esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation
### Problem Formulation  
In Figure 1, the beam, colored black, points directly to its serving satellite without considering the co-channel interference generated by other satellites. Actually, the beam pointing of the UT antenna needs to be shifted to other directions for interference mitigation, represented as the green beam in Figure 1. Moreover, interference avoidance for the time-varying dense LEO networks is challenging.  
To solve the time-varying interference problem, we will decompose the continuous time-varying interference situation into multiple discrete time slots. Then, we intend to maximize the SINR of the communication link at the receiver to obtain the optimal beam pointing of the receiving antenna at every moment, and the problem is formulated as follows:  
\[\max_{g_{r}^{\mathcal{C}u},\sigma_{r}^{\mathcal{C}U}}\{\mathrm{ SINR(n)}\}, \tag{7}\] \[s.t. \theta_{r}^{\mathcal{S}}(\hat{n})-\Theta_{3\mathrm{dB}}\leq \widehat{\theta}_{r}^{\mathcal{C}u}(\hat{n})\leq\theta_{r}^{\mathcal{S}}(\hat {n})+\Theta_{3\mathrm{dB}},\] (7a) \[\varphi_{r}^{\mathcal{S}}(\hat{n})-\Phi_{3\mathrm{dB}}\leq \widehat{\varphi}_{r}^{\mathcal{C}u}(\hat{n})\leq\varphi_{r}^{\mathcal{S}}( \hat{n})+\Phi_{3\mathrm{dB}}, \tag{7b}\]  
where \(\Theta_{3\mathrm{dB}}\) and \(\Phi_{3\mathrm{dB}}\) are the horizontal and vertical 3 dB beamwidth of the main beam of the UT antenna, \(\theta_{r}^{\mathcal{S}}\) and \(\varphi_{r}^{\mathcal{S}}\) are the elevation and azimuth angles of AoA of the communication link. (7a) and (7b) represent the range of the UT antenna beam center adjustment. The above constraints can help to reduce the beam search space.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 2 Preliminaries  
### Quadcopter Dynamics Model  
Here, we briefly summarize the quadcopter dynamics model as it has already been presented previously [45; 46].  
Let \(\phi,\ \theta\), and \(\psi\) denote the three Euler angles roll, pitch, and yaw, respectively, where, \(\left|\phi\right|<\pi/2\), \(\left|\theta\right|<\pi/2\), and \(\left|\psi\right|\leq\pi\) (see Figure 1). The symbols \(x,y\), and \(z\) respectively represent the position of the quadcopter along the \(x,y\), and \(z\) axes in an earth-fixed frame \(\left\{E\right\}\). \(J_{x},\ J_{y}\), and \(J_{z}\) denote the moments of inertia along the \(x,y\), and \(z\) axes, respectively; \(m\) represents the mass, \(l\) the arm length of the vehicle, and \(g\) the gravitational acceleration. The quadcopter dynamics model can be described as follows [45; 46]:  
\[\left\{\begin{aligned} \ddot{\phi}&=\left(\frac{J_{y}-J_{ z}}{J_{x}}\right)\dot{\phi}\dot{\psi}+\frac{l}{J_{x}}U_{2}\\ \ddot{\theta}&=\left(\frac{J_{z}-J_{x}}{J_{y}} \right)\dot{\phi}\dot{\psi}+\frac{l
# An Interpretable Method for Anomaly Detection in Multivariate Time Series Predictions
## 3 Interpretable Method Design  
### Scheme Framework  
In this chapter, we propose an interpretable method based on gradient optimization to address the problem of locating anomalies in anomaly detection in ICSs. The overall framework of this method is shown in Figure 1. Although the explanation module needs to receive the predicted results of the anomaly detection model, it does not rely on the internal structure or parameters of the original model, so there is no need to make any modifications to the original detection model. On the premise of not affecting the performance of anomaly detection, this module only starts when an attack behavior is detected, to determine which input features have a significant impact on the final decision when under attack, thereby helping managers to identify the location of the anomaly occurrence. Inspired by DeepAID [16], we have also reinterpreted the explanation of unsupervised deep anomaly detection models as finding the reasons for abnormal data deviating from normal data. Specifically, the interpretation of anomalies after being attacked is transformed into solving a normal \"reference\" optimization problem, while the interpretation of anomalies is obtained by the accumulation of gradients and values and the difference between normal \"reference values\" and anomalies. Initially, DeepAID only addressed the interpretability issue of univariate discrete time series. We further extended the interpreter to handle the problem of multivariate time series. In the case of multivariate time series, the complexity of the optimization problem will significantly increase, which requires us to not only consider the independent effects of different variables but also to simultaneously consider the relationships and mutual influences between variables.
## 6 Results and Discussion
### Disscussion of Earthquake Magnitude and Epicentral Distance  
Since the MSI at a location is related to the earthquake magnitude and epicentral distance, the SVR model with and without these two parameters are also discussed. As shown in Figure 10, the performance of the SVR of PGA is almost the same as those SVRs considering earthquake magnitude and epicentral distance. This means that it is enough to use ground motion parameters for predicting MSI, and it is not necessary to include magnitude and distance terms in the SVR model.
## 2 Materials and Methods
### Network Connection Architecture with Cache Storage  
It is necessary to capture the traffic in plain text and then either respond from the cache or encapsulate it to pass through the channel to the cloud and obtain a response from a remote point to identify the queries made by VAs and store them in the cache. This part of the work proposes a network architecture, Figure 3, which includes several components and functions described below.  
1. The first part of the process includes the VA, which must have a microphone, speaker, and communication protocol. This generates the initial data flow (user queries) sent through the network;
2. The second part consists of the cache memory, which stores queries and responses. This can be a local memory, an internal network database, or a cache proxy at the network perimeter. An algorithm then checks if the query is in the cache;
3. The third part involves external network elements, such as cloud servers, routers, and connections with AI systems, where queries not found in the cache will be processed. These responses will be returned to the end user, leaving a copy of the query in local memory.  
Thus, this device should respond to future queries, ensuring greater speed in the process and energy savings by not requiring a constant connection to the cloud. It is important to note that the time an object will remain in the cache depends on the specific configuration. Therefore, the Time-To-Live (TTL) of cached objects must be carefully considered to ensure that outdated or incorrect responses are not stored. This point will be addressed in future work. In Section 2.4 of this work, the VA is implemented, and in Section 3 experimental results are presented.  
Figure 2: Percentage of battery consumption of VAs in active or passive state.
## 3 Results
### Pathway Enrichment Analysis of Differentially Accumulated Metabolites (DAMs)  
As a multivariate statistical analysis method, orthogonal partial least squares discriminant analysis (OPLS-DA) can maximize group differentiation, which is helpful for finding differentially expressed metabolites. As shown in Figure 5, pairwise comparisons were obtained based on the OPLS-DA model, and distinct metabolic differentiations were observed between three groups. Q2 is an important parameter for evaluating the model in OPLS-DA. The differences between W and R (R2X = 0.758, R2Y = 0.982, Q2 = 0.964), W and B (R2X = 0.738, R2Y = 0.975, Q2 = 0.957), and B and R (R2X = 0.702, R2Y = 0.993, Q2 = 0.979) are shown in Figure S2. All Q2 values in the three comparison groups were greater than 0.9, demonstrating that these models were meaningful. Then, the variable  
Figure 4: Statistical analysis of metabolome data: (**A**) Classification of identified metabolites. (**B**) [PERSON]’s correlation analysis between samples. (**C**) Heatmap of the quantified identified metabolites. (**D**) Principal component analysis (PCA) score plot.  
importance in projection (VIP) values of the OPLS-DA model and the fold change (FC) were combined to further screen the DAMs. An identification criterion of FC \(\geq\) 2 or \(\leq\)0.5 together with VIP \(\geq\) 1.0 was used to screen DAMs between the comparison groups. The screening results are shown in Supplementary Table S2. There were 491 DAMs between the W and B groups (322 downregulated, 169 upregulated), 630 DAMs between the W and R groups (288 downregulated, 342 upregulated), and 285 DAMs between the R and B groups (227 downregulated, 58 upregulated). The DAMs were divided into seven subgroups with different numbers using k-means cluster analysis (Figure S3). Thus, it can be concluded that the DAMs responded to specific light quality in unique ways.  
Furthermore, the DAMs from each comparison group were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Most noteworthy, the DAMs were predominantly enriched in some overlapped KEGG pathways in the W vs. B, W vs. B, and B vs. R comparison groups, including the biosynthesis of secondary metabolites, monoterenoid biosynthesis, phenylalanine metabolism, plant hormone signal transduction, limonene and pinene degradation, etc. (Figure 6).  
Figure 5: The score plots of orthogonal projections to latent structures discriminant analysis (OPLS-DA) pairwise comparisons of differentially accumulated metabolites (DAMs): (**A**) Comparison groups of W vs. B. (**B**) Comparison groups of W vs. R. (**C**) Comparison groups of B vs. R.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 4 Proposed Protocol
### Registration Phase  
This phase is performed to provide the exchange of important parameters used in the next phases, which regards authentication. Each device must have its _IMSI_ registered in the HSS. This registration occurs offline and is executed by the manufacturer.  
Next, it is necessary to register the health center, patients and doctors in the cloud server. This registration occurs through a secure channel. Each entity generates \(k\) different random numbers \(R_{k}\) and calculates a set of temporary identities, \(TID_{x}=h_{I}(ID_{x}\parallel R_{k})\), which are individually used at each authentication session initiated by the entities. The use of real identities associated with a random number in the calculation of temporary identities guarantees its uniqueness. They send their real identity \(ID_{x}\), and temporary identities \(TID_{x}\) to the cloud server, which stores the data to be used in the following phases. If all temporary identities of a certain entity are used, a new registration phase is performed. If a real identity is revoked, it is necessary to perform a special registration phase, indicating which was the identity revoked and the new equivalent identity. Only registered entities can perform the following phases.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### Wireless Performance Metrics  
The wireless network performance is measured using throughput and energy efficiency measurements. The total achievable throughput (\(R_{total}(t)\)) of the network can be expressed as follows [30]:  
\[R_{total}(t)=\sum_{k=1}^{U}\sum_{i=1}^{N}BW\log_{2}(1+SINR_{i,k}) \tag{9}\]  
Figure 2: Flow chart of the proposed system.  
where \(N\) is the number of BSs, \(U\) is the number of end-users, and \(SINR_{i,k}\) is the received signal-to-interference-plus-noise-ratio at the kth end-users served by base station BS\({}_{\text{i}}\).  
The ratio of total power and network power consumption can be expressed as energy efficiency (EE). The EE metric at time \(t\) can be represented by [25]:  
\[\eta_{EE}=\frac{R_{Total}(t)}{P_{BS}(t)} \tag{10}\]  
where \(P_{BS}(t)\) is the total power consumed in all BSs at time \(t\) and can be calculated using Equation (6).
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 3 Results  
In this section, we present some experimental results. Since our main interest is in forward prediction, we only compare with STARFM and FSDAF, which require one pair of Worldview and Planet images at an earlier time, and one Planet image at the time of prediction. We did not compare with the ESTARFM and STAARCH methods, which, in addition to requiring one Planet image at the prediction time, also require two pairs of Worldview and Planet images: one pair at an earlier time and another pair at a future time. In other words, ESTARFM and STAARCH are suitable for processing archived data, not for forward prediction.  
In order to make our paper self-contained, we include the following specifications of Planet and Worldview images in Table 4. Only four bands have similar spectral ranges.  
We used WV-2 images that have eight bands, of which we only used the RGB bands for ease of demonstration/visualization. In addition, because Planet images only have four bands, using the RGB bands in both WV-2 and Planet images will be sufficient for both visualization and performance evaluation. The spatial resolution of WV-2 images is 2 m. Most of the Worldview images are off-nadir images in order to have a large coverage area. Moreover, because there is only one Worldview satellite, the revisit times are not as frequent as the Planet satellite constellation of 120 Cubesats.  
Each PlanetScope satellite is a CubeSat 3U form factor (10 cm by 10 cm by 30 cm). The complete PlanetScope constellation of approximately 120 satellites will be able to scan the entire land surface of the Earth every day [20]. In Planet image archive, there are Rapideye and Planetscope images. In this study, we used Planetscope images (orthorectified). The image resolution is 3.125 m. For Planet images, only top of atmosphere radiance (TOAR) data products are available. For WV images, surface reflectance images are available. In order to use compatible data, our collaborators at Digital Globe generated WV TOAR images for this study.  
Our area of interest is a border area near Tijuana and San Diego. From data archives of both Planet and Digital Globe, the following images were used in our study:  
Planet images: 19 July 2017, 27 July 2017, and 5 August 2017;  
WV images: 18 July 2017, 26 July 2017, and 3 August 2017  
It should be noted that it is difficult to retrieve Planet and WV images for the same dates. However, this also justifies our research, as our goal is to generate high spatial resolution images when high resolution WV images are not available. It is also emphasized that the registration of the two different types of satellite images is non-trivial, as WV images are not taken at nadir. As a result, automated registration algorithms using corner points from buildings may lead to large registration errors at ground level pixels. In this research, we manually selected ground feature points such as road intersections for image alignment.  
From the above collected images, we focused on three specific sub-areas representing different scenarios.
## 3 Formulation of the Mathematical Model
### Modified Effective Pore Diameter with Effects of Adsorption/Desorption and Stress-Sensitivity
#### Adsorption/Desorption  
In the process of shale gas production, desorption of adsorbed gas caused by decreasing reservoir pressure leads to surface diffusion. Gas adsorption characteristics of shale reservoirs can be described by adopting the Langmuir isotherm model [37]:  
\[V_{i}=\frac{V_{L}\times p_{i}}{p_{i}+p_{L}}, \tag{17}\]  
where \(V_{i}\) is the adsorbed gas content correspond to a given reservoir pressure \(p_{i}\), \(V_{L}\) is the Langmuir volume, and \(P_{L}\) is the Langmuir pressure.  
Figure 3: Fracture conductivity multiplier curves for Barnett Shale and Marcellus Shale.  
The Langmuir isotherm model assumes that the adsorbed gas on the pore surfaces is monolayer adsorption. Thus, the coverage of adsorbed gas on the pore surfaces is defined as [3]:  
\[\theta_{i}=\frac{V_{i}}{V_{L}}=\frac{p_{i}}{p_{i}+p_{L}}. \tag{18}\]  
As stated above, the change in effective pore diameter caused by the adsorption/desorption is expressed as:  
\[\Delta d_{a}=-2\theta_{i}d_{CH_{4}}. \tag{19}\]
## 3 Results
### PHCs Removal and Degradation in Soil  
Although plants face toxicity when pollutants are above a certain level, they have the ability to remove a certain amount of organic pollutants from soil through different mechanisms [64; 65]. In this study, individual use of biochar, mungbean plants and _Enterobacter_ sp. MN17 could remove a substantial amount of PHCs from soil. However, mungbean plants in the presence of microbes and biochar could further improve the removal of PHCs from soil [1]. This is because a beneficial microbe can influence the PHCs availability to the plants through a number of processes including the formation of organic acids, chelation, protonation, chemical transformation of chemicals, and solubilization of phosphate [66]. Another mechanism involved in the reduction of PHC-concentration in soil amended with biochar might be the adsorption of PHCs on the surface of biochar [63; 64; 65; 66; 67]. This because it has been reported that biochar can sorb PHCs due to the presence of diverse functional groups and provide micro-pores for aeration and microbial growth that further improve the removal or degradation of PHCs [4; 22; 68].  
To the best of our knowledge, this is the first report on the interactive effects of _Enterobacter_ sp. MN17 and biochar on mungbean growth and PHCs remediation from contaminated soil. Findings from the present study enhance our knowledge on plant-microbe interactions in PHC-contaminated soil and provide new information on the usage of beneficial microbes and biochar in improving PHCs remediation and crop productivity under PHC stress and open new areas for studying the significance of biological interactions in hydrocarbon-polluted environments.
## 5 Results and Discussion
### Effects of Using Different Loss Function  
For the purpose of evaluating the effect of using different loss functions, we conducted an experiment.  
As shown in Table 3, we reached the conclusion that the Weights Cross Entropy Loss Function and the Focal Loss Function achieve a higher accuracy than the Mean Square Error Loss Function and Cross Entropy Loss Function, for the reason that category imbalance always exists in concrete crack datasets because the number of crack pixels is always much smaller than the number of normal pixels in a concrete crack image. However, the Weights Cross Entropy Loss Function and the Focal Loss Function uses special parameters to increase the weight of crack pixels in loss function to compel the model paying more attention on crack pixels. Compared with the Weights Cross Entropy Loss Function, the Focal Loss Function could achieve slightly better results, as the Focal Loss Function uses more adjustable parameters to adjust the category imbalance.
## 4 Results and Discussion
### Analysis of Output Voltage
It can be observed from Figure 8 that the output voltage was minimum at 30 Hz excitation frequency and maximum at higher frequencies for a depth ratio of 0.6. Thus, the maximum voltage output is obtained at more than 30 Hz excitation frequency in the transverse direction. The variation in the output voltage at varied excitation frequencies is due to the confinement of the granular fill. Hence, the distance from the centreline of the load influences the efficiency of the _PZT_ patch embedded in confined granular fill, similar result was found by [PERSON] et al. (2018).
## 1 Introduction
To the best knowledge of the authors, this research represents the first in Portugal to address equity issues around the provision of shared e-scooter services. Additionally, this research contributes to expanding the knowledge of shared e-scooter usage patterns in urban contexts. It contributes to a better understanding of the interaction of this mode of transport with city dwellers and the range of influential aspects that need to be addressed such that e-scooter services could reach all individuals equally. Equity can be defined as \"the morally proper distribution of benefits and costs (burdens) over members of society\" [20; 21], and, according to [PERSON] et al. [22], more studies need to be addressed in this matter, since a clear definition for the concept is still missing, and the debate on the extent of equity within the field of transport is still open. For this, a case study in the city of Braga, located in the north of Portugal, is developed to better understand who the actual and potential users of the shared e-scooter service in the city are, and if this service is offered equally to the population despite their socioeconomic characteristics. Therefore, the research questions are:  
* Who are the actual and potential users of the shared e-scooter service in Braga, Portugal? * What are the social characteristics of the actual users and potential users of these shared e-scooters in Braga? * Is this service provided in an equitable way across the population of different socioeconomic profiles? The remainder of this paper is organized as follows: Theoretical Framework, Methodology, Results, where the case study is presented, Discussion, and Conclusion.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 3 LDPC-RS Concatenation Code
### Structure of the Proposed LDPC-RS Concatenation Code  
Typical concatenation code consists of inner and outer code. Data bits are encoded by the outer code, and the whole outer code word is treated as data that are part of the inner code; then, the inner encoder encodes it to form the inner (final) code word.  
The structure of the proposed LDPC-RS concatenation code is depicted in Figure 3. We chose RS (340, 320) and a shortened version of RS (511, 491) code for BP decoding, and the corresponding generation polynomial coefficients are 1, 58, 257, 157, 222, 388, 151, 62, 280, 137, 404, 286, 394, 61, 399, 73, 84, 145, 293, 369, and 331. We adopted 5G LDPC (7680, 3840) code for the inner code, with 780 bits shortened (filled with a known pattern which is not transmitted) and accustomed to the 3060-bit length of outer RS (340, 320) code. Therefore, there are \(320\times 9=2880\) bits of data, the total transmitted code length is \(340\times 9+3840=6900\) bits, and the code rate is \(2880/6900=0.4174\). The price of 0.09 rate-loss is acceptable considering rate and error floor trade-off.
## 4 Application of the Developed PV System Models  
The reliability impact of the different PV technologies is illustrated on the 150 kW SIPS explained previously. Many SIPS use the deterministic N-1 criterion, also known as the \"loss of the largest unit\" criterion, to determine the capacity reserve required in their system. This criterion ensures that the peak load can be satisfied in the event of the failure of the largest generating unit. The example SIPS with 80 kW peak load just meets the N-1 criterion. The LOLE and LOEE of the example system are 32.26 h/year and 483.46 kWh/year, respectively. This LOLE value is therefore chosen as the risk criterion in the following studies.  
Two case studies are carried out to investigate the reliability impacts of the different PV technologies. The first study examines the reliability contribution of adding PV generation to SIPS. The second study analyzes the capacity value of an installed PV systems. The three different PV topologies are taken into consideration for both studies. Table 6 presents all studies investigated in this work. Installed PV capacity levels of 15, 30 and 45 kW, corresponding to approximately 10%, 20% and 30%, respectively, of the SIPS capacity, are considered.