esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ecological Conditions of the Caspian Sea
The annual flow of petroleum products from the Ural River is 900 tons/year, i.e., 1.2% of the total river flow of petroleum products to the Caspian Sea. The second largest source of contamination is the industrial runoff (27.0%). In particular, the rivers draining the Caucasus Mountains make a significant contribution to the oil contamination of the Caspian Sea. The average flow of petroleum products to the Caspian Sea was 0.98 thousand tons/year from the Terek River and 0.44 thousand tons/year from the Sulak River [45] in 1978-2007. The main amounts of oil products river inflow to the Northern Caspian Sea belongs to the share of the river Volga. The volume of oil products transported by this river to the Caspian Sea varies significantly over the years. According to various estimates, it ranges from 13 thousand tons/year to 76.7 thousand tons/year [46; 47; 48]. It is shown that the content of petroleum hydrocarbons in the water of the Northern Caspian Sea ranged from 1 to 46.6 MPC (50-2330 \(\mu\)g/L) on average 4.8 MPC (240 \(\mu\)g/L) in 2002. They become mainly concentrated both in the Western Volga passage, on the border to the Middle Caspian Sea and in the mouth of the Volga and the Ural Rivers. According to the Caspian Fisheries Research Institute [49], concentrations of petroleum hydrocarbons in the northern Caspian Sea were 0-0.62 mg/L in April 2002. On average, the Caspian Sea's hydrocarbon contents in water exceeded the average annual level by 1.8 times in 2005 [50], which corresponds a steady trend of growing petroleum hydrocarbon concentrations throughout the Caspian Sea, including in the Kazakh sector. The problem of oil contamination of the Caspian Sea has become particularly acute and topical in connection with the large-scale development of hydrocarbon resources along the shelf of the Caspian Sea. It has been established that for every 1 million tons of oil produced in the world, an average of 131.4 tons of losses occurs [49]. With the specified volume of oil production, only in the water area of the Kazakh sector, about 8.0 thousand tons of oil can potentially be spilled annually. Thus, a large-scale development of oil and gas fields in the most productive Northern Caspian region is associated with an environmental risk. Studies have shown that even low concentrations of oil in water, below the maximum permissible (0.05 mg/L), lead to serious loss of functions of important organs of aquatic animals [33; 38; 39].
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 4 Experiments and Results
### Experimental Setup
#### 4.2.1 Examination of Pretrained Model and Data Configuration  
We build the MDD model by stacking one FC layer on the pretrained wav2 vec2.0 model, in which the parameters of the FC layer is randomly initialized. After that, we fine-tune the MDD model using training data of the L2-arctic and TIMIT datasets. To better understand the influence of the amount and type of training samples during fine-tuning, we train MDD models with the following four kinds of training data configurations (Source code and configuration files are available at [[https://github.com/vocaliodmiku/wav2](https://github.com/vocaliodmiku/wav2) vec2 mdd]([https://github.com/vocaliodmiku/wav2](https://github.com/vocaliodmiku/wav2) vec2 mdd)).  
* **Default** For this configuration, we merely use the training data from the L2-arctic corpus. The way of data partition for training and testing is consistent with [19] where the data of six speakers (NJS, TLV, TNI, TXHC, YKWK, ZHAA) are held as the test subset while the rest data of other 18 speakers are merged to build the training subset. Moreover, a development set is created by by randomly selecting 20% sentences from each speaker of the training subset. There is no overlap between the training and developing set.  
* \(-\)**33%** This configuration is designed to explore the feasibility of the pretrained model for ultra-low resource MDD. To this end, we reduce 33% of training data for each language by randomly excluding six speakers from the _Default_ training set.
* \(-\)**66%** Similar to the \(-\)_33%_ configuration, we further reduce 33% of training data for each language by randomly excluding another six speakers from the \(-\)33% training set. In this case, only one speaker is kept for each of the six languages of non-native speakers.
* **+TIMIT** This configuration is to explore the effectiveness of incorporating the native English data. Here, the original training subset of TIMIT corpus is merged into the _Default_ training set.  
Table 1 summarizes related duration statistics of these data configurations. We can regard the data configurations of \(-\)33% and \(-\)66% as the low-resource and ultra-low-resource MDD.
## 3 Uncertainty Modeling Techniques
### Latin Hypercube Sampling (LHS)
#### 3.1.2 Analytical Methods  
Analytical methods for uncertainty modeling in power systems offer mathematical frameworks to predict and manage the variability in power generation and demand, and PEMs are one of these key methodologies. PEMs are widely used to estimate an unknown parameter of a population using sample data. The goal of point estimation is to provide a single best guess or value for a parameter, such as the population mean or variance, based on observed data.
# Development and Validation of a Tool for VBOI (Virtual Body Ownership Illusion) Level Assessment
## 4 Results  
### Confirmatory Factor Analysis  
Confirmatory Factor Analysis (CFA) was conducted using R (Lavaan package) to assess the consistency of the sub-scales of the questionnaire developed. Table 3 presents the results of this analysis. After the first CFA, items 3, 8, and 14 were excluded due to factor loadings below 0.5. After the exclusion, in the second analysis, the test using non-parametric statistics indicated statistically significant results (x2 = 127,841, df = 67, \(p\) < 0.001). Thus, each item and scale can be considered appropriate as components for evaluating VBOI.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Subscales** & **Questions** & **Factor Loading** & **Cronbach’s Alpha** \\ \hline \multirow{7}{*}{ Embodiment} & Q1. Did it feel like the virtual body was your own body? & 0.894 & \\  & Q2. Did it feel like your (real) body was moving towards the virtual body or like the & & \\  & virtual body was moving towards your (real) body? & 0.742 & \\  & Q3. How much did it feel like there was more than one body besides your actual body? & \(-\)0.253 & \\  & Q4. How much did the virtual body resemble your (real) body? & 0.729 & \\  & Q5. How natural was the interaction with the virtual environment? & 0.873 & \\ \hline \multirow{7}{*}{ Presence} & Q6. How closely did the experience in the virtual environment match real-life & 0.857 & \\  & experiences? & 0.62 & 0.818 \\ \cline{1-1}  & Q7. Could you predict what would happen next based on your actions? & 0.322 & \\ \cline{1-1}  & Q8. How much delay did you experience between your actions and the expected & & \\ \cline{1-1}  & outcomes? & 0.322 & \\ \hline \multirow{7}{*}{ Visuo-proprioceptive} & Q9. Did it feel like your body was where the virtual body appeared to be? & 0.911 & \\ \cline{1-1}  & Q10. How well could you observe the virtual body from various perspectives? & 0.570 & \\ \cline{1-1}  & Q11. Was the sensation felt in the virtual body perceived to occur at the location where it & & \\ \cline{1-1}  & was seen to be touched? & & \\ \hline \multirow{7}{*}{ Visuo-tactile} & Q12. Was the sensation felt as if it originated from touching the virtual body? & 0.852 & \\ \cline{1-1}  & Q13. How strong was the sensation felt simultaneously in both locations (virtual & \\ \cline{1-1}  & body/real body)? & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confirmatory Factor Analysis results.  
The reliability values assessed by [PERSON]'s alpha for each of the sub-scales, Embodiment (\(\alpha\) = 0.822), Presence (\(\alpha\) = 0.818), Visuo-Proprioceptive (\(\alpha\) = 0.681), Visuo-tactile (\(\alpha\) = 0.742), and Visuo-motor (\(\alpha\) = 0.829), were all \(>\)0.6, indicating high reliability. Thus, we can conclude that the survey items we constructed are appropriately structured for each of the sub-scales.
## 4 Bringing CRKG into the Field: Securing Practical IoT Communications
### Short-Range IoT Communication Solutions  
#### 4.1.1 IEEE 802.15.4/Zigbee  
The IEEE 802.15.4 standard defines the physical and medium access control (MAC) layer for low-data rate and low-power transmissions, and ZigBee is a full communication suite based on the IEEE 802.15.4.  
Within the fast-secure project (de.fast-zwanzig20.de/industrie/fast-secure/), standard-conform USB dongles from metraTec, which realize the 802.15.4 standard, are applied. The hardware supports the ISM-band between 2.4 and 2.483 GHz. Periodically with 1 MHz, a beacon is sent by the devices to check connectivity. One measurement takes about 12 ms, while one complete sweep over all 83 MHz takes about 1.5 s. Based on a remote signal, the devices perform a measurement of the channel reported on the serial interface to the host laptop. Figure 6 shows a good agreement between the channel measurements of Alice and Bob in both directions for four trials (A-B represents the channel from Alice to Bob and B-A represents the channel from Bob to Alice). The applicability of CRKG is also investigated for the field of automation systems using channel parameters recorded in a real industrial environment [25]. Up to now, low secret key rates are reported to support low data rate links.
# Machine Learning Applications for Short Reach Optical Communication
## 3 Machine Learning for Short-Reach Optical Communication System Signal Processing
### Other Machine-Learning Based Signal Processing Applications for Short-Reach Communication Systems
#### 3.2.2 Digital Predistortion  
Digital predistortion (DPD) is a method widely used in power amplifiers for both linear and nonlinear impairments compensations. DPD is often realized using Volterra-based algorithms that employ sophisticated indirect and direct learning architectures [121].  
In order to find an alternative to the Volterra-based DPD method, a low-complexity DPD based on Extreme Learning Machine (ELM) is proposed, which has the capability of fast adaptive estimation and compensation for any MZM transfer function [108]. The low-complexity ELM-based DPD has been experimentally demonstrated based on an unamplified 40 km DCI link with 400 Gb/s dual-polarization 16-QAM signal. The ELM-DPD achieves the same link budget as the more sophisticated third order Volterra-based nonlinear equalizer. Additionally, an overall link budget gain of 0.85 dB, resulting in a total link budget of 19.1 dB, has been achieved over an unamplified 40 km link when ELM-DPD and Volterra post-equalizer were combined.  
ML-based (FFNN-based) DPD has also been used to improve the power efficiency of RoF links for analogue optical front-haul applications [121]. The FFNN-DPD model is trained using the received signal after ADC. The capability of FFNN-DPD is compared with the Volterra-based DPD method in this work. Results show that when the input power is 0 dBm, the FFNN-based DPD reduces the ACPR required in the system by 22 dB, while Volterra based method only reduces it by 14 dB. Since a lower ACPR means the better efficiency of the power amplifier, the FFNN based DPD method improves the RoF link performance with lower complexity compared with the Volterra-based approach.
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 1 Introduction
### Contribution  
The main contributions of this article are as follows.  
1. In order to ensure the secure communication between entities in AMI, a lightweight authentication and key agreement scheme based on Chebyshev chaotic map is proposed.
2. The chaotic map with a short key length, low memory occupation and a high secret strength is used to replace the time-consuming bilinear pairing and elliptic curve point multiplication operation, without additional storage certificate and key escrow, which saves the computation and communication overheads based on the premise of realizing the establishment of secure channel.  
3. According to the AMI architecture and message transmission mode, the authentication and key agreement mechanism between entities is designed, which establishes a secure channel for message transmission between entities. In addition, in order to solve the problem of the repeated transmission of data and repeated occupation of bandwidth, we also design a multicast key generation mechanism.
4. The security analysis results show that the proposed scheme satisfies the security characteristics such as anonymity and forward secrecy and can resist common attacks such as replay, impersonation and man-in-the-middle. The performance analysis results show that compared with the related schemes, the proposed scheme requires lower computation and communication costs and allows more entities to participate in the same hardware and software environment. The proposed scheme is more suitable for the AMI with limited resources.
# Practical Grammar Compression Based on Maximal Repeats +
## 2 Preliminaries
### Maximal Repeats  
Let \(s\) be a substring of text \(T\). If the frequency of \(s\) is greater than \(1\), \(s\) is called a _repeat_. A _left_ (or _right_) _extension_ of \(s\) is any substring of \(T\) in a form of \(ws\) (or \(sw\)), where \(w\in\Sigma^{*}\). We define \(s\) as a _left_ (or _right_) _maximal_ if left (or right) extensions of \(s\) occur a strictly less number of times in \(T\) than \(s\). Accordingly, \(s\) is a _maximal repeat_ of \(T\) if \(s\) is both left and right maximal. In this paper, we only consider strings with a length of more than \(1\) as maximal repeats. For example, substring \(\texttt{abra}\) of \(T=\texttt{abracadabra}\) is a maximal repeat, whereas \(\texttt{br}\) is not.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Blockchain and Artificial Intelligence  
Blockchain technology enables the creation of a distributed trust system. In fact, blockchain provides the same level of trust as traditional written contractual agreements due to its distributed and secure method of storing, managing, and transferring information and executing electronic transactions [62]. Specifically, the term blockchain refers to a public or private distributed ledger in which electronic transactions or data are connected in linked blocks of data. That renders them practically immune to malicious modifications due to a method that encrypts and hashes the block's information in an irreversible manner (one-way encryption) [62].  
While blockchain is extremely useful in order to provide security in the distributed IoT environment, its use will be further enhanced with a combination of artificial intelligence methods [63]. The use of blockchain's digital record to track the rationale behind AI predictions and thus enable people to trust them will be a big benefit of the AI-blockchain combination in 6G systems. In addition, adopting blockchain technology for archiving and distributing AI learning models would provide an audit record, which would enhance security. Blockchain also facilitates the development of AI by granting it access to vast amounts of verifiable data, thereby enhancing the reliability of the decisions it makes [64].
## 4 Experimental Results  
To justify the validity of the presented framework, quarterly sales of the three retailers, Walmart, Costco, and Kroger, are collected from 2005/Q1 to 2021/Q4. For visualization, Figure 5 displays that Walmart significantly surpasses Costco and Kroger, and all of them demonstrate seasonal variations. To help retail practitioners conduct sales forecasting, economic indicators [8] are treated as potential predictors. In particular, Lasso is applied to identify key performance indicators. As indicated by Table 3, CPI and regular wage are commonly identified for the three firms. PPI and oil price are only critical to Walmart because a hypermarket imports lots of goods (fashion clothes, home appliances, furniture, office supplies, sporting goods, electronic appliances, homemade tools, etc.) from foreign manufacturers, and it is more sensitive to the upstream variations. Specifically, DJT are critical to Costco and Kroger because they need frequent freight transportation to support logistics and inventory management. In contrast, GDP are key to Walmart and Costco, while PCE is influential to Walmart and Kroger.  
Very interestingly, lots of indicators, such as CCI, PMI, NMI, IMPI, EXPI, etc., are not critical to any of the three firms. Major product categories sold by a firm form a basis for the identified key predictors. Kroger is a chain supermarket selling uncooked food, such as vegetables, fruits, drinks, snacks, fishes, meat, bread, milk, etc., and Costco also sells lots of well-cooked food and bath supplies. According to different product categories sold by the three retailers, Walmart is closer to the upstream (producer) side while Kroger is closer to the downstream (customer) side. In contrast, Costco seems to be close to the median between Walmart and Kroger.
###### Abstract  
Odor and gaseous emissions from the swine industry are of concern for the wellbeing of humans and livestock. Additives applied to the swine manure surface are popular, marketed products to solve this problem and relatively inexpensive and easy for farmers to use. There is no scientific data evaluating the effectiveness of many of these products. We evaluated 12 manure additive products that are currently being marketed on their effectiveness in mitigating odor and gaseous emissions from swine manure. We used a pilot-scale system simulating the storage of swine manure with a controlled ventilation of headspace and periodic addition of manure. This dataset contains measured concentrations and estimated emissions of target gases in manure headspace above treated and untreated swine manure. These include ammonia (NH\({}_{3}\)), hydrogen sulfide (H\({}_{2}\)S), greenhouse gases (CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O), volatile organic compounds (VOC), and odor. The experiment to test each manure additive product lasted for two months; the measurements of NH\({}_{3}\) and H\({}_{2}\)S were completed twice a week; others were conducted weekly. The manure for each test was collected from three different farms in central Iowa to provide the necessary variety in stored swine manure properties. This dataset is useful for further analyses of gaseous emissions from swine manure under simulated storage conditions and for performance comparison of marketed products for the mitigation of gaseous emissions. Ultimately, swine farmers, the regulatory community, and the public need to have scientific data informing decisions about the usefulness of manure additives.  
odor mitigation; sustainable agriculture; air quality; gaseous emissions; environmental technologies; animal production systems; swine manure; waste management +
Footnote †: journal: Data Descriptor
## 3 Optimal Power Allocation (PA), Cooperative Relaying and Channel Models in PD-NOMA  
### Fuzzy Inference System for Optimal PA  
In comparison to conventional logic systems, fuzzy logic is largely reliant on human reasoning in spirit and is considerably closer to natural language. The fuzzy logic controller mimics the actions of a human operator by modifying the input signal. The proposed technique uses an FIS-based system to address the issue of power allocation. Normally, in the downlink PD-NOMA users' signals are assigned different power levels at the gNodeB but this is not the optimum solution for practical implementation of the NOMA system. To solve this issue, we proposed an FIS-based optimization technique that effectively distributes the total power among all the user signals at the gNodeB. The FIS system uses a fuzzy rule-based system (FRBS) that takes different input variables and mimics them identically as humans perform. The power optimization problem is solved using the FIS system, which allocates an optimal power factor to each user surrounding the gNodeB. Fuzzy logic (FL) is relatively less complicated and simpler to configure the model for solving non-convex and multi-objective systems with greater efficiency [41]. A normal fuzzy system is composed of three different levels: fuzzification, rule base, and defuzzification. The input values are transformed to fuzzy values in the fuzzification stage. The output of the fuzzification stage is executed depending on the different fuzzy rules from the fuzzy rule matrix (FRM) in the rule base. The fuzzy results from the rule base are turned into final output values via defuzzification.
# The Additive Manufacturing of Glass: A Critical Review
## 3 A Focus on Semi-Solid Processing for Glass AM
### Low-Viscosity Glass Feedstock Methods  
Depicted in Figure 15, one of the earliest approaches to semi-solid glass AM involves a molten glass feedstock [10; 15; 64]. This method employs a crucible kiln mounted at the top of the system, storing the molten feedstock while printing. The filling (and replenishment) of the crucible kiln can be accomplished through two methods: the manual ladling of molten glass from a separate furnace or the gradual heating of glass cullet within the crucible. A smaller secondary kiln, named the nozzle kiln, is positioned at the base of the crucible kiln. Housed in the nozzle kiln is a heated ceramic nozzle, regulating glass flow via temperature. Unlike traditional EAM, which employs mechanically assisted extrusion, this method utilises gravity-fed processing. The nozzle extends into an annealing chamber, where a ceramic printing platform actuates in all dimensions, X, Y, and Z. During the printing of 'System 96' soda-line glass, temperatures for the crucible kiln, nozzle kiln, and annealing chamber were set to 1040 \({}^{\circ}\)C, 1010 \({}^{\circ}\)C, and 480 \({}^{\circ}\)C, respectively.  
Figure 14: Semi-solid processing of glass in glass AM. (**a**) High-viscosity glass feedstock method [10]. (**b**) Low-viscosity glass feedstock method (Maple Glass Printing, 2023).  
During the initial heat-up process, the nozzle kil is typically set to a lower temperature (\(\sim\)800 \({}^{\circ}\)C) to prevent unwanted flow. To begin printing, the nozzle kil is set to 1010 \({}^{\circ}\)C, where glass will begin flowing spontaneously. As described in the general synopsis of semi-solid glass AM, the first-layer speed is reduced to promote adhesion to the platform. A layer height of 4.5 mm has been reported, controlled via the distance between the nozzle tip and the printing platform. Due to the gravity feed, the flow rate into the nozzle cannot be set directly; it is a function of the volume and viscosity of molten glass within the crucible. Therefore, layer width is dictated by printing speed and nozzle outlet size. A 10 mm nozzle was reported; however, the process may be varied to result in a line width of 19.5 +/ - 3.5 mm and 9.5 +/ - 0.5 mm. This may be achieved with printing speeds of 4.5 mm/s and 6.1 mm/s, respectively. Once the print process is complete, the nozzle must be cooled to <800 \({}^{\circ}\)C to stop glass flow. The process can produce components with exceptional aesthetic appeal, as is demonstrated in Figure 16.
## 3 Medium Access Control (MAC) Layered UWAC Protocols
### Contention-Based
#### 3.3.1 Hand-Shaking (HS)
When the back-off timer on underwater sensor nodes expires, a transmission opportunity to the target destination node becomes open. In order to transmit information to the target destination node, the source node that acquires the transmission opportunity broadcasts an RTS to neighbor nodes that includes the target node's ID. After receiving RTS, the destination node sends a clear-to-send (CTS) message to the source node, instructing it to conduct FD communication. After receiving the CTS, the source node and the destination node send the sensed data to each other in the order specified in the transmission order.