esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Improving the Scattering Coefficient Accuracy by Introducing SATD  
The traditional geophysical model function (GMF) primarily depends on wind speed, wind direction, and incident angle, without considering the effect of _SATD_. This omission would limit the accuracy of the scattering coefficient evaluated by the GMF. In this section, the influence of _SATD_ on the accuracy of the GMF is analyzed.  
Figure 9: The distribution of the comprehensive scatter plots of the different regional monthly mean marine environmental parameters, (**a**) _RMMSATD_, (**b**) _RMMAT_, (**c**) _RMMSST_, and (**d**) _RMMWS_, and the coupling coefficient \(k_{satda}\) in the four sea areas. The red dots represent the ARC sea area, the green square dots represent the BMC sea area, the blue diamonds represent the KE sea area, and the rose-red triangles represent the GS sea area.  
The QSCAT scatterometer operates in the Ku-band; therefore, the Ku-band GMF is used for the calculation of the scattering coefficient. Figure 10 displays the global scattering coefficient data on 1 January 2008. In Figure 10, the vertical axis (_real-sigma_) represents the scattering coefficient measured by the QSCAT, while the horizontal axis _uz-sigma_ in Figure 10a and _sstd-sigma_ in Figure 10b denote the scattering coefficients evaluated by the Ku-band GMFs with and without considering the influence of _SATD_ on the ECMWF wind speed. Comparing the values of the _BIAS_ and the _RMSE_ in Figure 10b with those in Figure 10a, we can find that the accuracy of the scattering coefficient calculated by the GMFs can be improved when the influence of _SATD_ is considered.  
The ECMWF wind speed, _SATD_, and scattering coefficient acquired by the QSCAT in January 2008 were employed to further evaluate the _BIAS_ and the _RMSE_ of the scattering coefficient calculated by the GMFs with and without considering the effect of _SATD_ on the ECMWF wind speed. As shown in Figure 11, it can be observed that the _BIAS_ and the _RMSE_ decrease by about 0.5 dB and 0.7 dB when the effect of _SATD_ on the ECMWF wind speed is considered. This result validates that the accuracy of the scattering coefficient calculated by the GMFs and can be improved by introducing the impact of _SATD_.  
Figure 11: The inter-month variation curves of the _BIAS_ (**a**) and the _RMSE_ (**b**) of the scattering coefficient calculated based on GMFs with and without considering the effect of _SATD_ on the ECMWF wind speed.  
Figure 10: The comparison between the QSCAT–measured scattering coefficient and the scattering coefficient calculated using the (**a**) ECMWF wind speed and (**b**) ECMWF wind speed with a sea–air temperature difference correction.
## 2 Data and Methods
### Data
#### 2.1.2 Study Field Petroleum System  
Major hydrocarbon occurrence in the studied field is illustrated in a generalized stratigraphy in Figure 2a. According to [PERSON] and [PERSON] [13], the main hydrocarbons at play in this area are Oligocene-Early Miocene Cycles 1 and 2 coastal plain sands, sealed by intra-formational shales and trapped in wrench related anticline. The identified plays are clastic reservoirs of the late Miocene to Early Pliocene age. The abundance of gas in the study field was suggested due to factors of humic and/or gas-prone marine source rocks in the Cycle 1 and 2 kitchen areas and the contrasting maturation and trap-timing histories. Sedimentation in Cycle 1 (or Cycle 2) to Cycle 8 is cut by the NE-SW strike faults that, for most of them, are not sealed and have very deep roots until sourced at the depth of Cycle 1 and Cycle 2 level. The faults act as major paths of hydrocarbon migration from the source rock to the main reservoir SC3 in the studied field (Figure 2b).  
The hydrocarbon accumulation is identified by high amplitude anomalies in the maximum trough amplitude, as shown in the seismic section passing three available wells (Figure 2c). Abrupt changes and structural conformability of SC3 seismic anomalies directly indicate the hydrocarbon occurrence. The interval velocity at the SC3 level is about 2400 m/s, so the tuning thickness here is about 12 m. Since the average thickness of SC3 sand is around 18 m to 28 m, the top and bottom reflectors of the SC3 reservoir can be tracked based on full stacking seismic data. However, due to the strong AVO impacts, the spill point location and the fluid variation inside the reservoir cannot be defined accurately.  
The three available wells encountered six reservoir layers above and below the SC3 pay with variable thicknesses. SC3A is one of the six encountered reservoirs. The 1140 sand  
Figure 2: Petroleum system and seismic data interpretation. (**a**) Generalized stratigraphy of the studied province showing the major hydrocarbon occurrences ([PERSON] and [PERSON] 1999), (**b**) Regional E–W seismic section showing gas generation occurred from source rock at depth (Cycle 1 and 2) and migrated to the Field’s sands (SC3) by deep rooted faults. (**c**) NW–SE seismic section and seismic horizons interpretation of studied zone. Overlaid log is well site seismic trace (Courtesy Petronas Carigali).  
is indicated as a non-hydrocarbon reservoir. SC3 reservoir sand has sufficient thickness, therefore, is distinct and shows good seismic amplitude anomaly (Figure 2c), whereas the SC3A sand and other discovered pays are thin. The structure is quite subtle in nature, with very small relief, which might have formed due to differential compaction. The above SC3 reservoir horizons are very character consistent and continuous over the field 3D survey. However, the below SC3 horizons are relatively weak and discontinuous.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 6 Discussion
### Use of Constrained Devices  
Although our considered use case is IoT-based, our solution is not designed with constrained devices in mind. Nevertheless, we believe that it should be possible to apply our solution even in these devices. EdDSA signatures can be implemented even in devices with limited capabilities. As [PERSON] et al. [11] reported, the EdDSA signature can be performed in 14 million cycles on an 8-bit device, while on a 32-bit low-cost ARM Cortex-M0 core, the same operation undergoes 3.6 million cycles. Therefore, Cortex-M0 devices, which are available for less than half a dollar in large quantities and run at up to 48 MHz, can perform up to 13 EdDSA signatures per second. Similarly, the power required to generate an EdDSA signature in a Cortex-M0 processor is 20-34 \(\upmu\)J [11].
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 2 D2D, IOT, and Context-Awareness
### Context-Aware 5G Systems with Mobile Edge Computing and D2D  
The development of 5G D2D systems for intelligent applications is continuously expanding toward smart homes, industrial plants, and intelligent transportation systems [16]. The 5G systems are expected to play a critical role in supporting the IoT with their con  
Figure 1: 5G D2D Based Smart Applications with Mobility and LLC Issues: (**a**) The Smart Factory Scenario and (**b**) The Social Networking Scenario.  
nectivity and ubiquitous coverage [7]. The IoT needs computation and storage ability to support the sensors and actuators. The smart service based on cognitive computing with the IoT will be fulfilled for time-constrained applications. Recently, mobile edge computing has been increasingly used in various application designs. In the 5G mobile systems, mobile edge computing has been recognized as a key enabling technology for a wide range of applications and scenarios [8]. D2D networks are a novel paradigm for mobile edge computing in the 5G and beyond 5G networks because of energy efficiency, reduced delay, limited interference, and so on [17]. A lot of mobile devices execute sustainable computation tasks without connecting the base station or the core network in D2D networks [18]. Furthermore, D2D networks support the applications for the sensing data to the mobile edge. However, there might be many hurdles for reliable and fast data delivery over D2D networks since D2D is composed of mobile devices, and they configure multi-hop mesh networks where connection links between devices can be frequently changed [13].  
Cognitive computing for context-awareness has been developing for the last couple of years to generate information or intelligence eventually. The state-of-the-art studies have been based on DNN, which is denoted by deep learning. The DNN technologies such as multi-layered neural network, convolutional neural network (CNN), and recurrent neural network (RNN) are exploited in various environments for smart applications [19]. These studies have been worked on communication or IoT related services such as cognitive radio networks [20], indoor localization [21; 22], wireless signal identification [23], mobile data sensing [24], and so on. These studies are also following the sequence of service loop mentioned above, and all the cognitive computing in them are based on one model: train offline-use online. Data acquisition is fulfilled as pre-operation separately; then, data training with DNN model is performed at the mobile edge as an offline operation. The trained weight of the DNN is used for the real-time sensory data. However, these methods for the DNN are not suitable for D2D based time-critical application cases because D2D network connectivity highly suffers from the mobility of component devices and this situation frequently happens before making connection to the mobile edge. Thus, on-node cognitive computing should be considered.
## 4 Cost Prediction Performance Analysis  
### Correlation of Features  
In the study, the correlation between 10 selected features was analyzed to confirm their mutual independence. The influencing factors used to predict construction costs should not overlap with each other.  
Figure 4 shows a heatmap of the correlation between each influencing factor. When the correlation is close to 1, the correlation is high, and when it is close to 0, the correlation is low. As shown in the figure, the 10 selected influencing factors show values close to 0, showing that these influencing factors have high independence.  
Along with data correlation analysis, feature importance for the final construction cost was analyzed using Random Forest analysis. The results of feature importance analysis are as shown in Figure 5.  
Figure 4: Correlation of Features.  
As shown in Figure 5, the total area is the most important feature, followed by the total height, site area, parking lot, ground floor, underground, and building area. Since initial construction costs are generally predicted based on the total area, the total area is the factor that has the greatest influence on the construction cost. Factors other than the total area can be seen as factors that can predict detailed fluctuations in construction costs that can be predicted by the total area. Factors related to the vertical direction of a building are factors that can change the construction cost even in the same area, and as shown in the figure. The factor in the vertical direction is the second most important construction cost influencing factor.
## 2 Methodology
### Dynamic Cloud Mask  
In order to consider the impacts of clouds on the snow retrievals observations obtained via LiDAR, a daily cloud mask is employed to simulate the cloud cover distribution. The cloud mask is extracted from the quality flag (i.e., Coarse Resolution Internal Cloud Mask) of the 0.05-degree MODIS Aqua daily reflectance collection 6 product (MYD09 CMG, [[https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09) CMG]([https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09](https://ladsweb.modaps.eosdis.nasa.gov/filespec/MODIS/6/MYD09) CMG) accessed on 20 December 2021) [53]. The 0.05-degree cloud mask is then interpolated to 0.01-degree grid using the nearest-neighbor algorithm. The cloud mask is not available during the nighttime-only polar winter given the passive (optical) nature of the MODIS sensor. Therefore, a gap filling strategy is adopted to simulate the cloud distribution when the data is missing. A set of gap-free cloud masks collected during the polar summer (i.e., from March 22 to September 20) is employed. When cloud retrievals are missing during the polar winter (i.e., from 22 September to 21 March), the gap-free cloud masks from the summer are used as a reasonable surrogate to estimate the impact of cloud attenuation on optical sensor retrievals collected from space. Even though this method does not exactly reproduce the cloud conditions that existed, the filled cloud map serves as a reasonable proxy to represent the true cloud variability across space and time.
# A Scheduling Solution for Robotic Arm-Based Batching Systems with Multiple Conveyor Belts +
## 2 Related Work  
### Scheduling Problems for Robotic Arms in Automated Systems  
As highlighted in the introduction, robotic arms have been applied to various industrial sectors, including manufacturing and production systems due to their contribution to the overall productivity, reliability, and quality of a target system. A key element for a successful implementation of robotic arms into such systems is to schedule movements of a robotic arm to perform atomic tasks, such as welding, drilling, and spray painting. This scheduling problem is known as the robotic task sequencing problem (RTSP) [7].  
Given that a robotic arm has a single configuration to perform a task, the RTSP can be formulated as the traveling salesman problem (TSP) [8]. However, a robotic arm could have multiple configurations especially to avoid collisions during operations and to reduce the duration of the movement, which is known as inverse kinematics solutions. With inverse kinematics, the RTSP can be formulated by the generalized TSP (GTSP), where a location for a task is represented as a bin with multiple nodes, and a travel time between two tasks depends on what nodes in the corresponding bins are visited. Given this setting, a robotic arm should visit all the bins for the target tasks, visiting only a single node within the bins [9]. Please refer to [10; 11] for a detailed review on the RTSP.  
The extended RTSP can be found in a flexible manufacturing system (FMS), where a robotic arm is often installed to transport items from an input station to multiple workstations and from the workstations to an output station. In this case, not only the robotic arm but also the workstations should be scheduled so that the overall system performance (e.g., workstation utilization and throughput of the system) can be optimized. To address this scheduling complexity, Petri nets are often applied to represent a target system as a discrete-event system [12; 13].  
The RTSP can also be extended for multiple robotic arms. Here, a goal is to operate robotic arms to move objects to available slots with a minimum total travel distance. The resulting multiple robotic arm scheduling problem can be formulated as a parallel machine scheduling problem for a single-stage production [14]. Note that the multiple robotic arm scheduling problem is in general solved for static task positions; therefore, a solution to the problem is often adjusted by a model predictive control (MPC) planning algorithm that addresses dynamic collision avoidance constraints of the robotic arms [15; 16].
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 5 Publicly Available Datasets  
This section provides a list of publicly available datasets for FRSs. These datasets cover a wide range of topics, including movie ratings, TV show ratings, music tastes, consumer preferences, and more. Each dataset has been carefully curated to contain relevant information for developing FRSs. Those datasets are available for free download and can be used to train and test the performance of FRSs. In Table 5, we show the list of those datasets and discuss the details in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Category** & **Variables** & **Data Type** & **License** & **Format** \\ \hline Amazon [183] & Reviews & Users, Reviews & Text, Numeric & Open-Source & CSV \\ \hline MovieLens [184] & Movies & Ratings, Genres & Numeric & Open-Source & CSV \\ \hline Yelp [185] & Reviews & Users, Reviews & Text, Numeric & Open-Source & JSON \\ \hline Film Trust [186] & Movies & Ratings, Genres & Numeric & Open-Source & CSV \\ \hline Goodreads [187] & Books & Reviews, Ratings & Text, Numeric & Open-Source & XML \\ \hline Netflix [188] & Movies & Ratings, Genres & Numeric & Open-Source & JSON \\ \hline LastFM [189] & Music & Listeners, Genres & Numeric & Open-Source & CSV \\ \hline Douban [190] & Movies/Books & Reviews, Ratings & Text, Numeric & Open-Source & XML \\ \hline BookCrossing [191] & Books & Reviews, Ratings & Text, Numeric & Open-Source & XML \\ \hline \hline \end{tabular}
\end{table}
Table 5: Publicly available datasets that can be used for the development of FRSs.
## 5 Cryptosystems Choice
### Cryptosystem for Product Encryption  
When the customer spends an e-coin to acquire some product, the vendor encrypts it under the \(Q_{R}\) public key included in the spent e-coin (Section 4.5, step 6). The customer is only able to decrypt such ciphertext if they know the corresponding private key. This public key is generated differently depending on whether the e-coin it is contained in is valued or not.  
* If the e-coin is valued, the customer generates private key \(v_{R}\) and then the corresponding public one \(Q_{R}\) (Section 4.3, step 3).
* If the e-coin is no-valued, public key \(Q_{R}\) is obtained pseudo-randomly (Section 4.4, step 3).  
Therefore, the cryptosystem for such \(\{v_{R},Q_{R}\}\) key-pairs must satisfy the following requirements:  
* It allows public key data encryption;
* It provides a relatively high probability of obtaining a valid public key by means of a pseudo-random process;
* It cannot be determined whether a given public key has been generated together with its private counterpart (Section 4.3 step 3) or through a pseudo-random process (Section 4.4, step 3).  
The RSA [21] cryptosystem would not be a suitable option since the probability that a pseudo-random integer \(N\) is composed of two large prime factors is rather low.  
We next detail two suitable public key encryption schemes.
## 4 Results
### Identification and Division of High-Frequency Sedimentary Cycles  
Based on the integrated data analysis and previous research experience in high-frequency sequence stratigraphy using Milankovitch orbital cycle theory [26; 32; 45; 46; 47], the long eccentricity cycle curve corresponds to the medium-term base-level cycle, classified as a fourth-order cycle with a duration of 405 kyr. The short eccentricity cycle curve corresponds to the short-term base-level cycle, classified as a fifth-order cycle with a duration of 100 kyr. The obliquity cycle curve corresponds to the ultra-short-term base-level cycle, classified as a sixth-order cycle with a duration of 18.4 kyr. The maxima of the filtered curve were used as the basis for base-level cycle division. In combination with the Jurassic-Cretaceous boundary (ca. 145 Ma) at the base of the Yixian Formation, as constrained by U-Pb dating in comparable basins [48; 49], this boundary was used as an anchor point. Incorporating relevant sequence stratigraphic theories, high-frequency cycle division and correlation were achieved in the study area.  
As shown in Figure 4, a total of six 405 kyr long eccentricity cycles were identified from the long-eccentricity-filtered curve of Well Nai-10. These correspond to six fourth-order sequences. These cycles align with six high-energy clusters in the wavelet time-frequency spectrum, with each cycle having an approximate thickness of 35 m, representing six medium-term base-level cycles. Similarly, the short eccentricity filtering curve identified 24 short eccentricity cycles of 100 kyr, each with a cycle thickness of approximately 9 m, corresponding to 24 short-term cycles. The sedimentation duration is calculated by multiplying the number of identified cycles by the duration represented by each cycle. Using the bottom age of the Yixian Formation as an anchor point and referencing the short eccentricity cycle filtering curve, the deposition duration of the studied interval was estimated to be approximately 2.4 Ma from bottom to top. Based on the sedimentation rate of each short eccentricity cycle, the average sedimentation rate of the lower Yixian Formation was calculated to be 8.36 cm/kyr. Additionally, the filtering curve indicates that, in terms of orbital parameter modulation, when the peak amplitude of the long eccentricity (405 kyr) filtering curve is high, the short eccentricity (100 kyr) amplitude also increases, as observed near the first, second, fifth, and sixth long eccentricity cycles. Moreover, obliquity and precession exhibit the same pattern, demonstrating the modulation effect of long-period cycles on short-period cycles.  
Figure 3: Spectrum analysis (**a**) and wavelet transform (**b**) of Well N10 in the lower Yixian Formation.
## 2 Related Works
### Resource Scheduling Algorithms for LEO Satellite Networks  
The works of [26, 27, 28] examine the challenge of satellite transmission delays. Specifically, Ref. [26] explores the use of ground stations, high-altitude platforms, and LEO satellites to offer data offloading services to ground users. Meanwhile, Ref. [27] investigates using satellite chains for data offloading to ground stations, suggesting optimization algorithms to enhance the download throughput of data at these stations and for data transfer among cooperative satellites and from satellites to ground stations. Ref. [28] develops a transmission scheduling algorithm to maximize bandwidth usage in satellite constellations by enhancing inter-satellite link capacity. Furthermore, Ref. [29] examines the integration of LEO satellite networks with terrestrial networks, proposing a matching algorithm aimed at maximizing data transmission rates and user access, factoring in various capacity constraints.  
Additionally, Refs. [30, 31, 32, 33] delve into the scheduling of computing and storage resources in LEO satellite networks. For instance, Ref. [30] introduces a hybrid satellite-ground network utilizing software-defined networking, focusing on optimizing the long-term usage efficiency of communication, storage, and computing resources despite variable network, storage, and computing states, employing deep Q-learning algorithms for joint resource allocation optimization. Ref. [31] considers LEO satellites equipped with MEC platform computing and storage resources, proposing a service request scheduling approach to optimize resource use and service quality while reducing system costs. Ref. [32] suggests a strategy for satellite network storage resource scheduling to maintain user experience quality, and Ref. [33] explores the joint optimization of wireless resource allocation and task offloading in satellite networks to leverage the computing power of edge nodes effectively.