esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Neural Machine Translation
#### 2.1.1 Formulating the NMT Task  
In the MT task, the language model (LM) can actually give the most important information: the emergence probability of a particular word (or phrase) that is conditioned on previous words. Thus, the key to improve the translation performance is to build a better language model. The NMT task is designed as an end-to-end learning task. It directly processes a source sequence to a target sequence. The learning objective is to find the correct target sequence given the source sequence, which can be seen as a high dimensional classification problem that tries to map the two sentences in the semantic space.  
Given a parallel corpus \(C\) having a set of parallel sentence pairs \((x,y)\), the training objective is to maximize the likelihood \(L\) in terms of \(\theta\), which is shown in Equation (1) \(L\):  
\[L_{\theta}=\sum_{(x,y)\in C}\log_{p}(y|x;\theta), \tag{1}\]  
where \(x=x_{1},\ldots,x_{n}\) denotes an input sentence, \(y=y_{1},\ldots,y_{n}\) represents its translation, and \(\theta\) is a set of parameters to be learned. Given the source sentence, the probability of a target sentence is calculated as shown in Equation (2):  
\[p(y|x;\theta)=\prod_{j=1}^{m}p(y_{j}|y_{<j},x;\theta) \tag{2}\]  
where \(m\) is the number of words in \(y\), \(y_{j}\) is the current generated word, and \(y_{<j}\) are the previously generated words. At the inference time, beam search is typically used to find the translation that maximizes this probability.
## 3 New Generation Sensor Web Enablement
### 3.3. Evolvement of SWE Interface Model
#### 3.3.1 Common SWE Service Aspects  
A major change in the design of the new generation SWE is a common model for SWE services. Many aspects of SWE service specifications can be commonly defined. This includes service operations and exceptions, among others. To harmonize these aspects the SWE Service Model (SWES) standard [51] has been developed. With the intention that SWE service specifications reference this standard, interoperability is improved through more consistent specifications and reuse of common types. So far, SPS 2.0 and SOS 2.0 are based on this common SWE service model. The main advancements introduced through this new specification are outlined in Table 2 and detailed in the following.  
Figure 4: The new generation of the SWE interface model. _Green boxes_: specifications approved as standards (or in standardization process); _Red boxes_: best practice specifications which have not been approved as standard; _Beige boxes_: discussion papers; _Solid arrow_: “evolvement to”; _Dashed arrow_: “dependent on”.  
Common Capabilities Content Model  
Both SPS 1.0 and SOS 1.0 used the _offering_ concept to structure their service metadata. Both services had their own way of realizing the offering in their conceptual model. Despite some commonalities regarding the requirement to list the sensor identifier(s) and observed/observable properties in their offerings, SOS 1.0 and SPS 1.0 each defined their own data types to convey the information to clients. The SWE Service Model defines an _abstract offering_ type that provides the information. It is reused in the conceptual models of SOS 2.0 and SPS 2.0. SWES thereby also defines a mechanism with which the amount of redundant information and therefore the size of a service's capabilities document can be reduced significantly. This mechanism is called _property-inheritance_. It has been derived from a similar approach that OGC's Web Mapping Service uses to reduce the size of its capabilities document.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 5 Current Advances in Biotechnological Remediation
### Advances in Phytoremediation  
1. Microbial Inoculation: Bioaugmentation of plant roots with beneficial microbes such as _Bacillus_ and _Rhizobium_ enhances degradation capacity and stress resilience [108]. * Nanomaterial-Assisted Phytoremediation: Nanomaterials like metal oxide nanoparticles, biochar-based composites and nanoscale zero-valent iron have emerged as powerful tools in phytoremediation. They help in phytoremediation by removing pollutants, improving pollutant uptake, facilitating enzyme activity and stimulating root exudates that attract pollutant-degrading bacteria [109]. For example, nanoparticles can bind to pollutants, making them easier for plants to absorb. They can also deliver pollutants to specific parts of the plant such as the roots or leaves. However, the use of nanotechnology in phytoremediation is still in its early stages but it has the potential to make it more effective and efficient [110]. * Genetic Improvements: Genetic modifications have been used to enhance traits such as heavy metal tolerance, root depth, exudate diversity, and enzymatic degradation capacity [111]. An example is the use of engineered Indian mustard (_Brassica junca_) to tolerate lead uptake and clean contaminated soil. However, the release of genetically modified plants into natural environments raises ethical and regulatory concerns [110]. * Development and Enhancement of Hyperaccumulator Plants: Hyperaccumulator plants have the natural ability to absorb and accumulate high levels of heavy metals in their tissues; hence, they are ideal for phytoremediation, and they can be used for the removal of heavy metals from contaminated soil and water bodies. Selective breeding, tissue culture and genetic modification have been employed by researchers to enhance or improve hyperaccumulator plant abilities for practical application.  
However, their use is only applicable in specific soil or waters and may pose environmental risks including the reintroduction of metals into food chains through plant biomass [110].
* Artificial Intelligence and Smart Monitoring: The application of artificial intelligence (AI) and remote sensing technologies offers a data-driven approach to phytoremediation management. AI algorithms can model contaminant dispersion, forecast plant growth, and optimize species selection, while drones and satellite imagery allow real-time monitoring of pollutant reduction, biomass accumulation, and vegetation health across large-scale remediation sites [110].  
Despite these advances, phytoremediation research still focuses more heavily on inorganic pollutants and freshwater systems, with limited studies targeting organic pollutants in saline marine contexts. Nonetheless, the potential of coastal plants remains untapped and is a promising direction for future work [112; 113]. Table 3 provides a comparative overview of key research studies focusing on how various biotechnological applications are used to remediate organic pollutants.
# Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games
## 3 Methods  
### Definition  
Let \(\mathcal{S}\) denote the set of state space, which includes all possible states that an agent can observe. Let \(\mathcal{A}\) represent the set of action space, representing all possible actions the agent can perform. In continuous-action control tasks, \(\mathcal{A}\) typically refers to a continuous vector space. Let \(\mu_{\theta}(s)\) denote the actor network with parameters \(\theta\), which maps a state \(s\in S\) to an action \(a\in\mathcal{A}\). Similarly, \(\mu_{\theta^{\prime}}(s)\) represents the target actor network with parameters \(\theta^{\prime}\). The target actor shares the same architecture as the main actor but is update via soft updates or periodic copying to improve training stability. Let \(Q^{\mu}_{\omega}(s,\ a)\) denote the critic network with parameter \(\omega\), which estimates the Q-value for a state-action pair \((s,\ a)\). The critic takes both the current state \(s\) and action \(a\) as inputs and outputs an estimate value. \(Q^{\mu}_{\omega^{\prime}}(s,\ a)\) denotes the target critic network with parameters \(\omega^{\prime}\), which has the same structure as \(Q^{\mu}_{\omega}\) and is update via soft or periodic updates. \(M\) represents the replay buffer. This buffer is use for sampling experiences during training to reduce temporal correlation and stabilize updates. Let \(\gamma\in(0,\ 1]\) denote the discount factor, which controls the trade-off between immediate and future rewards. A higher \(\gamma\) places more weight on long-term rewards. Let \(N\) represent the noise model use to promote exploration. Noise is added to the actions selected by the actor network, typically using Ornstein-Uhlenbeck or Gaussian processes during training. Let \(\tau\in(0,\ 1]\) denote the target update coefficient for soft updating the target networks. The soft update rule is Equation (1):  
\[\left\{\begin{array}{l}\theta^{\prime}\leftarrow\tau\theta+(1-\tau)\theta^{ \prime}\\ \omega^{\prime}\leftarrow\tau\omega+(1-\tau)\omega^{\prime}\end{array}\right. \tag{1}\]when \(\tau\) is very small, the target network updates relatively slowly; alternatively, a \"hard\" update can be used, including every fixed number of steps \(\theta^{\prime}\) and steps \(\omega^{\prime}\). Finally, the target \(Q\)-value \(y_{i}\) used for critic updates is computed as Equation (2):  
\[y_{i}=r_{i}+\gamma Q_{\omega^{\prime}}^{\eta_{\theta^{\prime}}}(s_{i+1},\ \mu_{ \theta^{\prime}}(s_{i+1})). \tag{2}\]
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 2 Technologies for Industrial IoT  
Reliable, controlled operation, and use of standardized technologies are key to the adoption of WSNs in industrial applications [32]. In best-effort type networks, all devices in the network obtain an unspecified data rate and delivery time, depending on the traffic load [33]. As a result, data is delivered without any quality of service guarantees. However, in general, industrial process automation applications have stringent latency requirements. For instance, monitoring applications should guarantee an average latency of 100 ms; control applications should guarantee 10 ms to 100 ms latency; and, lastly, safety applications should guarantee a maximum latency of 10 ms [14]. To meet these requirements, IWSNs may have to pre-allocate network bandwidth and physical resources, thus avoiding statistical effects that lead to insufficient bandwidth, uncontrolled jitter, and congestion losses. With these constraints in mind, this section surveys the main IWSN technologies and standards (namely, IEEE 802.15.4, ZigBeePRO WirelessHART, ISA100.11a and WIA-PA), highlighting their main features, monitoring functionality, and applicable management protocols.
# Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning
## 2 Related Works  
In recent years, the rapid development of artificial intelligence [8] technology, coupled with traffic road perception technology [9], has become quite mature. Therefore, the application of artificial intelligence and transportation can develop rapidly. Traffic road perception devices are capable of collecting various data in traffic, including traffic flow, steering ratios, number of lanes, road types and vehicle types. Machine learning is characterized by the ability to use collected data to train high-performance models. One of the most commonly-used ones is deep learning in machine learning, which can be classified into several categories, such as supervised learning, unsupervised learning and reinforcement learning, as shown in Figure 1. It should be noted that machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning.  
Figure 1: The classification of artificial intelligence related technologies.  
The reinforcement learning method, one of the machine learning methods, has achieved many outstanding results in interactive games. One of the most famous examples is AlphaGo [10], using deep learning combined with reinforcement learning to enable machines to transcend humans in the field of Go. This inspires the design of traffic light cycle configuration using the reinforcement learning schemes, such as Q-learning, regarding the interaction of traffic lights and machines. Q-learning is a model-free reinforcement learning algorithm, so it can handle problems using stochastic (random) transitions and rewards. That is, it can learn a policy and assign an agent to take actions under certain conditions, without requiring pre-defined adaptations. In this paper, we design a modified Q-Learning mechanism which optimizes traffic light period configuration to find the minimum average vehicle delay time (AVDT).
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 3 The Proposed EPT-VMD-TPA-TCN Model and the Restructured EPT-VMD-FE-TPA-TCN Model
### EPT-VMD Decomposition Layer
#### 3.2.2 Vmd  
In the experiment, in order to reduce the volatility and randomness of the \(Re(t)\) component, VMD is used to decompose it so as to obtain several relatively smooth IMF components. According to Section 2.2, the number of IMFs, i.e., the value of \(k\), must be determined before using VMD to decompose \(Re(t)\). If the value of \(k\) is too small, the decomposition result is inadequate and increases the error of prediction. If the value of \(k\) is too large, the decomposition is overdone, thus increasing the overhead of the  
Figure 11: Area 2 EPT decomposition results.  
Figure 10: Area 1 EPT decomposition results.  
decomposition. In this paper, we will use the method in reference [47] to determine the number \(k\) of the decomposed modal functions using the decomposed residuals (\(R_{res}\)), as in Equation (31):  
\[R_{res}=\frac{1}{M}\sum_{t=1}^{M}\left|\frac{Tr(t)-\sum_{i=1}^{k}u_{i}(t)}{Tr(t)}\right| \tag{31}\]  
where \(Re(t)\) is the residual fluctuation component, \(u_{i}(t)\) is the IMF subsequence obtained by VMD decomposition, the number of samples is \(M\), and the number of IMF subsequences is \(k\).  
In this paper, when the value of \(R_{res}\) is less than 1% and there is no obvious downward trend, the value of \(k\) is the appropriate number of decompositions for VMD. The results of the \(\text{Re}(t)\) decomposition using different values of \(k\) are shown in Table 3.  
As can be seen in Table 3, in Area 1, when the value of \(k\) is 11, \(R_{res}=0.00338015<0.001\), and when \(k>11\), the decreasing trend of \(R_{res}\) tends to be stable. In Area 2, when the value of \(k\) is 11, \(R_{res}=0.00198062<0.001\), and when \(k>11\), the decreasing trend of \(R_{res}\) tends to be stable. In this paper, the VMDIn this paper, the fuzzy entropy values are calculated by taking the sequence length N as the total sample length, the phase space dimension \(m=2\), the similarity tolerance limit \(r=0.3S_{d}\), and \(n=2\). The fuzzy entropy values of each IMF component are shown in Figure 14.  
In Figure 14, we can see that the fuzzy entropy of IMF components varies widely but there are some components with similar fuzzy entropy values, which indicates that there is some similarity among them. The fuzzy entropy of IMF1 is significantly lower than other components, while the fuzzy entropy of IMF7 is the highest. The IMF components with similar fuzzy entropy values are reorganized, as shown in Table 4.  
Figure 12: Area 1 VMD results.  
Figure 13: Area 2 VMD results.  
Figure 14: Fuzzy entropy of each IMF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Recombinant Sequences** & **FE-IMF1** & **FE-IMF2** & **FE-IMF3** & **FE-IMF4** & **FE-IMF5** & **FE-IMF6** \\ \hline Components & IMF1 & IMF2 & IMF3 + IMF10 & IMF4 + IMF7 & IMF5 + IMF6 & IMF8 + IMF9 + IMF11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FE-IMF composition.  
Figure 15: FE reorganization results for Area 1.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Validation  
The effectiveness of the downscaling method is measured by using the following metrics: root mean square error (RMSE), BIAS (Bias) and Correlation Coefficient (CC). CC ranges from 0 to 1, with an optimal value of 1, which indicates the strength of the correlation between station observations and downscaled precipitation values. Bias describes the degree to which downscale precipitation estimates are over or underestimated, with an optimal value of 0. RMSE represents the size of the error estimate, and the smaller the RMSE, the more reliable the downscaled precipitation. Its mathematical expression is as follows:  
\[\rm CC= \frac{\sum_{\rm i=1}^{N}\left(O_{\rm i}-\bar{O}_{\rm i}\right)\left(P_ {\rm i}-\bar{P}_{\rm i}\right)}{\sqrt{\sum_{\rm i=1}^{N}\left(O_{\rm i}-\bar{O }_{\rm i}\right)\left(P_{\rm i}-\bar{P}_{\rm i}\right)}} \tag{24}\] \[\rm BIAS= \frac{\sum_{\rm i}^{N}P_{\rm i}}{\sum_{\rm i}^{N}O_{\rm i}}-1\] (25) \[\rm RMSE= \sqrt{\frac{\sum_{\rm i=1}^{N}\left(P_{\rm i}-M_{\rm i}\right)^{2 }}{N}} \tag{26}\]  
where \(\rm O_{\rm i}\) (mm) and \(\rm P_{\rm i}\) (mm) are the observed precipitation and reduced precipitation at the location of the i-th weather station, respectively; \(\bar{O}\) (mm) and \(\bar{P}\) (mm) represent the mean values of \(\rm O_{\rm i}\) and \(\rm P_{\rm i}\), respectively; N is the total number of values in the corresponding dataset.
## 3 Results and Analysis  
To validate the proposed weighting model and the single-frequency cycle slip detection algorithm considering LEO onboard receiver clock jumps, the Luojia-1A onboard GNSS data description and the basic processing configuration are discussed first. The GPS/BDS single-frequency KOD considering the above model and algorithm strategy is then analyzed and validated.
## 3 Search Results and Classification
### Classification of Articles  
Figure 2 shows the number of scientific articles published per year and country related to the development of industrialized construction systems (prefabricated and modular). There has been a noticeable increase in research on prefabricated and modular elements starting in 2019. Between 2004 and 2018, the publication of articles on these topics fluctuated between 1 and 7, with an average of 2.5 publications per year. However, from 2019 to 2023, the average rose to 14 publications per year, with 2023 having the highest number of publications at 38 articles. This represents a 253% increase in publications in 2023 compared to 2022 (the second-highest year for publications). Additionally, 42% of the analyzed articles were published in 2023.  
Although the concept of industrialized construction was first used in the United Kingdom [6], Figure 2 shows that most of the scientific research has been conducted in China (24%). This can be attributed to the Chinese government's various measures to encourage and promote the industrialization of construction and the application of modern methods in their projects, which has also increased the associated research [94]. An example of this is the construction of two hospital buildings in less than two weeks [27]. Additionally, in 2018, the national area of prefabricated construction was 290 million square meters, meaning that 14% of all new buildings were prefabricated [95]. Furthermore, the figure shows that the countries publishing work on industrialized construction systems have become more diverse. In 2004, only China recorded such work, while in 2014 and 2015, only Italy did. From 2004 to 2019, aside from China and Italy, other countries, like the United Kingdom and Chile, also became involved. Since 2020, other countries have joined the research in this field, leading to a much more heterogeneous participation. The trend is expected to continue with an increase in publications from countries like Australia, the United States, and Portugal. It is important to mention that this analysis is directly related to scientific publications in the area, according to database queries described in the method, and, therefore, the development at the business level, which is not necessarily reported in this type of literature, is outside the scope of this analysis.
## 3 Results and Discussion
### Toxicity Tests in Laboratory
#### 3.2.3 Paracentrotus lividus Toxicity Tests  
_P. lividus_ Fertilization Bioassay  
The percentage of successfully fertilized _P. lividus_ eggs in each sampling station is shown in Figure 2c. A gradient of toxicity was observed between the downstream station (SE) with the lowest values of fertilization success and the upstream station AR, which showed the highest values (excluding the control). Significant differences (\(p<0.05\)) were observed in all the stations compared with the control (RSP). The control station showed the highest success (more than 90%), confirming the validity of the test.  
_P. lividus_ Embryo Development Test  
The success percentage of normally-developed _P. lividus_ larvae at each station are shown in Figure 2d. An increased number of normal _P. lividus_ larvae were observed in the Guadalquivir River stations (AR, AL and SE) and ranged from 12% (AR) to 25% (SE). These results indicated the presence of adverse effects (percentage of normal larvae  
Figure 2: (**a**) _A. hrevicornis_ mortality (%) after 10 d exposure. (**b**) IC\({}_{50}\) results (mg L\({}^{-1}\) dw sediment) obtained from the BSPT Microto\({}^{\circledR}\) samples. (**c**) Results of the _P. lividus_ fertilization bioassay test: average percentage of fertilized eggs after exposure of the sperm to the sediment samples under laboratory conditions. (**d**) Results of the _P. lividus_ embryo-larval bioassay test: average percentage of normal development of larvae after exposure of the fertilized eggs to the sediment samples under laboratory conditions. Asterisk (*) indicates significant differences (\(p<0.05\)) between samples and the control (RSP).  
development \(<\) 80%) and showed significant differences (_p_ \(<\) 0.05) with the control station (RSP), which showed more than 90% normal larvae development. The results confirm the validity of the test with the RSP station as a control.  
_Tubifex tubifex_ Chronic Bioassay  
In this bioassay the results of the control station (RSP) also confirmed the validity of the bioassay according to the [33] criteria. Dissolved oxygen was maintained above 60% (except twice: once in the control at 34%, and again in AR at 59%), unionized ammonia (NH\({}_{3}\)) was maintained above 1 mg L\({}^{-1}\), and pH ranged from 6-8.22 (BAR), 7.59-8.59 (AR), 7.98-8.60 (AL), and 7.85-8.62 (SE).  
Figure 3 represents the results for growth (TGR) and reproduction (TYG). A decreasing gradient of growth was shown downstream of the Guadalquivir River. TGR was significantly reduced (_p_ \(<\) 0.05) in sediments from AL and SE (SE being very close to zero). The TYG was significantly reduced (_p_ \(<\) 0.05) in the sediments from AR and AL, comparing with the control BAR. Nevertheless, no spatial gradient was shown. Regarding mortality, no significant differences in mortality were observed with respect to the control since mortality was not found in any station.