esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Pore Type Classification  
Pore type classification was carried out only using the unbiased approach with seven features as well as synthetic minority oversampling, based on performance for the pore presence classification. All three classifier types and voxel sizes were tested and the results for overall accuracy for both validation and test data are shown in Table 4. The bagged trees classifier achieved significantly higher accuracy than the other models for both validation and test data; however, the accuracy in any case is not high. The training dataset contained approximately 80% of pores characterised as lack-of-fusion and 20% as keyhole, indicating that lack-of-fusion is highly dominant in this dataset. However, this result cannot be generalised as the relative likelihood of each pore type has been shown to be very dependent on process parameters, particularly energy density [9; 26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Voxel Size (mm)** & **Model Type** & **Validation Accuracy (\%)** & **Test Accuracy (\%)** \\ \hline \multirow{3}{*}{0.5} & \multicolumn{2}{c}{Bagged trees} & 69.3 & 59.8 \\ \cline{2-4}  & KNN & 65.4 & 54.6 \\ \cline{2-4}  & NN & 60.3 & 63.0 \\ \hline \multirow{3}{*}{1.0} & \multicolumn{2}{c}{Bagged trees} & 67.7 & 52.6 \\ \cline{2-4}  & KNN & 64.6 & 52.5 \\ \cline{2-4}  & NN & 65.1 & 54.5 \\ \hline \multirow{3}{*}{2.0} & \multicolumn{2}{c}{Bagged trees} & 78.8 & 73.0 \\ \cline{2-4}  & KNN & 72.8 & 62.5 \\ \cline{1-1} \cline{2-4}  & NN & 68.0 & 62.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of classifiers for porosity type: keyhole or lack-of-fusion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **Model Size (MB)** & **Prediction Rate (s\({}^{-1}\))** & **Training Time (min)** \\ \hline Bagged Trees & 15–2000 & \(6\times 10^{4}\)–\(1\times 10^{5}\) & 0.25–50 \\ \hline Nearest Neighbours & 1–200 & \(8\times 10^{3}\)–\(4\times 10^{4}\) & 0.05–4 \\ \hline Neural Network & 0.1–14 & \(1\times 10^{6}\)–\(2\times 10^{6}\) & 3–280 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranges for model size, prediction rate, and training time.
## 4 Implementation of Optimal Attack Strategy
### Estimation of Unknown Parameter  
For the \(l\)-th observation system, the data \(s_{l,k}^{0}\) output by the binary observation in the structure shown in Figure 1 is binomially distributed. Based on Assumption 1, the probability that \(s_{l,k}^{0}\) is 1 is given by the following equation  
\[\Pr\{s_{l,k}^{0}=1\} = \Pr\{d_{l,k}<C_{l}-y_{l,k}\} \tag{12}\] \[= F(C_{l}-\phi_{l}^{T}\theta)\]  
Denoting \(\xi_{l,k}=\frac{1}{k}\sum_{i=1}^{k}s_{l,k}^{0}\), when \(N\rightarrow\infty\), there is \(\xi_{l,k}=F(C_{l}-\phi_{l}^{T}\theta)\). Thus, for \(m\) systems, we have the following \(m\) equations  
\[\phi_{1}^{T}\theta = C_{1}-F^{-1}(\xi_{1}),\] \[\vdots\] \[\phi_{m}^{T}\theta = C_{m}-F^{-1}(\xi_{m}).\]Noting that \(\Phi=[\phi_{1},\cdots,\phi_{m}]^{T}\) and \(\omega_{k}=[C_{1}-F^{-1}(\xi_{1,k}),\ldots,C_{m}-F^{-1}(\xi_{mk})]^{T}\), an estimate of the system parameter, \(\theta\), can be obtained when the matrix, \(\Phi\), has full rank,  
\[\hat{\theta}_{k}=\Phi^{-1}\omega_{k} \tag{13}\]
## 3 Results  
### Tibiofemoral Joint  
The peak ligament and compressive forces on the TF joint for all the simulated PE insert thickness cases are depicted in Figure 3. The peak MCL, LCL, and PCL forces increased by 38 N, 74 N, and 125 N, respectively, as PE insert thickness changed from 9 mm to 11 mm; the corresponding peak force increase for a 4 mm greater thickness relative to the reference case was 80 N, 157 N, and 286 N. Conversely, the peak force in the MCL, LCL, and PCL decreased by 36 N, 52 N, and 98 N, respectively, when using a 2 mm thinner PE insert compared to the reference case, while with a 4 mm smaller thickness than the reference, the peak decrease was 57 N, 70 N, 177 N, respectively. Table 1 summarizes the peak as well the average percentage variations relative to the reference case. Varying PE insert thickness simultaneously changed the ligament individual bundle strain. Both medial and lateral collateral ligaments appeared to stretch in the same strain range of about 0-6% for all the simulated thickness cases, whereas the PCL exhibited a maximum strain around 10% at 90\({}^{\circ}\) of knee flexion with 13 mm thickness. Force and strain patterns of the MCL, LCL, and PCL at varying thicknesses are provided in Appendix A.  
Figure 2: Illustration of three custom postoperative cases simulated in this study. The tibial polyethylene (PE) insert thickness cases of 9 mm (reference), 11 mm (+2 mm), and 13 mm (+4 mm) are depicted, plus the tibial metal baseplate fixed to the resected proximal tibia.  
Changes in PE insert thickness affected the TF compressive force, denoting a larger effect at 90\({}^{\circ}\) of knee flexion angle at which the highest peak occurred at 4.5 times body weight (BW) with a 13 mm PE insert (Figure 3). Compared to the reference case, the peak TF compressive force increased by 0.3 BW and 0.8 BW with 2 mm and 4 mm greater PE insert thicknesses, respectively. Using 2 mm and 4 mm thinner PE inserts than in the reference case showed a peak decrease of 0.2 BW and 0.3 BW, respectively. Detailed peak and average percentage variations of the TF joint compressive force are also provided in Table 1.
## 3 5G Signal and Detection Performance Analysis
### Features of 5G Signal Physical Layer
#### 3.2.2 Frame Structure  
In a 5G signal, the time length of a wireless frame is 10 ms, consisting of 10 subframes with a length of 1 ms. The time scheduling in 5G NR is based on slots, with each 1 ms sub-frame containing several slots, and each slot containing 14 or 12 OFDM symbols. The number of slots contained in a subframe is determined by the subcarrier spacing. The quantity relationship between subframes, slots, and OFDM symbols with different parameters \(\mu\) is shown in Table 3[10]. \(N_{\text{slot}}^{\text{frame}}\) is the number of slots in a frame, \(N_{\text{slot}}^{\text{subframe},\mu}\) is the number of slots in a subframe, and \(N_{\text{sym}}^{\text{slot}}\) is the number of OFDM symbols in a slot.  
The subcarrier spacing of the 5G signal used in this paper is 30 kHz, i.e., \(\mu=2\). When the subcarrier spacing is 30 kHz, each subframe contains two slots, that is, each slot is 0.5 ms [11]. Additionally, when the type of cyclic prefix is a normal cyclic prefix, a slot contains 14 OFDM symbols; when it is an extended cyclic prefix, a slot contains 12 OFDM symbols [11].
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item Name** & **Description** & **Qt** & **Provider/Company** & **Cost/Unit (US \$)** & **Total for the Unit** \\ \hline
**Power Source—Option 1** & & & & \\ \hline Solar Panel 150 W & Charge the generator & 1 & 4 Patriots.com & 2500 & - \\ \hline Generator 1800 W & Main power provider for use in remote setting & 1 & 4 Patriots.com & & \\ \hline
**Power Source—Option 2** & & & & \\ \hline
12 V 100 Ah battery & \begin{tabular}{c} Rechargeable Sealed \\ Lead Acid \\ \end{tabular} & 1 & \begin{tabular}{c} Mighty Max \\ Battery \\ \end{tabular} & 170 & 170 \\ \hline Solar Panel 100 W & Charging the battery & 1 & Renogy & 100 & 100 \\ \hline Power Invertor & \
\begin{tabular}{c} 12 V DC Battery Power to \\ AC (600 W) \\ \end{tabular} & 1 &
\begin{tabular}{c} Kinverch \\ \end{tabular} & 70 & 70 \\ \hline Solar Charger Controller & 12 V/10 Amp & 1 & Renogy & 20 & 20 \\ \hline
\begin{tabular}{c} Any bee \\ equipment \\ vendors \\ \end{tabular} & 80 & 80 \\ \hline
**Electronics** & & & & \\ \hline Speedway Reader & \begin{tabular}{c} R420 UHF RFID Reader \\ (4-Port) \\ \end{tabular} & 1 & \begin{tabular}{c} Impinj \\ \end{tabular} & 1500 & 1500 \\ \hline Small 5 dBi antenna & \begin{tabular}{c} Antenna for precision item \\ location/(5 \(\times\) 6) cm \\ \end{tabular} & 32 & \begin{tabular}{c} Impinj \\ \end{tabular} & 25 & 800 \\ \hline Antenna RF cable & \begin{tabular}{c} Connect Hubs to bee colony \\ (6 m) \\ \end{tabular} & 32 & \begin{tabular}{c} echain Technology \\ \end{tabular} & 10 & 320 \\ \hline Adapter Kit (4-Port) & \begin{tabular}{c} Control up to \\ 4 Antenna Hubs \\ \end{tabular} & 1 & \begin{tabular}{c} echain Technology \\ \end{tabular} & 150 & 150 \\ \hline Antenna Hub & \begin{tabular}{c} Contains 8 ports each \\ \end{tabular} & 4 & \begin{tabular}{c} echain Technology \\ \end{tabular} & 800 & 3200 \\ \hline RFID transponder & \
\begin{tabular}{c} Tags to fix on bees \\ (2.5 \(\times\) 2.5 \(\times\) 0.4) mm \\ \end{tabular} & As needed & Hitachi & 1 & 30 \\ \hline Portable Computer &
\begin{tabular}{c} With at least 4 GB of RAM \\ (processing memory) \\ \end{tabular} & 1 & Any provider & - & - \\ \hline
**Software** & & & & \\ \hline ItemTest Software & \begin{tabular}{c} Freely available, \\ version: 2.3.0 \\ \end{tabular} & 1 & \begin{tabular}{c} Impinj.com \\ \end{tabular} & Free & - \\ \hline MultiReader Gen2 RFID & \begin{tabular}{c} Freely available, \\ version: 6.6.13 \\ \end{tabular} & 1 & \begin{tabular}{c} Impinj.com \\ \end{tabular} & Free & - \\ \hline Speedway Connect SW & \
\begin{tabular}{c} Licensed software, \\ version: 6.2.2.240 \\ \end{tabular} & 1 &
\begin{tabular}{c} Impinj.com \\ \end{tabular} & 350 & 350 \\ \hline
**Total** & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of all hardware and software required to build the 32-antenna RFID unit used to track honey bee activity and behavior in the field. Provider and vendor names are given for the sole purpose of identifying the products used in this study.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 5 DrI-Based Executor  
DCoS implements a DRL agent to act as the executor and solve the scheduling problem above. We propose the Adaptive Placement Graph (APG) as the input to the scheduling process, providing a structured way to represent both intra-transaction and inter-transaction dependencies.  
**Graph structure.** The APG is defined as:  
\[G=(\mathcal{T}=\mathcal{T}\cup\{\mathcal{O}_{start},\mathcal{O}_{end}\}, \omega,\varphi) \tag{6}\]  
where \(\mathcal{O}_{start}\) and \(\mathcal{O}_{end}\) are the dummy starting and ending of scheduling sequence. The set \(\omega\) consists of intra-transaction dependency edges, capturing the execution order of operations within a single transaction. The set \(\varphi\) represents resource edges, which link pairs of operations requiring the same thread worker for execution, as dictated by \(\mathcal{M}(O_{ij})\). Resource edges consist of a mix of directed and undirected arcs:  
* **Undirected arcs:** Represent intra-transaction dependency, indicating that two operations contend for the same resource without enforcing a strict execution order.
* **Directed arcs:** Represent write-read dependency, ensuring that a write operation must be committed before a dependent read operation is executed.  
Figure 5 illustrates how DCoS schedules contention-intensive transactions in Example 2. By updating the resource edges (from undirected to directed), DCoS can generate a schedul  
Figure 4: Fine-grained scheduling process with a DRL executor.  
ing solution that optimizes the processing of transactions across available threads, ensuring minimal makespan while respecting the dependencies between transactions.  
Moreover, DCoS leverages historical workload traces containing transaction-processing metadata (e.g., procedure parameters, execution timestamps, query parameters). Using this data, DCoS estimates per-thread operation processing times set \(\epsilon=\{\rho_{ijk}\}(O_{ij}\in\mathcal{T},C_{k}\in\mathcal{C})\) based on current thread-worker state \(S(C)^{t}=\{S(C_{1})^{t},\ldots,S(C_{m})^{t}\}\).
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 4 Simulation Results and Discussion
### Error Analysis  
In this study, 100 speed tracking simulation experiments were conducted under road surface adhesion coefficients of 0.5, 0.6, and 0.8. The set of parameters with the smallest speed tracking error for the autonomous vehicle was selected as the optimal parameters for the controller. \(K_{p}=136.36\), \(K_{i}=6.34\), and \(K_{i}=1.712\). The speed errors of the vehicle under different road surface adhesion coefficients with these parameters are shown in Table 2.  
From Table 2, it can be seen that when the road surface adhesion coefficient is 0.5, the maximum speed tracking error of the autonomous vehicle is 0.22 m/s, the average error is 0.063 m/s, and the standard deviation is 0.124 m/s. When the road surface adhesion coefficient is 0.6, the maximum speed tracking error decreases to 0.18 m/s, the average error is 0.056 m/s, and the standard deviation is 0.099 m/s. When the road surface adhesion coefficient is 0.8, the maximum speed tracking error further decreases to 0.179 m/s, the average error remains at 0.056 m/s, and the standard deviation is 0.098 m/s.
# Improving Transaction Speed and Scalability of Blockchain Systems via Parallel Proof of Work +
## 1 Introduction  
In conventional financial systems, a third party is constantly required to verify transactions. For example, if a person wants to buy a product from a market using a credit or debit card, the transaction is verified by a bank or other financial institution. If s/he wants to use cash for the purchase, s/he first needs to withdraw money from the bank, which means that the third party is always involved directly or indirectly for validating or verifying a transaction. In this sense, transactions are centralized through a third party. As a result, there is always a probability of a single point of failure. The objective of blockchain, which can be either permissionless or permissioned, is to build up a decentralized framework [1]. A cryptocurrency uses public or permissionless blockchain so that everyone can participate in performing the transactions. In contrast, permissioned blockchain networks allow the network to appoint a group of participants who are given authority to take part in a block validation process. This can be applied within a private organization or network. For transactions, this provides a disseminated record which contains the history of each affirmed transaction. It also offers a shared system where the clients themselves can check the exchanges of different clients without the incorporation of any outsider association. Moreover, this blockchain also keeps all the transactionsand user information anonymous and provides a copy of the continuous growing ledger to every user of the system.  
However, as with all other systems, blockchain presents some concerns [2; 3], one of which is the major issue of scalability--the rate at transactions are processed on the Bitcoin network. Hundreds of cryptocurrencies on the market currently use the blockchain network for transactions, mining and maintaining ledgers. All cryptocurrencies face scalability issues, but VISA, a traditional transaction provider, has already reached a peak of 10,547 transactions per second [4]. The transaction speed for different cryptocurrencies is different due to their respective protocols. Table 1 shows the transaction speed and confirmation time of different cryptocurrencies which is adapted from [5].  
Blockchain offers some unique components or features which differentiates it from traditional systems. To understand blockchain, it is necessary to understand those properties.
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 2 Dusty Channel Model  
### Experimental Setup  
To simulate atmospheric dust conditions under controlled circumstances, a chamber was constructed with dimensions of \(90\times 40\times 40\) cm\({}^{3}\), as shown in Figure 1a, with complementary front and side views provided in Figure 1b.  
This size was selected to provide sufficient propagation length for the optical beam, enable uniform dust dispersion using internal fans, and maintain manageable particle concentrations under safe laboratory conditions. Moreover, this controlled laboratory setup eliminates the need for sporadic and unpredictable natural dust events, enabling continuous data collection for studies of the FSO system. The chosen dimensions are consistent with previously reported FSO experimental setups in the literature, which typically employ chambers with lengths close to 100 cm [9; 10; 12; 13].  
For optical signal irradiance fluctuation modeling, a Keysight N7714A laser diode (LD) operating at 1550 nm with an output power of 4 mW was employed. Transparent glass panels were placed at both ends of the chamber to minimize transmission losses. Dust particles were dispersed into the chamber volume via fans mounted at the base of the chamber. The dust sample used in the experiment was collected from a real dust storm and has a 17.3-um average diameter.  
Beam propagation in the free space was realized through a pair of identical fiber-coupled collimators with aspheric lenses placed at the chamber input and output windows. Each collimator produced a 3.6 mm diameter beam with a full-angle divergence of 558 \(\upmu\)rad. The received optical signal was captured and quantified using a Thorlabs PM320E dual-channel power meter.  
The received signal was sampled and recorded through a synchronized data acquisition platform. A dataset comprising 160,000 samples was created. For further details regarding the chamber configuration and measurement reliability, please refer to our earlier publication [14].  
Although such a chamber does not replicate the full physical scale of outdoor dust storms, it enables precise control of particle density and flow, which is critical for extracting meaningful statistical and physical insights into dust-induced attenuation. Furthermore, in our previous work using this chamber [14], we proposed a deterministic attenuation model for dust-laden FSO channels. This model was later validated through an outdoor FSO experiment conducted under actual dust storm conditions, where the measured attenuation  
Figure 1: **(a)** Experimental setup for measuring the FSO signal fluctuations, (**b**) chamber side view (left) and front view (right).  
matched closely with the chamber-predicted value (1.0 dB measured vs. 1.6 dB predicted over a 100 m link) [6]. This consistency supports the use of the chamber as a valid platform for simulating the optical effects of large-scale dust storms within a laboratory setting.
## 5 Conclusions  
A thorough comprehension of the shear behavior of non-persistent joints is essential for assessing rock mass stability and reinforcement. This study examined the influence of joint aperture, joint persistency, and normal load on the shear behavior. Indoor direct shear experiments were conducted to examine the effects of shear stress, normal displacement, and failure patterns under various conditions. A FEM-CZM model was developed to simulate the shear failure mechanism of non-persistent joints and to analyze the stress evolution during the shear process. The major conclusions are as follows:  
Figure 12: Evolution process of shear stress of non-persistent joints: (a) joint aperture; (b) joint persistency; (c) normal stress.  
1. The non-persistent joint shear process can be divided into four stages: elastic, damage, strength recovery, and residual stages. Within the range tested, the peak shear strength of a non-persistent joint is negatively correlated with joint aperture and joint persistency and positively correlated with normal stress. Residual shear strength is negatively correlated with joint persistency and positively correlated with normal stress.
2. The results of the tests carried out show that the peak normal displacement is inversely proportional to the joint aperture and normal stress, while the final normal displacement is inversely proportional to the joint persistency and normal stress.
3. Numerical results show that the stress concentration area changes during the shearing of a non-persistent joint. This indicates that the maximum stress area is located at the joint or crack extension area before the rock bridge breaks. After the rock bridge breaks, the maximum stress area is located at the contact point on the shear surface.
4. The experimental and numerical results show that the non-persistent joint failure pattern is affected by internal stress. As the joint aperture, joint persistency, and normal stress increase, the concentration of stress at the rock bridge grows, the width of the shear failure zone decreases, and the failure pattern changes from tensile or mixed failure to shear failure.  
Methodology, data management, writing and editing: G.W. and H.L.; Experimental operation, writing and editing: H.L. and Z.L.; Photo record: G.W.; Software: H.L.; Review and edit: G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 52209141 and 52079077).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Author [PERSON] was employed by the company Bejing Anke Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# YOLOv7 Optimization Model Based on Attention Mechanism Applied in Dense Scenes
## 5 Application  
As an important application in the field of computer vision, object detection has wide application prospects due to its ability to identify and locate objects in images or videos and reduce labor costs. The current practical needs for real-time detection of dense scenes are mainly in the field of public safety in public places such as airports, stations, subways, and other crowded places, where real-time monitoring of people is needed, or in the field of agriculture, where real-time detection and localization of crops and animals are needed to achieve intelligent agricultural management and precision agricultural production. It is of great significance for improving production efficiency and ensuring public safety. Therefore, how to achieve efficient and accurate real-time detection of dense scenes is the current challenge that needs to be solved for computer vision and object detection. However, small object detection in dense scenes needs to overcome a variety of  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **mAP50** & **Parameters (M)** & **GFLOPs** \\ \hline YOLOv5s & 0.829 & 7.06 & 16.0 \\ YOLOv7 & 0.863 & 36.58 & 103.6 \\ YOLOv7H-CA & 0.875 & 36.57 & 103.5 \\ YOLOv7H-SimAM & 0.877 & 36.56 & 103.4 \\ YOLOv7H-CBAM & 0.873 & 36.38 & 103.8 \\ YOLOv7B-SimAM & 0.827 & 36.56 & 103.4 \\ YOLOv7B-CA & 0.829 & 36.58 & 103.5 \\ YOLOv7B-CBAM & 0.878 & 36.58 & 103.8 \\ \hline \end{tabular}
\end{table}
Table 2: VOC dataset results.  
difficulties, specifically, the difficulties of small-object detection in dense scenes include the following aspects:  
1. High density: there are a large number of objects in the scene, and their mutual occlusion and overlap can increase the difficulty of detection.
2. Small objects: small objects usually have small pixel sizes and are difficult to be accurately detected and localized in the image.
3. Diversity: objects in dense scenes may have different classes, shapes, colors, textures, and other features, which can increase the difficulty of training and testing the model.  
In this case, achieving fast real-time detection is a challenging task. The YOLOv7B-CBAM model, whose accuracy has been verified on the VOC dataset, is used to test its accuracy and confirm its application in the public safety and agricultural domains on the mask dataset and the tomato dataset, respectively. The tomato and mask datasets used in this paper are both open-source datasets found on the Kaggle website, and the picture size is uniformly 640 \(\times\) 640 \(\times\) 3 during training.