esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Twenty-six patients with sABI outcomes were included in the study during the period from April 2023 to September 2024. The clinical and demographic characteristics of the two groups are listed in Table 1. Statistical analysis showed no statistically significant differences between the two groups. Regarding clinical variables, intragroup analysis showed statistically significant improvements in both groups in most of the clinical scales, as seen in Table 2. HuG showed statistically significant advances in all clinical scales considered, while CoG showed statistically significant improvements in all clinical scales except MFIS (\(p\) = 0.116) and its physical (\(p\) = 0.253), cognitive (\(p\) = 0.304), and psychosocial (\(p\) = 0.700) subscales. Comparison between groups revealed statistically significant differences in favor of HuG in TIS (\(p\) = 0.013), MI-LL affected side (\(p\) = 0.046), mBI (\(p\) = 0.001), EQ-5D VAS (\(p\) = 0.011), physical subscale of MFIS (\(p\) = 0.002) and total MFIS (\(p\) = 0.017) (Figure 2). Regarding instrumental variables, within-group analysis showed statistically significant improvements only in HuG for static and dynamic balance, LOS and static balance with raised seat, as seen in Table 3. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & \
\begin{tabular}{c} **HuG** \\ **(n = 14)** \\ \end{tabular} &
\begin{tabular}{c} **CoG** \\ **(n = 12)** \\ \end{tabular} & \(p\)**-Value** \\ \hline Gender, F vs. M & n & 4 vs. 10 & 4 vs. 8 & 0.997 \\ \hline Age, years & mean \(\pm\) SD & 58.99 \(\pm\) 13.76 & 52.79 \(\pm\) 15.93 & 0.104 \\ \hline Aetiology, TBI vs. nTBI & mean \(\pm\) SD & 3 vs. 11 & 3 vs. 9 & 0.991 \\ \hline Affected side, R vs. L & n & 5 vs. 9 & 7 vs. 5 & 0.667 \\ \hline Latency, months & mean \(\pm\) SD & 4.47 \(\pm\) 2.29 & 4.50 \(\pm\) 1.7 & 0.896 \\ \hline TIS T0 & mean \(\pm\) SD & 5.14 \(\pm\) 1.66 & 4.95 \(\pm\) 1.22 & 0.349 \\ \hline BBS T0 & mean \(\pm\) SD & 4.71 \(\pm\) 3.43 & 4.38 \(\pm\) 3.32 & 0.574 \\ \hline SPPB\_TOT T0 & mean \(\pm\) SD & 1.50 \(\pm\) 2.77 & 1.58 \(\pm\) 1.23 & 0.244 \\ \hline MI-LL AFF T0 & mean \(\pm\) SD & 51.57 \(\pm\) 29.93 & 40.58 \(\pm\) 10.49 & 0.399 \\ \hline MI-LL nAFF T0 & mean \(\pm\) SD & 79.21 \(\pm\) 17.53 & 75.50 \(\pm\) 18.96 & 0.584 \\ \hline LCF T0 & mean \(\pm\) SD & 5.19 \(\pm\) 0.91 & 4.92 \(\pm\) 0.29 & 0.499 \\ \hline DRS T0 & mean \(\pm\) SD & 16.36 \(\pm\) 1.28 & 16.92 \(\pm\) 1.13 & 0.674 \\ \hline mBI T0 & mean \(\pm\) SD & 12.14 \(\pm\) 4.26 & 14.58 \(\pm\) 5.44 & 0.688 \\ \hline EQ-5D\_VAS & mean \(\pm\) SD & 21.79 \(\pm\) 9.53 & 29.17 \(\pm\) 11.45 & 0.945 \\ \hline MFIS\_PHY T0 & mean \(\pm\) SD & 25.00 \(\pm\) 5.10 & 21.08 \(\pm\) 2.71 & 0.336 \\ \hline MFIS\_COG T0 & mean \(\pm\) SD & 12.86 \(\pm\) 4.83 & 13.42 \(\pm\) 3.32 & 0.749 \\ \hline MFIS\_PSY T0 & mean \(\pm\) SD & 4.57 \(\pm\) 1.79 & 5.58 \(\pm\) 1.83 & 0.711 \\ \hline MFIS\_TOT T0 & mean \(\pm\) SD & 42.43 \(\pm\) 1.10 & 40.08 \(\pm\) 6.36 & 0.249 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clinical and demographic characteristics of the sample at baseline. \begin{table}
## 4 Design of RBMM  
### Problem Statement  
In ICN multipath transmission systems, significant path differences and dynamic path changes result in numerous out-of-order packets, creating a major challenge for the gateway's reordering process. Given the limited resources at gateway nodes, providing extreme large buffer capacities is impractical. This necessitates the design of a buffer management method to improve reordering efficiency within limited resource availability.  
If the buffer's congestion assessment is too conservative and enters the \"restrictive state\" prematurely, it can waste resources and result in too many out-of-order packets being forwarded to the IP terminal, reducing transmission efficiency. Conversely, if the assessment is too lenient, the buffer may under immense pressure during traffic bursts. Network traffic volume peaks are unpredictable and can lead to a sudden surge in buffer usage at network nodes, increasing packet queuing delays and subsequently prolonging overall transmission completion time. This may degrade the performance of various network services and negatively impact user experience. When buffer resources are constrained, sudden traffic bursts may cause overflow, leading to packet loss and triggering retransmission mechanisms. This, in turn, further increases network load, consumes available bandwidth, and reduces effective throughput.  
Furthermore, the traffic composition in an ICN multipath transmission network is complex. Different application types generate traffic ranging from real-time flows (typically tens to hundreds of bytes per packet) to large-scale data transfers (with packets exceeding 1 KB), with the former requiring fast delivery and the latter occupying more buffer resources. At any given time, the buffer may contain multiple flows of varying sizes and requirements, competing for limited buffer space. If some flows occupy a large portion of the buffer for an extended period, it can prevent other flows that require from receiving the necessary service and could otherwise be quickly reordered and delivered (typically small-scale real-time flows).  
In summary, buffer management must address two key challenges. First, it needs to accurately evaluate congestion to enhance burst traffic handling capabilities under dynamic traffic conditions while avoiding excessive impact on resource utilization efficiency. Second, during congestion, it should prioritize the eviction of packets that have low delivery impact, ensuring fair resource allocation across different flow types and improving reordering service efficiency. We elaborate on how our Reordering Buffer Management Method (RBMM) responds to these two challenges in Sections 4.2 and 4.3.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 6 Control of PCO Constellation  
Let the following aim of controlling a constellation of satellites be posed: there is a master satellite that moves in an elliptical orbit, but with a small eccentricity, i.e., its orbit is close to a circular orbit. It is necessary to create and maintain a certain constellation of satellites, which are located in a certain formation relative to the master satellite and keep this formation over time. In the case of a circular master satellite orbit, the most fuel-efficient slave satellite orbits would be general circular orbit (GCO), PCO, and along-track orbit (ATO) since such orbits are stable solutions of the HCW (27) equations. Then, control will be spent only on counteracting disturbances and countering unaccounted nonlinearities in the model (27).  
The most interesting is the problem of positioning and maintaining the orbits of a constellation of satellites in the relative orbits of the PCO, even in the case of the master satellite's elliptical orbits. It is also interesting to investigate the possibility of simultaneously storing a formation of a large number of satellites in different PCO orbits. In this variant set, several orbits have different \(r_{dpc}\) in (29), and on the circles, the satellites are located with different offsets \(\phi\) so that a symmetric distribution of satellites is obtained.  
To maintain the slave satellite position in the formation, it should use control law in the form of (25) where expressions (29) are used as the target path \(\mathbf{x}_{d}(t)\). Configurable  
Figure 2: Configurations of satellite orbits. (**a**) Projected circular orbit. (**b**) Along-track orbit.  
parameters are matrix \(\mathbf{K}\in\mathbb{R}^{3\times 3}\) and vector \((\alpha_{1},\alpha_{2},\alpha_{3})\). A special case of the control law (25) is proportional-differential (PD) controllers of the following form:  
\[\begin{split}\bar{u}_{x}&=-K_{px}e_{x}-K_{dx}e_{x}\\ \bar{u}_{y}&=-K_{py}e_{y}-K_{dy}e_{y}\\ \bar{u}_{z}&=-K_{px}e_{z}-K_{dz}\dot{e}_{z}\end{split} \tag{30}\]  
where \(K_{px}\), \(K_{dx}\), \(K_{py}\), \(K_{dy}\), \(K_{pz}\), \(K_{dz}\) are the positive adjustable gains. In the vector notation (30) can be written as  
\[\mathbf{\bar{u}}=-\mathbf{K_{pd}e} \tag{31}\]  
where matrix \(\mathbf{K_{pd}}\in\mathbb{R}^{3\times 6}\) has the following form  
\[\mathbf{K_{pd}}=\begin{bmatrix}K_{px}&0&0&K_{dx}&0&0\\ 0&K_{py}&0&0&K_{dy}&0\\ 0&0&K_{pz}&0&0&K_{dz}\end{bmatrix}. \tag{32}\]
# Combined Optical Fiber Transmission System Based on QNSC and BER-LM
## 4 Results and Analysis
Figure 8: BER measured by [PERSON], [PERSON], and [PERSON] at different time points. Figure 7: (a) Q-factor vs. distance for PSK/QNSC and regular QPSK without PSK/QNSC. (b) DFP vs. OSNR for Eve at different OSNR values with encrypted signals of 220 PSK, 218 PSK, and 216 PSK. The method presented in this paper has the following advantages. This method does not require the use of a key provided by a third party. This method can provide a self-sufficient key for encrypted transmission. On the basis of the encrypted transmission system, this method only needs to improve the DSP algorithm without adding additional equipment overhead, which reduces the equipment cost and is conducive to large-scale popularization. At the same time, this method also has some limitations. Since the raw data and negotiation data are modulated into the same fiber channel at the same time, the negotiation data will interfere with the raw data, causing the Q-factor of the raw data to decrease. In order to reduce the interference of the negotiation data in the raw data, it is necessary to reasonably set the negotiation bit position. When the negotiated bit position was set to seven, the Q-factor of the signal of the 20 Gpsk, 200 km transaction system was nine, meeting the requirement of the 7% FEC limit.
# A Lightweight Forest Test Image Recognition Model Based on Improved YOLOv8
## 4 Experiments
### Evaluation Indicators  
In order to assess the detection performance of our proposed improved model, we utilize precision, recall, average precision (AP), mAP0.5, mAP0.5:0.95, number of model parameter, model size, and detection speed as evaluation metrics. The calculation formulas for each evaluation metric are as follows:  
\[\text{Precision}\,=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{14}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{15}\]  
\[\text{AP}=\int_{0}^{1}\text{Precision}(\text{Recall})\text{d}(\text{Recall}) \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Setup** & **Parameter** & **Setup** \\ \hline Epochs & 250 & Input image size & 640 \(\times\) 640 \\ Initial learning rate & 0.01 & Optimizer & SGD \\ Final learning rate & 0.0001 & \(\delta\) (WIoU v3) & 1.9 \\ batchsize & 16 & \(\alpha\) (WIoU v3) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The training parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline CPU & Intel(R) Core(TM) i5-10400F @2.90 GHz \\ GPU & NVIDIA GeForce RTX 3060 \\ GPU memory size & 12G \\ Operating systems & Windows 10 \\ Deep learning architecture & Python 3.8.8 + Cuda 11.7 + Pytorch 2.0.0 \\ Model & YOLOv8n \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training environment and hardware platform parameters.  
Figure 7: Augmented data results image. (**a**) Original image; (**b**) random scaling and cropping image; (**c**) random brightness and contrast adjustments image; (**d**) copying regions of less frequent classes, enlarging, rotating, translating, and pasting them back into the original image; (**e**) Mosaic data augmentation image; (**f**) Mosaic data augmentation training annotation image.  
\[\text{mAP}=\frac{\sum_{\text{i}=1}^{\text{N}}\text{AP}_{\text{i}}}{\text{N}} \tag{17}\]  
where TP represents the number of true positive samples correctly identified as positive, TN represents the number of true negative samples correctly identified as negative, FP represents the number of false positive samples incorrectly identified as positive, FN represents the number of false negative samples incorrectly identified as negative, and AP is the area under the precision-recall curve. N represents the number of classes (in this paper, N is 7). mAP0.5 represents the mean average precision when the detection model's IoU threshold is set to 0.5, and mAP0.5:0.95 represents the mean average precision when the detection model's IoU threshold is set to 0.5-0.95 (with a 0.05 interval).
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 4 Empirical Study
### Results  
According to the results of the experiments, using the approach based on trust and reputation decreases the \"waiting\" time up to 72 s on average, and the MAV ceases to be trusted after three iterations. \"Waiting\" time implies the time that MAVs needs to calculate the reputation value is implied. Under iteration, one cycle of updating reputation values is implied, i.e., after three recalculations, the MAVs are no longer be trusted by the reason of the low reputation level (if the reputation is reduced during the system's operation). The initial \"waiting\" time was set as 120 s. In other words, the implementation of the approach allows reducing this \"waiting\" time by 1.66 times.  
A series of simulations were conducted in which recalculation of the reputation value was required. Table 1 shows approximate averaged data for the values of reputation and the time elapsed from the occurrence of the conflict situation, in which it was necessary to take into account the reputation value.  
When MAV's reputation reaches a value of 0.25 or less, it is excluded from interacting with other MAVs. From the results of the simulations, one can see that, if the vehicle begins to send bogus data, then it was disconnected from the communication with others in 41 s on average. Without a reputation-based approach, the wait time was always 120 s. This value was chosen experimentally as the time during which we can be sure that the agent must be disconnected from the system. The implementation of the approach proposed allows calculating this time for each particular situation. Thus, in the event of a malfunction in the system, it stops to operate for a significantly short time, which is almost three times shorter compared to the operation without using the approach, therefore reducing the likelihood of a traffic accident occurrence.  
\begin{table}
\begin{tabular}{c c c} \hline
**Number of the Iteration** & **Reputation Value** & **Time (s)** \\ \hline
1 & 0.5 & 0 \\
2 & 0.367 & 30 \\
3 & 0.11 & 41 \\
4 & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Variation of the MAV’s reputation value during physical simulations.  
Figure 7: An example of two possible cases, which can occur at the intersection: **(a)** The case when the whole intersection is in the field of view of Vehicle (1). Therefore, Vehicle (1) itself checks for the presence of Vehicle (2) at the intersection. **(b)** The case when Vehicle (1) cannot see the whole intersection and needs to interact with others neighbors.
###### Abstract
They studied different climates (Ostersund, Norway; Turin, Italy; Abu-Dhabi, United Arab Emirates) and considered three representative days in summer, winter, and mid-season for the analysis. They assessed visual comfort in terms of DGP and Spatial Useful Illuminance (sEu). They showed that PCM glazing presented lower DGP values than selective glazing during overcast and sunny sky days with the sun in lower positions, or when the sun was not directly visible from the window. The main drawbacks of this study were the lack of validation of the model in Radiance for the PCM window and that only PCM in the solid-state was considered. To the best of our knowledge, there are a lack of experimental studies about the impact of PCM glazing on occupants' visual comfort. In summary, the literature review shows that the main identified research gaps are the lack of studies evaluating the effect of PCM glazing on building energy consumption, either by modeling or experimentally, and the absence of real-scale studies to determine the impact of PCM glazing on visual and thermal comfort. To address these research gaps, this paper aims to experimentally investigate the influence of PCM glazing on occupants' thermal and visual comfort and office energy performance under actual weather conditions. The research consists of a one-year real-scale experiment in two east-oriented facade twin offices; PCM glazing is installed in one office, while the other has double-clear glazing. The window-to-wall ratio (WWR) of both offices is 56%. The offices are located in Santiago, Chile, a semi-aridclimate classified as Bsk according to [PERSON] ([PERSON], 2018; [PERSON] _et al._, 2018) and 3C according to ASHRAE ([PERSON], 2018). Santiago's climate is characterized by high temperatures and solar radiation for more than half of the year, and high-temperature fluctuations between daytime and night-time. The results for energy consumption, thermal comfort, and visual comfort are shown on two timescales: seasonally, and daily. This paper provides an extensive analysis of the energy, thermal, solar, and daylighting performance of PCM glazing in the actual operational conditions of an office, quantifying the energy consumption and thermal and visual comfort. Architects and building engineers could benefit from the results shown in this paper to make informed decisions during the early building design stage. In addition, researchers could better understand this technology and use the results of this paper to validate thermal and daylighting models.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 4 Mobile LiDAR Sensing Circuit and Chip Design  
In this section, we present the design of the LiDAR sensing circuit and chip. We will describe in detail the function of each block, including LiDAR packet decoding, the CORDIC calculation architecture, and the chip design. At the same time, the algorithm for reducing the CORDIC iteration is used in the CORDIC block to calculate the values of COS and SIN, and finally the XYZ coordinate value of the 3D point-cloud image is calculated.
## 2 Literature Review
The extracted features are merged and sent to the detector, which uses them to distinguish between fake and real news. Furthermore, the presence of corrupted image data in the Weibo dataset influenced the results of BDANN. [PERSON] et al. [43] suggested a fake news detection mechanism with BERT based on a deep convolutional approach (fakeBERT). This fakeBERT is made up of various parallel blocks of a deep CNN, which is a one-dimensional (1D-CNN) with varying kernel sizes and filters, as well as the BERT. Various filters can extract valuable data from the training sample. BERT combined with 1D-CNN can handle both large-scale structured and unstructured text. As a result, the combination is advantageous in dealing with ambiguity. [PERSON] et al. [44] proposed a multi-modal framework that combines RNN and Graph Convolutional Network (GCN) for encoding fake news propagation in social media. The limited data on fake news is a significant issue in the NLP field. Thus, a few studies have tried to generate synthetic data to solve this problem. A study by [PERSON] et al. [30] presented Sequence Generative Adversarial Network (SeqGAN), which is a GAN structure that solves the gradient descent problem in GANs for discrete outputs using a reinforcement learning (RL)-oriented approach and Monte Carlo search. The authors fed actual news content into the GAN, then a classifier based on Google's BERT model was trained to distinguish actual samples from produced samples. However, seqGAN while can produce short text, it is not a better solution for generating entire news content with seqGAN, and may result in non-contextual news. The authors used DistilBERT to encode the representation of textual data such as fake news, tweets, and user self-descriptions. Furthermore, the authors improved their performance by employing an attention mechanism. Limited data on fake news is a significant issue in the NLP field. Thus, a few studies have tried to generate synthetic data to solve this problem. A study by [PERSON] et al. [30] presented seqGAN, which can only produce short text; it is not a better solution for generating entire news content with seqGAN, and may result in non-contextual news. Most research efforts have focused on English data. Other low-resource language-related fake news detection mechanisms are scarce in this domain. [PERSON] et al. (2018) published a Bangla dataset consisting of fake news and true news and performed classification with CNN, LSTM, and BERT. The authors classified news documents using the pre-trained multilingual BERT model. The multilingual BERT model is trained in 104 different languages. However, the model is not explicitly trained to have shared representations across languages. Thus, we propose a technique where we utilize the pre-trained multilingual BERT to generate synthetic news. Furthermore, we trained a smaller BERT from scratch for embedding and classification, and experimented with using a frozen BERT with our training samples.
# Performance Analysis of Storage Systems in Edge Computing Infrastructures
###### Abstract  
Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work inspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems.  
performance evaluation; blockchain; BigchainDB; MinIO; IFPS; object storage; file storage; databases; edge; secure storage Article