esa-sceva/satcom-chunk-collection huggingface dataset:
# Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases
## 3 Flight Planning Algorithm  
In this section, we describe the flight planning algorithm that is used to analyse the route network-based urban airspace. The algorithm that solves multiple sUASs flight planning problems consists of the inner loop algorithm and the outer loop algorithm. The inner loop algorithm optimises each flight plan of an sUAS that minimises the flight time while satisfying the time-based minimum separation requirement from other planned sUASs. Then, the outer loop algorithm allocates arrival and departure sequences of the sUASs.  
Definition 1: We define the flight planning problem as the following: (a) the flight route of each sUAS, (b) its separation-compliant speed profile including arrival times at each waypoint, and (c) the arrival and departure sequences (if it is a multiple sUASs flight planning problem).  
This section consists of five parts: (a) a description of the time-based separation concept; (b) flight planning problem formulation for each sUAS (inner loop); (c) the entire flight planning algorithm with two different outer loop algorithms (FCFS and LCFS); (d) a flight planning example using the proposed algorithm for a last-mile delivery case (1-to-\(\mathcal{M}\)); and (e) a description of four metrics to quantitatively analyse the urban airspace capacity.
# A NavCom Signal Authentication Scheme Based on Twice Two-Way Satellite Time Transfer
## 2 A Twice TWSTT NavCom Signal Authentication Scheme
### Fundamental Theory
In such scenarios, the ground terminal can ascertain the presence of repeater spoofing interference, as the inconsistency in the measurements serves as an indication. However, the previous analysis did not consider measurement errors, which can occur in real situations. The measurement of signal arrival time at the ground terminal can be subject to biases in twice TWSTT process. These biases may result in the same value of \(\Delta T_{1}\) and \(\Delta T_{2}\) when there are repeater spoofing interference signals, potentially leading to successful spoofing. Therefore, it is necessary to conduct further analysis and consider measurement errors when studying real scenarios [19].
## 3 Privacy Attacks in FL
### Membership Inference
Their study focused on the white-box setting, wherein the attacker has access to the model's loss and can compute the gradients of the loss with respect to all parameters using a simple backpropagation algorithm. The authors demonstrated that, in deep neural networks, the distribution of the model's gradients on members can be distinguished from the distribution of non-members. They explained that the initial layers of the neural network tend to contain less individual-specific information, requiring the attacker to devise specific attacks for each layer. The attack model proposed by [PERSON] et al. consisted of feature extraction components and an encoder component. To extract features from the output of each layer, they employed a fully connected network, incorporating the one-hot encoding of the true label and the loss. For the gradients, a convolutional neural network was utilized. The output from this step was then fed into an FCN encoder, which provided the membership probability of the input. Through experimentation with various datasets such as CIFAR100, Texas100, and Purchase100, the authors demonstrated that even well-generalized models are highly susceptible to white-box membership inference attacks. [PERSON] et al. in 2022 [38] proposed a membership inference attack named CS-MIA, which utilizes prediction confidence series (PCS) in federated learning. This attack takes advantage of the observation that the prediction confidence on training and testing data exhibit distinct changes over rounds in federated learning. The authors demonstrated that the variations of models across rounds in federated learning can be leveraged to differentiate between members and non-members of the target model. They trained a fully connected network to process the PCS and learn the discrepancies between training and testing data. The researchers designed membership inference methods for both local and global attackers and introduced an active global attack to enhance attack performance. To train the attack model, the authors drew inspiration from the shadow training technique introduced by [PERSON] et al. [33]. They generated shadow confidence series for member and non-memberinstances by involving members in a federated learning process. Subsequently, they computed the confidence of the shadow model on both member and non-member instances, using this information to train the attack model. Experimental results highlighted the vulnerability of federated learning privacy to the differences between training and testing confidence series. CS-MIA achieved a membership inference accuracy of over 90% on various benchmark datasets, indicating a significant threat to the privacy of federated learning.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 2 Methodology
### Comparative Analysis of LoRA and QLoRA Techniques  
#### 2.6.1 LoRA  
LoRA decomposes a large weight matrix into two smaller low-rank matrices (called update matrices) to make fine tuning more efficient. These new matrices can be trained to adapt to the new data while keeping the overall number of changes low. The original weight matrix remains frozen and has not received any further adjustments. The original and adapted weights are combined to produce the final results. Also, LoRA does not add inference latency when adapter weights are merged with the base model. The number of parameters in LoRA is determined by the rank and shape of the original weights. In practice, trainable parameters vary as low as 0.1-1% of all the parameters. As the number of parameters needing fine tuning decreases, the sizes of the gradients and optimizer states attached to them decrease accordingly. Thus, the overall size of the loaded model reduces.  
We leveraged LoRA for efficient parameter updates, as it reduces memory requirements by adding small trainable rank decomposition matrices to existing weights. This enables task-specific adaptations without modifying all the model parameters. In our experiments, we adapt LLIMA3 for downstream classification tasks using LoRA and QLoRA. The hyperparameter \(r\) sets the ranks of LoRA matrices, balancing the model's performance with memory use. The hyperparameter \(\alpha\) acts as a scaling factor to avoid overfitting. During inferences, LoRA involves running the original model with the entire parameter volume once (without needing gradients and optimizer states). So, as shown in Table 3, the memory requirement for inferences using LoRA is the same as the memory required by the number of parameters. Using low-rank adaptation (LoRA), Llama3 is loaded into the GPU memory as quantized 8-bit weights. However, during fine tuning using LoRA, a smaller model is trained that achieves nearly the same performance as that of the original model. Parameter types cannot be int8 or int4 to ensure model convergence during training. Generally, float fp32 is used, and for slightly lower performance, BF16 is an option.  
Full-parameter fine tuning took hours, but as shown in Table 1, fine tuning with LoRA completed in less than 30 min. LoRA also makes the process more efficient than QLoRA, as it reduces the fine-tuning time by 10\(\times\) times. This is because LoRA has fewer trainable parameters, which translate to fewer derivative calculations, and less memory is required to store and update weights. Also, when deployed, LoRA merges the trainable matrices (learned weights) with frozen (main) weights, introducing no inference latency compared to the fine-tuned model or adapter layers. The most significant benefit comes from reductions in memory and storage usages, which allowed us to train the model using a single GPU and avoid I/O bottlenecks. Another benefit is that it enables switching between tasks while deployed at a much lower cost by only swapping the LoRA weights as opposed to all the parameters.
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 3 Diversity Balancing for Two-Stage Collaborative Filtering
### User Interest Distribution  
#### 3.2.1 Acquisition of Item Category  
To construct a multi-interest model that can be used to obtain the interest distribution of a user, all item categories must be identified, and the user rating information for all item categories must be collected. The K-means clustering algorithm is generally used to cluster items [41]. The item sets are defined as \(I=\{i_{1},i_{2},\ldots,i_{m}\}\), the user sets are defined as \(U=\{u_{1},u_{2},\ldots,u_{m}\}\), and the item category sets after clustering are defined as \(C=\{c_{1},c_{2},\ldots,c_{k}\}\).
# Sensor-Based Solid Waste Handling Systems: A Survey
## 1 Introduction
In [13], a survey is performed to analyze the existing information and communication technologies (ICTs) and their use in solid waste management systems. It discussed the concerns and obstacles associated with implementing an ICT-enabled system. However, the latest technological approaches towards solid waste management systems are not included in this study. A systematic review of various technologies and decision support systems for the efficient handling of solid waste is presented in [14]. This work emphasizes much on decision support systems and does not provide a critical comparison of advanced ICT technologies adopted for the design of solid waste handling systems. A study on IoT frameworks for waste management in smart cities is presented in [15]. It compares various IoT reference models from the literature and highlights the research issues and challenges among them. However, the study does not analyze the identified solutions in terms of power consumption and communication range. An analysis of various ICT technologies, especially IoT and blockchain-oriented approaches towards the design and development of solid waste management systems, is described in [16]. It highlights the limitations of wireless communication technologies adopted for IoT architectures. However, from the analysis of previous studies and reviews on solid waste management systems, it is evident that none of the existing works presented a hierarchical approach on how evolution in communication technology revolutionized the solid waste management industry. Moreover, none of the existing works distinguish the WSN and IoT-based approaches for solid waste management systems. The main contributions of this study are as follows:  
* A comprehensive analysis of RFID, WSN, and IoT-based approaches towards the automation of solid waste handling systems. * Each category is analyzed with a typical system architecture. Significance and limitations for the same are discussed. * Recommends apt communication technology for IoT-based solid waste handling systems. * The critical open research issues on state-of-the-art solid waste handling systems are concluded from the review and recommendations for future directions are presented. The remainder of this article is organized as follows. Section 2 describes the methodology adopted for the survey. Section 3 presents RFID-based approaches for solid waste management systems. Section 4 describes various WSN-based approaches towards solid waste management systems. Section 5 presents various IoT-enabled approaches towards solid waste management systems. Section 6 presents the comparison and discussions on the various approaches in solid waste management. Section 7 describes research gaps and future directions for solid waste management. Finally, the article is concluded in Section 8.
## 5 DRL-Based Online Resource Allocation
### Mechanism of DRL  
Section 5.1 illustrates the necessity and advantage of the online mechanism and further briefly introduces the MDP problem. This section will further elaborate on the essential elements of the MDP problem and DRL method, namely action space, state space, and immediate reward.
## 3 Modeling and Linearization of the System  
### Modeling the Dynamics of the Machines  
In order to show the development proposed, the generalization of the oscillation equation of the electric machines is firstly contemplated. The modeling of the machines uses as a base Figure 1. The model used is called the \"two-axis model\", as it is a reduction of the complete model of the synchronous machine and does not include the subtransient terms and the flux linkage terms. For the detailed origin of this model, see [18].  
The main modification that the oscillation equation suffers is the introduction of the associated losses to the negative sequence. The following is considered for the  
Figure 1: Model of the two axes used in the modeling.  
synchronous machine of the current algebraic variable associated with the negative sequence, given by the term \(\frac{1}{2}R_{r_{i}}[(I_{R{e_{i}}}^{neg})^{2}+((I_{I_{Im_{i}}}^{neg}))^{2}]\).  
\[\begin{split}\frac{2H_{i}}{w_{s}}\dot{w}_{i}=& T_{M{i}}-E_{d{i}}^{{}^{\prime}}I_{d{i}}-E_{q{i}}^{{}^{\prime}}I_{q{i}}-(X_{q{i}} ^{{}^{\prime}}-X_{d{i}}^{{}^{\prime}})I_{d{i}}I_{q{i}}\\ &-D_{i}(\omega_{i}-\omega_{s})-\frac{1}{2}R_{r_{i}}[(I_{R{e_{i}} }^{neg})^{2}+((I_{Im_{i}}^{neg}))^{2}]\end{split} \tag{6}\]  
where:  
* \(\omega_{i}\) the angular speed of the machine (rad/s);
* \(\omega_{s}\) the synchronous speed of the machine (rad/s);
* \(H_{i}\) the inertia constant (s);
* \(D_{i}\) the machine damping (pu);
* \(T_{M_{i}}\) the mechanical torque applied to the machine;
* \(I_{d{-}q{i}}\) the direct current and of quadrature (pu);
* \(E_{d{-}q{i}}\) the direct equivalent voltage and of quadrature (pu);
* \(X_{d{-}q{i}}\) the direct transitory reactance and of quadrature (pu);
* \(I_{R{e_{i}}}^{neg}\) the current of the negative sequence, real and imaginary parts (pu);
* \(R_{i}^{{}^{\prime}}\) the resistance of the negative sequence (pu);
* \(R_{s_{i}}\) the resistance of the machine stator (pu);
* \(R_{r_{i}}=2*(R_{i}^{neg}-R_{s_{i}})\) the resistance of core losses due to unbalance (pu).
## 1 Introduction
Apart from calculating the EPs distribution directly from the equation derived from [PERSON]'s equation, inverse methods leverage a forward model for calculating B1 maps and a fitting method for evaluating the EPs distribution [27; 28; 29]. These reconstruction approaches avoid the locally homogeneous assumption and alleviate the noise effect by avoiding the differentiation of measured data. For example, [PERSON] et al. proposed global Maxwell tomography on the basis of the volume integral equation formulations [30]. [PERSON] et al. introduced contrast source inversion EPT (CSI-EPT) [31]. However, these methods require extensive numerical efforts and have not been investigated in vivo experiments. The recent applications of machine learning algorithms in EPT show promising results [32; 33]. These methods retrieve EPs from large datasets of transmit RF fields and their corresponding EPs via machine learning. [PERSON] et al. investigated the challenges of deep learning-based methods and found that the networks trained on realistic simulation introduce artifacts when applied to in vivo data [34]. In contrast to acquiring both the conductivity and permittivity from MREPT algorithms, the conductivity reconstruction can be conducted solely by phase information based on the fact that the phase map primarily dominates the conductivity calculation [35]. The transceive phase map is used to reconstruct the conductivity. Thereby, it bypasses the TPA and decreases the scan time without B1 mapping scanning. [PERSON] et al. introduced an electrical conductivity imaging method, which includes the gradient information of conductivity, to improve the reconstruction accuracy. However, this method involves setting the boundary value and the diffusion parameter [36]. In our previous study, we proposed a double regularization method that contains total variation and wavelet terms [37]. This method suppresses spurious oscillation and enhances the accuracy of reconstruction. However, the method needs the second spatial derivatives of the transceive phase, and staircase effects occur due to the total variation regularization. In this study, we proposed a novel conductivity imaging method on the basis of the gradient-based conductivity imaging method. We reformulated the central equation through divergence properties, leading to the free measurements of the Laplacian operator. With the finite-difference method, the optimization problem was obtained, and a second-order total generalized variation (TGV) term was used to remove the staircase effect [38]. The problem was solved using the alternating direction method of multipliers (ADMM) method [39]. Numerical simulation and experimental were applied to validate the performance of the proposed method. The reconstruction results show that the proposed method accurately determines the distribution of conductivity and is robust in terms of noise contamination compared with the conventional and double regularization methods.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 4 Results and Discussion
### On-Ground Voltage Profile Measurements  
Figure 4a illustrates voltage versus time data for 42 cycles of charging and discharging from both the BAn and the newly developed BMS. Each cycle consisted of approximately 1.5 \(\pm\) 0.2 h of charging, followed by 1.0 \(\pm\) 0.2 h of discharging. During the discharging phase, as depicted in Figure 4b, the voltage initially decreased rapidly before transitioning to a slower decline, consistent with the findings of [PERSON] et al. [42] and [PERSON] et al. [43]. Subsequently, the constant current charging phase led to a logarithmic increase in voltage, followed by a constant voltage charging period lasting over 0.5 \(\pm\) 0.1 h. The voltage data acquired by the BMS closely match those of the BAn, although a slight initial lag was observed in the BMS data, which subsequently converged with the BAn readings. The constant voltage portion of the discharge curve can be further investigated to establish a direct correlation with the SOH of batteries [44]. Differences in the voltage reading occur due to the 5-second data transfer rate of the BMS and a delay in the internal clock for both systems. The BMS operates on the battery's power, unlike the BAn, which uses an external power source that causes the initial lag. The observed average deviation of 0.2 V between the BMS and BAn, along with the logarithmic curve during the constant current phase, indicates a close match. The study by [PERSON] et al. [45] observed an accuracy error of just above 0.05 \(\pm\) 0.01 V while performing on-ground tests with a hardware-in-the-loop (HIL) simulator. Overall, the results suggested good alignment, with minor variations attributed to the power source and internal clock discrepancies between the BMS and BAn.  
Figure 4: A comparison of voltage readings collected from both BMS and BAn. (**a**) Voltage data for 42 cycles, highlighting the deviations between 0.2 V and 0.4 V. This indicates a close match between the BMS and BAn data. (**b**) The first cycle data, illustrating the discharging and charging profiles. Random deviations in voltage are shown compared to the BAn readings.
## 2 Contribution  
The future 6G network is expected to deliver high data rates to each user to run high-definition applications, which are facing various challenges with the current 5G network. Typically, the 6G network supports a diverse range of applications and services, including enhanced indoor coverage, large capacity for outdoor public spaces, improved spectral efficiency, and low power consumption [75]. To support essential user applications and increase the efficiency of communications among interconnected devices with low latency and high throughput, primary technologies are expected to achieve the goals of 6G [76; 77]. This study aims to describe the techniques involved in current 5G network design and their issues, in accordance with spectrum management for designing future 6G networks. The five major topics covered in this study are Carrier Aggregation (CA), CR, small cell, high-spectrum access, and M-MIMO (Figure 1). The main concept of each of the involved approaches and their potential results in current 5G is also presented. Furthermore, research issues are discussed in this study.  
**Carrier Aggregation:** The current 5G network is compatible with its predecessor's technologies, including Carrier Aggregation (CA). CA was first introduced in 4G LTE, and it is now implemented in the current 5G system. The core objective of CA is to utilize the spectrum resources of a cellular network effectively.  
**Cognitive Radio:** Another promising technology for the current 5G system is CR. It is an adaptive and advanced radio network technique that can automatically discover available channels in a wireless cellular spectrum.  
**Small Cell:** To support numerous users of up to almost 100 times more than that of the previous network, small cells with Full-Duplex (FD) communication are introduced to resolve the spectrum issues.  
**High-spectrum Access:** Accessing high-spectrum assets is inevitable due to the scarcity of available spectrum resources. A new high-spectrum space, i.e., mmWave frequency bands, ranges from 24 GHz to 300 GHz.  
**M-MIMO:** It allows the simultaneous transmission and reception of more than one signal over the same channel. Standard MIMO comprises two to four antennas, whereas the M-MIMO network contains several antennas for transmitting and receiving data. No preset figure is required, but in M-MIMO, we can implement 10 s and even 100 s of antennas for transmitting and receiving data over a common channel.  
Figure 1: Spectrum management issues.
## 5 Discussion
### Method Applicability  
#### 5.2.1 Tibet Province  
To evaluate the applicability of PSANet, we selected Tibet Province from the SARBuD 1.0 dataset, which has significant geographical differences compared to Anhui and Jiangsu Provinces. To facilitate knowledge transfer across regions, we employed transfer learning techniques [41], transferring the knowledge learned by the models from Anhui and Jiangsu to Tibet. Specifically, we froze the weights of all layers before the second to last upsampling module and trained the model using an SGD optimizer with a learning rate of 0.0003, momentum of 0.9, and weight decay of 0.0002 for a total of 30 iteration cycles and a batch size of 4. Then, we applied these trained models to evaluate the performance in Tibet Province.  
Tibet Province consists of 254 images collected in February 2019 in Tibet, as presented in Figure 13.  
Figure 14 shows the segmentation results of four representative images from the Tibet Province, from which it can be observed that all seven methods had certain errors. In particular, the segmentation results of method (a) contained more green pixels, while the segmentation results of method (f) contained more red pixels, indicating that these two methods fail to effectively transfer the learned knowledge, resulting in poor applicability on Tibet. In addition, our method performs better on large building areas compared to small building areas, which may be due to the more regular structural characteristics and larger size of large building areas, making them easier to distinguish in the image. Meanwhile, the fused encoded features can enhance the network's understanding of contextual information, which helps to accurately identify large building areas that typically contain more global information.