esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metrics of the Proposed Method
#### 2.5.1 Performance Metrics of the CNN  
The confusion matrix is a metric for the evaluation of the CNN as image classifier. A confusion matrix is defined by four terms, which are: true positive (\(TP\), elements predicted as elements that belong to a particular class, and that belong to that class); true negative (\(TN\), elements predicted as elements that do not belong to a particular class, and that do not belong to that class); false positive (\(FP\), elements predicted as elements that belong to a particular class, and that do not belong to that class ); false negative (\(FN\), elements predicted as elements that do not belong to a particular class, and that belong to that class).  
The _accuracy_ is defined as the percentage of the total number of predictions that were correctly classified and is calculated as:  
\[Accuracy=(TP+FN)/N \tag{4}\]  
where \(N\) is the total number of elements to be classified.  
The _precision_ is the ability to predict an element according to the class it belongs to and is defined as:  
\[Precision=TP/(TP+FP)\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline Algorithm optimizer & SGD \\ Number of epochs & 100.0 \\ Learning rate & 0.001 \\ Momentum & 0.900 \\ Batch size & 40.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters used in the training process. Stochastic gradient descent (SGD).  
The _recall_ is the ability of the classifier to label all the positive cases and is calculated as:  
\[\textit{Recall}=\textit{TP}/(\textit{TP}+\textit{FN}) \tag{6}\]  
The _specificity_ is the ability of the classifier to label all the negative cases and is defined as:  
\[\textit{Specificity}=\textit{TN}/(\textit{TN}+\textit{FP}) \tag{7}\]  
The _F-Score_ determines the precision of our classifier and is calculated as:  
\[\textit{F-Score}=(2*\textit{Precision}*\textit{Recall})/(\textit{Precision}+ \textit{Recall}) \tag{8}\]  
The receiver operating characteristic (ROC) is a graph of Recall versus Specificity. This graph characterizes the ability of a CNN to identify positive cases as positive, and negative cases as negative. Thereby, the area under the ROC curve (AUC) is the probability that a couple of positive and negative cases chosen at random are correctly classified.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 3 Preliminaries
### RelCLAS  
Aggregated signatures are a digital signature technique that efficiently combines \(n\) independent signatures from \(n\) users into a single compact signature. This approach allows verifiers to ensure that these \(n\) users have indeed signed their respective \(n\) messages, effectively reducing the computational and communication burden during the verification process. In this way, aggregated signatures not only improve data processing efficiency but also optimize resource consumption during storage and transmission. The RelCLAS scheme proposed in [18] is employed in this paper. The RelCLAS scheme typically consists of the following steps:  
**Setup:** The AMF and AUSF receive security parameters to generate the system master keys \(P_{\text{pub}}\) and \(T_{\text{pub}}\), and the system parameters list params is published by the AMF.  
**Secret Value Generation:** Users randomly select \(msk_{\text{i}}\in Z_{q}^{*}\) as the secret value and compute the user's partial public key \(mpk_{\text{i}}\).  
**Pseudonym Generation:** After receiving the user's identity identifier \(ID\), AUSF performs an XOR operation on the \(ID\) to obtain the anonymous identity \(TID\).  
**Partial Secret Key Generation:** AMF randomly selects \(r_{i}\in Z_{q}^{*}\) as the secret value and generates the user's partial public key \(R_{i}\) and partial private key \(psk_{\text{i}}\).  
**User Key Generation:** After receiving the partial key from AMF, the user generates the key pairs \(\{mpk_{\text{i}},R_{\text{i}}\}\) and \(\{msk_{\text{i}},psk_{\text{i}}\}\).  
**Signature Generation:** Each user selects \(n_{i}\in Z_{q}^{*}\) to generate the secret value \(N_{i}\). Using the parameter list \(param\), some state information, message \(M_{i}\in M\) (where \(M\) is the message space), their anonymous identity \(TID_{i}\), and their private key pair \(\{msk_{i},psk_{1}\}\), they compute the signature \(\sigma_{i}\).  
**Aggregate:** The aggregate signature generator is the first user entering a new coverage area. It receives signatures from other users and aggregates these signatures to produce the aggregate signature \(\sigma\).  
**Aggregate Verify:** The aggregate signature verifier, i.e., t-gNB, uses the anonymous identities \(TID_{i}\) of \(n\) users, their corresponding public keys \(pk_{i}\), secret values \(N_{i}\), the system master key \(Ppub\), and the aggregate signature \(\sigma\) on messages \(M_{i},\ldots,M_{n}\) as input. If the aggregate signature is valid, it outputs true, otherwise, it outputs false.
# Optimization of Pin Type Single Screw Mixer for Fabrication of Functionally Graded Materials
## 3 Results and Discussion
### Verification Experiment  
With the aforementioned internally developed printing system equipped with three types of mixers, the response was the printing of a 70 \(\times\) 70 mm\({}^{2}\) extruded filament displaying gradient variations from 90% of material A (white color) to 10% of material B (green color), and vice versa, as illustrated in Figure 15. Prior to the experiment, the two extruders were supplied in accordance with the initial paste ratio (9:1), and the screw speed was adjusted to 25 rpm for purging the mixing chamber. Thereafter, the dual extruders were supplied at the desired ratio (1:9) until the filament was produced. The printing path followed a zigzag pattern with 5 mm intervals, with the starting point indicated by the arrow in Figure 15. The total length of the extruded filament amounted to 1120 mm. The experimental printing parameters were configured to match the simulation settings. The extrusion head diameter remained at 1 mm, the combined feed rate was 0.5 mm/s, and the extruded flow rate from the nozzle could be estimated at approximately 2.4 mm\({}^{3}\)/s. Therefore, to ensure the absence of noticeable overstacking and understacking defects in the printing process, the movement speed of the printing platform was set to approximately 3 mm/s, following the principles of flow conservation. Therefore, the total printing time for the extruded filaments was 373 s. Hence, the transitional delay distance can be computed based on the platform's movement speed combined with the transition delay time.  
As depicted in Figure 15, all three mixers are capable of inducing gradient changes in the extruded filaments while maintaining satisfactory geometric pr  
Figure 15: Three mixers extruded filaments with gradient variations.  
highlighted in Figure 15a, filament breakage is evident, primarily attributed to the introduction of air bubbles during the loading of paste-like material into the extruder. Mixers lacking pins exhibit suboptimal dispersal and mixing capabilities, with minimal change in the size of the entrapped bubbles. The occurrence of these bubbles during material loading proves challenging to circumvent, and their dimensions directly impact filament breakage. Therefore, increased volumes of bubbles result in geometric inaccuracies during the printing process and exert a negative effect on the mechanical characteristics of the FGM parts. Conversely, when air bubbles infiltrate screw configurations equipped with pins, the larger bubbles can be dispersed into smaller units through the multiple shearing actions of the pins. This, in turn, translates into enhanced geometric quality, as illustrated in Figure 15b,c.
According to Figure 1, clear clinical evidence is required to classify digital health medicine from digital health [10]. Digital health refers to technologies aimed at improving the user's well-being or health in everyday life, but it does not necessarily have to include clinical evidence. However, both digital medicine and digital therapeutics require a definite medical evaluation. In particular, to be classified as a digital therapeutic product, the medical technology must prove its effectiveness with real-world data results through clinical trials. Digital therapeutics is not just about digitizing existing medical treatment methods, but it represents a new type of treatment software with various new computer technologies applied in each process. From identifying specific user medical conditions to treating and managing diagnosed disorders, DTx requires multiple cutting-edge technologies [11]. In this paper, we explain the latest technologies that should be considered for developing DTx and provide examples of major depressive disorder DTx designs. The products in DTx consist of four main categories: data collection and preprocessing, data analysis algorithms, HCI (human-computer interaction) factors, and recommendation systems for treatment. The core technologies in DTx consist of four main categories: data collection and preprocessing, data analysis algorithms, HCI, and recommendation systems for treatment. MDDs include both diagnosis and treatment of diseases that digital treatments can perform,and all four categories are essential to consider in each process. With the development of smart devices, it is possible to identify symptoms in daily life. It also has the characteristics of mental illnesses that require the daily application of various nondrug treatments, so an expansion of the DTx system can be applied. The demand for mental illness treatment has soared worldwide after the pandemic, and non-face-to-face systems have increased, so the suggested DTx architecture will be a good stepping stone for human well-being. We expect DTx companies to improve their product performance and digital health researchers to expand the research scope to applicable methods in the growing digital therapeutics market.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Orbit Design  
The InnoCube satellite will not be launched with a dedicated launcher; instead, it will be brought into orbit as a secondary payload. A voluntary orbit cannot be chosen.  
Therefore, different launch opportunities are being examined.  
As the number of CubeSats launched have increased rapidly in the last few years, various vendors offer launch services for small satellites with different orbital parameters, perhaps the most common in popular media being SpaceX and its capabilities to offer piggyback services onboard their Starlink mission.  
The major requirements considering orbit design are defined by the mission lifetime. To fulfill a minimum on-orbit mission of one year, a minimum orbit altitude of 400 km is defined. A maximum altitude of 600 km is defined by compliance with the Space Debris Mitigation guidelines, adhering to deorbiting not later than 25 years after the end of operational life.
## 5 Experiment Results and Analysis
### Enhancement of PA Signal Using MPC  
Figure 7 shows the enhancement of the photoacoustic signal using the compact multi-pass cell in the measurement of 10 ppm methane. The photoacoustic signal obtained by the resonant cavity (which has a radius of 4 mm and a length of 36 mm) without a multi-pass cell was 0.065 mV, and the photoacoustic signal obtained by the same resonant cavity was 0.710 mV when using a multi-pass cell. The multi-pass cell enhanced the photoacoustic signal of methane by about 11 times. The light intensity in the resonant cavity should be about 12.83 times that of the case without multi-pass cells after taking into account of the silver-plated mirror reflectivity of 95% and the light loss due to the windows in the PA cell. This is close to an 11-fold enhancement of the photoacoustic signal in the experimental results. The enhancement factor of the multi-pass cell is slightly less than the theoretical value, which might be due to the additional loss caused by scattering and excess absorption during the propagation. The experimental results indicate that using a multi-pass cell can significantly enhance the magnitude of the photoacoustic signal.  
Compared to a traditional Herriott cell, our multi-pass cell could achieve the same number of reflections in a smaller resonant cavity. For the Herriott cell shown in Figure 2b, the cavity radius may need to be designed to be 10 mm or larger. According to Formula (3), a smaller cavity radius provides stronger signal intensity and better detection performance.  
Figure 7: The photoacoustic signal enhanced by the multi-pass cell and the single-pass photoacoustic signal at 10 ppm methane concentration.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### Neighborhood Knowledge Discovery  
The proposed communication protocol uses a vehicle's On-Board Unit (OBU) to acquire knowledge of neighboring vehicles that are at a distance of one hop. The OBU is a device with processing power mounted on vehicles that allows DSRC/802.11p communications with other OBUs and RSUs. Periodic vehicle beacons are sent out containing information such as the vehicle's ID and current location. As vehicles already send beacons, increased network overhead is avoided.  
Upon receiving a beacon, the protocol stores the vehicle's ID and location as well as the timestamp. The protocol stores this information in one of six lists representing the neighboring sectors (N, NE, SE, S, SW, NW) of the receiving vehicle according to the current position of the vehicle that sent the beacon. Figure 3 shows the vehicle positions and sectors and the neighbor list with the vehicle IDs and timestamps.  
The lists containing the aforementioned information are used to choose the next hop for message transmission. However, information from neighbors quickly becomes out of date due to vehicle mobility. Thus, the list must be constantly updated within a periodic update window. The update period was estimated according to the average time required for a vehicle to change sectors at an average speed for a given scenario. For example, in a scenario with an average speed of 54 km/h (15 m/s), the time required for a vehicle to leave a sector with a size of 160 m where it is located in the center is approximately 5 s. Several works have estimated a similar update window and used it to generate satisfactory results [20; 24; 25].
# Modulation of Solar Wind Impact on the Earth's Magnetosphere during the Solar Cycle
## 2 Methodology
The CC peak is observed at a lag time \(\ell_{\text{maximum}}\approx 0.4\) days (9 h) for the solar maximum period, and is slightly shifted to a larger scale during the minimum period, \(\ell_{\text{minimum}}\approx 0.8\) days (19 h). In both cases, the CC reaches the first zero-crossing for \(\ell\approx 10\) days. Moreover, for both cases a broad peak is present, a fingerprint of the fact that the analysis encompasses multiple phenomena that could give rise to variations in the \(Dst\) index. In fact, such a temporal lag represents the average of the broadly distributed shift times between the two datasets, or, in other words, the time required for the various geo-effective structures to transit from L1 to the Earth. The signature of the 27-day solar rotation is evident in the secondary peaks observed during the minimum period [32; 33]. Therefore, to determine the influence of the SW energy on geo-effectiveness, in the following analysis, the energy time series are shifted backward by a time interval \(\ell\) with respect to the \(Dst\) index: \(E(t)=E(t+\ell)\). Finally, the large temporal scale \(u_{\text{max}}\), representing the correlation length of the two datasets, is extrapolated from the CC as the time lag at which the peak in \(\mathcal{C}\) is reduced to a value \(e^{-1}\), resulting in \(u_{\text{max}}=73\) h for the period of maximum activity  
Figure 2: Cross-correlation function for total energy \(E\) and \(Dst\) index measured for the maximum activity period (red symbols) and minimum activity period (blue symbols) as a function of the lag time \(\ell\). and \(u_{\max}=96\) h for the period of minimum activity. For \(u>u_{\max}\) the two datasets are considered to be completely decorrelated.
## 4 Results and Discussions
### Emerging Digital Technologies
#### 4.1.8 Integration of Digital Technologies for Creating Advanced Digital Ecosystem
Moreover, the novelty of this study lies in its holistic synthesis of the interaction between DT and IM technologies and their collective enhancement of worker safety, ergonomic, and training effectiveness. The integration model shown in Figure 13 represents a novel contribution to the literature, particularly in addressing RQ3. While existing studies examine DT and IM separately, this work highlights how their convergence, along with supporting technologies such as CPS, AI, and big data analytics, can drive a shift from reactive safety strategies to a predictive, immersive, and adaptive human-centric OHS ecosystem. Unlike previous research, this study explores this interaction and presents a conceptual framework and technology taxonomy to guide both future research and practical implementation. Additionally, insights from bibliographic analysis further support this integrated model; for instance, research items related to immersive training, predictive ergonomic, and DTs consistently cluster together, indicating that these themes are thematically central to OHS innovation in Industry 5.0. The high link strength clusters consistently focused on immersive safety training, DT-driven ergonomic, and AI-based risk forecasting. Thesefindings confirm the ecosystem's relevance to current research priorities and its originality in combining these aspects. The proposed advanced digital ecosystem addresses RQ 3 directly and offers a roadmap for safety transformation in data-rich environments. It also acknowledges potential challenges, such as cybersecurity, transparency, and trust in AI-driven systems, which must be addressed to ensure the responsible and effective adoption of AI in manufacturing environments.
# Inter-Sentence Segmentation of YouTube Subtitles Using Long-Short Term Memory (LSTM)
## 3 Methods
### RNN (Recurrent Neural Network)  
RNN (Recurrent Neural Network) is a neural network model that is suitable for learning time-series data. In the previous neural network structure, it is assumed that the input and output are independent of each other. However, in RNN, the same activation function is applied to every element of one sequence, and the output result is affected by the previous output result. However, the actual implementation of RNN has a limitation in effectively handling only relatively short sequences. This is called vanishing gradient problem, and a long-short term memory (LSTM) has been proposed to overcome this problem.  
As shown in Figure 4, LSTM solved the vanishing gradient problem by adding an input gate, a forget gate, and an output gate to each path that compute the data in the structure of the basic RNN.  
Figure 3: Schematic of the program to learn and generating period using Word2 Vec and LSTM.  
These three gates determine what existing information in the cell will be discarded, whether to store new information, and what output value to generate.  
LSTM has been successfully applied to many natural language processing problems. Especially, it shows good performance in the language modelling [26], which calculates the probability of seeing the next word in the sentence, and the machine translation field [27], which decides which sentence to output as the result of automatic translation. In this study, we use this characteristic of LSTM to train the features of sentence segmentation of a series of consecutive words. We also create a model that can automatically segment sentences by creating a degree of segmentation of the sentence as the output value of the network.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 2 Materials and Methods
### Datasets
#### 2.1.2 Database Information  
The majority of the examinations were retrieved and anonymized from CHU-Dijon Hospital. The acquisitions were performed by using the AERA, SOLA and SKYRA scanner models from Siemens. Notably, the SKYRA model has a 3.0 Tesla field strength, while the other scanners operate at 1.5 Tesla. In both LAX orientations, an additional subset of 44 examinations from Siemens scanners, sourced from the DETERMINE LV Segmentation Challenge of the Cardiac Atlas Project validation database [38; 39] was included. These examinations were conducted by using the AVANTO, SONATA and ESPREE scanner models.  
For the LAX 4-chamber database, 219 examinations from the MnM2 challenge obtained by using Siemens scanners were also incorporated [23], thereby adding four additional sources to this database. The inclusion of MnM2 images contributes to the difference in the number of images between the two views, as mentioned in Section 2.1.1, given that the challenge did not specifically focus on the 2-chamber view. Existing annotations for examinations from these two additional sets were modified and adapted to the previously defined delineation procedures.  
Image matrix sizes ranged from 156 \(\times\) 192 to 290 \(\times\) 352 voxels for 2-chamber images and from 192 \(\times\) 156 to 352 \(\times\) 330 voxels for 4-chamber images. The image resolutions are isotropic in the plane and range from 0.99 to 2.08 mm on both databases. The out-of-planeresolution varies from 1.00 to 9.00 mm on both databases. Since these sequences consist of a single spatial slice, the out-of-plane resolution was not considered for the experiments.  
No selection based on pathology was made during the data collection. As a result, both datasets comprise multiple diseases in addition to normal examinations. The included conditions encompass myocardial infarction, cardiopathies and congenital conditions, many of which can potentially alter heart anatomy.