esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 UEP Using Spatial Modulation
### ABER of SM Information Carrying Units
#### 3.2.2 ABER of the Bits Carried by Constellation Diagram  
In conventional SM, the ML decoder is used to detect the information bits carried by the constellation modulation diagram. The estimated transmitted symbol using ML decoder \(x_{q}\) represented in Equation (8) can, alternatively, expressed as follows [18],  
\[x_{q}\ =\ arg\ \underbrace{min}_{x_{q}}\ \Big{[}\sqrt{\rho}\parallel\mathbf{u}_{b,q}\parallel_{\overline{b}}^{2}\ -2 Re\Big{\{}\mathbf{r}_{\overline{b}}^{H}\mathbf{u}_{b,q}\Big{\}}\Big{]}, \tag{20}\]  
where \(x_{q}\) is referred to the \(q^{\text{ th}}\) symbol of the constellation diagram, \(\mathbf{r}\) is the vector of received signal, \(\mathbf{u}_{b,q}=\ \mathbf{h}_{b}x_{q}\), \(q\ \in[1:M]\), and \(M\) is the modulation order. The error probability constellation diagram over underwater acoustic channel by using the maximum-receive ratio combining (MRRC) can be expressed as [19]  
\[SER=\ \frac{\ell}{\mathbf{N}}\Bigg{\{}\frac{1}{2}\bigg{(}\frac{2}{\rho+2} \bigg{)}^{N_{r}}-\ \frac{\ell}{2}\bigg{(}\frac{1}{\rho+1}\bigg{)}^{N_{r}}+\ (1-\ell)\sum_{w=1}^{\mathbf{N}-1}\bigg{(}\frac{B_{w}}{\rho+B_{w}} \bigg{)}^{N_{r}}+\ \sum_{w=\mathbf{N}}^{2\mathbf{N}-1}\bigg{(}\frac{B_{w}}{\rho+B_{w}} \bigg{)}^{N_{r}}\Bigg{\}}. \tag{21}\]  
\(\mathbf{N}\) is the number of Monte Carlo, \(\ell=\Big{(}1-\frac{1}{\sqrt{M}}\Big{)}\), \(=\ \frac{3}{M-1}\), \(m=\ log_{2}(M)\), \(B_{w}=2 sin^{2}\theta_{w}\), \(\theta_{w}=w\pi/4n\) and the bit error rate of bits carried by constellation diagram \(BER_{C}\) can be expressed as follows,\[BER_{C}\approx\frac{SER}{m} \tag{22}\]  
The total ABER of the SM can be calculated as  
\[ABER_{SM}=\frac{\left((log_{2}(Nt))\times BER_{AI}\right)+\left((log_{2}(M)) \times BER_{C}\right)}{R_{SM}} \tag{23}\]
## 3 Mathematical Investigations
### A Certain Solution  
We can potentially identify the steady-state solution fixed point of Equations (27)-(30) by inserting \(\dot{a}_{1}=\dot{a}_{2}=\dot{\phi}_{1}=\dot{\phi}_{2}=0\)  
\[\frac{1}{2}a_{1}a_{1}=-\frac{f}{2\omega_{1}}\cos\phi_{1}+\frac{\lambda_{1}}{2 \omega_{1}}a_{2}\sin\phi_{2}, \tag{31}\]  
\[\frac{3}{8\omega_{1}}\beta a_{1}{}^{3}+\frac{5}{16\omega_{1}}\mu a_{1}{}^{5} -2a_{1}\sigma_{1}=\frac{f}{2\omega_{1}}\sin\phi_{1}+\frac{\lambda_{1}}{2 \omega_{1}}a_{2}\cos\phi_{2}, \tag{32}\]  
\[\frac{1}{2}\alpha_{2}a_{2}=-\frac{\lambda_{2}}{2\omega_{2}}a_{1}\sin\phi_{2}, \tag{33}\]  
\[(\sigma_{2}-2\sigma_{1})a_{2}=\frac{\lambda_{2}}{2\omega_{2}}a_{1}\cos\phi_{2}. \tag{34}\]  
Squaring then adding both sides of Equations (33) and (34) to obtain the following one:  
\[\Big{(}4\omega_{2}{}^{2}(\sigma_{2}-2\sigma_{1})^{2}+\omega_{2}{}^{2}\omega_{ 2}{}^{2}\Big{)}a_{2}{}^{2}=\lambda_{2}{}^{2}a_{1}{}^{2}. \tag{35}\]  
From (33) and (34), we have the following:  
\[\cos\phi_{2}=\frac{2\omega_{2}(\sigma_{2}-2\sigma_{1})a_{2}}{\lambda_{2}a_{1}}, \tag{36}\]  
\[\sin\phi_{2}=-\frac{\omega_{2}a_{2}a_{2}}{\lambda_{2}a_{1}}. \tag{37}\]  
Inserting (36) and (37) into (32) and (33), we obtain  
\[\frac{f}{2\omega_{1}}\cos\phi_{1}=-\frac{1}{2}\alpha_{1}a_{1}-\frac{\lambda_ {1}\omega_{2}\alpha_{2}a_{2}{}^{2}}{2\omega_{1}\lambda_{2}a_{1}}, \tag{38}\]  
\[\cos\phi_{1}=-\frac{2\omega_{1}}{f}\bigg{(}\frac{\lambda_{1}\omega_{2}\alpha_ {2}a_{2}{}^{2}}{2\omega_{1}\lambda_{2}a_{1}}+\frac{1}{2}\alpha_{1}a_{1}\bigg{)}, \tag{39}\]  
\[\frac{f}{2\omega_{1}}\sin\phi_{1}=\frac{3}{8\omega_{1}}\beta a_{1}{}^{3}+\frac {5}{16\omega_{1}}\mu a_{1}{}^{5}-2a_{1}\sigma_{1}-\frac{\lambda_{1}\omega_{2} (\sigma_{2}-2\sigma_{1})a_{2}{}^{2}}{\omega_{1}\lambda_{2}a_{1}}, \tag{40}\]  
\[\sin\phi_{1}=-\frac{2\omega_{1}}{f}\bigg{(}2a_{1}\sigma_{1}-\frac{3}{8\omega_ {1}}\beta a_{1}{}^{3}-\frac{5}{16\omega_{1}}\mu a_{1}{}^{5}+\frac{\lambda_{1 }\omega_{2}(\sigma_{2}-2\sigma_{1})a_{2}{}^{2}}{\omega_{1}\lambda_{2}a_{1}} \bigg{)}. \tag{41}\]  
By squaring and summing the Equations (39) and (41), we obtain  
\[\bigg{(}\begin{array}{c}2a_{1}\sigma_{1}-\frac{3}{8\omega_{1}}\beta a_{1}{} ^{3}-\frac{5}{16\omega_{1}}\mu a_{1}{}^{5}-\\ +\frac{\lambda_{1}\omega_{2}(\sigma_{2}-2\sigma_{1})a_{2}{}^{2}}{\omega_{1} \lambda_{2}a_{1}}\end{array}\bigg{)}^{2}+\bigg{(}-\frac{\lambda_{1}\omega_{2} \alpha_{2}a_{2}{}^{2}}{2\omega_{1}\lambda_{2}a_{1}}-\frac{1}{2}\alpha_{1}a_{1} \bigg{)}^{2}=\frac{f^{2}}{4\omega_{1}{}^{2}}. \tag{42}\]  
Equations (35) and (42), known as the frequency response equations, are employed to explain how the system behaves in practical situations under steady-state conditions. These equations illustrate the system's response across different frequencies when exposed to constant input signals. i.e., \((a_{1}\
eq 0,a_{2}\
eq 0)\).
# A Grey Wolf-Based Method for Mammographic Mass Classification
## 4 Experimental Results
### Mammography Dataset (DDSM)  
To assess the proposed method, we used the DDSM public dataset which is considered the largest mammography dataset [30]. The dataset was collected by a research team at the University of South Florida. It consists of 2620 patients classified into 43 different volumes with each patient having four mammograms. Each mammogram is taken from two different views: Mediolateral oblique view (MLO) and craniocaudal (CC). The average size of the mammograms is (3000 \(\times\) 4800) pixels and each mammogram has its ground truth information specified by experts, showing whether the image is benign or malignant as well as the lesion localization [30]. Using the breast imaging-reporting and data system (BI-RAD) scores, the DDSM database is classified by experts into three classes: normal, benign and malignant [30].  
The dataset used to test the proposed method consists of 400 ROIs. A total of 200 ROIs are normal breast parenchyma and 200 ROIs are masses which are further classified into two sub-classes (100 benign and 100 malignant). A sample of the used dataset is illustrated in Table 1. A sample of the DDSM regions used in the study is shown in Figure 4.  
Figure 4: Samples regions of the DDSM dataset; first row is regions with normal breast parenchyma, second row is a set benign ROI, and malignant masses are shown in the third row.
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 5 Intrusion Detection System (IDS) in IoT
### Deep Learning (DL) Approaches
Accordingly, Bi-RNN runs inputs in two ways, one from past to future and the other one from future to past. Bi-RNN, with two hidden states combined, can preserve information from both the past and the future, different from LSTM, which runs only backwards to preserve information. Common unsupervised DL approaches include AE and DBM. Deep AE methods are introduced in [38] to produce better data representation resulting from dimensionality reduction. An AE, apart from the hidden layers of low-dimensional features, consists of an equal number of feature vectors for each input and output layer. An AE combines an encoder that extracts features from the entire dataset and learns to convert the input into a low-dimensional representation. The decoder receives the low-dimensional representations and reconstructs the original features [94]. RBM neural network introduced by [PERSON] et al. [95] contains fewer hidden layers and has been used in many domains, including securing IoT systems [96; 97]. RBM, unlike AE, consists of two types of layers: input layers, which are the visible layers, and hidden layers. The main motive behind RBM is to limit the number of features processed by each layer. RBM has limited feature-representation capability and is substantially stacked from two or more RBM layers to form a DBN. In [37], the authors produced a new learning algorithm based on a fully connected Boltzmann machine to enhance the RBM technique, so-called DBM, which is an undirected model, as is RBM. DBM, unlike RBM, consists of several hidden layers, whereas RBM only contains one or possibly two hidden layers. Unfortunately, RBM possesses drawbacks that are inapplicable to onboard devices with limited resources. However, investigators still employ RBM/DBM in securing IoT environments. Nevertheless, IoT architectures will perhaps be adapted to accommodate the use of DBM, thereby streamlining the use of ANN countermeasures. Hybrid or semi-supervised DL methods combine generative features in early phases and discriminative features at a later stage for data differentiation. Generative adversarial network (GAN) is a good example of hybrid deep learning. GAN has been adapted into the IoT environment for security purposes [98; 99]. GAN may show improved success because it can learn different attack scenarios that are combined to generate samples similar to a zero-day attack scenario. Such predictive capabilities represent a higher level of learning and require that such hybrid algorithms receive extra attacks samples to learn other than existing attacks [12] that approximate suspicious zero-day behaviors. This course aims to achieve lower false-negative rates, though perhaps at the expense of higher false-positive rates. Yet, some would argue that higher learning layers are necessary to anticipate unknown, sophisticated attack strategies.
## 5 Evaluation of Material Factors and Performance Indicators by One-Way Standard Deviation and GRA  
The objective of this section is to ascertain the impact of each material factor on the slurry performance indicators and the correlation between each performance indicator of the slurry and its optimal slurry ratio. The ultimate objective was to determine the optimal mix ratio of the grout. The key to determining the optimal mix ratio of alkali-activated GGBFS-zeolite powder grouping material was quantifying the effect of four  
Figure 8: SEM image of alkali-activated GGBFS–zeolite powder grouping slurry stone: (**a**) 2% CL; (**b**) 5% CL.  
material factors on performance indicators. The four performance indicators were the setting time, flowability, bleeding rate, and compressive strength. The four material factors were the ratio of the zeolite powder to the GGBFS, the set retarder dosage, the alkali activator dosage, and the water-binder ratio. The one-way standard deviation method was used to assess the influence of each material factor on the performance indicators. Subsequently, Grey Relational Analysis (GRA) was used to evaluate the correlation between each performance indicator and the optimal mix ratio, aiming to determine the optimal mix ratio.
# Monitoring the Health and Residence Conditions of Elderly People, Using LoRa and The Things Network
## 5 Discussion and Results
### Limitations  
The system was placed in a backpack and transported on some previously defined routes. It was tested by placing the equipment in this position for some time. The only position that was not tested with the prototype in the backpack was the fall backward, for obvious safety reasons. We fully understand that the prototype is not as robust as a consumer-grade product. Still, to demonstrate the approach's feasibility, we strongly argue that such a prototype serves its purpose.  
This limitation forced us to reuse the prototyping boards that we have available, which are much larger than what would be necessary, such as ESP32 and SIM808. We only used GPS in the prototype; however, it considerably increases energy consumption. Therefore, everything will have to be adjusted with much more efficient components in a commercial product, even boards with ESP32, including a GPS and accelerometer.  
The use of the protoboard allowed us to reorganize the components, making it easy to build a PCB board for soldering components. We also intend to look for the most adjustable sensors for human use to miniaturize the prototype for human use and monitor residences' habitability conditions. Our prototype covers these two parts, the accelerometer and GPS, being naturally disposable in homes, and it may have other sensors, for example, the detection of smoke or gas, among others.
# A Security and Privacy Scoring System for Contact Tracing Apps
## 1 Introduction
### Digital Contact Tracing Methods
#### 1.2.1 Bluetooth-Based Contact Tracing Methods  
In this approach, an app broadcasts an anonymous identifier over Bluetooth, while at the same time scanning for other devices broadcasting from the app. When two devices detect each other, they exchange identifiers and log them along with other auxiliary information, such as signal strength.  
There are two main designs currently in use. For the first, known as the _centralized_ system, a user that tests positive for COVID-19 releases the log of who they have been near to the server. The server then finds matches of these IDs in its database and performs a risk assessment. If the logged individual was close enough to the patient for the set exposure period, they are sent an alert [2]. Typically, the COVID-19 exposure requirement is that they were within 2 meters for at least 15 minutes.  
In the second, known as the _decentralized_ system, the patient sends their own ID values to the server. Every app periodically pulls the list of positive IDs from the server and looks for a match in its contact logs. The app performs the same risk assessment as before if it finds a match and alerts the user accordingly. The most widely used design for such a system is the Google/Apple Exposure Notification (GAEN) protocol [3].
###### Abstract  
To explore the advantages in the flexibility of ternary pumped storage units (T-PSUs) compared with fixed-speed pumped storage units (FS-PSUs) and variable-speed pumped storage units (VS-PSUs) in the day-ahead optimal scheduling of power systems, this paper firstly establishes the mathematical model of T-PSUs which is suitable for the target application, and establishes a new hybrid pumped storage model combining both FS-PSUs and T-PSUs for the purpose of improving existing FS-PSUs. Secondly, a day-ahead optimal scheduling model used for a wind-thermal-pumped storage bundled transmission system with the goal of obtaining the lowest operating cost is established. Additionally, various indicators are proposed to comprehensively evaluate the flexibility of different pumped storage unit types on day-ahead optimal scheduling. Finally, a sensitivity analysis is conducted from three aspects: covering the wind power capacity, load rate, and pumped storage capacity. The results indicate that T-PSUs are superior to FS-PSUs and VS-PSUs in maintaining the smooth, economic, and robust operation of thermal power units, as well as in promoting wind power consumption. This provides a significant reference for evaluating the technical and economic benefits of the different types of pumped storage units in applications of future power grids.  
speed regulation characteristics; flexibility assessment; ternary pumped storage units; day-ahead optimization scheduling; sensitivity analysis
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Motivation for Multiple Parameter Set Profiles  
Although range-based updating captured short-term network changes, there remain cases where fundamentally different fuzzy _profiles_ are required. For instance, a lightly populated network (e.g., 40 nodes) may favor the membership function where \((NN)\) plays a significant role less than a densely populated network (e.g., 280 nodes) and, likewise, different network conditions and parameters, such as the energy constraints of the topology, the importance of delay, or overall how topology scenarios--such as if nodes span across long distances, their communication frequency, etc.--might make the use of the initial three parameters throughout the network life ineffective.  
While our previous method adapted to network conditions by dynamically adjusting the ranges of fuzzy membership functions within a fixed parameter set and fine-tuned the BC/DBC ratio, Method B introduces a fundamentally new capability: profile switching. Instead of relying on a single set of parameters with adjusted ranges, the proposed approach supports multiple distinct parameter sets tailored for diverse network contexts, such as energy constraints or delay sensitivity. Each profile contains its own fuzzy logic configuration, enabling more context-aware decision-making across a wider spectrum of scenarios. This profile-level adaptability allows for the system to respond not only to gradual changes, but also to abrupt shifts in network topology or application requirements.The accompanying Algorithm 2 and 3, compares these models side-by-side, illustrating how profile evaluation and selection are integrated into the reinforcement learning framework. Consequently, Model B offers enhanced flexibility and responsiveness in dynamic and heterogeneous network environments.  
To address these challenges, we propose creating several parameter set profile candidates for use in each in membership function, and then adaptively selecting the most suitable profile based on real-time metrics. This approach aims to better capture large-scale and more abrupt variations in topology and mobility.  
```
1:Input: Metrics \(X=\{CT,\,NN,\,SS,\ldots\}\)
2:Update fuzzy range boundaries using sliding window
3:Apply fuzzy membership functions with adjusted ranges
4:Construct fuzzy state \(s_{t}\)
5:Select action \(a_{t}\in\{\texttt{BC,DNB}\}\) using \(\epsilon\)-greedy policy over \(Q(s_{t},a)\)
6:Execute action \(a_{t}\) and observe reward \(R_{t+1}\)
7:Update \(Q(s_{t},a_{t})\) using TD or MC method
8:Loop: Repeat at each adaptation interval \(\Delta t\)
```  
**Algorithm 2** Model A: Single-Profile Fuzzy Adaptation
## 3 Results
### Combination with IDA  
The second component of the standard \"7 + 3\" AML therapy regimen is an anthracycline drug (most frequently, daunorubicin or idarubicin). From a dose-response viability assay (see Supplementary Materials, Figure S1), a 100 nM concentration of IDA was chosen for further experiments. The effect of anthracycline drugs on survivin expression is poorly investigated, and studies reporting on both survivin level enhancement and attenuation can be found [18; 39]. Accordingly, we observed both these effects of 100 nM IDA treatment on survivin expression depending on the cell line (Figure 3a,b and Figure S2). Ten times lower IDA concentration was used for HL-60 cell line treatment as this line was reported to be very sensitive to IDA treatment [52]. Interestingly, cell viability was reduced by IDA treatment in all cell lines independently of survivin expression (Figure 3a) and PARP fragmentation was simultaneously detected (Figure 3b). We, therefore, tested a set of other apoptosis-related proteins, and we revealed considerably reduced expression of another member of the IAP family, XIAP (Figure 3b). Moreover, Mcl1 was suppressed in all cell lines except OCI-AML3 and expression of p21 increased in the cell lines with functional p53 (Figure 3b). Interestingly, upregulation of p53 was detected in MV4-11 and OCI-AML3 but not in OCI-AML2.  
Combination of Ara-C and IDA did not show any synergism of these two drugs in terms of the effect on cell viability. However, Ara-C-induced survivin overexpression was reversed by the combination with IDA in the lines where single IDA treatment  
Figure 3: Effect of 100 nM (10 nM for HL-60) idarubicin (IDA) and its combination with 5 \(\upmu\)M (0.5 \(\upmu\)M for HL-60) Ara-C on AML cell lines. (**a**) Viability (estimated by propidium iodide (PI) exclusion test) and survivin mRNA and protein expression in control (white), Ara-C (gray), IDA (white, hatched), or Ara-C + IDA (gray, hatched)-treated cells. Values represent an average of at least three independent measurements \(\pm\) SD. Significance of the difference is marked by asterisks (difference vs. control is indicated above the appropriate bar): * \(p<0.05\), **\(p<0.01\), ***\(p<0.001\), ***\(p<0.0001\). (**b**) Expression of apoptosis-related proteins. Blots are representatives of at least three independent experiments. Original blots with MW markers are provided in the Supplementary Materials, Figure S2.  
caused survivin depletion. Similar results were obtained also at the survivin mRNA level (Figure 3a). Immunofluorescence monitoring of survivin expression confirmed the results of immunoblotlot (Figure 4 and Figure S3) and revealed no mitotic cells in IDA-treated samples, although IDA induces G2/M (mitosis) cell-cycle arrest after 24 h [52].
# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 1 Introduction
In our work, topology estimation is formulated as iterative vascular connectivity prediction, which is specifically implemented as deep-learning-based pairwise classification between vessel pixels across a junction. We note that we will use the junction as a term including bifurcation and crossing in this work. In the proposed network, the connectivity classification is efficiently carried out for any pixel pair on top of vascular shape features, which are learned using thickness/orientation supervisions generated from segmentation ground-truths (GTs). This design is based on the observation that: (1) Shape is more distinct than appearance on thin vessels, (2) thickness/orientation changes at a bifurcation show specific patterns that can be learned [14], and (3) thickness/orientation are not abruptly changed on the same vessel at a crossing. Figure 1: Illustration of our topology-aware retinal artery–vein (AV) classification method, where estimated vessel topology is used to perform tree-wise AV classification. Red and blue colors represent classified arteries and veins, respectively. Different colors in the estimated topology map represent separate sub-trees. Tree-wise AV classification is used to correct the pixelwise classification errors on thin vessels, highlighted by white circles, which may occur due to their indistinct appearances. The main contributions of our work are as follows. (1) Our work is the first, to the best of our knowledge, to cast vessel topology estimation as an iterative connectivity prediction, which is implemented as deep-learning-based pairwise classification. (2) For both efficiency and effectiveness in the testing time, the connectivity prediction is carried out on top of convolutional feature maps learned using vascular shape supervisions, which enables a batch processing of a bunch of pixel pairs. (3) To evaluate the proposed method on the public DRIVE dataset, topology GTs are acquired for the set, and it will be made publicly available for the community ([[https://github.com/sysshin1014/VCP](https://github.com/sysshin1014/VCP)]([https://github.com/sysshin1014/VCP](https://github.com/sysshin1014/VCP))). We demonstrate the effectiveness of the proposed method by conducting comparative studies on two retinal image datasets.