esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Strategy for Fair and Efficient Energy Usage in WLANs in the Presence of Capture Effect
## 5 PHY Capture Induced Unfairness in WLAN and Proposed Solutions Based on Joint Strategy
### Adaptive Transmission Power Control (ATXPR)
#### 5.2.1 Performance Evaluation of Proposed Solutions  
Figure 9 compares the five proposed solutions, i.e., CWADJ, AIAD+, AIMD+, MIAD+ and MIMD+ with the baseline.  
As shown in Figure 9 CWADJ improved fairness in energy efficiency, though limited, without sacrificing the overall aggregate energy efficiency of the network. [PERSON]'s fairness index for CWADJ according to Equation (13) was 0.91, which was almost 12% improvement in fairness compared to the baseline. The limited improvement in fairness is because CWADJ mildly increases the number of transmission opportunities of Zone 2 nodes, however, Zone 2 nodes still suffered more transmission failures due to capture failure when their transmission overlap with the transmissions from Zone 1 nodes.  
Among the four adaptive algorithms, AIMD+ gave the best performance in terms of fairness whereas MIMD+ gives best performance in terms of aggregate energy at the cost of fairness. Overall all the four adaptive transmission power control algorithms performed better than the baseline and CWADJ. AIMD+ improved fairness by 22% and 8% compared to the baseline and CWADJ, respectively.  
The reason behind AIMD+ is that the slow increase in the transmission power gives more opportunity of capture for the simultaneously arriving packets. In contrast, the quick increase in transmission power in MIMD+ causes more collisions than capture for the transmissions from the nodes located far away from the AP. Table 4 compares the fairness and efficiency for all the six methods.  
Figure 9: Comparison of three proposed strategies with the baseline.  
To validate our argument of AIMD+ being superior to the rest of the three presented algorithms we determined the collision probability of nodes in each of the two zones and compare them in Figure 10. It can be seen from Figure 10 that AIMD+ significantly reduced the collision probability of Zone 2 nodes followed by AIAD+, MIAD+ and MIMD+. The feature of additive increase in transmission power in both AIMD+ and AIAD+ effectively reduced the collision probability in Zone 2 nodes and thereby increased fair energy consumption and efficiency whereas as the multiplicative increase in MIAD+ and MIMD+ increased the collision probability of Zone 2 nodes. From Figures 9 and 10, it can be concluded that AIMD+ performs better than the baseline and the remaining three transmission and CW control algorithms, i.e., MIAD+, AIMD+, and MIMD+. We also performed simulations when there were for more than two zones in the network and found similar results as above. Due to space limitation we do not illustrate those results here. Instead we performed some extended simulations and illustrate the obtained results in the next subsection.
## 5 Case Study: Cryptographic Hardware Coprocessor for the Secure Subsystem of Rhea GPP  
Similarly to the AMD PSP, the decision for the isolation strategy of the TEE inside the Rhea GPP was to isolate it from the main processing unit(s) at the physical level by implementing a Security Subsystem dedicated to all and only the security services. The outline of the Rhea GPP developed in the framework of the EPI project is illustrated in Figure 5, while Figure 6 focuses on the internal architecture of the Trusted Environment (i.e., the Security Subsystem).  
Our research group was involved in the development of the cryptographic hardware coprocessor (the blue box in Figure 6) that has the purpose of assisting the secure boot routine for enabling the Security Subsystem and providing high-performance security mechanisms [20]. The implemented module was named Crypto-Tile and offers cutting-edge security services [14]. According to the presented design methodology in Sections 5.1-5.3, we described the features integrated within the developed security module, respectively, at the security service level, interface security policy level, and physical implementation level.  
Figure 4: Recursive application of security partitioning model to the chain of elements forming the TEE.  
Figure 5: Simplified outline of Rhea GPP [14].
## 2 Experiments and Signal Acquisition
In the early stages of voltage reduction, the top window revealed the situation shown in Figure 3f, with liquid droplets beginning to accumulate below the upper trays but without a significant operational  
Figure 3: Fluid flow states in the column under different operating conditions: (**a**) normal; (**b**) potential fault 1; (**c**) flooding; (**d**) potential fault 2; (**e**) dry tray; (**f**) potential fault 3; (**g**) weeping. fault. The acoustic signal collected at this time was classified as T3, indicating a potential leakage fault. Over time, the bottom window showed that the condensed droplets began to flow down the inner wall of the distillation column, as shown in Figure 3g. Due to the reduced gas velocity, the bubble diameter significantly increased, and the generation and breakup process slowed down, with bubble coalescence becoming unobservable. The passive acoustic signal collected at this time was classified as F3. The Operating variables corresponding to each category are shown in Table 3.  
The duration of each data sample was 3.0 s, and the number of valid samples collected for each category was as follows: class N1 included 674 samples; class T1 included 702 samples; class F1 included 711 samples; class T2 included 727 samples; class F2 included 695 samples; class T3 included 236 samples; and class F3 included 379 samples.
## 2 Bidirectional DC-DC Converters
### Classification of Bidirectional DC-DC Converter  
As mentioned in the preceding section, bidirectional _DC-DC_ converters are of two types--_IBD_C and _NIBD_C--as they been classified in Figure 3, based on galvanic isolation; these are further described below.
## 5 Use Case
### Technical Safety Concept  
The technical safety concept comprises all technical safety requirements, defining the system-level implementation of functional safety requirements and guiding hardware (HW) and software (SW) development. Technical safety requirements are derived from functional safety requirements, providing specific technical solutions. Each functional safety requirement must have at least one corresponding technical safety requirement, which should be subdivided if it applies to multiple functional safety requirements or relates specifically to HW or SW to ensure traceability. These requirements must be detailed using predefined categories, with updates for values determined in later development phases, and must be assignable to specific ITEM functions. The diagrams of the model are shown in Figures A26 and A27.
# Content-Based Approach for Improving Bloom Filter Efficiency
## 4 Experiments and Results
### Hash Functions' Variation  
Here, we discuss the impact of using different numbers of hash functions on the number of false positives. The parameters of this set of experiments are the same as described in Table 1.  
The impact is shown in Figure 5. We varied the number of hash functions from 1 to 25. A very interesting behavior is observed in the frequency of false positives. In the beginning, the false positives reduced from 544 to 408 as the hash functions were increased from 1 to 2, representing a decrease of 25%. However, only a decrease of 14.7% in false positives is seen as three hash functions are employed. This difference is further reduced to 13.2% and 6.3%, as the number of hash functions is increased to four and five, respectively. In short, a more than 50% decrease is observed in the number of false positives by using just six hash functions.  
Figure 3: Role of the size-based content bits in the reduction in false positives.  
A very minimal decrease (not more than five) in the false positives is seen as the hash functions are increased beyond 11. This analysis shows that increasing the number of hash functions beyond a certain number is not very beneficial in reducing the false positives. More so, a significant overhead would be observed in the case of using too many functions.  
Figure 4: Variation in time with respect to increasing content bits.  
Figure 5: Effect of using more hash functions on the false positives.
# Application Layer Protocol Identification Method Based on ResNet
###### Abstract  
Most network attacks occur at the application layer, where many application layer protocols exist. These protocols have different structures and functionalities, posing feature extraction challenges and resulting in low identification accuracy. This significantly affects application layer protocol recognition, analysis, and detection. We propose a data protocol identification method based on a Residual Network (ResNet) to address this issue. The method involves the following steps: (1) utilizing a delimiter determination algorithm based on information entropy proposed in this paper to determine an optimal set of delimiters; (2) segmenting the original data using the optimal set of delimiters and constructing a feature data block frequency table based on the frequency of segmented data blocks; (3) employing a composite-feature-based RGB image generation algorithm proposed in this paper to generate feature images by combining feature data blocks and original data; and (4) training the ResNet model with the generated feature images to automatically learn protocol features and achieve classification recognition of application layer protocols. Experimental results demonstrate that this method achieves over 98% accuracy, precision, recall, and F1 score across these four metrics.  
Keywords:protocol identification; ResNet; information entropy; composite feature data _Article_
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems
### Poly(ortho esters)  
Poly(ortho ester) polymeric formulations are designed to achieve steady drug release subsequent to hydrolysis of polymer chains on the surface of the polymer. Poly(ortho ester) biopolymers were developed in the early 1970's and their main applications were in surgical repair materials and sutures [21]. In this decade, their applications in drug delivery systems for delivery of analgesics, DNA vaccines, and antiproliferative drugs have been studied widely [22].  
The first generation of poly(ortho esters) were fabricated by the addition of DETOSU (3,9-bis(ethylidene-2,4,8,10-tetraoxaspiro [5.5]undecane) to a diol to form a DETOSU-based poly(ortho ester). Ester hydrolysis takes place within the aqueous environment and the hydrolysis rate can be adjusted by inclusion of acidic or basic units into the polymer matrix. For example, acid-catalysed hydrolysis increased microparticle degradation rate and a zero-order release of 5-fluorouracil over a 15-day period was achieved [23]. In another case, basic excipients stabilized the matrix bulk and facilitated surface-only erosion. By this approach, controlled release of tetracycline over a period of weeks was achieved for the treatment of periodontal disease [24].  
Diffusion of acidic excipients out of the polymer matrix leads to unpredictable release kinetics. To address this issue, a new biopolymer structure containing the glycolide sequence was designed and self-catalysed hydrolysis without excipients was achieved [25]. Modification of a diol group by conjugation with an N-methyldiethanolamine can render the polymers pH sensitive in acidic environments [26]. Furthermore, because the poly(ortho ester) is highly hydrophobic, very limited water can penetrate into the matrix, which leads to a relatively low hydrolysis rate. By using poly(ethylene glycol) as the diol, polymer hydrophilicity was increased, which led to increased erosion rates [21].
## 3 Results  
Non-Overlapping Surface, Volume, and SI Using Mirroring Technique Based on Each Mandibular Midsagittal Plane Prior to Surgery  
This study included 29 patients (15 men, 14 women; average age, 23.1 \(\pm\) 6.9 years). The non-overlapping surface (3543.14 \(\pm\) 734.13 mm\({}^{2}\)) and volume (5088.85 \(\pm\) 2280.33 mm\({}^{3}\)) of the right anterior segment of the hemi-mandible based on cmAMP before surgery were significantly smaller than those with MSP, AMP, and mAMP as the reference plane (\(p<0.001\)) (Table 2). A similar trend was observed in the hemi-mandible left anterior segment. On the other hand, the SI with cmAMP showed a significantly higher value than that when using the other planes as the reference, reaching 0.83 \(\pm\) 0.04. These results suggested that cmAMP is a plane that can divide the anterior segment of the mandible most symmetrically left and right.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MSP**} & \multicolumn{2}{c}{**AMP**} & \multicolumn{2}{c}{**mAMP**} & \multicolumn{2}{c}{**cmAMP**} & \multicolumn{2}{c}{**\(p\)-Value**} \\ \cline{2-9}  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline Right segment & & & & & & & & \\ Non-overlapping & 4347.03 b & 775.29 & 4786.67 b & 1029.46 & 4423.69 b & 777.71 & 3543.14 a & 734.13 & \textless{}0.001 \\ Non-overlapping & 7942.44 ab & 2790.26 & 14,175.40 c & 6718.91 & 10,382.64 b & 5019.15 & 5088.85 a & 2280.33 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Non-overlapping surface, volume, and similarity index (SI) using a mirroring technique based on each plane before surgery.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Metrics Used by Our Proposed Algorithm
#### 3.1.2 Trust Level Value  
The value of trust in the beginning is same as all nodes. Anomaly detection algorithms lower this level if a node is functioning incorrectly. It is possible for an abnormal node to be a distrusted node or a malicious node. The nodes' trust level is set as [1,8]:  
Normal_Sensor_node: 0.7 \(\leq\) Ti \(\leq\) 1  
Distrusted_Sensor_node: 0.3 \(\leq\) Ti \(\leq\) 0.7  
Malicious_Sensor_node: 1 \(\leq\) Ti < 0.3  
In order for a cluster head to be elected, only normal nodes are allowed to participate. There is a possibility that the node could become malicious at any point in time, and thus Distrusted_Sensor_node and Malicious_Sensor_node cannot participate in the election of a cluster head. If a normal node's residual energy is less than the average residual energy of all nodes, then it is also considered as a malicious node [29].
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 2 Proposed Method
### The Proposed Algorithm  
#### 2.2.1 The Feature Detector PC-Harris  
At present, the feature detectors based on the PC maximum moment of the edge images become more prevalent since the PC-based algorithms are not sensitive to the geometric and radiometric differences between optical and SAR images. However, PC-based feature detectors are not compatible with the scale differences. Thus, to solve the scale difference problem between optical and SAR images, we propose a scale-insensitive feature detection method. It convolves the PC maximum moment with the log-Gabor filter to construct the scale space.  
First, the PC maximum moment \(M\) of the optical or SAR image \(I(x,y)\) is calculated. Second, the response map \(A^{\prime}{}_{s,o}(x,y)\) in the orientation \(o\) and the scale \(s\) is calculated by combining the log-Gabor filter and \(M\) according to Formulas (3) and (4). The response map \(A^{\prime}{}_{s_{i}}(x,y)\) of the maximum moment \(M\) on the scale \(s_{i}\) is obtained as follows:  
\[A^{\prime}{}_{s_{i}}(x,y)=\sum\limits_{j=1}^{o}A^{\prime}{}_{s_{i},o_{j}}(x,y) \tag{11}\]  
where the scale factor is \(s_{i}\in[1,\ s]\). The gradient amplitude \(D(s_{i})\) and orientation \(\theta(s_{i})\) are calculated by the gradient components \(D_{X}(x,y)\) and \(D_{Y}(x,y)\) as follows:  
\[D(s_{i})=\sqrt{D_{X}(x,y)^{2}+D_{Y}(x,y)^{2}} \tag{12}\]  
\[\theta(s_{i}){=}\tan^{-1}(D_{Y}(x,y)/D_{X}(x,y)^{2}) \tag{13}\]  
The proposed PC-based Harris scale space is obtained by \(D_{X}(x,y)\) and \(D_{Y}(x,y)\) as follows:  
\[M_{s_{i}}(x,y)=g_{\sqrt{2}s_{i}}\begin{bmatrix}(D_{X}(x,y))^{2}&D_{X}(x,y)D_{ X}(x,y)\\ D_{Y}(x,y)D_{X}(x,y)&(D_{Y}(x,y))^{2}\end{bmatrix} \tag{14}\]  
\[R_{s_{i}}(x,y){=}\det(M_{s_{i}}(x,y))-d\cdot tr(M_{s_{i}}(x,y))^{2} \tag{15}\]  
where \(g\) stands for a Gaussian kernel; \(*\) represents the convolution operator; \(d\) is an arbitrary parameter; and det and \(tr\) denote the values of the matrix determinant and the matrix trace, respectively.  
Here, experiments are conducted to analyze the performance of the proposed PC-Harris between the optical and SAR images. Figure 2 and Table 1 show the quantitative results of the keypoint repeatability based on the multi-scale Harris [12] and the proposed PC-Harris. Among them, the scale factor \(s\) is set to 4 and the threshold between repeatable keypoints is set to 3. On the premise that the two detectors extract similar numbers of keypoints, Table 1 shows that the proposed PC-Harris gains approximately 2% of points compared with the multi-scale Harris. Therefore, the proposed PC-Harris can improve the keypoint repeatability between optical and SAR images.
### Scope and Contribution of This Study  
In the present paper, two novel algorithms are proposed to identify all bearings' coefficients of multi-disc and multi-span rotor with m discs and n bearings from unbalance responses based on the continuous rotor dynamic analysis method (CRDAM). The matrix method is proposed to overcome the difficulties that equations of the inverse problem are non-linear transcendental, too many unknown variables are included in the equations and rotor unbalances and bearing coefficients are coupling together. Then, a linear function, which represents the relationship between the main complex coefficients and the cross-coupled complex coefficients, is proposed to obtain Algorithm I. However, the algorithm can only be applied to identifying the main stiffness and damping coefficients of rolling-bearing, of which the cross-coupled coefficients can be considered zero. To identify the cross-coupled coefficients together with the main coefficients, there is a difficulty that another set of equations cannot be easily developed based on the proposed function just by using doubled unbalance responses as input. The reason is the slope of the proposed linear function is constant when the rotating speed is maintained at a fixed speed. Changing the rotating speed slightly is proposed to obtain another equation set and Algorithm II is provided for estimating all the eight coefficients of a bearing. Algorithm II is suitable for the identification of coefficients of both rolling bearings and journal bearings in a rotor. The number of required measuring points of unbalance responses is m + n + 1. Three kinds of numerical simulations are conducted to validate the two algorithms. It is indicated that the two algorithms have high identification accuracy when the measurement errors of all input unbalance responses are zero or the same. There should be a measuring point, called an adjustment point, to achieve high identification accuracy. The adjustment point should be near the bearing, whose coefficients are to be identified. The proposed algorithms do not require a machine run-down/run-up and external exciters, and have the flexibility to incorporate any number of bearings and discs.
# Stability Analysis of the Longitudinal Slope Linear Shield Tunnel Excavation Face
## 4 Conclusions and Future Work
### Conclusions  
In this study, a transparent soil laboratory model test of a longitudinal tunnel was conducted, combined with a three-dimensional finite element model of a longitudinal tunnel established using FLAC3D software. This work contributes to bridging the research gap in excavation face stability under longitudinal slope conditions by integrating transparent soil testing and calibrated 3D numerical modeling.  
The stability of the excavation face under different slope conditions was analyzed, leading to the following conclusions:  
(1) Failure Mechanism: The transparent soil test clearly illustrates the destabilization process of the soil under different slope conditions. The excavation face destabilization primarily occurs in two stages: 1 The destabilized soil region in front of the excavation forms a 'bull's-horn-shaped' failure zone, which expands as the excavation retreat increases. 2 The destabilized region extends toward the surface, forming a failure mode dominated by vertical development. Additionally, larger longitudinal slopes (\(i=15\%\)) induce wider and more vertically developed failure zones, increasing the risk of surface disturbance.  
(2) Displacement Trends: Both horizontal and vertical displacements at the excavation face increase as the support pressure decreases. Under upward slopes, these displacements are significantly higher due to additional stress from overburden soil.  
(3) Support Requirements: For different burial depth ratios (\(C\)/\(D\)), the ultimate support ratio (\(\lambda_{\text{lim}}\)) increases significantly with increasing longitudinal slope, while the rate of increase remains approximately constant.  
(4) Stress Redistribution: The variation of vertical stress exhibits a double-inflection-point characteristic. As \(i\) increases, both inflection points shift upward, leading to an elevation of the soil loosening zone in the excavation face.
## 3 System Concept and Hardware  
For our work, we decided to use passive infrared arrays, also known as long-wave infrared (LWIR) sensor arrays, which provide thermal images. These sensors offer the same advantages as conventional black-and-white or RGB cameras (see [6; 8]), while the aspect of privacy is less significant due to the reduced lateral resolution and unfamiliar image content of the thermal image. Thermal cameras come with the added advantage of not requiring external lighting, and they are less susceptible to the influences of weather and ambient lighting compared with conventional cameras.
# Application of RFID to Soil-Erosion Research
## 3 Results
### Experiment 2
#### 3.2.2 Recording of Particles after the Rainfall Event  
Twenty-two particles remained on the plot after the rainfall event, 29 were collected with the runoff samples, and 49 left the plot during the intervals between collecting runoff samples. Of these, 26 left the plot before minute 9 of the experiment and the times for the remaining 23 were not recorded. Of the 22 particles remaining on the plot, 10 were from line one corresponding with the greatest distance from the centre of the depression. The fewest were from lines 2 and 5 (one and two particles, respectively), and intermediate numbers (four and five particles) from lines 3 and 4, respectively. Correspondingly, the fewest particles leaving the plot were from line 1 (10), the greatest number from lines 2 and 5 (19 and 18, respectively), and intermediate numbers from lines 3 and 4 (17 and 15, respectively). Particles leaving the plot before minute 9 showed only a slight bias towards the lower part of the array compared to those leaving after minute 9. Mean travel distances were 4.62 m and 4.76 m, respectively, but the difference is statistically significant (\(p=0.047\)).