esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Comparison of Different Auxiliary Input Types  
Table 5 compares the influence of different input auxiliary sequence generation methods. Human trans means to directly use the training target sequence that does not contain  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{\(i\)\(h_{schwa}\)} & \multicolumn{2}{c}{\(ah_{schwa}\)} & **PCR** & **Time** \\ \cline{2-5}  & **Pre/Rec** & **F1** & **Pre/Rec** & **F1** & **(\%)** & **(s)** \\ \hline original-streams + G2P & 0.64/0.67 & 0.66 & 0.62/0.55 & 0.58 & 81.8 & 244 \\ original-streams + char & 0.58/0.72 & 0.64 & 0.60/0.57 & 0.59 & 82.9 & 232 \\ multi-streams + G2P & 0.55/0.63 & 0.59 & 0.55/0.52 & 0.54 & 80.6 & 248 \\ multi-streams + char & 0.58/0.68 & 0.63 & 0.58/0.53 & 0.56 & 86.3 & 241 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of original streams and expanded multi-streams.  
Figure 3: The changing weight of encoder-level attention.  
schwa labels after preprocessing. Compared with Table 3 in Section 4.1, it suggests that both automatic methods are effective. Moreover, the method that uses the letter sequence and the G2P method achieve a comparable performance. As for the overall phoneme recognition performance (PCR), the former method (82.9%) is higher than the latter. The reason could be that the reference text letter sequence is closer to the phoneme recognition target than the G2P generated sequence. The G2P method also did not show a great performance improvement after the introduction of stress information. A possible explanation for this is that the gap between the automatically generated sequence and the actual pronunciation introduces interference to the model.  
Experiments using human transcription input are expected to explore this issue further. As shown in Table 5, a significant performance improvement can be observed when using human transcripts as an auxiliary. In other words, accurate context is constructive for vowel reduction detection. This conclusion is consistent with previous research conclusions in phonetics that the phenomenon of vowel reduction is affected by context.
## 6 Classification of Attacks in IoMT  
IoMT applications rely on a wide range of technologies with different types of embedded features, each of which has its own set of security vulnerabilities [31]. Hence, many widely used IoT protocols now lack fundamental security procedures. There are various types of attacks that not only compromise the patient's privacy, but can also cause irreparable financial and reputational damage [41]. As a recorded case, in October 2016, an IoT botnet conducted a DDoS attack on a DNS (Domain Name System) service provider. Mirai was the malware employed by the botnet. Large parts of the Internet, including Twitter, the Guardian, Netflix, Reddit, and CNN, were shut down as a result of the latter [44]. According to a recent Compartech report, these attacks have cost the healthcare industry more than $160 million since 2016 [44, 45]. It was also alleged that attacks on brain implants result in death [45]. The number of ransomware attacks against healthcare organizations increased by 94 percent between 2021 and 2022 according to a report from cybersecurity firm Sophos. More than two-thirds of U.S. healthcare organizations reported experiencing a ransomware attack in 2021; according to the study, this was up from 34 percent in 2020 [46]. Table 3 displays the attacks classified according to the IoMT's targeted layer and their influence on the system's security requirements. As a result of the heightened danger of a cyber-attack on the IoMT system, the creation and development of robust security solutions has become necessary. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type Layer** & **Attack** & **Brief Description** & **Effects** & **References** \\ \hline \multirow{8}{*}{**Perception**} & **Side-channel attack** & The information is obtained from the side channels of the encryption device. & Confidentiality, Integrity & [47, 48] \\ \cline{2-4}  & **Tampering devices** & The IoMT device is physically accessed to modify the data (modification in a device using RFID or communication link). & Confidentiality, Integrity & [49] \\ \cline{2-4}  & & An attacker might exploit data obtained through a successful side-channel attack or replicate data from a previously used tag. The cloned tag, for example, might be used to gain access to an unlawful facility or data, such as medical data (Using simple technologies, attackers may clone RFIDs). & Confidentiality, Authorization, Integrity & [50] \\ \cline{2-4}  & & This form of attack invades patients’ privacy. Attackers might obtain access to patients’ whereabouts or fake GPS data by using unsecured equipment. Other sensors, such as those used in fall detection, wheelchair management, and remote monitoring systems, can also be utilized to divulge sensitive data about patients. & Confidentiality, Authorization, Integrity, Privacy & [51] \\ \cline{2-4}  & & & \\ \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: IoMT potential attacks for each layer and their influence on the system’s security requirements.
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
###### Abstract  
In this paper, a stochastic approach is combined with field theory and circuit methods to study how the geometrical and electrical properties of holidays (defects or pores in the insulating coating) in a metallic pipeline influence the probability of exceeding the current density limit for corrosion. Three-dimensional FEM simulations are conducted to assess the influence of the shape and electrical resistivity of the pore on the computed spread resistance value. The obtained results are then used to evaluate the probability of exceeding a given current density value for different sizes of pore and soil resistivities. Finally, a case of 50 Hz interference along a pipeline-transmission line routing is examined. The probabilistic approach presented in this paper allows the pipeline sections more subjected to the induced AC corrosion risk to be identified to be used as an auxiliary tool for adopting preventive protection countermeasures. Lastly, unlike most papers devoted to assessing electromagnetic interference on pipelines, the present work uses a probabilistic rather than a deterministic approach, representing its main novelty aspect.  
Keywords:50-60 Hz electromagnetic interference; AC corrosion; FEM; probabilistic methods; pipelines integrity +
Footnote †: journal: Applied sciences
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 4 Localization
### Particle Filter  
In our system, the sampling rate of the PIR sensors is 15 Hz. The data stream will be segmented into data frames before further processing, as shown in Figure 7. Each data frame is 30 sampling points, which is about two seconds. The overlap between two successive data frames is 15 sampling points. In each data frame, the localization algorithm is applied to consecutive windows. The size of the window is 15 sampling points. As a result, there will be a maximum of 16 localization results in each data frame.  
Assume that there are \(L\) localization results in the \(t\)th data frame, denoted as \(\mathbf{y}_{t}^{l}=(x_{t}^{l},y_{t}^{l})\), where \(l=1,...,L\). The previous location of the human target is denoted as \(\mathbf{Z}_{t-1}=(x_{t-1},y_{y-1})\). Based on the idea of the particle filter (PF) [29], the weight of the \(l\)th sample can be defined as:  
\[w_{t}^{l}=p(\mathbf{y}_{t}^{l}|\mathbf{Z}_{t-1}) \tag{6}\]  
In our system model, we assume that the probability of current location \(\mathbf{y}_{t}^{l}\) given the previous location \(\mathbf{Z}_{t-1}\) is two-dimensional Gaussian, then Equation (6) can be rewritten as:  
\[w_{t}^{l}=N(\mathbf{y}_{t}^{l}|\mathbf{Z}_{t-1},\sigma) \tag{7}\]  
where \(\sigma\) is the variance of the observation model. \(\gamma\) is the threshold to validate \(\mathbf{y}_{t}^{l}\). If \(w_{t}^{l}<\gamma\), \(\mathbf{y}_{t}^{l}\) will be discarded. If \(w_{t}^{l}\) is too small, this means that the probability of \(\mathbf{y}_{t}^{l}\) generated by a false alarm is high. After discarding invalid localization results, \(L\leq 16\).  
Assume there are total \(L\) valid localization results in the same data frame; the weight of each result should be normalized as follows:  
\[\widehat{w}_{t}^{l}=\frac{w_{t}^{l}}{\sum_{l^{\prime}=1}^{L}w_{t}^{l^{\prime}}} \tag{8}\]  
Thus, the current location of the human target can be represented as:  
\[\mathbf{Z}_{t}=\sum_{l=1}^{L}\mathbf{y}_{t}^{l}\widehat{w}_{t}^{l} \tag{9}\]  
where \(\widehat{w}_{t}^{l}\) is the normalized weight of \(\mathbf{y}_{t}^{l}\).  
Figure 9: Data fusion strategy of adjacent sensor nodes.
# MAPSKEW: Metaheuristic Approaches for Partitioning Skew in MapReduce
## 1 Introduction
Recently, some approaches [2; 10; 11; 12] have been proposed by using prediction techniques to find the partition size even before the completion of the map tasks. These solutions dynamically allocate resources for reduce tasks according to their estimated value. Some of them adopt a linear regression-based prediction [2; 12] and can achieve good accuracy only with the completion of a few mappers. However, predictions are inaccurate due to the data imbalance in the map phase. If the skew data occur only in the end of the map phase, or much later in the training model, the accuracy model will be affected, limiting their applicability. Different from the previous work, we adopt metaheuristics to address the problem of how to efficiently partition the intermediate keys in the reduce phase of MapReduce framework, considering data skew. Our strategy aims to minimize an imbalance function that measures the load allocated to each machine by using three different metaheuristics: Simulated Annealing, Local Beam Search and Stochastic Beam Search. With metaheuristics, it is possible to guarantee fair distribution of the reducers' inputs without prior knowledge about the data behavior and without explore the entire solution space. Thus, our strategy, called MAPSKEW, finds solutions close to the global optimum while having relatively small complexity. Additionally, it allows the user to set the tradeoff between performance and quality, based on metaheuristic parameters. The main contributions of the paper are summarized below:  
1. A novel approach to balance the workload among the reduce tasks, since the partitioning problem is NP-hard and our proposal finds, in polynomial time, good partitionings online, i.e., without needing to stop the execution of any phase of MapReduce. A new partitioning method for general user-defined MapReduce programs. The method is highly adaptative since it uses metaheuristics, allowing the system manager to choose between performance and quality of the solution by adjusting hyperparameters. * An implementation of the proposed method in Hadoop 2.7.2 Yarn. Additionally, we have conducted several experiments with a real dataset, comparing the effects of different metaheuristics with related work and the native partitioning function of the Hadoop MapReduce on the Partitioning Skew.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network
### Power Consumption  
Since end devices are powered only by batteries, power consumption has to be as low as possible. In order to estimate its power consumption in the different modes (see Section 3.2.3), one end device with a 10-minute sampling period (the fastest possible) powered by a 2200-mAh battery was employed. The results are shown in Table 6.  
Basing on these results and assuming an 80% maximum discharge level for the battery, a battery life of 2.14 years was theoretically estimated for the end devices when using a 2200-mAh battery.
## 3 Results and Discussion
### Influence of Hen Diet on Nutritional Profile and Physical Characteristics of Eggs  
Hens fed with supplemented diet adapted well to the new feed, as proved by the zootechnical performance evaluation. Eggs were analyzed every week along the 28 days trial and compared with control samples (eggs from hens fed with standard diet). No significant differences were observed in the nutritional composition between samples throughout the assay (\(p>\) 0.05), with about 12% of protein, 10% of lipids and less than 1% of ash (Table 2), in agreement with the composition usually reported for whole eggs [31]. \begin{table}
\begin{tabular}{c c c c} \hline  & **Standard Feed** & **Supplemented Feed** & _N. oceanica_ \\ \hline Protein (\%) & 14.7 \(\pm\) 2.6 & 16.6 \(\pm\) 1.9 & 44.0 \(\pm\) 0.7 \\ Lipids (\%) & 3.96 \(\pm\) 0.37 & 3.96 \(\pm\) 0.39 & 20.9 \(\pm\) 2.7 \\ Ash (\%) & 11.44 \(\pm\) 0.89 & 10.70 \(\pm\) 0.32 & 8.77 \(\pm\) 0.20 \\ Fatty acids (\(\mu\)g/mg) & & & \\ \hline C16:0 & 4.10 \(\pm\) 0.34 & 4.89 \(\pm\) 0.15 & 22.38 \(\pm\) 0.53 \\ C18:0 & 0.74 \(\pm\) 0.08 & 0.63 \(\pm\) 0.07 * & n. d. \\ C16:1 n-7 & n. d. & 1.67 \(\pm\) 0.32 & 37.15 \(\pm\) 0.99 \\ C18:1 n-9 & 11.99 \(\pm\) 0.65 & 10.06 \(\pm\) 0.10 * & n. d. \\ C18:2 n-6 & 29.2 \(\pm\) 1.1 & 26.1 \(\pm\) 1.1 * & n. d. \\ C18:3 n-3 (ALA) & 1.21 \(\pm\) 0.05 & 1.15 \(\pm\) 0.43 & n. d. \\ C20:4 n-6 & n. d. & n. d. & 10.35 \(\pm\) 0.58 \\ C20:5 n-3 (EPA) & n. d. & 0.95 \(\pm\) 0.21 & 21.40 \(\pm\) 0.64 \\ \hline \end{tabular} For each parameter (same row), * means statistically significant differences (\(p<\) 0.05) between feeds (N = 5). ALA—a-linolenic acid; EPA—eicosapentaenoic acid  
\end{table} 
Table 1: Nutritional composition and fatty acid profile of hen feed (standard and supplemented) and of the microalgae _N. oceanica_. However, a remarkable increase in DHA and EPA content was observed at the end of week 1, reaching values two-fold higher than the control, which allowed eggs classification as high in n-3 PUFA (Figure 1). These values remained constant throughout the experiment, attaining values of 5.2 \(\pm\) 1.2 for EPA and 105 \(\pm\) 18 mg/100 g for DHA at the end of week 4. Conversely, no significant differences were observed in ALA content (\(p\) > 0.05) which was slightly higher than 50 mg/100 g in all samples. A similar accumulation pattern was described in other studies that used microalgae as a source of n-3 PUFA for eggs enrichment. [PERSON] et al. [41] reported an increase in the values of EPA (to 2.3 \(\pm\) 0.6) and DHA (to 44.9 \(\pm\) 6.6 mg/egg) while ALA content remained constant (15.9 \(\pm\) 2.0 mg/egg) after a 28-day trial, using 5.0% of _N. gaditana_ in the supplemented feed. Moreover, the use of twice the concentration of microalgae in feed did not result in a proportional increase of n-3 PUFA in eggs. [PERSON] et al. [42] also observed a consistently increase in EPA and DHA when 20% of _N. oculata_ was added to different standard diets. Feed supplemented with 5% _N. ocaanica_ had high efficiency for n-3 PUFA eggs enrichment, ranging from 85% to 60% at the end of week 1 and week 4, respectively (Figure 2).
# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications  
The taxonomy of XAI techniques, as mentioned in [32; 33; 34; 35], follows four aspects: (1) scope or level explanation; (2) purpose explanation; (3) implement or usage explanation; (4) data types. First, both global and local explanations are included at the explanation level. The models' operational and decision-making processes and their ability to be articulated are generally emphasized by each explanation level. For many purposes, selecting the explanation level depends on specific cases or subgroups. The survey scope with the local explanation level is the main priority of this paper. Second, there are post hoc and intrinsic (ante hoc) approaches regarding XAI's purpose. The characteristics, decision-making procedures, and rules of the models, such as linear models, decision trees, Bayesian networks, etc., provide internal justifications for the model type. Meanwhile, post hoc reasoning holds that black-box models' innermost workings and decision-making procedures are revealed after training. Numerous post hoc XAI solutions have been created, because post hoc explainers include anything from black-box models to interpretable models. Third, usage or implementation includes both agnostic and specific models. Model-specific approaches use the model's characteristics and properties to make it [36] interpretable. Such approaches' strengths come at a cost, because applying them to different models is challenging. What gives them their power is their access to model internals such as weights or structures. Meanwhile, post hoc explanations can be provided after the model has been trained using model-agnostic techniques, which can be used for any ML model. Since these methods can only examine input-output pairings, they have the drawback of being unable to benefit from model internals. Fourth, from an XAI-based data perspective, a data type can be tabular, text, image, time series, or any other type. Most proposed explainable AI systems from industrial applications have been tested, including autonomous systems and robotics, energy and building management, security and privacy, environment, finance, industrial engineering, healthcare, and smart agriculture. The year-by-year distribution of publications on the local explanation of industrial AI applications from 2020 to March 2023 is depicted in Figure 2. According to the literature review, researchers increasingly use local XAI approaches in industrial applications. In particular, the links between XAI approaches and different industrial application areas are shown in Table 2.  
Figure 2: Year-wise distribution of publications on the local explanation of industrial AI applications.
# A Preliminary Study of Model-Generated Speech
## 3 Method
### Three Models  
#### 3.2.1 Attention-Based Sequence-to-Sequence Method  
The sequence-to-sequence (seq2 seq) model is a popular method for natural language processing. The seq2 seq model is based on an encoder-decoder architecture constructed by two RNNs. One neural network is an encoder, which adopts the RNN to encode the input sequence into a context vector. This context vector is then passed to the next neural network to decode and generate an output sequence. For the RNN in the seq2 seq model, long short-term memory (LSTM) is usually adopted to avoid the vanishing gradient problem, which often happens in the training process of a general RNN. The objective function of the seq2 seq model can be defined as follows:  
\[p(y_{1},\dots,y_{T^{\prime}}|x_{1},\dots,x_{T})\ =\ \prod_{t\ =\ 1}^{T^{\prime}}p(y_{t}| \upsilon,y_{1},\dots,y_{t-1}) \tag{1}\]  
where \(x_{1},x_{2},\dots x_{T}\) is the input sequence given time step \(T\); \(\upsilon\) is the encoded context vector generated by the encoder; \(y_{1},\dots,y_{T}\) is the previously generated output sequence; and the function is to maximize the probability of the output sequence with the next token, \(y_{T^{\prime}}\).  
The seq2 seq model encodes the entire input sequence into a vector, but it is sometimes difficult to capture enough information when the input sequence is long. Furthermore, for some applications, such as machine translation, when we translate a word in a sentence into another language, we usually focus on some important words around it rather than the entire sentence. In order to give the seq2 seq model the ability to focus on some important portion of the input sequence while engaged in decoding, an \"attention mechanism\" is introduced. Attention is a technique that allows the neural network to devote greater focus to small but important parts of the input. In the network, each input token has its own weight. The \(i\)th input token has an attention weight, \(w_{i}\). For each input token, \(i\), the encoder RNN has its hidden state, \(\upsilon_{i}\). The weighted average, \(\sum w_{i}\upsilon_{i}\), is the output of the attention mechanism. An overview of the attention-based seq2 seq model is shown in Figure 1.  
Figure 1: Attention-based sequence-to-sequence (seq2 seq) model.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 1 Introduction
Therefore, this paper presents a structured literature review on IM issues and mitigation strategies in B5G networks. The review is organized around three key terrestrial components D2D communication, HetNets, and ICI, along with a dedicated discussion of emerging AI-based frameworks approaches. These areas represent foundational challenges in B5G deployment and are closely aligned with 3 GPP standardization efforts. We conducted this paper as a literature review to provide a structured and critical analysis of existing research on IM in B5G networks. By synthesizing recent contributions and comparing established techniques, the review offers a comprehensive perspective that helps identify effective practices and open research questions, especially those relevant to real-world deployments and standardization. In addition, a comparative visualization is included to quantitatively illustrate how optimization objectives, such as SE, power consumption, and outage probability, are prioritized across different IM domains. In this paper, we distinguish between IM and interference mitigation (IMI). IM refers to the broader set of techniques and strategies used to monitor, coordinate, and control interference in wireless networks, including detection, classification, and prevention. In contrast, IMI focuses specifically on techniques that reduce or suppress the impact of existing interference on communication quality, such as through POC, RA, beamforming (BF), and filtering. Thus, mitigation is a subset of the broader IM framework. The organization of the paper is as follows: Section 2 highlights the research motivations and contributions of this paper. Section 3 presents a classification and in-depth analysis of IM strategies in B5G networks, covering key categories and the current research addressing each type. In Section 4, the proposed IMI schemes and techniques by the 3 GPP are presented. Section 5 provides a critical analysis of the reviewed strategies and outlines open research challenges for future B5G networks. Finally, the conclusions are communicated in Section 6.