esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
## 2 Materials and Methods
### Training for Creating Models  
The software for building the DL method was developed with in-house MATLAB software (The Mathworks, Inc., Natick, MA, USA), and a desktop computer with an NVIDIA GeForce RTX 3080 graphics card (Nvidia Corporation, Santa Clara, CA, USA) was used. Three convolutional neural networks (CNNs) were used for image training, and they could be selected in MATLAB: VGG-16 (Visual Geometry Group) [19], ResNet-50 (Residual Neural Network) [20], and the original CNN. The original CNN was a simple CNN that was constructed by reducing the convolution layers in VGG-16. The architecture of the original CNN is shown in Figure 2. The classification method was applied, except for the angle correction. The regression method was applied for angle correction. In the regression method, the final layer was replaced with the regression layer, with an output size of 1. The dropout layer was added in front of the regression layer. The optimizer was Adam.  
The loss function was cross-entropy for the classification method and half-mean squared error for the regression method. Fivefold cross-validation was performed for all training. The following hyperparameters were used: maximum number of training epochs, 30; initial learning ratio, 0.0001; learning rate drop period, 5; and learning rate drop factor, 0.2. The early stopping method was used. The validation patience was 10 days, and the batch size was 16. In the classification method, the model with the highest area under the curve (AUC) was selected as the best model. In the regression method, the lowest mean absolute error (MAE) was selected as the best model.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 1 Introduction
### Natural Language Preprocessing Techniques in Short Text
#### 1.2.2 Lemmatization  
Lemmatization breaks words into their basic components and examines the words from a morphological viewpoint. The word lemmatizer maintains the original state of the word. In lemmatization, only the inflectional end of words is eliminated, which often returns to their base form. For example, the word \"saw\", can be either a noun or a verb [7; 8; 9; 10; 11; 12]. Based on the parts of speech (POS), the lemmatizer can separate both words. It can convert the saw to its basic form \"see\" and saw by itself. Consider the following example of lemmatization:  
Example:  
Text: [PERSON] saw a carpenter, using a saw.  
Lemmatized text: [PERSON] sees a carpenter, using a saw.  
Figure 2: Basic techniques of preprocessing pipeline.
## 2 Background
### Environmental Labelling and ISO  
ISO defines three types of environmental labels and declarations, which will be described later. An overarching standard that is common for the three is ISO 14020 ([PERSON] et al., 2016). It provides nine common guiding principles for the development and use of environmental labels and declarations.  
Type I environmental labels are defined by ISO 14024 ([PERSON] et al., 2016) as voluntary, multi-criteria based third-party programs (managed by a respective eco-labeling body) that award licenses for the use of environmental labels on products. Type I eco-labels are based on the concept of eco-efficiency ([PERSON] et al., 2019), which proclaims that the development of new products or the improvement of existing products should be done with an intention to reduce their damage on the ecological systems (i.e., doing more with less). To achieve the certification, a product should fulfill certain product environmental criteria that are also based on life cycle considerations. \"Product environmental criteria\" is the official term as per ISO 14024. However, in this paper, the terms \"awarding criteria\" and \"certification criteria\" (as per C2C Certified) are also used and accounted as synonyms. Type I eco-labels usually facilitate business-to-consumer (B2C) communication, and the awarded label indicates overall environmental preferences within a certain product category. Typical examples include the German Blue Angel (BA) ([PERSON] et al., 2016), the European Eco-label ([PERSON] et al., 2016), and the Scandinavian Nordic Swan (2017).  
Type II labels are self-declared environmental claims that are either issued in the form of a claim, a stamp, a label, a declaration, or a more complex rating system. It is not mandatory for such claims to undergo third-party certification. Although ISO 14021 ([PERSON] et al., 2016) seeks to harmonize the basic principles and requirements of such self-declared claims, nowadays, their availability and variability on the market is large, making it almost impossible to categorize average properties and characteristics. Thus, Type II claims are not further considered in this work (a further explanation for this is given in Section 3).  
Type III environmental declarations (known also as environmental product declarations, or EPDs) present third-party verified and quantified environmental information on the life cycle of a product. They are governed by ISO 14025 [16], and are based on an LCA study that was conducted according to specific product category rules (PCR). EPDs are intended for business-to-business (B2B) communication, although B2C application is not precluded [16]. Typical Type III programs (managed by a legal body called a program operator) include the Swedish International EPD(r) System [17] and the German Institut Bauen und Umwelt e.V. (IBU) [18].
# High Pressure and Pasteurization Effects on Dairy Cream +
## 3 Results and Discussion
### Microbial Analysis
coli_ counts on both 600 MPa treatments experienced a significant increase (\(p\leq\) 0.05), surpassing 6.00 log CFU/mL for 600/5 samples. However, on the following days, _E. coli_ counts on both 600 MPa treated samples presented values below 1.00 log CFU/mL, probably due to the fact that _E. coli_ is not able to survive after long exposures to low temperatures, as suggested by [PERSON] et al. (2001) [PERSON] et al. (2001). Despite the similar outcomes by the end of storage time, longer exposure to HPP at the same pressure appeared to be more effective in delaying microbial growth over time, given the lower counts registered on days 3 and 10. Previous works revealed that gram-positive bacteria are more resistant to HPP than gram-negative ([PERSON] et al., 2018; [PERSON] et al., 2018). In our study, _L. innocua_ loads increased in both 600 MPa-treated samples (\(p\leq\) 0.05) from the 28 th up to the 52 nd day of storage, suggesting that cells may recover from the injuries caused by HPP and grow during cold storage ([PERSON] et al., 2018). A larger number of L. monocytogenes cells on milk samples, after 10 days of refrigerated storage, was also observed by [PERSON] et al. (2018) [PERSON] et al. (2018). This is probably due to the higher-pressure  
Figure 2: Microbial growth of (**a**) TAP, (**b**) LAB, (**c**) _E. coli_, and (**d**) _L. innocua_ on initial raw cream after heat treatment (75 \({}^{\circ}\)C, 15 s) and after pressure treatment under 600 MPa for 5 min and 600 MPa during 15 min (second batch). Analyses were made on the initial cream and right after processing (**b**) and after 3 (**c**), 10 (**e**), 28 (**e**), and 52 (**f**) days of storage at 4 \({}^{\circ}\)C. Bars with \({}^{\star}\) and # represent microbial loads below the detection limit (lower than 1.00 log CFU/mL) and above 6.00 log CFU/mL, respectively. Different letters denote significant differences (\(p\leq\) 0.05) between storage days for each condition (A,B) and treatment conditions for each storage day (a–c). resistance of gram-positive bacteria, namely regarding their metabolic repair mechanisms, in comparison with gram-negative _E. coli_. Even though thermal pasteurization was able to reduce initial microbial loads and inhibit microbial growth on the first days of storage, it is possible to conclude that HPP has a more pronounced effect on slowing microbial growth rate over time, as evidenced by lower microbial counts on the final day of storage (52 nd). Since milk and dairy products follow very strict regulations worldwide, further research is also needed to accurately establish the safety of dairy cream processed by HPP, namely to overcome these regulatory issues. For instance, in the United States, pasteurization must inactivate _Mycobacterium tuberculosis_ and _Coxiella burnetti_ (which is more heat stable than the first one), while also resulting in a negative phosphatase reaction [26].
## 2 BDS/GNSS for Earth Observations
### GNSS Meteorology  
Tropospheric delay is one of the main error sources in GNSS positioning [23]. By utilizing ground-based GNSS stations alongside the BeiDou Navigation Satellite System, the technology of precise-point positioning (PPP) can effectively estimate tropospheric delay. In comparison to radiosonde data and the fifth-generation generation reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF) (ERA5), the GNSS provides high precision and enables real-time descriptions of short-term fluctuations in regional tropospheric delay [24]. A critical parameter in climate research, Precipitable Water Vapor (PWV) serves as a commonly used standard measure of atmospheric water vapor content [25]. ZTD (Zenith Tropospheric Delay) is closely related to meteorological monitoring and early warning systems. By utilizing suitable methods, the tropospheric delay can be translated into PWV [26], which enables the assessment of atmospheric water vapor levels and provides dependable technical assistance for meteorological measurements. Studies have shown that the PWV in China exhibits clear seasonal variations (Figure 1), and this cyclical pattern provides a theoretical basis for PWV research. Therefore, the monitoring of PWV is essential for climate studies and for the ability to issue early warnings related to extreme weather events [27].  
In GNSS meteorology applications, various meteorological parameters play crucial roles in the revealing of weather changes. These parameters include atmospheric pressure, vapor vapor, temperature, and weighted average temperature. The atmospheric weighted average temperature (Tm) is a critical parameter for the determination of PWV. [PERSON] et al. [28] developed a Tm model (LSTM-ERATM) utilizing a long short-term memory (LSTM) approach that leveraged atmospheric reanalysis data provided by the EAR5. The findings of their study indicated that this model delivered highly accurate temperature information tailored for users within the specified region. In comparison to existing models such as UNB3, GPT3, and Bevis, the LSTM-ERATM demonstrated a significant reduction in the root mean square error (RMS) during its validation against ERA5 data with achieving reductions of 50.8%, 37.4%, 26.2%, and 18.9%, respectively. Furthermore, when validated with radiosonde data, the LSTM-ERATM model also showed impressive performance, with RMS reductions of 31.3%, 27.2%, 35.9%, and 8.6% for the corresponding models. These results highlighted the superior accuracy of the LSTM-ERATM model in providing reliable temperature data.
## 3 Simulation Examples
### Mode Shape Extraction Using Displacement Approximation of Contact Points
#### 3.3.3 Mode Shape Extraction with CEEMDAN-IWT
Figure 24: The first three beam mode shapes from the approximate displacement residual STFT-based extraction after being denoised by IWT under noise levels of 1%, 5%, and 10%, compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**). CEEMDAN-IWT could enhance the accuracy of the second mode shape under noise levels of 1%, 5%, and 10%, as shown in Figure 25. Moreover, in the lower-amplitude time-frequency region around the third mode, as shown in Figure 14, the extracted third mode shape exhibits good improvement. This indicates that both CEEMDAN-NSPCA and CEEMDAN-IWT can deal with the small-amplitude high-frequency signal fraction well, and CEEMDAN-IWT is better than CEEMDAN-NSPCA in terms of stability.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 3 TDS-OFDM-NOMA Scheme
In this paper, we use \(f_{c}=3072\) Hz, \(N=1024\) subcarrier, \(T_{s}=1\) s, \(B=1024\) Hz; hence, the subcarrier spacing is 1 Hz. Unlike the conventional CP-OFDM and ZP-OFDM, where the data stream from the IFFT block is converted into the serial stream to append the cyclic prefix and prevent inter-symbol interference, the TDS-OFDM inserts a PN sequence as a guard interval. As in [24], in this paper, the PN training sequence is used for the frequency domain to relax the underwater Doppler estimation and multiplexing. The TDS-OFDM signals' blocks can be written as:  
\[s(t)=\frac{1}{\sqrt{N}}\mathrm{\sum}_{k=0}^{N-1}s(k)e^{j2\pi k\Delta ft}+\frac {1}{\sqrt{M}}\mathrm{\sum}_{k=N}^{N+M}c(k)e^{j2\pi k\Delta ft} \tag{10}\]  
Figure 3: The TDS-OFDM-NOMA communication system. The transmitted signal is then passed over the UWA channel in the passband as:  
\[s(t)=Re\bigg{\{}\bigg{[}\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}s(k)e^{j2\pi k\Delta ft }+\frac{1}{\sqrt{M}}\sum_{k=N}^{N+M}c(k)e^{j2\pi k\Delta ft}\bigg{]}\times g(t) \times e^{j2\pi f_{c}t}\bigg{\}} \tag{11}\]  
and \(t\in\big{[}0,T+T_{g}\big{]}\), where \(T\) is the data symbols' duration; \(T_{g}\) is the guard interval duration; and \(g(t)=1,\ t\in\big{[}0,T]\). Through its transmission over the UWA channel, the transmitted signal is corrupted due to the underwater channel multipath, ambient noise, Doppler shift, and additive white Gaussian noise (AWGN). The TDS-OFDM-NOMA is contentious for two branches at the receiver side, the energy harvesting part, and the information decoding part. These two parts will be explained in detail in separated sections below.
## 5 Experimental Results
### Experiment Evaluation
#### 5.2.1 Effectiveness Evaluation
The first part analyzed the influence of different user clusters on the search efficiency of nearest neighbor users. According to the results of item clustering, the number of clusters and nearest neighbors was respectively 40 and 30. While, for user clustering, the number of nearest neighbors was 30, the regulating parameter \(\rho\) was 0.5, the similarity threshold \(\theta\) was 0.7. Figure 7 shows the diagram of search efficiency with different clusters varied from 30 to 50 with a step of 10. Y-coordinate denotes the ratio of searched nearest neighbors of users, and X-coordinate denotes the percentage of users that have been searched. The results are similar to that of item clustering. When the percentage of users being searched increased, the ratio of searched nearest neighbors increased. Through user clustering, more nearest neighbor users can be found within fewer search spaces; the greater the number of clusters was, the higher the search efficiency was. Likewise, in the second part, to investigate the relation between the relevant parameters of user clustering and the accuracy of rating prediction, we varied the number of clusters from 30 to 100 with a step of 10 to compute the average MAE under different circumstances, i.e., the number of nearest neighbors was set to 10, 20, 30, 40, 50 respectively. Values of the regulating parameter \(\rho\) and the similarity threshold \(\theta\) remained the same as above. Overall, as shown in Figure 8, MAE values decreased first and then rose with the increase in the numbers of clusters. As shown in Figure 8, the lowest MAE appeared with 0.75 when the clusters were 50 or 60; the MAE values were relatively lower with 0.85 when the nearest neighbors were 30, 40; all the curves dropped to their lowest points when the number of clusters was 50. Thus, in the following experiments, the number of clusters for user clustering was chosen as 50, while the number of nearest neighbors was indeterminate, which can be 30 or 40. Figure 8: Impact of the number of nearest neighbors on MAE for user clustering. Figure 7: Impact of the number of clusters for user clustering. Figure 9 shows the effect of the regulating parameter on rating prediction. In view of above analyses, the number of clusters and nearest neighbors was respectively 40 and 30 for item clustering, while for user clustering, the number was 50 and 30 respectively, and the similarity threshold \(\theta\) was 0.7. We varied the regulating parameter \(\rho\) from 0 to 1 with a step of 0.1. It can be found in Figure 10 that MAE achieved the lowest value when \(\rho\) = 0.4. Accordingly, the regulating parameter can be set to 0.4 in the following experiments. Note that we obtained the same conclusion if we only changed the number of nearest neighbors for user clustering to 40.
## 4 Sensor Packet Loss Compensation  
### Principle  
Considering Figure 1, the first source of potential information losses in the control loop lies in the wireless link between the sensor and controller nodes. Under the assumption that a large proportion of samples will be successfully delivered in a well-designed wireless network, one possible way to add resilience against such information losses in the loop is to use buffering. Since the control loop is real-time, only known information at sample index \(k\) can be transmitted by the sensor node at this time instant; therefore, the measured process output at sample \(k+1\) cannot be transmitted. Nevertheless, a prediction of its value could still be formed in the controller node. The adaptive compensator proposed in this paper therefore consists of three main elements which mainly reside inside the controller node. Suppose that the sensor-controller data packet transfer (DT1 in Figure 2) has an effective deadline of \(t_{s}\) time units, where \(t_{s}<T\). This effective deadline is such that if the controller node does not begin the process of control calculation and initiation of data transfer DT2 in Figure 2, the actuator node does not have a chance of receiving the data before the next actuation event. A packet omission for the \(k^{th}\) iteration of the loop can easily be detected in the controller node by the absence of a new packet at time \(kT+t_{s}\). The schedule decoder/packet loss detector is responsible for providing the indication of successful/ unsuccessful packet receipt to a switching mechanism. In the case of a successful receipt, \(y(k)\) may be retrieved from the packet and forwarded to the control module. In the case of unsuccessful receipt, the switching mechanism instead forwards an estimate of the omitted packet's content \(\hat{y}(k)\) to the controller. This estimate is produced by an adaptive process model. The model continually uses a technique--to be described below--to predict the values of missing samples, by monitoring the time history of the control signal \(u(k)\) and the actual/estimated process values \(y(k)\) and \(\hat{y}(k)\) respectively. When packets arrive successfully, the actual process output is employed to apply corrections to the process model parameters, to ensure it remains \"up to date\". Information loss is further minimized by buffering and transmitting multiple samples in the sensor node, such that at sample index \(k\), measured values for \(y(k)\), \(y(k-1)\), \(y(k-2)\),..., \(y(k-H)\) are always transmitted for some horizon length \(H\). Under the assumption that the horizon \(H\geq\beta\), good resilience to burst errors is achievable since any information lost during a burst can be recovered upon the arrival of the first packet. Since a DCS sensor sample is often encoded as a 16-bit integer, the overhead of such an approach is typically only \(2H\) extra bytes per packet which is relatively small.
###### Abstract
However, collecting a sufficient variety of signals in practical applications is challenging. This makes these methods difficult to implement in real-world scenarios. In recent years, the rapid development of machine learning and artificial intelligence has significantly advanced UGW monitoring, particularly in the field of temperature compensation [34; 35; 36; 37]. However, these methods require substantial computational resources, prior knowledge, and high-quality training samples, which limits their applicability. Currently, pipeline temperature compensation algorithms typically assume uniform temperature changes across the pipeline. Methods combining OBS and BSS, along with their improved versions, can effectively handle uniform temperature variations. While advanced technologies like OMP, machine learning, and neural networks offer strong compensation capabilities, they require a complete signal database or high-quality learning samples, which are difficult to obtain in practice. The DTW method is time consuming and may obscure defect features. Therefore, there is a need for a convenient, stable, and accurate nonuniform temperature compensation algorithm to meet the UGW monitoring requirements under nonuniform temperature changes. In addressing nonuniform temperature compensation in pipelines, this paper first establishes a simulation model to analyze the effects of temperature on material properties and UGW signals. It then proposes a new method named local phase matching (LPM). LPM primarily leverages the local similarity between two signals to perform signal matching, effectively addressing the matching challenges caused by uneven changes throughout the entire signal. Initially, two sliding windows of different sizes divide the baseline signal (BS) and monitoring signal (MS) into various sub-sequences. Subsequently, cosine similarity is employed to locally match these sub-sequences, identifying the most similar local signal sub-sequences. A damage index is then defined to quantitatively assess the matched signal sub-sequences as indicators of defects. To validate the effectiveness of the algorithm, this paper conducts three heating experiments on pipelines under nonuniform temperature fields. These experiments thoroughly assess the temperature compensation capabilities of LPM and its defect detection abilities across different temperature regions. Additionally, a comparison is made with the BSS method.