esa-sceva/satcom-chunk-collection huggingface dataset:
# Populating the Mix Space: Parametric Methods for Generating Multitrack Audio Mixtures
## 2 Theoretical Framework
### Generating Gain Vectors by Sampling the Mix-Space
#### 2.2.2 Perceptual Mixing Model  
Rather than a simple vocal boost, what is required is a more informed choice of instrument levels. In [7], a simple 4-track mixing exercise was reported, where participants created mixes of vocals, guitars, bass and drums using only volume faders. This experiment was expanded to an 8-track format, as in this paper, and is reported in [15]. Participants were asked the same task, only this time stereo-panning and a basic 3-band EQ was added. The median instrument levels obtained from this experiment are shown in Equation (8). Since participants had the ability to pan sources; the median levels were available for left and right channels separately, which are shown in Equations (10) and (11). Figure 6b shows the mixes obtained when the target vector is based on these median track levels, known as \(\mu_{\text{informed}}\). It can be seen that the levels of bass guitar and kick drum are higher than average, while drum overheads have been attenuated. Vocals are set high in the mix, as seen in the mono experiment [7; 15] and other previous studies [16; 17]. Matlab code for generating sets of mixes, as in Figure 6, is available for download ([[https://github.com/alexwilson101/PopulateMixSpace](https://github.com/alexwilson101/PopulateMixSpace)]([https://github.com/alexwilson101/PopulateMixSpace](https://github.com/alexwilson101/PopulateMixSpace))).  
\[\mu_{\text{informed}}=[0.2254\ \ \ 0.2282\ \ \ 0.3221\ \ \ 0.2679\ \ 0.4437\ \ 0.3616\ \ \ 0.3221\ \ \ 0.5387] \tag{8}\]
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 2 The Mimblewimble Protocol
### Authentication of Transactions  
Consider that [PERSON] has received \(v\) coins and this was recorded in the blockchain as \(r.G+v.H\), where \(r\) was chosen by her to keep it private. Now she wants to transfer these \(v\) coins to [PERSON]. As a consequence, [PERSON] looses \(v\) coins and [PERSON] receives the very same amount, which means that the transaction adds to zero: \(r.G+v.H-(r.G+v.H)=0.G-0.H\). However, [PERSON] now knows [PERSON]'s blinding factor because it must be the same chosen by her (so the transaction is balanced). In order to protect [PERSON] from being stolen by [PERSON], MW allows [PERSON] to add his blinding factor, \(r_{B}\), in such a way that the transaction is recorded as:  
\[(r+r_{B}).G+v.H-(r.G+v.H)=r_{B}.G-0.H,\]  
although now it does not sum zero. However, this _excess value_ is used as part of an authentication scheme. Indeed, [PERSON] uses \(r_{B}\) as a private key to sign the empty string (\(e\)). This signed document is attached to the transaction so in the blockchain we have:  
* Input: \(I\).
* Output: \(O\).
* [PERSON]'s signed document: \(S\).  
This way, the transaction is valid if the result of decrypting \(S\) with \(I-O\) (in the group generated by \(G\)) yields \(\epsilon\). If \(I-O\) does not yield something in the form of \(r_{B}.G-0.H\), then \(\epsilon\) will not be recovered and so we know there is an attempt to create money from thin air or there is an attempt to steal [PERSON]'s money.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 4 Results and Discussion
### Performance Parameters, Evaluation and Comparison
#### 4.1.6 The Matthews Correlation Coefficient  
MCC in another metric used to further evaluate the model in addition to simple accuracy. It utilizes the parameters in confusion matrix to evaluate the model, and specifically considers true negatives, unlike other metrics. This metric is useful to apply when the classification is multiclass type, or the data is unbalanced. Therefore, it is employed to further evaluate the ProSenAD. Equation (7) is used to calculate the MCC.  
\[\text{MCC}=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+ FN)}} \tag{7}\]  
Figure 14: Derivation of variables for CK calculation.  
Figure 16 shows the MCC results of ProSenAD, LGBM, GB, and XGBoost, in RPL-based IoT environment for protocol-specific and SN-inherited attack detection. The comparative analysis shows that the proposed model outperforms the approaches used as benchmarks. For instance, the comparison with GB shows the proposed model's high performance with an 18% improved MCC. XGBoost performs with 0.825 MCC value, as compared to ProSenAD. The latter performs better with 0.942 MCC, indicating an 11.7% increase in the comparative performance. Lastly, the LGBM shows an MCC value of 0.893. ProSenAD exceeds in performance by 4.9% based on this metric when compared to LGBM. Overall, the proposed model outperforms the benchmarks with an average improvement of 11.5% based on MCC evaluation metric.
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 3 Materials and Methods
### Proposed Method  
#### 3.3.1 Finsformer Overview  
The Finsformer model is a deep learning model specifically designed for financial attack detection, combining the Transformer architecture with an innovative cluster-attention mechanism to enhance the accuracy and efficiency of detecting anomalous transaction behaviors in financial systems. The following sections provide a detailed overview of the Finsformer model's overall construction, its characteristics, and operational mechanism. Based on the Transformer architecture, widely applied for its outstanding performance in processing sequential data, the original Transformer model comprises multiple encoder and decoder layers, each containing a self-attention mechanism and a feed-forward neural network. In the Finsformer model, this structure has been partially modified and optimized to suit the characteristics of financial data. The operational mechanism of the Finsformer model involves several steps:  
1. Input processing: The raw financial transaction data undergo preprocessing, including feature extraction, data cleansing, and normalization. The data, in the form of time series, include transaction amounts, timestamps, and account information. To effectively process these data, feature extraction and normalization are first carried out, transforming the raw data into a format that the model can handle.
2. Clustering and attention mechanism application: The cluster-attention mechanism is applied to the preprocessed data. Through cluster analysis, the model identifies key patterns in the data and focuses attention on these patterns. Differing from the traditional self-attention mechanism, the cluster-attention mechanism first clusters input data based on similarity, then applies the attention mechanism to these clusters. This approach enables the model to focus more on key patterns in the data, thereby enhancing the accuracy of detecting attack behaviors.
3. Feature extraction: After processing with the cluster-attention mechanism, the data are passed to the encoder and decoder layers. These layers further extract and process features, preparing for the final classification task.
4. Classification and detection: Finally, the model classifies the transactions based on the extracted features, determining whether each transaction is normal or an attack behavior.  
By introducing the cluster-attention mechanism, the Finsformer model operates more efficiently in processing large volumes of financial transaction data, especially in identifying complex attack patterns. Compared to traditional models, Finsformer can more accurately detect abnormal patterns in financial data, which is crucial for detecting complex financial attack behaviors. The Finsformer model can be adjusted to suit different financial scenarios, exhibiting good flexibility and scalability.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 2 Materials and Methods
### RNAP Levels in Time  
Cells (strain LK134) were grown in LB rich medium to OD\({}_{600}\) 0.3 (time point 0). Subsequently, every 30 min 10 mL of cells were pelleted and OD\({}_{600}\) was measured. Pellets were washed with Lysis Buffer (20 mM Tris-HCl, pH 8, 150 mM KCl, 1 mM MgCl\({}_{2}\)) and frozen. Next day, pellets were resuspended in Lysis Buffer (100-500 \(\upmu\)L, according to the size of pellet) and disrupted by sonication 2 \(\times\) 1 min, with 1 min pause on ice between the pulses. After centrifugation (5 min, 4 \({}^{\circ}\)C) to remove cell debris, the amounts of proteins were measured with the Bradford protein assay and 5 \(\upmu\)g was resolved by SDS-PAGE and analyzed by Western blotting, using mouse monoclonal antibodies against the \(\upbeta\) subunit of RNAP (clone name 8 RB13, dilution 1:1000, Genetex, Irvine, CA, USA) and anti-mouse secondary antibody conjugated with HRP (dilution 1:800,000, Sigma, Munich, Germany). Subsequently, the blot was incubated for 5 min with SuperSignal\({}^{\text{TM}}\) West Femto PLUS Chemiluminiscent substrate (Thermo scientific, Waltham, MA, USA), exposed on film and developed.
# eJamar: A Novel Exergame Controller for Upper Limb Motor Rehabilitation
## 3 Materials and Method
### GC Functioning
#### 3.2.1. Peter Jumper Game  
Peter Jumper (PJ) is a 2D game set within a 3D environment where the character navigates a pathway amidst an array of obstacles like boxes, towers, and fences. Figure 6 illustrates the second level of gameplay and the customization screen. [PERSON] must leap to avoid these obstacles; failure results in a life being lost. Adhering to exergame development guidelines [44], the game offers customizable features allowing users to adjust game parameters such as duration, difficulty levels, speed, haptic feedback intensity, effects activation, audio volume, and game modes. PJ encompasses two distinct game modes. In the gripper squeeze mode, the user must exert force on the ejamar's gripper. When the applied force surpasses the preset threshold for that level, the character executes a jump. In the controller movement mode, the user moves the ejamar vertically upwards, employing radial/ulnar deviation movements in the vertical sub-mode and flexion/extension in the horizontal sub-mode. The previous Figure 5 illustrates the hand-wrist movements corresponding to the various working modes used in the PJ game.  
Prior to gameplay, the system measures the user's maximum force (MAX) either through force or controller movement. Subsequently, thresholds for all levels are calibrated based on this value, incrementing with each progressive level. Additionally, the character can collect coins and hearts; every 5 coins or each heart acquired adds a life. Colliding with obstacles deducts a life and triggers vibration feedback via the ejamar. If all lives are lost,  
Figure 5. ejamar modes of use.  
Figure 6. Peter Jumper environments.  
users can restart the level at that point. Table 1 details the specific attributes of each PJ level, outlining their unique characteristics and challenges.  
By engaging users in these game modes with varying movement patterns and challenges, the rehabilitation system promotes targeted exercises and entertainment while aiding in the recovery of hand and wrist mobility.  
The aim of this game is to engage users in active participation in rehabilitation exercises, specifically targeting the enhancement of hand grip strength and wrist movements involving flexion, extension, and lateral deviations. Upon completion of the three levels, played within a specified time frame, all grip strength and movement data recorded by the ejamar are meticulously stored. This comprehensive data collection allows for a comprehensive understanding of the user's entire exercise regimen. Analyzing this data across multiple rehabilitation sessions enables a comparative analysis, facilitating a detailed assessment of the patient's progression over time.
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 6 Discussion
Thanks to this fact, the drop of performance in the _inactive duration_ is not important and it is only noticeable for networks of size \(5\times 5\) and greater. As the size of the networks grows, the number of packet retransmissions in the mesh network increases, and therefore the probability of secondary collisions. For the scenarios studied, a WO value of 5 has shown to provide good video quality without worsening the throughput. This can be better observed by watching the received videos since, as mentioned in the previous section, values of PSNR values may be misleading for videos with high motion. It has to be noted that, due to the tendency of worsening PSNR and packet loss with network size, for networks larger than thestudied ones, higher values of WO should be considered. In this case, increasing the WO by one implies a more than twofold increase of the _inactive duration_ and the number of slots (see Table 5). Therefore transmissions in the _inactive duration_ are sparser and the probability of collision decreases. In view of the simulation results, it can be affirmed that it is feasible to transmit video using the IEEE 802.15.5 standard. Using appropriate WO-AO values, it can achieve the necessary reliability for a satisfactory video quality. On the other hand, the main drawback of the standard is that it does not achieve high throughput values, which hinders its applicability to real-time video applications, although this is anyway very challenging for multi-hop networks. In order to improve throughput, some enhancements are necessary that allow more than one packet per _wakeup interval_ to be reliably sent. This can be achieved by tackling the diverse problems that have been discussed in this section in order to arrange transmissions so that multimedia flows can use more than one slot per _wakeup interval_, but also by applying enhancements in the directions given in the Related Work section. In this line, the standard has an important feature, the possibility of allowing transmissions to any other node of the network, which facilitates the development of collaborative or multi-sink applications.
## 4 Experiments  
This section evaluates LBMR compared with AOMDV and AODV, especially that AODV is used by numerous Zigbee based medical monitoring systems [10; 11; 12; 20]. Evaluation metrics include load balancing, unreachable nodes, and packet delivery ratio. We use ns2 as the simulation platform, whose environment parameters are listed in Table 3.  
In the simulation topology, we use two different ways to deploy nodes: grid and random uniform distribution. In the grid topology, the number of nodes is 85 and the distance between nodes is 40 m. In the random topology, the number of nodes is 100. The field size of both network topologies is 250 m \(\times\) 250 m. The traffic type of data sending is called Constant Bit Rate, and half the sensor nodes send data to the gateway. The coordinator node's energy is infinite, while other nodes' energy is 5 J. The transmission consumption is 0.0744 W and data-reception consumption is 0.0648 W. To compare LBMR performance with AOMDV performance, the following experiments consist of three evaluation scenarios: load balancing, route maintenance, and delivery ratio.
## 2 Soil-Bentonite Vertical Cut-Off Wall Service Performance and Improvement Measures
### Soil-Bentonite Vertical Cut-Off Wall Material Improvement Measures  
The montmorillonite content in bentonite typically exceeds 85%, granting it abundant adsorption sites within interlayer spaces, outer surfaces, and edges. This structural feature underpins bentonite's strong adsorption capacity, making it a critical component in containment systems for waste disposal or contaminated sites. However, pure bentonite's low mechanical strength limits its standalone use in vertical cut-off walls. To address this, bentonite is often blended with soil mixtures. The fine particles of bentonite disperse within soil pores, where hydration-induced swelling fills voids and reduces permeability. Concurrently, its high specific surface area enhances pollutant adsorption, particularly for heavy metals [76].  
Despite these advantages, soil-bentonite (SB) vertical cut-off walls face limitations under diverse pollutant types and environmental conditions. To overcome these challenges,researchers have explored additives to optimize SB material performance. For instance, [PERSON] [77] and [PERSON] et al. [78] demonstrated that incorporating sawdust (SD), controlled amounts of zeolite, or activated carbon significantly reduces hydraulic conductivity and improves impermeability (Figure 4a). Notably, [PERSON] et al. [78] cautioned that excessive activated carbon increases hydraulic conductivity, as shown in Figure 4b. Beyond permeability enhancements, compressibility adjustments are also critical. [PERSON] et al. [79] reported that adding powdered or crushed recycled tires (PRT/CRT) elevates vertical strain in SB walls, thereby enhancing compressibility, as demonstrated in Figure 5a,b.  
Internationally, modified bentonite products have emerged to address seepage control limitations. Innovations such as organic clay bentonite (OB) [80], trisoplast bentonite (TSP) [81], multi-swollen bentonite (MSB) [82], and sodium carboxymethylcellulose-modified bentonite [83] exhibit superior performance compared to conventional bentonite. Further advancing this field, [PERSON] et al. [62] developed an alkali-activated slag-bentonite-soil composite, which outperformed unmodified bentonite in compressive strength, permeability, Zn\({}^{2+}\) adsorption, and chloride diffusion resistance. This paper synthesizes optimal additive ratios for minimizing hydraulic conductivity in SB walls, as illustrated in Figure 6 [62; 78; 84; 85; 86].  
Figure 4: Effect of additives on hydraulic conductivities [77; 78]: (**a**) Hydraulic conductivity of SB materials under different vertical stresses by various additives. (**b**) Bentonite hydraulic conductivity at different zeolite and activated carbon dosages.  
Figure 5: Variation in different forms of tire doping on SB vertical cut-off wall in vertical strain [79]: (**a**) Addition of powdered recycled tire (PRT) blending over vertical strain. (**b**) Addition of crushed recycled tires (CRT) blending over vertical strain.
## 3 Background
### Overview of Target Algorithms
#### 3.2.4 Counter Modes of Operation  
The block cipher operation method refers to a procedure that repeatedly and safely uses a block cipher in one key. Since block ciphers operate in blocks of a specific length, to encrypt variable-length data, they must first be divided into unit blocks. The method used to encrypt the blocks is called an operation method. There are various block cipher operation methods, and typical block cipher operation methods are ECB, CBC, and CTR modes. The ECB operation mode has the simplest structure, and the message to be encrypted is divided into several blocks to encrypt each. In the CBC operation mode, each block is XORed with the previous block's encryption result before being encrypted, and, in the case of the first block, an initialization vector is used. In CTR operation mode, the counter value for each block is combined with nonce and used as the input of the block cipher. The encrypted input is XORed with plaintext. The structure of the CTR operation mode can be seen in Figure 3. In the case of CTR mode, since the plaintext is not encrypted, it is XORed with the encrypted counter value to make the ciphertext, thus using this feature enables efficient encryption on the GPU. In addition, due to the nature of CTR mode, encryption can be performed in parallel for each block, making it easy to implement on GPUs. Therefore, many studies have been conducted to operate the block cipher in the CTR mode, and it is utilized in various fields using the cipher, such as CTR_DRBG and Galois/Counter mode.
## 2 Global-Local Problem Formulation
### Global-Local Algorithm  
The Algorithm 1 presents the stages of the implemented global-local method.  
```
while\(\eta>\eta^{crit}\)do  Solve linear global problem \(K_{G}\cdot u_{g}^{n}=f_{G}^{d}+C_{\Gamma G}\cdot(\lambda_{A}^{(n-1)}-\psi_{LG} \cdot\lambda_{L}^{(n-1)})\) \(\triangleright\)\(\lambda_{A}\) and \(\lambda_{L}\) are 0 for the first iteration  Solve linear auxiliary problem \(K_{A}\cdot u_{A}^{n}=f_{A}^{d}\) \(\triangleright\) with prescribed displacements, \(u_{A}^{\Gamma\,n}=u_{G}^{\Gamma\,n}\)  Compute \(\lambda_{A}\)  Solve nonlinear local problem (Newton Raphson) \(K_{L}(u_{L}^{n})\cdot u_{L}^{n}=f_{L}^{d}\) \(\triangleright\) with prescribed displacements, \(u_{L}^{\Gamma\,n}=\psi_{GL}\cdot u_{G}^{\Gamma\,n}\)  Compute \(\lambda_{L}\)  Compute residual \(r^{n}=C_{\Gamma G}\cdot\{(\lambda_{A}^{n}-\psi_{LG}\cdot\lambda_{L}^{n})-( \lambda_{A}^{n-1}-\psi_{LG}\cdot\lambda_{L}^{n-1})\}\)  Compute error criteria \(\eta=\frac{||r^{n}||_{2}}{||r^{n}||_{2}}\) endwhile
```  
**Algorithm 1** Primal-Dual Global-Local strategy
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
On the other hand, such a sightseeing support method has some problems. Usually, ordinary visits involve rushing to famous places in the area. However, local habits and unique sights that only local people know will not appear in such information sites except in rare cases. Even when they do, it is in the corner of guidebooks and websites and easy to miss. Therefore, if we could provide various types and levels of information according to tourists' needs, we could expect an improvement in their satisfaction. In the past, human guides develop interactions between a guide and tourists [4]. They have played roles such as increasing the value of travel, especially at historical areas [5; 6]. Moreover, \"local tour guides are key players in mediating tensions between concurrent processes of localization and globalization\" [7]. However, with the diversification of travel, this is becoming more difficult. For example, if a traveler visited a city before sightseeing, he/she would like to see a different town area. However, for a traveler who has not visited there, he/she would like to see the most popular sightseeing point. In another case, if it is too cold and raining, travelers would like to visit a museum rather than climbing a hill. Therefore, each traveler needs different information during a trip. How can we use ICT to increase tourists' satisfaction and provide them with detailed information? We have been performing research to support tourism in Nikko, a World Heritage area in Japan, since 2014. Recently, the number of visitors to Nikko has decreased, so we aim to promote the Nikko area by supporting travelers using ICT. This paper outlines our research history to develop a sightseeing support application by applying Bluetooth Low Energy (BLE) beacons and various psychological effects, especially for inbound travelers, and outlines the application based on behavioral economics developed last year to enable the optimum distribution of information. The structure of this paper is as follows. Section 2 mentions related works. Section 3 introduces the enhancement of CogInfoCom model to meet our new sightseeing support application. In Section 4, we explain the history of studies on sightseeing support applications including psychological effects. Then, details of the behavior of the information delivery method based on behavioral economics are described in Section 5. Section 6 reports on the results of the experiments. Finally, a summary of our studies is given in Section 7.