esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Area  
The research was conducted along the shoreline of Pulau Baai Port with coordinates ranging from a longitude of 102\({}^{\circ}\)16\({}^{\prime}\)00\({}^{\prime\prime}\) to 102\({}^{\circ}\)18\({}^{\prime}\)30\({}^{\prime\prime}\) east and latitude of 03\({}^{\circ}\)53\({}^{\prime}\)00\({}^{\prime\prime}\) to 03\({}^{\circ}\)55\({}^{\prime}\)30\({}^{\prime\prime}\) south, covering an outer water area of 2183.47 ha and an inner water area of 1000 ha [2; 3; 13]. As can be observed in Figure 2, it is evident that there has been a noticeable change in the coastline conditions at the study location from 2019 to 2022 (Figure 2A-D). Figure 2A shows that in 2019, a significant portion of the coastal land area was still present at the research site. However, in 2020 and 2021, as depicted in Figure 2B,C, it is apparent that the coastal land has begun to erode due to ongoing erosion, reaching its most severe condition, as indicated in Figure 2D. In this last image, the extent of coastal land has become severely limited, and it is on the verge of transforming into a fully aquatic region. These changes are attributed to the impact of strong waves, causing coastal erosion and abrasion [28; 29]. This phenomenon has consequences for the submerged pipeline installations within the shoreline, leading to exposure to the external environment [27; 30; 31], as depicted in Figure 3. The image was captured during a site survey to assess the condition of existing pipeline installations.  
Figure 1: Flowchart of the assessment scheme for the study.  
Figure 2: Research location and the comparison of the coastline over the past four years (A–D) around Pulau Baai Port, Bengkulu (Source: Google Earth Pro, 2022).  
From this inspection, it is evident that the pipelines are significantly exposed from the shoreline, revealing the entire pipeline structure. Given the challenges observed at the port, a study on abrasion and wave dynamics will be conducted to obtain parameters for determining suitable alternatives for shore protection to be implemented at the study site. This study aims to address issues related to coastal morphology similar to those found at Pulau Baai Port, Bengkulu, Indonesia.
## 3 Experiments and Results  
In this section, three groups of postearthquake RS images were used to compare the proposed method with two other methods, including the classical SIFT (referred to as SIFT henceforth) and the SIFT method using the same patch matching approach (referred to as Patch-SIFT) used by the proposed method. The details of the three datasets (such as the satellite, resolution, size, acquired date, and disaster information) are introduced in Section 3.1. The evaluation criteria are presented in Section 3.2. The comparison results are displayed in Section 3.3.  
The experiments were performed on a computer with an Intel Core i7-4770 CPU 3.40-GHz processor and 14.0 GB of physical memory, using Visual Studio 2013 (C#) as the programming environment.  
Figure 7: Comparison of the spatial distribution of the initial matched tie points (**a**) and the remaining tie points after removing some tie points with high density (**b**).
## 4 Experimental Results and Discussion
### Experimental Setup and Evaluation Metrics
#### 4.1.4 Evaluation Metric  
Involving physiology, psychology, and colorimetry, the evaluation of color correction algorithms is very challenging. The evaluation methods are usually composed of two parts: the subjective evaluation and the objective evaluation. The subjective evaluation is to visually evaluate the color quality of the color samples by a certain number of observers. This method is intuitive and reliable.  
In general, two colors appear different from a visual perspective if their chromatic distance \(\Delta E\) is out of a threshold. It is commonly considered in the range if \(\Delta E_{ab}<3.0\) in the CIE Lab color space [16; 26]:  
\[\Delta E_{ab}=\sqrt{(\Delta L)^{2}+(\Delta\alpha)^{2}+(\Delta\beta)^{2}}, \tag{11}\]  
For subjective evaluation, we captured tongue images for testing. We can conduct the subjective evaluation by making comparisons between the captured and the corrected tongue images.
## 1 Introduction  
Barley grain is used as a main ingredient for several traditional foods or beverages. The production of barley increases year by year to supply the demand market. During the grading process for food, defective barley grains are separated and become an agricultural by-product, mainly used as energy sources for animals. Meanwhile, hydroponic fodder production could be an alternative approach to improve its economic value [1; 2; 3]. In addition, some of the barley grain could be pre-sprouting, pre-germinating, gape, splitting, or skinning during harvesting, storage, or distribution due to environmental factors. Without any processing, those defective barley grains may have low economic value and could significantly reduce the farmer's income. The germination of grains changes the nutrient compositions of dry grain by converting starch into sugar, increasing protein content, or converting protein and fat into amino acids and fatty acids, respectively [1]. Sprouted barley (SB) is a dry barley grain that germinates and sprouts for 8 days [2; 3; 4]. It results in green shoots and roots that can provide green fodder as a fiber source for ruminant requirements. According to [PERSON] et al. [3], dry matter (DM) concentration of barley grain decreased with sprouting activity, but crude protein (CP) and mineral concentrationsincreased. Supplementation of SB in the steer diet could improve DM digestibility [4], rumen fermentation [5], and animal growth performance [6]. In sheep or goats, dietary SB increased total DM intake, feed efficiency, and body weight [7], and it also improved fermentation and enzyme activity in the rumen [5]. In addition, replacing wheat bran and wheat straw with SB had no adverse effect on milk yield and milk composition of lactating goats; still, it showed beneficial effects on health conditions, mortalities, conception rates, and abortion [8]. Moreover, supplementation of SB for non-ruminants such as chickens increased the egg-laying rate, fertility, and number of hatched chicks [9].  
Nevertheless, the application of SB as a feed source did not always promise beneficial effects for the animal [10; 11]. The cultivation stage of SB might be a significant influence on the success of SB application for ruminants. It was supported by many previous studies that reported no advantage of the SB over the barley grain because the digestibility of SB decreased along with an increase of cultivation stage [10; 11]. The structural carbohydrates such as neutral detergent fiber (NDF) and acid detergent fiber (ADF) were reported to increase as the growths of root and green fodder [10; 11]. In the appropriate cultivation stage, SB can present beneficial effects for ruminant diet. Therefore, the present study was conducted to investigate the effect of SB with different cultivation stage on fermentation characteristics and degradation kinetics of DM and NDF in the rumen.
## 2 Literature Review
### Buffer Allocation Problem  
Buffer allocation problems focus on finding optimal buffer capacities to be allocated to buffer areas in the production system for a specific objective. BAP is an NP-hard combinatorial optimization problem [16], and it was first presented by [PERSON] [17]. [PERSON] et al. [18] provided a systematic review of the studies published since 1998. They classified the literature based on four aspects: the line topology, the objective functions, the evaluative method, and the generative method. The authors also concluded that \"handling BAP in a multi-objective manner still seems an important research issue\". Recently, [PERSON] et al. [19] and [PERSON] et al. [20] suggested studying multi-criterion optimization problems instead of optimizing a single-objective BAP.  
Since the ultimate goal of BAP is to optimize the performance of the production line, an interesting research area called the bowl phenomenon has emerged under an unpaced production environment. It was introduced by [PERSON] and [PERSON] [21], while a review of the phenomenon can be found in [22]. This research area considers a relevant problem that is the simultaneous allocation of the workload and the buffer capacities in an unpaced production line. [PERSON] and [PERSON] [23] tried to approach this kind of problem for a reliable production line with no parallel stations. During their further research [24], exact numerical results were shown for small lines (no more than four stations) with exponential or Erlang distributions of processing time, while some possible heuristic methods and simulation studies were provided for other situations where the production lines had more stations or different processing time distribution. [PERSON] et al. [25] proposed a simulation-based multi-objective optimization (MOO) approach. Four aspects of production systems were considered when solving the problem, which were throughput, work in process, average idle times, and average buffer level. It was also concluded that \"the ultimate goal of this kind of studies is to propose a Pareto-based MOO methodology for studying the simultaneous effects of workload balancing and buffer allocation to real production lines with complex routings and various sources of variations.\" It is worth noting that most of these approaches assumed that the total amount of the workload remains certain throughout the line and can be arbitrarily divided at each station. This represents the complexity of solving TLBP, where the workload of a station is determined by a set of indivisible machining operations, and each operation is constrained more or less by different types of constraints (such as precedence constraints) during the allocation.
## 2 Materials and Methods
### On-Chip Layout Design  
The mixer chip often contains direct current (DC) bias lines, electrodes and so on. Figure 3a,b show the presented geometry of the on-chip layout. Stub tuners are loaded at different positions on bowtie antennas, integrated with YBCO bicrystal JJs. The arm length \(l\) and width \(w\) of the bowtie are 135 \(\upmu\)m and 515 \(\upmu\)m, respectively. A pair of stubs with length of \(l_{\text{s1}}\) = 30 \(\upmu\)m, width of \(w_{\text{s1}}\) = 30 \(\upmu\)m and an interval of 10 \(\upmu\)m, acting as lumped elements [32], are put in the middle of the bowtie, which forms as Antenna 1. Another pair of stubs with length of \(l_{\text{s2}}\) = 225 \(\upmu\)m, width of \(w_{\text{s2}}\) = 10 \(\upmu\)m and an interval of 10 \(\upmu\)m, placed vertically in the middle of another bowtie, have open ends [33]. Thus it forms as Antenna 2. In addition, electrodes, DC bias lines, and choke filters [34] are integrated on a MgO substrate with the dielectric constant of 9.6. The DC bias lines are connected to the part of bowtie with the weakest current distribution to prevent alternating current (AC) power leakage from the antenna. Choke filter with a bandgap structure [35] is applied on the DC bias lines to avoid AC power leakage to the CPSs. The optimized parameters at 0.21 THz by CST MWS are as follows: \(d\) = 32 \(\upmu\)m, \(l_{0}\) = 246 \(\upmu\)m, \(w_{0}\) = 15 \(\upmu\)m, \(l_{1}\) = 165 \(\upmu\)m, \(w_{1}\) = 90 \(\upmu\)m, \(l_{2}\) = \(l_{3}\) = 90 \(\upmu\)m and \(l_{4}\) = \(l_{5}\) = 135 \(\upmu\)m. The length of each section of choke filter is \(\sim\)\(\lambda_{\text{g}}\)/4 [36] with  
\[\lambda_{\text{g}}=\lambda_{0}/\sqrt{(1+\varepsilon_{\text{r}}t)/2} \tag{4}\]  
where \(\lambda_{0}\) is the wavelength in free space, and \(\varepsilon_{\text{r}}t\) is the dielectric constant of the substrate. As seen in Figure 3c, currents leaked from both antennas are effectively blocked by choke filters, respectively. At the other ends of CPSs, four electrodes are applied in the four-terminal measurement to exclude the influence of contact resistance.  
Figure 2: (**a**) Model of the hyper-hemispherical lens with bowtie feed in CST MWS. (**b**) Radiation pattern of the hyper-hemispherical lens with the radius of 4.5 mm at around 0.21 THz.
# Design, Implementation and Power Analysis of
## 3 Results  
### Energy Assessment Parameters Introduction and Formulation  
For the purpose of investigation, four energy estimation parameters are used in this paper. They are energy consumption ratio of base scenario contrary to smart scenario (\(\text{ECR}_{\text{BS}}\)), energy consumption ratio of manually controlled scenario counter to smart scenario (\(\text{ECR}_{\text{MS}}\)), relative energy saving ratio  
Figure 4: Flowchart of PARSLAD.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Expt \multicolumn{2}{c}{**Perceptive Light Automation with Buzzer Activation Algorithm in Alert (AL) Mode of Operation**} \\ \hline \hline
**Light State** & **Buzzer State** & **Condition and Logic to be Satisfied** \\ \hline ON & \multicolumn{2}{c}{Intervention detection _AND_ Value of Illuminance \textless{} 300 \(\mathrm{lx}\)} \\ DIM & ALARM ON & Intervention detection _AND_ Value of Illuminance \textgreater{} 300 \(\mathrm{lx}\) \& \textless{} 500 \(\mathrm{lx}\) \\ OFF & \multicolumn{2}{c}{Intervention detection AND Value of Illuminance \textgreater{} 500 \(\mathrm{lx}\)} \\ \hline OFF & ALARM OFF & No intervention OR Value of illuminance \textgreater{} 500 \(\mathrm{lx}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Condition and logic to be satisfied for PLABA.  
of base scenario contrary to smart scenario (RESR\({}_{\text{BS}}\)) and relative energy saving ratio of manually controlled scenario counter to smart scenario (RESR\({}_{\text{MS}}\)).  
ECR\({}_{\text{BS}}\) and ECR\({}_{\text{MS}}\) are the ratios of energy consumed for a particular strategy by PARSLAD deployment to the energy consumed in the base and manually controlled scenario, respectively.  
RESR\({}_{\text{BS}}\) and RESR\({}_{\text{MS}}\) is the ratio of energy saved through use of PARSLAD device in a specific condition relative to the energy consumed in base and manually controlled scenario, respectively under the same specific conditions:  
\[\text{ECR}_{\text{BS}}=\frac{\text{E}_{\text{S}}}{\text{E}_{\text{B}}}\times 100\% \tag{3}\]  
\[\text{ECR}_{\text{MS}}=\frac{\text{E}_{\text{s}}}{\text{E}_{\text{M}}}\times 10 0\%, \tag{4}\]  
\[\text{RESR}_{\text{BS}}=\frac{\text{E}_{\text{B}}-\text{E}_{\text{s}}}{\text{E }_{\text{B}}}\times 100\% \tag{5}\]  
\[\text{RESR}_{\text{MS}}=\frac{\text{E}_{\text{M}}-\text{E}_{\text{s}}}{\text{E }_{\text{M}}}\times 100\%, \tag{6}\]  
where, E\({}_{\text{B}}\) is the energy expended in base Scenario, E\({}_{\text{M}}\) is the energy consumed in the manually controlled scenario and Es is the energy spent in a smart scenario in KWh.
## 3 Design Optimization
### \(h_{b}\)  
As mentioned above, the TM mode and complex mode in this hybrid waveguide can be regarded as compound polarizations supported by the vertical, as well as the horizontal Ag-SiO\({}_{2}\)-Ag structure. The MD, PL and Re(\(N_{\rm eff}\)) of the proposed modulator as a function of  
Figure 4: Fermi level \(\mu_{\rm c}\) of graphene versus the change of applied external voltage.  
Figure 5: Comparison of normalized electric field distributions and ohmic loss profiles under different waveguide dimensions. When \(h\) and \(g\) are 50 nm, (**a,b**) are electric field distribution and ohmic loss profile for the TM mode, respectively; (**c,d**) are corresponding cases for the complex mode. When \(h\) and \(g\) are 200 nm; (**e,f**) are electric field distribution and ohmic loss profile for the TM mode, respectively; (**g,h**) are corresponding cases for the complex mode.  
bottom Ag slab thickness \(h_{\text{b}}\) are shown in the Figure 6. The analysis of \(h_{\text{b}}\) is implemented by a similar way adopted in the MIM waveguide model analysis, because it is also with thin metal layers [21]. Compared to complex mode, the TM mode is almost dominated by the longitudinal polarization, therefore the dimension change in vertical direction has a larger impact on the TM mode. Figure 6a shows that \(\text{Re}(N_{\text{eff}})\) of the TM mode is more sensitive to \(h_{\text{b}}\) than that of complex mode. The \(\text{Re}(N_{\text{eff}})\) of theTM mode keeps decreasing with the rising of \(h_{\text{b}}\), which decreases from 1.6993 to 1.6464 at \(\mu_{\text{c}}\) = 1 eV and 1.718 to 1.6654 at \(\mu_{\text{c}}\) = 0 eV. Meanwhile, \(\text{Re}(N_{\text{eff}})\) of complex mode slowly decreases from \(h_{\text{b}}\) = 50 nm, and reaches to a stable value at \(h_{\text{b}}\) = 150 nm. In addition, as shown in Figure 6c, the change in \(h_{\text{b}}\) has a significant impact on the PL of the TM mode that decreases with the increasing of \(h_{\text{b}}\) and reaches a stable value at \(h_{\text{b}}\) = 150 nm. In contrast, PL of complex mode remains at around 0.15 dB/\(\upmu\)m that is caused by the fraction of the TM mode distributed in the metal layer. This leads to a larger optical loss. The MD change of two modes versus \(h_{\text{b}}\) is very small, which is related to the limited influence of \(h_{\text{b}}\) on the light field distribution. The \(\text{Re}(\Delta N_{\text{eff}})\) of two modes versus \(h_{\text{b}}\) is shown in Figure 6b. Here, \(\text{Re}(\Delta N_{\text{eff}})\) is determined by  
\[\text{Re}(\Delta N_{\text{eff}})=\left|\text{Re}(N_{\text{eff}})_{\text{TM mode}}-\text{Re}(N_{\text{eff}})_{\text{Complex mode}}\right| \tag{5}\]  
Since a low \(\text{Re}(\Delta N_{\text{eff}})\) is more desirable, \(h_{\text{b}}\) is chosen to be 180 nm.
## 3 Experimental Verification Using Benchmark Test Functions
### Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Fun** & **Measure** & **IRSA** & **RSA** & **BMO** & **PSO** & **GWO** & **AAO** & **DGCO** \\ \hline
**F14** & **Best** & \(0.9980\) & \(2.9821\) & \(2.0481\) & \(1.913\) & \(0.9980\) & \(7.8740\) & \(0.9980\) \\ \hline  & **Worst** & \(2.9834\) & \(3.0006\) & \(11.7187\) & \(2.981\) & \(10.7632\) & \(12.6705\) & \(2.9821\) \\ \hline  & **Average** & \(\mathbf{2.1313}\) & \(2.9901\) & \(6.4665\) & \(2.523\) & \(5.1025\) & \(10.7579\) & \(2.1964\) \\ \hline  & **STD** & \(0.8325\) & \(0.0139\) & \(3.0067\) & \(0.7341\) & \(4.9092\) & \(1.7957\) & \(0.6274\) \\ \hline
**F15** & **Best** & \(0.00030\) & \(0.00068\) & \(0.00033\) & \(0.00038\) & \(0.00030\) & \(0.00036\) & \(0.00035\) \\ \hline  & **Worst** & \(0.01550\) & \(0.00896\) & \(0.00654\) & \(0.00142\) & \(0.02036\) & \(0.05758\) & \(0.001224\) \\ \hline  & **Average** & \(0.00406\) & \(0.00180\) & \(0.001710\) & \(0.000813\) & \(0.004417\) & \(0.009030\) & \(\mathbf{0.000 Table 3 delineates the performance of the IRSA and other MH techniques at varying dimensions of 30, 100, and 500, considering 300 iterations. From the table, it can be observed that the IRSA provides the best results on 33 out of a total of 39 test evaluations, which represented nearly 84%, and it ranks second of five other evaluations. These results clearly demonstrate the effectiveness of the proposed algorithm in solving high-dimensional complex optimization problems.
## 1 Introduction
Little is known about the seasonal ice dynamics of the branches and their governing mechanisms. In this study, we use a dense time series of TerraSAR-X and TanDEM-X satellite data (2011-2016) to estimate surface velocities and changes in frontal position of Columbia Glacier. We also measure the relative elevation changes of the radar scattering phase center (PC) over time (with reference to 06/18/2011) applying bistatic SAR interferometry on the TanDEM-X data. Elevation of the PC depicts the surface elevation during the melt seasons as the radar signal is attenuated due to the water content on the glacier surface. Combining velocities, limited surface elevation data, elevation changes of the PC and changes in front position, we examine the ice dynamics of Columbia Glacier after its split and the potential controlling processes.
## 1 Introduction
### Paper Contribution  
The primary goal of the paper is to conduct an experimental investigation into the security vulnerabilities present in open-source 5G projects, namely Open5 GS and OAI. Specifically, our focus centers on analyzing the security landscape of the 5G core network, with a pronounced emphasis on evaluating the Service-Based Interface (SBI), the novel communication interface connecting network functions (NFs) that was introduced with the 5G standard. Our scrutiny will be directed towards two pivotal components within the Service-Based Architecture (SBA) NFs: the Access and Mobility Management Function (AMF) and the Network Repository Function/Network Exposure Function (NRF/NEF).  
In summary, the paper's central contribution lies in its rigorous experimental exploration of the security vulnerabilities inherent in the AMF and NRF/NEF implementation of the above-mentioned open-source 5G projects. By focusing on the core network's SBI and scrutinizing externally exposed NFs, this analysis not only sheds light on potential security gaps but also underscores the critical role of 5G vendors and MNOs in ensuring the implementation of robust security measures. Furthermore, in the backdrop of the evolution towards NFV, the paper underscores the significance of secure development practices to bolster the integrity of 5G network functions. To provide a visual summary of our experimental tests, an accurate comparison of the main issues identified in the two implementations is also presented in the final section.  
Finally, all the traffic captures and the scripts used in the experimental analysis are available in the GitHub repository [6], so that readers can not only reproduce the paper results but also check the evolution of the frameworks' releases in terms of security (or adapt the scripts to other frameworks of interest).