esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Range Compression and the Semantic Descriptor Aggressive
## 1 Introduction
### Research Aims  
Thus, the work in the current study aims to address several pertinent research questions relating to the use of compression during mixing and the semantic nature of its sonic signature. The following research questions are addressed in this article. Firstly, how do professionals describe the sonic signature of the 1176 compressor when processing a range of sources, but most specifically vocals? Secondly, and derived from the results of the first question, what does the subjective descriptor aggressive mean at an objective level?  
To answer these questions, three studies were carried out. Initially, a qualitative study was conducted that asked several experienced users to describe the sound quality of the 1176 when compressing a range of audio sources. Based on their responses, they were then asked to rate the appropriateness of commonly used descriptors in a similarity matrix. The results suggested the descriptor aggressive was a synonym for distortion. Thus, the second stage of testing conducted a subjective listening test using the Audio Perceptual Evaluation (APE) method from the Web Audio Evaluation Tool (WAET) [20]. This tested whether listeners rated mixes with vocals compressed by 1176 hardware and using settings measured to have larger amounts of Total Harmonic Distortion (THD) as the most aggressive. Finally, a subsequent listening test was carried out to ascertain whether distortion, timing behaviour or a mixture of both were the most important factors in creating compressed audio perceived to be aggressive. The reason for this test was due to the 1176's reputation as a fast-acting compressor (particularly when working with time constant settings, which will be addressed in Section 4). Therefore, it could be that argued its fast timing creates the aggressive sonic signature, rather than distortion. This was examined in the second listening experiment, which had vocals processed with a clean software compressor (measured with 0% THD) and set to mimic the timing behaviour of the 1176 as well as material compressed with 1176 hardware and measured to have 1.58% THD. The Klanghelm DC8C software compressor [21] was used for this test as it allows user control over a range of design traits that can be used to match the behaviour of several compressors. Most importantly, when used in its clean mode, it does not generate any distortion, even at the fastest time constants.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 7 Implementation
### OOB Communication with a Blinking LED
In addition, since the character set of the OOB message only consists of ASCII characters, we can use seven-bit encoding. This optimization allows for more data to be encoded within each captured image. In this variation, shown in Figure 5, the preamble is followed by a seven-bit sequence number and a seven-bit payload, both of which are Manchester encoded into 14-bit values. This means that each captured image can contain 2-3 characters. The theoretical maximum bitrate with a \(800\times 600\) resolution and a 30 FPS capture rate is approximately 18,000 bps, or 18 kbps (600 bits \(\times\) 30 FPS). However, because of the way timers work in Contiki-NG, controlling the width of the band is difficult. Moreover, on and off require different timers to produce the same bandwidth. We noticed that using long bit-sequences causes some of the messages to cut short, which causes data to be lost. This is noticeable at the edges of the captured frames and causes the actual bitrate to be lower than the theoretical bitrate. Furthermore, due to the Zolertia Firefly mote's LED being small, the camera needs to be placed directly on top of the LED to perceive the rolling-shutter effect. To help the user in scanning the small blinking LED, we implemented a circular UI design that is  
Figure 4: Data structure of a three-character block. Figure 5: Data structure of a sequence-numbered character block. Figure 3: Screen capture of a blinking LED through a smartphone camera. Figure 2: The electronic rolling shutter effect. Capturing a blinking LED leaves bright and dark bands on the image. shown in Figure 6. Once the message is scanned, the complete URL is displayed to the user. By clicking the link, the user is taken to the website to complete the authentication process. In EAP-NOOB, the OOB message can be encoded as a URL that consists of a domain name, a 22-byte identifier (PeerId), a 16-byte nonce (Noob), and a 16-byte hash (Hoob). With the first transmission method we were able to successfully read the message in approximately two seconds. With the second method the corresponding time was six seconds. However, the slower method based on sequence numbers is more robust against movement and scanning the OOB message can be done in parts. Conversely, while the repeating method is faster, it is more prone to errors if the user accidentally moves their hand during the scanning.
###### Abstract  
In situ electric heating is an important method used to increase production capacity during the extraction of natural gas hydrates. This work numerically evaluated the sensitivity of different heating parameters on gas production behavior with a vertical well depressurization in the Shenhu Sea area hydrate reservoir, the production pressure difference of 4 MPa, and continuous depressurization for 1080 days. The results showed that the in situ electric heating method can effectively enhance production capability by promoting hydrate dissociation and eliminating secondary hydrates. Compared with scenarios without heating, implementing whole wellbore heating (100 W/m) increases cumulative gas production (\(V_{g}\)) by 118.56%. When intermittent heating is applied to the local wellbore (15 m) located in the three-phase layer (with an interval of 30 days) and stops heating in advance at 480 days, there is no significant difference in \(V_{g}\) compared to the whole wellbore heating case, and the cumulative heat input is only 4.76%. We recommend considering intermittent heating of the local wellbore and stopping heating in advance during vertical well depressurization as this approach significantly reduces heating energy consumption while simultaneously improving production capacity.  
natural gas hydrate; electric heating; depressurization; local wellbore; intermittent heating; TOUGH+HYDRATE
## 3 Hierarchical Clause Annotation
### HCA Corpus
#### 3.3.4 Dataset Detail  
The resulting HCA-AMR2.0 dataset was based on AMR 2.0, which contains 39,260 sentences, and 19,376 (49.4%) sentences were paired with an HCA tree, while the rest were simple sentences with only one clause. The train, dev, and test set split followed the original split in AMR 2.0. Detailed statistics are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Occurrence** & **Relation** & **Occurrence** \\ \hline \# Sentences (S) & 39,260 & _MulSnt_* & 3249 \\ \# of S with HCA & 19,376 & _And_ & 14,952 \\ \# of S in Train Set & 17,885 & _Or_ & 974 \\ \# of S in Dev Set & 740 & _But_ & 3704 \\ \# of S in Test Set & 751 & _Subjective_ & 992 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main statistics of the hierarchical clause annotation dataset based on AMR 2.0 (HCA-AMR2.0). * means that some input sequences contain multiple sentences, and the coordination \(MulSnt\) is necessary for these inter-sentence relations in these cases. * indicates that _Adverbial_ can be divided into nine sub-types such as _Condition_, _Concession_, and _Purpose_. Note that _“#_” represents the number of the subsequent item.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Annotator**} & \multicolumn{3}{c}{**Clause**} & \multicolumn{4}{c}{**Interrelation**} \\ \cline{2-7}  & **P** & **R** & **F\({}_{1}\)** & **Span** & **Nuc.** & **Rel.** & **Full** \\ \hline
1, 2 & 99.9 & 99.8 & 99.8 & 97.9 & 97.5 & **94.3** & **94.1** \\
1, 3 & **100** & **100** & **100** & **98.1** & **97.8** & 94.2 & **94.1** \\
2, 4 & 99.8 & 99.5 & 99.6 & 97.5 & 97.3 & 93.9 & 93.
# State Evaluation of Electrical Equipment in Substations Based on Data Mining
## 1 Introduction
The proposed methodology presents several advantages compared to existing electrical equipment status assessment techniques:  
* The proposed state assessment algorithm integrates the a priori algorithm with a Naive Bayes classifier. The former efficiently identifies frequent itemset, revealing operational patterns and correlations, including co-occurrence of equipment failures under specific conditions. This provides valuable input for subsequent state classification. The Naive Bayes classifier is a powerful approach to identify the presence of a certain amount of information. The proposed method is based on the concept of a \"good\" state classification. Bayes classifier utilizes the association rules discovered by a priori to swiftly classify device states, enhancing predictive capabilities in specific contexts. * Gaussian mixture models are employed to unify state characteristics of electrical equipment across varying environments, loads, and operating conditions. These models provide a cohesive description of state features, identifying commonalities and differences across states, offering high flexibility and adaptability. This approach minimizes the impact of environmental interference and varying working conditions on status monitoring data, enhancing the reliability of final evaluation results. This paper is structured as follows: Section 1 provides a basic introduction, discussing substation development and current trends in electrical equipment evaluation methods. Section 2 presents a comprehensive explanation of the proposed algorithm. It begins by introducing characteristics of substation electrical equipment status assessment, including equipment types and status data acquisition methods. Then, it explores effective information dataset extraction techniques and details the collaborative application of a priori algorithm and Naive Bayes classifiers for device state classification and evaluation. Finally, this section validates the efficacy of the proposed data mining-based smart substation state evaluation method and algorithm. Section 3 summarizes this paper and proposes future research directions.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 4 System Model
### Network Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Node ID**} & **Location on the Body** & **Health** & **Data Rate** & **Bandwidth** & **Type** & **Power Consumption** \\  & **the Body** & **Parameter** & **(kbps)** & **(Hz)** & **Type** & **Consumption** \\ \hline
1 & Head & EEG Sensor & 43.2 & 0–150 & Wearable & Low \\  & Right &
\begin{tabular}{c} Pulse \\ \end{tabular} & 10–16 & 0–1 & Wearable & High \\  & shoulder & Sensor & & & & \\
3 & Chest & ECG Sensor & 288 & 100–1000 & Implanted & Low \\  & Abdomen & Lactic acid & 400 & 0–100 & Implanted & Low \\  & left & Sensor & 260 & 100–1000 & Wearable & High \\  & Face &
\begin{tabular}{c} EMG Sensor \\ \end{tabular} & 320 & 0–10,000 & Wearable & Low \\  & Abdomen & Glucose & 1600 & 0–50 & Implanted & Extremely \\  & right & Sensor & & & & Low \\
8 & Left Hip & Respiration & 320 & 0–10,000 & Wearable & Low \\  & Blood & & & & & \\
9 & Right arm & Pressure & 10–16 & 0–1 & Wearable & High \\  & & Sensor & & & & \\
10 & Right foot &
\begin{tabular}{c} Motion \\ Sensor \\ \end{tabular} & 35 & 0–500 & Wearable & High \\  & & Central & & & & \\
11 & Left U-Hip & Node & 4 Gbps & - & Wearable & High \\  & Controller–L & & & & \\  & & Central & & & \\
12 & Right U-Hip & Node & 4 Gbps & - & Wearable & High \\  & Controller–R & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Biological SNs details with SNs IDs [30]. (5) The IEEE 802.15.6 BAN channel modeling project [18] is used for path loss. It is shown in Equation (3):  
\[Pl\left(f,\,d\right)=Pl\left(f\right)\times Pl\left(d\right) \tag{3}\]  
where \(Pl\left(f\right)\) and \(Pl\left(d\right)\) represent path loss due to frequency and distance. (6) The Euclidean distance formula equation is used for the calculation of distance among SNs and Sink Node. The path loss also effects on distance which necessary to be considered while computing distance as shown in Equation (4):  
\[\mathrm{P.L_{db}(d)}=PL_{0}(db)+10\eta\frac{d}{d_{0}} \tag{4}\]  
where \(P.L\) and \(PL_{0}\) represent the path loss and path loss at a specific distance \(d_{0}\) (reference distance). \(\eta\) is path loss exponent/constant.
## 2 Related Work  
### Characteristics of a Smart Campus  
It is first important to note that in the literature, some authors use the term smart campus to refer to digital online platforms to manage learning content [30; 31] or to strategies or solutions to increase the smartness of the students [32; 33; 34]. In this article, the term smart campus is used for referring to the hardware infrastructure and software that provides smart services and applications to the campus users (i.e., to students and to the university staff). In this regard, a smart campus, such as a smart city, can be modeled along six different smart fields [35]:* Smart governance. It provides users with mechanisms to participate in decision-making or in public services.
* Smart people. It deals with social issues, including the engagement in campus events and learning activities.
* Smart mobility. This field is related to the accessibility of the campus, including the use of efficient, clean, safe, and intelligent transport means.
* Smart environment. It contemplates the monitoring and protection of the environment, as well as the sustainable management of the available resources.
* Smart living. The technologies used in these fields can monitor diverse living aspects in the campus facilities, such as personal safety [36], health [37] or crowd sensing [38].
* Smart economy. It is related to the competitiveness of the campus in terms of entrepreneur, innovation, or productivity.  
These smart campus fields can be further refined to determine specific smart services and solutions that should be ideally provided by a smart campus [39]:  
* Smart living services: room occupation, classroom/lab equipment access control, health monitoring and alert services, classroom attendance systems, teaching interaction services, or context-aware applications (e.g., guidance or navigation solutions).
* Smart environment solutions: they include solutions for monitoring waste, water consumption, air quality (e.g., pollution) or the status of the campus green areas.
* Smart energy systems: they control and monitor the production, distribution, and consumption of energy in a campus.  
These novel smart services and solutions make use of a growing number of enabling technologies, being the most relevant represented in Figure 1.  
Figure 1: Most relevant enabling technologies and applications in a smart campus.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 2 Materials and Methods
### Evaluation Criteria
#### 2.4.3 Visual Analysis  
Despite the need for numerical evaluation means, visual analysis remains an important supplementary tool for inspecting GAN outputs, especially for identifying obvious differences. Researchers often plot real and synthetic time series side-by-side to detect anomalies like flat lines, unnatural spikes, or lack of variability, which are common issueswhen the GAN fails to learn sufficient diversity or overfits specific training samples. Visualization also helps assess whether the GAN captures typical behavioral patterns, such as daily cycles or seasonal shifts.  
Additionally, dimensionality reduction techniques like t-distributed stochastic neighbor embedding (t-SNE) are used to project high-dimensional time-series data into two dimensions. By plotting the t-SNE embeddings of both real and synthetic data, one can visually inspect whether the clusters overlap, indicating similarity in distribution and structure. Another useful visual diagnostic is the loss curve over training epochs, for both the generator and discriminator. Stable convergence, absence of mode collapse, and oscillation patterns in the losses provide qualitative insights into training dynamics and model stability.  
The combined usage of those evaluation methods forms the evaluation framework of the proposed methodology. Based on the requirements of the model, as well as the specifics of the problem, more precisely, the generation of synthetic data for load profiles, and the literature review, the following acceptable values for each evaluation criterion are defined and summarized in Table 1.
## 4 Discussion
### Limitation  
This study has several limitations that need to be acknowledged. One notable limitation is the absence of periapical resistance simulation, which can be achieved using materials like floral foam and agar gel. In the present study, no such materials were utilized to simulate this resistance. This decision was made due to reported issues with the absorption of foam and the challenge of establishing a definitive value for the thickness of agar gel at the apex to mimic the size of the apical lesion [92; 93].  
Another limitation is the heterogeneity of the instruments analyzed in the studies included in this systematic review. The authors used a variety of different instruments to assess the quantity of extruded debris, and some even used endodontic instruments specifically designed for retreatments. It is unclear how this heterogeneity may influence the extrusion of apical debris. Despite these limitations, it is important to note that in vitro studies like these have their own set of limitations when extrapolating findings to clinical practice. However, they do allow for addressing internal validity effectively.  
Additionally, there is a lack of standardization across the studies. Some authors evaluated incisors, while others assessed premolars or molars, and some focused solely on root portions. This lack of standardization makes direct comparisons difficult and challenges the ability to draw definitive conclusions. To address these limitations and provide more comprehensive insights into the effects of different NiTi alloys on apical transportation, further research is warranted. On a positive note, this article adheres rigorously to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors followed a well-defined protocol and conducted a comprehensive literature search, ensuring transparency and reproducibility in the review process. The inclusion of a registered protocol number further enhances the study's credibility. Moreover, the involvement of two independent researchers and consultation with a senior researcher for discrepancies adds to the robustness of the study, increasing the reliability of the findings and strengthening the overall quality of this systematic review.
# TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts
###### Abstract
Although existing distributed-collaborative-learning architectures have made a lot of efforts in privacy protection [6], incentive mechanisms [7] and protection against malicious attacks [8], they still assume that the collaboration is mainly performed between trusted environments and mutually trusted collaboration nodes and rely to some extent on third-party platforms. Security and mutual distrust have seriously affected the confidence and willingness of data owners to collaborate. Therefore, how to ensure the credibility and fairness of the collaboration process has become the core issue of distributed collaboration based on decentralised data. The development of blockchain technology [9] has filled a gap in many fields and also brought development opportunities for distributed-collaborative-learning architecture. As a decentralised infrastructure, blockchain provides trusted distributed storage with good features such as the immutability, traceability and security of stored content. The application of smart contracts [10] has greatly enhanced the scalability of the blockchain platform, giving it Turing-complete programming capabilities and providing trusted distributed computing. Locating the problem of scattered data resources and the low trustworthiness of existing distributed-collaborative-learning architectures, we propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. The mechanism uses the blockchain as a distributed collaborative execution environment, and the cooperative behaviour and specifications are defined and encapsulated by smart contracts. At the same time, a model-fusion method based on feature fusion is proposed to realise model collaborative training to improve the model performance. The mechanism strives to provide a trusted and fair distributed-collaboration infrastructure, enabling all collaboration parties to perform trusted distributed collaboration in the absence of mutual trust and third-party intermediary dependence. The innovations and contributions of this paper are as follows:  
* We use smart contracts to define and encapsulate collaborative behaviours, relationships and specifications between distributed-collaboration nodes. * Based on the idea of smart contract collaboration architecture and transfer learning, we propose a model-fusion approach based on feature fusion to replace the direct sharing of local data resources with distributed-model collaboration training. The remainder of this paper is organised as follows. Section 2 summarises the work involved. Section 3 details the architecture and design details of the collaborative-learning mechanism. In Section 4, we implement a formal verification of the mechanism to prove its trustworthiness. In Section 5, the usability of the mechanism is demonstrated by means of experiments and a case study. The conclusion is presented in Section 6.
## 4 Results and Discussion
### Further Experiments and Results
#### 4.1.1 APS Failure at Scania Trucks Results  
In Table 11, we present extended experimental results from an FD task on the imbalanced APS Failure at APS Failure at Scania Trucks dataset [61]. First, we compare our method DLWP loss against all the other baselines when no class-balancing weight parameter has been applied to the loss function. DLWP loss with no reweighting and no selected logit weight vector (DLWP-None-None) shows improved Precision (0.83) and F1 (0.87) macro scores over the counterparts CE-None, Focal-None, and LDAM-None. The variations in this category of no reweighting, DLWP-None-ENPr, DLWP-None-DENPr, both achieve improved and highest Precision (0.84) and F1 (0.88) macro scores for the same dataset. Comparing the methods from the different categories, Figure 5 shows the method variations that yield the best Type I error count and Type II error count on the APS Failure at Scania Trucks dataset.  
We observe that higher recall scores are associated with lower misclassification costs across all the compared methods. Applying RPLr generated weights as logit perturbation weights is effective in minimizing recall and misclassification costs compared to other weight schemes. Notably, even baselines reweighted using our proposed RLPr weight generation scheme (CE-RLPr, Focal-RLPr, and LDAM-RLPr) achieve higher recall scores compared to the baselines reweighted with strategies proposed in [23; 46; 47]. Figure 6, compares the misclassification costs across the different categories revealing DLWP-RPLr as best performing.  
The reweighting strategy DRW involves a delay that, in effect, applies reweighting during the later stages of training (beyond 80% epoch). We note that for all methods, this delay causes no effect as the result is similar to the one in methods where the reweighting is not at all applied. Method pairs (CE-None and CE-DRW), (Focal-None and Focal-DRW), (LDAM-None and LDAM-DRW), and (DLWP-None-None and DLWP-DRW-None) all have similar results, showing that there is no effect of delaying the reweighting in these cases.