esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Pseudonym-Based Privacy Schemes
### Public Key/Asymmetric-Based Pseudonym Schemes  
In asymmetric based schemes, public key infrastructure (PKI) is used. A public key-based pseudonym scheme proposed by [PERSON] et al. [86], in which several unidentified asymmetric keys are utilized so that the safety messages sending vehicle cannot be tracked. However, the limitations of this approach are, large storage is required to store enormous asymmetric keys and computational cost in terms of CRL entries checking. A technique called Expedite Message Authentication Protocol (EMAP) [87] uses hash function along with PKI to reduce the time checking process in CRL. However, the communication overhead is high, as hash codes are also exchanged along with CRLs. Also, large storage is required to store hash codes of CRLs. The schemes presented in [88; 89] use ECC, to reduce communication and computational overheads. However, the privacy of source (sending  
Figure 8: Symmetric cryptographic approach.  
Figure 7: Asymmetric cryptographic approach.  
safety messages vehicle) is low. ECC [90] is based on the algorithmic artifact of oval arcs closed specific plots. ECC is basically a type of asymmetric encryption. However, only the signature with the messages cannot ensure the authenticity of messages. The messages are appended with digital certificates. ECC computational and transmission costs are affected by a number of vehicles. Similarly, speed affects delay in ECC [90]. Computational cost, transmission cost, and packet delivery ratio in dense and sparse scenarios are affected in ECC [90]. ECC schemes work well in sparse scenarios [91], but in dense scenarios, the efficiency of ECC is affected. ECC based schemes [91] alone do not provide the feature of non-repudiation and is prone to DoS attack. Authentication is the most important service in ITS and guarantee security. However, in order to achieve efficiency, an augmented authentication mechanism is still a big challenge [92].  
[PERSON] et al. [92], proposed a security scheme based on PKI. The scheme uses Merkle tree (MT) and elliptic curve digital signature algorithm (ECDSA) but incurs high computational delay. Construction of MT is a time-consuming activity. The scheme [92] considered no privacy for vehicles. There is a transmission overhead by sending the Merkle verification direction. However, only ECDSA-based authentication schemes are open to DoS attack.  
In summary the performance, security, and privacy analysis of asymmetric encryption based pseudonym approaches are shown in Tables 4-6, respectively.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 2 System Architecture
Practice shows that this part of open communication lasts 2 to 10 s. As the communication is open, the leakage of information about open speech S to the eavesdropper does not play any role; that is, previously identified sources of common randomness can be used for the safe distillation of secret keys._  
Following the previous logic, a good source of common randomness could be \(CR\big{(}\hat{S}_{IA},~{}\hat{S}_{IB}\big{)}\). Signals \(\hat{S}_{IA}\) and \(\hat{S}_{IB}\) were obtained using local LP syntheses with the same LSP parameters about which [PERSON] has no information. Let us imagine two First In First Out (FIFO) buffers of identical secret random content on [PERSON]'s and [PERSON]'s side; see Figure 3. If we interpret this content as a set of randomly selected LSP parameters about which [PERSON] has no information, by synchronized reading, both sides can synthesize the required signals \(\hat{S}_{IA}\) and \(\hat{S}_{IB}\). In that case, [PERSON] and [PERSON] can use the SKD protocol over the source \(CR\big{(}\hat{S}_{IA},~{}\hat{S}_{IB}\big{)}\) to distill secret keys without [PERSON] receiving a single bit of information from the public channel about the input speech signal S. Namely,  
\[I\big{(}S,~{}\hat{S}_{IA}\big{)}=0,I\big{(}S,\hat{S}_{IB}\big{)}=0, \tag{3}\]  
having in mind the way the \(\hat{S}_{IA}\) and \(\hat{S}_{IB}\) signals were generated. The FIFO buffer is continuously replenished with just-distilled secret keys, while the secret key sequence \(C\) is synchronously read on the receiving and transmitting side. By summarizing modulo 2 with the output sequence of the MELPe analyzer \(X\),  
\[Y=X\oplus C, \tag{4}\]  
we form a perfectly secret Vernam cipher. A necessary condition for maintaining the system in continuous perfect secrecy is that the filling speed of the FIFO memories on the transmitting and receiving side must not be less than the reading speed, i.e.,  
Figure 1.0  
In (5), \(R_{K}\) is the secret key distillation rate, \(R_{C}\) is the Vernam cipher secret key consumption rate, and \(R_{E}\) is the consumption rate of the LP synthesizer. Note that \(R_{C}\) must be equal to the output sequence rate of the MELPe vocoder in the main channel  
Figure 1.1  
Figure 4 shows the generic scheme of this APS-VCS concept.
## 2 Different Methods of Hydrogen Generation
### Latest Hydrogen Production Process Advances
#### 2.3.8 Solhyde Technology  
A startup called Solhyde Technology is developing a hydrogen panel that produces hydrogen from air humidity using sunlight and a photocatalyst. This innovation makes it perfect for decentralized hydrogen generation in homes, enterprises, and communities. The panel works by collecting airborne water molecules and storing them in a tube structure. When the panel's photocatalyst is activated by sunshine, the stored water is divided into pure hydrogen and oxygen. In comparison to conventional electrolysis methods, this technology has a number of advantages, such as the lack of a requirement for clean water or energy, lower operational costs, and a lower environmental impact. Moreover, it has the ability to produce hydrogen locally and on demand, making it a potentially beneficial technology for use in transportation, energy storage, and other fields. A huge roof would produce 2 to 4 tons of hydrogen annually, whereas 20 hydrogen panels on a small roof at home would produce 240 kg annually [59]. Table 3 provides a concise summary of the advantages and disadvantages associated with the latest advancements in hydrogen production processes. It highlights the key benefits and drawbacks of each method, offering valuable insights into their respective strengths and limitations. By presenting this information in a structured format, the table allows for easy comparison and evaluation of the different approaches, enabling researchers and industry professionals to make informed decisions regarding the most suitable hydrogen production method for their specific needs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology** & & **Pros** & **Cons** & **Ref** \\ \hline C-Zero & 1. & Thermocatalysis is used in the process to convert natural gas into turquoise hydrogen and solid carbon through methane pyrolysis and heat. & & \\ C-Zero & 2. & This method enables the production of hydrogen without carbon dioxide emissions. & 1. & The potential release of methane during the distribution phase poses a challenge to achieving a fully environmentally [60; 61; 62; 63; 64] \\ C-Zero & 4. & The C-Zero process provides a solution for countries heavily dependent on fossil fuel exports. & 2. & Probability of leaks of methane \\ C-Zero & 5. & It allows these countries to meet their green energy goals while still utilizing their natural resources. & 2. & Probability of leaks of methane \\ C-Zero & 6. & This process enables a transition towards sustainable energy while maximizing the use of existing fossil fuel reserves. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of hydrogen synthesis technologies.
# A Method Improves Speech Recognition with Contrastive Learning in Low-Resource Languages
## 4 Experimental Methods
### Implementation Details  
All experiments were conducted using fairseq [40] and were conducted on 2 NVIDIAATX A5000 Graphics Cards. For the pre-training stage, we used the open-source wav2 vec 2.0 base model. The CNN layer in our model comprises of 7 hidden layers, where each layer includes a temporal convolution, layer normalization, and a GELU activation function. The temporal convolution of each block has 512 channels, the width of the convolution kernel is (10, 3, 3, 3, 3, 2, 2, 2), the stride size is (5, 2, 2, 2, 2), the stride length is about 20 ms, and the receptive field is 25 ms. The self-attention layer consists of a 12-layer, 768-dimensional Transformer layer with 8 self-attention heads. The encoder layer dropout is 0.5, and dropout input and dropout features are 0.1. For the mask operation, the value of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Language** & **Durations** & **Format** & **Sampling Rate** & **Speakers** & **Units** \\ \hline LibriSpeech-train-clean-100 h & English & 100 h & FLAC & 16 kHz & 251 & subword \\ AISHELL-1 & Mandarin & 150 h & WAV & 16 kHz & 340 & character \\ THCHS-30 & Mandarin & 33.5 h & WAV & 16 kHz & 40 & character \\ Common Voice Corpus 11.0-Uyghur & Uyghur & 113 h & MP3 & 32 kHz & 747 & subword \\ Common Voice Corpus 11.0-Uzbek & Uzbek & 258 h & MP3 & 32 kHz & 2025 & subword \\ \hline \hline \end{tabular}
\end{table}
Table 1: Corpus Information.  
\(p\) is 0.065, and \(M\) is 10. The quantization module has two codebooks with 320 entries in each, and each entry has a dimension of 128. For English, Uyghur, and Uzbek, we used gradient accumulation to implement a batch size of 11.2 m samples, and for Mandarin, we used gradient accumulation to implement a batch size of 22.4 m samples, with the peak learning rate 0.0001 and warm-up steps 32,000. The pre-training is 100 epochs, batch size is 8, and the other parameters were basically the same as the base model configuration in [27]. For the loss function (3), we set \(\alpha=0.1\), and \(\epsilon=0.05\). The network structure of the model is shown in Figure 7.  
In the fine-tuning stage, Mandarin fine-tuned for 100 epochs, Uyghur fine-tuned for 200 epochs, Uzbek fine-tuned for 400 epochs, and English fine-tuned for 30 k steps. Using gradient accumulation to implement a batch size of 7.68 m samples for English, Mandarin, and Uyghur, using gradient accumulation to implement a batch size of 5.76 m samples for Uzbek, with the peak learning rate 0.00005, 8 batch size, and warm-up steps 3000, and the other parameters were basically the same as the base model configuration in the fine-tune experiments of [27]. The same language uses the same language model, where EN language model is 4-g from LibriSpeech-train-clean-960h text made by kenLM [41], MA language model is 4-g from The People's Daily text made by kenLM, and UG and UZ are 3-g from respective text made by kenLM.
## 1 Introduction
Both reports agree that the Northwest of Mexico reached the maximum values, the states of Sonora, Baja California, Coahuila, and Chihuahua being the main producers/receivers. Recently, diverse reports have studied the relations between some meteorological variables and the electric power generated in a photovoltaic system. In Reference [26], an analysis of the climatic factors and solar data from the Andes site was performed; nonetheless, all the data gathered for the study was averaged every 10 min and the power measurements were estimated. In [27], the effect of diverse meteorological variables (outdoor temperature, air pressure, humidity, wind speed, and solar radiation) on the generated energy are mentioned, although a representative model of the plant was not obtained and all the data was experimentally generated. In Reference [28], a statistical method was applied to forecast the energy generated by a solar plant, though a database of 30 plants from different locations is necessary and although it does not have real power measurements from the site and the computational load is heavy. In Reference [29], an artificial neural network (ANN) is used to obtain a model to forecast the photovoltaic energy; however, the solar radiation was the only meteorological variable analyzed. Higher Education Institutions (HEI) currently play a major role in the generation of human capital and the associated impact on societal development; HEIs are ideal locations to focus the resources in terms of the deployment and experimentation of decarbonization technologies to demonstrate the best practice for a further replication within wider society [30]. Figure 2: The Mexican solar radiation atlas [24]. Careful planning is required to manage the future electricity demand of PV systems due to its increasing potential demand in Mexico [31; 32]. Therefore, it is vital to understand the influence of meteorological variables on energy consumption in which a better understanding of it can contribute to a more useful strategy in meeting the energy efficiency goal for the country. According to the above and considering References [1; 13; 30], the aim of this work is to present a statistical analysis based on the gradient descent method that is easy to implement and has a low computational load to estimate the electric power generation from the meteorological data such as the solar radiation, outdoor temperature, wind speed, and daylight time collected from PV systems located in Mexico City and Sonora [33]. This is important because most of the current photovoltaic system deployments do not monitor these factors or employ them in adaptation and prediction tasks [34; 35; 36]. The proposed methodology achieves a satisfactory estimation of the PV power with a high determination coefficient and a fair error percentage value.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study
### Uplift Simulation of Self-Drilling Anchor Bolts
#### 3.3.2 Correction Method for Pile Elements  
This study modifies the pile element based on a residual strength (strain-softening) model, which includes a three-segment post-peak softening model. The first segment represents the elastic deformation stage before the peak, the second segment represents the stress reduction stage after the peak, and the third segment represents the phase where the stress remains at a lower residual strength after a sharp drop, as shown in Figure 11. It should be noted that, from a practical engineering perspective, the interface stress model is not an absolute linear model but rather resembles the model shown in Figure 12. However, if we wish to extract data from all points, the only approach currently available is to consider it from a theoretical derivation standpoint, which also becomes the target direction for our next step of work.  
Figure 11: Residual strength (strain-softening) model.  
Figure 10: Initial stress contour map.  
Based on the above, the following effect can be achieved in FLAC3D. It is important to emphasize that in the modified pile element model, the implementation approach involves reading key points from the experiment, converting them into the anchorage interface unit cohesion (\(C_{g}\)) using Equation (6), and finally rewriting the linear curve in the Fish language. This curve is then assigned to the pile structural element using the table function to represent the characteristics of the anchorage interface. The method is universal for anchor bolts under different parameters, but the values assigned may differ. Therefore, this study focuses on simulating and analyzing the RS6-01 anchor bolt. As a result, we obtain the two curve models for the modified pile element shown in Figure 13. These curves describe the variation in unit bond strength at the interface and the ultimate uplift capacity of the anchor bolt. All subsequent simulation result analyses are based on this model.  
Figure 12: Shear stress-displacement curve.  
Figure 13: Modified relationship between ultimate uplift capacity and unit bond strength in pile elements.
###### Abstract  
Ultrafast chip nanocalorimetry opens up remarkable possibilities in materials science by allowing samples to be cooled and heated at extremely high rates. Due to heat transfer limitations, controlled ultrafast cooling and heating can only be achieved for tiny samples in calorimeters with a micron-thick membrane. Even if ultrafast heating can be controlled under quasi-adiabatic conditions, ultrafast controlled cooling can be performed if the calorimetric cell is located in a heat-conducting gas. It was found that the maximum possible cooling rate increases as \(1/r_{0}\) with decreasing radius \(r_{0}\) of the hot zone of the membrane. The possibility of increasing the maximum cooling rate with decreasing \(r_{0}\) was successfully implemented in many experiments. In this regard, it is interesting to answer the question: what is the maximum possible cooling rate in such experiments if \(r_{0}\) tends to zero? Indeed, on submicron scales, the mean free path of gas molecules \(l_{mfp}\) becomes comparable to \(r_{0}\), and the temperature jump that exists at the membrane/gas interface becomes significant. Considering the limitation associated with thermal resistance at the membrane/gas interface and considering the transfer of heat through the membrane, we show that the controlled cooling rate can reach billions of K/s, up to \(10^{10}\) K/s.  
chip-nanocalorimetry; ultrafast nanocalorimetry; interfacial thermal resistance +
Footnote †: journal: Applied Sciences
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 2 Microbial Polymers
### Polylactide (PLA)  
Polylactide or polylactic acid, also known as PLA, is a microbial biopolymer that functions identically to polyolefins and is capable of being processed utilizing standard approaches like blow molding and protrusion to produce green bioplastics. Polylactic acid comes under the class of polyester. It is comprised of lactic acid monomers. Sugars are generally converted into lactic acid with the aid of bacterial and algae strains and are afterward polymerized to polylactic acid [46, 47]. A study by [PERSON] et al. [224] concluded that advanced ring-open polymerization with catalysts like tin and zinc is beneficial to declining the cost of PLA. PLA is a biodegradable and biocompatible polymer that exists in two enantiomeric forms L-PLA and D-PLA can be selected for material properties. PLA is a promising biopolymer that can be extracted from municipal waste [225], lignocellulosic [226], and food waste streams with the aid of enzymatic and fermentation processes. Most waste biomass is comprised of the prevalent natural flora of lactic acid bacteria. Therefore, it can be anticipated that sugars in biowaste are naturally fermented to lactic acid [225]. [PERSON] et al. [227] explored the performance of PLA in protracted marine environments. [PERSON] et al. [228] emphasized the use of PLA-based films as an environmentally satisfying alternative to traditional plastic mulch. Another notable application of PLA-based fibrous membranes was analyzed for oil-water separation by [PERSON] et al. [229]. In comparison to polymers made from petroleum, this microbial biopolymer is high-priced and has inferior mechanical characteristics. Now, copolymerization can be used to overcome this difficulty and to scale up at an industrial scale [230].
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Multi-Stage Graph  
A multi-stage graph is a directed graph where nodes are grouped into stages and edges connect nodes from one stage to the next stage [140]. In a multi-stage graph, the goal is to find the shortest path from the first node to the last node, so, sometimes it is called as the shortest path problem.  
The multi-stage graph has been used in several multi-stage shop scheduling problems such as multi-stage flow shop systems discussed in Section 3.2.4[141; 142], and classic open shop and job shop scheduling problems [36]. Within these applications, the combinations of the shop scheduling problems and the shortest path problem are studied using multi-stage graphs [142].  
This so-called combination problem can be formally defined as follows: Let \(G=(V,E)\) be a directed graph with two distinguished vertices \(s,t\in V\) as a start and finish points which represents a manufacturing system with \(m\) machines and \(p\) jobs. Also, edges \(j\in E\) correspond to jobs \(j_{j}\in J\), (\(j=1\), \(2\), \(\ldots\), \(p\)) and the weights on edges represent the processing time of jobs on the machines (\(p_{1j}\), \(p_{2j}\), \(\ldots\), \(p_{mj}\)). Then, the problem is to find a path from node \(s\) to \(t\) such that the appropriate schedule of jobs on machines yields the minimum makespan (or generally, with optimum performance measure) [36]. This is a combination of shop scheduling problem and the graph shortest path problem. It should be noted that the scheduling problem and the shortest path problem individually are two special cases of this so-called combination problem.  
For instance, consider Figure 18A for a system with two machines and \(n\) jobs. Since in this figure, there is only one unique path from \(s\) to \(t\), our problem is reduced to the two-machine shop scheduling problem. However, if we assumed that processing times on the second machine is zero, then, as shown in Figure 18B, then our problem is reduced to the shortest path problem [36].  
Figure 17: The alternative graph depicts a partial system with two operations \(i\) and \(j\) that cannot be processed at the same time.  
Figure 18: Special cases of the combination problems using multi-stage graph. (**A**) a system with two machines. (**B**) the reduced problem to the shortest path problem.
## 2 Materials and Methods
### Landsat 8 Dataset  
This study utilized Landsat 8 level 1 TP products. Level 1 TP is the most elevated quality of level 1 product that is reasonable for pixel-level time series analysis. The image  
Figure 1: Study area.  
acquired on 28 March 2018, was used for model implementation. Meanwhile, the image acquired on 16 April 2019, was elaborated for validation purposes. Images acquired on 23 November 2018, 17 September 2017, 26 June 2016, and 23 July 2020, were used to develop the TSS concentration map over the study area. These images were carefully selected to ensure minimal cloud cover. All images were downloaded from the EarthExplorer website and, after that, processed using ENVI 5.1. Atmospheric correction was executed to eliminate the effect of the atmospheric haze present in the image. A few atmospheric corrections are accessible. The DOS atmospheric method was used in this study due to its effectiveness in eliminating atmospheric hazards. This atmospheric correction method has been widely used by many researchers in order to obtain water quality parameters around the globe [3; 44; 45; 46; 47; 48; 49; 50]. To realize this objective, radiometric calibration was performed to convert radiance, and the surface brightness was measured directly by the satellite, to the unit with less surface reflectance utilizing metadata which approximated the procurement time and sun elevation when the image was taken [50]. From that point, the DOS calculation was performed. DOS searched each band for the darkest pixel value. Expecting that dark objects do not reflect light, any esteem more prominent than zero must have come about from atmospheric scattering. Such scattering was removed by subtracting this esteem from every pixel within the band. This basic procedure is compelling for haze correction in multispectral information, but ought to not be utilized for hyperspectral information. Clouds that were shown within the image were masked utilizing Landsat 8 cloud mask product. During this process, the classification of pixel QA bands was analyzed. The pixel QA band quality conditions are expressed as a cloud confidence level. In this study, only pixels with a clear condition (no cloud), low cloud confidence (LCC) and low cloud shadow (LCS) were used in the cloud masking method [51; 52; 32].