esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Limitations  
It is noteworthy to mention that the study focuses on the bearing capacity of the footing above square and circular cavities. However, since the results of the square cavities represent a worse scenario, our solution is built based on the results of the square cavities, and thus fewer results of the circular cases were shown. In addition, some researchers confirmed that the shape of the cavity has negligible influence on the bearing capacity characteristic [8; 27]. This study is limited to the cases where the soil strengths, especially, are not as large as in rock-like materials. This means that, in the case of a material strength higher than 100 kPa, the solution may not be sufficient. In addition, the cavity size should be within the range of the parameters used in the present study to provide a realistic solution. This being said, future research can focus on much larger cavity sizes and stronger geomaterials. Other aspects in which clarifications are needed should consider the cases of non-homogenous soil materials (layered ground), foundation load inclinations, and most interestingly an examination of the water table level and drainage conditions  
Figure 13: Illustration of the model of the footing-cavity system by [PERSON] et al. [30].  
as the changes in groundwater levels can affect soil properties and the behavior of the footing-cavity system.  
In light of our comprehensive study on the influence of cavities on the bearing capacity of footings, we propose a set of recommendations for incorporation into construction practices and codes. It is imperative that construction practitioners and regulatory bodies recognize the potential impact of subsurface voids on the stability of foundations. Our findings underscore the need for applying a straightforward formula to assess the stability level when a cavity is discovered. We suggest the integration that the formula be integrated into the use of real case projects so that it can be improved if necessary. Furthermore, we recommend the development of guidelines within construction codes to address the stability concerns associated with cavities, including specific design methodologies and safety factors. Emphasizing the importance of proactive cavity detection and mitigation measures in construction regulations will contribute to enhanced structural stability and, ultimately, safer built environments. Continuous collaboration between geotechnical engineers, construction professionals, and regulatory authorities is crucial to ensuring the effective implementation of these recommendations in construction practices.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Drift Detection Algorithms
#### 6.2.5 Reoccurring Concept Detection  
A unique form of concept drift is that of reoccurring concepts. For example, one can think of a measurement series subject to seasonal variations. In this case, standard methods would forget essential information, which may become necessary again later. Therefore, particular interest is given to detecting reoccurring concepts in data streams. The aim is to keep models which have become unusable in reserve for reuse at a later point in time. Conventional for this scenario is the use of classifier ensembles. For example, the Concept Profiling Framework (CPF) uses a collection of different classification models [167]. A drift detection mechanism triggers the archiving of new labeled instances when a threshold for the classification error is reached. Based on this archived set, all existing classifiers are tested. If no suitable model is found, a new model is trained. Likewise, the similarity of the classifiers is evaluated so that the collection remains diverse, and the new model replaces the most previously similar classifier. In order to prevent the erroneous deletion of a classifier, in the extended version, both models initially remain. Only if it could be ensured over an extended period that both represent the same concept is the old model discarded [168]. While [PERSON] et al. based the drift detection on the model error, the detection of reoccurring concepts could also be conducted by comparing data distributions in the stream (see data-based methods).  
Promising research directions are the handling of imbalanced data [118], drift detection in case of missing values [112], as well as the inclusion of temporal dependencies [155]. Ref. [36] states that drift detection methods should be enhanced in terms of their ability to provide information about drift types and regions. Further, ref. [157] see potential in the automated parameter tuning of drift detection and stream classification algorithms.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l|c c c c c c} \hline \hline
**Sample** & **PR1/24h** & **PR2/24h** & **PR3/24h** & **PR1/48h** & **PR2/48h** & **PR3/48h** \\ \hline S\({}_{\text{BET}}\), m\({}^{2}\)/g & 35 & 4 & 30 & 9 & 11 & 38 \\ S\({}_{\text{ext}}\), m\({}^{2}\)/g & 30 & 3 & 27 & 7 & 8 & 35 \\ V\({}_{\text{\mu}}\), cm\({}^{3}\)/g & 0.0020 & 0.00050 & 0.0013 & 0.0011 & 0.0012 & 0.0011 \\ V\({}_{\text{p}}\), cm\({}^{3}\)/g & 0.13 & 0.010 & 0.069 & 0.025 & 0.027 & 0.077 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data derived from N\({}_{2}\) adsorption–desorption measurements: S\({}_{\text{BET}}\), V\({}_{\text{\mu}}\), and V\({}_{\text{p}}\) of all synthesized product samples. closely matching their total surface areas and indicating that the surfaces are predominantly external. Micropores account for a mere 12%, 17%, and 8%, respectively, of the total surface area for these specimens, indicating the mesoporous nature of the materials [31]. This assertion is further substantiated by the uniformity in micropore volumes across all samples (V\({}_{\mu}\approx\) 0.0011-0.0012 cm\({}^{3}\)/g), thereby confirming the predominance of meso- to macroporous characteristics in the material [32]. The significantly higher surface area and pore volume of PR3/48h, attributed to its finer particle morphology, likely provide a more open and biologically favorable structure. This is consistent with previous findings that sintered WH granules with macroporous structures enhance bioactivity and support bone tissue integration due to improved fluid and nutrient transport [13]. Studies on porous calcium phosphate ceramics have shown that elevated surface area and porosity significantly enhance protein adsorption, osteoblastic differentiation, and ionic interactions essential for new bone formation [28; 33]. Therefore, PR3/48h may offer superior bioactivity compared to the other samples. In contrast, the denser microstructures and underdeveloped mesoporosity of PR1/48h and PR2/48h may negatively impact their performance in surface-driven biological processes [30].
# Topology Optimization with Explicit Components Considering Stress Constraints
## 3 Problem Formulation
### Global Stress Control  
In the studies of other pioneering researchers, most stress constraints are as follows:  
\[g_{j}=\frac{|\sigma_{j}|}{\sigma_{lim}}-1\leq 0,\quad 1\leq j\leq n_{e} \tag{11}\]  
In Equation (11), \(\sigma_{lim}\) is the stress limit of the structure, and \(n_{e}\) is the number of solid elements. To address the difficulties caused by considering the stress constraint problem mentioned in Section 1, this paper uses the well-known lower-bound KS aggregation model. [PERSON] et al. proved that using the lower-bound KS function can be considered a special case of using the original upper-bound KS function to aggregate constraints combined with \(\varepsilon-relaxation\), and thus, no additional relaxation techniques are required [32]. They pointed out that the value in the aggregation parameter \(P\in[20,40]\) can achieve the best results. The lower-bound KS function is expressed as follows:  
\[\Psi_{KS}^{L}=\Psi_{KS}^{L}-\frac{1}{P}ln(N)=\frac{1}{P}ln\left(\frac{1}{N} \Sigma_{i=1}^{N}e^{p_{fi}}\right). \tag{12}\]  
In the above equation, \(P\) represents the aggregation parameter, and \(N\) denotes the number of elements. Utilizing Equation (12), we can reformulate the global stress constraint described in Equation (11) accordingly.  
\[g_{j}=\frac{1}{P}ln\left(\frac{1}{N}\sum_{i=1}^{N}e^{p_{\tilde{g}_{i}}}\right) \leq 0, \tag{13}\]  
where  
\[\tilde{g}_{i}=H\big{(}\mathbf{\Phi}(\mathbf{x};\mathbf{D}_{g})\big{)}\left(\frac{\sigma_{ VM,i}}{\sigma_{lim}}-1\right) \tag{14}\]In Equation (14), \(\sigma_{VM,i}\) is the von Mises stress calculated at the center of the i-th element, which is defined as follows:  
\[\sigma_{\text{VM,i}}={\left(\sigma_{i}^{T}V\sigma_{i}\right)^{1/2}}, \tag{15}\]  
where  
\[\text{V}=\begin{pmatrix}1&-1/2&0\\ -1/2&1&0\\ 0&0&3\end{pmatrix} \tag{16}\]  
\[\sigma_{i}=C_{0}Bu_{i} \tag{17}\]  
\(\sigma_{i}=C_{0}\) is the stress-strain matrix related to [PERSON]'s modulus \(E\) and Poisson's ratio, \(B\) is the strain-displacement matrix, and \(u_{i}\) is the displacement vector of the \(i\)-th element.
## 2 Single-Channel Feedforward ANC System and Causality Constraint
### Causality Constraint of Feedforward ANC System  
In the feedforward ANC system, the anti-noise is generated by the convolution of the noise control filter and the reference signal obtained by the reference microphone. Then, the anti-noise is radiated from the secondary source and cancels the unwanted noise. The anti-noise should reach the error microphone earlier than the unwanted noise. In other words, the total delay of the reference path, the noise controller, and the secondary path should be smaller than that of the primary path. This is called the causality constraint, and if this condition is not satisfied, the causality constraint is violated and the feedforward ANC system cannot reduce the unwanted noise. This causality constraint can be represented by:  
\[D_{\mathrm{P}}>D_{\mathrm{R}}+D_{\mathrm{C}}+D_{\mathrm{S}}, \tag{4}\]  
where \(D_{\mathrm{P}}\) and \(D_{\mathrm{R}}\) are the propagation delay from the noise source to the error microphone and reference microphone, respectively. \(D_{\mathrm{C}}\) is the processing delay due to the calculations to generate the anti-noise. \(D_{\mathrm{S}}\) is the response delay of the analogue-to-digital converter, digital-to-analogue converter,  
Figure 1: Block diagram of a single-channel feedforward active noise control (ANC) system.  
analogue low-pass filters, loudspeaker used for the secondary source, and the propagation delay between the secondary loudspeaker and the error microphone. Here, inequality (4) can be rewritten as:  
\[D_{\text{P}}-D_{\text{R}}>D_{\text{C}}+D_{\text{S}}. \tag{5}\]  
The left-hand side of inequality (5) represents TDOA between the reference microphone and the error microphone.
## 4 Conclusions  
A novel UTMWPT system has been presented that is capable of transmitting electric energy through a 40-mm-thick stainless steel plate and output 15.7 W of DC power regulated at 5 V with an overall efficiency of up to 27.7%. An RF AC-DC converter with input impedance matching is used in this system to achieve the high RF AC to DC conversion efficiency. The performance of the full system was tested using a computer-controlled measurement method, which has been presented as well. The practical effectiveness of the system has also been successfully demonstrated using ARM(r) Cortex(tm)-A8 evaluation boards with LCD panels. In addition, the authors believe that higher power transfer efficiency and higher output power could be achieved with the use of custom-designed transducers, inductors and capacitors, as well as a more powerful PA. This system can be applied in many industrial and military applications to power sensors and electronic circuits operating inside or outside hermetic metal containers, such as submarines, space crafts, planes and nuclear storage facilities, etc., for increased safety and reduced maintenance costs.  
The author gratefully acknowledges the co-authors for their support and contribution to this work. [PERSON] conceived the work and undertook the experiments. [PERSON] and [PERSON] are his thesis supervisors and provided critical comments and guidance for the work. [PERSON] provided funding and facilities support for the experiment. All the authors have given final approval of the version to be published.  
This work was supported by the IACAS Young Elite Researcher Project QNYC201722 and the National Natural Science Fund of China under Grant 11472307.  
The authors declare no conflict of interest.
## 1 Introduction
The on-board fault diagnosis system was investigated at the University of Southampton, installed on a passenger train, and then utilized Perpetuum wireless sensors to measure train vibrations. The system transmits raw data and calculates measurement data to the cloud, with train number, wheel position, recording time, speed, position, direction, ambient temperature, and bearing and wheel health indexes. The operators and maintenance personnel can access real-time data through the website, monitor the health status of bearings and wheels, and determine whether failures occur [15]. Until the end of the 20 th century, the railways in China began to pay attention to the detection and fault diagnosis of train bearings by utilizing the infrared axle temperature detection system and on-mounted axle temperature detection device. China Railway also adopts monitoring technology at the infrastructure management level as a diagnostic solution to improve the safety and reliability of railway operations. By deploying a large number of sensors and intelligent monitoring systems along the railway, these devices can monitor the vibration, temperature, stress, and other parameters during the operation of the train, especially the health of the axles and the status of the welding points. Although these systems are useful for fault warning, usually when the alarm is issued, the bearing damage has typically advanced to a more serious stage, still posing significant safety risks [16]. Based on the above reasons, herein, the obtained data are first methodically pre-processed, which mainly includes missing values' treatment, outlier treatment, and normalization treatment. The axial temperature feature data with a strong thermal level is difficult to obtain, which is generated by the optimized adversary-generated network, and the objective function and discriminator in the network are optimized to enhance the effectiveness and diversity of the generated data. Finally, an optimal integration model based on the transformer module is designed to detect the alarm level. The novelty of this approach lies in the utilization of an optimized adversary-generated network to create realistic and diverse strong heat state data, overcoming the scarcity of such data. This approach ensures better generalization and robustness in the model's performance, enhancing its ability to accurately detect alarm levels. In this way, when the abnormal increase in bearing temperature is detected, the system will immediately issue a high-temperature warning, and convey in a timely manner the warning information to the driver and relevant service departments (such as maintenance workshop, DEPO), to ensure that potential problemsare quickly responded to and dealt with, and ensure the safe operation of the train and the maintenance efficiency of the equipment to the greatest extent.
# Survey Solutions for 3D Acquisition and Representation of Artificial and Natural Caves
## 3 Obtained Results
### D reconstruction of the Gazzano Cave  
The cave was surveyed according to the proposed methodology (described in Section 2.4), validated as a result of the surveys in the tunnel. The entire image dataset was captured according to the following camera settings: focal length 18 mm; iso 6400; exposure time 1/13 sec; f-number 3.5 and processed in Agisoft Metashape where the point cloud was reconstructed. Prior to the calculation, the images were registered to the cave survey (Figure 2b) to obtain the final result in geographical coordinates (Figure 7a). The registering was achieved with a decimeter-level accuracy (Table 7).  
\begin{table}
\begin{tabular}{c c c} \hline  & \(\Delta\Delta\)**[m]** & \(\Delta\)**B [m]** \\ \hline Start & 0.07 & 0.05 \\ \hline Center & 0.01 & 0.05 \\ \hline End & 0.02 & 0.04 \\ \hline \end{tabular}
\end{table}
Table 6: SfM/TLS comparison results.  
Figure 6: Center section measurements (red: TLS cloud; black: SfM cloud).  
As the images' quantity was noteworthy (1597), the survey was processed in four separate groups and then merged into a unique 3D model (Figure 7b). The use of image groups is vital to ease the processing of images and reduce the processing time. This is encouraging as the final output can represent the cave and its features.  
Moreover, the SfM capability allows portraying the cave interior, too (Figure 8). This feature can constitute the basis for realizing a low-resolution representation of the cave for virtual exploration and tourism purposes. The only limit of the proposed methodology is the complete representation of the final chamber. The lighting and humidity conditions did not illuminate its upper sector adequately, as it reaches 27 m (Figures 1 and 2a). A possible solution is adopting more acquisition points at different elevations inside the final chamber using cavey techniques to climb the wall and reach the top of the chamber. The final result is a continuous model of the cave with a good resolution and accuracy. For traditional methods, the use of SfM allows the reconstruction of a 3D model without the manual interpretation of cavey that should not connect the different benchmarks with the manual representation of the cave.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Targets** & **RMSE X (m)** & **RMSE Y (m)** & **RMSE Z (m)** & **RMSE XY (m)** & **Total RMSE (m)** \\ \hline
20 & 0.165 & 0.147 & 0.246 & 0.221 & 0.331 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SfM co-registering errors.  
Figure 7: 3D model of the cave with the locations of control points (**a**) and camera shots (**b**).  
Figure 8: Cave 3D model interior pictures.
## 4 Proposed ML Attestation Method and Workflow
### Remote Attestation Workflow  
The remote attestation procedure involves different actors, as shown in Figure 3. We utilize the RATS terminology to define a possible attestation scenario workflow, ensuring a standardized approach to describing the roles and interactions of key entities.  
The device, referred to as the prover, generates a nonce derived from the random boot seed created during system boot. The relying party, which relies on attestation results to make trust decisions, sends an attestation request to the device along with the specified type of token to be generated and its public key. If the ML-EAT token is selected, the label encoding can be further chosen as integer-encoded or string-encoded.  
Upon receiving an attestation request from a relying party, the public key received in the attestation request is used to encrypt a nonce, which is then returned to the relying party.  
The relying party processes the nonce to compute a hash value, \(H(\text{nonce}\,||\,H(w)G)\), and returns it to the device. When the relying party requests ML attestation, it employs a zero-knowledge proof over the hash of the model weights. Here, \(H(w)\) represents the hash of the model weights or the hash over the ML model object. \(H(w)G\) represents a point encoded on a secp256k1 elliptic curve. The device verifies that the hash of the current nonce combined with the encoded point retrieved from secure storage matches the received value. Depending on the verification result, the device either aborts the process or generates the attestation proof as requested.  
The nonce is incremented with each attestation request to prevent replay attacks. The device generates authentication tokens only if the remote relying party can compute the same hash as the expected one. Additionally, a denial-of-service (DoS) attack from a malicious verifier attempting to overwhelm the device with multiple attestation queries can be mitigated. Since attestation is not a frequent process, rate limiting can be implemented to restrict the number of requests allowed within a given time frame. A Man-in-the-Middle attack is mitigated by design, since the tokens are signed with private keys that are only used in the secure environment and by the verifier party. The integrity of the token and its claims cannot be altered in transit, only if the private key is known.  
The verifiers might be an independent entity or integrated into the relying party's system. With the help of a verifier entity, the relying party can evaluate the evidence of the produced tokens and determine its validity.  
Figure 3: Remote attestation mechanism. Attestation request from a remote relying party.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 4 Discussion
### Limitations and Future Perspectives  
This study presents a viable approach for determining regional rainfall thresholds but also faces several limitations that require further exploration. Although the model has proven effective in Italy, its transferability to other regions requires further validation due to data availability constraints. Future research directions should include expanding the training dataset to encompass multi-regional environments or implementing transfer learning methodologies to improve model adaptability across diverse geological and climatic settings. Utilizing satellite rainfall data facilitated model development through enhanced data accessibility and provided satisfactory results. Subsequent research could enhance accuracy by incorporating ground-based rain gauge measurements to reduce uncertainties, particularly in complex terrains. Additionally, soil moisture, a crucial variable for landslide prediction, was treated as a static initial environmental factor to characterize antecedent wetness conditions prior to landslide occurrence. This approach enabled the model to evaluate landslide probability under varying initial soil moisture conditions, thereby enhancing generalizability across diverse real-world scenarios. Nevertheless, this approach may constrain the model's capacity to capture dynamic moisture changes during rainfall events, which significantly influence landslide initiation. Addressing this limitation in subsequent studies could involve integrating physically based hydrological models or sequential modeling techniques--such as time series analysis or advanced deep learning models (such as LSTM)--to better simulate and incorporate real-time soil moisture dynamics into landslide prediction frameworks.