esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 3 Ultrasound--US
### Microbiological Quality and Safety  
At present, ultrasonication, in combination with other methods, is rarely used to inactivate the microorganisms or extend the shelf-life of fish and seafood products during storage. To the best of the authors' knowledge, only a few studies have been published describing the US's antimicrobial effects on fish and seafood products, which are of exclusive interest in this review.  
In a recent investigation, [PERSON] et al. (2021) [120] studied the effect of high-power ultrasound as a new method to improve thawed cod fillets' quality. During this investigation, they evaluated the hydration medium's pH controlled with NaOH (from pH 8.5 to 10.5) combined with US at 25 kHz and its impact on the fillets' microbiological quality at the end of the hydration process. The combination of US and hydration media of pH 8.5 and 9.5 allowed to limit or even to inactivate the total aerobic bacteria of cod fillets when pH was higher (10.5) after five (5) days. It is mentioned that the use of NaOH to control the pH of the hydration media can have a high bactericidal effect [125]. It can be implemented with the simultaneous use of US, due to increased contact among microorganisms and the chemical agent [120]. It is also common to use US in combination with traditional heating to increase the microbial inactivation on different meat products, such as fish and seafood. In the case of ready-to-eat whole brown crab (_C. pagurus_), [PERSON] et al. (2018) [122] assessed for the first time the application of US (up to 900 W) in a water bath containing or not 5% (\(w/v\)) NaCl together with heat at 75 \({}^{\circ}\)C simulating the industrial heat processing of RTE crustaceans' products. In compliance with other authors, reporting the synergistic effect of US treatment with heat [126], they found that ultrasound-assisted cooking showed significantly higher microbial load reduction in white and brown crab meat. Particular mention has to be made in the study of [PERSON] et al. (2017) [127]. They explored the exclusive effect of high-intensity US at 30 kHz in an ultrasonic bath for 5 to 45 min in several fish species. After the application of US, the total psychrophilic and mesophilic counts of oily fish species, such as salmon (_S. salar_) and mackerel (_Somber scombrus_), reduced up to 1.5 and 1.1 log CFU/g, respectively. The ultrasonication effect in white fish species, namely cod (_G. morhua_) and hake (_Merlucius merlucius_), was minimal and not higher than 0.5 log CFU/g.
## 6 Conclusions  
The communication tests performed during the shallow inland-water experiment with the use of the laboratory model of an OFDM system have shown that it is possible to achieve reliable data transmission with Bit Error Rate less than \(10^{-4}\). The tested UAC channel was about 1 km long and the depth was variable along this distance from 20 to 40 m. Thus, it was a typical case of a very shallow channel characterised by numerous reflections of the transmitted signal from the bottom and surface of the water. Obtaining such a low BER required the use of two techniques of ISI suppression, namely pilot tones and Error-Correction Coding. Achievable BER and data transmission rates were measured for four different configurations of OFDM signal parameters, selected to match the physical layer of data transmission to the transmission parameters of the channel, calculated on the basis of the measured Time-Varying Impulse Responses.  
The lowest BER was obtained in the case of subcarrier spacing which was less than the coherence bandwidth measured at the threshold level of 0.9 of the maximum value of the Space-Frequency Correlation Function (SFCF) of the channel. At the same time, the subcarrier spacing was much larger than the maximum Doppler spread measured at the threshold level of \(-20\) dB relative to the maximumvalue of Doppler Power Spectral Density. The OFDM symbol duration was longer than the delay spread of the channel measured at the threshold level of \(-20\) dB relative to the maximum value of Power Delay Profile.  
In case of the largest number of subcarriers, i.e., 512, reliable data transmission could not be obtained. For the smallest number of subcarriers, i.e., 64, the data transmission rate of 72.42 bps was obtained after using the BCH code of rate of 0.051. For 128 and 256 subcarriers, transmission rates of hundreds of bps were achieved, which is sufficient for most applications of reliable underwater communications, such as submarine-to-submarine or submarine-to-surface platform communications, monitoring of bottom installations or AUV remote control.
## 2 Methods
### Method of Study  
In this study, we analyzed the distribution of abandoned houses and the variables affecting the occurrence by using a spatial regression model on 11,966 abandoned houses in 308 submunicipal-level divisions (eup, myeon, and dong) and 18 municipal-level divisions (cities and counties) in Gyeongsangnam-do.  
First, we examined the distribution of abandoned houses to understand the reality of housing abandonment. We constructed data by transforming the addresses of abandoned houses into spatial variables. Based on this, we checked the distribution and the regional clustering of housing-abandonment phenomena. We identified the exact locations of abandoned houses using the geographic information system (GIS) program and identified the degrees of dispersion using a heat map.  
Second, we used the main variables that were selected, after determining the status of an abandoned house, by setting a buffer centering on the boundary to measure the distance between the spatial variable and the abandoned houses. To measure the distance between the main variable and the abandoned houses, we used the GIS program.  
Third, to identify the variables affecting the occurrence of housing abandonment, we performed a spatial regression analysis. Because abandoned houses and the surrounding areas can have mutual influence, spatial autocorrelation can be detected. We used the spatial regression model to increase the reliability of the statistical estimations because general regression models do not take autocorrelation into account and can produce distorted results. Prior to the analysis, we measured Moran's I and local indicators of spatial association (LISA) to determine the presence of spatial autocorrelation and its goodness of fit with the spatial econometrics model. After reviewing the ordinary least squares (OLS) regression model, spatial lag model (SLM), and spatial error model (SEM), we selected SEM as a suitable research model. To analyze the spatial regression model, we used GeoDa software shown as Figure 1.  
Figure 1: Study method and flowchart.
## 5 Engineering Application Examples  
The fatigue life cases in the literature [31] were adopted, and the structure shown in Figure 2 is as follows: the thickness of steel bridge deck motherboard and asphalt pavement is T1 = 14 mm and T2 = 50 mm, the width of the motherboard is W = 100 mm, and the length is L = 380 mm. The structure was subjected to variable amplitude loading, and its stress grade, acting frequency, and design life are shown in Table 1. This paper aimed to design and analyze the fatigue life of asphalt pavement structure, calculate its stress ratio, and determine its stress state grade.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level \(i\)**} & \multicolumn{2}{c}{**Three Levels**} & \multicolumn{2}{c}{**Four Levels**} & \multicolumn{1}{c}{**Designed Lifetime**} \\ \cline{2-5}  & \(\sigma_{i}\)/Mpa & \(n_{i}\)/\(\times\)10\({}^{7}\) & \(\sigma_{i}\)/Mpa & \(n_{i}\)/\(\times\)10\({}^{6}\) & \(N\)/\(\times\)10\({}^{7}\) \\ \hline
1 & 0.85 & 0.3 & 0.85 & 0.2 & \\
2 & 0.70 & 0.3 & 0.70 & 0.2 & \\
3 & 0.55 & 0.4 & 0.65 & 0.2 & \\
4 & - & - & 0.55 & 0.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Load spectrum of steel bridge deck asphalt pavement structure.  
Based on the reference of similar tests in China and abroad, the load mode without intermittent sine wave was decided to be used for loading, as shown in Figure 3, with the loading frequency set as 10 Hz.  
Figure 3: Examples of load spectrum under bending amplitude variation.  
Figure 2: Schematic illustration of fatigue test for steel deck–asphalt pavement beam.  
For steel bridge deck asphalt pavement structure, its equivalent stress can be calculated by both nominal stress and pavement layer stress [34; 35]. The form of asphalt pavement is a typical structure of steel bridge deck asphalt pavement. In this paper, the medium effect force was calculated and analyzed according to the nominal stress. On the basis of the linear and nonlinear cumulative damage theory, the design was conducted according to the loading sequence from high to low and from low to high. The fatigue strength value \(\Delta\sigma_{\text{ref}}\) under the specified number of cycles was first calculated according to the steps of fatigue life design. According to the CN-JT; 43. JTG E20-2011 standard [33], the structure is highway class I, with the reference design life of \(N_{\text{ref}}=1.2\times 10^{7}\) and the confidence of 97.5%. The fatigue strength can be calculated by the following formula [31]:  
\[N_{f}=\Gamma\frac{\sigma_{m}(0)}{\left[\frac{d\sigma_{m}(N)}{dN}\right]_{N=0}} \tag{18}\]  
where \(\sigma_{m}(0)\) and \(\sigma_{m}(N)\) are the stress at the bottom edge of the upper beam section under repeated initial action of cyclic load and repeated action C times. The calculation of parameter \(\Gamma\) value is independent of the loading sequence, which can be obtained directly from the calculation of relevant parameters, \(\Gamma=0.394\). We can solve for \(\Delta\sigma_{\text{ref}}=0.6237\,\text{MPa}\).
## 2 Related Work
Using two sequences, a sequence-to-sequence approach from machine translation can be applied. In [20], the authors proposed a neural architecture, namely, Morpheus, which is based on sequential neural encoder-decoders. It jointly solves the lemmatization and morphological tagging task. It uses a two-level LSTM network that produces context-aware vector encodings, and takes this vector as input for the decoders. The outputs are both the morphological tags associated with each word and the minimal edit operations required to transform the surface words into their respective lemmas. Previous work [21] employed an encoder-decoder framework with a bidirectional LSTM for encoding, paired with an attention mechanism during decoding to better understand the semantic relationships between the suffixes of words and other elements in a sentence. These approaches have been validated across multiple languages, including Finnish, Turkish, and English, demonstrating that such models can achieve performance that rivals or exceeds existing state-of-the-art techniques. Overall, the shortcomings of existing approaches can be summarized as follows:(i) The variety and complexity of morphological analysis result in a large number of unique labels. This not only increases label data sparsity but also leads to the potential issue of out-of-vocabulary labels. (ii) The issue of long-term dependency, such as in HMM-based approaches, fails to capture the long-term dependencies between tags. While CRF-based approaches may mitigate the issue found in HMMs, they require feature engineering since features are extracted from the analysis. Additionally, CRFs still operate under a second-order HMM assumption, making it impractical to fully consider all the possible paths of different analyses. The approach proposed in this work attempts to address these problems using contrastive learning methods. It models the analyses separately and employs a Transformer architecture to process input at the sentence level, incorporating knowledge encoded in LLMs. To our knowledge, there are few papers available on the MD task with LLMs, and the work presented here not only represents one of the first efforts to apply LLMs to this task but also introduces a contrastive learning method for the approach.
## 2 Newly Proposed Evaluation Method and Analysis Procedure
### Typical Procedure of Newly Proposed CRITIC-GRA Evaluation Model
#### 2.1.2 GRA Multi-Variate Statistical Analysis Method  
Grey relational analysis (GRA) [28; 29] is a method for statistical analysis of multi-indicators. This method calculates the grey correlation degree of the comparative schemes by comparing the geometric relationship between the weight values of all the considered factors and the preset comparative schemes. The advantage of the GRA method lies in its ability to assess the quality of a plan based on the similarity of the geometry of the sequence curves, without being constrained by sample size or regularity.  
The reference sequence is a hypothetical sequence containing the same number of indicators as the comparison sequence. The indicators in the reference sequence are derived from the optimal values of each indicator extracted from the scheme matrix.  
Assuming that the reference sequence is represented by \(x_{0}=\{x_{0}\,(j)\mid j=1,\,2,\ldots,\,n\}\) and the desired comparison sequence is shown by \(x_{i}=\{x_{i}\,(j)\mid j=1,\,2,\ldots,\,n.\,\,i=1,\,2,\,\ldots,\,m\}\), where \(m\) represents the number of preset schemes, and \(n\) represents the number of considered indicators, the same as \(m\) and \(n\) shown in Equation (7).  
The grey correlation coefficient [30; 31] between the comparative sequence and the reference sequence essentially reflects the disparity in their geometries, which can be quantified in terms of the magnitude of their differences. The grey correlation coefficient \(\zeta\) (\(j\)) of sequence \(x_{i}\) with respect to the reference sequence \(x_{0}\) on the indicator \(j\) is calculated by Equation (15).  
\[\zeta_{i}(j)=\frac{\min_{i}\min_{j}\lvert x_{0}(j)-x_{i}(j)\rvert+\rho\max_{i }\max_{j}\lvert x_{0}(j)-x_{i}(j)\rvert}{\lvert x_{0}(j)-x_{i}(j)\rvert+\rho \max_{i}\max_{j}\lvert x_{0}(j)-x_{i}(j)\rvert} \tag{15}\]  
where, \(\rho\) is the resolution coefficient which ranges from 0 to 1, normally takes 0.5 here.
## 4 Results and Discussion
### Lstm--Word2 Vec/GloVe Embeddings  
Our approach employs LSTM networks alongside Word2 Vec and GloVe embeddings for spam classification. The text data are initially preprocessed via tokenization and padding to generate fixed-length input sequences. For instance, the Word2 Vec embedder, sourced from the Gensim library, was used to transform the processed text into embeddings, which were then inputted into the LSTM model for spam classification. Algorithm 1 presents the overall process for training LSTM models with Word2 Vec and GloVe embeddings on the Twitter and YouTube datasets for spam classification. After the dataset is loaded and preprocessed by converting text to lowercase, removing URLs/mentions/hashtags/numbers, and trimming extra whitespace, the data is then converted to binary labels for spam/nonspam and balanced to ensure equal representation of both classes. The dataset is split into training and testing sets. For Word2 Vec/GloVe, an embedding model is trained only on the training data to learn vector representations of words. The maximum text sequence length is determined to enable padding sequences to a fixed length. Helper functions are defined to convert text to corresponding index  
Figure 7: Performance metrics of embedding Word2 Vec and Glove. sequences based on the trained embedding model and pad them to the max length. This numerical conversion of text enables feeding as input to the LSTM models. An embedding matrix is constructed from the learned vectors. The LSTM architecture is defined using this matrix to represent text input. The model is compiled, trained on the training set, and finally evaluated on the testing set. Learning distributed embeddings through predicting words in context encodes meanings based on similarity. GloVe enhances this by using log-bilinear regression modeling on word-context co-occurrence statistics. Through the pretrained vectors capture detailed contextual meanings, enabling the downstream LSTM models to process text sequences as real-valued vectors instead of sparse one-hot encoded inputs. This incorporation of modern word embedding techniques like Word2 Vec and GloVe enriches the textual feature representation fed into the LSTM models.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### LV and MV Monitoring and Management
#### 4.7.5. Bidelek  
The Bidelek project, realised in Spain and is led by the Spanish Iberdrola and the Basque Energy Agency, has to do with monitoring and automation of the MV network and the deployment of future substations. The project does not only include the implementation of smart meters, but it is also planned to configure 1100 transformer stations with various services, like remote management (250), remote management and supervision (700) and remote management plus automation and supervision (165) [122].
## 1 Introduction
A real-time communication framework based on wireless sensor networks (WSNs) and cellular for transmission line monitoring has been proposed in several works [12; 13; 14; 15; 16]. These works focused on network architectures [12; 13; 14] and optimization models [15; 16] in order to realize reasonable deployment and minimize costs for monitoring application. This application was established for the purpose of transmitting an enormous of information gathered from every sensor node and conquering the bottleneck of packet collision near substations, which differed from end-to-end information transmission in protection. Consequently,  
Figure 1: Functional setup of current differential protection. [PERSON] and [PERSON] [17] developed a laboratory model using peer-to-peer Wi-Fi communication protocols for differential protection by only two wireless access nodes at both ends of the transmission line. However, affected by factors such as wireless transmission distance and survivability of wireless relaying nodes in extreme environments, the reliability and time-delay performance of multiple-hop manners should be taken into account, which essentially concerns the node arrangement and routing protocols. Since the transmission lines are set up along limited and longitudinal ways, the WSN nodes are deployed along elongated geographic areas, and thus a linear network is formed [18]. Due to the specific features of this kind of network, the following issues for routing protocols can be concluded:  
1. Complicated environment factors may influence the connectivity of network. When the route reply is missing due to poor link quality or node damage, the routing processes will initialize in succession, which is time-consuming and unfavorable for delay constraint. 2. Conventional routing protocols consider routes on a two-dimensional plane, while the protective data only needs directional propagation in one dimension. 3. The WSN nodes of RCCs are deployed fixedly and almost lined up straight; but the WSN routing protocol has considered mobile ad hoc situations, which need requirement-based improvement. To handle the aforementioned application requirements, a novel scheme utilizing WSN-based monitoring systems for communication channel reconstruction is proposed in this work. More specifically, the application scope of the RCC is presented, followed by the recommended structure of a WSN-based monitoring system. For reliable system arrangement, theoretical planning of the WSN for differential protection is formulated for reliability and cost optimization while satisfying connectivity. Additionally, considering challenges such as data preparation, capacity analysis and time synchronization, the overall design of RCC that addresses was presented. Furthermore, an improved routing protocol is proposed in Section 4 to guarantee end-to-end delay. Section 5 is devoted to experiments and performance evaluation followed by conclusions in Section 6.
## 3 Results
### Production of Reactive Oxygen Species  
#### 3.5.1 Production of Singlet Oxygen  
In our previous research, we established that different types of carbon dots (carbon quantum dots (CQDs), graphene quantum dots (GQDs) and doped carbon quantum dots (DCQDs)) produced reactive oxygen species under blue light irradiation [45]. Recently, we found for the first time that CPDs produced singlet oxygen and superoxide anions under blue light irradiation at 470 nm [18]. Until now, there have been two proposed mechanisms of ROS production: [PERSON] et al. claimed that GQDs produce singlet oxygen through energy transfer to molecular oxygen [46], whereas [PERSON] et al. found that besides singlet oxygen, superoxide anion could be involved in the production of ROS [47]. Thus, electron transfer is an intermediate step for the generation of singlet oxygen of GQDs under blue light irradiation.  
Figure 4 shows the production of singlet oxygen of CPD/PU (Figure 4a) and C\({}_{60}\)/PU (Figure 4b) composite films and the production rate of singlet oxygens of CPD/PU and C\({}_{60}\)/PU samples (Figure 4c). Figure 1a shows that the intensity of the absorption peak of SOS at 533 nm increased with blue light irradiation time. A similar phenomenon occurred for C\({}_{60}\)/PU samples (Figure 4b). But from Figure 4c, we can observe that the production rate of singlet oxygen is higher for the CPD/PU samples.  
Figure 4d,e represent the decay of the absorption peak of ABDA molecules in the presence of blue-light-irradiated CPD/PU and C\({}_{60}\)/PU samples. From this figure, we can observe the photobleaching of ABDA for different time intervals (60, 120 and 180 min), respectively. As can be seen from Figure 4d,e, the photobleaching of ABDA is much higher for the CPDs/PU samples. In the case of the CPD/PU sample, the decolorization of ABDA was complete after 180 min of blue light irradiation. At the same time, the ABDA decolorization, i.e., the ABDA absorbance intensity, decreased in the presence of C\({}_{60}\)/PU composite films by only 15% after 180 min of blue light irradiation.