esa-sceva/satcom-chunk-collection huggingface dataset:
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 4 ILP Problem Formulation: OptTs
### Objective Function  
With finite \(P_{ES}\) and \(P_{D}\) values, the objective to minimize the unused or remaining available power can be conceptually expressed as the difference between these parameters, as shown in Equation (8). In each time slot \(i\), this objective function of can be expressed, as shown in Equation (9).  
\[minimize\ \{P_{ES}-P_{D}\} \tag{8}\] \[\forall i\in W:minimize\left\{\sum_{i=1}^{W}\left(P_{ES}[i]-P_{D} [i]\right)\right\} \tag{9}\]  
The difference between \(P_{ES}\) and \(P_{D}\) is defined as Residual Power (\(P_{RES}\)), and it is given by:  
\[\forall i\in W:P_{RES}=\sum_{i=1}^{W}\left(P_{ES}[i]-P_{D}[i]\right) \tag{10}\]  
Thus, the objective of the proposed adaptive energy management model is to minimize the \(P_{RES}\), given the constraints presented below.  
Domain Constraints  
The role of energy provisioning by the ES and the non-negative \(P_{RES}\) is ensured by Equations (11) and (12), respectively.  
\[P_{ES}[i]\geq 0 \tag{11}\] \[(P_{ES}[i]-P_{D}[i])\geq 0 \tag{12}\]  
Given that, mathematically, the \(P_{D}\) can be greater than \(P_{ES}\), a situation that occurs due to a low generation or high load, this negative difference or missing power is denoted as Lack Power (\(P_{LACK}\)). The \(P_{LACK}\) represents the amount of energy that is needed to process all demands under the current conditions of the system. This parameter can be expressed as:  
\[P_{LACK}[i]=\left\{\begin{array}{ll}|\,P_{ES}[i]-P_{D}[i]\,|&\text{ if }P_{ES}[i]<P_{D}[i],\\ 0&\text{ otherwise.}\end{array}\right. \tag{13}\]
## 4 Discussion
Without uniformity in data collection and reporting, any comparison between different interventions remains speculative rather than conclusive. To address these methodological deficiencies, the authors of this review have developed studies proposing more controlled experimental models, with the goal of initiating a new phase in scientific literature on PIPJ. The first study explored the influence of total end-range time (TERT) on PIPJ recovery, identifying that this factor may play a key role in ROM improvement. Instead of merely analyzing the effectiveness of a specific treatment, this study established a standardized framework for measuring ROM progression within clinical research [50]. The second study focused on the dose-response relationship between TERT and ROM improvement, proposing a quantifiable model for assessing therapeutic progression. This approach seeks to eliminate ambiguities in the existing literature and facilitate more homogeneous comparisons between future studies [50]. The third study, from a biomechanical perspective, analyzed differences between elastic-tension orthoses and conventional devices, evaluating parameters such as pressure distribution and the risk of skin injuries. Through a more structured methodology, objective criteria were established to compare different therapeutic approaches in future research [51]. The fourth study proposed an adaptive protocol based on the patient's ROM progression, allowing for gradual treatment adjustments. This dynamic model has the potential to serve as a reference for future research, ensuring that interventions are evaluated with reproducible and well-defined criteria [52]. Building upon these prior findings, [PERSON] et al. [53] conducted a cadaveric study aimed at refining the understanding of PIPJ biomechanics and its implications for therapeutic strategies. Their research demonstrated that the mid-flexion position (30-40\({}^{\circ}\)) maximizes capsular space volume, facilitating ligament shortening and arthrofibrosis, which reinforces the need to prioritize proximal interphalangeal joint (PIPJ) extension in clinical settings. Through high-pressure silicone and latex injections, they identified that joint position plays a crucial role in capsular space distribution and mobility restriction, supporting previous findings on TERT and ROM improvement. Their results emphasize that immobilizing the PIPJ in extension with dorsal compression may be the most beneficial strategy to prevent contractures and arthrofibrosis, aligning with previous recommendations on structured rehabilitation protocols. Furthermore, their study provides a strong anatomical and biomechanical foundation to justify the application of manual therapy in mid-flexion to maximize joint accessory movements while minimizing friction. By integrating these findings with previous controlled experimental models, this research contributes to a more standardized and evidence-based approach to PIPJ rehabilitation, offering clinicians a clearer framework for intervention and supporting the development of adaptive, patient-specific treatment protocols.
# Treatment of Water Contaminated with Diesel Using Carbon Nanotubes
## 3 Results
### Thermal Characterization of Post-Adsorption Carbon Nanotubes  
Figure 4a shows a typical trend of the TG and DTG thermal curves of a sample of nanotubes used in the diesel adsorption process. Regardless of the quantities of nanotubes used during the test, the TG and DTG curves always presented the same profile. In particular, an initial weight loss at about 200 \({}^{\circ}\)C, not present in the original CNTs, i.e., before being used in the adsorption process, can be seen, referable to the loss of the adsorbed gas oil. The thermal analyses of the original nanotubes before the adsorption tests are reported in our previous articles [61; 68]. A second weight loss is visible at about 600 \({}^{\circ}\)C, a typical behavior of carbon nanotubes attributable to their combustion. The DTG curve has two minima at these two temperatures.  
Figure 4b shows the DTA thermal curve of the same sample. At about 220 \({}^{\circ}\)C, there is an endothermic peak due to the loss through evaporation of the adsorbed gas oil. An exothermic peak at 600 \({}^{\circ}\)C confirms that the nanotubes used in the experimental process are in no way chemically \"attacked\" by the gas oil itself, given that they maintain the same combustion temperature as the pre-adsorption samples and, therefore, the same degree of graphitization.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Device-to-Device (D2D)  
The persistent demand for a maximum data rate with reduced end-to-end delay is one of the most significant defiance facing telecommunication providers. One method to accomplish it is via D2D communication. D2D communication allows nearby devices to communicate between them directly without passing into the BS. Due to the very low  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
latency associated with D2D communication, it has garnered considerable attention from researchers operating on promising B5G cellular communication networks [81; 82]. D2D communication can use both licensed and unlicensed cellular spectrum which is referred to as in-band and out-band communication, respectively. In the first situation, both D2D and cellular communication can coexist on the same licensed cellular spectral; this is referred to as the underlay mode of D2D communication. However, it causes increased interference between cellular and D2D users. To address this issue, a novel communication mode called overlay D2D communication was suggested, which allows D2D users to utilize a portion of cellular resources that are not allotted to typical cellular users. To avoid spectrum waste, the effectiveness of recourse allocation must be considered in the overlay mode. Concerning D2D communication performance, it provides more advantages in comparison with traditional cellular communication, when it is viable technically. The D2D communication technique is characterized by transparency besides being extremely effective in terms of massive spectrum efficiency, low energy consumption, and low latency. Thus, local traffic management becomes easier for user Ues that communicate directly in a certain vicinity. Another advantage of D2D communication is that it allows for computational offloading. D2D users under the environment of a static network can utilize D2D links for offloading computationally intensive activities to adjacent D2D users [83]. The mechanism of mode selection in D2D communication enables devices to simply move from the infrastructure communication path to the direct communication path. This helps to decrease network congestion. Economically, D2D communication has a significant role to play in commercial, e-commercial, and social apps, among others, where users can immediately share important information locally [84; 85]. Figure 7 shows the typical D2D communication scenario [86].
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
###### Abstract  
An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility. The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity. The monitors were developed and tested in situ for three months in several overlapping deployments, before a full cohort of 40 was deployed in a heavy vehicle manufacturing facility for a year of data collection. The monitors collect data from each sensor and report them to a central database every 5 min. The work includes an experimental validation of the particle, gas and noise monitors. The \(R^{2}\) for the particle sensor ranges between 0.98 and 0.99 for particle mass densities up to 300 \(\mu\)g/m\({}^{3}\). The \(R^{2}\) for the carbon monoxide sensor is 0.99 for concentrations up to 15 ppm. The R\({}^{2}\) for the oxidizing gas sensor is 0.98 over the sensitive range from 20 to 180 ppb. The noise monitor is precise within 1% between 65 and 95 dBA. This work demonstrates the capability of distributed monitoring as a means to examine exposure variability in both space and time, building an important preliminary step towards a new approach for workplace hazard monitoring.  
wireless sensor network; occupational health and safety; sensor arrays; particle sensors; carbon monoxide gas sensors; oxidizing gas sensors; noise sensors; occupational medicine; aerosol exposure; personal exposure 2018184111 doi:10.3390/s18051411  
1
## 3 Results and Discussions
### Microscopic Analysis  
Scanning electron microscopy (SEM)--electron dispersive X-ray spectroscopy (EDS) was carried out on a Tescan MIRA3 GMU Univac SEM with a secondary electron detector and Drawbeam advanced/offline software to examine the Co\({}_{3}\)O\({}_{4}\) NPs' morphology and structural configuration. Figure 3a,b depicts stable hollow spherical entities which consist of interconnected nanoparticles. It was observed that the Co\({}_{3}\)O\({}_{4}\) NPs spontaneously arranged themselves in a sieve-like pattern and adhered to the surface, creating interior cavities and loose structures. Agglomeration could also be observed because of the reduced size, greater surface area, and the presence of biomolecules [50]. Image J software (1.53v) was also used to identify the particle size by the histogram, as shown in Figure 3c, with an average diameter of about 42.7 nm.  
Moreover, in order to further confirm the pores' structure, TEM observations were carried out using an FEI Tecnai 20 transmission electron microscope, operating at 200 kV (Lab6 emitter). The high-magnification TEM images (Figure 4a-g) indicate that the pores structure consists of interconnected nanoparticles with an average diameter of about 42.7 nm. In the large area TEM image, it can be seen that a few nanoparticles are aggregated together in the form of chains.  
Figure 3: \(Cont\).  
Figure 2: XRD analysis of _H. thehubica_ Co\({}_{3}\)O\({}_{4}\) NPs.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 2 Method  
### Network Architecture  
To more effectively detect multi-scale vessels in SAR image data, especially small vessels in complex backgrounds, we propose an optimized ST-YOLOv8 algorithm for multi-scale vessels and complex scenarios in SAR images. This algorithm maintains robust performance for multi-scale vessel detection. In the backbone network of the baseline model, the C_OREPA module and the ASPP module were integrated, and the SA module was added to the neck network. Firstly, the preprocessed SAR images are passed through a convolutional group and then into the feature extraction network reconstructed by OPERA for feature extraction. Subsequently, these features are processed through the ASPP module for feature fusion, resulting in enhanced spatial and semantic data (P1, P2, P3). Feature maps of various scales, augmented by SA at each stage, are then upsampled before entering  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FasterR-CNN** & **RetinaNet** & **SSD** & **YOLOv8n** \\ \hline Architecture & Two-Stage & Single-Stage & Single-Stage & Single-Stage \\ GFLOPs & 112 & 83 & 31.2 & 14.5 \\ Parameters (M) & 41.3 & 38.6 & 26.3 & 9.3 \\ AP\_S (COCO) & 34.7 & 33.2 & 24.3 & 32.1 \\ Inference FPS & 23 & 58 & 89 & 89 \\ Memory Footprint (MB) & 3212 & 2984 & 1640 & 784 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architectural comparison of object detection frameworks.  
the detection network. Finally, the W-IoU loss function is used to continuously optimize the prediction results. The results of detection are obtained through Non-Maximum Suppression (NMS). Figure 2 shows us the model structure of ST-YOLOv8.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 2 Materials and Methods
### Image Preprocessing  
When a robotic vision system performs real-time monitoring of kiwifruit picking, the recognition effect is mainly affected by factors such as light intensity, robot arm vibration, branch, leaf shading situation, and fruits overlapping. In order to make the training model have better generalization ability (Figure 2), the original image is first Hue, Saturation, Value (HSV) transformed to simulate the lighting condition of scaffolded kiwifruit; the data features are enhanced using linear enhancement techniques to reduce the probability of sample inhomogeneity; to enhance the recognition of small target fruits, the image scaling is controlled and gray bars are added to the edges; padding is used to enrich the data set while avoiding learning unnecessary features; introducing Gaussian noise and pretzel noise to simulate the disturbance of the actual picking to the robot vision system and enhance the network model's ability to capture the target fruits.  
The network uses the Mosaic data enhancement method to traverse four images at a time (Figure 3): firstly, the fixed area of the image is intercepted by using the matrix, and the images are inverted, scaled, and transformed by HSV color gamut; secondly, the four images are stitched into one image, and the combination of images and frames is performed, the frames beyond the image are removed, and the stitched images are edge processed; finally, the enhanced images are passed into the neural network for normalization calculation, and four images are calculated at a time, which enriches the detection background of the target fruit and speeds up the model learning efficiency.  
Figure 2: Manipulations of the kiwi image dataset. (**a**) Original image; (**b**) Applying salt and pepper noise; (**c**) Padding; (**d**) HSV (Hue, Saturation, Value) transforming.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### Neural Architecture
#### 3.5.1. Inputs  
The network takes as inputs normalized log-mel-spectrograms \(\mathbf{X}\in[0,1]^{T\times F\times 1}\) that are derived from the audio. First, all audio data are resampled to 16 kHz and processed bycomputing a 1024-bin Fast Fourier Transform on successive 1024-long Hanning windows shifted every 512 samples. Therefore, each of the \(T\) spectrogram frames has a duration of 32 ms. The linear frequency scale is compressed into \(F=128\) Mel bins.
## 3 A Proposed Approach
### Path Planning Approach
#### 3.3.3 Path Optimization  
Since the path is generated based on the position of dynamic obstacles at the current moment, there may still be a potential risk for the OS navigating along this path. Hence, a path optimization method needs to be specified for the safety of the OS. The proposed path optimization scheme can be summed up in the following steps:  
Step 1 is to predict the trajectories \(\boldsymbol{p_{os}}\) and \(\boldsymbol{p_{ts}}\) based on Equations (4) and (5) and path \(P_{t_{1}}\).  
Step 2 is to calculate the minimum relative distance \(d_{min}\) and the corresponding time \(t_{2}\) based on the trajectories of OS and TS.  
Step 3 is to forecast the position \(\boldsymbol{p_{ts}}(t_{2})\) at time \(t_{2}\) of TS with Equation (5).  
Step 4 is to update the dynamic obstacle representation \(M_{d}(t_{2})\) based on \(\boldsymbol{p_{ts}}(t_{2})\) and regenerate the planning space representation \(M_{\text{e}}(t_{2})\) with Equation (6).  
Step 5 is to plan the new path \(P_{t_{2}}\) for collision avoidance.  
The proposed path optimization is an iterative process. The iteration will be finished when the minimum relative distance \(d_{min}\) converges to the maximum and meets the safety requirements of OS. Otherwise, it is necessary to reconfigure the parameters of the FM method and re-execute the path planning algorithm.  
Figure 5 shows the path optimization process. The three iteratively generated paths \(P_{t_{0}}\), \(P_{t_{1}}\), and \(P_{t_{2}}\) are denoted by the solid red, solid green, and solid blue lines, respectively.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 2 Traditional Methods before Deep Learning
### Svms  
The support vector machine (SVM) is a classic machine learning method that is widely used in computer vision fields such as image classification, object recognition, and object tracking. The main idea of an SVM is to map data to a high-dimensional space and find the optimal hyperplane in that space for classification. This method can handle non-linear and high-dimensional data, making it widely applicable for solving practical problems. SVMs have also been widely used in image semantic segmentation tasks. Generally, an SVM is employed as a pixel classifier; each pixel in the image is regarded as a sample, and an SVM is used to classify each pixel to obtain the semantic segmentation result of the image. SVMs have good generalization performance and can achieve good results for some small image semantic segmentation problems. However, SVMs can be used only for binary classification problems and are ineffective for multiclass problems. In image semantic segmentation tasks, multiclass problems such as segmenting multiple objects in an image are commonly encountered. In addition, SVMs have a high training complexity and require much time and computational resources, so their application in some large-scale image segmentation problems is limited. In comparison with SVMs, deep learning methods can better handle multiclass classification problems and have more advantages in training time and computational resources; thus, they are gradually becoming the mainstream methods in image semantic segmentation.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 2 Materials and Methods
### Inverse Adding-Doubling (IAD) Solution  
In addition to the forward problem, the GPU AD algorithm demonstrated utility in solving the inverse scattering problem. The human skin model used for this purpose consisted of two layers: epidermis and dermis (Figure 4). The epidermis and dermis are characterized by various optical parameters, which are discussed in more detail in Appendix A. The diamond initialization was used to simulate an initial thin skin layer, and the incident and reflected light were divided into 20 fluxes to ensure accurate results. To perform non-linear least-squares fitting, the Levenberg-Marquardt (LM) algorithm (Appendix B) was adopted on the GPU; the maximum number of iterations for the LM algorithm was limited to 200. A large batch of HSI reflectance spectra in the range of 430-750 nm with a step of 5 nm was fitted at once, enabling a rapid solution of the inverse scattering problem. The total number of fitting parameters was 11; however, only 6 were considered free variables: (a) volume fraction of melanin, \(f_{m}\), (b) volume fraction of deoxyhemoglobin, \(f_{Hb}\), (c) volume fraction of oxyhemoglobin, \(f_{HbO2}\), (d) scattering factor \(a\), where \(a=\mu_{s}\) (\(\lambda\) = 500 nm), (e) scattering power, \(b\), and (f) fraction of Rayleigh scattering, \(f_{Ray}\). Among these free parameters, (a) and (d-f) were fitted for the epidermis, and (b-f) were fitted for the dermis. Several fixed parameters were also defined: (a) volume fraction of bilirubin, \(f_{turbb}\), was set to 0.01, (b) volume fraction of reduced cytochrome oxidase, \(f_{COO2}\), was set to 0.01, (d) epidermis thickness, \(d_{epi}\), was fixed at 0.2 mm, and (e) dermis thickness, \(d_{d\sigma}\), was set to 1 cm. While the LM algorithm allowed the fitting of all parameters, the above parameters were fixed to prevent crosstalk between different sets of parameters and, therefore, improve the robustness of the inverse problem-solving process.  
To test the reliability and robustness of the GPU LM algorithm, a healthy participant's forearm was imaged with the HSI-integrated imaging system [22] before, during, and after applying a blood pressure cuff. Prior to cuff application, a steady-state image was acquired. The cuff was then inflated to about 200 mmHg to obstruct the blood flow completely, and the image was recorded three minutes after the initial inflation. Subsequently, the cuff was deflated, and one minute later, another image was captured. Hyperspectral images were acquired and processed as described in detail in our previous publications [22; 23; 24].