper (0-1 cm) layer was sampled. At a given site, sediments were collected at several points using an iron spatula, and the collected ca. 1 kg material was homogenized.
# Observation and Numerical Simulation of a Windshear Case at an Airport in the Qinghai-Tibet Plateau
## 3 Results and Discussion
### Mechanical of the Easterly Jet  
The simulation results provide the basis to understand the occurrence of low-level jets as shown in the LIDAR's velocity data. For that purpose, the time-height cross section of the simulated horizontal wind and vertical wind at the location of the LIDAR is shown in Figure 8a. It could be seen that, at the times of the occurrence of the pulses of the stronger easterly wind between around 22:00 and 22:30 local time, the simulation result gives quite significant downward velocity. As such, the pulses might be related to the downward transfer of horizontal momentum from heights as high as 1000 m to 1200 m above ground.  
Figure 8: _Cont._  
At the same time, the horizontal distribution of wind is also inspected. The LIDAR at the airport has also been configured to perform range-height indicator (RHI) scans, but such Doppler velocity images are not available. Nonetheless, simulated RHI images could be obtained from the simulation data, and a sample is shown in Figure 8b. At the height of the easterly pulses, there are, in fact, streaks of strong horizontal winds (darker blue in the order of 12 m/s for Doppler velocity).  
Based on the above results, downward transfer of momentum could be a possible mechanism for the occurrence of the easterly pulses, coupled with the horizontal transfer of the higher wind streaks at the height of the easterly pulses. The simulation results provide a novel view of the mechanism of the easterly pulses for this airport.
## 3 Test Results
### Variation Patterns of Various Factors on SRNR Performance
#### 3.3.1 Variation Patterns of Various Factors on Skid Resistance Performance  
As shown in Figure 9, as the water-binder ratio increases, the pendulum value gradually rises; with the increase in the sand ratio, the pendulum value first increases and then decreases. For both water-binder ratio and sand ratio, the maximum value does not exceed 2.5% compared to the minimum value. This indicates that the influence of water-binder ratio and sand ratio on the skid resistance performance of EAC surfaces is not significant.  
As the coarse aggregate volume ratio increases, the pendulum value first increases and then decreases. When the coarse aggregate volume ratio increases from 40% to 42%, the exposed aggregate area ratio changes slightly, only increasing by a small margin of 0.59%, while the exposed aggregate depth increases by 0.22 mm. The rise in pendulum value is primarily attributed to the increased exposed height of coarse aggregates on the EAC surface, enhancing the frictional effect of the coarse aggregates and thereby improving surface skid resistance performance. When the coarse aggregate volume ratio increases from 42% to 44%, the exposed aggregate area ratio increases by 5.93%, while the exposed aggregate depth decreases by 0.15 mm. According to research by the International Road Federation (IRF), the skid resistance performance between tires and pavement primarily depends on the pavement surface micro-texture under low-speed driving conditions [35]. On EAC surfaces, the mortar possesses richer micro-textures. The pendulum friction tester simulates low-speed vehicle driving; therefore, the primary reason for the decrease in pendulum value at this stage is that the increased coarse aggregate area ratio obstructs contact between the pendulum friction tester's rubber slider and the cement mortar, thereby reducing the EAC surface skid resistance performance.  
As the proportion of aggregates > 9.5 mm increases, the pendulum value gradually increases. When the proportion of aggregates > 9.5 mm increases from 30% to 50%, the exposed aggregate depth only decreases by 0.01 mm, and the exposed aggregate area ratio decreases by 3.74%, while the pendulum value only increases by 1.00 BPN. This indicates that when the exposed aggregate area ratio is from 66.90% to 70.64%, the impact of the exposed aggregate area ratio on the pendulum value is relatively small. When the proportion of aggregates > 9.5 mm increases from 50% to 70%, the exposed aggregate depth decreases by 0.30 mm, and the exposed aggregate area ratio decreases by 5.79%, while the pendulum value increases by 4.34 BPN. This indicates that when the exposed aggregate area ratio is from 61.11% to 66.90%, the impact of the exposed aggregate area ratio on the pendulum value is relatively large. Although the frictional resistance of the coarse  
Figure 9: The variation patterns of the pendulum value and noise value.  
aggregate decreases, the contact area between the rubber slider of the pendulum friction tester and the cement mortar continuously increases, enhancing the surface skid resistance of the EAC.
## 2 Ground-Based Interferometric Radar  
A radar detects and ranges objects using electromagnetic waves. It transmits microwave signals and receives echoes from targets within its antenna field of view Figure 1 shows a simple scheme of the ranging performed by real aperture radar, aimed at observing a bridge deck. Different parts of the structure are associated with \"radar bin\" [23]. With respect to conventional radar, which is only capable of measuring the amplitude of the received signal, an interferometric radar can measure the phase with a sensibility that is a small fraction of its wavelength: for this reason, sub-millimeter displacements can be measured.  
The signal to noise ratio (SNR) is a parameter of the radar backscattering quality: the higher, the better is the achievable accuracy [33]. To separate two or more targets in the radar range direction, the distance between them must be larger than the radar range resolution, which is dictated by the bandwidth of the radar. A radar bin is an elemental space unit, containing the response of a radar sampling footprint. The amplitudes of the received radar signals in all the range bins make a range profile, in which the outstanding peaks indicate the best reflecting part of the structure at hand.  
IBIS-S, manufactured by IDS (Ingegneria Dei Sistemi S.p.A.), is a commercial radar system with interferometric capability. The system consists of a sensor module for transmitting and receiving signals, a control PC for system controlling and data collecting, a power supply unit and data processing software. The radar sensor is mounted on a tripod equipped with a rotating head to adjust the bearing of the antenna towards the investigated structure. The sensor transmits an electromagnetic signal at a central frequency of 17.2 GHz (Ku-band) with a maximum bandwidth of 300 MHz, corresponding to a range resolution of 0.5 m. Range resolution is intended here as the minimum distance required between two targets to be separated--i.e., seen as two different subsequent bins. For details on the radar equipment and the used radar data processing please refers to [33]. The main characteristics of the sensor are summarized in Table 1.
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 3 General Framework for ERO-ATSC  
This section presents a brief transition from the standard ER technique to the ERO framework. Again, the section provides a detailed formulation of two comprehensive frameworks of the ERO algorithm. While the first framework combines ERO with a dual memory and an ATSC, the second framework puts together ERO, a double memory, an ATSC and a TSC mechanism. The section further shows an investigation of the effect of different retention strategies and buffer sizes on the stability and performance of the two frameworks, considering the varying sampling ratios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Algorithm** & \
\begin{tabular}{c} **Sampling** \\ **Uniform** \\ \end{tabular} &
\begin{tabular}{c} **Strategy** \\ **Priority** \\ \end{tabular} & \multicolumn{3}{c}{**Retention Strategy**} \\
**Sequential Uniform** & **Priority** \\ \hline
[8]** & Deep Q-Network (DQN) & \(\surd\) & & \(\surd\) & & \\
[9]** & Prioritised Experience Replay (PER) & \(\surd\) & & \(\surd\) & & \\
[11]** & Deep Deterministic Policy Gradient (DDPG) & \(\surd\) & & \(\surd\) & & \\
[12]** & Episodic Memory Deep Q-Network (EMDQN) & \(\surd\) & & \(\surd\) & & \\
[13]** & Selective Experience Replay (SER) & \(\surd\) & & & \(\surd\) & \(\surd\) \\
[14]** & Prioritised Sequence Experience Replay (PSER) & \(\surd\) & \(\surd\) & & & \\
[15] & Experience Replay Optimisation (ERO) & \(\surd\) & \(\surd\) & & & \\
[16] & Attentive Experience Replay (AER) & \(\surd\) & \(\surd\) & \(\surd\) & & \\
[17] & Double Experience Replay (DER) & \(\surd\) & \(\surd\) & & & \\
[26] & Prioritised Stochastic Memory Management (PSMM) & \(\surd\) & & & & \(\surd\) \\
[30] & Twin Delayed Deep Deterministic Policy Gradient (TD3) & \(\surd\) & & & \(\surd\) & \\
[31] & Proximal Policy Optimisation (PPO) & \(\surd\) & & \(\surd\) & & \\
[33] & Dual Memory Structure (DMS) & \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some RL algorithms and their ER strategies \(\surd\).
## 3 Experiments
### Multi-Band Beamforming and Data Fusion
Therefore, we can directly perform pulse compression on the fused signal, as shown in Figure 5h. The pulse compression results for the fused LFM signal with a 4 GHz bandwidth match the simulation results well. The range resolution with the four bands of pulse compression is 15.4 cm, 15.7 cm, 33.0 cm, and 15.1 cm, respectively. Compared to pulse compression of a single band, the range resolution of the fused pulse compression is improved by nearly four times to 3.85 cm. Data fusion and pulse compression processing are performed on 100 sets of continuously collected four-band signals. All results showed consistent peak positions, confirming both inter-band phase coherence and system stability. Table 2 presents a performance comparison of this work with recent studies. As indicated, our approach extends the operational frequency bands to four (S/X/Ku/K) and achieves enhanced range resolution by leveraging great inter-band coherence and synchronization. These specifications enable precise target identification in complex scenarios, demonstrating the method's suitability for high-resolution multi-band radar applications. Moreover, the inherent synchronism and coherence significantly simplify the signal processing workflow, thereby contributing to the reduction in computational latency and the improvement in radar response.
# Experimental and Theoretical Study of One-Way Cyclic Lateral Responses of Piles in Sloping Ground
## 2 Experimental Setup and Procedure
### Test Procedure  
To investigate the effects of slope and one-way cyclic lateral loading on the pile responses, a series of one-way cyclic lateral loading tests were performed on the model pile in different cases of cycle number, load amplitude, and slope angle, as listed in Table 2. For comparison, the lateral static loading tests in both level ground and sloping ground were conducted as well. All the lateral loads were controlled by the computer system (load-controlled) and applied by an electric servo cylinder (YJ-DG-500-300, load range 0-50 kN, frequency 0-1 HZ), connecting to the pile head with a steel strand.  
In the one-way cyclic lateral loading tests, the loading was controlled by the self-coded spectrum, following the loading paths of trapezoidal waves [11] illustrated in Figure 4. The loading period and loading frequency were constant in all the one-way cyclic lateral loading tests, that is, \(T=30\) s and \(f=0.03\) Hz, and the experimental data were gathered once a second. The loading was terminated when the cycle number \(n\) reached 2500. As to the lateral static loading tests, the loading was applied as per the slow maintenance loading method [38] with a load increment of 1/8 to 1/10 of the estimated ultimate bearing capacity, and it was terminated when the pile head deflection \(y_{0}\) was greater than 30 mm. The loading of the next stage was applied while the increasing rate of successively measured pile head deflection in a minute was less than 0.1 mm/h.  
The pile head deflections and subgrade reactions can be directly measured by the displacement gauges and earth pressure cells, respectively, whereas the bending moments \(M\) can be indirectly inferred by the measured strains from strain gauges [11; 16; 28], as presented in Equation (8). Note that although the measured pile head deflections cannot be directly used to predict the prototype pile response, their variations with cycle number, load amplitude, and slope angle would be applicable for the prototype pile. Furthermore,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Test** & **Loading Regime** & **Load Amplitude/N** & **Slope Angle/\({}^{\circ}\)** \\ \hline \multirow{4}{*}{One-way cyclic lateral loading tests} & C1 & & 30 & 30 \\  & C2 & Trapezoidal wave & 30 & 45 \\  & C3 & loading (cycle & 30 & 60 \\  & C4 & number \(n=2500\)) & 20 & 45 \\  & C5 & & 40 & 45 \\ \hline Lateral static loading tests & S1 & Slow & – & 0 \\  & S2 & maintenance loading & – & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test program.  
Figure 3: Gradation curve of unsaturated river sand.  
the measured pile head deflections can also be used to validate the subsequent theoretical study on predicting one-way cyclic lateral responses of piles in sloping ground.  
\[M=EI\cdot\frac{\varepsilon_{\text{t}}+\varepsilon_{\text{c}}}{D} \tag{8}\]  
where \(\varepsilon_{\text{t}}\) and \(\varepsilon_{\text{c}}\) symbolize the tension strain (+) and compression strain (-), respectively.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods
### Reverse Process  
The reverse process is critical in DDPMs and aims to reconstruct the original data from the noisy data generated in the forward process. This process converts an isotropic Gaussian distribution back into the target data distribution. While estimating \(q(x_{t-1}|x_{t})\) directly is challenging, the posterior distribution \(q(x_{t-1}|x_{t},x_{0})\) can be analytically derived:  
\[q(x_{t-1}|x_{t},x_{0}):=\mathcal{N}(x_{t-1};\bar{\mu}_{t}(x_{t},x_{0}),\bar{ \beta}_{t}\mathbf{I}), \tag{4}\]  
where  
\[\bar{\mu}_{t}(x_{t},x_{0}):=\frac{\sqrt{\bar{\alpha}_{t-1}}\beta_{t}}{1-\bar{ \alpha}_{t}}x_{0}+\frac{\sqrt{\bar{\alpha}_{t}}(1-\bar{\alpha}_{t-1})}{1-\bar{ \alpha}_{t}}x_{t}, \tag{5}\]  
\[\bar{\beta}_{t}:=\frac{1-\bar{\alpha}_{t-1}}{1-\bar{\alpha}_{t}}\beta_{t}, \tag{6}\]  
and \(\beta_{t}:=1-\alpha_{t}\), which represents the added noise variance at step \(t\). In these equations: \(-\bar{\mu}_{t}(x_{t},x_{0})\) is the mean of the posterior distribution, combining contributions from both \(x_{0}\) and \(x_{t},-\bar{\beta}_{t}\) is the variance of the posterior distribution, modulating the uncertainty in the reverse process.  
During testing, the true value of \(x_{0}\) is unknown, but it can be approximated using the learned parameters:  
\[\mathbf{y}_{\theta}(x_{t}):=\frac{1}{\sqrt{\bar{\alpha}_{t}}}\Big{(}x_{t}- \sqrt{1-\bar{\alpha}_{t}}\epsilon_{\theta}(x_{t})\Big{)}, \tag{7}\]  
where \(\epsilon_{\theta}(x_{t})\) is the predicted noise from the denoising network, trained to estimate the noise added during the forward process, and \(\mathbf{y}_{\theta}(x_{t})\) serves as an approximation of the original data \(x_{0}\).  
This leads to a refined reverse process formulation:  
\[p_{\theta}(x_{t-1}|x_{t}):=\mathcal{N}(x_{t-1};\bar{\mu}_{t}(x_{t},\mathbf{y }_{\theta}(x_{t})),\bar{\beta}_{t}\mathbf{I}). \tag{8}\]Here, the mean \(\hat{\mu}_{t}(x_{t},\mathbf{y}_{\theta}(x_{t}))\) incorporates the estimated \(x_{0}\) approximation \(\mathbf{y}_{\theta}(x_{t})\), and the variance \(\hat{\beta}_{t}\) ensures appropriate uncertainty in the reconstruction process. This formulation iteratively refines noisy data into high-quality outputs while leveraging the learned noise predictions from the denoising network.
## 4 Results
### Fourier-Transform Infrared Spectroscopy  
The FTIR spectra of PP/PE, PP/PE/SCB, and PP/PE/SW blend composites are shown in Figure 2. The spectra of PP/PE and all blend composites show main absorbance peaks at around 2915 cm\({}^{-1}\), 1716 cm\({}^{-1}\), and 1469 cm\({}^{-1}\). These peaks are associated with the -CH asymmetric, -CH\({}_{3}\) symmetric, and the scissor modes of the -CH stretching of both PP and PE, respectively. Moreover, there are other small peaks at around 1158 cm\({}^{-1}\) and 718 cm\({}^{-1}\), which are associated with -CH\({}_{3}\) symmetric deformation and -CH\({}_{2}\) rocking vibration [15; 22]. The peak attributed to the hydroxy (-OH) group from fiber materials, as well as intramolecular and intermolecular hydrogen bonds at 3340 cm\({}^{-1}\), is more  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Cellulose (\%)** & **Hemicellulose (\%)** & **Lignin (\%)** \\ \hline SCB & 39.75 \(\pm\) 0.7 & 38.03 \(\pm\) 0.1 & 22.01 \(\pm\) 0.6 \\ SW & 40.00 \(\pm\) 0.5 & 24.00 \(\pm\) 0.1 & 37.0 0.5 \\ \hline \end{tabular}
\end{table}
Table 2: Sample compositions of SCB and SW fibers.  
Figure 1: SEM images of (**a**) PP/PE/SCB untreated 5%, (**b**) PP/PE/SCB cellulose 5%, (**c**) PP/PE/SW untreated 5%, and (**d**) PP/PE/SW cellulose 5% blend composites.  
pronounced in the blend containing cellulose [26; 27]. This may be due to the stretching and wagging of the groups as cellulose possibly migrates to a more compatible phase. The peaks at 1716 cm\({}^{-1}\) and 1046 cm\({}^{-1}\) are completely absent with the addition of SCB and SW fiber or cellulose, indicating an interaction between the blend and fibers. Furthermore, the PP/PE blends spectra exhibit -CH\({}_{2}\) rocking vibrations around 718 cm\({}^{-1}\), which are not present in the cellulose containing blend composites [28]. This disappearance is caused by the decrease in intermolecular hydrogen bonding between the treated fiber and PP/PE.
## 2 System Description
### Models of Main Devices
#### 2.2.5 Es  
ES, as a flexible resource in the system, charges to store electricity when there is an electricity power surplus and discharges to balance consumption during electricity power shortages. The constraints that need to be satisfied are as follows:  
\[0\leq SoC_{t}=SoC_{t-1}+\Delta t\left(\eta_{ES}P_{cls,t}^{E}-\frac{P_{dis,t}^{ E}}{\eta_{ES}}\right)\leq\overline{SoC},\,\forall t\geq 1 \tag{15}\]  
\[SoC_{t}=\frac{\overline{SoC}}{2},\,t=0 \tag{16}\]  
\[0\leq P_{dis,t}^{E}\leq\overline{P_{dis,t}^{E}}u_{dis,t} \tag{17}\]  
\[0\leq P_{cls,t}^{E}\leq\overline{P_{cls,t}^{E}}u_{cls,t} \tag{18}\]  
\[u_{dis,t}+u_{cls,t}=1 \tag{19}\]  
\[u_{dis,t},\,u_{cls,t}\in\{0,1\} \tag{20}\]  
where \(SoC\) refers to the capacity of ES, of which \(\overline{SoC}\) is the maximum value. \(\eta_{ES}\) denotes the charging and discharging efficiency of ES. \(P_{cls,t}^{E}\) and \(P_{dis,t}^{E}\) represent the charging and discharging power, while \(u_{cls,t}\) and \(u_{dis,t}\) are the corresponding state indicators, respectively.
# Market-Oriented Procurement Planning Leading to a Higher Service Level and Cost Optimization
## 3 Design of the Conceptual and Simulation Models
### Inventory Planning  
Inventory planning comprises the determination of the needed safety stock (SS) and stock for procurement order release [43]. For this, consistent inventory management and forecast values from the demand planning are needed [43]. The reason for safety stocks is motivated by the uncertainties coming from stock deviations, delivery dates, replenishment time (RT) deviations, quantity deviations, and demand volatility. The level of SS depends on the target service level (SL). In this context, Table 12 in Appendix A shows the value of the safety factor, k, for the required service level. The calculation of the stock for procurement order release or reorder point(s) as well as for the calculation of safety stock are performed considering the replenishment times and demand deviations. Therefore, the model considers the stochastic demand and replenishment time and deterministic procurement quantity and stock level in each period. For the calculation of the safety stock in Table 3:  
The safety factor, k, is determined by the required service level. Initially, the model has a service level goal of 99%, and, therefore, k is equal to 2.33 [43]. By using a dynamic calculation of safety stocks and of the reorder point notification considering the market and production deviations, it is possible to obtain systematic inventory planning depending on the required service level and reducing the probability of stockouts without unnecessarily increasing the inventory level [43]. In the simulation models, the safety stock depends on the replenishment method used. In the EOQ, the safety stocks (SS) are equal to zero. In the EOQ method, this is due to the nature of the method that always sends a certain quantity every fixed value of weeks, as this is a method to deal with non-variability demands and where forecast errors are small [42]. The stock for procurement order release are presented for the reorder point and for the EOQ method in Table 4: