esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
## 2 Materials and Methods
### Temporal Convolutional Network  
Recurrent neural networks and their variants, such as LSTM and GRU, are structures that have demonstrated superior capabilities of capturing dynamic characteristics compared to convolutional models when dealing with time-series data. However, serial computing during forward propagation requires excessive memory to store intermediate results for each time step, which leads to more inefficient training.  
TCN is a time-series modeling method based on convolutional operations that can work on entire sequences in parallel, enhancing the utilization of computing resources. Compared to LSTM, the architecture of TCN is simpler and there are fewer parameters, which further reduces the risk of overfitting [29].  
TCN combines the causal convolution and dilated convolution to expand the receptive field of the filters without losing any input information. Additionally, layers are stackedas the residual structure to eliminate the instability that arises from the increase in model depth. Figure 3 displays the TCN architecture.  
Causal convolution is a unidirectional structure, which implies that the outcome of \(d\) layer at \(t\) moment is only related to the factors before moment \(t-1\) in layer \(d-1\). Given an input sequence \(X=(x_{1},x_{2},x_{3},\ldots,x_{T})\) and a filter \(F=(f_{1},f_{2},f_{3},\ldots,f_{d})\), the convolution operation at \(x_{s}\) can be calculated via Equation (6) as follows [30]:  
\[\left(F\times X\right)_{(s)}=\sum_{d=1}^{D}f_{d}x_{s-D+d} \tag{6}\]  
Instead of utilizing pooling to increase the receptive field like CNN, TCN allows interval sampling of inputs. A standard convolutional layer can adjust the number of kernel intervals by a hyperparameter denoted dilation rate \(r\) to control the size of the convolution window, as shown in Equation (7).  
\[m=(k-1)\times r+1 \tag{7}\]  
where \(k\) is the filter size. Considering the dilated convolution injects \(m-1\) empty units into the one-dimensional convolutional layer, it is necessary to make padding with the same number of zeros at the end of each layer.  
As presented in Figure 3, each residual block in TCN consists of two dilated causal convolutional layers and ReLU nonlinear mapping layers. Moreover, the weight normalization and dropout layer are inserted after them to regularize the network, avoiding the gradient vanishing or explosion problems. To prevent the performance of TCN from degrading with an increasing number of layers, the input is directly added to the extracted features from the second convolutional layer. Since the two tensor shapes can be different, a common convolutional layer with a kernel size of \(1\times 1\) is applied for shape regularization before the residual summation.
## 3 Proposed Method
### Image Fusion
#### 3.1.2 Mk-Mmdb  
The most crucial aspect of transfer learning domain adaptation is reducing the distribution discrepancy between the source domain and the target domain. MMD is a commonly used metric for measuring the distance between two probability distributions in regenerative kernel Hilbert space (RKHS) [25]. The equation for MMD is defined as follows:  
\[L_{MMD}(P_{s},P_{t})=||\mathbb{E}_{P_{s}}[\phi(x^{s})]-\mathbb{E}_{P_{t}}[\phi (x^{s})]||_{\mathcal{H}}^{2} \tag{12}\]  
where \(\phi(\cdot)\) represents the mapping function.  
However, the MMD method is limited by the use of a single kernel function, which often performs poorly when the distribution differences between the two domains are complex and diverse. MK-MMD improves upon this by using multiple kernel functions to compute a combined distribution discrepancy, taking into account the various relationships between features. The specific kernel defined by \(m\) kernel functions can be expressed as follows:  
\[K\overset{\Delta}{=}\{k=\sum_{u=1}^{m}\beta_{u}k_{u}:\sum_{u=1}^{m}\beta_{u}=1,\beta_{u}\geq 0,\forall u\} \tag{13}\]  
where \(\beta_{u}\) are the weight parameters for the different kernels. The MK-MMD loss can be expressed as follows:  
\[L_{MK-MMD}=\sum_{u=1}^{m}\beta_{u}L_{u} \tag{14}\]  
where \(L_{u}\) denotes the MMD loss, computed using the kernel function \(k_{u}\). Thus, the total MK-MMD loss function can be expressed as follows:  
\[L=L_{cel}+\lambda_{MK-MMD}L_{MK-MMD}(D_{s},D_{s}) \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Layers** & **Out\_Channels** & **Kernel\_Size** & **Padding** \\ \hline Conv1 & Conv2d & 64 & 7 & 3 \\ Maxpool & MaxPool2d & 64 & 3 & 1 \\ H1 & Conv2d & 64 & 3 & 1 \\  & Conv2d & 64 & 3 & 1 \\  & Conv2d & 64 & 3 & 1 \\ H2 & Conv2d & 128 & 3 & 1 \\  & Conv2d & 128 & 1 & 1 \\  & Conv2d & 128 & 3 & 1 \\  & Conv2d & 128 & 3 & 1 \\ H3 & Conv2d & 256 & 3 & 1 \\  & Conv2d & 256 & 3 & 1 \\  & Conv2d & 256 & 1 & 1 \\  & Conv2d & 256 & 3 & 1 \\  & Conv2d & 256 & 3 & 1 \\ Avgpool & AdaptiveAvgPool2d & 1 & / & / \\ Fc & KABlock & / & / & / \\ Output & KABlock & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: KAResNet parameters.  
where \(L_{cell}\) represents the classification loss, and \(\lambda_{MK-MMD}\) is a weight parameter.
## 3 Results and Discussion
### Bulk Density  
The impact of superplasticizers on the bulk density of OPC and LC\({}^{3}\) systems, cured for 28 days in water and dried at 50 \({}^{\circ}\)C before testing, is illustrated in Figure 7. When CS was introduced to the mixtures, the specimen density increased proportionally with its percentage dosage for both studied systems, as anticipated (Figure 7a). These results align with the reported range of bulk density (2200-2400 kg/m\({}^{3}\)) by [PERSON] et al. [60].  
In contrast, the bulk density data for OPC and LC\({}^{3}\) decreased with the addition of the AVM plasticizer and an increase in its dosage (Figure 7b). However, at 2.5 wt.% AVM, both OPC and LC\({}^{3}\) systems exhibited a density range typical for conventional structural concrete. The results for CS dosage show that a 2 wt.% dosage increased the density of OPC and LC\({}^{3}\) specimens by 3% and 2%, respectively, compared to the control at 0 wt.% dosage. Conversely, a 2.5 wt.% AVM dosage in OPC reduced the density by 1%, while in LC\({}^{3}\) it remained unchanged.  
However, both systems recorded the lowest value of density at 10 wt.% AVM dosage. These results can be attributed to the nature and properties of concrete mixture constituents, such as the specific gravity of cement, limestone filler, CS, AVM, aggregates, and water-powder ratio, as discussed in more detail in [61].
## 2 Materials and Methods
### Control System
These parameters are considered in the safety procedures that limit or decrease the controller's output to prevent violation of any safety requirements. Since the device should be positioned flat on the table during operation and the motion is executed only in the sagittal plane, the effect of gravity on the device is known and can therefore be compensated based on the joints' positions. Figure 3: Multithreaded approach to robot control and embedded hardware communication. This compensation is performed in order to minimise the steady-state error. The motion reference is determined according to the prechosen parameters, while the proportional and derivative constants were tuned for critically damped behaviour. In the assistive mode, the device facilitates motion in parts of the movement, which the patients cannot yet perform themselves--only assisting the motion when needed. The device acts as a mass-damper-spring system in this mode, allowing the patient to deflect the device from the reference position by implementing an impedance control approach. Figure 5 depicts the block diagram of the assistive mode. It differs from the passive mode mainly in substituting the PD regulator for an impedance control block. This block calculates the desired force based on the current position, velocity, acceleration, and other high-level parameters--i.e., the level of assistance and the parts of movement where they are applicable. In the active mode, where the patient is performing the movement through the set ROM without any active assistance from the device, the latter acts only as a measuring tool, gathering the signals from the incremental encoders, installed at motor axes, and force sensors, installed at the distal finger rest. Direct force control is implemented in the resistive mode, where the device generates controlled resistance, opposing the patient's motion. Figure 6 depicts the block diagram of the resistive mode. It is again similar to the passive mode, with the difference being that the force, instead of position, is regulated. Force feedback is provided by the strain gauges installed on the distal finger rest, while the reference force is determined by the position of the wrist joint, gradually decreasing in extension. Figure 4: A block diagram for the passive mode of operation with all the used hardware and signals. Figure 5: A block diagram for the assistive mode of operation with all the used hardware and signals. In all training modes, the data from the encoders and the interaction forces between the patient's hand and the distal finger rest can be used for therapy progress evaluation. Even though this is still in the development stage, the high-level control already encompasses a human-controlled feed-forward position control and a passive training modality that guides the patient's hand and wrist through cyclical flexion and extension every five seconds.
_applied_  
_sciences_  
_Article_  
**An Analytical Model to Predict Foot Sole Temperature:**  
**Implications to Insole Design for Physical Activity in Sport**  
**and Exercise**  
**[PERSON] 1,*(c) and [PERSON] 2(c)**  
Footnote 25: https://Cooling feet is important for two reasons: first, thermal comfort is related mainly to skin temperature [13], and for feet, this comfort depends more on skin temperature rather than moisture [14]. The second and even more important reason, is that excess foot sole temperature is associated with diabetic foot ulcers [15; 16; 17].  
Few experimental methods to measure the foot temperature and footwear thermal insulation assessment exist [2; 3; 5; 18; 19]. To assess the footwear thermal insulation, a plastic bag filled with hot water is usually placed inside the shoe to occupy the shoe space perfectly, and then, the temperatures of the shoe's different parts are measured during the time [5; 20]. Some other experiments involve measuring the temperature in situ, by thermocouples during walking or running [2; 3; 18; 21; 22; 23]. A comprehensive study in this field was conducted as a series of experiments by [PERSON] et al. [2; 3; 23; 24].  
Despite the relatively developed models to assess the mechanical behavior of footwear [25; 26; 27], there is currently no accepted model to justify the experimental results and predict the foot sole temperature. Recently, [PERSON] et al. [28] proposed an analytical method to predict the foot sole temperature during running and jogging at different speeds. In their model, they accounted for both the thermal and mechanical properties of the shoe sole. The model was then validated against experimental data and found to have a very good agreement between the measured foot sole temperatures and the predicted values. That model enables the designer to compare, for example, the softness of PVA12 and the superiority of the thermal properties of EVA08. The model has an analytical solution that can be used to accurately predict the arch area temperature for the initial duration of activity as up to 20 min during slow walking at 3 km/h and for up to 15 min for running at 9 km/h. However, after these times, the model over-predicts the foot sole temperature. [PERSON] et al. claimed that this may be due to the fact that the effect of sweating was not considered in their study [28]. Since the foot is capsulated inside the shoe, it is not possible to exchange heat to ambient environment by radiation or convection, efficiently. Therefore, sweating is the only effective temperature-controlling mechanism which relies on latent heat. While sweating is an effective factor in cooling feet, further care is required in modeling to assess the effect of sweating on the foot temperature.  
The main aim of this study was to develop a model which can predict the temperature at the ball area and arch area in the first instance and to assess the accuracy of the model in predicting the foot sole temperature.
# Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
## 9 Deployability on Micro-Controllers  
Deployability on tiny MCU and performance characterization were studied in terms of the memory occupied by the tiny neural networks and the number of multiply-accumulated operations (MACC) required by their execution. The evaluation run on two MCUs typically employed in the Automotive and Internet of Things (IoT) solutions. The SR5E1-EVBE3000D [78] MCU is dedicated to automotive applications and deployed in electric vehicles. It is equipped with a dual split-lock ARM Cortex-M7 process, an internal RAM memory of 488 KiB and an internal FLASH memory of 2 MB, with a clock frequency of 300 MHz. STM32H5731-DK [79] MCU, instead, is for IoT applications, equipped with an ARM Cortex-M7 processor at 280 MHz, an internal RAM memory of 1184 KiB and an external RAM memory of 16 MB, an internal FLASH memory of 2 MB and an external FLASH memory of 64 MB, and relies on 128 Mbits SDRAM. The STM32 Cube.AI Developer Cloud tool eased the deployment of the pre-trained networks on these two MCUs. It automatically converted pre-trained ANN models into optimized ANSI C code for these low-power edge devices. The results achieved by deploying the fp32 neural architecture on the two MCUs are shown below. Tables 7 and 8 report the RAM and FLASH memory occupancy. Tables 9 and 10 report the memory, in KiB, occupied by the storage of activation tensors and the weights assigned to the connections between neurons in the layers, respectively. Tables 11 and 12 report the number of operations executed by the MCUs and the inference time in milliseconds. The LMU layer was not supported by the STM32 Cube.AI platform at the time this study was performed. Therefore, it was not included in the deployability analysis and this is left to the future work.  
As per Table 3 and 4, parallel TCNs architecture achieved the best accuracy on both test sets, however it featured the highest MACC and inference time on both MCUs. On the other hand, the dense architecture was the highest in KiB size for storing the activations in RAM on both MCUs. The deployability results of the dense architecture and parallel TCNs were due to the higher number of parameters compared to the other networks. LSTM stateful, having been the model characterised by the lowest number of parameters, was expected to achieve low MACC count and inference time and indeed achieved the lowest, on both SR5E1-EVBE3000 and STM32H5731-DK, FLASH memory footprint.
## 4 Theoretical Derivation
### Verification of the Theoretical Model  
In order to verify the accuracy of the theoretical model, the calculation results of the theoretical model were compared with the Equation calculation results in [PERSON], [PERSON] et al., (2023) [16], field monitoring data, and simulation data generated by the numerical model, as shown in Figure 10. The comparison results are shown in Figure 10. It can be seen from Figure 10 that the simulation results are highly consistent with the measured data, which proves the feasibility of the numerical simulation method. However, due to the limitation of simplified conditions, the influence of the Timoshenko beam theory on the longitudinal deformation of the pipeline is not fully reflected in the numerical simulation, resulting in a small width of the predicted settlement curve.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 5 Our PQC Incorporation and Implementation
### SUCI in Existing 5G  
This section describes the SUCI calculation in existing 5G that builds on Section 2.2.1. In the current standard of 5G AKA, two protection schemes X25519 and secp256r1 exist. Below are the steps for generating the SUCI protection scheme output fields for both the ECDH algorithms. We refer to each relevant field in the SUCI format depicted in Figure 2.  
The CN generates public-private key pairs of the elliptic curve (EC) and shares the public key with the user by integrating it into the USIM. This is the core network public key discussed in Section 2.2.1. Further discussion on each of these steps follows below.  
1. Public-Private Key Generation at UE: During the registration process, the UE generates the EC public-private key pair. The UE's public key is used by the CN to de-conceal the SUPI from the received SUCI while the UE's private key serves as input for the key agreement function in the subsequent step.  
Key Agreement: The input to this function includes the CN's public key stored in the USIM and the UE's private key obtained from step 1, resulting in the generation of a shared key. In contrast to the use of ECDH in other contexts that exchange the public keys via digital networking, our key agreement makes use of the public keys shared during the UE registration, as described in Section 2.2.
3. Encryption Key: The UE then uses a key derivation function on the shared key to generate an encryption key \(k_{enc}\) for the symmetric cipher, an initial counter block (ICB), and a \(k_{mac}\) for the message authentication code (MAC).
4. Symmetric Encryption: The UE uses the encryption key \(k_{enc}\) and the ICB to encrypt the plaintext (SUPI or IMSI) depending on the SUPI type field in the SUCI format. Also, the UE generates the MAC field using \(k_{mac}\). The output ciphertext value becomes the part of the scheme output of the SUCI. The UE then transmits the finalized output of the UE-side encryption process to the CN for registration and identification. This output consists of the concatenation of the UE's public key, ciphertext, and MAC.
5. Decryption: When the CN receives the registration request along with the SUCI, it uses the UE's public key (from step 1) and CN's private key to generate the shared key at the CN side. The shared key is further used to generate a decryption key \(k_{dec}\) that is the same as \(k_{enc}\) to decrypt the SUCI, using a key derivation function.  
In our experimentation, we implement steps 1, 2, and the shared key part of step 5 for our performance analysis of the existing SUCI calculation. Steps 3 and 4 use symmetric cryptography, which is not in our research contribution.
## 2 Methods
### Geometry Model for the Eroded Soil Maximum Transport Distance
#### 2.3.2 Geometry Model for Sediment Maximum Transport Distance  
Figure 7 describes the geometric relationship between the eroded sediment and the slope. We inferred a geometric relation between the maximum transport distance (\(L_{x\text{max}}\)) and area (\(S_{xz}\)).  
Figure 5: Sediment volume calculation by Golden Surfer (at 2 min under 25 mm/h rainfall intensity): (**a**) whole volume (sediment volume + initial slope volume); (**b**) initial slope volume; (**c**) using the whole volume to reduce the initial slope volume.  
Figure 6: Vertical topographic view of eroded sediment (at 5 min under 25 mm/h rainfall intensity).  
The processes are as follows:  
\[S_{xz}=L_{x\mathrm{max}}h/2 \tag{1}\]  
\[h=\mathrm{tan}L_{x}=\mathrm{tan}\beta(L_{x\mathrm{max}}+L_{x}) \tag{2}\]  
\[S_{xz}=L_{x\mathrm{max}}L_{x}\mathrm{tan}\alpha/2 \tag{3}\]  
\[L_{x}=L_{x\mathrm{max}}\mathrm{tan}\beta/(\mathrm{tan}\alpha-\mathrm{tan}\beta) \tag{4}\]  
\[S_{xz}=L_{x\mathrm{max}}\mathrm{tan}\beta\mathrm{tan}\alpha/2(\mathrm{tan} \alpha-\mathrm{tan}\beta), \tag{5}\]  
where \(S_{xz}\) is the lateral area; \(h\) is the height of debris; \(L_{x\mathrm{max}}\) is the maximum transport distance (here, we consider all the \(L_{x\mathrm{max}}\) to be in the \(Y\)-direction in the same time domain); \(L_{x}\) is the length of the sediment shadow minus the \(L_{x\mathrm{max}}\); \(\alpha\) is the angle of the slope; and \(\beta\) is the angle of sediment.  
In Figure 6, we consider the volume element \(\Delta V^{i}\) in the \(Y\)-direction to equate to the \(S_{xz}\) times \(\Delta L_{y}{}^{i}\) element. The whole volume of sediment is shown in Equation (9).  
\[\Delta V^{i}=S_{xz}\Delta L_{y}{}^{i} \tag{6}\]  
\[\sum_{1}^{i}\Delta V^{i}=\sum_{1}^{i}S_{xz}\Delta L_{y}{}^{i}=S_{xz}L_{y} \tag{7}\]  
Putting Equation (5) into Equation (7) provides Equation (8) as follows:  
\[V=S_{xz}L_{y}=L_{x\mathrm{max}}{}^{2}\tan\beta\mathrm{tan}\alpha/2(\mathrm{tan }\alpha-\mathrm{tan}\beta)L_{y} \tag{8}\]  
\[L_{x\mathrm{max}}\mathrm{=}(2V(\mathrm{tan}\alpha-\mathrm{tan}\beta)/\mathrm{ tan}\beta\mathrm{tan}L_{y})^{1/2} \tag{9}\]  
The model boundaries limit the rate of sediment transport in the direction \(L_{y}\). Thus, \(L_{x\mathrm{max}}(t)\) is bigger than \(L_{\mathrm{ymax}}(t)\). Another issue is that, in order to reduce calculation complexity, \(S_{xz}(t)\) and \(\beta(t)\) in the \(L_{y}\) direction are approximated to be the same for each time domain.
## 4 Experimental Verification
### Analysis of Experimental Results and Visualization of Training Classification Process
FFT signal processing combined with the network model proposed in this paper (Propose + FFT) under the three working conditions obtained an average diagnostic accuracy of 96.969%, 97.846%, and 97.904% higher than other models. The standard deviation was also significantly smaller than other models, which reflects the stability of the proposed model. During the experiments, both the correct and loss rates of the model almost stabilized in the training set and the validation set, and the fit was good. The accuracy of the validation set was maintained at about 90%, as shown in Figure 12.  
After the model training is completed, the test set is used to verify the model. The confusion matrix results are shown in Figure 13, and its values of 0, 1, 2, and 3 correspond to the fault type labels in Table 5. It can be seen that the prediction results are almost all correct. The real category 3 label of Working Condition 3 is the mixed fault of the inner and outer race rolling element, and 6% of the samples are predicted to be the inner-race fault of label 0, which is tolerable in the actual bearing fault diagnosis. Figure 11: Comparison of diagnostic results of different models. Figure 12: The trend of accuracy and the loss function of the model training process under three working conditions: **(a)** Working condition I; **(b)** Working condition II; **(c)** Working condition III.