esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MJTAR Protocol
### MJTAR Overview  
Our proposed MJTAR protocol incorporates two fundamental mechanisms: (1) the E-IFTIS mechanism, capable of estimating traffic density up to two-hop junctions; (2) the OMSS applies the ACO algorithm to select the optimal multiple junctions.  
MJTAR uses a GPS receiver to determine its position and speed. Additionally, it leverages the digital map provided by the vehicle's navigation system to ascertain the position of neighboring junctions and acquire road-level information. For the source vehicle to determine the position of the destination vehicle, GLS [36] is employed, utilizing a network of wireless sensors in urban environments to discover the destination's position. Furthermore, each vehicle is assumed to maintain a neighborhood table containing its neighbors' position, speed, and direction. This table is constructed based on all vehicles' periodic Hello (beacon) packet transmission. Our proposed MJTAR draws inspiration from the modified version of ant colony optimization (MACO) [37] and applies the ACO algorithm. MACO is an algorithm that mimics the behavior of artificial ants on their way to work, recognizing congestion when encountering a strong concentration of pheromones and consequently seeking less-congested routes. In contrast, MJTAR's algorithm identifies high pheromone concentration as indicative of high traffic density and navigates routes with strong wireless connectivity. Subsequently, we define a probabilistic algorithm capable of selecting the optimal multiple junctions using the weight of the shortest distance to the destination in conjunction with the permeant and the destination.
# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
## 3 Materials and Methods
### Preparation of the Training Dataset  
In the resulting spring composite, an area with visually the densest fill network and the presence of major land-use classes of water, anthropogenic, forest, and agricultural features was selected and clipped. For this area of more than 2500 square kilometers, continuous manual fill erosion recognition was performed to create a set of reference data samples (Ground truth, GT) (Figure 2). Manual recognition was made by QGIS tools using Sentinel-2 data and ultra-high resolution (UHR) images (Bing, Google, and so on, available as a substrate at no charge). The main selection was made on the S2 images. In controversial cases, the UHR images were used. They were also used for the non-selection of permanent gullies. Due to the difficulty of classifying erosion forms on such a vast territory with different natural conditions, the differentiation of erosion forms was not carried out. Ephemeral gullies\" and \"rills\" were defined as all erosion forms that can be interpreted on satellite images but do not belong to permanent gullies. Considering the resolution of the satellite image, rills are the most minor erosion form, which transforms into an ephemeral gullThe resulting sample was rasterized and reduced to the resolution of the satellite image fragment, and then both images were cut into 256 \(\times\) 256 pixels patches. In total, 10,933 satellite image-binary mask pairs have been obtained this way (Figure 3). The resulting rasters were further randomly transformed to augment the number of rasters by three times artificially. The resulting dataset was divided into training and test sets in the ratio of 1/5.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
## 4 Simulation Results and Evaluations
### Simulation Results
#### 4.2.1 Overall Residual Energy  
Considering the limitation of energy nodes, one of the goals of this paper was to present an energy-efficient algorithm. The decrease in the overall residual energy for all nodes leads to an increase in the network lifetime. This shows how the energy consumption rate in all nodes is distributed uniformly and optimally in different rounds. The residual energy is calculated as follows:  
\[\sum_{n=1}^{N}E_{n,X}=E_{overall} \tag{25}\]  
where \(E_{n,X}\) is the overall energy consumption by a sensor node (n) after X rounds of data collection from the area-of-interest of the network, and \(E_{overall}\) is determined as overall energy consumption by N number of total nodes after X number of simulation rounds of data collection in the network [42]. The mean of \(E_{total}\) until round X is defined as the average residual energy in this paper. According to Figures 8 and 9, although PSO-HSA [29], which is highly energy-efficient, is one of the most novel algorithms, this method could not compete with our method in forest fire scenarios (Figure 7).  
As shown in Figure 8, because of the random selection of CHs, the residual energy of LEACH drops to zero in the early rounds (around 15 rounds). Until 40 rounds of simulation have passed, it can also be seen in Figure 9 that PSO-HSA has a better average residual energy than TPSO-CR because the efficient searching of HS complements the dynamic capacity of PSO and also improves the balance between exploration and exploitation. In other words, the proposed method has better energy dissipation distributions than other methods. Additionally, the proposed method is improved in terms of the overall residual energy by about 1.04-1.42 times compared to PSO-HSA, by about 1.24-1.46 times compared to TPSO-CR and by about 1.70-1.77 times compared to LEACH for the different sink locations according to Figure 8. The reasons for this improvement are the optimum improvement of the structure of PSO-HSA and the attention paid to non-uniform events in the routing phase.
# Copper Price Prediction Using Support Vector Regression Technique
## 2 Related Works  
In the general case of the financial time series, the support vector machine (SVM) and SVR methods are widely used for making forecasts [13]. Basically, when the SVM method extends to nonlinear regression problems, it is called SVR [10; 11; 12]. The SVR method belonging to the field of data statistics was first proposed by [PERSON] et al. [10] at the end of the twentieth century. A characteristic of this method is that it solves the problems of \"high dimensionality\" and \"overlearning\" to a certain degree. Furthermore, it achieves a significant effect in solving the problem of small samples. Consequently, support vector regression is used to solve the prediction problems of nonlinear data in engineering areas.  
It is important to consider some works, such as the work done by [PERSON] [14], which presents daily forecasts (using SVR) of the trend of change in the Korean Composite Stock Price Index (KOSPI), in which it uses 2928 days of data between January 1989 and December 1998. In another work, [PERSON] et al. [15] use SVR to predict the stock index of the Sao Paulo State Stock Exchange (BOVESPA), the Shanghai Stock Exchange Composite (SSEC) and the Dow Jones, using data from April 2006 to April 2010. Similarly, [PERSON] et al. [16] predict the market prices of Microsoft, Intel and the National Bank shares using a set of data from November 2007 to November 2011. In these works, the prediction is always a day before and depends on a certain amount of past data (\(l\)), i.e., if \(\dot{p}_{t+1}\) is the price foretold in \(t+1\), it has \(\dot{p}_{t+1}=f(p_{t},p_{t-1},\ldots,p_{t-l})\).  
Additionally, we must consider the work of [PERSON] et al. [17], which proposes to make predictions 10, 15 and 30 days in advance using a two-stage system based on SVR, neural networks and random forests, which are trained with nine technical indexes. Among these indicators is the stochastic index %\(K\), that compares the closing price at a particular time with the price range during a given period, and %\(D\), which is the first moving average. In addition, it uses the relative strength index (RSI) that indicates the price change rate and the average data rate, among others. The author uses historical data from January 2003 to December 2012 of the CNX Nifty stock index and S&P BSE Sensex.  
On the other hand, several studies include copper and other metals as products of interest in the evaluations of the prediction to improve the forecasts of price. Such studies employ different methods and mathematical models such as autoregressive integrated moving average (ARIMA) models combined with wavelets [18], meta-heuristics models [19; 20], neural networks models [2; 21] and hybrid models [5; 6; 7; 8; 9]. The Fourier transform [22] is used to analyze the variability of the prices of various metals. In addition, there are works in the literature that study the relationships of commodity and asset price models, such as the case of oil prices and their effects on copper and silver prices [23].
## 3 Results and Discussion
### Linearity
#### 3.2.3 Linearity Checks (Linear Range)  
Residual-concentration chart:  
The plot of residuals with respect to DEHP concentration is constructed, Figure 6. The points in this plot are randomly distributed around zero and do not show any particular pattern [19]. Therefore, no deviation from linearity is seen.  
Sensitivity plot:  
For its construction, the response factor (area/concentration) was first calculated. In the sensitivity diagram, Figure 7, it is observed that there is no point outside the inter  
Figure 5: Calibration curve for DEHP determination method. The diagram shows: the 95% confidence and prediction intervals and the coefficient of determination, r\({}^{2}\).  
Figure 6: Plot of residuals in terms of DEHP concentration, to check for linearity.  
val \(\pm\) 5% from the average of the response factor [11]. This confirms the linear range of the method for the range of standard solutions used 0.3-1.5 mg/L DEHP.
# An Automated IoT Visualization BIM Platform for Decision Support in Facilities Management
###### Abstract  
Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy-saving options for rooms with different functions (mechanical/electrical equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions.  
Keywords:building information modeling; industry foundation classes; Internet of Things; smart campus; environmental sensors; Dynamo +
Footnote †: journal: Applied Sciences
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
### Background  
The BSS methods estimate the desired source speech signals from the observed convolutive mixture containing noise. However, accurate identification of the targeted speech signal in a noisy reverberant environment is the fundamental goal of the speech processing systems. The traditional BSS methods are limited to multiple speech signals and sensors, where the de-noising process is challenging. Nevertheless, various signal processing methods, such as Single-channel Blind Source Separation (SBSS), Sparse Component Analysis (SCA), and Variation Mode Decomposition (VMD), can tackle this issue. The VMD method is applied to decompose a single channel into two channels, and then SCA separates the speech signals. This approach shows enhancement of speech signal in under-determined conditions [22].  
Another approach, AdaGrade, is proposed in [23] for blind audio speech extraction that uses the gradient-based algorithm. The gradient learning rule is modified by preconditioning the input signal and using the AdaGrade update. In this method, the natural gradient method with two-step pre-processing suppresses the noise in the receiving reverberant mixture. First, the bias removal method followed by the least-square method is applied to de-noise the noisy convolutive mixture. Then, a joint algorithm with a gradient method estimates the noisy signals and the mixing matrix [24]. Moreover, in [25], the BSS involves Eigen filtering, which receives the dominant frequencies of the signal, and then Wavelet de-noising is applied. It suppresses the noise components and retains the speech signal regardless of its frequency components. The authors of [26] propose an alternate method based on temporal predictability to obtain the individual independent noise signal where a non-negative matrix factorization algorithm enhances the speech signal. The performance is improved by adding time-correlation to the objective function, which restricts the time-varying gain of the noise [27]. Moreover, masking techniques can also be applied to separate the desired speech signal from the received mixture, where the time-frequency masking rule can define the BSS method [28]. In [29], the authors propose an EM algorithm to suppress the noise in the convolutive mixture for the complex-Gaussian signal model and the unknown deterministic model. The statistical model is defined for both models, and the EM algorithm is developed for these models to estimate the speech signal and its acoustic parameters.  
Recently, unsupervised speech enhancement algorithms are gaining interest that use a Real-Time (RT) two-channel BSS algorithm. In this method, a non-negative matrix factorization (NMF) dictionary is combined with a generalized cross-correlation (GCC) spatial localization approach. The RT-GCC-NMF operates in a frame-by-frame manner, comparing individual dictionary atoms with the desired speech signal or interfering noise based on the time-delay arrivals [30].
## 4 Discussion on Failure Behavior and Mechanical Problems of Engineering Rock Mass  
Currently, the reduction of crustal stress, stability analysis, and control technologies for engineering rock masses are hot research topics. The intelligent inverse method combining a particle swarm optimization (PSO) algorithm with a back propagation (BP) neural network was applied to the inverse analysis of crustal stress by [PERSON] et al. [10]. The application was carried out using an underground powerhouse of Shuangjiangkou Hydropower Station as an example. The results indicated that the method improved the stability and accuracy of the inversion results. Referring to existing tunnel projects, nonelectronic equipment and electronic detonators were used for blasting tests and determining the impact of blasting construction on the range of rock loosening zones and the degree of rock fragmentation, respectively [11]. The results indicated that the range of loose rock circles around the tunnel, the decrease in wave velocity of loose rock masses, and the degree of rock fragmentation caused by normal blasting with nonelectronic detonators were larger than those caused by electronic detonators. Then, the surrounding rock stability of the excavated geologically weak section of the #2 diversion tunnel in the Xulong Hydropower Station was assessed by [PERSON] et al. [12] using a numerical model. The results indicated that during the excavation process, the surrounding rock of the tunnel section was most significantly damaged near the arch crown and corner of the side wall with tensile failure. As the excavation progressed, the number of microseismic events at the corners of the arch and side walls gradually increased, and the energy of acoustic emissions steadily accumulated. Therefore, the likelihood of a collapse and rock burst in the area increased. Furthermore, the effect of the fracture geometric parameters on the failure mechanism and mechanical parameters of engineering rock mass in the dam site area of Lianghekou Hydropower Station were investigated using the ShapeMetrixX\({}^{\text{3D}}\) system and RFPA\({}^{\text{3D}}\) in an uniaxial compression test [13]. The results showed that the fractured rock mass mainly represented a compressive-shear composite. Moreover, the influence of the fracture geometric parameters on the uniaxial compressive strength was greater than that of the elastic modulus.
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### The Ciphering Method  
The security of the xAAL bus is ensured by:  
* A symmetric key, pre-shared into all participating devices;
* Poly1305/Chacha20 as the only cryptographic algorithm, and used according to RFC 7905 recommendations (i.e., with a 96 bits nonce and a 256 bits key);
* A binary nonce build as a timestamp since the Epoch ( seconds (64 bits) + microseconds (32 bits) );
* An acceptance window for the timestamp of messages;
* The list of targets is clear, but covered by the signature;
* A Security Layer in CBOR, as described above;
* An Application Layer in CBOR, as described above;
* The Application Layer is CBOR serialized to produce binary data, ciphered into a binary buffer, which is placed into the Security Layer as a CBOR definite byte string.  
Figure 3: CDDL specification of the xAAL Application Layer.  
Figure 2: Concise Data Definition Language (CDDL) specification of the xAAL Security Layer.  
Notes:  
* The target field of the Security Layer is a CBOR definite byte string. This is not an array of UUIDs, this is the CBOR serialization of an array of UUIDs. This byte string may be seen as a buffer of bytes and can directly be used as the _public additional data_ for the Poly1305/Chacha20 algorithm, to be covered by the cryptographic signature.
* The binary nonce (96 bits) to be used with Poly1305/Chacha20 is composed of the seconds and microseconds (in this order): first a 64 bits big-endian unsigned integer (seconds), followed by a 32 bits big-endian unsigned integer (microseconds).  
Recommendations:  
* To build the cryptographic key from a passphrase: The Poly1305/Chacha20 algorithm uses a binary key on 256 bits. A fine way to select a good key is to build it from a passphrase using a cryptographic hashing algorithm. It is proposed to use the dedicated function provided for this purpose in the reference Chacha20 library (the _sodium_ library), and derived libraries:
* function: crypto_pwhash_scryptsalsa208 sha256()
* for the salt: a buffer of zeros
* for the _opslimit_: crypto_pwhash_scryptsalsa208 sha256_OPSLIMIT_INTERACTIVE (512k cycles)
* for the _memlimit_: crypto_pwhash_scryptsalsa208 sha256_MEMLIMIT_INTERACTIVE (16 Mbytes)
* To choose a window of acceptance for the timestamp: An acceptance window of two minutes should be fine.
* To have several keys on the same bus: If several key or xAAL versions are needed in a facility, it is recommended to use different xAAL buses (UDP ports).
## 3 Packet Delays within a Router
### Effect of Number of Rules in ACL Using a Basic OS  
Further work was carried out to investigate the delay experienced by packets matched against an increasing number of rules for both standard and extended ACLs. Figure 4 shows that for a Basic OS increasing the number of rules in the list has a significant effect on the delay. The effect of increasing the number of rules in an Extended ACL from 10 to 15,000 in a Basic OS has the effect of increasing the overall delay by approximately 1400% which is a pretty significant problem.  
Figure 4: Delay through router with Basic OS.  
Figure 3: Delay through router with Access Control List (ACL) running Basic OS.
## 3 Results  
The new IRIDE space program will allow for a technological transfer in the remote sensing services framework. Several areas of application and domains will be involved, thanks to the advanced temporal and spatial resolution of the satellite sensors of the various IRIDE missions. In collaboration with other national and European space systems, this constellation is poised to support public administrations, with an initial focus on civil protection. Distinguished by a diverse array of sensing technologies and tools, the constellation stands out as a unique entity. The data it collects will be accessible at no cost and will be permanently stored in a cutting-edge storage infrastructure. As previously mentioned, IRIDE will be equipped with synthetic aperture radar and optical payloads, offering resolutions of 2-3 m, and in certain cases, even less than 1 m, contingent upon the acquisition mode for specific sensors.