esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 4 Opportunistic Routing Protocol for a WBAN Based on a Time-Varying Link
### Expected Transmission Cost of the Remaining Opportunistic Routing  
The reliable one-hop path does not guarantee the overall routing reliability. Taking into account the interaction between the links, the transmission of the WBAN is divided into anycast routing and remaining opportunistic routing. In the opportunistic routing, when receiving a packet, the node with the highest priority will forward the data first, otherwise the candidate node with the second priority will forward it. That is, if node \(v_{j}\) forwards the packet, all candidate nodes with higher priority have failed to receive it. Taking periodic data as an example, when the candidate set \(Ca_{i}\) of node \(v_{i}\) forwards packets to the destination node, the required expected transmission energy efficiency is:  
\[\begin{array}{ll}Ee_{i}&=de_{iCa}+Cf_{i}\\ &=\frac{1}{1-\prod\limits_{j\in Ca_{i}}(1-p_{ij}^{t})}(1+\sum\limits_{j=1}^{| Ca_{i}|}p_{ij}^{t}\frac{E_{0}}{E_{j}}+p_{i1}^{t}\frac{E_{0}}{E_{j}}+\sum\limits_{j= 2}^{|Ca_{i}|}p_{ij}^{t}\frac{E_{0}}{E_{j}}\prod\limits_{p=1}^{j-1}(1-p_{iv}^{t}) )\end{array} \tag{11}\]  
According to Equation (10), for periodic data, the influence of their residual energy on the network energy balance is considered when selecting the next hop. However,for emergency data, try to choose a transmission path with a small expected number of transmissions and transmit the data to the sink as soon as possible. For important data, both energy loss and link reliability are considered.  
\[\begin{array}{ll}Ee_{i}&=de_{iCa}+Cf_{i}\\ &=\begin{cases}\frac{1}{1-\prod\limits_{j\in\mathcal{C}_{i}}\left(1-p_{ij}^{t} \right)}\left(1+\sum\limits_{j=1}^{|Ca|}p_{ij}^{t}\left(\frac{E_{i}}{E_{i \gamma}}+\frac{LCS_{i}}{l-1}\right)+p_{i1}^{t}\left(\frac{E_{i}}{E_{i\gamma}}+ \frac{LCS_{i}}{l-1}\right)+\sum\limits_{j=2}^{|Ca|}p_{ij}^{t}\left(\frac{E_{i }}{E_{i\gamma}}+\frac{LCS_{i}}{l-1}\right)\prod\limits_{v=1}^{j-1}\left(1-p_{iv }^{t}\right)\right)&\phi=1\\ &\frac{1}{1-\prod\limits_{j\in\mathcal{C}_{i}}\left(1-p_{iv}^{t}\right)}\left(1 +\sum\limits_{j=1}^{|Ca|}p_{ij}^{t}\frac{LCS_{i}}{l-1}+p_{ii}^{t}\frac{LCS_{i }}{l-1}+\sum\limits_{j=2}^{|Ca|}p_{ij}^{t}\frac{LCS_{i}}{l-1}\prod\limits_{v= 1}^{j-1}\left(1-p_{iv}^{t}\right)\right)&\phi=0\end{array} \tag{12}\]
## 5 Results
### Performance Metrics  
The results for the performed experiments were evaluated using the mAP50, mAP50-95, precision, and recall on the validation dataset, and mAP, F1, and frames per second (fps) on the test dataset. mAP50 (mean average precision at 50% intersection over union) is a common metric used to evaluate the performance of object detection models. It measures the average precision of the model at different levels of confidence when the overlap between the predicted bounding box and the ground truth bounding box is at least 50%. In other words, it measures the accuracy of the model in localizing objects in the image, and it takes into account both the precision and the recall of the model. A mAP50 score of 1.0 means that the model has achieved perfect precision and recall, while a score of 0 means that the model has failed to detect any objects. The mAP50-95 (mean average precision at the 50-95% intersection over union range) is another common metric used to evaluate the performance of object detection models. It measures the average precision of the model at different levels of confidence when the overlap between the predicted bounding box and the ground truth bounding box is between 50% and 95%. The mAP50-95 is often considered a more stringent evaluation metric compared to the mAP50, as it requires the model to detect objects with higher levels of precision and recall. A higher mAP50-95 score indicates that the model is more accurate in localizing objects in the image, and it takes into account both the precision and the recall of the model over a wider range of intersection over union thresholds.  
Precision measures the proportion of true positive predictions (i.e., the number of correctly identified positive instances) out of all positive predictions made by the model (i.e., the sum of true positives and false positives). A high precision score indicates that the model is making accurate positive predictions and has a low false positive rate. Precision can be calculated using:  
\[\text{Precision}=\frac{TP}{(TP+FP)} \tag{9}\]  
Recall measures the proportion of true positive predictions (i.e., the number of correctly identified positive instances) out of all actual positive instances in the data. Recall can be calculated using:  
\[\text{Recall}=\frac{TP}{(TP+FN)} \tag{10}\]  
The F1 score was calculated using (11), which takes into account both the precision and recall of the algorithm in order to provide a single measure of its effectiveness. The F1 score ranges from 0 to 1, with a score of 1 indicating perfect precision and recall, and a score of 0 indicating that the model did not correctly identify any objects.  
\[F1=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{11}\]
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 3 Theoretical Framework and Novel Methods
### Dual-Thread Concurrent Processing Model  
Simultaneous motor control and audio recording require the coordination of both tasks so that one does not interfere with the other. The dual-thread architecture facilitates separating those concerns through a priority-based concurrent execution model, which can be abstracted as a set of tasks \(T\) with different priority and execution constraints:  
\[T=\{\tau_{1},\tau_{2},\ldots,\tau_{n}\} \tag{6}\]  
Each task \(\tau_{i}\) is marked by its execution time \(e_{i}\), period \(p_{i}\), and deadline \(d_{i}\). In this system, the execution time for motor control commands is short, but deadlines are strict to maintain responsive control, while audio sampling tasks take longer to execute but have looser deadlines.  
The schedulability of this task set can be analyzed using Rate Monotonic Analysis (RMA), which assigns priorities based on task periods. For a set of \(n\) periodic tasks, the sufficient condition for schedulability is  
\[\sum_{i=1}^{n}\frac{e_{i}}{p_{i}}\leq n(2^{1/n}-1) \tag{7}\]  
With these two tasks (motor control and audio sampling), a theoretical utilization bound of \(\sim\)83% is achieved by this two-task implementation. The task utilization measured falls below this limit so that motor control takes about 5% and audio 60-70%, guaranteeing a schedule under nominal conditions.  
The motor control thread operates with a polling frequency of 100 Hz, checking for incoming commands and updating motor states accordingly. This frequency provides a theoretical minimum command latency of 10 ms, though practical implementation considerations including Bluetooth transmission delays increase this to approximately 100-150 ms, still well within acceptable limits for teleoperation applications.  
The audio acquisition thread operates at a higher frequency, dictated by the I2S sampling rate of 16 kHz. However, audio data is buffered and processed in chunks of 64 bytes, representing 8 ms of audio, to optimize both CPU utilization and Bluetooth transmission efficiency. This buffering introduces a small but consistent latency in the audio stream, which is acceptable for the target applications of environmental monitoring and acoustic scene analysis.
# Design of a Lung Lesion Target Detection Algorithm Based on a Domain-Adaptive Neural Network Model
###### Abstract
These models' heavy reliance on source domain data often leads to a significant drop in performance when applied to target domain data [12]. Moreover, the morphology and size of lung lesions in medical images exhibit a high degree of variability, further increasing the difficulty of detection. To mitigate these challenges, domain adaptation techniques have been incorporated into medical image analysis. This study contributes to the field by addressing these critical challenges and proposes the following advancements:  
1. This study introduces the CNDAD--Net framework, which integrates domain adaptation and multi-scale feature fusion to address the challenges of cross-domain medical image analysis. This approach effectively mitigates performance degradation caused by cross-domain data distribution discrepancies, enhancing the model's generalization and adaptability. The proposed framework is particularly suited for analyzing medical image data across different devices and organizations. 2. A novel multi-dimensional feature Cross-Fusion Block (CFB) is designed and rigorously validated, enabling the deep fusion of 2D, 3D, and domain-adapted features across multiple scales. Through bidirectional interactions among features of varying dimensions, the CFB module significantly improves the model's capacity to detect diverse lesion regions, enhancing detection accuracy and robustness. This design reduces the likelihood of missed detections and false positives, offering scalable technical support for complex medical image analysis tasks. 3. The CNDAD--Net framework demonstrates superior performance on the LUNA16 dataset, substantially surpassing traditional target detection methods in precision, recall, and mAP. This achievement provides an innovative technological pathway for the intelligent analysis of medical images and the precise diagnosis of lung lesions, advancing the development and application of AI-driven diagnostic solutions in medicine. The remainder of this paper is structured as follows: Section 2 reviews related work on object detection, transfer learning, and domain adaptation. Section 3 presents the proposed CNDAD--Net framework in detail. Experimental setup and results are elaborated upon in Section 4, followed by concluding remarks at the end.
## 4 Description of the Proposed Topology Construction
### Different Phases of the Proposed Topology  
The proposed logical topology can be described using three phases, namely (i) topology formation phase; (ii) steady state phase; and (iii) topology update phase. Figure 5 demonstrates these phases with respect to a timeline. Additionally, Figure 6 demonstrates the transitions among different phases.  
Figure 4: A sample model of the proposed topology.  
Figure 3: Types of chains: (**a**) simple chain; (**b**) complex chain.  
At the initial stage of the sensor deployment in the target field, the topology formation phase starts. This phase takes place only once. The steady state phase and the topology update phase then follow. At the beginning of the topology formation phase, no sensor nodes recognise any other sensor node in the target field. Each of the deployed sensor nodes then reports its individual characteristics to all of its neighbouring sensor nodes using broadcasting. A sensor node, receiving broadcasted messages by its neighbouring nodes, calculates the distances between itself and the neighbouring nodes. Additionally, each sensor node aggregates the reports it collects from its neighbouring nodes. After reporting, all the sensor nodes negotiate with their neighbours and construct several chains. When the chain constructions finish, lower-level leaders are elected for each chain. Each lower-level chain then broadcasts the topology, describing the member nodes, successor-predecessor lists, and time division multiple access (TDMA) allocations. At this point, the topology formation phase ends, and the deployed sensors are ready for their normal operation.  
Figure 5: Timeline of the proposed topology.  
Figure 6: Transitions of different phases of the proposed topology.  
At the end of the topology formation phase, the steady state phase begins. In this phase, the sensor nodes start their normal operation. Without the loss of generality, it can be assumed that the sensors are deployed in the target field to collect some data. The steady state consists of several rounds. A round begins whenever the sensor nodes start their sensing. A round finishes when the higher-level leader collects all sensed data via the lower-level leaders, and then sends the data to the BS.  
After the end of a fixed number of rounds in the steady state, the topology update phase takes place. The tasks of this phase are to maintain the topology, such as selection of new lower-level leaders, construction of a higher-level chain, selection of a higher-level leader, and reconstruction of chains, if necessary.
## 2 Experimental Procedures
### Experimental Setup and Testing Method  
The main experimental setup used in the Brazilian test includes the loading device (MTS-Landmark servo-hydraulic loading system) and an image acquisition device (Point Grey-GS3-U3-123S6M-C), as shown in Figure 2b. Loading was carried out using displacement control at a rate of 0.2 mm/min. The specimens failed within about 5 min, which is within the recommended time of 1-10 min [13]. During loading, two cameras were used to capture images at intervals of 50 ms (20 fps). As shown in Figure 2b, the two cameras with 50 mm fixed focal length were installed in front of the specimen, and the axes of the cameras were at an angle of \(\sim\)20\({}^{\circ}\) from the axis of the specimen. Moreover, the two cameras were connected to a computer which was later used to analyze the images using three-dimensional visual image correlation software (VIC-3D). Further details of the camera setup are described in CorelatedSolutions [49]. To ensure the correct brightness of the specimen surface, two LED lamp sources were placed on both sides of the specimen. Additionally, image calibration was carried out before testing with the 3D-DIC technology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rock Type** & **Parameter** & **Mean Value** & **Standard Deviation** \\ \hline \multirow{3}{*}{White sandstone} & \(\sigma_{c}\) (MPa) & 37.7 & 0.6 \\  & \(E\) (GPa) & 9.2 & 0.2 \\  & \(\
u\) & 0.15 & 0.01 \\ \hline \multirow{3}{*}{Red sandstone} & \(\sigma_{c}\) (MPa) & 62.6 & 0.8 \\  & \(E\) (GPa) & 14.8 & 0.6 \\  & \(\
u\) & 0.18 & 0.01 \\ \hline \multirow{3}{*}{Marble} & \(\sigma_{c}\) (MPa) & 79.2 & 0.7 \\  & \(E\) (GPa) & 43.7 & 0.6 \\  & \(\
u\) & 0.21 & 0.01 \\ \hline \multirow{3}{*}{Granite} & \(\sigma_{c}\) (MPa) & 171.7 & 0.4 \\  & \(E\) (GPa) & 72.2 & 0.9 \\  & \(\
u\) & 0.23 & 0.01 \\ \hline \multirow{3}{*}{Basalt} & \(\sigma_{c}\) (MPa) & 158.4 & 3.3 \\  & \(E\) (GPa) & 61.9 & 2.1 \\ \cline{1-1}  & \(\
u\) & 0.25 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing results of the uniaxial compression tests.  
Figure 2: _Cont._
# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 4 Experiments  
In this paper, we propose an object detection framework based around the problems of small targets and the presence of too many negative samples in UAV object detection. In this section, we will discuss the following aspects:* Whether the Sample Balance Strategies (SBS) we proposed for Positive and Negative, and Easy and Hard Samples can improve the AP performance for the selection of positive candidates.
* Whether the weight-GAN subnet can effectively enhance the local features of objects and improve the accuracy of small object detection.
* Verify the detection framework we proposed that combines the Sample Balance Strategies and weight-GANs subnet for the detection of objects from drones, and evaluate it against CornetNet [27], FPN [20], FCOS+ATSS [40], Perceptual GANs [46] and so forth.  
For the software environment, all experiments are implemented based on the Pytorch [54] and mmdetection [55] frameworks. The hardware environment of this experiment is the Intel Core i7-6500k CPU 3.4 GHz, and with two GPUs of TITAN RTX 24G memory. We initialize our backbone networks and FPN network with the weights pre-trained on ImageNet [49]. We verify our detection framework on the VisDrone2020 DET dataset [12], which consists of 10,209 images in unconstrained challenging scenes, including 6471 images in the training subset, 548 in the validation subset, 1580 in the test-challenge subset, and 1610 in the test-dev subset. Since our experiment is mainly based on the mmdetection framework, which requires the COCO data format, we convert the Visdrone2020 datasets into the COCO data format. During the training process, we first train the weight-GAN based on the dataset we generated. Then, by the forward propagation of weight-GAN, the local features will be enhanced after the generation of the candidates' boxes. Finally, the classification and regression will be regressed by the framework we proposed.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.1 LoRaWAN - Classes  
LoRaWAN has three main channel access strategies grouped as Class A, B and, C. Class A is mandatory and all end devices have to implement protocol specification defined by [7]. Table 3 presents a brief info about the classes.  
LoRaWAN adopts ALOHA-type random access to keep network complexity as simple as possible and maximize energy saving. In Class A devices, downlink (DL) is controlled by the end node and devices don't have an active DL receive windows. Only two DL receive windows are activated after an uplink (UL) transmission performed. Any packet to be transmitted from GW to the node have to wait next UL message. Thus, Class A devices are the most energy efficient.  
Class B devices control the receive windows with pre-determined time slots and end devices open DL windows only scheduled times. End-node timers are controlled Beacon frames by the gateway.  
Class C devices are the least efficient devices since DL receive windows are always active. Class A and B devices are higher latency when compared to Class C nodes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Battery Consumption** & **Description** \\ \hline A & Most energy efficient & Must be supported by all the End-Nodes. DL after TX \\ B & Efficient with controlled DL & Slotted communication synchronized with beacon frames \\ C & Least efficient & Devices listen continuously. DL without latency. \\ \hline \hline \end{tabular}
\end{table}
Table 3: LoRaWAN class comparison.  
Figure 9: LoRaWAN classes.
## 4 Discussion
### Limitations  
There may be some possible limitations of our study. All of the methods of human body composition assessment are considered 'indirect'; therefore, they have limited accuracy. Even though an 'indirect' method for assessing BF% does not exist, both the BIA and the CUN-BAE methods are considered 'indirect' methods for estimating BF%. In the present study, the reference outcomes were estimated using the BIA; however, the gold standard may possibly require other more complex techniques since BIA measurements can produce random errors due to inter-individual differences in body composition, geometry, and size, resulting in non-uniform conductivity [14].  
Secondly, the raw estimations obtained via BIA differ amid devices depending on technological and biological factors; therefore, healthcare professionals should be aware of these factors when extrapolating prediction equations established for body composition assessment [53].  
The third limitation may be related to the changes that can occur in an athlete's body weight status resulting from the seasonality of their physical activity (in terms of pre- and in-season training); therefore, further cohort studies should be continued on the basis of the data obtained in this single cross-sectional study.  
Finally, whilst the representative sample size of the athletes was modest, the cross-sectional study we conducted may have held some biases because such research is not able to establish cause-and-effect relationships between the variables studied. However, taking into account the representative sample size and the probability sampling technique used, these study results can be generalized and transferred to populations of physically active people exclusively through population-based studies.
## 1 Introduction
A notable disadvantage of using cryogenic conditions is the costs associated with specialist equipment installation, which can also occupy a large space. In addition, working with liquid nitrogen or liquid helium poses potential human health risks. Dry machining and dry burnishing can, in some cases, be a viable alternative to traditional flood lubrication treatment. Due to the accessibility of the cutting area, turning and milling represent excellent opportunities for eliminating cutting fluids [34]. This is also true for static burnishing processes, as their kinematic schemes are similar to those of turning. However, there are relatively few studies to date [27; 29; 30; 31; 35; 36] on the effects of dry DB on the SI characteristics and operational behavior of machined metal components, and among these, only [36] describes CNASS. Another potential compromise option for realizing dry DB while reducing heat generation is to use a vortex tube [37; 38] to supply cold air to the plastic deformation zone; however, no studies to date have investigated this aspect in the context of CNASS. The effects of DB on the SI and operating behavior of CNASS components are studied in [1; 6; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. The most commonly studied grade of CNASS is 316 [36; 41; 42; 43; 44], followed by 304 [1; 6; 39; 40], 317 [45; 46; 47], and 321 [48; 49]. Most studies to date have explored simple correlations between DB governing factors and SI characteristics [1; 6; 36; 39; 40; 42; 43; 44; 45; 48; 49]; however, significantly less research has investigated the correlation between DB governing factors and operating behavior in terms of corrosion resistance [1], wear [42; 45], and fatigue behavior [1; 6; 42]. Explicit correlations between the surface integrity and fatigue limit of diamond-burnished CNASS were obtained in [50]. In particular, there have been no comprehensive studies on the influence of lubrication and cooling conditions in the DB process on the SI and fatigue behavior of CNASS components. To address these limitations, the present study aims to evaluate the influence of lubrication and cooling conditions in the DB process on the SI and fatigue limit of CNASS and, on this basis, identify a cost-effective and sustainable DB process.