esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Reliable communication is the enabler of black start in autarkic DER powered microgrids. LTE/5G-based PLMNs can be used to provide reliable communications to MT  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transition** & **Transition Purpose: Time to** & **Value (hours)** \\ \hline T\_eNodeB & Mean time for eNodeB failure & 0.002 \\ T\_Core\_Net & Time to bring up the DERs generation & 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Core Network, eNodeB mean times to failure [23].  
Figure 8: Test Case 2: CTMC models of communication link failure model (**a**) with and (**b**) without IOPS.  
Figure 9: Test Case 2: Probability (**a**) and mean time of failure (**b**) of the MAS communications with/without IOPS.  
even during a blackout by differentiating the microgrid MTC devices from other MTC and H2H users and giving them preferential handling in emergencies.  
Using LTE/5G-based PLMN for microgrid communications allows re-use of any existing PLMN which covers the area of the microgrid. This means that reliable MTC can be achieved without having to build a separate network, which cannot be achieved with other wireless technologies such as LoRa.  
CTMC modelling of the use of PLMNs shows blackout recovery time being reduced by a factor of the order of 100 compared to the conventional black start method. The direct consequence is reduced power outage duration locally in the microgrid. Furthermore using the DERs for blackout recovery will reduce the need for central generators to provide black start ancillary service.  
PLMN-based Access Barring, which is available in commercial PLMNs, can provide this preferential, differentiated handling and its use to achieve reliable MTC communication during blackout is demonstrated in this work. However, its use requires an inefficient static pre-allocation of radio network resources to the microgrid MTC devices into a separate PLMN. In future, reliable MTC communication during blackout may be achieved more efficiently by using the Enhanced Access Barring (EAB) Override or Application specific Congestion control for Data Communication (ACDC) features instead, when these features are available in commercial PLMNs.  
Conceptualization, P.M., A.S. and S.R.; methodology, P.M., A.S. and S.R.; software, A.S. and Z.M.; validation, A.S. and Z.M.; formal analysis, A.S.; investigation, P.M., A.S. and S.R.; resources, S.R.; data curation, A.S., Z.M.; writing--original draft preparation, P.M., A.S.; writing--review and editing, P.M., visualization, P.M., A.S.; supervision, P.M.; project administration, P.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the German Ministry for Industry and Energy, grant number 03 ET7549A).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Roughness Analysis  
#### 2.4.1 Surface Roughness Characterization  
Each milled surface roughness is characterized according to the diagram presented in Figure 3 (Figure 3, Step B). Two dimensional (2D) and three dimensional (3D) roughnesses are computed according to ISO 4287, 4288 standards [25; 26] for 2D roughness applications and ISO 25178 standards [28] for 3D applications. The 3D roughness parameters are computed on \(0.8\times 1\) mm\({}^{2}\) planar surfaces. Two surfaces per sample are recorded and 3D roughness are computed in the middle of the milled surface. Then, the mean and standard deviation are calculated. In accordance with standard EN 623-4 [29], aberrant points are excluded from the area, if necessary. From each 3D surface recording, 2D roughness profiles are selected and extracted perpendicularly to the feed rate direction (Figure 4a). Three profiles (around 1 mm length) per sample are selected in the middle of the milled surface. Then, the mean and standard deviation are calculated.
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 1 Introduction
Various fusion methods, including voting-, averaging-, maximum-, and probability-based approaches, are employed. 2. Our fall detection system is based on skeleton data, which means that the accuracy of person detection and pose estimation significantly influences the fall detection performance. Through comprehensive performance comparisons with several detection models and pose estimation models, we propose suitable person detection and joint point estimation algorithms for fall detection. In the experiment, YOLOv3 [28] and YOLOv7 [29] were employed to detect individuals. Moreover, AlphaPose [30], a convolutional neural network (CNN)-based approach, and ViTPose [31], which is based on a vision transformer (ViT) [32], were utilized to estimate joint points. This paper is organized as follows: In Sections 2 and 3, we provide a comprehensive overview of relevant studies that have attempted to detect falls and the details of the entire system process and of each module used to determine falls, respectively. The dataset, experimental setup, and experimental results are presented in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Fishing Gear Type Identification from Automatic Identification System Trajectory Data
### The Proposed Deep Neural Network Model for Fishing Gear Type Identification  
Each fishing gear type has its own unique fishing activity which can be characterized by the fishing ship movement and marine environment data changes such as position, ship course, speed, and sea depth. To determine the fishing gear types of a fishing ship, it is necessary to examine its local motions and sequential motion changes over a given voyage. Even though each fishing gear type exhibits some patterns, it is difficult to directly determine the fishing gear type of a ship. Machine learning is an excellent tool to address this kind of problem and is an effective technique for extracting or identifying patterns within a complicated data set. Deep learning is a kind of machine-learning technique that has recently created many success stories in complicated application domains with many types of data. It is one excellent approach for finding ideal models using neural network models [25].  
This paper proposes a deep-learning based fishing gear type identification method that uses a deep neural network model, of which front parts are a combination layer of a convolutional neural network and a fully connected neural network and the backend is a fully connected neural network. The fishing gear type identification problem is a classification problem in which the input is the AIS trajectory data of a fishing ship trip, and the output is the label of fishing gear type. The proposed  
Figure 3: Ship speed distributions for fishing gear types over the time spans: the morning time span of 5:00 to 12:00 in blue, the afternoon time span of 12:00 to 20:00 in black, and the night time span of 20:00 to 5:00 in red.  
deep neural network model reads in AIS trajectory and marine environment data and determines the corresponding fishing gear type class.  
The proposed deep neural network model combines both the 1D CNN model for fishing ship movement and sequential trajectory-pattern learning and the fully connected neural network (FCNN) model for fishing-environment feature extraction, as shown in Figure 4. The proposed model consists of the input data preparation module, the feature extraction module for fishing activity and environments, and the fishing gear type prediction module.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 4 Method Implementation and Validation
### Imperceptibility  
The objective here is to evaluate the similarity between the stego and host signals in a quantitative way. The higher the similarity, the higher imperceptibility is, a desired feature in steganography methodologies [19].  
Consolidated results from the 600 imperceptibility tests are shown in Figures 4 and 5. The measurement parameter is _PCC_ (Pearson Correlation Coefficient) between the stego and host signals, where the ideal value is 1 and the null correlation value, 0. The first analysis (Figure 4) is performed by arranging the results from each host signal that was used during the process (10 groups with 60 tests each, i.e. 10 secret messages by 6 \(BH\) values). This graph shows confidence intervals, where each box gathers 95% of the results. According to them, it can be inferred that the selection of a host signal has little influence in the secret message imperceptibility. In other words, regardless of the selected host signal, a very high imperceptibility is expected, since the _PCC_ value in the average test was higher than 0.988, and is over 0.985 in at least 95% of all the tests.  
Figure 3: Example signals: Host, secret and stego signals. Source: [18].  
The second analysis involves the influence of the \(BH\) parameter on imperceptibility. In order to see this, results are grouped according to secret message and \(BH\) value. Thus, 60 groups are set (6 groups by secret message, 10 secret messages in total) as shown in Figure 5. This graph clearly shows how the imperceptibility grows as the \(BH\) value increases. The higher the \(BH\) value, the more similar the stego and host signals are in an exponential manner. For instance, an improvement in \(PCC\) between \(BH\) 1 and 2 is much higher than the improvement in \(PCC\) between \(BH\) 5 and 6.  
In either case, even with a \(BH\) of 1, the similarity between both signals is very high, with a minimum \(PCC\) value of 0.975. From a \(BH\) of 3, the \(PCC\) value exceeds 0.955. Another feature that was validated through tests is that imperceptibility results are stable, meaning that the selection of the secret message does not have an influence on the quality of the stego signal. For different secret messages, the approximate same \(PCC\) values were obtained.
## 1 Introduction  
In the era of the Internet of Things (IoT), the unprecedented proliferation of devices connected to the Internet has made them omnipresent. These devices, ranging from simple sensors to complex systems, are increasingly becoming the backbone of many critical applications across various sectors, including healthcare, agriculture, driving, smart cities, or industrial automation [1].  
Nonetheless, the diversity of IoT technologies, including different communication protocols and network standards, poses significant challenges in achieving seamless interoperability. Specifically, the integration of 6 LoWPAN (IPv6-based) devices within legacy IPv4 networks--classical internet networks--remains a critical hurdle, hindering the realization of the full potential of IoT applications [2; 3].  
The transition from IPv4- to IPv6-based computer machinery and networking has not been accomplished yet, with no real coexistence between the two [4; 5; 6]. In fact, there are still several foundational issues to address for this movement to be completed, such as the following: (i) providing support and compatibility for certain operating systems, hardware and platforms; (ii) resolving unstable routing or name services; and (iii) managing discordant security policies.  
The situation is not very different in the IoT scope, and is even worse. IoT devices used to count on fewer resources; they have less ease of expansion, and lack up-to-date, long-term service usage. Moreover, the heterogeneity and vast number of IoT devices exacerbate the challenges associated with achieving a seamless transition and interoperability between IPv4 and IPv6 in this very field [7; 8; 9]. This is compounded by the diversity of communication protocols that are unique to IoT ecosystems, which further complicate the will to integrate.  
Thus, it is of paramount importance to deploy devices/systems that not only facilitate communications between IPv4 and IPv6 networks, but also address the unique constraints and very protocols of IoT devices. Such solutions require innovative, yet simple approaches to ensure compatibility among players, whilst providing efficacious, reliable connectivity in a greatly heterogeneous, resource-constrained environment.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Preprocessing** & **Highest-Performing** & **Matthews** & \multirow{2}{*}{**Confusion Matrix of Algorithm Trained on the Main**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**(\(n\) = 650 Spectra)**} \\
**Approach** & **Classification Algorithm** & & & & \\  & & **(\(MCC\))** & & & \\ \hline \multirow{4}{*}{Unfiltered} & Subspace kNN & \multirow{4}{*}{0.72} & True Class & 1 & 96 & 24 \\  & (learners = 30, subspace dimension = 48) & & & 2 & (True negatives) & (False positives) \\  & & & & 1 & 2 \\  & & & & & Predicted Class \\ \hline \multirow{4}{*}{Filtered} & Fine kNN & \multirow{4}{*}{0.75} & True Class & 1 & 96 & 24 \\  & & & & (True negatives) & (False positives) \\ \cline{1-1}  & Fine kNN & \multirow{4}{*}{0.75} & & 2 & 25 & 505 \\ \cline{1-1}  & & & & (False negatives) & (True positives) \\ \cline{1-1}  & & & & 1 & 2 \\ \cline{1-1}  & & & & & Predicted Class \\ \hline \end{tabular}
\end{table} 
Table 2: Overview of the performance characteristics of the highest-performing classification algorithm trained on unfiltered and filtered spectra, respectively. A fine kNN algorithm trained on the filtered dataset of n = 65 teeth achieved the highest \(MCC\), indicating its remarkable performance despite the imbalanced distribution of the sample. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Occlusal Photograph** & \
\begin{tabular}{c} **HSI-Based Classification** \\ \end{tabular} &
\begin{tabular}{c} **Histologically** \\ **Decrimmed** \\ **Anamel State** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clinical simulation of enamel health predictions using HSI-based automated classification as a decision-support system. Five additional teeth (two stained [(a) and (b)], three stained lesion [(c) through (e)]) were imaged and classified by the previously trained fine kNN classification algorithm (i.e., the samples in this table were not included in algorithm training). Stained sound enamel is displayed green, whereas lesions (stained or unstained) are displayed red. Polarization microscopy images from each tooth’s sectional plane (dotted lines) serve as reference. All scale bars = 1 mm.
## 2 Materials and Methods
### Analytical Model
For this test, the resultant load is the symmetrically acting pressure difference \(p\) [kPa] between the gap and the environment. Figure 3: Test scheme: (**a**) initial parameters; (**b**) final parameters. The IGU dimensions should be understood as the distance between the supports (measured \"in situ\"); in this test, it is 488 mm \(\times\) 488 mm. The following glass parameters are assumed: [PERSON]'s modulus \(E\) = 70 GPa, [PERSON]'s ratio \(\mu\) = 0.23, and glass thickness \(d\) = 6 mm. For the above data and assuming the simple connection of the component panes with the spacer, the dimensionless factors are: \(\alpha_{\rm v1}\) = \(\alpha_{\rm v2}\) = 1.75189 \(\times\) 10\({}^{-5}\) m\({}^{5}\)/kN. The gap volume and conduit volume are also determined based on \"in situ\" measurements (the width of the edge, i.e., the spacer and the seal, are taken into account); in this case, \(v_{\rm st}^{*}\) = 3.68993 \(\times\) 10\({}^{-3}\) m\({}^{3}\). Based on the base model, considering the actual conditions during the measurement of \(p_{\rm a}\) and \(T_{\rm a}\), the values \(\Delta p_{\rm sim}\) and \(\Delta T_{\rm sim}\) were calculated, which define the value of the pressure or temperature change simulated by a specific series. Table 1 shows the equivalent values of change in the atmospheric pressure and gas temperature inside the gap, which correspond to the amount of air injected or withdrawn into/from the gap during the experiments. These values were obtained based on the analytical model presented in [8; 9]. In order to understand the meaning of these values, it should be noted that in real IGUs the baseline is the initial conditions in the gap, which are not always known. For example, if one takes the reference levels \(T_{0}\) = 20 \({}^{\circ}\)C and \(\Delta T_{\rm sim}\) = 15.908 \({}^{\circ}\)C, the temperature in the gap increased to almost 36 \({}^{\circ}\)C (test series 4). This effect may result from sun exposure, see [38]. The fluctuations in the atmospheric pressure of \(\pm\)5 kPa may also result from natural weather variations or the difference in altitude above sea level between the place of production and the place of operation of the IGU [3; 7]. Thus, the values presented in Table 1 provide possible loads under certain weather conditions that correspond to the amount of air injected or withdrawn into/from the gap during the experiments. It should be noted that the base model [8; 9] is compatible with similar models described in the literature in terms of determining the resultant load from climatic influences. The additional analysis carried out showed that, in terms of data from Table 1, the results do not differ by more than 2.39% from the results according to [1] and [3].
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 4 Band Expansion through Porous Materials
### Porous Materials Arranged in Series  
In order to further improve the absorption performance beyond 6000 Hz, a composite metasurface is obtained by placing a melamine foam, as a porous layer, on the structure's surface. Given that the layer's area corresponds exactly to the incident area, i.e., \(\eta=1\), its acoustic resistance can be incrementally enhanced to optimize the absorption performance for the high-frequency sounds. As depicted in Figure 6a, the influences on the sound absorption performance of the foam thickness are first pronounced. The parameters of the foam here are different from those of the porous cells, which are the porosity \(\phi=0.97\), tortuous factor \(\alpha_{\infty}\)= 1.02, flow resistance \(\sigma=19\),780 Pa\(\cdot\)s/m\({}^{2}\), viscous characteristic length \(\Lambda=90.3\)\(\upmu\)m, and the thermal characteristic length \(\Lambda^{\prime}=178.5\)\(\upmu\)m. As the thickness increases, the absorption coefficient experiences a notable enhancement, accompanied by a gradual shift in the peak frequency towards lower frequencies. At a thickness of 10 mm, the foam exhibits an average absorption of 80% in the frequency range above 6000 Hz. However, when the thickness is 20 mm, although the absorption performance is enhanced, its thickness is almost the same as that of the HR structure. This doubles the thickness of the structure, which is not desirable.  
The foam with a thickness of 10 mm is, therefore, adopted as the porous layer. As shown in Figure 6b, the metasurface achieved an average absorption over 85% across the frequency range of 750-10,000 Hz. The low-frequency sound can penetrate the porous layer and be absorbed by the HR structure behind it. Owing to the additional resistance of the porous layer, the overall resistance of the metasurface is augmented, leading to a decrease in the absorption peaks and a smoother absorption spectrum. When the high-frequency sound  
Figure 6: Sound absorption coefficients of **(a)** the melamine foam with different thicknesses and **(b)** the metasurface, HR structure, porous cells and porous layer.  
waves strike the metasurface, the HR structure loses the absorption ability and functions as a rigid wall, which reflects all incoming sound waves. Consequently, the sound wave is absorbed by the porous layer, thus maintaining its original absorption performance.
## 6 Other Technologies  
### Ultrawide Bandwidth Technology  
Ultrawide bandwidth radio is a fast emerging technology with uniquely attractive features that has attracted a great deal of interest from academia, industry, and global standardization. bodies. The most widely accepted definition of a UWB signal is a signal with instantaneous spectral occupancy in excess of 500 MHz or a fractional bandwidth of more than 20%. One of the most promising UWB techniques, especially for WSN applications, is named Impulse Radio-UWB (IR-UWB) [37; 38]. The IR-UWB technique relies on ultra-short (nanosecond scale) waveforms that can be free of sine-wave carriers and do not require IF processing because they can operate at baseband. The IR-UWB technique has been selected as the PHY layer of the IEEE 802.15.4a Task Group for WPAN Low Rate Alternative PHY layer [39]. The baseline of 802.15.4a is based on two optional PHYs consisting of a UWB impulse radio (operating in unlicensed UWB spectrum) and an other option operating in unlicensed 2.4 GHz spectrum, where the former will be able to deliver communications and high precision ranging.  
Figure 13: The mean energy spent by an 802.15.4 node working in non beacon-enabled mode, belonging to a network of \(N\) nodes organised in a star topology.
## 4 Game Theoretic Client Selection Model  
This section introduces our RBPS as a centralized strategy with cooperative intent, framing it within the context of a cooperative game where clients contribute to a shared objective by participating in the FL training process based on a common reward-based scoring strategy. By applying the concept of NE, we identify stable selection outcomes in which no client can improve its reward by deviating alone. Figure 1 shows the main structure of our system, where RBPS is used to determine which clients should join the FL process. In this setup, each client is evaluated based on its current battery level, data sensitivity, and potential accuracy contribution. The server selects the top-scoring clients to participate, collects their updates, and sends back the improved model. Clients that are not selected do not participate and do not receive the model, helping conserve battery and protect privacy. We use NE to reach a cooperative and stable situation, where no client has an incentive to change its state unilaterally, and where selection benefits both individual clients and the global model performance.