esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Problem of Shrinkage in 3D Printing of Armlet with Wet Circuit Measurement  
Three-dimensional printing using Fused Deposition Modeling (FDM) technology, also known as Fused Filament Fabrication (FFF) or Layer Plastic Deposition (LPD) in the case of the ZORTRAX brand (ZORTRAX, Olsztyn, Poland), poses certain challenges. FDM technology was developed by the American-Israeli company Stratasys (Prairie, MN, USA; Rehovot, Israel) in 1988, and the patent for this technology expired in 2009, allowing other companies to use and further develop it. FDM is the most commonly used 3D printing technology, which involves extruding heated thermoplastic material through a print head.  
One of the issues that can occur during 3D printing, depending on the material used, is material shrinkage. This shrinkage negatively affects the entire process, and typically, the parts where this phenomenon occurs are not suitable for use. The main reason for shrinkage is the cooling of the printed layers, which causes detachment from the print bed and prevents the correct printing of subsequent layers. This results in warped models and deviations from the intended dimensions, particularly in the Z-axis. When this problem arises, it is usually necessary to abort the print. This problem is most commonly encountered with large models printed with ABS. However, using different materials or ABS with additives that minimize or eliminate shrinkage can help mitigate the issue.  
In addition, PVC straps in the form of flat bars were tested during the research. PVC is an interesting and relatively inexpensive material that can be used for constructing enclosures for potential armlets with wet circuit measurement in closed channels. The strap enclosure consists of a PVC flat bar with corner brackets attached to its ends. The purpose of these brackets is to provide the necessary pressure for the armlet with a wet circuit measurement system against the walls of the sewer pipe. Additionally, silicone cones can be used, which are inserted between the corner brackets to increase the stability of the strap attachment in the sewer pipe. Another interesting material for building measurement straps was found to be thin-walled PVC. This solution is recommended, especially for larger sewer pipe diameters where using 3D-printed straps would be too costly. To ensure the seamless flow of both sewage and water within the sewer channel, the use of a relatively slender strap is conventionally favored. Considering the essential structural strength, we advise a minimum thickness of 4 mm for 3D printing applications. In situations where preserving the cross-sectional area integrity of the channel is of utmost importance, opting for an armlet with a wet circuit measurement thickness of up to 7 mm is vital.
## 3 Methodology  
### Frequency Sweep Rheological Tests  
Frequency sweep tests were carried out on bitumen samples to evaluate their viscoelastic properties at temperatures of 15 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. These two temperatures were selected to represent intermediate service conditions commonly experienced by asphalt pavements. The lower temperature, 15 \({}^{\circ}\)C, corresponds to early spring or nighttime conditions in cooler regions, making it suitable for evaluating fatigue cracking behavior. The higher temperature, 25 \({}^{\circ}\)C, reflects typical daytime temperatures in moderate to warm climates. This selection is consistent with prior studies and testing guidelines and provides a reliable framework for comparing the temperature sensitivity of modified and unmodified binders.  
The tests utilized an Anton Paar Rheometer with RheeCompass software (v. 1.35.1394) and were performed under two stress levels: Glover-Rowe (G-R) parameter = 180 kPa and G-R = 600 kPa. These stress levels were selected to simulate moderate and severe traffic loading conditions, respectively. Specifically, 180 kPa represents typical service-level stress experienced under standard vehicle loads, while 600 kPa reflects heavier axle loads encountered in more demanding traffic scenarios. This selection aligns with established practices in LAS testing, including recommendations in NCHRP Report 927 [41], to enable comprehensive evaluation of binder fatigue behavior under varied loading intensities. Sample preparation adhered to standardized rheological protocols, ensuring consistency across all experiments. The primary focus of the rheological tests was on three key aspects: G-R index analysis, the frequency-dependent behavior of storage and loss moduli, and the relationship between complex viscosity and the loss factor (tan \(\delta\)). The tests employed a parallel plate setup to conduct dynamic frequency sweeps, covering a frequency range from 0.01 rad/s to 100 rad/s, providing a thorough evaluation of the material's performance over a broad frequency spectrum.  
All frequency sweep measurements--including storage modulus (G\({}^{\prime}\)), loss modulus (G\({}^{\prime\prime}\)), complex viscosity (\(\eta\)), and phase angle (\(\delta\))--were conducted in accordance with ASTM D7175 [42] using a Dynamic Shear Rheometer (DSR) with parallel plate geometry.
## 2 Materials and Methods
#### 2.1.2 Operating Principle
The beaker has an inner diameter (ID) of 15.43 mm and a height of around 53 mm with a total weight of around 1.7 g. This results in a usable weight (0.8 g oil, 1.9 g water, 1.7 g beaker) of around 5.7 g.  
A challenge is to insert the needle through the oil film so that the tip of the needle is at the end roughly in the middle of the water below the oil film. The needle tip must not be too close to the oil film but also not too close to the bottom of the beaker. Both scenarios would have a negative impact on the measurements. To realise the exact positioning of the needle in the beaker, a traverse system was built at RISE Central Workshop, which enables the exact lifting and lowering of the needle. The traversing system consists mainly of a Bosch Rexroth guide unit and a fine-thread rod with hand knob. One turn corresponds to an axial displacement of 1.0 mm. The weighing scale and the traversing system are accurately aligned with each other by means of guide rails. Depending on the flow rate, different needles with various inner and outer diameters are available for the measurements. During the measurements (filling process) the buoyancy of the needle must be considered. For testing volumetric infusion pumps and volumetric infusion controllers, the IEC/ISO 60601-2-24 [11] standard proposes the use of an 18G needle with a nominal outer diameter (OD) of 1.270 mm and a nominal inner diameter (ID) of 0.838 mm. As the focus is on lower flow rates, a 20G needle with a nominal OD of 0.908 mm and a nominal ID of 0.603 mm was used in most cases shown in the following. In this configuration, any change in the level in the weighing container (beaker) results in a necessary correction compared to water of 0.000 636 mL mm\({}^{-1}\). The corresponding corrected weight value can then be calculated by means of the actual temperature-dependent water density. All tests shown here were carried out at an ambient and water temperature of 23.0 \({}^{\circ}\)C \(\pm\) 0.5 \({}^{\circ}\)C.  
For most measurements, it is important to keep the infusion lines as short as possible to minimise the so-called dead volume. For this reason, the tripod table was built in such a way that the measuring set-up, including the DUT can be placed on the table and the scales can be placed under the table to save space. For a typical measurement set-up, the hoses in the measuring section are 1/16\" OD Polyether ether ketone (PEEK) tubing (with IDs from 0.13 mm to 0.75 mm depending on the DUT and the flow rates) and the hoses in the refill line are usually 1/8\" OD Swagelok Perfluoroalkoxy (PFA) tubing (PFA-T2-030) with an ID of 1.65 mm.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Matrix Performance Result  
In the segmentation, the most popular matrix performance output was the Dice coefficient. The highest Dice value was found for UH segmentation with a gain of 0.94 \(\pm\) 0.02 with a CNN network architecture with smooth muscles, while the highest precision was that of the cross-sectional area of the rectus femoris with a value of 0.93 \(\pm\) 0.02 based on a CNN architecture with skeletal muscles. Furthermore, the accuracy results were the most popular for classification. VGG-16 shows the highest results for gender classification with 95.20% accuracy.
# Evolution towards Smart and Software-Defined Internet of Things
## 2 IoT Classification and Applications
### IoT for Data Analytics  
After the emergence of IoT, big data analytics experienced a boom. Millions of devices are continuously communicating with each other, and a large amount of data are accumulated. The data accumulated in this fashion are quite huge and can be used to perform data analytics. The machine learning and data mining techniques can be used to extract some useful features from the huge data [41]. The data are the fuel for today's digital transformation. Although the management of this huge data is a concern in IoT, the analysis of these data can be used for the healthy benefit of society. Analyzing big data can effectively be used for disaster management [42].
## 3 Experimental Results
### Experimental Setups
#### 3.1.2 Development of the Neural Network in the Study  
In this study, a neural network was constructed for use. The neural network consists of one input layer with 14 neurons, two hidden layers with 128 neurons each and one output layer. The input layer with 14 neurons represents a 14-dimensional state variable received from the environment. The study defines three action spaces as the output of the network: 0: accelerate, 1: decelerate and 2: maintain speed. The values of _ts_, \(q\), _tlsc_ and _de_ in the state variable were obtained with the help of sensors in the traffic light. The 14-dimensional state space provided to the input layer of the network is expressed in Equation (3).  
\[S=[s,t,c,ts,tlsc,d,q,de,a,f,ca] \tag{3}\]  
In Equation (3), \(s\) represents the instantaneous speed of the vehicle, \(t\) denotes the time required for the vehicle to pass through a green light at the traffic signal on its route, \(c\) indicates the current position of the vehicle, _ts_ signifies the status of the traffic light (red, green, yellow), _tlsc_ represents the cycle value of the traffic light, \(d\) denotes the distance traveled by the vehicle up to that time, \(q\) represents the number of vehicles in the queue at the traffic light, _de_ indicates the vehicle density at the traffic light, \(a\) represents the previous action, \(f\) denotes fuel consumption and \(ca\) signifies carbon dioxide emission. The design of the neural network used in the study is illustrated in Table 4.  
The parameters used in designing the neural network model shown in Table 4 were directly employed without modification for the other three algorithms that will be used for performance comparisons in the study. The additional model parameters used in the study are presented in Table 5.  
The parameters shown in Table 5 are from the four methods described in Section 2, and each method utilizes one or more of these parameters.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer Name** & **Number of Units** & **Activation Function** \\ \hline Inputs & 14 & Relu \\ Dense & 128 & Relu \\ Dense & 256 & Relu \\ Dense & 512 & Relu \\ Dense & 256 & Relu \\ Dense & 128 & Relu \\ Dense & 64 & Relu \\ Outputs & Action Space & None \\ Optimizer & - & Adam \\ \hline \end{tabular}
\end{table}
Table 4: Neural network layer parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Value** \\ \hline Epsilon & 1.0 \\ Min\_epsilon & 0.01 \\ Lr (Learning rate) & 0.005 \\ Batch size & 64 \\ Gamma & 0.99 \\ Policy clip & 0.2 \\ Gale laanda & 0.95 \\ Epsilon\_dec & 0.0001 \\ Loss & Mse \\ \hline \end{tabular}
\end{table}
Table 5: Model parameters used in the study.  
For training deep learning neural networks, an average system specification computer was used. The training process was conducted on a computer equipped with an AMD Ryzen 5 processor (AMD, Santa Clara, CA, USA), 32 GB of RAM and a 192-bit graphics card with 12 GB of memory.
# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 3 Datasets and Implementation Details
### Android Deployment  
Neural networks have significantly pushed forward the limits in various applications; however, they frequently require considerable computational resources. To incorporate modern networks into edge devices that have stringent power and compute constraints, it is crucial to minimize the power consumption and latency of neural network inference. Quantizing neural networks stands out as a highly effective method for achieving these reductions, although it may introduce additional noise that could result in decreased accuracy [20].  
NCNN is a high-performance neural network inference computing framework optimized for mobile platforms [21]. This research employed the NCNN framework to quantify the model and construct a real-time expiration date identification and character recognition application utilizing the quantized model. Figure 4 illustrates the quantization procedure.  
The Android implementation is verified on an Oppo Find X5 running with Android version 13 with processor Qualcomm Snapdragon 888, a Realme GT Neo2 running Android 13 with Snapdragon 870, and Redmi 10 Pro running Android 13 with Snapdragon 732G. The smartphones have an application that was developed based on Java Development Kit 21.0.3, NCNN 20241226-android-vulkan, and Android Studio 2024.2.2 Patch 1.  
The expiration date real-time recognition application created in this study is composed of three main components (Figure 5): the video acquisition module, the processing module, and the result display module. The video acquisition module uses the smartphone's camera to capture food packages in real time; the processing module inputs these video streams into the detection model frame by frame while keeping the resolution size of the image fixed at \(480\times 640\), and the detected area is cropped out; then, it preprocesses the cropped image and sends it to the recognition model; after the recognition receives the correct date, the date time parser tries to transform the date format into a unified format; the result display module then shows the predicted bounding boxes and the parsed characters in real time, providing the non-maximum suppression (NMS) threshold bar and the confidence threshold bar to adjust in the expiration date recognition app developed in this research. As shown in Figure 6, the app's overall interface features an image with working unit CPU or GPU of the smartphone, the size of the image, the inference time, frames per second  
Figure 4: Flow chart of NCNN quantization.  
(FPS), and the average FPS. Users can adjust the NMS threshold bar and the confidence threshold bar to change how the detected area would be shown on the interface.
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 2 Methods  
### YOLOv8n Model  
YOLOv8 (You Only Look Once Version 8) [35] is a one-stage, real-time object detector released by Ultralytics in 2023. Its speed, accuracy, and ease of use make it an excellent choice for object detection. YOLOv8 is available in five versions, differentiated by network depth and feature map width: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. Among these, YOLOv8n has the smallest number of parameters, making it suitable for deployment on devices with limited computational power, while YOLOv8x has the largest number of parameters and offers higher detection accuracy. Compared to YOLOv5 and YOLOv7, YOLOv8 enhances both object detection accuracy and processing speed through refined improvements in its network architecture. Specifically, YOLOv8n offers improved accuracy over YOLOv5n while being more efficient in terms of parameter count and computational cost compared to YOLOv7-tiny. Therefore, this paper selects YOLOv8n as the benchmark model to meet the real-time detection requirements on edge devices. Its architecture consists of a backbone, neck, and prediction head, as shown in Figure 1.  
The backbone module mainly extracts rich, shallow feature information from images through convolution operations. The backbone includes a convolution block (Conv), a CSP bottleneck with two convolutions (C2f) modules, and a spatial pyramid pooling (SPPF) module. The Conv block consists of 2D convolution layers (Conv2d), a batch normalization (BN) layer, and a sigmoid linear unit (SiLU). Its primary function is to further extract features and perform downsampling to obtain shallow features at multiple scales. Meanwhile, the residual module facilitates model convergence and mitigates the issue of gradient vanishing. The C2f module integrates the Conv block, split layer, bottleneck block, and concatenation layer. Its multi-branch design enhances the model's feature extraction capabilities while maintaining a lightweight structure. The SPPF module combines the Conv block, pooling layer, and concatenation layer. The use of multiple small pooling layersreduces computational load and fuses feature information at different scales, improving the model's robustness to object scale variations.  
The neck module primarily fuses multi-scale features extracted by the backbone. It includes an upsampling layer, the C2f module, and a concatenation layer. The neck adopts the FAN [36] and PAN [37] structures, enhancing feature map representation through bottom-up and top-down feature aggregation.  
The head module is responsible for final object detection and classification. To improve efficiency, a decoupled head structure is employed, with separate detection and classification heads. This design allows the tasks of detection and classification to be handled independently. By leveraging feature maps at different scales, the model predicts object categories and locations at multiple scales. The decoupled head design enhances both training and inference efficiency, thereby improving the overall performance of YOLOv8n.
## Results and Discussion
### Radical Scavenging Activity  
#### 3.4.1 DPPH Scavenging Activity  
As shown in Figure 7A, three isolated peptides (MMP-4, MMP-7, and MMP-12) strongly scavenge DPPH: in a dose-dependent manner. The EC\({}_{50}\) values of MMP-4, MMP-7, and MMP-12 were 0.39, 0.62, and 0.51 mg/mL, respectively, but their activities were still lower than that of the positive control of GSH at the same concentration. The EC\({}_{50}\) value of MMP-4 was lower than those of most APs from protein hydrolys of red stingray cartilages (VPR: 4.61 mg/mL, IEPH: 1.90 mg/mL, LEEEE: 3.69 mg/mL, and IEEEQ: 4.01 mg/mL) [6], loach (PSYV: 17.0 mg/mL) [35], salmon pectoral fin (TTANIEDRR: 2.50 mg/mL) [36], Spanish mackerel skins (PFGPD: 0.80 mg/mL, PYGAKG: 3.02 mg/mL, and YGPM: 0.72 mg/mL) [37], skipjack tuna bone (GADIVa: 0.57 mg/mL) [23], and miuuy croaker swim bladders (FPYLRH: 0.51 mg/mL; GIEWA: 0.78 mg/mL; YLPYA: 3.63 mg/mL; VPDDD: 2.87 mg/mL) [20]. Therefore, the present result suggested that three isolated peptides (MMP-4, MMP-7, and MMP-12), especially MMP-4 could contribute an electron or hydrogen radical to suppress the DPPH: reaction.  
Figure 6: Mass spectra of three isolated peptides (MMP-4 (**A**), MMP-7 (**B**), and MMP-12 (**C**)) from protein hydrolysate of monkfish (_L. litulon_) muscle.  
Figure 7: DPPH (**A**), HO. (**B**), and O\({}_{2}^{-}\). (**C**) scavenging activities of three isolated peptides (MMP-4, MMP-7, and MMP-12) from protein hydrolysate of monkfish (_L. litulon_) muscle. The data are presented as the mean \(\pm\) SD (\(n=3\)). \({}^{a-d}\) Values with the same letters indicate no significant difference of different samples at the same concentrations (\(p>0.05\)).
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 3 Results
### Simulated Study
#### 3.1.2 Circular Anechoic Cyst in Speckle  
Figure 8 shows the beamformed images of a simulated 8 mm radius cyst using the CPWC and our CPWC + SVD method over a 60 dB display dynamic range. From Figure 8 it can be observed that the proposed beamformer has an improvement over CPWC, which indicates the effectiveness of our coherence-based method for cysts. The window-weighted CPWC also has a better performance in terms of contrast, but the details are obscured due to the low resolution. To compute the mean values in the cyst region and in the background, we considered two circles with diameters of 4 and 6 mm, located at 30 mm in the depth, and 0 and 6 mm in the lateral direction, respectively. Table 3 lists the relative CR and CNR for all beamforming methods. As seen, the performance of the proposed beamformer is significantly better in terms of both CR and CNR than the CPWC performance. Specifically, the proposed beamformer offers the contrast enhancement of 5.6 dB and improvement of CNR of about 12% in comparison to CPWC. CPWC + SVD also outperforms CPWC with Hamming apodization in CR and CNR.
## 3 RCU-Style Non-Blocking Synchronous Algorithms
### Read Log Update (RLU)
#### 3.2.2 Use of RLU APIs  
Since RLU is API compatible with RCU, many applications based on RCU can be replaced with RLU. The main APIs of RLU are as follows.  
* **rlu_reader_lock0**, **rlu_reader_unlock0**: APIs for entering and exiting a RLU section of threads. rlu_reader_lock() reads global clock and stores it as local clock in the thread's metadata. rlu_reader_unlock() performs a commit for an update and log reclamation.
* **rlu_try_lock0**: Acquire exclusive rights to modify an object and make a copy in the log.
* **rlu_assign_pointer0 or rlu_assign_ptr0**: Used by write threads to modify a pointer of a node. For instance, it is used to change the next pointer of node \(A\) to copy version \(B^{\prime}\) in Figure 1(a).
* **rlu_dereference0** or rlu_deref0**: An API used by threads to read shared data. It guarantees a consistent view of shared objects according to local clock of a thread.
* **rlu_free0**: An API to logically delete an object.  
Figure 1(b) shows a thread relationship and the API usages required in the procedure of Figure 1(a).  
All threads in RLU enter the RLU section through rlu_reader_lock(). A thread reads a copy of an object (a version) if the node has a version whose write clock is less than or equal to local clock; otherwise, it reads an original node. In Figure 1(b), the two rlu_deref(B)s performed by R1 and R2 for the first time read node \(B\), that is, the original node \(B\), when entering the RLU section. Thread W acquires the exclusive right to modify node \(B\), creates a new version \(B^{\prime}\) in its log (), and modifies the value. Additionally, global clock + 1 is stored as the update clock of the version B' (denoted as write clock ). This procedure is represented in Figure 1(a)'s  and Figure 1(b)'s try_lock(B) and assign_pointer(B). Then, thread W calls rlu_synchronize() and waits for the threads reading node B to terminate RLU section. This is expressed as Grace Period in Figure 1(b). When the Grace Period ends, thread W overwrites the version \(B^{\prime}\) with node \(B\) (denoted as Figure 1(a)'s  and Write Back in Figure 1(b)). While the thread W is modifying the node, the threads entering the RLU section read the copied node \(B^{\prime}\), which appears in the second RLU section of R1 and R2. In this way, by allowing reads of copies (versions) that exist in the log of other threads, both  
Figure 2: Example of updating a node in a linked list with RLU. The assumptions and notations of Figure 1 are followed in the same way.  
reads and writes can be performed at the same time, but the waiting time for recycling the per-thread log is pointed out as a performance bottleneck.