esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Research
### Edge Computing-Based Solutions  
A design method for a smart building (where various subsystems must be integrated to develop intelligent facilities), comprised of a two-level architecture (edge and fog layers) and embedded devices in edge or fog nodes, was proposed and implemented in [25]. A resource preservation net (RPN) framework for emergency department (ED) systems was proposed in [26]. The proposed framework was designed to model non-consumable resources and was simulated, highlighting significant improvements in length of stay (LoS), resource utilization, and patient waiting time. A video analytics-based intelligent indoor positioning system that provides centimeter-level positioning accuracy was implemented in [27]. The system modularized a video analytics process that improves its efficiency, demonstrating a lower response delay than a traditional cloud computing model. A knowledge-sharing framework based on edge computing was proposed in [28], which enables an IoT device to share its knowledge with other devices at the edge in the vicinity. The authors found that the proposed framework reduces the response time for intelligent IoT devices and the power consumption for computation. The study in [29] proposed the use of an IoT edge platform and a social computing framework to build an energy efficient system in a public building scenario. The system has advantages such as reduced data transfer from the IoT edge to the cloud and reduced computing and network resource costs.
# Cybersecurity in the Internet of Things in Industrial Management
## 4 Discussion
### Blockchain and Cloud Computing  
The decentralized architectures of IoT devices are a current debate. The latest developments in Blockchain technologies, for example, have allowed the use of a smart ecosystem able to support cybersecurity mechanisms across distinct sectors such as smart home installations, focusing on the immutability of users and devices as well as the dynamic and immutable management of blocked malicious IPs [25]; in multiple industrial applications, healthcare, finance, and government [20] in terms of solutions for cybersecurity problems such as accountability, traceability, and identification [12].  
The key point is how to improve the security of systems architecture to protect against malicious internal users and malware implanted inside the system, which can be solved through an effective sensor network [70], inherent to Blockchain security architecture [19]. Blockchain may contribute to privacy, security, and non-repudiation of an IoT system, through the large amount of data generated and variety of sensors and devices adopted [2], as Blockchain technology builds a scalable and decentralized end-to-end secure IoT system [14]. Furthermore, the IoT can be enhanced with an AI at the gateway level to detect and classify suspected activities [14]. Moreover, Blockchain technology is also of use in parallel with cloud computing for higher education, in terms of primary infrastructure topology, putting together machine learning and artificial intelligence on training opportunities [35].  
Cloud computing is therefore highly correlated with Blockchain technology, namely in preventing attacks against, for example, radio-frequency (RF)-enabled hardware, Internet of Things (IoT) firmware, and wireless protocols [50]. Interconnectedness of intelligent devices and the use of public networks is at the center of debate in smart cities due to interconnected services for their citizens, in which cybersecurity has become a major concern [29], namely in issues such as communication infrastructures, cloud computing, smart health, and energy management [26].  
The discussion on cloud computing covers subthemes of cybersecurity of supply chains based on software and networks, to minimize risks of purchasing and disconnection of key machines from networks [46]. To summarize, 5G and 6G networks can provide novel communication networks infrastructure, although IoT systems will still have the same energy capacity for hackers to take advantage of these weaknesses. There is a need for a system that may identify and counter potential threats in next generation networks and decentralized systems such as Blockchain [15].
## 9 Conclusions  
In this paper, based on the Dejiang tunnel in Tongren City, Guizhou Province, the SPH-FEM coupling calculation method is used to analyze the water inrush conditions of the tunnel under different rock wall thicknesses, and the minimum outburst prevention thickness of the karst tunnel under blasting is determined according to the analysis of the unit stress data of the calculation model. The main conclusions are as follows:  
1. Compared with the independent effects of the blasting load and water pressure, the coupling effect of the blasting stress wave and water pressure will increase the depth of damage to the tunnel rock wall and increase the risk of water inrush inside the tunnel;
2. Under the coupling action of the blasting load and water pressure, as the thickness of the rock wall increases within a certain range, the stress, vibration velocity, and displacement of the unit at the junction of the rock wall and the aquifer show a variation law that first decreases, then increases, and then decreases, and there is peak stress, peak vibration velocity, and peak displacement. In order to reduce the risk of water inrush during tunnel construction, the minimum thickness of the rock wall for outburst prevention should be at least greater than the width of the rock wall corresponding to the peak stress, peak vibration velocity, and peak displacement;  
Figure 15: On-site diagnosis and treatment measures: (**a**) over-drilling. (**b**) grouting reinforcement.  
* Comparing the minimum outburst prevention thickness analyzed based on the actual geological parameter modeling with the existing theoretical calculation formula for the minimum outburst prevention thickness of karst tunnels under drilling and blasting conditions, the error between the two is only 9.7%. The minimum anti-outburst thickness guides the construction and ensures the construction safety of the supporting project.  
Writing--original draft preparation, Y.D.; writing--review and editing, X.Z. ([PERSON]), X.Z. ([PERSON]) and X.O. All authors have read and agreed to the published version of the manuscript.  
Supported by the National Natural Science Foundation of China (51978671), the Graduate Research Innovation Project of Hunan Province (CX20200366), the Graduate Innovation Project of Central South University (1053320213141), the Natural Science Foundation of Hunan Province (2021 JJ40592), the National Natural Science Foundation of China (52008039).  
Not applicable.  
[PERSON], [PERSON], [PERSON] and [PERSON] authors made significant contributions to the manuscript, including providing the geological parameters required for the analysis of the manuscript, conducting engineering field tests, and also revising the manuscript, among other tasks. They played a vital role in the improvement of the manuscript.  
The authors declare no conflict of interest.
## 4 Evaluation  
### Data Sets and Experimental Settings  
We evaluated our model on a DailyDialog dataset [22], which has been widely used in recent studies. DailyDialog has 13,118 daily conversations for English learners. The datasets are separated into training, validation, and testing as 8:1:1. Bilingual evaluation understudy (BLEU) [23; 24], bag-of-words (BOW) embedding [25], and Distinct [7] were used as performance measures. BLEU measures the number of generated responses that contain word \(n\)-gram overlaps with gold responses, as shown in the following equation:  
\[\text{BLEU}=\text{min}(1,\,\frac{\text{length of a generated sentence}}{\text{length of a gold sentence}})(\prod_{i=1}^{n}\text{precision}_{i})^{\frac{1}{n}} \tag{7}\]  
where \(n\) is the maximum number length of \(n\)-grams considered and is commonly set to 4, and \(precision_{i}\) is a word \(i\)-gram precision (i.e., the number of correct word \(i\)-grams divided by the number of word \(i\)-grams in a generated sentence). Precision of BLEU is an average score of BLEUs of 10 generated sentences per query, and Recall of BLEU is a maximum score among BLEUs of 10 generated sentences per query. The BOW embedding metric is the cosine similarity of BOW embedding between generated and gold responses. The BOW embedding metric consists of three metrics: Greedy [26], Average [27], and Extrema [28]. In our test, the maximum BOW embedding score among the 10 sampled responses was reported. The distinct score, such as Intra-dist or Inter-dist, computes the diversity of the generated responses. Dist-\(n\) is defined as the ratio of unique word \(n\)-grams over all word \(n\)-grams in the generated responses. Intra-dist is defined as the average of distinct values within each sampled response, and Inter-dist as the distinct value among all sampled responses.
In this study, controlling the orientation and spatial location of a non-spherical micro-particle by synthesized sound field is proposed, where the fewest transducers are to be employed. The working principle is firstly introduced with a detailed description of the employed sound field synthesis technique. The rotating and transporting transformation of the desired field, which determines the particle orientation and position, is theoretically demonstrated. To validate the proposed method, the prototype device is developed. Calibration of the employed transducers is performed before measuring the synthesized and transformed sound field. Finally, a silica rod is used as the target to perform the experiment.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 3 Methodology
### Sampling Phase in SEMMA  
In the Sample stage of the SEMMA process model, the data collection procedure is performed. Various network models are designed and implemented in the Contiki Cooja simulator, which is designed for simulating the resource-constrained IoT networks with RPL enabled for data communication and routing. The next subsections explain the data generating models developed in this study which are broadly classified into the Normal Traffic Network Model, PS-R Attack Model, and SN-W Attack Model.  
Due to the scarcity of publicly available protocol-specific and SN-inherited data, researchers generate the datasets using simulations and capture the packets using the 6 LoWPAN analyzer. It is a frequent practice observed in literature. For example, Ref. [12] have created network models depending on the requirement of the attack dataset and simulated the network scenarios for data collection. Therefore, similar practice is followed for data collection in this paper using the Cooja simulator. The network models of interest are designed and implemented in Cooja, which include, normal traffic network model, PS-R attack network model, and SN-W attack network model. The network size, distance coverage, and node positioning parameters considered in this paper are supported through relevant literature, a summary of which is given in Table 3.  
The first attack model is designed to simulate the PS-R attack, where the attacker nodes relay false rank values in addition to ideal conditions as a prospective parent using DODAG Information Object (DIO) messages to attract the child nodes. The attacker nodes are deliberately implemented in close proximity to the sink to speed up the parent selection process and for recording the attack effects. The malicious node initiates with a delay of 60 s after the network is stabilized. The victim nodes consider the attacker as a parent and eventually joins it. In the second attack model, i.e., SN-W attack, two nodes create a tunneling effect logically by exploiting the control messages. They probe the neighbor nodes using the DODAG Information Solicitation (DIS) control message to join a node and initialize the traffic transmission process. Other control messages including DODAG Information Object (DIO), DODAG Advertisement Object (DAO), and DAO acknowledgment are used to illegitimately present the shortest path towards the root node to attract the victim nodes. A similar strategy to the previous attack is used in this attack as well, in terms of attacker node placement, where they are placed near the sink and victim nodes for speeding up the attack process. The closeness of malicious nodes to the sink appears favorable to the victim nodes and, as a result, they join the attacker parent(s). The  
Figure 2: SEMMA process model adopted for ProSenAD development.  
effects of the two attacks on the network resources and routing mechanism are presented in the previous section.
###### Abstract  
With the development of bridge engineering, U-shaped girder bridges have been applied in numerous bridge designs due to their structural characteristics. However, the U-shaped girder bridge is sensitive to solar radiation effects, leading to uneven temperature distributions that can affect the service performance of the structure. Thus, this study proposes an analysis method for the temperature distribution of U-shaped girder bridges and develops a prediction model to estimate temperature gradients. First, an improved ASHRAE clear sky model is proposed to calculate the structural shadow areas under sunlight, which provides a basis for the numerical simulation of U-shaped girder bridges under solar radiation effect. Then, a three-dimensional finite element model of the U-shaped girder bridge is established, and its correctness is verified by comparing with the actual temperature data. The temperature distribution of the U-shaped girder bridge under solar radiation is simulated using the verified model to obtain the maximum temperature difference and temperature variation characteristics. Finally, a prediction model for the temperature gradient is developed using nonlinear fitting approaches, and its accuracy is confirmed through comparison with actual data. The results indicate that the temperature distribution of the U-shaped girder bridge has minor changes along the longitudinal direction, while there are significant changes in the transverse distribution; the temperature distribution exhibits nonlinear changes in the height direction of the two side webs and the lateral direction of the bottom slab, with the maximum temperature difference reaching 17 \({}^{\circ}\)C; the fitting effect of the prediction model is very good, the correlation coefficients of the fitting curve and the actual data are all greater than 88%, providing a basis for the analysis of the temperature effects on U-shaped girder bridges and its application in design specifications.  
U-shaped girder bridges; solar radiation; temperature distribution; field application; finite element model; temperature gradient prediction model +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Attention Modules
#### 3.4.1 Attention Gate  
[PERSON] et al. [46] propose the Attention Gate for their Attention U-Net model. Said AGs automatically learn to focus on relevant structures in the images without the need for additional supervision [46]. They do so by suppressing feature activations in irrelevant regions without adding a significant number of parameters to the model.  
The AG module is positioned before the concatenation step between a layer in the decoder and a corresponding layer in the encoder. It receives a gating signal \(g\), which is the feature map of a layer in the decoder, and an input \(x^{l}\), which is the feature map obtained from a corresponding layer in the encoder and has a coarser resolution. Then, both \(g\) and \(x^{l}\) go through a convolutional layer that allows their dimensions to match. Next, the resulting feature maps are summed element-wise and then go through a ReLU activation function. Then, the result goes through a \(1\times 1\) convolution layer and a sigmoid activation function that scales the resulting vector to a range between zero and one. This output contains the _attention coefficients_, where values closer to one indicate relevant features. Then, the _attention coefficients_ are up-sampled through trilinear interpolation to the original dimensions of the \(x^{l}\) input. Finally, the up-sampled _attention coefficients_ are multiplied element-wise to the \(x^{l}\) input. Lastly, the resulting feature map \(\hat{x}^{l}\) is then concatenated to the corresponding layer in the decoder, just as in a regular U-Net model. Figure 12 shows the structure of the AG proposed by [PERSON] et al.  
Finally, the authors incorporate their proposed AG to a regular U-Net architecture and test it on biomedical image datasets, showing an increase in the segmentation performance with different types of images and different sizes of the training set.
## 3 Conclusions  
In summary, we successfully fabricated the PHZ polymer electrolyte membrane by incorporating the metal-organic framework material ZIF-8 into PVDF-HFP and employing UV light-curing rapid prototyping for full polymerization. The porous network structure of ZIF-8 formed additional ion transport channels within the polymer matrix, accelerating lithium-ion migration. The uniform Li\({}^{+}\) flux distribution regulated by ZIF-8 channels effectively reduced dendrite growth risks and improved battery cycling stability. The PHZ polymer electrolyte membrane with 4 wt% ZIF-8 exhibited an ionic conductivity of 1.17 \(\times\) 10\({}^{-3}\) S/cm, an electrochemical stability window of 4.7 V, and a lithium transference number of 0.7. The solid-state battery assembled with PHZ polymer electrolyte demonstrated an initial discharge capacity of 168 mAh g\({}^{-1}\) at a 0.1 C discharge rate, with a capacity retention rate of 95.8% and a Coulombic efficiency of 99.5% after 100 cycles. at 1 C, it was 48 mAh g\({}^{-1}\); and at 2 C, it was 28 mAh g\({}^{-1}\). These results indicate excellent rate and cycling performance. Therefore, the PHZ electrolyte material offers a novel approach and method for future lithium metal batteries research and development.
## 3 Results
### Quality Assessment  
The quality assessment of the 60 studies included in this review revealed a diverse methodological landscape with varying degrees of strength and reliability. Most empirical studies, comprising experimental, observational, and mixed-methods research, were judged as suitable for appraisal using the Cochrane RoB 2.0 tool. Most demonstrated low to moderate risk of bias, contributing valuable real-world insights despite occasional limitations in reporting transparency or measurement standardization. For example, [PERSON] et al. (2024) conducted a meta-analysis on white noise in NICUs [19], while [PERSON] et al. (2025) and [PERSON] et al. (2023) offered good observational assessments of ICU noise levels [24; 34]. [PERSON] et al. (2022) used clinical trial methodology to measure stress reduction via music therapy in dental clinics, showcasing strong experimental validity [44]. Further, systematic and scoping reviews, generally demonstrated moderate confidence. These included comprehensive literature syntheses, such as [PERSON] et al. (2024), which identified architectural research gaps in inpatient environments [7], and [PERSON] & [PERSON] (2018), which summarized evidence on natural soundscapes and stress reduction [63]. While structured and insightful, some of these lacked formal risk-of-bias evaluations for included studies, slightly limiting their generalizability. Moreover, narrative reviews, conceptual papers, and book chapters, appraised through qualitative tools, were found to offer strong theoretical contributions, especially in areas like biophilic design and acoustic psychology [40; 46; 47].  
These sources enriched the review by framing the role of sound beyond mere decibel measurements, though their lack of empirical data constrains their direct applicability to clinical design contexts.  
In general, the assessment confirms a solid foundation of evidence, with a balanced mix of empirical investigations and theory-driven contributions. This blend affirms the growing maturity and interdisciplinary richness of research in healthcare and dental acoustic environments. In Table S1, the detailed quality appraisal results for each included study are summarized.
## Appendix A Derivation of the Matrices \(\mathbf{D}_{0}\) and \(\mathbf{D}_{1}\) in (24)
A multiplication of this inequality from the left and right with the symmetric block diagonal matrix \(\mathrm{blkdiag}(\mathbf{Q},\ldots,\mathbf{Q}):=\mathrm{blkdiag}\big{(} \mathbf{P}^{-1},\ldots,\mathbf{P}^{-1}\big{)}\succ 0\) of appropriate dimension yields  
\[\mathbf{D}_{0,\mathcal{C}}\otimes\mathbf{Q}+\mathbf{D}_{1,\mathcal{C}}\otimes \left(\mathbf{Q}\cdot\left(\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K}_{y} \mathbf{C}_{y}^{+}\bar{\mathcal{C}}\right)^{T}\right)+\mathbf{D}_{1,\mathcal{C }}^{T}\otimes\left(\left(\mathbf{A}_{v}-\mathbf{B}_{v}\mathbf{K}_{y}\mathbf{C }_{y}^{+}\bar{\mathcal{C}}\right)\cdot\mathbf{Q}\right)\prec 0\] (A13)  
and  
\[\mathbf{D}_{0,\mathcal{C}}\otimes\mathbf{Q}+\mathbf{D}_{1,\mathcal{C}}\otimes \left(\mathbf{Q}\mathbf{A}_{v}^{T}-\mathbf{Q}\big{(}\mathbf{C}_{y}^{+}\bar{ \mathcal{C}}\big{)}^{T}\mathbf{K}_{y}^{T}\mathbf{B}_{v}^{T}\right)+\mathbf{D}_{ 1,\mathcal{C}}^{T}\otimes\left(\mathbf{A}_{v}\mathbf{Q}-\mathbf{B}_{v} \mathbf{K}_{y}\mathbf{C}_{y}^{+}\bar{\mathcal{C}}\mathbf{Q}\right)\prec 0\enspace,\] (A14)  
which by applying the variable substitution  
\[\mathbf{NC}_{y}^{+}\bar{\mathcal{C}}=\mathbf{K}_{y}\mathbf{MC}_{y}^{+}\bar{ \mathcal{C}}=\mathbf{K}_{y}\mathbf{C}_{y}^{+}\bar{\mathcal{C}}\mathbf{Q}\enspace,\] (A15)  
resulting from the equality constraint (12) turns into  
\[\mathbf{D}_{0,\mathcal{C}}\otimes\mathbf{Q}+\mathbf{D}_{1,\mathcal{C}}\otimes \left(\mathbf{Q}\mathbf{A}_{v}^{T}-\left(\mathbf{C}_{y}^{+}\bar{\mathcal{C}} \right)^{T}\mathbf{N}^{T}\mathbf{B}_{v}^{T}\right)+\mathbf{D}_{1,\mathcal{C}}^ {T}\otimes\left(\mathbf{A}_{v}\mathbf{Q}-\mathbf{B}_{v}\mathbf{NC}_{y}^{+} \bar{\mathcal{C}}\right)\prec 0\enspace.\] (A16)  
**Remark A2**. : _Although (A16) is an obvious substitute for (13), it should be pointed out that the output feedback approach derived in this paper was designed on the assumption of the stabilizability of the plant by the considered system outputs; see the discussion of the control law (6) in Case 3. Imposing the additional constraint (A11) instead of purely demanding \(\Re\{s\}<0\) may make the design tasks infeasible due to the fact that a fully free, independent eigenvalue placement is typically impossible by means of a pure output feedback control._
## 2 Materials and Methods
### Statistical Analysis  
To assess reliability among the readouts and due to the ordinal structure of the data, inter-readout agreement was quantified using Krippendorff's alpha coefficients. Agreement was interpreted as follows: 0.00-0.20 = poor, 0.21-0.40 = fair, 0.41-0.60 = moderate, 0.61-0.80 = substantial, and 0.81-1.00 = almost perfect agreement.  
Differences in image quality ratings across reconstruction methods were assessed using the non-parametric Friedman test for repeated measures. In the case of significant results, post hoc pairwise comparisons were performed using Wilcoxon signed-rank tests. Resulting \(p\)-values were corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discovery rate. All statistical tests were two-tailed, and \(p\)-values \(<\) 0.05 were considered statistically significant. Data are presented as median (interquartile range) for ordinal variables and mean \(\pm\) standard deviation (SD) for continuous variables. All analyses were performed using R statistical software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria; [[https://www.R-project.org/](https://www.R-project.org/)]([https://www.R-project.org/](https://www.R-project.org/)), accessed on 1 June 2024).