esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation and Complexity Review
### Complexity of the Problem  
This Section first considers the decision version of the TSAFP. Formally, we are given a set of \(n\) tasks, each with period \(p_{i_{\text{s}}}\) offset \(o_{i_{\text{s}}}\) computational cost \(c_{i_{\text{s}}}\) and a positive integer \(b\). The question that is asked is as follows: _is the maximum demand for processor time over the hyperperiod \((c_{Max})\) less than or equal to \(b\)_? From Figure 1, it can be seen that TSAFP is in coNP, since the existence of a counterexample (task overrun) in the \(j^{\text{th}}\) tick can be verified in polynomial-time by setting tick: \(=\)\(j\) and executing lines 5,6 and 7 of the TTC algorithm, summing the execution times of all tasks that are run.  
**Theorem 1**: TSAFP is strongly coNP-Complete.  
**Proof**: Short (2009) [20], Transformation from the complement of Simultaneous Congruences Problem (SCP) which is known to be strongly NP-Complete [11].  
It should be noted at this stage that it was previously (incorrectly) assumed by some authors, that a particular version of the TSAFP problem was actually in P [21, 23]. Considering next the decision version of TSOAP, again we are given a set of \(n\) tasks each with period \(p_{i_{\text{s}}}\) computational cost \(c_{i_{\text{s}}}\) and a positive integer \(b\). The question that is asked is as follows: _is it possible to assign a non-negative offset to each task such that the maximum demand for processor time over the hyperperiod \((c_{Max})\) is less than or equal to \(b\)_? Given the previous result, this problem is clearly coNP-Hard; however under the assumption that P \(\
eq\) NP, it is worthwhile investigating where the problem lies on the so-called \"polynomial hierarchy\" [16]. From (3), it can be seen that the task offsets for 'Yes' instances of this problem can be encoded in a number of bits that is less than or equal to the task periods, and hence the size of the problem instance. Given the previous Theorem, the resulting asynchronous schedule is verifiable in polynomial time by a Turing machine with an oracle for the feasibility problem; the problem resides in \(\Sigma_{2}\)\({}^{\text{p}}\). The lower bound that shows the problem is complete for this complexity class is achieved by a transformation from the Periodic Maintenance Scheduling Problem (PMSP); this problem is known to be \(\Sigma_{2}\)\({}^{\text{p}}\)-Complete [11, 19].  
**Theorem 2**: TSOAP is \(\Sigma_{2}\)\({}^{\text{p}}\)-Complete.  
**Proof**: Short (2009) Transformation from PMSP [20].  
Given these results, it can be seen that there are two (closely related) issues to be addressed in order to efficiently solve this problem, even approximately. In order to begin to address these complexity issues, the coNP-Hard problem of determining \(c_{Max}\) for a given asynchronous schedule will first be addressed. From (7), it can be seen that the length of the hyperperiod that needs to be examined is potentially proportional to the product of the task periods, a largely undesirable situation. An algorithm to circumvent this problem is described in the following Section.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 3 Experimental Setup
### Database for Training and Evaluation
#### 3.1.2 Fecgsyn Dab  
The synthetic NI-FECG signals provided are generated by the open-source FECSYN toolbox, which synthesizes physiologically plausible fetal and maternal ECG signals by simulating the propagation dynamics of fetal and maternal ECG signals in the maternal abdomen. The simulator adopts a source mixing approach, treating each signal component (fetal ECG, maternal ECG, and noise) as an independent signal source to provide separable waveforms and accurate adult and fetal heartbeat annotations. The database simulates ten different pregnancies, each of which contains seven types of physiological events, including the following:  
* Baseline: Abdominal mixture (no noise or events).
* Case 0: Baseline (no events) + noise.
* Case 1: Fetal movement + noise.
* Case 2: MHR/FHR acceleration/decelerations + noise.
* Case 3: Uterine contraction + noise.
* Case 4: Ectopic beats (for both fetus and mother) + noise.
* Case 5: Additional NI-FECG (twin pregnancy) + noise.  
Each simulation had a duration of 5 min and was sampled at 250 Hz with a 16-bit resolution (WFDB MIT format) for a total of 34 channels (channels 1-32 are FECG channels, and channels 33-34 are maternal reference ECG channels). By superimposing multiple signal components, a noisy NI-FECG signal can be obtained to support the algorithm verification of this work.  
This study extracted two event types: baseline (noise-free abdominal mixture) and Case 0 (baseline + additive noise). From these, four datasets were constructed--each comprising 50 records (10 subjects \(\times\) 5 records)--with defined noise levels:  
* SYN_origin: Pure baseline signals (no noise).
* SYN_6/SYN_9/SYN_12: Case 0 signals at SNR of 6 dB, 9 dB, and 12 dB, respectively.  
The full database is publicly accessible at [[https://physionet.org/content/fecgsyndb/1.0.0/](https://physionet.org/content/fecgsyndb/1.0.0/)]([https://physionet.org/content/fecgsyndb/1.0.0/](https://physionet.org/content/fecgsyndb/1.0.0/)) [accessed on 28 July 2025].
## 5 Practical Issues
### Prototype  
We developed and tested the protocol in a real environment, and the implementation code is accessible through the link provided in [32]. First, we review the structure and size of each packet in the network interface, followed by the fields of the ARP table. Next, we discuss the elements of our proof of concept, including the base station and the drone program. Lastly, we compare the use of LoRa technology with other possible alternatives.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Features** & **[PERSON] [14]** & **[PERSON] [15]** & **[PERSON] [16]** & **Our Scheme** \\ \hline
**Hash** & 13 & 6 & 12 & 8 \\
**PUF** & 2 & 2 & - & 1 \\
**EC multiplication** & - & - & 10 & 4 \\
**EC addition** & - & - & 3 & - \\
**Interactions** & 5 & 3 & 8 & 4 \\
**Storage cost at the UAV** & 96 bytes & 44 bytes & 152 bytes & 18 bytes \\
**Communication cost** & 224 bytes & 200 bytes & 280 bytes & 384 bytes (180 bytes) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number and execution time of operations for new drone.  
Figure 8: Execution time of registration on different devices.  
The LoRa network protocol generates packets. Each packet has a max size of 255 bytes and consists of two parts: header (Table 5) and payload (Table 6). Messages over 246 bytes are split into multiple encrypted packets using AES-GCM before transmission. The receiving end then rebuilds the message and checks the CRC (cyclic redundancy check) before processing it. Although CRC is the default in LoRa, the AES-GCM we use also ensures data integrity protection. Replay attacks are prevented by using a different IV with every message. An expiration date must be given for each IV, which is not too long and not too short, taking the hardware capabilities and security requirements into consideration. Each message must have its identifier, for example, _GPS coordinates for the next waypoint_.  
The ARP table has multiple fields for each registry (see Table 7), all necessary for tracking message activity on the network. Each registry has to contain the device's address on the network, the exchanged secret key, and the used initialization vectors with an expiration period. The device status serves as info about the state of the communication (e.g., if the key exchange is in progress or lost connection). Since, for the drone, it is vital that attackers do not have access to the memory and, thus, to the ARP table inside, we require that the fields of the ARP table are encrypted with the PUF key.
## 4 Discussion
EPR spectroscopy was used to identify the ROS generated on the surface of the tested materials, helping to understand their photocatalytic behavior. The results indicate that the most effective material is the PMMA + (2) composite, as its EPR spectrum shows the least distortion, suggesting efficient charge transfer. Composites containing titanium oxo-clusters (TOCs) exhibit similar EPR patterns, with signals corresponding to O\({}^{-}\), O\({}_{2}\)\({}^{-}\) radicals, and Ti\({}^{3+}\) species, which are key contributors to photocatalysis [30]. Among the studied TOCs, (2) shows a distinct free electron signal, while **(1)** and **(3)** display typical features of Ti\({}^{3+}\) and superoxide radicals, confirming their role in ROS generation [32; 98]. For ruthenium(III) complexes, the EPR spectra indicate that Ru(III) centers are the only paramagnetic species present, with no ROS signals detected. This suggests that photocatalysis in these materials follows a different mechanism, likely involving Ru(III) reduction to Ru(II) upon irradiation. Because Ru(II) is diamagnetic, it does not produce an EPR signal, supporting the idea that electron transfer occurs within the ruthenium center rather than through ROS formation [85]. Notably, only the composite containing 20 wt.% of (5) exhibits a relatively intense signal, indicating a higher concentration of Ru(III) centers (Table S7 and Figures S9 and S10) [99]. These findings align with the observed photocatalytic activity trends, confirming that titanium-based composites primarily operate through ROS-mediated processes, while ruthenium-based materials rely on direct electron transfer mechanisms.
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 1 Introduction
[33] proposed a weakly supervised learning method that utilizes multi-scale dilated convolutional neural networks and Swin-Transformer to aggregate local and global features for rough extraction of solid waste boundaries. However, these methods cannot obtain the actual distribution edges of solid waste piles to achieve pixel-level classification, and are generally limited to small-scale areas or single scenarios [22; 34; 35]. Therefore, they are not suitable for monitoring and controlling large-area illegal solid waste that this study focuses on. Semantic segmentation can obtain detailed edges of segmented objects, but there is a problem of missing high-quality training images [23; 24; 30], and existing semantic segmentation models are mainly used in street scenes such as Cityscapes [36], CamVid [37], Mapillary Vistas [38], and BDD100K [39]. However, solid waste piles are of different scales, diverse types, varied shapes, and widely distributed, which greatly increase the difficulty of existing models to distinguish objects from backgrounds. It remains questionable whether existing models can be applied to the segmentation of large-area solid waste. In summary, the semantic segmentation method for large-area solid waste piles is still in the development stage and requires further in-depth research. This paper conducts research in the Nanhu District of Jiaxing City. Compared with existing research, the contribution of this work can be summarized as follows:  
1. Approximately 450 km\({}^{2}\) of high-resolution images of the Nanhu District in Jiaxing City and some neighboring areas were collected using drones, and a high-precision solid waste pile segmentation dataset with multi-scene distribution was created by pixel-wise labeling. 2. A dual-branch structured semantic segmentation model was proposed, which achieves a more comprehensive feature representation by fusing contextual feature branches and spatial feature branches. In comparison with other semantic segmentation methods, it achieves state-of-the-art performance in the high-resolution solid waste pile segmentation dataset, demonstrating excellent capability in segmenting complex edges of solid waste piles in multiple scenes. 3. This work provides new ideas for large-area and high-precision solid waste monitoring, effectively reducing the cost of manual inspection and supervision. It can provide scientific theoretical support for the ecological civilization construction and sustainable development of cities, and play an active role in protecting land resources and the ecological environment.
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
###### Abstract  
In this paper, we estimate the uplink performance of large-scale multi-user multiple-input multiple-output (MIMO) networks. By applying minimum-mean-square-error (MMSE) detection, a novel statistical distribution of the signal-to-interference-plus-noise ratio (SINR) for any user is derived, for path loss, shadowing and Rayleigh fading. Suppose that the channel state information is perfectly known at the base station. Then, we derive the analytical expressions for the pairwise error probability (PEP) of the massive multiuser MMSE-MIMO systems, based on which we further obtain the upper bound of the bit error rate (BER). The analytical results are validated successfully through simulations for all cases.  
large-scale multi-user MIMO systems; SINR distribution; MMSE detector; convolutional code; performance analysis +
Footnote †: journal:  
0000-0002-4999-4300-3820][PERSON]. [PERSON] 0000-0002-0002-3824][PERSON]
The two main modules for detecting breast mass in the CAD system are detection of regions of interest (ROIs) and suspicious region identification based on segmentation and classification modules, which classify identified ROIs into benign or malignant categories [18; 19]. A major phase that can strongly impact the classification rate is feature extraction [20; 21]. Thus, this study aims to develop an efficient CAD system to detect breast cancer masses in mammogram images. Relevant features must be extracted effectively to enhance the efficiency and precision of CAD systems. Additionally, the success of accurate mammogram image retrieval is based on the extracted features. Parameters such as texture, shape, and color are typically used to characterize images. However, mammogram images contain useless regions, such as dark background and bright interesting regions with various colorless shapes located in the foreground. Therefore, texture is a convenient descriptor in mammogram images for the detection of masses. Consequently, many studies have used texture characteristics, such as the gray aurora matrix, Gabor characteristics, gray-level cooccurrence matrices (GLCMs), gray-level run-length matrices (GLRLMs), grey-level difference matrices (GLDMs), and gray neighbor matrix, for retrieval [22]. The following are the main contributions of this study:  
* Wavelet transform (WT) based on Bayes-Shrink soft thresholding is applied to suppress noise from each produced block by capturing high-frequency information and processing it to reduce noise that might decrease accuracy. * A new mechanism is used to produce multi-fractal dimension, called multi-FD technique. The traditional FD method has been enhanced by a new thresholding technique to produce features that can help identify the risk of malignant cancer. * Different threshold values are produced and then placed on each block to create different binary images to extract multi-FD features from each block. To compute the values of thresholds that are distributed uniformly and to determine the range of grayscale, the interval's ROI intensity is calculated. Based on that, sensitive threshold values are produced to obtain different binary images. * A fusion technique is used to fuse different classifiers trained to classify the features of each block. The fusion process uses the majority vote for decision-making. The rest of the organization of this paper is as follows: Section 2 presents related work of the study. Section 3 presents the proposed M-FD model and is divided into several subsections. In Sections 4 and 5, the experimental results are discussed in detail with significance and the limitations of the study are presented, respectively. Lastly, the conclusions and future work of this study are presented in Section 6.
## 2 Theoretical Background
### Split-and-Merge Combined with Region Growing  
The simplest segmentation approaches use a global threshold applied on pixel intensity to partition the original image: pixels with an intensity greater than threshold \(T\) are assigned to one region, while those below the threshold \(T\) are assigned to another one [20; 21; 22; 23]. In this way, a binary image is created providing the segmentation of the original image with respect to the chosen threshold value. Split-and-Merge and Region Growing (SMRG) is basically a threshold-based approach that combines Split-and-Merge (SM)--composed of a first split (top-down) phase of the image followed by a merge (bottom-up) phase--with RG for the refinement of the identified regions [23].  
The SM algorithm represents a valid alternative to thresholding, because it can find homogeneous regions in terms of uniformity criteria [24; 25; 26; 27]. Unlike SM, the RG algorithm, starting from one or more seed-points, identifies an ROI through a growing procedure guided by appropriate similarity properties that describe ROI intensity features. Generally SR and RG are used individually for image segmentation, but the use of both together allows us to exploit the overall potential [28; 29].  
With more details, the idea behind the SM algorithm involves successive splits of the whole image into disjoint regions lead by a logical predicate \(\mathsf{P}\). The algorithm starts with an arbitrary partition \(\mathcal{R}\) of the original image (i.e., the whole image) and yields an output composed of uniform sub-regions \(\mathcal{R}_{i}\), for \(i=1,2,\ldots,n\), according to the logical criterion expressed by \(\mathsf{P}\). At the generic step \(t\), if \(\mathsf{P}(\mathcal{R}_{i})=\mathsf{FALSE}\), each region \(\mathcal{R}_{i}\) is split into four sub-regions (also called 'quad-regions'): this process iteratively continues until a quad-region such that \(\mathsf{P}(\mathcal{R}_{i})=\mathsf{TRUE}\) or with an area smaller than a certain threshold is found. The logical predicate chosen for this work allows us to find the quad-regions with a mean intensity that is greater than the threshold value yielded by the [PERSON]'s method [30]. If only splitting is used, the final partition contains adjacent regions with identical properties: this drawback can be overcome by merging only adjacent regions where the combined pixels satisfy the predicate \(\mathsf{P}\).  
After the initial rough ROI identification obtained by means of SM, the RG algorithm expands this initial region to properly identify the lesion. According to the classic RG algorithm, each region begins its own growth from a single pixel (seed-point). Instead, when the SM is exploited, the seed-point can be obtained by using the ROI yielded by the SM algorithm (more correctly, a seed-region) [23]: the seed-region is iteratively grown by evaluating, for each pixel on the boundary, its 8-neighborhood as candidate for the growth. A stopping rule is necessary for interrupting the growing procedure if no more pixels match the membership criterion, which often refers to the proximity of the pixel intensities.
## 4 Interaction Between the Gut Microbiota and Cardiovascular Diseases
### Short-Chain Fatty Acids
It also stimulates the expression of mucin 2 (MUC2), which strengthens the mucus barrier against pathogenic agents, and enhances AMP production, both essential components of the host's first defence line [179]. In the case of atherosclerosis, SCFAs, particularly butyrate and propionate, exhibit protective effects through multiple mechanisms, as demonstrated in both in vitro and in vivo studies. In vitro experiments using human umbilical vein endothelial cells (HUVECs) have shown that these SCFAs inhibit NF-kB activation and decrease the expression of pro-inflammatory cytokines such as TNF-\(\alpha\) and IL-6, primarily through the inhibition of histone deacetylases (HDACs). This modulation leads to the expression of IL-10, an anti-inflammatory cytokine, and reduced expression of vascular adhesion molecules like VCAM-1 and ICAM-1, which are critical for monocyte adhesion and early plaque development. In addition to this HDAC-dependent mechanism, SCFAs, particularly butyrate, can also activate intracellular signalling pathways by binding to FFAR2 on leukocytes. This interaction leads to AMPK activation, which stimulates PGC-1\(\alpha\) and its downstream effector PPAR-\(\alpha\), further contributing to NF-kB inhibition and the suppression of ICAM-1 and VCAM-1 expression. These converging anti-inflammatory pathways underscore the multifaceted protective role of SCFAs in atherosclerosis [180; 181; 182; 159]. In vivo studies using apolipoprotein E-deficient (ApoE\({}^{-/-}\)) mice, a well-established model for atherosclerosis, have shown that dietary supplementation with butyrate attenuates atherosclerotic lesion development, decreases macrophage infiltration, and improves endothelial function. These outcomes are associated with the downregulation of oxidative stress pathways, including the suppression of NADPH oxidase activity and inhibition of the NLRP3 inflamasome [183; 184; 185; 159]. Regarding cholesterol metabolism, propionic acid is a potent inhibitor of cholesterol synthesis, while the three main SCFAs increase hepatic cholesterol uptake from the blood and accelerate its excretion, contributing to a reduction in plasma cholesterol levels. Additionally, SCFAs can regulate lipolysis and adipogenesis. Specifically, acetic acid and propionic acid inhibit endogenous lipolysis, while propionic acid regulates extracellular lipolysis by promoting lipoprotein lipase expression, resulting in decreased plasma lipid levels. Both of these metabolic processes contribute to reducing the risk factors associated with the development and progression of atherosclerosis [186].
## 3 The Proposed Algorithm
### Decryption Process  
The decryption process is an inverse process of the encryption process. Each encrypted facet is also extracted from the encrypted 3D triangle mesh. The key value **K** and the index of each facet is used to generate the interpolating vector and the curvature coefficients as shown in Equation (2) and Equation (6) in Section 3.2. These features are then encrypted by the key value **K** that is used in Section 3.3. The encrypted interpolating vector and three vertices of the encrypted facet are used in the re-distortion process to compute three new vertices. These vertices are then used with the encrypted curvature coefficients to re-compute three encrypted control points. Three encrypted control points are then decrypted by the key value **K** in order to get three control points. Finally, three control points and the curvature coefficients are used to decrypt three vertices of the facet. The decrypted 3D triangle mesh is a set of decrypted facets.  
The purpose of the proposed algorithm is to prevent illegal copying or illegal access from unauthorized users and attacks from hackers. Thus, if an authorized user or receiver would like to use the encrypted 3D printing models for 3D printing, they only need the secret key to decrypt and use them as 3D printing inputs. The decryption does not affect the printing process. To print a real object from a 3D triangle mesh, the 3D triangle mesh must be cut into a set of 2D slices and stored in G-code format. The content of the G-code format consists in paths that contain 2D coordinates for the CNC machine in a 3D printer. Consequently, the decryption process does not affect other aspects of the 3D printing process.