esa-sceva/satcom-chunk-collection huggingface dataset:
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 5 Discussion  
### Advantages of the Proposed Siamese-SAM  
From all the experimental results, it is clear that the Siamese-SAM we proposed holds great potential for change detection tasks. The Siamese-SAM model cleverly integrates the Siamese network architecture with the powerful feature extraction capabilities of the SAM. This combination not only effectively captures the difference information between the two-phase images, but also enhances the understanding of the global context, resulting in more reliable change detection results. The comprehensive and well-conducted ablation experiments demonstrate that the three modules we designed (GIEM, DIEM, and DGIF) each play a crucial role in improving model performance. GIEM and DIEM significantly enhance the model's ability to understand global context and extract differential features. DGIF effectively integrates both global context and differential features, allowing them to contribute more effectively to accurate change detection.
# Validation of Lumbar Compressive Force Simulation in Forward Flexion Condition
## 3 Result  
### CF Estimation  
Figure 4 shows the comparison between the estimated CF obtained with the proposed simulator, the model by [PERSON] [15], and the model by [PERSON] [16], and the modified invasive measurements for forward flexion angles of 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\). The mean values of L5/S1 at the different forward flexion angles for all estimations (models and experiment) are shown in Figure 4. The CF estimated by the CF simulator at 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\) is 416, 841, 1244, and 1546 N, respectively. Table 3 presents the difference in the CF between adjacent flexion angle ranges of 0\({}^{\circ}\)-10\({}^{\circ}\), 10\({}^{\circ}\)-20\({}^{\circ}\), and 20\({}^{\circ}\)-30\({}^{\circ}\), hereinafter referred to as AR1, AR2, and AR3, respectively. These differences are different among the different models: the increase in CF obtained by the CF simulator between AR1 and AR2 and between AR2 and AR3 is \(-\)2.2 and \(-\)10.1 N/deg, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Researcher** & **Information** & **Disc Level** & \(\dfrac{\mathbf{A}_{disc}}{\text{(cm}^{2})}\) & **Postures** & **IDP (MPa)** & **CF (\(N\))** \\ \hline \multirow{3}{*}{[PERSON] [30]} & Two subjects & & & & & \\  & 49–52 y & L4/L5 & 19.7 & Standing & 0.60 \({}^{\ast}\) & 780 \\  & 67–75 kg & & & & & \\  & 163–175 cm & & & & & \\ \hline \multirow{3}{*}{[PERSON] [31]} & One subject & & & Standing & 0.5 & 594 \\  & 45 y & L4/L5 & 18 & Forward flexion at 36\({}^{\circ}\) & 1.08 & 1283 \\  & 70 kg & & Standing (19.8 kg) & 1 & 1188 \\  & 168 cm & & & lifting (19.8 kg) & 2.3 & 2732 \\ \hline \multirow{3}{*}{[PERSON] [32]} & Eight subjects & & & & & \\  & 22–29 y & L4/L5 & 15.9 & Standing & 0.53 & 556 \\  & 60–90 kg & & & Forward flexion at 30\({}^{\circ}\) & 1.32 & 1385 \\  & 166–181 cm & & & & & \\ \hline \multirow{3}{*}{[PERSON] [33]} & Three subjects & & & Standing & 0.34 & 556 \\  & 24–26 y & L4/L5 & 19.1 & Forward flexion at 30\({}^{\circ}\) & 1.21 & 1385 \\ \cline{1-1}  & 70–77 kg & & Standing (10 kg) & 0.416 & 428 \\ \cline{1-1}  & 170–180 cm & & & Forward flexion (10 kg) & 1.46 & 1525 \\ \hline \hline \end{tabular} \({}^{\ast}\)[PERSON]’s measurement is corrected by [PERSON] et al. [36].  
\end{table}
Table 2: Information of invasive intradiscal pressure (IDP) measurements.
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
## 3 Results  
### Evaluation of PIM Composition  
Divalent metals such as Zn and Cu can be accumulated in the receiving solution through the PIM. The incorporation of D2 EHPA in the membrane allows the extraction and back-extraction of metal ions simultaneously. As reported in several studies [22; 23] the mechanism of the divalent ion complexation with the carrier D2 EHPA can be described by the following stoichiometric equations (Equations (2) and (3)).  
\[\text{Feed phase}:\ \text{M}^{2+}{}_{(\text{aq})}+\overline{3/2(HR)}{}_{2} \leftrightarrow\overline{MR_{2}{\cdot}HR}+2\text{H}^{+}{}_{(\text{aq})} \tag{2}\]  
\[\text{Receiving phase}:\ \overline{MR_{2}{\cdot}HR}+2\text{H}^{+}{}_{(\text{aq})} \leftrightarrow\text{M}^{2+}{}_{(\text{aq})}+\overline{3/2(HR)}{}_{2} \tag{3}\]  
where \(HR\) denotes the carrier (D2 EHPA) molecule and M\({}^{2+}\) the divalent cations, Zn\({}^{2+}\) or Cu\({}^{2+}\).  
Even though the carrier has the central role in the transport process through the membrane, it is well known that the other components of the PIM composition are also of great importance for the transport of the metals [24]. Therefore, different PIMs were prepared (Table 1) and their effectiveness was investigated in terms of Zn accumulation at different times. The results are presented in Figure 2. As can be seen, for M1 (PIM without any plasticizer) the accumulated Zn in the receiving phase increased with time. However, this membrane was highly brittle and difficult to manipulate. In order to improve the mechanical characteristics of the membrane, the addition of the plasticizer NPOE to the formulation of the PIM was investigated (M2 and M3). It was found that M2 had similar behavior to M1, but that the addition of plasticizer improved its mechanical stability. In the case of M3, the metal accumulation was less efficient. According to [PERSON] et al., this could be due to an excess of plasticizer causing an additional barrier to zinc transport by forming a film on the membrane surface [25]. Consequently, further experiments were performed using M2.
# Vehicle Collision Prediction under Reduced Visibility Conditions
## 4 Experiments
### Experiment Results
#### 4.2.4 Comparison with Different Methods
As shown in Table 5, we experimented with 5 different sets of experiments, with a total of 567 datasets and 153 cases of target visibilities, the results show that ViCoWS gives early warnings in each case compared to FCPI. In Set A, the earliest warning by ViCoWS is at 4.6 s before collision which is safe (PRT should be at least 2.36 s), whereas FCPI warns by only 0.6 s before the collision, which is unsafe. The situations are similar for Sets B, C, D, and E. On average, ViCoWS is early by around 5.0 s which is 45.38% when compared to the full duration of car following before tentative collisions. From Table 5, we can conclude that ViCoWS is not only a valid method for giving early warnings of possible rear-end collisions in car following situations, but it is also much safer than the conventional \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time Slot** & \(D_{vb}\) **(m)** & \(T_{pr}\) **(s)** & \(T_{ph}\) & \(T_{ViCoWS}\) **(s)** & \(T_{FCPI}\) **(s)** \\ \hline
1 to 19 & 400 & 0.8397 & 1 & No & No \\
20 to 39 & 120 & 2.0864 & 23 & 4.5–3.2 & No * \\
40 to 71 & 160 & 1.6101 & 22 & 3.1–2.1 & 0.6 * \\ \hline \multicolumn{6}{l}{\({}^{*}\)Dangerous because Warning Time is less than Safe PRT.} \\ \end{tabular}
\end{table} 
Table 4: Prediction Horizons under different visibility for congested traffic scenario. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Set** & \begin{tabular}{c} **Dataset** \\ **Size** \\ **Cases** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Visibility** \\ **Cases** \\ \end{tabular} & \begin{tabular}{c} **Target** \\ **Visibility** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **PRT \(T_{pr}\)** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Refection** \\ **Horizon** \(T_{ph}\) \\ **(Time Slots)** \\ \end{tabular} & \begin{tabular}{c} **Max** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Max** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Max** \\ **(s)** \\ \end{tabular} \\ \hline A & 71 & 29 & 106–444 & 2.36–0.79 & 25–18 & 4.6 & 0.6 * & 4.0 & 56.34 \\ B & 116 & 45 & 39–516 & 6.48–0.74 & 13–1 & 6.5 & 1.3 * & 5.2 & 44.83 \\ C & 118 & 22 & 37–44 & 7.11–5.83 & 25–16 & 9.7 & 1.4 * & 8.3 & 70.34 \\ D & 141 & 44 & 50–488 & 5.08–0.76 & 23–3 & 6.3 & 1.4 * & 4.9 & 34.75 \\ E & 121 & 13 & 221–515 & 1.24–0.74 & 25–20 & 4.3 & 1.8 * & 2.5 & 20.66 \\ \hline
**Total** & **567** & **153** & \multicolumn{6}{c}{**Average Earlier by**} & **5.0** & **45.38** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing ViCoWS and FCPI with more experiments.
## 5 Discussion
To address this issue, we introduced a resampling scheme and correlation analysis for estimating the correlation between base learners within the ensemble. When constituting the ensemble, it is desirable that the predictions produced by base learners be weakly correlated as the similar predictions may not contribute to the ensemble performance. In this study, the PCC values under different resampling times (with the 20, 30, and 40 times, respectively) showed a high correlation between the SVM and LR. This result means the SVM generates very similar or even the same predictions as the LR for ensemble modeling in most cases, reducing the effect of assembling predictions. In other words,the SVM and LR played an equal role in the ensemble of SVM-ANN-NB-LR, which led to similar performances between the SAN model and the ANL model, as well as similar distribution patterns of developed susceptibility maps. In fact, the SANL model also behaved similarly to the SAN model and the ANL model. We argue that the performance evaluation for the candidate used for composing the ensemble is important but may not be conclusive, because all base learners yielded good predictive accuracies in this work. In this case, a correlation analysis is helpful to determine eligible candidates for \"optimal\" landslide ensemble modeling. We further verified this by removing the NB from the SANL model, and it was found that the SAL model underperformed the other ensemble models. The correlation analysis showed pairs containing the NB had the lowest PCC on average results. The decreased performance of ensemble modeling after excluding the NB indicated that weakly or not highly correlated candidates should be included in the ensemble. Overall, our results suggest that the candidates serving as base learners should be skillful but diverse, allowing the ensemble method to determine how to combine the best solutions from base learners for performance refinement [85; 86]. However, some limitations should be noted. When it comes to such geologic factors as modeling input, we considered the physical and mechanical properties of engineering rock groups alone without taking into account geologic factors such as the angle of rock bedding and the orientation of bedding relative to slope direction that are related to slope failure. As detailed knowledge of geology is critical for landslide susceptibility studies, we will focus more on these important geologic factors and examine their effect in the future work.
## 7 The Case Study
### Background  
The energy consumption of the Al-Haram Mosque (Masjid Al-Haram) in Saudi Arabia is a critical area of focus because of its vast size, continuous operation, and the immense number of visitors it attracts [101]. Located in Mecca, the Al-Haram Mosque is the largest Mosque in the world and surrounds Islam's holiest site, the Kaaba. This section discusses the mosque's energy demands, primary energy sources, the integration of innovative self-powered systems, and its overall sustainability approach.  
The Al-Haram Mosque hosts millions of visitors annually, with peak numbers during the Hajj pilgrimage and the month of Ramadan [101]. These visitors require a consistent and reliable energy supply to ensure comfort, safety, and uninterrupted religious activities [102]. The mosque operates 24/7, with energy demands encompassing lighting, cooling, ventilation, escalators, water supply, and large-screen displays, among other needs.  
Several core areas contribute to the Al-Haram Mosque's significant energy consumption:  
Lighting: The mosque is illuminated by thousands of high-intensity LED lights, both indoors and outdoors, to accommodate continuous prayer times and ensure visibility and safety [102]. This lighting is essential for guiding pilgrims at night and illuminating significant structures such as the Kaaba.  
Cooling and Ventilation: Saudi Arabia's climate is characterized by high temperatures, particularly in Mecca, where summer temperatures can exceed 40 \({}^{\circ}\)C (104 \({}^{\circ}\)F) [103]. Cooling is a significant energy consumer within the mosque, especially given the density of people congregating in one area. The cooling system includes massive air conditioning units, ventilation fans, and chillers to regulate temperatures, ensuring visitors' comfort despite the extreme heat.  
Escalators and Elevators: The Al-Haram Mosque spans multiple levels, and to facilitate the movement of millions of pilgrims, it features an extensive network of escalators and elevators. These systems are particularly beneficial for elderly and disabled visitors.  
Water Supply: The provision of water for ablution areas and drinking is another critical energy component. Thousands of liters of water are required daily, with energy expended on water distribution, desalination (for drinking water), and wastewater management.  
Audio-Visual Systems: Large screens and a high-quality audio system support religious activities, providing announcements and guidance to pilgrims. The audio system is extensive, covering the vast area of the mosque to ensure all worshippers can hear prayers and recitations clearly.  
While exact figures for energy consumption are not frequently disclosed, reports and estimates provide insight into the mosque's immense energy needs. The Al-Haram Mosque consumes approximately 118 MVA annually, comparable to the energy demands of a small city. Energy consumption escalates during Ramadan and Hajj because of the increased number of pilgrims, with demand rising by an estimated 20-30% [102; 103].
## 2 Materials and Methods
### Action Registration in the System
#### 2.3.1 Data Storage on the BC Network  
To carry out the actual storage of data on the decentralized data network that constitutes BC, it was necessary to define a unique structure (see Figure 4). This structure hosts information about the challenge itself, the action related to the former fulfilled, the user invoking the SC, and whether the action has already been rewarded or not.  
The invocation of the SC triggers several consequences. The first one is that an \"event\" is emitted on the network itself. This \"event\" (defined as shown in Figure 5) notifies all participants on the network that a certain user has performed a particular action within the framework of the challenge.  
Within the framework of this platform, when it is appropriate to register the action associated with the challenge, the code shown in the following image (Figure 6) will be executed. This code stores in a secure manner the data with a proper timestamp and emits the event to let other participants know about this new piece of information.  
The sending of Ether will be carried out within the SC itself once its availability has been validated and the fulfilment of its conditions has been verified (Figure 7).  
The reader can verify how the functions in Figure 7 check the completion of a plank in each of the last 15 days, as required for the Planking challenge. Additionally, the SC takes care of marking those actions that have already been awarded to avoid duplicate payments for a single action.  
Figure 4: Structure for the action registry.  
Figure 5: Code for the message sending.  
Figure 6: Event registry code.
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 5 The Performance Measurement Terms
\[\text{Sp}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{5}\]  
The false alarm rate (false positive rate or fallout) is equivalent to one minus the specificity. It is calculated by dividing the number of ADLs incorrectly detected as falls by the total number of ADL instances. \[\text{FAR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{6}\]  
Three elements determine the validity of a fall detection algorithm: sensitivity, false alarm rate, and miss rate [116]. In order to fully determine the efficacy of a fall detection approach, both the precision and the sensitivity should be calculated. Nevertheless, the improvement of one of these elements is generally made at the expense of the other. For example, in the case of high sensitivity and low precision, most of the falls are correctly identified (low FN), along with a lot of false positives, and vice versa. Thus, the performance estimation tends towards calculation of the specificity and F-score. Three more terms used in the performance evaluation deserve to be mentioned here. The F-score (F1 score) combines the precision and sensitivity by calculating the harmonic mean; it provides the algorithm's capacity to detect all judicious situations. \[\text{F-score}=2\bigg{(}\frac{\text{Precision}\ \times\ \text{sensitivity}}{\text{ Precision}\ +\ \text{Sensitivity}}\bigg{)} \tag{7}\]  
The kappa value is mainly a measure that compares the system to how it would have worked merely by chance; it judges the inter-rater reliability of fall detectors [18,117]. The best value of kappa is when it approaches 1. \[\text{k}=\frac{\text{Accuracy - Expected Accuracy}}{\text{1- Expected Accuracy}} \tag{8}\]  
\[\text{Expected Accuracy}=\frac{\frac{\text{(TP}+\text{FN)}\times\text{(TP +FP)}}{\text{TP}+\text{FP}+\text{TN}+\text{FN}}+\frac{\text{(TN}+\text{FN)} \times\text{(TN}+\text{FP)}}{\text{TP}+\text{FP}+\text{TN}+\text{FN}}}{ \text{TP}+\text{FP}+\text{TN}+\text{FN}} \tag{9}\]  
The area under the Receiver Operating Characteristic (ROC) curve (AUROC) is obtained from the ROC curve, which shows the variation in sensitivity and specificity as a function of the decision threshold. It measures the precision of an approach for all possible decision thresholds. The fall detection approach is perfectly discriminating if the AUROC approaches 1. In addition, the principle of evaluation by cross-validation has been used in several approaches based on machine and deep learning, in the same context as estimating performance in practice. A fall detection system is considered perfect if the above performance terms have a value close to 1 (100%) and a fallout with the error rate very close to zero.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Model
#### 3.4.3 Definition of Hyperparameters  
Once the DL models were defined, the dataset was split into 70.00% for training, 15.00% for validation, and 15.00% for testing. Next, optimal performance was evaluated through hyperparameter tuning. To define an RNN, a sequential network is created, which can be either unidirectional or bidirectional, followed by additional layers for multiclass classification. In particular, a layer with 64 neurons was added to the model, configured to return all sequences instead of a single value. A dropout layer with a dropout rate of 20.00% was included to prevent overfitting during training, as it randomly deactivates 20.00% of units at each step. A Global Max-Pooling 1D layer was introduced, extracting the maximum value along the temporal dimension to reduce the output dimensionality of the RNN layer. Then, a dense layer with five neurons, each corresponding to one of the  
Figure 5: LSTM model architecture.  
Figure 6: BiLSTM model architecture.  
predefined classes in the sample section, and a softmax activation function were added. The softmax activation layer transforms the outputs into probability distributions for each class. During the training stage for all models, the categorical_crossentropy loss function was employed, with 20 training epochs and the 'adam' optimizer, and model performance was evaluated using the accuracy metric, given that it is a multiclass classification problem. All parameters used during the experimentation are detailed in Table 8.
# Survey of Artificial Intelligence Model Marketplace
## 4 Model Pricing and Trading
### Model Development and Trading Platforms
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference, Year** & **Scheme or Technique** & **Objective (s)** & **Approach** & **Future Research Direction** \\ \hline Ref. [99], 2018 & Danku Protocol; Blockchain & Trustless, Broker-free, model generating market & Leverages Ethereum blockchain to yield a model development marketplace & Encryption-based, computation-efficient model development markets \\ \hline Ref. [100], 2019 & Hyperledger Fabric; Blockchain & Collaborative, decentralized model development market & The Hyper-ledger-based market deployed on the cloud facilitates model development between the stakeholders in an ownership-preserving manner & Homomorphic encryption to complement federated learning in decentralized model markets \\ \hline Ref. [106], 2021 & Primal; Cloud-based marketplace & Model training data efficiency testing broker & The platform facilitates collaboration between the model owner and the data shopper in privacy-preserving manner & Primal framework for distributed trading marketplaces \\ \hline Ref. [90], 2021 & Golden Grain; Blockchain & Broker-free, fair model trading market & Leverages SGX to test and validate model performance, then passed to Ethereum blockchain for pricing and transaction & Multi-buyers and fault-tolerant trading markets \\ \hline Ref. [103], 2021 & Distributed ledger; Blockchain & Model trading in IoT-driven market & Shapley value determines model quality and guides the fair interaction/transaction between the data owners and model buyers & Fault-tolerant, distributed trading markets \\ \hline \hline \end{tabular}
\end{table} 
Table 11: A summary of model development and trading platforms. In summary, we have reviewed the existing pricing schemes and trading platforms. It is evident from the existing efforts that pricing schemes and trading platforms that are efficient (arbitrage-free, computation-efficient) and robust against privacy and security threats remain open for further research. Among the feasible directions for broadening this area of research is marketplace design (i.e., auction mechanisms) that balance performance efficiency and robustness against privacy and adversarial threats in future AI model marketplaces.
# 3D Visualization in Digital Medicine Using XR Technology
## 1 Introduction  
### Extended Reality  
The emergence of extended reality (XR) technologies, including augmented reality (AR), virtual reality (VR) and mixed reality (MR) provide many new opportunities that started to be exploited in the medical field in the following areas:  
* Diagnostic data visualization [1]
* Education [2; 3; 4]
* Consultation [5]
* Examination environment customization [6]  
The first appearance of VR technology can be traced back to the 1980s, but the devices of that time were still expensive and inconvenient for users due to the display and the weight of the device. In recent years, leaps and bounds have been made in this area as well. The new generation of head-mounted devices (HMD) have taken a big step to be able to meet the technical and ergonomical requirements that are essential for everyday use in routine pathology [7, 8]. Such properties include:  
* Adequate display resolution
* Proper image refresh time
* Adequate viewing angle
* Adequate weight* Adequate motion tracking In our research, we focused on the application of virtual reality technology. The reason behind this decision, that from the 3 extended reality subcategories, only the wired VR headsets has good enough display resolution.