esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of Ensemble Learning Model for Home Appliance Demand Forecasting
## 3 Experiment and Discussion  
The overall network framework is implemented, and the research results in this paper are mainly based on model accuracy. Four indicators, \(MAE\), \(MSE\), \(RMSE\), and \(R^{2}\), are chosen as the evaluation indexes of the model with the following formulas:  
\(MAE\) (Mean Absolute Error) is used to measure the average absolute difference between the predicted and actual values. The smaller the \(MAE\) value, the higher the model accuracy.  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}\abs{\hat{y_{i}}-y_{i}} \tag{7}\]  
\(MSE\) (Mean Squared Error) is a measure of the average of the sum of the squares of the differences between the predicted and actual values. The smaller the \(MSE\) value, the more accurate the prediction of the model.  
\[MSE=\frac{1}{n}{\sum\limits_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}} \tag{8}\]  
\(RMSE\) (Root Mean Squared Error) is a measure of the mean squared difference between actual and predicted values. The smaller the value, the smaller the model bias.  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}} \tag{9}\]  
\(R^{2}\) (R-squared) is the goodness of fit, which measures the extent to which all the characteristic variables explain the variance of the target variable, and can intuitively reflect the model regression fitting effect. The range of results is \([0,1]\). The closer the \(R^{2}\) value is to 1, the better the model fitting effect, and the closer it is to 0, the worse the model fitting effect.  
\[R^{2}=1-\frac{\sum\
olimits_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}{\sum\
olimits_ {i=1}^{n}(\overline{y_{i}}-y_{i})^{2}} \tag{10}\]  
In the above four equations, \(n\) is the number of samples, \(i\) is the current sample, \(\hat{y_{i}}\) is the predicted value, \(y_{i}\) is the true value, and \(\overline{y_{i}}\) is the mean of the true value.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 3 Energy Balance Analysis  
### Required Flight Power  
The force on an aircraft during cruise flight is as in Equation (14):  
\[\begin{array}{l}L=W\\ T=D\end{array} \tag{14}\]  
Since power is defined as the amount of work done per unit time, the required flight power for cruise flight for a day, as in Equation (15), consists of propulsion multiplied by cruise speed and output efficiency.  
\[P_{req}=\frac{TV}{\eta_{propulsion}}=\frac{DV}{\eta_{propulsion}} \tag{15}\]  
\[\eta_{propulsion}=\eta_{motor}\eta_{propeller} \tag{16}\]  
where \(\eta_{propulsion}\) represents the efficiency of factor(s) affecting output and is the product of motor efficiency (\(\eta_{motor}\)) and propeller efficiency (\(\eta_{propeller}\)).  
The total weight utilized in the calculation of required flight power is as in Equation (17):  
\[W_{T}=W_{S}+W_{P}+W_{SC}+W_{MP}+W_{B} \tag{17}\]  
where \(W_{S}\) is the structure weight, \(W_{P}\) is the payload weight, \(W_{SC}\) is the solar cell weight, \(W_{MP}\) is the motor and propeller weight, and \(W_{B}\) is the battery weight.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flight Conditions** & **Value** & **Unit** \\ \hline Flight altitude & 18 & km \\ Flight location & Anhneung & – \\ Latitude & 36.45 & deg \\ Longitude & 126.19 & deg \\ Solar attenuation factor & variance & – \\ Flight date & 06/22 & – \\ Flight speed & 18 & m/s \\ Turbidity & 100 & \% \\ \hline
**Design Parameters** & **Value** & **Unit** \\ \hline Wing span & 24.2 & m \\ Aspect ratio & 18.1 & – \\ Wing area & 30.3 & m\({}^{2}\) \\ Payload mass & 5 & kg \\ Payload power & 50 & W \\ Solar cell fill factor & 0.8 & – \\ Solar cell efficiency & 0.2 & – \\ Solar cell specific mass & 0.5 & kg/m\({}^{2}\) \\ Battery energy & 10 & kWh \\ Battery specific energy density & 300 & Wh/kg \\ Battery efficiency & 0.9 & – \\ Airframe weight adjustment factor & 1.2 & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Flight condition and design parameters.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 3 Preliminaries  
In a typical TRS, there are three sets of entities: a user set \(U=\{u_{1},\ldots,u_{M}\}\), an item set \(I=\{i_{1},\ldots,i_{N}\}\), and a tag set \(T=\{t_{1},\ldots,t_{L}\}\), where \(M\), \(N\), and \(L\) are sizes of the corresponding set. Different from the traditional recommendation systems, the user-iteminteractions in TRS can be represented by a ternary relation set: \(S\subseteq U\times I\times T\), where an element \((u,i,t)\in S\) means that user \(u\) has marked item \(i\) with the tag \(t\). Mathematically, we often represent the ternary relation as a three-order tensor \(Y\in\mathbb{R}^{M\times N\times L}\), whose element is defined as:  
\[y_{u,i,t}=\begin{cases}1&(u,i,t)\in S\\ 0&\text{otherwise}\end{cases}, \tag{1}\]  
where \(y_{u,i,t}=1\) means that user \(u\) has interacted with item \(i\). Let \(R\) be the user-item interaction matrix, where \(R(u,i)=1\) if the tagging behavior between user \(u\) and item \(i\) is observed; otherwise, \(R(u,i)=0\). The positive samples for training can be easily obtained by selecting one pair \((u,i)\) from the observed user-item interaction set.  
In our work, the user-item-tag tensors are first decomposed into two adjacent matrices: user-tag matrix and item-tag matrix, as shown in Figure 1. To be specific, we define the set of observed user-tag interactions \(\Omega_{ut}\) and item-tag interactions \(\Omega_{it}\) as:  
\[\begin{array}{l}\Omega_{ut}=\{(u,t)\mid\exists i\in I:(u,i,t)\in S\},\\ \Omega_{it}=\{(i,t)\mid\exists u\in U:(u,i,t)\in S\}.\end{array} \tag{2}\]  
Each entry \(e_{u,t}\) in the user-tag matrix is set as: \(e_{u,t}=1\) if \((u,t)\in\Omega_{ut}\), and 0, otherwise. Analogously, we set \(e_{i,t}=1\) in the item-tag matrix as: \(e_{i,t}=1\) if \((i,t)\in\Omega_{it}\), and 0, otherwise. According to the corresponding matrix, we can then establish two undirected bipartite graphs: user-tag graph \(G_{u}=(U\cup T,\Omega_{ut})\) and item-tag graph \(G_{i}=(I\cup T,\Omega_{it})\). Note that user-item interactions are constructed by the related tagging behaviors. The task of our work is to mine the implicit feedback in tagging records, explore tag-based graph collaborative information, and learn a recommendation model for providing satisfactory recommendations to each user.
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 2 Literature Review
### Theory
#### 2.1.4 Multimodal Interaction Effects of Sound and Image  
The synergistic effect of crossmodal integration arises from the complementary and dynamic interaction of multisensory information channels. The combination of auditory and visual stimuli initiates a \"dual encoding-cross-reinforcement\" mechanism: while speech input activates phonological word representations, pictorial stimuli engage semantic networks, with both modalities converging to strengthen memory traces through associative encoding in the hippocampus. Empirical evidence by [PERSON] & [PERSON] supports this mechanism--for instance, simultaneous presentation of the word \"bird\" with corresponding visual imagery enhances long-term memory retention through the cross-activation of phonological and visual coding systems [49].  
Oculomotor indicators provide microscopic evidence for multimodal integration as well. For example, a series of studies by [PERSON], [PERSON] & [PERSON], [PERSON] found that the audiovisual congruent group switched gaze 40% more frequently than the unimodal group, suggesting that multimodal information facilitates dynamic integration and that switching paths are more focused on semantically critical areas [50]. It has also been found that the frequency of gaze switching was significantly higher in the multimodal group than in the unimodal group (25.00% more on average), reflecting the fact raised by [PERSON], [PERSON] et al. that learners dynamically integrate content between audiovisual information more frequently [51]. This all suggests that multimodal input enhances learning efficiency by optimizing resource allocation.  
However, [PERSON] (2001) suggests that multimodal designs must strictly adhere to the \"consistency principle\". Inconsistent audiovisual information by [PERSON] et al. induces competing representations in working memory, leading to memory fragmentation [52]. Empirical evidence from eye-tracking studies confirms this effect: [PERSON] et al. observed a 30% increase in eye movements in response to verbal stimuli in an incongruent multimodal condition, suggesting that learners are repeatedly attempting to reconcile conflicting information. This inefficient integration process depletes cognitive resources and ultimately affects learning efficiency [52].  
In summary, the current study focuses on the learning effects of sound, pictures, and multimodality and analyzes them inductively. However, two key limitations remain: firstly, analyses of multimodal synergies have relied heavily on traditional statistical methods (e.g., ANOVA) and lack dynamic quantitative tools to distinguish between nonlinear interactions between auditory and visual stimuli over time. Secondly, assessments of cognitive resource allocation often rely on subjective reports or isolated behavioral metrics (e.g., reaction time), failing to use micro-behavioral data from eye tracking (e.g., average saccade [ASC], standard deviation of fixation duration [SD-FD]) to quantify the process of attentional reallocation.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.3 Mystery Gray Panel Accuracy  
Figure 12 provides the accuracy measurement results for the Mystery Gray panel, which had a reflectance from 0.22 to 0.50 dependent on wavelength. Each ASD instrument approach is included along with the UAS-based measurements. Unfortunately, the panel size was too small for the GSD of the UAS SWIR sensor, so data from these wavelengths are not included. The figure shows measured reflectance minus calibrated reflectance, or accuracy error. Results indicate typical accuracy for the SDSU ASD system of 0.02 reflectance units or less, while the EROS ASD systems were biased and slightly lower, at approximately 0.04 reflectance units. The UAS results were similar in bias to the EROS measurements but somewhat larger in magnitude.  
Figure 11: Spectral profile of Mystery panels.  
Figure 12: Reflectance measurement accuracy using the Mystery Gray panel.
# Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aldar
## 4 Results and Analysis
### Range-Doppler Imaging for Rotating Targets  
We also employed this method for the ISAL imaging of rotating extended targets. We designed a '1-2-1' ladder target, as illustrated in Figure 13. The steps are positioned at intervals of 5 cm to meet the range resolution requirements corresponding to the LFM signal bandwidth, which is 3.32 cm.  
Figure 13: ISAL images of rotating targets: (**a**) target layout; (**b**) 650 m fiber with uncompensated phase noise; (**c**) 650 m fiber with compensation; (**d**) 650 m fiber with compensation and multi-view; (**e**) 1460 m fiber with compensation; (**f**) 1460 m fiber with compensation and multi-view; (**g**) 2110 m fiber with compensation; (**h**) 2110 m fiber with compensation and multi-view; (**i**) narrow linewidth laser signal with 2110 m fiber; and (**j**) multi-view processing.  
We utilize a widely adopted rotation imaging module, wherein the target rotates around a central axis to generate Doppler information. The target rotation speed is 10\({}^{\circ}\) per second with 64 pulses contributing to azimuth coherent accumulation. As an illustrative example, we consider the imaging results obtained after a delayed optical fiber transmission of 650 m. Figure 13b depicts the range-Doppler image without compensation; however, the target location and overall layout are entirely unrecognizable. After compensation via the dual-channel method, the image defocus induced by laser phase noise is significantly mitigated, allowing for the discernment of strong reflection points from the '1-2-1' target, as illustrated in Figure 13c. When imaging requirements allow, we implement multi-view processing, which involves the incoherent superposition of multiple sub-views. While this approach sacrifices azimuth resolution, it effectively suppresses coherent speckle noise interference in the image. Figure 13d demonstrates that the target is more identifiable following multi-view processing with the image and target arranged similarly.  
Figure 13e,g present the ISAL imaging results following fiber transmissions of 1.46 km and 2.11 km, respectively, utilizing the DRC method to mitigate phase noise. Figure 13f,h display the corresponding images obtained after the multi-view processing at these two distances.  
Additionally, we compare these results with those from a 100 Hz narrow linewidth laser. Figure 13i illustrates the two-dimensional compression result, while Figure 13j depicts the image after multi-view processing. It is evident that when the round-trip distance exceeds one hundred times the laser coherence length, we can achieve high-resolution ISAL imaging of dynamically expanding targets by applying the DRC method to compensate for phase noise.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 1 Introduction
### Literature Review
#### 1.2.3 Related Research on Ship Behavior Mining
However, most of them suffer from limited accuracy and poor data quality, leading to algorithm failure. Additionally, machine learning approaches can achieve better results with small identification errors and high accuracy. However, due to the complexity of training, real-time identification of ship behavior is subject to a certain degree of latency. Furthermore, most collision avoidance behavior identification studies focus on a single characteristic of avoidance behavior without recognizing the encounter situations. Moreover, past encounter situation classification models typically use a single criterion, such as the bearing angle or the course difference, to judge ship encounter situations, which has limitations in terms of misjudgment. To address the aforementioned issues, this paper proposes a collision avoidance behavior mining model considering encounter scenarios. The objective is to accurately identify ship collision avoidance behaviors in near-collision scenarios across various encounter scenarios within the AIS dataset through feature analysis of ship AIS trajectories while excluding data interference. The model facilitates the establishment of a collision avoidance behavior database for different encounter scenarios containing avoidance information for ships at various distances and bearings. This research contributes to more comprehensive mining and analysis of collision avoidance strategies for multiple encounter scenarios, providing a crucial data foundation for future studies on autonomous collision avoidance decision making for intelligent ships, thereby advancing the development of intelligent waterborne transportation. The structure of the remaining sections of this paper is as follows. Section 1 reviews relevant work; Section 2 introduces the methods for ship AIS trajectory analysis, collision avoidance behavior mining, ship encounter pair searching, and encounter scenario identification; Section 3 presents the experiments and the analysis; Section 4 discusses the proposed method; and Section 5 concludes the paper.
## 2 Materials and Methods
### Data and Field Work
#### 2.2.1 Satellite Data and Preprocessing
Level-1 products provide four calibration look-up tables (LUTs) to produce \(\beta^{0}\)i, \(e^{0}\)i and \(\gamma_{i}\) or a digital number (DN). The LUTs apply a range-dependent gain, including the absolute calibration constant. Independently of the selected LUT (in this case, \(\sigma^{0}\)i was chosen), for any pixel \(i\) that falls between points in the LUT, the \(A_{i}\) value is found by bilinear interpolation. A terrain correction was also applied to the images because, due to the topographical variations of a scene and the tilt of the satellite sensor, distances can be distorted in SAR images. The range Doppler orthorectification method was used to geolocate all of the SAR images using available orbit state vector information in the metadata, radar timing annotations, slant-to-ground range conversion parameters and reference DEM data. NASA's Shuttle Radar Topography Mission (SRTM) DEM sampling at 3 arc-seconds was adopted. The SPOT-5 Take-5 experiment consists of using SPOT as a simulator of the image time series that ESA's Sentinel-2 mission will provide. SPOT-5 Take-5 images were distributed both as Level-1C and Level-2A in a 10-m spatial resolution, similar to Sentinel-2 products. In analogy to the previous SPOT-4 Take-5 experiment, on 5 April 2015, SPOT-5 was placed in a 5-day cycle orbit, acquiring data from 150 selected sites every 5 days under constant angles until 15 September 2015. These Sentinel-2-type  
Figure 2: Sentinel-1A and SPOT-5 Take-5 acquisition timeline, from 26 March 2015 (DOY 85) to 4 October 2015 (DOY 277). time series were made available to the scientific community by ESA and CNES to support the development of time series analysis in preparation for the exploitation of the Sentinel-2 mission. Level-2A SPOT-5 images were used in this study due to their improved pre-processing level. The highest quality product (ORTHO_SURF_CORR_PENTE) was chosen because it provides surface reflectances corrected from atmospheric effects (top of atmosphere), including adjacency effects and even terrain effects. Therefore, no further pre-processing steps were required for these images.
# A Review of Physical Layer Security Techniques for Internet of Things: Challenges and Solutions
## Challenges of the PLS Protocol Design for Internet of Things
Fourth, future IoT is expected to support various application scenarios with diversified wireless services. Different types of services have totally different requirements on security, delay, throughput, and transmission reliability. However, the majority of the existing PLS protocols simply aim at optimizing the secrecy rate or the secrecy outage probability. Thus, it is impossible to provide comprehensive QoS assurance for IoT applications. To overcome this difficulty, the PLS protocol design should jointly consider various aspects of the user demands including delay, reliability, throughput, and secrecy as well.
## 3 Results
### Energy Consumption  
#### 3.4.1 Specific Energy Consumption for the Mineralization of TMP  
To determine the energy consumption required for the mineralization of TMP, we calculated the kWh per g of total organic carbon reduction in the solution, as described in [47]. The energy consumption in the experiment came from the simulated solar radiation lamp (with a power of 489 W) and the stirrer (with a power of 10 W). The thermostatic bath, used to maintain a constant temperature of the solution, was not included in the calculation. During the three-hour experiment, the average electric energy consumption was 499 W.  
The photocatalytic degradation of TMP without pH adjustment resulted in a TOC reduction of 4.59 mg/L, with a starting TOC value of TOC\({}_{0}\) = 5.85 mg/L and a final value of TOC\({}_{180}\) = 1.26 mg/L after three hours.  
The energy consumption for this photocatalytic system with a nanostructured photocatalytic film was quite high, resulting in a 3261 kWh/g TOC reduction, which is higher than in the comparative photocatalytic studies (a 5.4-120.0 kWh/g TOC reduction, described in [47], or a 150.34-406.63 kWh/g TOC reduction described in [29]). In this case, the used lamp and reactor dimensions are not optimally matched, as the radiation area of the lamp is larger than the surface area of the reactor and could irradiate at least four such reactors, resulting in unused radiation potential in this experiment. Furthermore, energy consumption optimization was not the primary aim of this study.  
It should be noted that the use of a photocatalyst in the form of a film is practically more favorable than in the form of a suspension, as in the comparative studies [29; 47]. The degradation of organic matter can be achieved without the need to recycle the photocatalyst, simply by adding a new amount of polluted water to the reactor.
## 9 Conclusions  
In this paper we have presented a first, novel energy-efficient secure routing and key management scheme for mobile sinks in sensor networks, namely SCODE_plus_. It is a significant enhancement of our previous study, _Secure COodination-based Data dissEmination protocol for mobile sinks_ (SCODE) [12]. Besides several improvements, this study was recognized by a careful consideration of security (key management scheme) during the design time. Those not only enhance security but also increase efficiency of the proposed scheme.  
SCODE_plus_ takes advantages of a coordination network based on GAF and a hexagonal deployment model to apply an energy-efficient secure routing and key management scheme for mobile sinks. In SCODE_plus_, the sensor network plane is partitioned into a hexagonal structure. An efficient key management based on [PERSON]'s scheme is applied to reduce memory requirements and increase the security level. The cell size is optimal to increase network connectivity and reduce the number of transmission hops. Sensor nodes in each cell negotiate among each other so that only one node, called coordinator, stays awake and the others fall into sleeping mode for the sake of energy. After an interval, all nodes wake up and re-elect a new coordinator. By doing this, nodes do not quickly run out of its energy, thus prolonging the network lifetime. Communication between sources and sinks are established via coordinators and the shortest path is calculated.  
Through analysis, we show that SCODE_plus_ can defend against several common attacks in the sensor network routing including spoofed routing information, selective forwarding, sink-hole, wormhole, Sybil, and HELLO flooding attacks. The enhance key management in this version can perfectly eliminate the impacts of node compromise attacks on links between non-compromised nodes which most existing key management schemes have faced. Our simulation results have shown that SCODE_plus_ achieves much better performance compared with our previous scheme SCODE as well as existing ones, _Two-Tier Data Dissemination_ (TTDD) [17] and _Directed Diffusion_ (DD) [18]. We have shown that SCODEplus reduces much more energy consumption, delay time, and increases the success ratio (always above 97%, which means SCODE_plus_ delivers most packets to mobile sinks successfully).
## 2 Routing Protocols for UAV Communication
### Dynamic Source Routing (DSR)  
DSR is one of the first reactive routing protocols, allowing a network to self-configure and operate without the need for infrastructure [26]. In DSR, the source node only makes a connection towards the destination when required. There are two steps involved in this routing protocol, such as route discovery and maintenance. In route discovery steps, the source node discovers the path through the destination; in the case of link failure, maintenance steps are needed. The use of definite source routing across a set of nodes enables the sender to choose and control the routes employed for its own packets, hence improving network performance by allowing numerous paths for every destination (load balancing) [27]. Multiple routes between source and destination can be recovered and maintained using DSR. As a consequence, each sender may choose the optimal forwarding route based on network stability or load balancing among numerous accessible routes. Loop-free routing and support for unidirectional networks are two more features of this routing protocol. Its dynamic nature, thus, enables swift recovery whenever the topology of the network changes. Furthermore, based on the authors of the paper [28], DSR is most suitable for FANET. However, in the case of military-based UAV communication networks, the topology can be highly dynamic, and DSR is not suitable in this case.