esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decentralized Receiver in Gaussian Interference
## Appendix C Proof of Conditional Capacity  
**Remark 7**: _The capacity of the system is \(R_{\overline{DC}}(L)\) under the following restrictions:_  
* \(\Sigma\) _is diagonal (no interference)._
* _The base does not have its own full-precision observation of the broadcast (_\(h_{0}=0\)_)._
* _The broadcaster must transmit a Gaussian signal._
* _Construction of helper messages is independent of the transmitter's codebook_ \(\mathcal{X}\)_._  
Proof.: By Theorem 5 in Reference [1] (variable names altered, constants adapted for complex variables), the capacity of the system under the assumed restrictions is  
\[\max_{\begin{subarray}{c}\mathbf{r}\in\mathcal{R}_{\text{LAN}}(L),\\ \mathbf{V}\in\mathcal{V}\end{subarray}}C_{\mathbf{r},\mathbf{V}} \tag{100}\]  
\[C_{\mathbf{r},\mathbf{V}}\triangleq\min_{S\subseteq[1:N]}\left\{\sum_{m\in S}[r_{m}-I (V_{m};Y_{m}|X)]+I(\mathbf{V}_{S};X)\right\}. \tag{101}\]  
In Equation (101), \(\mathcal{V}\) is the collection of random vectors \(\mathbf{V}=(V_{1},\ldots,V_{N})\) whose components are of the form \(V_{n}=Y_{n}+\tilde{W}_{n}\) with independently distributed \(\tilde{W}_{n}\):  
\[\tilde{W}_{n}\sim\mathcal{CN}\left(0,\sigma_{n}^{2}\cdot\frac{2^{-2v_{n}}}{1- 2^{-2v_{n}}}\right) \tag{102}\]  
for any \(v_{n}\geq 0\). Since both the variance in Equation (102) as a function of \(v_{n}\) and Equation (26) as a function of \(r_{n}\) are injective on \((0,\infty)\), then for any \(\mathbf{V}\in\mathcal{V}\) there is some \(\mathbf{\rho}\) (in the context of Equation (37); either inside or outside \(\cup_{\lambda\in\mathbb{R}}\mathcal{R}_{DC}^{\lambda}(L)\) ) which will yield a variable \(\mathbf{Z}(\mathbf{\rho})\) with identical distribution to \(\mathbf{V}\).  
Fixing helper rates \(\mathbf{r}\), for any \(S\subseteq[1:N]\), we can instead write \(C_{\mathbf{r},\mathbf{V}}\) as:  
\[C_{\mathbf{r},\mathbf{V}} =\max\left\{c:\forall S\subseteq[1:N],\ \ c\leq I(\mathbf{V}_{S};X)+\sum_{m\in S}r_{m}-I(V_{m};Y_{m}|X)\right\} \tag{103}\] \[\quad\leq\max\left\{c:\forall S\subseteq[1:N],\ \ c-I(\mathbf{V}_{S};X)+I(\mathbf{V}_{S};\mathbf{Y}_{S}|X)\leq \sum_{m\in S}r_{m}\right\}\] (104) \[\quad\leq\max\left\{c:\forall S\subseteq[1:N],\ \ [c-I(X;\mathbf{V})]+I(\mathbf{Y}_{S};\mathbf{V}_{S}|\mathbf{V}_{S ^{C}})\leq\sum_{m\in S}r_{m}\right\}\] (105) \[=\max_{\lambda\in\mathbb{R}}\max\left\{I(X;\mathbf{V})-\lambda: \forall S\subseteq[1:N]I(\mathbf{Y}_{S};\mathbf{V}_{S}|\mathbf{V}_{S^{C}})\leq\sum_{m\in S }r_{m}+\lambda\right\} \tag{106}\]  
where \(I(\mathbf{V}_{S};\mathbf{Y}_{S}|X)\leq\sum_{m\in S}I(V_{m};Y_{m}|X)\) follows because, for each \(n\), \(V_{n}\leftrightarrow(X,Y_{n})\leftrightarrow(\mathbf{V}_{\{j\
eq n\}},\mathbf{Y}_{\{j \
eq n\}})\) is a Markov chain. Maximizing over \(\mathbf{r}\), Equation (106) is identical to the set of rates shown in Theorem 7.
# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 2 Related Works
[35] proposed an intuitionistic fuzzy-set-based approach for the detection anomaly from time series data. [PERSON] et al. [36] proposed the definition correlation coefficient of intuitionistic fuzzy sets. [PERSON] et al. [37] introduced the details of fuzzy similarity relations. In [38], the concepts of \(\alpha\)-cut of a fuzzy relation and fuzzy equivalence relations were introduced in detail. Rough set theory was introduced by [PERSON] [39] to deal with imprecision, uncertainty, or vagueness that exist in any datasets. Using properties to equivalence relation, [PERSON] et al. [40] proposed a rough-set-based classification method on discrete datasets. [PERSON] et al. [41] proposed an anomaly-detection-based method on a highly scalable approach to compute the nearest neighbor of objects using rough set theory. [PERSON] et al. [42] proposed an efficient fuzzy-rough-set-based approach for the feature selection. [PERSON] et al. [43; 44], proposed two density approaches based on neutrosophic sets and fuzzy proximity relations for the detection anomaly. [PERSON] et al. [45] introduced a neural-network-based classification algorithm using the fuzzy-rough lower and upper approximations to classify test objects or to predict their decision value. [PERSON] et al. [46; 47] not only proposed the structure of nanotopological space in terms of lower and upper approximation but also applied it in medical diagnosis. [PERSON] et al. [48] first introduced the concept of the covering-based rough fuzzy nanotopology, the covering-based rough intuitionistic fuzzy nanotopology, and the covering based rough neutrosophic nanotopology. In [49], the authors introduced the concept of fuzzy-rough set theory. [PERSON] et al. [50] applied fuzzy-rough set for relevant genes selection from microarray data. [PERSON] et al. [51] proposed an anomaly-based IDS, which used fuzzy-rough clustering method. In [30], the authors conducted the experimental studies with two well-known datasets: KDDCUP'99 [52] Network Anomaly Detection dataset and Kitsune [53] Network Attack dataset.
# Seismic Response Effect on Base-Isolated Rigid Structures by Mass Eccentricity in Nuclear Plants
## 1 Introduction  
The recent earthquakes in Gyeongju and Pohang have been of the largest magnitude among recorded earthquakes in South Korea, raising significant national concerns due to their considerable impact. Furthermore, concerns about the safety of nuclear power plants located near earthquake-prone areas have led to the development of technologies to enhance the seismic performance of these nuclear facilities. Within nuclear power plants, there are numerous large and small pieces of equipment and structures. Typically, during the design phase, efforts are made to avoid eccentricity in mass or stiffness as much as possible. However, in some cases, unavoidable structural characteristics or modifications during operation, such as the installation of additional equipment, can result in a shift in the center of mass (or stiffness). For example, equipment such as emergency diesel generators and spent fuel storage racks within nuclear power plants may unavoidably exhibit significant mass eccentricities due to structural arrangements dictated by their design functions. When the mass center is offset, the natural frequencies or mode characteristics of the structures can change, ultimately leading to a potential increase in seismic response. There have been many studies introducing the seismic response effect and warning about response increases in building structures due to mass or stiffness eccentricity. However, there arevery few studies on the effect of mass eccentricity on the seismic response of base-isolated equipment in building structures or operating plants.  
In recent years, it has been proposed to apply seismic isolation technology to major individual facilities with high safety importance to improve the seismic performance of operating nuclear power plants (NPPs). The application of small laminated rubber bearing (LRB) can be a good option for the purpose. Therefore, this paper aims to investigate the influence of mass eccentricity on the seismic response of base-isolated equipment. To achieve this, sensitivity analysis (SA) of the seismic response to mass eccentricity was performed to analyze its impact on the maximum acceleration (and displacement) response during design-basis earthquakes. Some specific eccentricity models conducive to experimentation were set up, and simulation analyses using the test models were conducted to predict the test results. Subsequently, shake table tests were performed to compare and validate these analytical results.
## 3 Application Protocol for Wireless Intelligent Sensor Networks
### Sensor Multi-Agent Structure
#### 3.1.3 Communication Agent  
The communication control agent incorporates the application protocol, which allows sensors to communicate with other sensors, neighboring sensors, and with a computer connected to the WSN by means of an access point. The main functions of this agent are the following:  
1. Communication with other sensors and the computer.
2. Control the interval between consecutive communications with the computer. This communication interval may differ from previous intervals (sleep mode interval and application execution interval).
3. Reception of commands from the computer. The set of commands and responses used in the application protocol is shown in Section 3.2.
4. Transmission of measurements, inferences, and alarms (_i.e._ values of variables out of range defined by thresholds) that are generated in the sensor.
5. Reception of KBs used by FRBSs and collaborative FRBSs.
6. Transmission of alarms to a collaborative group, _i.e._ the set of neighboring sensors that are working in a collaborative FRBS application.
7. Control the power transmission, taking into account factors such as the quality of the reception messages from the neighborhood and the battery level.
8. Provide the agent state.
## 3 Model and Background
### Network Structure
#### 4.1.1 Selection of Aggregators  
We run the secure data aggregator selection protocol (SANE) [29] to select data aggregators which satisfy two conditions:  
1. There are at least \(T\) nodes, called forwarding nodes, on the path between any two consecutive aggregators.
2. Each aggregator has at least \(T\) neighboring nodes.  
Figure 2: The network structure of FESA, where the three nodes within a dotted line are in the same group and share one group key.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 8 Prototype Implementation and Results  
A prototype of the approach proposed in this manuscript may be useful in a limited-scope demonstration of its effectiveness. In the prototype implementation, the authors implemented data acquisition and processing technologies at a \"downstream\" location inside a live distribution network, as well as at an \"upstream\" location inside the substation servicing the distribution network. In each  
Figure 7: Distribution Network with Multiple Monitoring and Control Points [48]  
location, sensitive current transformers were used to detect current profiles, and data acquisition systems were employed to acquire time-series data of the current profiles.  
At the downstream location, a series of current disturbances were manufactured using a real-time current modulation system. These waveforms were introduced directly into a conventional low-voltage outlet, captured with a data acquisition system, converted to a DCP data packet via limited ML analysis, and transmitted using an out-of-band network to a central location for comparison with UCP data captured at the servicing substation. Unfortunately, the distribution system servicing our prototype test environment was not equipped with an actual \"smart meter.\" As a result, we implemented a data acquisition system with localized processing and formatting immediately outside of the conventional meter which services our test environment. We refer to this as an \"emulated smart meter.\" The DCP data packet created by our emulated smart meter contained characteristic information of the current disturbances (anomalous characteristics) as well as geospatial information (location) and timing information (time-stamps). The primary objective of the DCP data packet was to create a \"matching template\" for further processing and comparison with high-resolution UCP data in order to prototype the DCP/UCP localization process.  
At the upstream location, high-resolution data acquisition equipment was attached to the low-side of current transformers which are typically mounted on circuit breakers of high-voltage distribution feeders. The current sensing capabilities of the feeder circuit breakers are sufficient for acquiring DCP current anomalies which have transited the distribution network upstream from the low-voltage network to the substation bus-bar. This acquisition process creates UCP \"data blobs\" which contain waveforms of average for each phase of a three-phase feeder. In this fashion, UCP data is time-registered and normalized for comparison with DCP data packets using any of several signal processing and/or ML algorithms. In our test environment, our ML analysis was limited to various frequency and amplitude vectors characteristic of the manufactured disturbances, and the DCP/UCP comparison was accomplished via a correlation template.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Sensor Fusion  
Wearable sensor systems relying on single or multiple sensors present limitations such as sensor deprivation, limited spatial coverage, and imprecision [100; 101]. Sensor fusion is an effective solution to address these problems. Sensor fusion can be _competitive_, _complementary_, and _cooperative_[102]. Competitive fusion uses multiple equivalent sources of information to obtain redundancy and self-calibration. In complementary fusion each sensor captures different aspects of what is being monitored to improve system accuracy and reliability. Cooperative fusion is when multiple sensor signals are needed to obtain information that wasn't obtained by any of the signals independently [103]. When it comes to data processing level, sensor fusion is divided into three categories:  
* _data-level fusion_: implements de-nosing, feature extraction, data classification, and data compression,
* _feature-level fusion_: creates a new high-dimension feature set that represents the input for classification or pattern recognition, and
* _decision-level fusion_: utilizes the abstracted information from either data-level or feature-level fusion to make a decision [103].  
The fusion level used in a wearable sensor system will affect other issues such as processing, information loss, and performance. There are instances when sensor fusion is necessary to provide more accurate measurements. For example, when researchers want to know the orientation of an IMU, it may not be sufficient to just use an accelerometer because this may yield inaccurate results. It is not possible to extract heading of the sensor with just an accelerometer but fusing an accelerometer and magnetometer provides this additional information critical to examine postural control and ROM [98]. Fusing the accelerometer, gyroscope, and magnetometer data helps to improve the accuracy of these measurements.  
From the studies included in this review, \(87.50\%\) used data-level fusion, \(33.93\%\) used feature level fusion, and \(33.93\%\) used decision-level fusion.
## 3 Operational Requirements
### Technical Specifications
#### 3.4.5 Processing and Control  
The processing unit in a fruit-harvesting robot is the heart of the control system, responsible for executing the algorithms necessary for the robot's autonomous operation. Given the complexity of the tasks that the robot must perform, the processing unit must be extremely powerful and efficient. Multi-core processors, such as advanced CPUs and GPUs (Graphics Processing Units), are essential for handling the data-intensive workloads that artificial intelligence (AI) algorithms require. These processors enable parallel processing of multiple data streams, significantly improving the robot's speed and efficiency [66].  
A lot of RAM and fast storage (such as SSDs) is crucial to handle large volumes of data that are generated during robot operation. This includes high-resolution imagery, 3D maps of the environment, and real-time sensor data.  
Optimization algorithms, such as the particle swarm algorithm (PSO) and the genetic algorithm (GA), are used to adjust routes in real-time based on sensor data. This allows the robot to adapt to changes in the environment, such as the appearance of new obstacles or variation in fruit density [67].  
Real-time control systems allow the robot to respond quickly to changes in the environment and instructions from AI algorithms. These controllers adjust the robot's actuators, such as traction motors and harvesting arms, to perform precise and coordinated movements [68].  
A modular software architecture allows for easy integration and upgrade of different control system components. This includes modules for perception, planning, control, and communication, which can be developed and optimized independently [69].
## 2 Results
### Macrophages Infiltrate and Phagocytose CD47-Depleted, IgG-Opsonized B16F10 Nodules  
To assess phagocytosis in the melanized tumor nodules, late-stage lung sections were immunostained for macrophage marker F4/80 (Figure S5). We also noted that the DAPI-stained nuclei in nodules are large and consistent with B16's high-modal karyotype [28]. Importantly, CD47 KO tumor nodules treated with anti-Tyrp1 show many F4/80-positive cells that are round in morphology and contain a single large nucleus plus some melanization (Figure 3 Ai,ii). Such features indicate in vivo phagocytosis of a B16F10 cell. Lymphatics-localized F4/80+ macrophages are distinct and always appear more spread. Bone marrow-derived macrophages in culture are similarly spread on plastic unless they have phagocytosed an anti-Tyrp1-opsonized B16F10 cell, in which case they are round and melanized (Figure 3 Aiii). Increased tumor infiltration by immune cells is further suggested by a marker-free analysis of nuclear size and intensity (small and bright) (Figure S6).  
Untreated tumor nodules show the same low density of phagocytic F4/80+ macrophages at days 16 and 20, and 2.6-fold more on day 16, with 2.1-fold more on day 20 (Figure 3 Bi). The modest decrease is consistent, with rapid B16 proliferation and tumor growth (Figure 2 Biii) progressively dominating the clearance by these effector immune cells. We model the net growth rate of metastatic tumors as an exponential growth solution to a differential equation where the tumor growth rate increases with tumor cell proliferation and decreases as a function of macrophage phagocytosis (Figure 3 Bii, equations).  
Figure 3: Tumor-opsonizing antibodies increase macrophage infiltration in tumors. (**A**) Phagocytic macrophages in treated CD47 KO lung tumor nodules. (i) Confocal images of anti-Tyrp1-treated CD47 KO tumors show F4/80+ macrophages that are rounded in morphology and contain melanin and large DAPI+ nuclei, indicating phagocytosis of B16F10 cells. F4/80+ macrophages in control lymph tissue (lymphatics-localized) show diffuse, non-phagocytic morphology. (ii) Treated lung nodules show more phagocytic macrophages than untreated control lungs. (iii) Rounded macrophage in vitro after engulfing an opsonized B16F10 cell. Scale: 50 μm. (**B**) (i) Phagocytic macrophages per tumor area increase with anti-Tyrp1 treatment in CD47 KO lung tumor nodules. Mean \(\pm\) s.e.m. (* \(p\) < 0.05, **_p_ < 0.01, Student’s _t_-test, \(n\) = 3 representative samples per group, closed circles indicate day 16 samples and open circles indicate day 20 samples). (ii) Relationship between the lung tumor area and density of phagocytic macrophages: exponential-type growth versus phagocytic suppression. Tumor burden, B, is modelled by an ordinary differential equation in which growth is suppressed by phagocytic interactors, P. P\({}_{0}\) refers to inactivated macrophages.  
This exponential fits well with the total tumor burden versus the number of phagocytic macrophages (Figure 3 Bii, plot), and illustrates the disproportionate potency of tumor clearance possible when CD47 disruption is combined with IgG opsonization.
## 4 Experiments and Results
### Performance for Repetition  
In the denoising scheme subsection, we described the average of the multiple outputs used as the final outputs of the proposed method with Equation (19). In this subsection, we analyze the effects of the number of outputs on the performance of the method. Figure 7 displays the changes in the PSNR and SSIM values according to the number of outputs. The PSNR and SSIM exhibited similar trends according to the number of outputs and started to converge at about \(N=25\) in Equation (19). By adjusting the number of iterations, we could practically obtain the result within an acceptable processing time.
## 2 Lempel-Ziv Data Compression  
Lempel-Ziv compression is a dictionary-based technique. In fact, the factors of the string are substituted by _pointers_ to copies stored in a dictionary which are called _targets_. LZ1 (LZ2) compression is also called the sliding (dynamic) dictionary method.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 4 TB-CSPN: A Hybrid Architecture for Semantic and Structural Coordination
### Semantic-Coordination Architectural Separation  
TB-CSPN's hybrid design addresses the fundamental tension between formal verification and scalable intelligence through strategic architectural separation:  
_Semantic Layer:_ Topic-based tokens carry rich semantic content including natural language descriptions, probability distributions, and contextual metadata. LLM agents operate primarily at this layer, extracting topics, refining semantic content, and making intelligent decisions based on token semantics. This layer scales independently of system size through shared topic spaces.  
_Coordination Layer:_ Colored Petri Nets manage token flow, enforce coordination constraints, and provide formal guarantees about system behavior. The CPN structure remains relatively static--coordination patterns for 10 agents are identical to those for 100 agents, with only token volumes changing.  
_Integration Interface:_ Topic relevance thresholds and rule-based activation mechanisms bridge the semantic and coordination layers, enabling formal verification of coordination properties while preserving semantic flexibility and agent autonomy.
## 3 Background
### International Regulations  
Globally, frameworks include the NIST Cybersecurity Framework (USA) [31], which offers voluntary guidelines focused on identifying, protecting, detecting, responding to, and recovering from threats; the NIS2 Directive (EU, 2023) [32], which mandates annual penetration tests and 24-h incident reporting for critical situations; and PCI DSS [33], the international standard for payment data security, requiring encryption, continuous monitoring, and regular testing. Furthermore, DORA (EU, 2025) [34] emerges as a pivotal regulation, concentrating on operational resilience and IT risk management, while Singapore's MAS Guidelines [35] necessitate independent audits and offensive testing (red teaming).  
In Brazil, the Central Bank has issued specific regulations to strengthen cybersecurity in the financial system. CMN Resolution No. 4,968/2021 [10] requires financial institutions to conduct periodic security tests on their IT systems, partially aligning with PCI DSS but without specifying a minimum frequency. CMN Resolution No. 4,879/2020 [36] mandates internal audits to assess system reliability and integrity, resembling NIS2 but lacking requirements for rapid failure reporting. Meanwhile, CMN Resolution No. 4,893/2021 [37] establishes continuous control mechanisms and periodic testing, reflecting aspects of DORA but without mandating crisis simulations. Finally, BCB Normative Instruction No. 305/2022 [38] introduces mandatory annual penetration testing for Open Finance, aligning with NIS2 and PCI DSS, although its scope is limited compared to global standards that cover the entire financial sector.  
Despite progress, gaps persist in Brazilian regulations. There are no strict deadlines for incident reporting (unlike NIS2's 24-h requirement), no obligation for offensive testing (red teaming), and regulatory coverage remains fragmented, with Open Finance-specific rules instead of a unified approach for the entire financial system. To enhance resilience, Brazil could benefit from adopting clear timelines for incident reporting, expanding penetration testing beyond Open Finance, and better integrating policies across the Central Bank, CVM (Securities Commission), and ANPD (Data Protection Authority), following models like those of the EU.