esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A progressive paradigm shift from centralized to distributed network architectures has been consolidated since the 4G communication standard, calling for novel decision-making mechanisms with distributed control to operate at the network edge. This situation implies that each base station (BS) must manage resources independently to meet the quality of service (QoS) of existing human-type communication devices (HTC), as well as the emerging machine type communication (MTC) devices from the internet of things (IoT). In this paper, we address the BS assignment problem, whose aim is to determine the most appropriate serving BS to each mobile device. This problem is formulated as an optimization problem for maximizing the system throughput and imposing constraints on the air interface and backhaul resources. The assignment problem is challenging to solve, so we present a simple yet valid reformulation of the original problem while using dual decomposition theory. Subsequently, we propose a distributed price-based BS assignment algorithm that performs at each BS the assignment process, where a novel pricing update scheme is presented. The simulation results show that our proposed solution outperforms traditional maximum signal to interference plus noise ratio (Max-SINR) and minimum path-loss (Min-PL) approaches in terms of system throughput.  
Keywords:base station assignment; distributed algorithm; IoT; resource allocation +
Footnote †: journal: Sensors
# Vulnerabilities and Limitations of MQTT Protocol Used between IoT Devices
## Security of the Internet of Things
### Security Overview of MQTT  
As previously mentioned, MQTT features different security mechanisms, but most of them are not configured or provided by default, such as data encryption or entity authentication. Authentication mechanisms, such as using the physical address of the device (MAC), exist and are controlled by the broker by registering a device's information once it tries to connect. Access authorization can be done by the broker using a mechanism called an Access Control List (ACL). The ACL, as the name implies, contains records of information such as the identifiers and passwords of the different clients that are allowed to access different objects and can also specify what functions the client can perform on these.  
According to Reference [4], confidentiality is a major requirement of a secure system and can be accomplished at the application layer by encrypting the message that needs to be published. This method of encryption can either be implemented as client-to-broker or end-to-end. In a client-to-broker type of encryption, the broker decrypts the information that is being published to a topic and respectively encrypts the values that it needs to send to other clients. In an end-to-end situation, the broker cannot decrypt the information being published to topics and it forwards the ciphertext to other devices. In the latter method, the broker needs fewer computational resources and less energy as it only functions as a courier and does not require any additional modules that can encrypt/decrypt messages.  
Nonetheless, additional security mechanisms can also be implemented on lower layers. According to Reference [3], one way to reliably ensure the security of a communication channel at the transport layer is by using Transport Layer Security protocol (for TCP) or even Datagram Transport Layer Security (in the case of UDP). Additionally, according to [4], encryption at the link layer can be achieved by using one of the many algorithms available, such as Advanced Encryption Standard (AES) in Counter Block Mode or AES in Counter with CBC-MAC mode, also called CCM mode. This type of security mechanism provides some additional advantages compared to other methods, such as increased efficiency due to the hardware acceleration capabilities found on radio chips.
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
###### Abstract
In the case of a given user distribution, the distribution table is used to directly derive the confidence interval at a given significance level. Random constraints are converted into general constraints, and the dynamic programming method is then used to solve the optimization problem. For the case where the user distribution is unknown, we estimated the density of users on the basis of trajectory data, and derived the dynamic programming recursion formula on the basis of the estimated density. Our main contributions are listed below:  
1. In order to meet the high requirements for reliability in 5G wireless networks, we propose to model the user-BS selection strategy problem as an optimization problem with reliability as the object. 2. In view of the drawbacks in commonly used indicators, we chose to define reliability by the RSSI for the first time, which could easily be obtained in practical scenarios. Considering the time-varying nature of the user's position, we transformed the original problem into a resource allocation problem under probability constraints, and solved it with dynamic programming and the time-space density estimation method. 3. We conducted two comparative simulations to verify the superiority of our algorithm in terms of the density estimation effect and the reliability approach GeoLife GPS Trajectories dataset [12]. Simulation 1 showed that, under different sampling frequencies, our time-space density estimation method improved accuracy by 2.84% compared to the two-dimensional kernel density estimation method. Simulation 2 showed that, compared with the distance-based selection strategies, our RSSI-based selection strategies improved communication reliability by an average of 3.57% under the three scenarios above. The rest of this paper is organized as follows. Section 3 presents the system model and formulates the BS-user selection strategy problem. In Section 4, the wireless BS selection strategy optimization problem based on identified distribution is solved by dynamic programming. The wireless BS selection-strategy optimization problem based on user trajectory is addressed in Section 5, where a time-space density estimation method is proposed. Simulation results are presented in Section 6 to illustrate the performance of the proposed time-space density estimation method under different RSSI scenarios. Lastly, Section 7 concludes this paper.
## 1 Introduction
All the pulse sequences from all the channels are then summed up with their respective time slots to create a unified pulse sequence. It is noteworthy that all the pulse sequences are not necessarily orthogonal to each other; however, due to the orthogonality property of the multi-order MHP pulses, as the pulse sequences are added up with their respective time slots, the individual pulse sequence is not affected or destroyed. With the proposed scheme, in a single-channel UWB system, the data rate has been improved by \(n\)-folds for \(n\)-number of MHP orthogonal pulses in a single time slot. For instance 16 distinct composite pulses can be represented through the employment of 4 MHP orthogonal pulses, resulting in a data rate enhancement of 4 folds in a single channel. On the other hand, for \(n\)-number of MHP orthogonal pulses the channel number has been increased to \(n(n-1)\). Hence, a unique pattern sequence has been developed by encoding the MHP orthogonal pulses to transmit binary high bits for each channel. By using 4 orthogonal pulses the proposed scheme can generate distinctive pattern sequences for 12 channels. The organization of this paper is as follows. Section 2 briefly describes the process of composite pulse generation while Sections 3 and 4 describe the single-user and multiple-user UWB systems, respectively. In Section 5, the bit error rate (BER) analysis has been illustrated. Finally in Section 6, the paper is summarized.
## 5 Results and Discussion
### Quantitative Evaluation  
We assess performance using PSNR, SSIM, and LPIPS (for perceptual quality), as well as FLOPs and parameter count (for efficiency). Table 2 summarizes the results on the LOL test set.  
Our model achieves the best trade-off between visual quality and computational cost. While SID achieves slightly higher PSNR, it is significantly larger and slower. The full set of raw quantitative metrics for individual test images from the LOL dataset is available in Appendix C to support reproducibility and detailed analysis.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 4 Attitude Determination Using the Optimal-REQUEST Algorithm
### Measurement Update  
When the measurement vectors and reference vectors are obtained based on the position loci, the initial attitude matrix \(\mathrm{C}_{b(0)}^{n(0)}\) can be determined by minimizing the following formula:  
\[\frac{1}{2}\sum\limits_{i=1}^{n}a_{i}\left\|\mathrm{b}_{i}-\mathrm{Ar}_{i} \right\|^{2}=1-\mathrm{q}^{\mathrm{T}}\mathrm{Kq} \tag{29}\]  
where \(\mathrm{b}_{i}=\frac{\beta_{p}}{\left\|\beta_{p}\right\|}\), \(\mathrm{r}_{i}=\frac{\alpha_{p}}{\left\|\alpha_{p}\right\|}\) and \(\sum\limits_{i=1}^{n}a_{i}=1\). The matrix \(\mathrm{A}\) is the initial attitude matrix \(\mathrm{C}_{b(0)}^{n(0)}\). The construction method of the K-matrix is shown below:  
\[\mathrm{K}_{k/k}=\begin{bmatrix}\mathrm{S}_{k}-\sigma_{k}\mathrm{I}_{3}& \mathrm{z}_{k}\\ \mathrm{z}_{k}^{\mathrm{T}}&\sigma_{k}\end{bmatrix} \tag{30}\]  
where the parameters in Equation (30) are defined as follows:  
\[\mathrm{B}_{k}\triangleq\sum\limits_{i=1}^{n}a_{i}\mathrm{b}_{i}\mathrm{r}_{ i}^{\mathrm{T}},\,\mathrm{S}_{k}\triangleq\mathrm{B}_{k}+\mathrm{B}_{k}^{ \mathrm{T}} \tag{31}\]  
\[\mathrm{z}_{k}\triangleq\sum\limits_{i=1}^{n}a_{i}\mathrm{b}_{i}\times \mathrm{r}_{i},\,\sigma_{k}\triangleq tr(\mathrm{B}_{k}) \tag{32}\]  
According to the REQUEST algorithm, the propagation formula of the K-matrix is given by:  
\[\mathrm{K}_{k+1/k}=\Phi_{k}\mathrm{K}_{k/k}\Phi_{k}^{\mathrm{T}} \tag{33}\]  
During the coarse alignment, the initial attitude matrix \(\mathrm{C}_{b(0)}^{n(0)}\) stays constant. Thus, the propagation coefficient matrix \(\Phi_{k}=\mathrm{I}_{4}\).  
When the new observation vectors are obtained at time \(k+1\), we construct the corresponding incremental K-matrix, namely \(\delta\mathrm{K}_{k+1}\). The specific construction method is the same as Equations (30)-(32), and the only difference is that the vectors acquired at time \(k+1\) are used to calculate \(\delta\mathrm{K}_{k+1}\).  
Next, the estimated \(\mathrm{K}_{k+1/k+1}\) can be updated by the combination of prior estimation \(\mathrm{K}_{k+1/k}\) and the incremental matrix \(\delta\mathrm{K}_{k+1}\) at time \(k+1\), namely,  
\[\mathrm{K}_{k+1/k+1}=(1-\rho_{k+1})\frac{m_{k}}{m_{k+1}}\mathrm{K}_{k+1/k}+ \rho_{k+1}\frac{\delta m_{k+1}}{m_{k+1}}\delta\mathrm{K}_{k+1} \tag{34}\]where \(m_{k}\), \(m_{k+1}\) and \(\delta m_{k+1}\) are scalar coefficients, and the specific relationship of the three scalars is as follows:  
\[m_{k+1}=(1-\rho_{k+1})m_{k}+\rho_{k+1}\delta m_{k+1} \tag{35}\]  
The fading memory factor \(\rho_{k+1}\) is the core of the REQUEST algorithm and the Optimal-REQUEST algorithm. The REQUEST algorithm uses the fading memory concept to treat the measurement noise, and this makes the algorithm suboptimal. However, the proposed OP-PL algorithm can optimally filter the measurement noise by calculating the value of the fading memory factor adaptively.
## 4 Experiments
### Evaluation of Acoustic Summary Statistics Extraction Scheme  
We next experimentally validate the proposed summary statistics extraction. In the previous experiment, the TFR obtained by CQT transform had been proved to be superior for ASC task. In this test, we further investigate the feature weighting scheme for acoustic summary statistics extraction. Figure 9 presents the contribution of employing feature-enhancing weighting. It was evident that the proposed feature weighting scheme is universally applicable to generate efficient acoustic features for ASC. In addition, we further establish multiple spectro-temporal feature aggregation scheme to achieve superior ASC classification. Figure 10 presents the class-wise precision obtained by the proposed method. According to the results, our scheme produced ideal accuracies, i.e., over 98.5% accuracy, over the classes of bus, car, kid game, restaurant and high speed-train. While, there were several cases that current method failed in making a confident classification, such as for classifying the best street, quiet street, and shop cases. In Table 2, we compared our result with state-of-the-art performances reported by the latest publications. It can be seen the proposed ASC scheme achieved superior accuracy compared to other methods. Notably, our approach also outperformed a very recent work using the optimal fusion of multiple convolution neural networks (CNNs) [24]. It is no doubt that CNNs is quite powerful learning method for general pattern classification tasks; however, in this study, the data size is small, and may induce under fitting of CNN models. That can be the main reason that our method can outperform CNNs. The comparison confirmed the superiority of the proposed scheme for ASC task.
## 4 Verification
### Li-Ion Battery RUL Prognosis  
In this section, the constructed indirect HI series is divided into two parts: the 1 to 81 series data is training data; the remaining data is used as testing data.  
Firstly, the neuron, the factor for dropping the learning rate, and the factor for L2 regularization (three major parameters of DLSTM) are set as 200, 0.3, and 0.1 respectively. Then, the proposed DLSTM, LSTM, ANN, and ARMA are used to predict the indirect HI series respectively. Finally, the prediction results of four methods are transferred to Li-ion battery capacity using the mapping relationship (in Section 4.1), and the RUL is calculated according to the failure threshold (described in Section 2.2).  
Figure 6a-d show the RUL prognosis processes of four methods; the prediction performance comparison of four methods is listed in Table 1. Comparing Figure 6a,b, the proposed DLSTM has better dynamic performance than LSTM, and the error of RUL is very small. From Figure 6a-d and Table 2, it can be seen that the _RMSE_, _MAE_, and \(RUL_{error}\) of DLSTM are better than LSTM, ANN and ARMA.  
Figure 5: Indirect HI construction process: (**a**) capacity curve; (**b**) indirect HI curve; (**c**) correlation curve between indirect HI series and Li-ion battery capacity series; (**d**) mapping construction using polynomial fitting.  
To summarize, the proposed DLSTM method based on indirect HI has good dynamic performance, and is more suitable for satellite Li-ion battery long-term RUL prognosis.
## 5 Experimental Results
### Evaluation Metrics  
The peak-signal-to-noise ratio (PSNR) and normalized correlation coefficient (NCC) metrics evaluate how effectively the proposed watermarking system works. These are discussed in detail in this section.
# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 4 Experiments
### Evaluation Metrics  
**The miss rate (MR)** is a metric used to evaluate the results of human body detection, where lower values indicate a more accurate performance. The accuracy of human body detection is primarily reflected in two aspects: detecting as many human targets as possible and minimizing false positives. Therefore, the MR and false positives per image (FPPI) are usually considered together to evaluate the pedestrian detection performance; often, the decision threshold (or confidence score) in the detection algorithm must be adjusted to achieve a balance between the two. If a lower decision threshold is set, the MR decreases and the FPPI increases; conversely, if a higher decision threshold is set, the MR increases and the FPPI decreases. Hence, to more accurately represent the performance of human body detection under different decision threshold conditions, Dollar [17] proposed calculating the logarithmic average of the MR within a certain range of FPPI as a quantitative metric, referred to as the log-average miss rate (LAMR). The LAMR is calculated as follows:  
\[LAMR=\exp(\frac{\sum_{i=1}^{N}(MR(FPPI_{i})}{N}) \tag{9}\]  
where FPPIi represents the FPPI value corresponding to the selected sampling point i and N denotes the number of sampling points. The FPPI was sampled in 100.25 intervals within the range of [10\({}^{-2}\), 100] to reflect the miss rate of the detector under low-false-positive conditions and ensure fair comparisons with existing methods. The logarithmic average miss rate (LAMR) in this state is referred to as the miss rate and denoted as MR-2 in this paper.  
The average precision (AP) represents the average accuracy of all image detections belonging to a certain class. The calculation formula is as follows:  
\[\text{AP}=\frac{\sum_{n}\text{Precision}}{n} \tag{10}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{11}\]  
where n represents the total number of images belonging to a certain class.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 5 Application of Silica Mesopores in Drug Delivery
### Glucose-Responsive Drug Delivery  
[PERSON] et al. synthesized a nano-carrier mesoporous silica nanoparticle-based drug delivery system for glucose-responsive controlled release [199]. The glucose-responsive controlled release consisted of insulin and cyclic adenosine monophosphate (cAMP). Gluconic acid-modified insulin (G-Ins) was attached to the surface of the mesoporous MSN carrier, and cAMP molecules were encapsulated inside the MSN. This system is suitable for the treatment of diabetes because it responds to just the glucose levels in the blood. In addition, the cyclic adenosine cAMP, known as an insulin secretion stimulating agent, can be encapsulated inside the pores, and then released subsequent to G-Ins diffusion to regulate blood glucose levels (Figure 19). [PERSON] et al. have described the use of glucose oxides conjugated with cyclodextrin as pore blockers of MSNs, which was previously functionalized with propyl benzimidazole groups in order to form an inclusion complex with the cyclodextrin [200]. When glucose is added, glucose oxide decomposed this molecule into gluconic acid and resulted in a pH drop which induces the protonation of the benzimidazole, opening the pore. [PERSON] et al. designed an  
Figure 18: Matrix metalloproteinases (MMPs) based on MSNs (mesoporous-peptide-BSA-LA) responsible for releasing DOX in vivo.  
insulin delivery based on MSNs, which showed positive correlation with the species and concentration of saccharides [201]. They proposed that the designed insulin delivery system presented glucose responsiveness even in the serum but also a good biocompatibility toward liver cells.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 4 Conclusions
Due to the poor satellite geometry of GLONASS-only kinematic (especially when stations are located in the low or the middle latitude) and BDS-only kinematic PPP, deleting eclipsing satellites will not always ensure a better solution, even resulting a worse solution than that of S2. This situation can be solved when multi-GNSS data are available. With the increase of the satellite geometric strength, the accuracy improvement is gradually reduced. When data of four systems are introduced into PPP model, the slight improvements of S1 and S3 are nearly the same. In the future, when the modeled attitudes are more consistent with the actual attitude control model, especially for GALILEO FOC satellites, the positioning accuracy of single-system PPP will be further improved, which also benefits the multi-GNSS PPP. The authors gratefully acknowledge IGS Multi-GNSS Experiment (MGEX) for providing GNSS data and precise products. The work is partially sponsored by the National Natural Science Foundation of China (Grant No. 41674031) and partially sponsored by Natural Science Foundation of the Anhui Education Institutions of China (Grant No. KJ2016A019). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; and [PERSON] wrote the paper. The authors declare no conflict of interest.
## 5 Discussion and Future Research Directions
### Signal Processing and Data Preparation
#### 5.2.3 Hybrid Approaches and Emerging Perspectives  
Hybrid methods that combine Fourier or wavelet transforms with advanced machine learning architectures (e.g., CNNs) or statistical tests (e.g., kurtosis-based indices) have shown improved robustness against noise. For instance, wavelet energy features can feed into a CNN classifier, directly capturing localized impulse-like behaviours caused by fault signatures. Likewise, envelope detection (e.g., Hilbert transform) can emphasize modulated fault frequencies before applying wavelet-based techniques. Nevertheless, the rising popularity of deep learning--where models ingest raw time-series data or spectrograms--shifts the emphasis onto automated feature extraction, potentially reducing manual signal processing but increasing reliance on large labelled datasets and higher computational budgets.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 2 Related Work
### Privacy-Preserving Federated Learning  
Although the training dataset is divided and stored separately, federated learning (FL) cannot protect training data privacy because the exchanged global model and local gradients still contain a large amount of sensitive information. Three popular privacy-protecting federal learning (PPFL), i.e., differential privacy (DP), homomorphic encryption (HE), and secure multi-party computation (MPC), have been proposed to address this problem.  
DP-based PPFL protects the local updates against inference attacks through adding random noises [18; 19; 20]. However, it is challenging to bring a trade-off of accuracy loss. SMC-based PPFL collaboratively computes the aggregated values between multi-parties' inputs through secure multi-party computing in nature [21; 22; 23; 24; 25; 26]. SMC-based PPFL has been constructed in one [27], two [28], three [29], and four [30] servers. However, although SMC, which is a cryptographic primitive, has powerful privacy, it is inefficient. HE-based PPFL uses homomorphic encryption to support aggregation operation on cipher-texts, and protects training data privacy from curious parameter servers without external learning accuracy loss [31]. EaSTFLy [32] uses \((T,N)\)-Shamir' secret sharing and Paillier partially homomorphic encryption (PHE). BatchCrypt [33] encodes quantized gradients into signed integers and then encrypts them, achieving training speedup compared with full-precision encryption. However, most of them contain massive, complicated arithmetical operations, which brings considerable computation overhead.  
In this paper, threshold fully homomorphic encryption (TFHE), which combines \((T,N)\)-Shamir' secret sharing and CKKS fully homomorphic encryption (FHE) [34], is used as a privacy protection mechanism, which greatly reduces the computing and communication overheads.