esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Operation and Control
### Operation Support Tools
#### 2.5.4 Online Tools and Simulators
To summarize, Table 5 shows all the work described in this section. In the rows are shown types of strategies used (agent and MAS, distortion, catastrophes and online tools) while in the columns is shown the application rate on operation support tools (operation control: online and simulation, reliability of the control structure, etc.). As can be seen in the previous table, the operation and control are in need of online and offline process tools. In addition, these tools can be developed under the _MAS_ paradigm, and in some cases, they will be used to detect catastrophes and distortions in the electrical infrastructure.
# Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets
## 3 Preliminary
### Timed Colored Petri Net for the Multi-Type Robot Systems
Note that \([0,+\infty]\) was used to indicate that no time constraint was attached to a task region. Given an MTRS and its moving environment \(R=\{r_{1},r_{2},\ldots,r_{n}\}\) with \(n\) regions, a _task requirement_ for an MTRS that contains logic requirement and time windows is the sequence. (To simplify the notation, we ignore the time window \([0,+\infty]\) in \(\varphi\) in the remainder of the paper for all regions of interest with no time constraints.) \[\varphi=(r_{i_{1}},\Delta(r_{i_{1}}),[e_{i_{1}},l_{i_{1}}]),(r_{i_{2}},\Delta (r_{i_{2}}),[e_{i_{2}},l_{i_{2}}]),\ldots,(r_{i_{k}},\Delta(r_{i_{k}}),[e_{i_ {k}},l_{i_{k}}]) \tag{2}\]  
where \(r_{i_{j}}\in R^{I}\) is an interest region; \(\Delta(r_{i_{j}})\) is a state function defined in Equation (1), and \([e_{i_{j}},l_{i_{j}}]\) is a given time window corresponding to region \(r_{i_{j}}\). The following task requirement for the MTRS depicted in Figure 1,  
\[\varphi=(r_{1},E^{1},[2,5]),(r_{8},S^{1}),(r_{8},S^{2}),(r_{10},U,[20,30]),(r _{7},A), \tag{3}\]  
requires that the task in region \(r_{1}\) can only be performed by a \(C_{1}\) robot in the time window \([2,5]\); the task in region \(r_{8}\) should be performed by two types of robot, i.e., \(C_{1}\) and \(C_{2}\); the task in region \(r_{10}\) can be performed by any type of robot in time window \([20,30]\); and all robots should avoid region \(r_{7}\). Note that the task in region \(r_{8}\) does not have a time window constraint. In this paper, we considered an MTRS that contained \(K\) different types of mobile robots moving in a static surrounding \(R=\{r_{1},r_{2},\ldots,r_{n}\}\). For a given task requirement \(\varphi\) that contains logic constraints and time windows as defined in Equation (2), the goal of this paper was to find optimal trajectories for all robots to meet the task requirement, while the total travel distance of the MTRS was minimized.
## 3 Results and Discussion
### Method Performance  
The method for determining 26 PhACs using SPE cartridges and LC-MS/MS technique was validated. Table 2 gives an overview of the validation parameters. The initial linear range for each PhAC was narrowed until all data accomplished the specified criteria of the statistical tests. The method showed good working range, precision, and accuracy as well as low method detection and method quantification limits (MDL, MQL). The calibration curves of all the compounds were linear over a concentration range from 0.76 to 87 \(\mu\)g/L (depending on the analyte) with a determination coefficient (R\({}^{2}\)) between 0.9983 (E2) and 0.9999 (IBUP), coefficient of variation between 1.55% (SMX) and 6.66% (GTD), and all the PG values were lower than the F tabulated values for the corresponding degrees of freedom (F (0.05; 4;3) = 9.11). The MQLs ranged from 6.81 ng/L (DCF) to 223 ng/L (E1).  
The obtained recovery values were between 43.9% (GTD) and 160% (FLX). Despite the lower and higher recoveries of certain PhACs, the variability of the data was satisfactory with an RSD \(\leq\) 24%. (Table 2). Considering the complexity of matrices and the concentration ranges, the obtained recoveries were considered acceptable.  
The expanded uncertainty (\(U\)) of the analytical method was lower than 41%, with a confidence level of 95%.
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 2 Background and Motivation
### Threat Model  
**Adversarial capabilities:** We consider the cloud servers and the key servers as honest-but-curious, which means that they will honestly follow the storage protocol and execute the assigned tasks in the system. However, they would like to learn as much information as possible about the encrypted outsourced data via inferring the plaintext data from the ciphertext data stored in the cloud. Moreover, some clients are also curious and would try to access unauthorized data out of the scope of their privileges. Specifically, there are two types of adversaries considered in the system:  
* An inside adversary refers to a cloud storage service provider (owns the cloud servers) or any of the key servers. The inside adversary is assumed to be honest-but-curious and its goal is to infer the plaintext data from the outsourced ciphertext data it possesses to obtain useful information.
* An outside adversary refers to any external entity that attempts to gain unauthorized access to the data stored in the cloud. The outside adversary plays the role of a client, interacting with cloud servers and key servers.  
**Assumptions:** Our threat model makes the following assumptions: (i) All communications among the clients, the key server, and the cloud server are protected via SSL/TLS against tampering. (ii) The key manager rate-limits the client's key generation requests to defend against online brute-force attacks [1] (see Section 2.1). (iii) To ensure data availability, we deploy remote auditing [19] for data integrity and deduplication-aware secret sharing [14] across multiple storage sites for fault tolerance.
## 3 Method
### Multi-Task Loss  
#### 3.3.1 Multi-Task Supervised Loss of Labeled Data  
About the labeled data, shadow images are input and paired with annotated binary shadow masks as region ground truth (\(G\prime\)). To generate edge ground-truth (\(Ge\)), we employ the Canny operator [47] to the masks. With these targets, the multi-task supervised loss \(Ls\) for image (\(x\)) is computed by summing the supervised losses for shadow region detection (\(L_{r}\)'s) and shadow edge detection (\(L_{e}\)'s), i.e.,  
\[L^{s}(x)=L_{r}^{s}+\alpha L_{\rm e}^{s}+\beta L_{\rm c}^{s}\]  
where  
\[L_{r}^{s}=\sum_{j=1}^{9}\Phi_{BCE}(P_{r}(j),G_{r}),\]  
\[L_{e}^{s}=\Phi_{BCE}(P_{e},G_{e}),\]  
\(P_{r}(j)\) and \(P_{e}\) are the predicted shadow maps and shadow edge map, respectively. \(\Phi_{BCE}\) and \(\Phi_{MSE}\) are binary cross-entropy and MAE loss functions, respectively. We set weights \(\alpha=10\) and \(\beta=1\) for training.  
The region loss \(Lrs\) provides pixel-level supervision for segmentation. The edge loss \(L_{\rm es}\) gives boundary-aware guidance using both learning methods and focuses on accurately distinguishing subtle illumination transitions.  
By coupling region and contour cues, the model learns a holistic shadow representation. The joint modeling provides complementary signals--what is shadowed and where are the edges. This combination addresses inherent ambiguities better than a single target.  
In summary, shadow masks provide region supervision, Canny edges provide boundary supervision, and the multi-task loss combines BCE for regions and MAE for edges to enable holistic shadow learning through complementary region and contour cues.
# Optimization of Glass Edge Sealing Process Using Microwaves for Fabrication of Vacuum Glazing
## 2 Experimental Setup
### Basic Experiment  
The basic experiment was carried out to set the process and reaction variables. The factors that affected the quality of the sealed edge when the glass edges were sealed using microwaves were examined. Furthermore, different factors were derived in this stage to verify the characteristics of the sealed edge.  
The microwave radiation and the glass edge sealing were carried out at the glass transition temperature in this experiment. The sealing result showed inadequately pressed parts on the edges, thus creating unsealed areas. A liquid penetrant examination was conducted to verify the sealing [9; 10]. The liquid permeated through the glass to areas that were not sealed. Figure 3 shows an image of the liquid penetrant examination for the resulting edge sealing in the basic experiment.  
Figure 3: Liquid penetrant examination for the basic experiment.  
Figure 2: Schematic and image of experimental setup. (**a**) Schematics of the chamber for glass sealing; and (**b**) picture of the fabricated chamber.  
If a high pressure was applied to the four edges, the probability of complete sealing increased. However, in that case, a large step height between the sealed edges and the center of the glass was generated. This may increase the surface stress of the glass after the experiment, which could lead to breakage. Thus, appropriate fabrication conditions were required such that the step height between the center of the glass and the edges were minimized while allowing for completely sealed edges.  
To derive the minimal step height, five basic experiments were carried out with different step heights. In each experiment, the sealed glass was divided into four sections vertically and horizontally, resulting in 16 separate subsections. To measure the height of the separate regions, a laser displacement sensor (CD-33-30N-422; Optex, Ogoto, Japan) was used. Figure 4 shows the layout of the step height measuring system. Figure 5 shows the schematics of the step height measurement.  
After the glass height measurement, the measured glass sections were cut using the water jet technique. The exposed surfaces were put through liquid penetrant examination to ensure complete sealing. The sealed edges were further analyzed using Dino-Lite, which is a digital microscope. Figure 6 shows the cross-sections of the microwave sealed edges. Figure 6a shows an unsuccessfully sealed specimen, whereas Figure 6b shows a suc  
Figure 4: Step height measurement system layout.  
Figure 5: Step height measurement schematics.  
The experimental results showed that if the step height was less than 0.66 mm, a significant portion of the sealed edge was not sealed. Even when the step height was the highest, i.e., 0.93 mm, there existed regions where the sealing was incomplete. This suggested that when the step height was 0.93 mm, sealing faults could occur in specific sections. Therefore, a minimum step height of 0.93 mm is required for complete sealing.
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 4 Application of the Trained Network Models
### Filtering Results  
In the filtering process of this paper, after filtering, the displacement curve changed from unqualified to qualified. When approaching the reasonable filtering frequency obtained using the traditional method, there was no significant difference in the displacement curve picture, so it would be judged as qualified prematurely. The conventional data binary classification problem considers that as long as the prediction probability is greater than 50%, it is considered to belong to the class. In order to avoid premature judgment as qualified, stricter criteria should be adopted. Therefore, probability restrictions were added to determine whether the filtered displacement curve was qualified using the deep network model. Only when a higher probability is judged to be qualified can it be considered qualified and not be the traditional 50%. After comparison, this paper argued that more than 99.9% of the probability being considered qualified is appropriate. Figures 8 and 9 show the relationship between the high-pass cut-off frequency obtained by considering the probability and not considering the probability and the high-pass cut-off frequency obtained by using the traditional method when judging whether the displacement after filtering is qualified.  
As seen from Figures 8 and 9, almost all of these scatter points were distributed below the black line, indicating that the values obtained using the method in this paper were generally smaller than those obtained using the traditional method, and the unqualified displacement curve was prematurely judged as qualified. The black line here was y = x; if two values were equal, they were distributed on the black line.  
Figure 8: The high-pass cut-off frequency was obtained using a model that only trains the fully connected layer (unit: Hz). (**a**) The VGG19 when not considering probability; (**b**) the VGG19 when considering probability.  
Figure 9: The high-pass cut-off frequency obtained by training the model of all layers (unit: Hz). (**a**,\(c\),**e**,**g**) are the results of VGG19, ResNet50, InceptionV3, and InceptionResNetV2 when probabilities are not considered, respectively; (**b**,\(d\),\(f\),**h**) are the results of VGG19, ResNet50, InceptionV3, and InceptionResNetV2, respectively, when considering the probability.
# Signal Separation Based on Knowledge Representation
## 4 Numerical Simulation
### Separation of Mixed Handwritten Digits via Data-Driven Knowledge Representation  
By mixing different handwritten digit signals together, experiments were conducted to separately mix and superimpose the digits 1 and 9, digits 3 and 8, as well as digits 4 and 7. Taking the separation of the mixed handwritten digits 1 and 9 as an example, this study selected a large number of handwritten digit samples related to digit 1 and a large number related to digit 9 (excluding real examples of mixed data). During training, the two branches of the U-Net network were constrained by knowledge representation results driven by the samples. This constraint allowed one branch of the network to learn features related to digit 1, another branch to learn features related to digit 9, and simultaneously  
Figure 11: Knowledge-guided P- and S-wave separation results. **(a)** P-wave prediction result and residual. **(b)** S-wave prediction result and residual. **(c)** Reconstruction result and residual.  
Figure 12: P- and S-wave separation results without knowledge guidance. **(a)** P-wave prediction result without knowledge guidance and residual. **(b)** S-wave prediction result without knowledge guidance and residual. **(c)** Reconstruction result without knowledge guidance and residual.  
Figure 10: FWI realizes knowledge representation via RNN. **(a)** P-wave knowledge representation result and residual. **(b)** S-wave knowledge representation result and residual. **(c)** FWI data result and residual.  
learn the feature differences between digits 1 and 9. Ultimately, this led to the separation of the mixed handwritten digit signals. The separation results are shown in Figure 13.  
The experimental results demonstrate that the data-driven knowledge representation effectively guides neural networks in separating mixed handwritten digits. The network branches can efficiently learn the shape features of digits and the feature differences between different digits. Despite the presence of certain differences in amplitude compared to real data and some degree of blurriness, the separated results closely resemble the shapes of the real data, effectively achieving signal separation.
## 6 Limits and Future Work  
This article only studied the jamming recognition in the Gaussian channel. In a real-life environment, other more complex channel types might appear. Therefore, the research results might not be able to be used directly in more diverse communication environments. In addition, the mode decision fusion rule based on the multi-node collaborative compound jamming recognition algorithm proposed in this article is relatively simple. This rule directly assumes that each node contributes the same to the final decision result. However, in the real environment, each node might appear to have a different JNR. In this case, the node with higher JNR obviously needs to be assigned higher priority. Finally, in the process of recognition, the silent period is accepted by default. Only jamming and noise were considered. Yet, other communication signals could also exist in the real communication scene. These could be reserved for future study.  
Conceptualization, Q.Z. and S.H.; Methodology, Q.Z. and S.H.; Software, Q.Z., S.H. and W.C.; Validation, Q.Z. and W.C.; Formal Analysis, J.H.; Investigation, Q.Z. and S.H. and J.H.; Resources, Q.Z. and S.H.; Data Curation, Q.Z., S.H. and W.C.; Writing--Original Draft Preparation, S.H.; Writing--Review and Editing, S.H. and C.G.; Visualization, Q.Z., W.C. and J.H.; Supervision, C.G.; Project Administration, C.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article. Conflicts of Interest: Author [PERSON] was employed by the company Power China Sichuan Electronic Power Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Protocol Design
### Registration Stage  
In the registration stage, users purchase the corresponding cryptocurrency from the ground authentication center as a prerequisite for obtaining satellite services. Authentication center transfers a certain amount of cryptocurrency from the authentication center address to the user address, which is used to provide data authentication, as shown in Figure 4, which is divided into four steps.  
1. The user uses the key generator provided by the authentication center to generate the private key, public key and corresponding address. The user sends the public key, address, quantity of cryptocurrency to purchase, timestamp, and signature to the authentication center. The calculation method is shown in (1). \[m1=\mathit{Pk},\mathit{Addr},\mathit{Amount},\mathit{Timestamp},\mathit{Sign}( \mathit{Pk},\mathit{Addr},\mathit{Amount},\mathit{Timestamp})\] (1)
2. After receiving the request, the authentication center verifies the correctness of the public key, address, cryptocurrency quantity, timestamp, and signature. If the verification succeeds, the authentication center returns to the payment channel and waits for the payment. Otherwise, the authentication center rejects the request. The calculation method is shown in (2). \[m2=\mathit{Enc}_{pk}(\mathit{Addr},\mathit{Amount},\mathit{Timestamp}), \mathit{Sign}(\mathit{Addr},\mathit{Amount},\mathit{Timestamp})\] (2)
3. The user pays and asks for confirmation.
4. If the authentication center confirms the successful payment, it will submit a transaction to the nearest available satellite node. The transaction content includes the authentication center transferring the corresponding amount of cryptocurrency to the user address. Otherwise, the request will be rejected.
# MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
## 2 Applications
### Colorectal Cancer  
Colorectal cancer is a global health issue with high incidence and mortality [29]. The American Joint Committee on Cancer classifies patients into five stages (from 0 to IV) based on the presence of tumors (T), the number of lymph node metastases (N), and the presence of distant metastases (M). Although early-diagnosed and localized tumors can be easily removed (five-year survival rate \(>\) 90%), advanced-stage tumors have poor survival rates and only half of the patients survive within five years of diagnosis. Currently, a colorectal cancer diagnosis is achieved by coupling colonoscopy and histopathological examinations. However, due to their invasive nature, many patients are reluctant to undergo these tests, which are also hindered by several issues. Therefore, alternative detection methods for colorectal cancer are clearly needed.  
The extracellular proteome of colorectal cancer cells was the target of an immunostreening approach [30] for the identification of tumor biomarkers. The secretome of five colorectal cancer cell lines was separated by 2D-E, immobilized on polyvinylidene fluoride membranes, and used for serological screening with serum from 21 colorectal cancer patients compared to 24 healthy controls. Autoantigen candidates were defined based on immunoreactivities and the corresponding proteins isolated, which were identified by MALDI-MS and/or by nano-HPLC/ESI-MS/MS and confirmed by Duplex Western blotting. Glod4, a poorly characterized glyoxalase domain-containing protein, and a C-terminal fragment of agrin, a prominent large heparan sulfate proteoglycan resident in basement membranes, were proposed as potential markers for colorectal cancer. EVs released from different colon cancer cell populations, CCL-228 as the primary tumor, the lymph node metastasis CCL-227, and subclones resistant to different doses of the chemotherapy agent 5-fluorouracilacil, were successfully differentiated by MALDI protein profiling [31]. EVs were collected from cell culture supernatant by ultracentrifugation, the proteins were solvent-extracted and eventually analyzed using different MALDI-TOF-MS instruments. Hierarchical clustering, principal component analysis, and partial least-squares data analysis allowed to obtain protein patterns able to discriminate EVs secreted from different cell lines, making the approach a promising noninvasive tool for colon cancer diagnosis and therapy monitoring.
## 2 Related Work & Background
### System Architectures for Industry 4.0
#### 2.1.1 Reference Architectures for Industry 4.0  
Effective and intelligent data processing is fundamental for Industry 4.0. Reference architectures like RAMI 4.0 and IIRA provide structured frameworks for data integration, automation, and decision-making [24].  
* RAMI 4.0 (Reference Architecture Model for Industry 4.0) offers a three-dimensional framework that structures the product lifecycle, together with enterprise hierarchy levels and integration layers [25]. It facilitates Industry 4.0 project planning and implementation, focusing on interoperability, standardization, and security.
* IIRA (Industrial Internet Reference Architecture) complements RAMI 4.0 by emphasizing IT and OT system integration within IIoT environments. It provides guidelines for architecture decisions, security strategies, and scalability [26].
## 3 Survey Results
### Sensing
When considering sensor sampling rate, a pattern can be observed (Figure 4). Most of the deployments are low sampling rate examples, where the more has a very low duty cycle and the sampling rate is less than 1 Hz. Other, less popular application classes use sampling in the range 10-100 Hz and 100-1,000 kHz. The former class uses accelerometer data processing, while the latter is mainly representative of audio and high sensitivity vibration processing. A significant part of applications have a variable sampling rate, configurable in run time. _Design rule 5_: The operating system must set effective low-frequency, low duty-cycle sampling as the first priority. High performance for sophisticated audio signal processing and other high-frequency sampling applications is secondary, yet required. GPS localization is a widely used technology, in general; however, it is not very popular in sensor networks, mainly due to unreasonably high power consumption. It is used in less than 18% of deployments. A GPS module should not be considered as a default component.