esa-sceva/satcom-chunk-collection huggingface dataset:
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 7 Experiment in Laboratory Environment
### Testing Positions and Microphone Data Pre-Processing  
As aforementioned, the source signal is played by an array of 30 loudspeakers, so the ground truth source positions are identical to the locations of the loudspeakers. Figure 11b and Table 4 illustrate the ground truth positions of loudspeakers represented in the interaural-polar coordinate system with  
Figure 11: The hardware setup and the loudspeaker positions. (**a**) The loudspeakers are positioned on the middle of edges of a dodecahedron frame, and the dummy head simulator with two microphones are placed in the center of the speaker arrays; (**b**) The ground truth position of sound sources and their corresponding labels  
Figure 12: System hardware. (**a**) Dante audio network system; (**b**) U-PHORIA UMC202 HD audio interface.  
the label indices. The testing loudspeaker positions are not presented in the training positions. Thus, a correct estimation should be the nearest location label to the ground truth. Notably, there are six loudspeakers placed beyond the training region, which are marked by \({}^{*}\) in Table 4. Those loudspeakers are muted during the following tests, since the localization of these speakers is meaningless.  
One issue that must be noted is that the impulses of the two microphone channels cannot be identical in the real world, and small differences lead to inaccuracy in localization. Therefore, in the binaural recording system, the raw signals captured by the binaural microphones are equalized before passing to the localization model, which aims at minimizing the amplitude difference between channels. The equalization is performed by multiplying a generic compen coefficient \(\zeta_{l}\) on the left-ear channel, and this coefficient is pre-measured in two steps. First, one 2 s white noise signal is played as the source signal by each loudspeaker, and the captured signals are notated as \(x_{1,l,k}(t,\Theta)\) and \(x_{1,r,k}(t,\Theta)\). Second, the connection of two channels is switched and the same source signals are played by each speaker again. The signals captured by the switched channel are notated as \(x_{2,l,k}(t,\Theta)\) and \(x_{2,r,k}(t,\Theta)\). Ideally, if the two channels are identical, we would have \(x_{1,l,k}(t,\Theta)=x_{2,r,k}(t,\Theta)\) and \(x_{1,r,k}(t,\Theta)=x_{2,l,k}(t,\Theta)\). Therefore, the estimated left-ear compensation coefficient \(\hat{\zeta}_{l}\) can be estimated by averaging all frames and positions as:  
\[\hat{\zeta}_{l}=\sum_{k,\Theta}\Big{[}\frac{x_{2,r,k}(t,\Theta)}{x_{1,l,k}(t, \Theta)}+\frac{x_{1,r,k}(t,\Theta)}{x_{2,l,k}(t,\Theta)}\Big{]} \tag{20}\]  
and hence, the binaural signals for To are calibrated by,  
\[\begin{split} x_{l}(t,\Theta)&=\hat{\zeta}_{l}\cdot x _{1,l,k}(t,\Theta)\\ x_{l}(r,\Theta)&=x_{1,r,k}(t,\Theta)\end{split} \tag{21}\]  
Given that the calibration is applied on the left channel for all source positions in the time domain, it is spatial and frequency independent, and does not introduce external information to estimate the sound source position.
## 1 Introduction
### Coordination of ESs or ES with renewable energies
A multi-agent system (MAS) is a network structure composed of multiple agents dispersing physically or logically, and they complete complex control tasks or problems through negotiation and coordination [40; 41]. MAPSO applies the topology of PSO to MAS architecture, in which each agent is equivalent to one particle in PSO. In addition, each agent competes and cooperates with its neighbor's agent, and it can converge faster and more accurately to global optimal solutions combining evolutionary mechanisms and particle update rules of PSO. Previous scholars integrated PSO with MAS [42; 43]. In addition, MAPSO was mostly applied to power systems. [PERSON] et al. [44] used MAPSO to solve the problem of reactive power optimization. On the basis of combining multi agent technology, [PERSON] et al. [45] put forward MAPSO to handle a distribution network reconfiguration problem. [PERSON] et al. [46] presented a MAPSO based on hybrid PSO technology applied to the economic load dispatch which is a non-linear constrained optimization problem. Moreover, [PERSON] et al. [47] applied MAPSO for the optimization of maximizing loadability limit of power systems. In summary, most scholars studied the coordination of ESs or ES with renewable energies from a technical point of view, and there is little literature that takes ESC as the topic in EI. Moreover, EI involves a variety of energy sources and energy conversion, and the structure and mode of operations are more complex and diverse. Hence, both the capacity and type of ESs are in great demand, and diverse functions of EI can be achieved with abundant uses of ESs. Therefore, according to the overall demand of an EI system, three scenarios with the optimization of EE and EUE (uniting EE and EUE) are demonstrated in this study. MAPSO is applied here to handle this optimization problem. The remainder of this paper is organized as follows. Section 2 analyzes the necessity and principles of ESC in EI. In Section 3, the mathematical formulation of ESC is constructed. In Section 4, PSO, MAS and MAPSO are described in detail. Section 5 presents a numerical example and a comparison with PSO to verify the feasibility and superiority of the proposed algorithm is given. Finally, Section 6 summarizes the main conclusions and research limitations.
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
## 5 Results
### DNN Results
The DNN model uses the detection results of the two techniques. After \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Technique**} & **Detection Probability** & **Detection Probability** & **False Alarm Rate** \\  & **One PUE** & **Four PUs** & \\ \hline DNN & 95\% & 90\% & 10\%—Multiple PUs \\ SVM & 98\% & 0\% & 5\%—Single PUE \\ KNN & 97\% & 0\% & 5\%—Single PUE \\ \hline \hline \end{tabular}
\end{table} 
Table 4: DNN PUE detection comparison with other techniques. Figure 17: Probability of detection of a PUE attack (source: own). the learning process, the PD results increased to 95% with one PUE and 90% with the four PUE attacks, showing an increment of 5% for authentication and 3% for energy with one PUE attack and 4% in authentication for multiple PUEs. The false alarm rate is 13% for energy detection and 14% for authentication detection with multiple PUEs. For DNN, it decreases to 10% with multiple PUE attacks. The DNN creates a new interaction between the input variables, allowing for higher detection results with multiple PUE attacks while keeping lower values for false alarm results. The time for calculation after the learning process is lower than that of the other techniques, allowing for an average detection time of 5 s, faster than the 20 s that the authentication process needs in a cooperative environment.
# Towards the Real-World Deployment of a Smart Home EMS: A DP Implementation on the Raspberry Pi
###### Abstract  
As the adoption of distributed generation and energy storage grows and the attention to energy efficiency rises, Energy Management is assuming a growing importance in smart homes. Energy Management Systems (EMSs) should be easily deployable on smart homes and seamlessly integrate with the Internet of Things (IoT) ecosystem, including generators and storage devices. This paper redesigns a previously presented EMS to reduce its computational complexity, implement it on a Raspberry Pi, and make it compatible with the IoT paradigm. The EMS manages the power flows between smart home loads, renewable generators, electrical storage, and power grid. It communicates with a network of wireless sensors for electrical appliances and with a cloud-based utility data aggregator. The EMS uses Artificial Intelligence and a Dynamic Programming algorithm to fulfill two objectives at the same time: lowering the end user's electricity bill and reducing the uncertainty on the power exchanged between the end user and the grid manager. The latter goal is obtained by an effective compensation of forecasting errors. A test bench emulating four smart homes was used to measure the effectiveness of the EMS and the efficiency of the proposed implementation. The results show an uncertainty of the aggregated exchanged power of only 2.88% and a reduction of the electrical bill for end-users of up to 3.23%. Furthermore, the EMS can complete its most onerous task in less than 9 min. The good performance of the proposed EMS makes it a candidate for fast adoption by the market.  
Energy Management System; embedded implementation; IoT; Raspberry Pi; optimization algorithm; energy efficiency; demand uncertainty
# Product Demand Prediction with Spatial Graph Neural Networks
## 2 Related Works  
### E-Commerce Demand Prediction  
#### 2.1.1 Characteristics of Online Advertising Data  
Customers' willingness to purchase on e-commerce platforms is influenced not only by historical data but also significantly by the quality, thoroughness, authenticity, and context of advertisements [1]. This complexity underscores the multifaceted nature of online advertising data. Such data encompass numerical data (e.g., historical demand), image data (e.g., ad visuals), text data (e.g., product descriptions), and location data (e.g., geographic targeting). Detailed descriptions and contextual cues, such as geographic location and the presence of similar ads, play a significant role in shaping customer behavior [2]. Moreover, advertising demand often follows seasonal patterns and short-term and long-term trends, necessitating models that can capture these temporal dynamics [3]. The dynamic nature of online markets introduces volatility and noise, further challenging models to provide accurate predictions amidst these fluctuations. These interconnected characteristics highlight the need for advanced models capable of processing and analyzing diverse and complex data types to enhance demand prediction accuracy.
## 1 Introduction
As recognized by the Infectious Disease Society of America (IDSA), a faction of antibiotic-resistant bacteria, namely _Enterococcus facetium_, _Staphylococcus aureus_, _Klebsiella pneumoniae_, _Acinetobacter bauumanii_, _Pseudomonas aeruginosa_, and _Enterobacter_ spp., acronymically dubbed the ESKAPE pathogens, can effectively \"escape\" the biocidal action of antibiotics [31]. These pathogens cause a number of lethal diseases and antimicrobial resistance (AMR) creates significant challenges in treating bacterial infections in both animals and humans [32; 33]. As described by IDSA, these opportunistic ESKAPE pathogens are significantly associated with a high degree of morbidity, mortality, and increased costs. The rise and spread of ESKAPE pathogens with AMR pose a serious threat to global health. _K. pneumoniae_ are one of the dangerous ESKAPE organisms and are a major cause of hospital- and community-acquired infections, including pneumonia, liver abscesses, and sepsis [34; 35]. Given that the antibacterial activities of the fruit extracts of _P. cauliflora_ were established [36; 37], the molecular targets in _K. pneumoniae_ inhibited by extracts of _P. cauliflora_ should be explored. Thus, we assessed here whether the fruit extract of _P. cauliflora_ had activity against _K. pneumoniae_ SSB (KpSSB). The pharmacological inhibition of _P. cauliflora_ extract against KpSSB may be useful in suppressing the DNA replication and decreasing the virulence of _K. pneumoniae_. Considering that DNA replication is an essential process for cell survival [38], DNA replication proteins should be suitable targets for drug development. In this study, we identified that KpSSB, an essential DNA replication protein in _K. pneumoniae_, could be inhibited by the fruit extract of _P. cauliflora_ obtained using 50% ethanol (Figure 1B). Given that human RPA [30] and SSB [20] are different in structure and function, bacterial SSB should be a prime target in antibiotic development. By using gas chromatography-mass spectrometry (GC-MS), the chemical content of this extract was determined. We have recently solved the crystal structure of KpSSB, and thus, the three most abundant compounds in the extract of _P. cauliflora_ were further selected for docking analysis of KpSSB to realize the possible inhibition modes. Given that many natural products have anti-skin-cancer activities [39; 40], we also investigated whether the extract of _P. cauliflora_ exhibited any antiproliferative effects on the growth of B16F10 melanoma cells. Additionally, we found that the extract of _P. cauliflora_ could induce apoptosis and inhibit the migration of B16F10 cells. The results of this study collectively suggest that _P. cauliflora_ holds potential pharmacological benefits, warranting further exploration for therapeutic applications.
## 4 Simulation Experiments: Security Analysis and Comparison
### Known-Plaintext Attack  
A known-plaintext attack [50] is a cryptographic attack, where an attacker has access to some pairs of plaintext and its corresponding ciphertext, aiming to discover the secret key. As our cryptographic algorithm is based on an elliptic curve, it is crucial for the attacker to possess extensive knowledge about the specific curve being used. Our algorithm offers a wide range of options for the prime \(p\). Additionally, for each prime value, there are numerous possible values of \(b\), and for each \(b\), there are vast options of point \(G\) on the corresponding elliptic curve. Therefore, for an attacker to succeed, they would need to exhaustively try all possible primes and, for each prime, use all \(b\), then for each \(b\), iterate through all points on the elliptic curve. This process is highly time consuming and presents a significant computational challenge. As a result, the proposed text encryption scheme exhibits a high level of security against the known-plaintext attack.
## 2 Experience Design for Virtual Museums
### Hybridization between Virtual and Real Museums
#### Mixed Reality: Focus on Holographic Showcases  
The intent of creating a 'dramatic dimension' around the museum's object and pushing the user's sensory involvement within its stories, has convinced researchers and creatives of CNR ITABC to apply an ancient representation technique born with XVI century photography and diffused in theatres in the XIX century. This technique is the Pepper Ghost effect (so called from the English professor that brought it to fame, [PERSON]). It has been brought inside the museum's showcases, in presence of the artifacts, in order to produce an effect of mixed reality (Figures 3 and 6).  
The basic idea of a hologram is an apparition of something that seems tangible, in an empty space. An illusion of reality, of something that does not exist but appears as if it was just in front of our eyes. Taking advantage of the available digital technologies in the current market, the challenge is to realize a new form of scenography and dramaturgy around the museum's objects, with a simple technology, compatible with the museum's preservation and maintenance needs [25].  
Why holograms should be used to tell the story of an object, rather than a common 2D screen, a traditional projection or even a mobile device with a multimedia? It is not only to surprise and produce an astonishing reaction in the visitors. Through the hologram we change the traditional paradigm: if we include the real artifact inside the holographic showcase, the attention still remains focused on it. It stays as the real protagonist of the story. Not its virtual replica, but the original itself, is the center of our attention along the time of the whole experience: virtual animations, fragments of stories originate from its real figures and details.  
We can mount and dismount, virtually restore, move, and transform the object, overlapping virtual projection on the real artifact, and playing with lights.  
Through holograms we do not immerse ourselves in virtual reality using particular viewers and managing controllers, but it is the virtual reality that enters our space.  
Due to this integration, the museum's object becomes alive, enforced and not weakened by multimedia. This is not a marginal difference, because it greatly influences perception, engagement and comprehension, favoring a deeper relation between the visitor and the observed original object.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Provide Transportation in Areas Where Road Infrastructure Is Limited  
Flying taxis could potentially provide transportation in areas where road infrastructure is limited, such as remote or rural areas with limited roads or in cities with heavy traffic congestion. Flying taxis do not require roads or bridges to travel, and they can fly over obstacles like mountains, lakes, and rivers, making it easier to reach destinations that are difficult to access by traditional ground transportation. However, it is important to note that the availability of flying taxi services in these areas would depend on factors such as demand, infrastructure, and regulatory approval [172].
## 3 Simulation Experiment
### Experimental Design and Results Analysis
Figure 6: A comparison of the predicted and measured values when the working face advanced to the 108 th day. LOS deformation (strike line + dip line) on the 84 th day was 1.58 mm, with an absolute error of 0 to 5.12 mm. The mean error of the subsidence fitting was 10.35 mm, with an absolute error of 0 to 24.24 mm. The mean error of the east-west horizontal movement fitting was 6.18 mm, with an absolute error of 0 to 27.77 mm. The mean error of the north-south horizontal movement fitting was 1.63 mm, with an absolute error of 0 to 3.82 mm. The mean error of the LOS deformation (strike line + dip line) fitting on the 108 th day was 8.77 mm, and the absolute error was between 0 and 30.42 mm. The mean error of the subsidence fitting was 10.76 mm, and the absolute error was between 0 and 24.46 mm. The mean error of the east-west horizontal movement fitting was 6.17 mm, and the absolute error was between 0 and 27.77 mm. The mean error of the north-south horizontal movement fitting was 4.38 mm, and the absolute error was between 0 and 16.36 mm. Based on the above analysis, the InSAR-DIB prediction model based on the edge correction model established in this paper can be used to predict the three-dimensional deformation of mining subsidence with high predictive accuracy, thus verifying the feasibility of the model.
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 3 Preliminaries and Definitions
### K-Medoids Clustering Algorithm  
_K-medoids_ is a robust clustering algorithm. It is used to partition a given set of data points into \(k\) disjoint clusters ([PERSON] et al., 2017). In contrast to the \(k\)-means, which use the mean value of the data points in each cluster as a cluster centroid, \(k\)-medoids chooses an actual data point, called a medoid. The medoid is the most centrally located point in a given cluster. Therefore, \(k\)-medoids are more robust to outliers and noise than other points. The algorithm works by arbitrarily choosing a set of \(k\) initial cluster medoids from a given set of data points, where \(k\) is preliminarily specified. Then, each data point is assigned to the cluster whose center is the nearest, and the cluster centers (medoids) are recomputed. This process is repeated until the points inside every cluster become as close to the center as possible, and no further item reassignments take place.  
In our FedCO algorithm, we use \(k\)-medoids for partitioning the available workers into groups of similar workers with respect to their local updates. Furthermore, 2-medoids are used in the iteration phase of the algorithm for conducting cluster splitting.
# IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
## 4 IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
### Overview  
The existing systems have flaws because they contain single unifunctional sensors for each target portion in the residential place. However, if an event occurred in a particular place and the sensor of that region was not working, then it would be highly improbable to detect the incident at its initial stage. The second major problem is the generation of false alarms. The proposed smart home fire detection system consists of four major parts: (i) sensor; (ii) processing unit as the main home sink; (iii) GSM communication system; (iv) alarm system. In the sensor unit, we deployed multi-sensors, i.e., smoke, gas, and heat sensors for each portion of the smart homes. All these sensors have their own event detection mechanism. Figure 5 shows the complete model of our proposed work. The processing unit contains a home sink, which communicates with the sensors through the ZigBee Protocol. The decision of fire detection is taken on the sink on the basis of the information received from the sensors and of the user's response. If a single sensor node sends fire alert to the sink, it automatically activates the GSM communication and sends an alert message to the user. The sink make decisions on the basis of the user's response or of the alert notification from the other sensors. After getting confirmation of a fire event either from two or more sensors or from the user, the sink generates an alarm. Simultaneously, the system shares the event information with the cloud and with the local server which helps to spread the information to the basic service units. The local server is connected with the surrounding inhabitants to inform the other houses about the current situation. We present the detail design of each unit in the following subsections.
## 3 Experimental Results and Discussion
### Comparison of Yield Loads of Studies Specimens  
Experiment results showed that yield and ultimate load of TRM specimens were higher than those of the control specimen except T1, T1N, and T2A in ultimate load. The ratio of increase of yield load was especially higher than that of the ultimate load. After the yielding point of the TRM specimen, textile rupture occurred in T1, T1N, and T2A, and ductility of all specimens was lower than that of the control specimen. Therefore, considering the structural safety and serviceability, the strengthening effect of reinforced concrete with TRM using non-impregnated textile could be compared within yield load. Figure 13 shows yield load of all TRM specimens. Loads increased in proportion to the reinforcement ratio of T1, T2, and T3. The other specimens except T1M showed similar yield loads to that of the T3 specimen, which had the largest reinforcement ratio. Therefore, TRM strengthening using non-impregnated textiles could improve load-bearing capacity until yielding by using small mesh size, textile lamination, and fixing the textile with mechanical end anchorage.  
Figure 11: Comparison of ductility index of TRM specimen.