esa-sceva/satcom-chunk-collection huggingface dataset:
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 3 Results
### Precision  
Bandwise average CV% for all 28 images and for each of the 22 cumulative percentile steps of the six SoCal QIAs were combined into one dataset to examine how precision is affected by increasing reflectance for both CMAC and Sen2 Cor output (in Supplementary Materials as \"SoCalCV%.xlsx\"). These cumulative percentile-step averages were plotted against TOAR to represent the unbiased trend from low to high reflectance for both methods (Figure 12).  
The resulting pattern of bandwise CV% is instructive. Moving rightward from the origin toward higher TOAR, CV% is initially high, decreases to a trough and then increases in a quadratic-like manner. This behavior is exhibited in all four bands for both CMAC and Sen2 Cor despite very different workflows and data inputs. This result provides support that the CMAC conceptual model, as expressed in Figure 3 with its  
Figure 12: Average CV% distribution for the 22 percentile steps combined for the six QIAs; (\(n=132\)) of CMAC and Sen2 Cor. Though approached very differently, both methods show similar trends.  
response, correctly portrays the phenomenology of atmospheric aerosol effects. According to the CMAC model, the greatest precision is expected to occur at the axis point where the TDL crosses the x-axis and the affected image's surface and TOAR are equivalent. The statistical distribution in the region around the axis point will, therefore, tend to have minimal scatter. It should be noted, however, that the axis point is a concept and not a fixed value. The axis point migrates rightward as Atm-I increases; hypothetically, this trend is caused by forward scatter.  
In Figure 12 the trend in CV% can be seen to increase above a scaled TOAR reflectance level roughly between 2300 to 2800 DN depending upon band and method, but the values become widely spaced and, for each dataset, highly scattered. Uncertainty increases at higher reflectance, as can be seen in the spreadsheets and tables that summarize the QIA CV% data available through the link supplied in the Supplementary Materials.
## 1 Introduction
Another limitation is that the Hessian matrix must be positive definite, and its dimension had better not be too large, taking the computational load into consideration [20]. Thus, the first-order gradient-based optimization algorithms, i.e., gradient descent and its variants are widely used in deep learning problems [21]. If the first partial derivatives of the cost function are available with respect to parameters, the gradient descent method is a very effective optimization method. To properly adjust the parameters, in every training iteration, the optimization algorithm calculates a gradient vector. Then the parameters is changed in the opposite direction of the gradient vector. It is worth noting that the commonly used tap estimation algorithms in time-domain equalizers (TDE) with the traditional structures, i.e., least mean square (LMS) and recursive least square algorithms, are based on first-order gradient of the cost function. Therefore, it is very possible to optimize the TDE with traditional structure by using gradient descent and its variants. In optical communications, especially short-reach optical communications, the distortion models are almost certain. The traditional TDE have been widely applied in optical communication systems and achieved good performance. Therefore, NNs are not always required in some communication systems with deterministic distortion models, taking the cost of training into consideration. In this paper, we propose and evaluate the tap estimation schemes for the equalizer with traditional structures in optical fiber communications using the optimization algorithms commonly used in the NNs. The experimental results show that adaptive moment estimation algorithm and batch gradient descent method perform well in the tap estimation of equalizer. In conclusion, the optimization algorithms of NNs are useful in the tap estimation of equalizer with traditional structures in optical communications. The rest of this paper is organized as follows. In Section 2, we first review the mathematical model of FFE. Section 3 presents the principle of the proposed tap estimation schemes using optimization algorithms of NNs. The experimental setup is described in Section 4 and the detailed results and discussions are provided in Section 5. Finally, Section 6 concludes the paper.
## 3 Overview of HO Management in 5G UDSC Network
### Classification of HO Types  
There are HOs types according to the network and according to the frequency and according to the techniques. The details of classification as followed:  
1. Horizontal HO; This type of HO is executed when the networks are same e.g., HO occur between 3G to 3G is called horizontal HO. This type of HO is also called intra-technology HO.
2. Vertical HO; When HO executed between base stations of different network is called vertical HO. For example, HO is occurring between 3G to 4G. To proceed this type of HO, layer 2 and 3 of OSI model play an important role.
3. Intra-frequency HO; When two distinct base stations work on the same operating frequency bands, then it supports intra-frequency HO.
4. Inter-frequency HO; When two distinct base stations work on the different operating frequency bands, then it provides inter-frequency HO.
5. Soft HO; It follows the make-before-make strategy where first new connections are built between UEs and wireless links before breaking the previous ones.
6. Hard HO; It follows the break-before-make strategy where all the wireless links are first removed from UEs to build new wireless communication connections.
7. Controller based HO; This type of HO is executed by mobile station. There are further three type of classifications: network controlled HO (NCHO), Mobile controlled HO (MCHO), and mobile assisted HO. In NCHO, the decision step is detained by a controller while mobile station takes initiation step, In MCHO, mobile station takes both steps initiation step as well as the decision step, while in a MAHO, network  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Handover Information Gathering** & \multicolumn{3}{c}{**Handover Decision Making**} \\ \hline
**Network Related** & **Mobile Terminal Related** & **Criteria Based** & **Strategy Based** \\ \hline Cost Based & Velocity Based & RSS Based & ANN Based \\ Coverage Based & Stations Based & Velocity Based & Function Based \\ Link Quality Based & User Preference Based & Security Based & Traditional Based \\ Quality of Service Based & & QoS Parameters & Fuzzy Logic Based \\  & & Bandwidth Based & User Centric Based \\  & & Battery Usage Based & Context Aware Based \\  & & Available RATs Based & Multiple Attribute Based \\  & & User Preferences Based & \\  & & Operator Performance Based & \\ \hline \hline \end{tabular}
\end{table}
Table 3: HO Information Gathering and Decision Making.  
takes the decision and mobile only collect and send basic information i.e., received signal strength indication, and signal to interference-plus-noise ratio [77].  
There are also some advantages and disadvantages of HO schemes based on spectrum, overhead, reliability, QoS, latency, and designated channels. Following the guidelines in [78], Table 4 illustrate advantages and disadvantages of HO schemes.
###### Abstract  
With the rapid development of intelligent information technologies, remote sensing object detection has played an important role in different field applications. Particularly in recent years, it has attracted widespread attention in assisting with food safety supervision, which still faces troubling issues between oversized parameters and low performance that are challenging to solve. Hence, this article proposes a novel remote sensing detection framework for multi-scale objects with a rotating status and mutual occlusion, defined as EYMR-Net. This proposed approach is established on the YOLO-v7 architecture with a Swin Transformer backbone, which offers multi-scale receptive fields to mine massive features. Then, an enhanced attention module is added to exploit the spatial and dimensional interrelationships among different local characteristics. Subsequently, the effective rotating frame regression mechanism via circular smoothing labels is introduced to the EYMR-Net structure, addressing the problem of horizontal YOLO (You Only Look Once) frames ignoring direction changes. Extensive experiments on DOTA datasets demonstrated the outstanding performance of EYMR-Net, which achieved an impressive mAP0.5 of up to 74.3%. Further ablation experiments verified that our proposed approach obtains a balance between performance and efficiency, which is beneficial for practical remote sensing applications in transportation monitoring and supply chain management.  
agricultural environmental supervision; remote sensing imaging detection; rotated annotation analysis; deep learning technology; convolutional block attention Article
## 3 Methodology
### Generalized Expansion Method (GEM)
Notations and Definitions  
\(M\) Number of nodes  
\(\Lambda_{j}\) Input rate node \(i\) (\(i\) = 1,2,... \(M\))  
\(T\) Number of product classes \(t\) (\(t\) = 1,2,... \(T\))  
\(\mathrm{B_{i}}\) Buffer capacity at node \(i\) excluding those in service  
\(\mathrm{K_{i}}\) Buffer capacity at node \(i\) including those in service  
\(\alpha_{j}\) The probability of jobs leaving the system from node \(j\) (\(j\) = 1,2,..., \(M\))  
\(C_{j}\) Cost of a buffer space in node \(j\)  
\(L_{sj}\) Mean number of jobs (queue length) at queue \(s\) in node \(j\)  
\(\lambda_{h}\) Arrival rate to holding node \(h\)  
\(\mu_{h}\) Mean service rate at holding node \(h\)  
\(P_{kj}\) Blocking probability that node \(j\) is at capacity \(K_{i}\)  
\(P^{\prime}{}_{Kj}\) Feedback blocking probability  
\(\Theta\) Mean throughput of the system  
\(\Theta_{j}\) Output rate at node \(j\)  
\(U_{j}\) Utilisation rate at node \(j\)  
\(S^{2}\) Squared Coefficient of variation of the service time  
\(\rho=\frac{\lambda_{h}}{\mu}\) Server busy time as a proportion of arrival and service rate  
\(\rho_{h}=\frac{\lambda_{h}}{\mu_{h}}\) Server busy time as a proportion of arrival and service rate at node \(h\)  
\(\beta\) Maximum budget allocated for buffer space allocation  
\(D_{s}\) Mean waiting time at queue \(s\)  
Figure 3: GEM steps for a tandem network (adapted from [PERSON] and [PERSON] [73]). \(N\) Network population (for CQN)  
An M/G/1 queue is a queueing system where customer arrivals follow a Poisson process (M), service times have a general distribution (G), and there is a single server (1) serving customers one at a time. Based on the two-moment approximation technique, a closed-form approach can be used to calculate the blocking probability using the following equation [73; 81]. \[P_{Kj}=\frac{\rho^{\frac{(2+\sqrt{\rho^{2}-\sqrt{\rho}^{2}+2(K_{j}-1)}}{(2+ \sqrt{\
u\varsigma^{2}-\sqrt{\rho}})}}(\rho-1)}{\rho^{\frac{(2+\sqrt{\rho^{2} -\sqrt{\rho}^{2}+2(K_{j}-1)}}{(2+\sqrt{\
u\varsigma^{2}-\sqrt{\rho}})}}-1} \tag{1}\]  
According to [PERSON] et al. [82], the output rate of node \(j\) can be calculated using the following for an OQN system. \[\Theta_{j}=\Lambda_{j}\big{(}1-P_{Kj}\big{)}+\lambda_{h}\big{(}1-P^{\prime}_{ Kj}\big{)}^{\rho_{h}}\cdot\big{(}1-P_{Kj}\big{)}^{\rho_{j}} \tag{2}\]  
According to [PERSON] and [PERSON] [26], the total throughput of node \(j\) in a multi-class finite CQN with total population of \(N\). \[\Theta_{j}=\frac{\sum_{i=1}^{M}\sum_{t=1}^{T}\big{(}\lambda_{ij}\cdot(1-P_{Kj} )\big{)}}{N} \tag{3}\]  
According to [PERSON] and [PERSON] and [PERSON] [72; 73] the utilisation rate at node \(j\) in OQN and CQN can be calculated using the following equations, respectively. \[U_{j}=\frac{\lambda_{j}}{\big{(}\Theta_{j}\cdot K_{j}\big{)}} \tag{4}\]  
\[U_{j}=\frac{\Theta_{j}}{\mu_{j}} \tag{5}\]
## 4 Experiment and Results  
In this section, we will build the framework from the bottom up based on the structure presented above. In Section 4.1, we introduce the relevant software and hardware environment used in this experiment. Then, in Section 4.2, we provide a detailed description of the dataset division and discuss potential issues. Next, in Section 4.3, we introduce the evaluation metrics and visualization methods used in the experiment, explaining their specific applications. Finally, Section 4.4 presents the experimental results from various key parts, focusing on the model's performance, effectiveness, and comparisons with other methods: Section 4.4.1 discusses the fine-tuning details of ResNet50 and AlexNet and presents the results of deep feature extraction and selection; Section 4.4.2 describes the results of radiomics feature extraction and their practical physical characterization properties; Section 4.4.3 lists the comprehensive evaluation performance of the proposed method on the test dataset and preliminarily validates the interpretability of the model. Furthermore, a further comparison is made with 15 state-of-the-art methods to highlight the superiority of the proposed method. In Section 4.4.4, ablation experiments are conducted to verify the effectiveness of each strategy, and various uncertainty quantification metrics are used to emphasize the clinical decision reliability of the proposed method.
## 3 Results Analysis
### Experimental Results for Time Measurements  
Time measurements were performed with _TSC_ timers and _clock_gettime()_ System Call in three scenarios: Host OS, where the operating system is placed on bare hardware; VMware ESXi and Xen for hypervisor-based virtualization; and Virtual Box and QEMU for virtual monitor based virtualization.
# Deep Transformer Language Models for Arabic Text Summarization: A Comparison Study
## 4 Experiments
### Used Transformer Language Models (TLMs)  
There are a number of pre-trained TLM models that are proposed, fine-tuned, improved upon, and put into use primarily for ATS tasks in several natural languages, including Arabic. Next, we discuss the models that are adopted and considered in this comparative study.  
* **mBART**: Following BART, mBART [47] is constructed using a seq2 seq model with denoising as a pre-training objective. It models architecture that combines an encoder and a decoder using a typical seq2 seq. The pre-training assignment incorporates a new approach where text ranges are exchanged with a single mask token and modifying the starting sentences order randomly. The autoregressive BART decoder is controlled for developing sequential NLP tasks such as text summarization. The denoising pre-training objective is strongly tied to the fact that the data are taken from the input but altered. As a result, the encoder's input is the input sequence embedding, and the decoder's output is produced autoregressively. BART only pre-trained for English, but mBART thoroughly investigated the impacts of pre-training on many sets of languages (e.g., Japanese, French, German, and Arabic). It utilized a common sequence-to-sequence Transformer design with 12 layers of encoders and 12 layers of decoders on 16 heads (corresponding to around 680 M parameters). The training was stabilized by adding a layer-normalization layer on top of the encoder and decoder.
* **mT5**: Transfer learning is the principle underpinning the mT5 [51] model, which is an extended version of T5. The original model was initially trained using transfer learning on a task with a lot of text before being fine-tuned on a downstream task to help the model develop general-purpose abilities and knowledge that can be used for tasks such as summarizing. T5 employed a sequence-to-sequence creation technique that produces an autoregressive output from the decoder after feeding it the encoded input through cross-attention layers. T5 only pre-trained for English; however, mT5 came to carefully examine the effects of pre-training on various natural languages, including Arabic.
* **PEGASUS**: A sequence-to-sequence model, PEGASUS [48] separates out important lines from the input text and compiles them as independent outputs. Additionally, selecting only pertinent sentences works better than selecting sentences at random. As it is analogous to the work of reading the complete document and producing a summary, this style is chosen and preferred for abstractive summarizing.
* **AraBART**: The architecture of AraBART [11], which has 768 hidden dimensions and 6 encoder and 6 decoder layers, is based on that of BART Base. AraBART has 139 M parameters in total. To stabilize training, it has a normalization layer on top of the encoder and the decoder. Sentencepiece is used by AraBART to construct its vocabulary. A randomly chosen subset of the pre-training corpus, measuring 20 GB in size, was used to train the sentencepiece model. The size of the vocabulary is 50 K tokens.
## 3 Results and Discussion
### Relationship between Modulus of Elasticity, Toughness and Apparent Void Volume  
In this sub-section, we try to relate the AVV with MOR and MOE. We used the Ryshkewitch's model, which is widely used for describing the relationship of mechanical properties of porous materials as a function of the solid fraction (equal to 1--air void fraction) [45]. It can be seen in Figure 9(a),b that the experimental results are generally in good agreement with the models, with the correlation factors, R\({}^{2}\), being higher than 0.8 for both MOE and MOR. Figure 9(c) shows that the toughness is increased with the increase of AVV. However, the correlation is not good, with an R\({}^{2}\) of 0.51 for a linear relationship. The toughness is not only affected by AVV but also depends on the loading rate and interfacial zone between the fiber and matrix. Therefore, the uncertainty in measuring toughness is quite high as shown in Figures 4(c) and 5(c), which may partially contribute to the poor correlation. In fact, AVV is not directly linked to the toughness, instead other factors such as MOE have a better correlation (shown later in Figure 9(a)).  
In Figure 9(a), we explore the role of MOE to understand how it influences the toughness. In the literature [46], a simple linear regression has become one of the most widely used analytical models for describing the relationship between the MOE and toughness. The results showed a good agreement between the model and experiment data. This agreement is expected because of the fact that toughness can be obtained by integrating the load vs. deflection curve, therefore, the slope or MOE has a major effect on the toughness as shown previously in Figure 10(b).  
Figure 9: Average values of physical properties of non-carbonated and carbonation curing with different rolling pressure (\(\Delta\) NCC, \(\blacklozenge\) 5 h ACC): (a) Apparent void volume (AVV); (b) bulk density (BD) and (c) water absorption (WA).  
Figure 11: Relationship between modulus of elasticity (MOE), modulus of rupture (MOR), and toughness: MOE vs. toughness (**a**); MOR vs. toughness (**b**); MOE vs. MOR (**c**).  
Figure 10: Relationship between mechanical properties and apparent void volume (AVV): (**a**) MOE; (**b**) MOR; and (**c**) toughness.  
Figure 11b shows that MOR is significantly reduced with the increase of toughness up to 5 kJ/m\({}^{2}\). With further increase in toughness, MOR slightly decreases. This can be the reason why the required value for toughness is set at a minimum of 5 kJ/m\({}^{2}\). The best fit with experimental data of MOR and toughness is a logarithmic model, despite that the correlation factor is poor (R\({}^{2}\) = 0.51). The MOE also linearly increases with an increase of MOR, but again the correlation is rather poor (R\({}^{2}\) = 0.47) as shown in Figure 11c.
# Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications
## 3 Results and Discussions
### Measurement Results
The parasitic effects can vary between PIN diodes and SPDT switches, leading to different impacts on the antenna performance. On the other hand, variations occur due to the chemical etching process since manufacturing variations can lead to slight differences in the antenna dimensions and affect its performance parameters such as resonant frequency, and impedance. Table 3 outlines the comparison of the proposed novel antenna structure with the other frequency reconfigurable antennas in the literature. It is clear from Table 3 that the proposed antenna outperforms the other frequency reconfigurable antennas in the literature in terms of compactness, wide operating frequency range, and low cost. Furthermore, the control method of the used components, which provide reconfigurability features, is also compared. The proposed novel antenna can be integrated into many devices including access points, gateways, smartphones, and IoT kits due to its tiny size and easy control mechanism. Figure 11: Comparison of the numerically calculated and measured reflection coefficients for the proposed antenna configurations with (**a**) OFF-OFF, (**b**) ON-ON, (**c**) ON-OFF, and (**d**) OFF-ON.
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 2 Numerical Modelling
### Convergence and Validation
#### 2.4.2 Honeycomb Mesh Convergence  
A detailed mesh convergence analysis was also performed on the honeycomb core structure. The computational run time (CPU) was monitored for finite element meshes of the honeycomb with different element sizes, ranging from 0.4 to 3.2 mm, with the corresponding results shown in Figure 10. The convergence analysis simulation was set up with an SPH density of 5 particles/mm to minimise computational run time, and all remaining models were set up with the optimal 7 particles/mm density. Al2024-T3 was used for all components of the shield, and all parameters other than the mesh element size of the honeycomb shell were kept constant. The results in Figure 10 indicate that the optimum solution in terms of reducing CPU time whilst retaining an accurate solution, is to use a 1 mm element size.  
Figure 9: Average impact pressure \(p\) at time \(t=0.5\) μs and computational run time (CPU) against the SPH particle density, with corresponding trendlines.