esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
###### Abstract  
Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model.  
smart grid; Home Energy Management Systems (HEMS); Simulink; load categorization; green effects; energy cost minimization; CHEMA Article
# A Survey on Parameters Affecting MANET Performance
## 3 Simulation in MANETs
### Attacks on MANETs' Routing Protocols
Flooding attack: In a flooding attack, malicious nodes tend to waste network resources such as memory, bandwidth, and battery by flooding the network with bogus packets [34]. For example, flooding RREQ packets prevents the MANET from functioning normally. 3. DDoS attack: In a DDoS attack, attackers tend to keep the targeted legitimate node busy by continuously requesting RREQ messages from collaborative attackers at the same time without respecting the TTL time [35]. 4. False removal of working route: In this attack, the malicious node advertises a false state of the link with the destination node as if the link is broken. This enforces the source node to re-initiate route discovery protocol to find another path to reach the destination. Additionally, it slows down packet transmission. False removal of working route attack could be used with another collaborative attack to isolate the targeted legitimate node from MANET. 5. Node isolation attack: Attackers isolate an innocent node by blocking routing information about this targeted node from the entire network [36]. This leads to an ignorance of the presence of this innocent node. 6. Routing table poisoning: In this attack, the attacker sends false RREQ packets with a higher sequence number to force all nodes to delete the old genuine route to a destination and update this route with a corrupted one. 7. Blackhole attack: The attacker tends to change the routing protocol packets to be the best route known for a targeted destination, and when it is requested to forward data packets to the destination node, it starts discarding the received packets to slow down the network performance [37]. 8. Grayhole attack: Grayhole attack is an instance of a blackhole attack where an attacker selectively drops some data packets and normally forwards others [38], or drops all packets but only at a certain time. This makes the attack difficult to detect. 9. Wormhole attack: In a wormhole attack, two attacking nodes cooperate where one attacker at a specific location encapsulates some packets and tunnels them to the second attacker, bypassing all intermediate nodes to introduce itself as the fastest route to a destination and then drop the data packets later [39]. It can also be used to replay the received data packets in the other side of the network to disrupt the routing protocol. 10. Rushing attack: In a rushing attack, the malicious node sends RREQ messages with high-power transmission to introduce itself as the shortest path to any destination with only one hop count [40], this manipulates all network nodes to use this routing path. The rushing attack is most likely used alongside another attack such as dropping the network packets that need forwarding.
# Architectural Patterns for Blockchain Systems and Application Design
## 4 Pattern Description
### Interactional View Patterns
#### 4.2.1 Blockchain Broker Pattern  
**Summary:** This pattern addresses concerns about communicating the decoupled components of a blockchain application as a distributed system.  
**Context:** A blockchain application consists of decoupled off-chain and on-chain components.  
**Solution:** The blockchain broker enables communication between the decoupled components in distributed systems. Each node in the network can act as a message producer  
Figure 4: The centralized off-chain pattern for blockchain applications.  
Figure 3: The distributed off-chain pattern for blockchain applications.  
or consumer, depending on whether it submits transactions or receives blocks from the network. For example, smart contracts can act as message brokers by enabling communication and coordination between different nodes. Digital wallets and APIs can act as message clients that consume messages from the network and perform various actions based on them, such as updating balances, providing market data, and providing services to third-party applications or users. As shown in Figure 5, there are two types of blockchain brokers:  
1. Side-chain broker: By design, blockchains are heterogeneous and not able to communicate with each other since each blockchain has its own set of rules and tools that make communication difficult. A side-chain broker allows blockchain interoperability through cross-chain bridges. It enables the transfer of assets between one blockchain (on-chain) and another (side-chain). Typically, a side-chain broker involves locking or burning tokens through a smart contract on the source chain and unlocking or minting equivalent tokens on the destination chain through another smart contract.
2. Off-chain broker: This type of service communicates among blockchain and client applications. A client API handles the input from the client UI and passes it to a dedicated blockchain endpoint. The blockchain endpoint node passes the input data to the blockchain and returns output data to the client API, which, in turn, passes the data to the UI. The client API and blockchain endpoint API are service broker components that connect off-chain and on-chain components.  
**Known uses:** Dtube is a Steem-based video-sharing application. It uses the JSON-RPC web service and the RPC endpoint of Steem's public service node to communicate the off-chain components with the Steem blockchain. CryptoFlip Cars is an Ethereum-based game application. It uses the JSON-RPC web service and the RPC endpoint of Ethereum public nodes to communicate the off-chain components with the Ethereum blockchain. PUML is a health and fitness loyalty program. It uses the JSON-RPC web service and the RPC endpoint of EOS public nodes to communicate the off-chain components with the EOS blockchain and the PUML side chain.  
**Related patterns:** This pattern shows how the different tiers in the on-chain-off-chain pattern are connected.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
###### Abstract  
The upwelling spectral radiance distribution is polarized, and this polarization varies with the optical properties of the water body. Knowledge of the polarized, upwelling, bidirectional radiance distribution function (BRDF) is important for generating consistent, long-term data records for ocean color because the satellite sensors from which the data are derived are sensitive to polarization. In addition, various studies have indicated that measurement of the polarization of the radiance leaving the ocean can used to determine particle characteristics ([PERSON] et al., 2007; [PERSON] et al., 2016; [PERSON] et al., 2001). Models for the unpolarized BRDF ([PERSON] et al., 2002; [PERSON] et al., 2011) have been validated ([PERSON] et al., 2007; [PERSON] et al., 2012), but variations in the polarization of the upwelling radiance due to the sun angle, viewing geometry, dissolved material, and suspended particles have not been systematically documented. In this work, we simulated the upwelling radiance distribution using a Monte Carlo-based radiative transfer code and measured it using a set of fish-eye cameras with linear polarizing filters. The results of model-data comparisons from three field experiments in clear and turbid coastal conditions showed that the degree of linear polarization (_DOLP_) of the upwelling light field could be determined by the model with an absolute error of \(\pm 0.05\) (or 5% when the _DOLP_ was expressed in %). This agreement was achieved even with a fixed scattering Mueller matrix, but did require in situ measurements of the other inherent optical properties, e.g., scattering coefficient, absorption coefficient, etc. This underscores the difficulty that is likely to be encountered using the particle scattering Mueller matrix (as indicated through the remote measurement of the polarized radiance) to provide a signature relating to the properties of marine particles beyond the attenuation/absorption coefficient.  
polarization; ocean optics; upwelling radiance distribution; remote sensing +
Footnote †: journal: Applied Sciences
## 3 Creating Loads and PV Generation Data
### Secondary Substation PV Generation Data  
Since the PV plants considered here are fictive, their power generation data cannot be taken from measurements. Instead, the production profiles of PVs are built using irradiation data converted to output power. There are several online tools that provide such information by choosing the geographical location of interest. In this paper, the SARAH2 database of PVGIS was used, which is a tool offered by the European Commission. It is possible to obtain the data for a unit 1 kWp PV plant and build the unit production profile for that geographical territory. That is much easier than processing parameters such as  
Figure 4: Active power of the 41 SSs.  
Figure 5: Reactive powers of loads.  
DNI (Direct Normal Irradiation), DHI (Diffuse Horizontal Irradiation), or GHI (Global Horizontal Irradiation).  
Upon building the unit production vector, it can be used for all other PVs, as they are all subject to the same irradiation, only their installed peak power changes. Next, that process is explained in steps.  
First, using the 1 kWp PV plant's outputs for two years, the unit production vector is built.  
\[I_{pv}=\left(0,0,0,\ldots,0.89,0.93,0.88,\ldots\right)_{1\times 17,520} \tag{1}\]  
Then, another vector containing the installed peak powers of the PVs is built:  
\[P_{pv}=\left(PP1,PP2,\ldots,PP9\right)_{1\times 9} \tag{2}\]  
When multiplying \(I_{pv}\) transposed and \(P_{pv}\), we get the matrix which contains the hourly power outputs for all nine plants for the entire two years:  
\[P_{0}=\left({I_{pv}}^{\prime}\times P_{pv}\right)_{17,520\times 9}\]  
The PV outputs for the first half of the data are shown in Figure 6. There are nine joint characteristics, so a zoomed-in picture is shown in Figure 7.  
Similar to the loads, the matrix's columns represent a 2-year characteristic for each PV. Those columns are exported individually to separate '.csv' files. They are first used in PowerFactory, and later, they will also be used as inputs to build and train the DNN. The reactive components of the PVs are added although their power factor is assumed to be fixed in these simulations.  
Figure 6: PV outputs of the first half of the analysis.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 4 Conclusions  
In this paper, the pseudo-static tests of both the precast columns using grouted splice sleeves to connect steel bars and the cast-in-situ columns have been conducted. According to the analysis and discussion of the test results in the above sections, several main conclusions are drawn as follows:  
1. The failure modes between the prefabricated columns and cast-in-situ columns are basically the same, which are both bending failure. The cracks widen visibly at the tensile edge of the concrete, and the columns fail by crushing of the concrete in the compressive zone and tensile yielding of the vertical reinforcement bars in the tensile zone. The ultimate displacement angle of the prefabricated columns are between 1/104 and 1/54, which satisfies the regulation in rare earthquake situations and demonstrates that the grouted splice sleeve is reliable in the connection between the prefabricated members.
2. For the cast-in-situ columns, there is a horizontal interconnected crack in the interface between the column foot and pedestal, while for the prefabricated columns, horizontal cracks appear above the sleeve, and then develop into diagonal cracks, and finally, many cracks with large width form within the range of height of 300 mm above the column foot.
3. The horizontal load-displacement curves of the prefabricated columns are basically the same with those of the cast-in-situ columns. The energy-dissipation capacity and ductility of the prefabricated columns are better than those of the cast-in-situ columns. Furthermore, the load-carrying capability of the prefabricated columns is lower than that of the cast-in-situ columns, whereas their stiffness degradation is slower than that of the cast-in-situ columns.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 3 Approach
### Network Model and Assumptions  
A wireless sensor network is composed of a set of battery-powered sensors and a sink. The latter is connected to a power source and so we disregard its energy consumption in this paper. Communication between the sensors is achieved through wireless links if they are within range of each other, or through other intermediate sensors if not.  
Thus, a WSN can be modeled as a unit disk graph [32; 33]\(G=(V,E)\) where \(V\) is the set of all nodes including the sink and \(E\in V\times V\) is a set of possible communication links. We assume our graph is connected.  
\(S=\{s_{1},\,s_{2},\,...,\,s_{N}\}\) represents a collection of \(N\) sensor nodes that are distributed randomly in a two-dimensional plane to monitor a set of target nodes \(T=\{t_{1},\,t_{2},\,...,\,t_{M}\}\).  
Given two nodes \(s_{1}\) and \(s_{2}\) (one of them should be a sensor), they are connected by an edge \((s_{1},s_{2})\in E\), if the first sensor is within the sensing coverage range of the second sensor. Thus, two nodes \(s_{1}\) and \(s_{2}\) can communicate with each other (there is an edge between them) if \(\mid d(s_{1},s_{2})\mid\mid\,\leq 1\), where \(\mid\,\mid d(s_{1},s_{2})\mid\mid\,\) is the Euclidean distance between \(s_{1}\) and \(s_{2}\).  
Each sensor node \(s_{i}\in S\), can operate into a number of sensing ranges \(r_{1}\), \(r_{2}\),..., \(r_{p}\) where each \(r_{k}\) consumes energy \(e_{k}\), \(1\leq k\leq P\). The initial energy of each sensor node is \(E\). A sensing node \(s_{i}\in S\) can cover a target \(t_{i}\in T\) using sensing range \(r_{k}\) if the Euclidean distance \(d(s_{i},t_{j})\) is less than or equal to \(r_{k}\), denoted as \(t_{j}\to s_{i}\). Then, we define the target subset \(T_{i}=\{t_{j}\mid t_{j}\to s_{i},\,1\leq j\leq m\}\) as the target set of the sensor \(s_{i}\).  
We assume that at regular time intervals, the information sensed by a network node has a fixed unit size of \(b\) bits that can be stored at the sensor in a buffer of infinite capacity, modeled as a First In First Out (FIFO) queue. We also assume that the channel bandwidth for all links is \(W\) bps and that all transmitted packets have a unit size of \(b\) bits. Only one packet can be transmitted in each time slot \(t\), which is defined as \(t=b/W\) seconds. Time-division multiple access (TDMA) is used at the MAC layer.
# Visual Tracking Using Wang-Landau Reinforcement Sampler
## 4 Experiments  
### Experimental Settings  
The proposed method was compared with 30 non-deep learning methods (e.g., SCM [36], STRUCK [37], ASLA [38], TLD [39], CXT [40], VTD [41], VTS [42], CSK [43], MEEM [44], Staple [45], and SRDCF [46]) on 50 test sequences in the OTB dataset [47]. Furthermore, our method was compared to state-of-the-art deep learning-based methods: C-COT [48], SINT [29], SINT-op [29], ECO [49], ECO-HC [49], SiamRPN++ [32], TADT [50], DAT [51], and SiamDW [52]. Moreover, the proposed visual tracker was compared based on the VOT2017 [53] dataset with 10 recent visual trackers, including CFWCR [54], CFCF [55], CSRDCF [56], MCCT [57], and LSART [58]. Our proposed visual tracker was also compared for the LaSOT [59] dataset with 8 state-of-the-art visual trackers, namely ECO [49], SiamRPN++ [32], GlobalTrack [31], ATOM [60], DASiam [30], CFNet [61], SPLT [62], and StructSiam [63]. Please note that we compared the proposed method with 30 non-deep learning visual trackers but reported only top 10 visual tracker in Figure 2 to visualize precision and success curves for each tracker more clearly.  
We used the precision, success rate, and AUC as evaluation metrics for testing these methods [47]. For precision, we calculated the \(l2\)-norm distance between the estimated bounding box \(E_{t}\) and the ground truth bounding box \(G_{t}\). Then, we depicted the precision plot, which shows the percentage of frames such that the \(l2\)-norm distance less than a specific threshold. For the success rate, we calculated the intersection of union \(IoU=\frac{|E_{t}\cap G_{t}|}{|E_{t}\cup G_{t}|}\), where \(|\cdot|\) indicates the number of pixels. We considered visual tracking at each frame to be successful, if \(IoU\) is greater than a specific threshold. Moreover, we calculated the success rate, which is the ratio of the number of successful frames to the number of total frames. We then illustrated the success plot, which presents the success rates with different thresholds, and we calculated the area under curve (AUC).  
For fair comparison, we used the best visual tracking results reported by the authors in the original papers and followed their experimental settings. For example, SiamRPN++ [32] was pretrained on ImageNet [64] and used ResNet [33] as a backbone network. In addition, SiamRPN++ was trained using the training datasets of COCO [65], ImageNet DET [64], ImageNet VID, and the YouTube-Bounding Boxes dataset [66]. All experiments were conducted using a desktop with an Intel CPU i7 3.60 GHz and GeForce Titan XP graphics card for the proposed method. Throughout the experiments, hyperparameters were fixed as follows: \(\gamma=0.9\) in (8), \(N=2000\) in Algorithm 1 and \(w=0.8\) in (12).
## 5 Numerical Study
### CGA vs. Monte Carlo (MC) Method
The plots presented in Figure 5 depict the improvements in the fitness function value obtained using the CGA vs. the MC method for the twenty-seven-node topology. Figure 5a-c illustrates the optimization process convergence for the fitness function coefficients that were adjusted to (\(\gamma=1\), \(\varphi=1\)), (\(\gamma=1\), \(\varphi=10\)), and (\(\gamma=10\), \(\varphi=10\)), respectively. All the cases showed that CGA allowed one to find the optimal solution in a smaller number of iterations than the MC approach. This is a key observation for the analyzed class of real-life systems, which require high computational investments when determining a single fitness function value. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number** & **Optimization** & **Fitness Function** & \multirow{2}{*}{**Performance Indicators**} & **Best Fitness** & **Fitness Value** \\
**of Nodes** & **Method** & & **Coefficients** & & **Function** & **Difference** \\ \hline  & & \(\gamma\) & \(\varphi\) & **Holding Cost** & **Fill Rate** & & \\ \hline
5 & MC & \(1\) & \(1\) & 3086 & 94\% & 0.84691098 & 0.0013 \\
5 & CGA & \(1\) & \(1\) & 3043 & 94\% & 0.84820807 & 0.0013 \\ \hline
5 & MC & \(1\) & \(10\) & 7715 & 100\% & 0.75242282 & 0.0097 \\
5 & CGA & \(1\) & \(10\) & 7412 & 100\% & 0.7621462 & 0.0097 \\ \hline
5 & MC & \(10\) & \(1\) & 676 & 73\% & 0.58623815 & 0.0031 \\
5 & CGA & \(10\) & \(1\) & 660 & 73\% & 0.58932219 & 0.0031 \\ \hline
14 & MC & \(1\) & \(1\) & 8068 & 94\% & 0.90066472 & 0.0427 \\
14 & CGA & \(1\) & \(1\) & 9076 & 99\% & 0.94339654 & 0.0427 \\ \hline
14 & MC & \(1\) & \(10\) & 31,326 & 100\% & 0.83752243 & 0.1008 \\
14 & CGA & \(1\) & \(10\) & 11,892 & 100\% & 0.93832014 & 0.1008 \\ \hline
14 & MC & \(10\) & \(1\) & 4438 & 88\% & 0.69718233 & 0.0485 \\
14 & CGA & \(10\) & \(1\) & 3593 & 90\% & 0.74566725 & 0.0485 \\ \hline
27 & MC & \(1\) & \(1\) & 59,828 & 99\% & 0.92887945 & 0.0425 \\
27 & CGA & \(1\) & \(1\) & 18,210 & 99\% & 0.97139658 & 0.0425 \\ \hline
27 & MC & \(1\) & \(10\) & 105,453 & 100\% & 0.89118056 & 0.0763 \\
27 & CGA & \(1\) & \(10\) & 31,488 & 100\% & 0.96750679 & 0.0763 \\ \hline
27 & MC & \(10\) & \(1\) & 42,873 & 96\% & 0.61059353 & 0.02361 \\
27 & CGA & \(10\) & \(1\) & 13,086 & 97\% & 0.84669328 & 0.2361 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed results of the Monte Carlo (MC) method vs. the CGA. The execution of the optimization procedure with the CGA lasted on average 45, 311, and 1268 s for the five-, fourteen-, and twenty-seven-node networks, respectively. Thus, the computation time grew exponentially with the network size, albeit linearly with the planning horizon. The CGA performance in this aspect may yet be improved, e.g., by recurring using the hardware-assisted parallelization techniques [56].
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 5 Results
### Dynamical Optimization  
The _dynamical optimization_ inverse kinematics has been implemented using iDynTree multibody kinematics library [51], and the inverse differential kinematics is solved using OSQP [38]. Figure 8 highlights the rate of convergence of the error from a given initial zero configuration (\(s(0)=0\)) towards a target static pose. When the gain is zero, there is no velocity correction and the error remains constant. However, increasing the magnitude of \(K\), the error converges to its steady state value in less than one second. A large value of the gain \(K\) leads to system instability as mentioned in Section 3.1.  
From Figure 7, the _dynamical optimization_ orientation error is mostly comparable with the results achieved with _instantaneous optimization_. The only case in which it shows worst orientation accuracy is with the _Human66_ model during running task. Concerning the angular velocity error, the performances are again comparable during dynamic motion, while it is higher for the T-pose with the constrained models. This angular velocity error may be due to the fact that a corrected angular velocity is used in place of the measured link angular velocities, and the joint constraints may introduce a constant constraint error because of an unfeasible configuration. Concerning the computational load, this method seems to outperform the others not only in terms of a mean computational time, having an average always below \(3\,\mathrm{ms}\), but also for its consistency in different scenario.  
Experiments with real-robot show the feasibility of the inverse kinematics solution. The robot is able to track real-time the motion of the human by following the computed joint configuration with a delay lower then \(300\,\mathrm{ms}\), as shown in Figure 9 (evaluation of the controller is out of the scope of this work). Moreover, the joint limit avoidance strategy successfully constraints the joint angles within the robot physical limits that are never exceeded. More in details, Figure 3 shows the trajectory of some joints concerning both position and velocity, and it can be observed that the joint configuration remains within the joint constrained configuration space \((s,\dot{s})\).  
Figure 8: Convergence of the dynamical inverse kinematics optimization for static _T-pose_ using a 66 degrees of freedom (DoF) model, starting from zero configuration. Convergence rate depends on the magnitude of the gain \(K\).