esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Symmetrical Exercises in Scoliosis by Using Thermal Scanning
## 2 Materials and Methods
### Measurements with the Thermal Imaging Camera  
Four scans with the thermal imaging camera were performed for all subjects. At each scan the subject was positioned standing and the image capture was performed only in the frontal plane from the posterior. The FLIR B200 camera was used in the experiment The acquisitions were made respecting the examination conditions imposed by the European Thermography Association [16]. All assessments were performed by the same person under the same conditions: the temperature inside the room was 23 \(\pm\) 1 \({}^{\circ}\)C and the imagine capture was taken from 1.5 m away from the investigated subject. The main specifications of the camera, acquired by Polytechnic University of Timisoara in 2012, are given in Table 1[17].  
\begin{table}
\begin{tabular}{l l} \hline Field of View (FOV)/Close Focus Limit & 25\({}^{\circ}\)\(\times\) 19\({}^{\circ}\)/0.4 m (1.31 ft.) \\ \hline Detector Type & Focal Plane Array (FPA) microbolometer \\ \hline IR resolution & 200 \(\times\) 150 \\ \hline Spectral range & 7.5 to 13 \(\upmu\)m \\ \hline Digital zoom and pan/focus & 1x \(-\) 2x continuous/auto and manual focus \\ \hline Display & Built-in touch-screen LCD display, 3.5 in. \\ \hline Video lamp & 1000 cd \\ \hline Visible light camera resolution & 1280 \(\times\) 1024 m (1.3 megapixels) \\ \hline Object temperature range & \(-\)20 \({}^{\circ}\)C to +120 \({}^{\circ}\)C (\(-\) 4\({}^{\circ}\)F to +248 \({}^{\circ}\)F), optional up to +350 \({}^{\circ}\)C \\ \hline Accuracy & \(\pm\)2 \({}^{\circ}\)C (\(\pm\)3.6 \({}^{\circ}\)F) or \(\pm\)2\% of reading \\ \hline Measurement modes & 5 Spot meters, 5 Box areas, Isotherm, Auto hot/cold spot \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the thermographic camera.  
The images were taken in four sequences: before the exercises, one minute after exercises 1, 2 and 3. The height of the camera was adjusted to the patient's height, so that the back of the patient to be captured in the field of view.  
Thermal imaging proved to be appropriate for complementary investigation, as it is non-invasive and painless for the patient. The images can be taken anywhere, as the apparatus is small, mobile and light. It is easily handled by the therapist and provides instant results.  
The information provided by thermal image are qualitative, but quantification would not bring substantial extra information. The thermal image is unique in regard with its intuitive character. No other imaging procedure provides such an easy-to-read result. The therapist can take images at the beginning of the session and during the exercise program and acquire valuable information regarding the efficiency of the exercise.
## 5 Discussion  
Our architecture is inspired by the design of the helium integration network [100], where IoT devices can transmit sensor data to remote application servers or users. We modified the design of the architecture in a multi-layered manner to achieve more control over the network. Our architecture represents a true hotspot coverage network in which many hotspots can be placed within the coverage area of each hotspot. In this way, IoT devices can transmit messages on a hop-by-hop basis. We used the dew computing paradigm to minimize the Internet dependency and improve accessibility for the end devices. The hotspots can be integrated with dew servers to enhance their ability to act as one of three types of hotspots, such as: (i) full-fledged hotspot miners, (ii) light-weight hotspot miners, and (iii) data-only hotspots. A dew-enabled hotspot can earn crypto tokens, based on its specified activities.  
The implicit architecture enables a new type of incentive for hotspot owners to set up hotspots, according to their needs. Dew computing leverages the synchronization aspect, which does not require a direct Internet connection. Dew computing hotspots can communicate with each other to create a distributed ad hoc network that operates independently of the Internet. This provides high reliability and availability for the IoT devices that want to transmit sensor data to remote locations. IoT devices do not need to be directly connected to the Internet, which can dramatically reduce the costs of the network and the traffic on the cellular network. Our architecture provides two types of hotspot technologies: (i) Wi-Fi, and (ii) LoraWAN. Wi-Fi-enabled dew-based hotspot miners can be used when high data speed is required and there is an Internet connection between the hotspot network layer and the remote network service providers or the end users. LoraWAN can be used when a low data rate is required and there may not be an Internet connection. The Things network can be added as an exchange. Various APIs can be integrated into the LoraWAN-enabled network platforms to forward the IoT sensor data to the remote network servers and application processes.
###### Abstract  
Background: Pelvic floor muscle (PFM) dysfunctions, such as urinary incontinence (UI), significantly impact women's quality of life. The abdominal hypoppressive technique (AHT), a breathing and posture-based intervention, has emerged as a promising approach to improve PFM function. Despite growing evidence on its efficacy, limited research explores its effect on PFM contractility. Objective: This study aimed to evaluate the effects of an 8-week AHT program on pelvic floor muscle (PFM) strength in adult women with no prior experience with AHT. Methods: A randomized controlled trial was conducted with 42 females (mean age 43 \(\pm\) 9 years) assigned to an experimental group (EG, n = 21) or a control group (CG, n = 21). The EG completed 24 AHT sessions (30 min/session, 3 sessions/week) over 8 weeks. PFM strength, measured via maximum voluntary contraction (MVC), was assessed pre- and post-intervention using the Phenix Biofeedback Kit. Secondary outcomes included load absorption and muscle efficiency. Muscle efficiency and load absorption were also registered. Results: After the 8-week intervention, significant improvements were observed in the EG for maximal voluntary contraction (MVC1 (from 672.2 \(\pm\) 344.1 g/cm\({}^{2}\) to 890.3 \(\pm\) 435.8 g/cm\({}^{2}\), \(p\) = 0.002) and load absorption (from 83.9 \(\pm\) 36.8 to 103.1 \(\pm\) 37.3, \(p\) = 0.001), with no significant changes in the CG. Between-group differences for MVC1 (\(p\) = 0.001), MVC2 (\(p\) = 0.016), and load absorption (\(p\) = 0.008) were statistically significant. High adherence (96%) and no adverse events were recorded. Conclusions: An 8-week AHT program significantly improved PFM strength, load absorption, and muscle efficiency in women from the present study comparing with the CG. AHT could be considered a safe, non-invasive, and effective intervention for pelvic floor rehabilitation, with potential benefits for addressing UI and enhancing pelvic floor functionality. Future research should focus on long-term outcomes and comparisons with alternative therapies.  
abdominal hypoppressive technique; pelvic floor muscle; maximum voluntary contraction; randomized controlled trial; muscle strength; urinary incontinence
# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 6 Conclusions and Future Work  
This paper focused on addressing the room classification problem for social robots. The CNN deep learning approach was adopted for this purpose because of its superiority in the areas of object detection and classification. Several CNN architectures were examined by fine-tuning them on five room classifications of the Places dataset, in order to find out the best model for real life experiments. It was found that VGG16 is the best adopted model, with 93.29% of validation accuracy after cleaning the dataset by excluding all mislabeled images. In addition, we proposed and examined a combination of CNN with ECOC, a multi-binary classifier approach, in order to address the error in practical prediction. The validation accuracy reached 98.5% in one of the binary classifiers and 95.37% in the average of all binary classifiers. The CNN and the combination model of CNN and ECOC in both forms, i.e., CNN-ECOC and CNN-ECOC-REG, were evaluated practically on a NAO humanoid robot. The results show the superiority of the combination model over the regular CNN.  
There are many challenges that should be considered for future work. First, the real time experiments for domestic robots need their own dataset which is compatible with most social robots' heights. For example, the images captured by NAO robots are almost at the level of 0.5 m from the floor. This implies that the real time prediction will be negatively affected if the model is trained using the existing datasets. Second, a final prediction based on one image is practically not sufficient for social robots, as the captured images depend on the angle of the view or other image factors, e.g., resolution or light. Therefore, combining many images of the same room or many frames from a video are important for getting the right decision. Third, corridors in houses are an important class that should be added for future work. Lastly, detecting and locating the door through the same CNN architecture is significant for the purpose of indoor navigation.  
[PERSON] is a PhD candidate undertaking his research under the supervision of A.B.R.  
This research received no external funding.  
The first author acknowledges the financial support of Umm Al-Qura University in Saudi Arabia that represented by the Saudi Arabian Cultural Bureau in Canada. This research was enabled in part by support provided by WestGrid ([[https://www.westgrid.ca/](https://www.westgrid.ca/)]([https://www.westgrid.ca/](https://www.westgrid.ca/))) and Compute Canada ([[https://www.computecanada.ca](https://www.computecanada.ca)]([https://www.computecanada.ca](https://www.computecanada.ca))).  
The authors declare no conflict of interest.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
## 3 Proposed Scheme
### System Initialization Phase  
In the remote server node initial phase, the remote server node \(S\), which provides telemedicine services and is certified by healthcare certification authority, sets up parameters by performing following steps.  
Step 1: The healthcare certification authority issues a certificate \(\mathit{Cert}_{HCA\to S}\) to remote server node \(S\) which provides telemedicine services and is certified by healthcare certification authority.  
Step 2: The remote server node \(S\) generates a secret value \(s_{0}\), a big prime \(p\), and random number \(x\in(-\infty,\ +\infty)\) and computes \(Q_{0}=T_{s_{0}}(x)\) mod \(p\).  
Step 3: The remote server node \(S\) choses a symmetric encryption algorithm \(E_{k}(.)\), a symmetric decryption algorithm \(D_{k}(.)\), collision-resistance one-way hash functions \((H_{1}(.),\ H_{2}(.),\ H_{3}(.))\), and a collision-resistance secure one-way chaotic hash function \(h_{k}\) (.).  
Step 4: The remote server node \(S\) outputs public parameters \(\{Q_{0},\ p,\ x,\ H_{1}(.),\ H_{2}(.),\ H_{3}(.),\ h_{k}(.),\ E_{k}(.),\ D_{k}(.)\}\) and private parameters \(s_{0}\).  
Step 5: The gateway node \(GW_{i}\) generates two large random primes \((p_{i},q_{i})\), \(N_{i}\), and \(q_{i}\) as follows. Then, the gateway node \(GW_{i}\) selects a random integer \(e_{i}\), where \(1<e_{i}<q_{i}\) and gcd \((e_{i},\ q_{i})=1\), and makes it public. After that, the gateway node \(GW_{i}\) computes \(d_{i}\), where \(1<d_{i}<\varphi_{i}\) and \(e_{i}d_{i}\equiv 1\) (mod \(\varphi_{t}\)) and keeps it secretly.
## 2 Preliminaries
### Lifting Wavelet Transform  
In order to generate bi-orthogonal wavelets, a novel method known as the lifting wavelet transform (LWT) was developed. A flaw of the conventional discrete wavelet transform resulted in the development of LWT. The direct evaluation in the integer domain is used in this approach, which greatly simplifies the solution to the reversibility problem. Since LWT is superior to DWT in time and space, it is widely employed in the image-processing industry. If the lifting process is changed, this technique can produce wavelets from a second generation. Because it uses an in-place realization technique for the wavelet transform, the lifting wavelets technique can significantly reduce the processing time and  
Figure 1: Flowchart: Particle Swarm Optimization for Watermarking Scheme.  
memory requirements [46]. It outperforms DWT in terms of time and space; as a result, it may find extensive use in image processing [47]. The signal is split into its component parts across three stages during the LWT process: split, predict, and update, as shown below.  
* **Split**: Separate, non-overlapping sets of odd and even samples of the signal \(\Phi(n)\), calculated using Equation (1). \[\Phi_{e}=\Phi(2n)\text{ {and} }\Phi_{o}=\Phi(2n+1)\] (1)
* **Predict**: By abstracting the difference, the split samples can be utilised to forecast each other if they are associated. The values predicted can be calculated using Equation (2). \[\Psi(n)=\Phi_{o}(n)-\text{Y}[\Phi_{e}(n)]\] (2) where \(\text{Y}[\Phi_{e}(n)]\) and \(\Psi(n)\) are the predict operator and high-frequency components, respectively, which are used to describe the difference between the original sample value and the anticipated value [48].
* **Update**: After updating the even samples \(\Phi_{e}(n)\) with the update operator \(Updt(n)\), the low-frequency component \(lfc(n)\) is represented by the abstract difference \(\Psi(n)\), which is a rough approximation of the original signal \(\Phi(n)\) defined in the following Equation (3) [48]. \[lfc(n)=\Phi_{e}(n)+Updt(G(n))\] (3)
## 3 Results and Discussion
### THz Spectra of Lard  
Figure 4 shows experimental data of the ATR spectra measured from different lard thicknesses \(d\) with a back-reflecting Au-mirror on the top of the rendered lard. Most of the synchrotron radiation was in the spectral window of 20-40 cm\({}^{-1}\) (this window includes CSR, which was not used in this particular experiment). This region provides possibility to determine lard absorption with the highest signal-to-noise ratio. This spectral window around 1 THz is shown in Figure 4b; for sake of comparison normalisation to transmission of ATR in air is shown as well. Our method uses a top back-reflecting Au mirror to reflect the scattered synchrotron radiation back in the detection direction, and spacers to set the thickness of lard sample \(d\). It is noteworthy that the spectral window of 0.5-1.5 THz has many strongly absorbing narrow lines in the vibrational-rotational manifold of molecular water spectrum [28]. This would require samples to be measured in vacuum and the ATR modality of transmission measurement is critically important.  
Figure 3: Simulation of ATR reflectance (**a**) \(R_{s}\) and \(R_{p}\) vs. absorbance coefficient and (**b**) reflectance vs. frequency THz according to Equations (2) and (3). The refractive indices are modeled by \(n_{1}=2.38\) (diamond) and lard by \(n_{2}=1.48\) at the sampling frequency of 0.9 THz (**a**). The angle of incidence \(\mathbf{45^{\circ}}\).
## 3 Data Augmentation and Model Design
### Integral Structure of the Algorithm  
The specific implementation of the algorithm proposed in this paper includes three main stages. First, design a dual-branch data augmentation framework to extend and enhance the standard images in the pre-processing data stage. Second, train the standard and enhanced datasets using the i_ResNet50 network with the modified attention mechanism in the model training stage. Last, construct a multi-branch loss function to decide the authenticity of the input images in the comprehensive decision stage. As can be seen in Figure 2, it shows the overall structure of the proposed algorithm.  
We propose a dual-branch augmentation framework for robust deepfake detection. The first branch applies the Augmix algorithm for randomized combination, while the second leverages our novel Augmax algorithm to generate adversarial examples. We modify ResNet50 by integrating the proposed MSE attention modules, replacing 3 \(\times\) 3 convolutions to enhance multi-scale feature learning on forgeries. The original and augmented images are fed to the improved i_ResNet50 models for parallel training with shared parameters. Their outputs are combined in the integrated decision stage. We use cross-entropy loss on original data as the raw loss. Critically, we introduce a Jensen-Shannon divergence consistency loss between original and augmented outputs to capture correlations. Their sum provides the total loss. For stable large-scale learning, we use AdamW for optimiza  
Figure 2: The overall architecture of the proposed deepfake detection algorithm based on dual-branch data augmentation framework and the modified attention mechanism.  
tion, which scales down gradients and provides automatic weight decay. To accelerate convergence, we apply cosine annealing to dynamically modulate the learning rate during training. We believe this adversarial data augmentation approach coupled with attention mechanisms will significantly enhance deepfake detection robustness.
# Structural Design and Optimization of Herringbone Grooved Journal Bearings Considering Turbulent
## 3 Results
### Oil Film Thickness and Pressure Distribution of Different Structures  
Figure 13 shows the oil film thickness diagram and pressure distribution diagram of the constant-width HGJBs and the newly designed convergent HGJBs calculated using the oblique coordinate system when the eccentricity is 0.3 and 0.5, where Figure 13a,b,e,f are the calculation results of the constant-width HGJBs, and Figure 13c,d,g,h are the calculation results of the convergent HGJBs.  
Figure 13: _Cont._  
Figure 13 shows that due to the presence of herringbone grooves, the oil film of HGJBs is distributed in steps, which can be divided into groove areas and ridge areas along the circumferential direction. Assuming that the herringbone groove is set on the shaft and the corresponding bearing bush is smooth, the shaft rotates at a certain speed and pumps the liquid on one side of the bearing. This phenomenon is called the pumping effect. Therefore, the pressure distribution diagram shows that the pressure of the HGJBs is distributed in a zigzag shape in the circumferential direction, and each groove will have a pressure peak. The pumping effect formed by the unique structure of the herringbone groove itself makes the pressure in the groove greater at the center of the herringbone groove, and the peak of the oil film pressure appears at the top of the herringbone groove. Since the herringbone pressure belt is encircled on the journal, the bearing can load in all directions, thereby improving the vibration resistance and stability of the bearing.  
The groove width of the constant-width HGJBs is equal everywhere, while the groove structure of the convergent HGJBs is convergent. The difference between the two can be well seen through the oil film thickness distribution diagram. Under small eccentricity, there is little difference in oil film pressure between the two. Under large eccentricity, there is a big difference in oil film pressure between the two. The oil film pressure of the convergent HGJBs is greater than that of the constant-width HGJBs.
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 3 MPCR-Net
### Tmpe-Net
#### 3.3.2 Network Architecture  
As shown in Figure 4, TMPE-Net mainly includes a point cloud feature perceptron, pooling layer, and transformation parameter perceptron. The architecture of the point cloud feature perceptron is the same as that of TPCC-Net. The architecture of transformation parameter perceptron is also similar to that of TPCC-Net, which consists of three convolutional layers of sizes 2048-1024, 1024-512, and 512-256, and two fully connected layers of sizes 256-128 and 128-7. Its main function is to estimate \(R\) and \(t\).  
Figure 3: The estimating process of the correspondence points.  
Suppose that the input partial point cloud of the \(j\)-th iteration is \(([S_{i}])_{j}\), and \(G_{j}\) is the corresponding estimated rigid body transformation matrix for \(([S_{i}])_{j}\). Use \(G_{j}\) to perform a rigid body transformation on \(([S_{i}])_{j}\) to form a new partial point cloud \(([S_{i}])_{j+1}\), and use \(\odot\) to represent the rigid body transformation operation.  
\[\big{(}[S_{i}]\big{)}_{j+1}=G_{j}\odot([S_{i}])_{j} \tag{10}\]  
Input \(([S_{i}])_{j+1}\) to TMPE-Net for the \((j+1)\)-th iteration and output the rigid body transformation matrix \(G_{j+1}\). Then, the difference \(\Delta G\) between the results of two consecutive iterations is calculated.  
\[\Delta G=\|\bar{G}_{j}\big{(}\bar{G}_{j+1}\big{)}^{-1}-I\| \tag{11}\]  
In Equation (11), \(\bar{G}_{j}\in\mathbb{R}^{4\times 4}\) and \(\bar{G}_{j+1}\in\mathbb{R}^{4\times 4}\) are the exponential mapping of \(G_{j}\) and \(G_{j+1}\) respectively, which refer to the rigid body transformation matrix of two consecutive iterations. \(\|\cdot\|\) represents the sum of squares of all elements of the matrix.  
Set the minimum rigid body transformation threshold \(H_{G}\) and the maximum number of iterations \(H_{N}\), and terminate the iterative training when \(\Delta G\leq H_{G}\) or the current iteration number \(N\geq H_{N}\). We assume that TMPE-Net performs \(m\) iteration calculations according to these conditions. Combine the rigid body transformation matrices from each iteration to obtain the final trained rigid body transformation matrix \(G^{\prime}\).  
If the total number of iterations is \(m\), the final estimate \(G^{\prime}\) can be computed during the iterative loop:  
\[G^{\prime}=G_{1}\otimes G_{2}\otimes\ldots\otimes G_{m}=\{R_{1}\times R_{2} \times,\ldots\times R_{n},t_{1}+t_{2}+,\ldots+t_{m}\} \tag{12}\]  
In Equation (12), \(\otimes\) represents the combined operation of all the iterated rigid body transformation matrices. The iterative estimation process of the rigid body transformation matrix is shown in Figure 5.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 5 Model of Injection
As shown in the table, networks such as the school's library or the school's department are more represented by the LBIM (have lower divergence from it), whereas the RAC environment is close to the RIM. For some network environments such as the hotel, depending on the interval duration, the network traffic could be simulated by either injection models. Therefore, both types of traffic injection models exhibited certain network traffic behavior that exists in real-world networks. Thus, we evaluate our new broadcast algorithm for both traffic injection models.