esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### General Components, SEM Images and Particle Size  
The general components and SEM images are shown in Figure 1. Energy and protein were highest in OBLP, followed by wheat flour and IBLP. Fat and ash were highest in OBLP, followed by IBLP and wheat flour. Carbohydrate was highest in the order of wheat flour, followed by IBLP and OBLP. These trends were in agreement with those reported by [PERSON] et al. (2015) [12].  
In SEM images, grains of 20-150 \(\upmu\)m were observed in wheat flour, and the grains were homogeneous. In IBLP, grains between 20 and 100 \(\upmu\)m were observed, and overall, most of the grains were around 20 \(\upmu\)m, but they were agglomerated with larger grains in some places. OBLPs were present as large clumps of 100-350 \(\upmu\)m, although some 20 \(\upmu\)m grains were observed.  
Barley flour had two peaks at 20 \(\upmu\)m and 300-450 \(\upmu\)m, with 20 \(\upmu\)m being starch granules and 300-450 \(\upmu\)m being the cell wall and cell mass [19]. IBLP is barley bran, but as it is close to the endosperm, it has many small 20 \(\upmu\)m granules, while OBLP is close to the outer skin part, and it was inferred that cell walls and cell clumps were mixed. BLPs and PBLPs were not processed for milling, so it was considered that the outer hull parts of the barley were directly mixed in and the grains were unevenly distributed.
## 4 Convergence Analysis
\right],\
abla_{1}=(1-\Theta(k))Q_{i}(1-\Theta(k))-Q_{i },\
abla_{2}=(1-\Theta(k))Q_{i}\Theta(k),\
abla_{3}=\Theta(k)Q_{i}\Theta(k)\). Let \(\lambda_{\min}=b^{2}/4\), using the inequality \(a^{2}+b^{2}\geq 2 ab\), we choose \(\lambda>\lambda_{\min}\) such that there exists a constant \(0<M<1\) satisfying:\[0<M\leq\frac{\phi_{a(k)}(k)\hat{\phi}_{a(k)}(k)}{\lambda_{a(k)}+\left|\hat{\phi} _{a(k)}(k)\right|^{2}}\leq\frac{b\left|\hat{\phi}_{a(k)}(k)\right|}{\lambda_{a(k )}+\left|\hat{\phi}_{a(k)}(k)\right|^{2}}\leq\frac{b}{2\sqrt{\lambda_{\min}}}=1 \tag{33}\]  
consequently, can have \(o_{1}<\Theta(k)<o_{2}\). Let us consider the event-triggered scheme (19) can have:  
\[\Delta V_{i}(k+1)\leq\ell^{T}(k)\Lambda\ell(k)+\delta-e(k_{r})^{T}\Omega e(k_{ r}) \tag{34}\]  
By utilizing the Schur complement lemma and combining it with Equation (34), have:  
\[\Delta V_{i}(k+1)\leq\ell^{T}(k)\aleph(k)\ell(k)<0 \tag{35}\]  
where \(\aleph=\left( \begin{array}{cc}\Xi_{1}&\Xi_{2}\\ *&\Xi_{3}\end{array} \right)\), \(\Xi_{1}=\left( \begin{array}{cc}(1-o_{1})Q_{i}(1-o_{1})-Q_{i}&(1-o_{1})Q_{i} \rho_{2}\\ *&o_{2}Q_{i}o_{2}-\Omega\end{array} \right)\), \(\Xi_{2}=\text{diag}\Big{\{}\delta^{\frac{1}{2}},\delta^{\frac{1}{2}}\Big{\}}\), \(\Xi_{3}=\text{diag}\{-I,-I\}\). The analysis demonstrates that \(V_{i}(k+1)\) is constrained within a certain range, indicating that the tracking error \(e(k)\) is similarly bounded. In summary, the tracking error remains within certain bounds, ensuring stable system behavior. The output \(y(k)\) is bounded because \(y_{d}(k)\) is constant and the tracking error \(e(k)\) converges.
## 3 Experimental Results and Analysis
### Ablation Experiment
The mAP and PR curve comparison results can be found in Figure 9. Relative to the baseline model, the CCS-YOLOv8 model demonstrates significant improvements across all detection metrics. The CCS-YOLOv8 model attained 84.1% precision, 82.2% recall, 84.4% [EMAIL_ADDRESS], 60.3% [EMAIL_ADDRESS], 53.6% [EMAIL_ADDRESS]:0.95, and 83.1% F1-score, evidencing its capability to accurately and efficiently recognize livestock of different species and ages against complex backgrounds. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **F1-Score (\%)** \\ \hline YOLOv8n & 83 & 74.3 & 78.6 & 53.7 & 48.8 & 78.4 \\ +C2 fCBAM & 85.2 & 74.8 & 79.7 & 55.2 & 49.8 & 79.4 \\ +CARAFE & 82.6 & 76.9 & 79.7 & 55.2 & 50 & 79.2 \\ +small object detection & 84.9 & 77.9 & 82.7 & 59 & 52.7 & 80.4 \\ layer & +C2 fCBAM+CARAFE & 82.8 & 77.2 & 80.2 & 56.2 & 51 & 79.4 \\ +C2 fCBAM+small & 84.9 & 79.3 & 82.4 & 59.8 & 52.8 & 82 \\ +CARAFE+small object detection layer & 83.9 & 80.6 & 82.6 & 59.6 & 52.7 & 81.6 \\ CCS-YOLOv8 & 84.1 & 82.2 & 84.4 & 60.3 & 53.6 & 83.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment results.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 4 An Efficient Conflict Detection Algorithm for Non-Prefix Fields
### Characteristic Analysis and Classification for Non-Prefix Field
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Filter**} & **Source IP** & **Destination IP** & \multirow{2}{*}{**Source Port**} & **Destination Port** & **Protocol** \\  & **Address** & **Address** & & & \\ \hline \(F_{1}\) & 000* & 01* & LO & WC & TCP \\ \(F_{2}\) & 100* & 01* & WC & WC & TCP \\ \(F_{3}\) & 100* & 01* & WC & LO & WC \\ \(F_{4}\) & 10* & 0* & WC & LO & TCP \\ \(F_{5}\) & 00* & 011* & LO & WC & WC \\ \(F_{6}\) & 00* & 011* & HI & WC & UDP \\ \(F_{7}\) & 0* & 0* & WC & WC & TCP \\ \(F_{8}\) & * & 000* & WC & Both & WC \\ \hline \hline \end{tabular}
\end{table} 
Table 6: After classification based on each non-prefix field value in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Value**} & **Description** \\ \hline WC & Wildcard (*) \\ HI & Ephemeral user port range [1024:65535] \\ LO & Well-known system port range [0:1023] \\ AR & An arbitrary range, for example [1000:5000] \\ EM & Exact value, for example [21:21] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Five classes of the source/destination port field. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Value**} & **Description** \\ \hline WC & Wildcard (*) \\ TCP & Transmission control protocol \\ UDP & User datagram protocol \\ Other & The other protocol values, for example ICMP, ESP, etc. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Four classes of the protocol field. Overlap: For port and protocol fields, when one of the two compared fields is a wildcard or the values of both two compared protocol fields are TCP or UDP, we can determine that the comparison results of this field must not be an empty set because the wildcard indicates that the value covers all ranges. If it is compared with any other classes, they will certainly overlap. 3. Requiring exact comparison: In this relationship, we must fetch the complete information of this field for an exact comparison to determine the final comparison results. For example, if the two compared port values are exact values 23 and 80, respectively, which are classified as LO, we know that they are disjoint only after an exact comparison. With the same method, if the two compared port values are an exact value 80 and a range [0:1023], respectively, we know that they are overlapping only after exact comparison. Similarly, if the protocol field belongs to Other, further exact comparison is required to determine the result. Table 7 lists all comparison combinations of two filters \(F\) and \(G\) in a single non-prefix field. When a single non-prefix field comparison is extended to all non-prefix fields comparison, we can list the classes of all comparison combinations of non-prefix fields based on the three conflicting combinations described in Section 4.1 because each field has several types of comparison results. We can then analyze the comparison results to identify those combinations that are known without exact comparison.
## 2 Background Theory
### General Signal Models for Pulse Position Modulation
The received signal from a single user can then be written as:  
\[r(t) = s(t)*h(t)+w(t) \tag{5}\] \[= \sum_{i=-\infty}^{\infty}\sum_{l=0}^{L-1}\alpha_{l}g_{s}(t-iT_{s} -a_{i}\Delta-\tau_{l})+w(t)\] (6) \[= \sum_{i=-\infty}^{\infty}\widehat{s}(t-iT_{s}-a_{i}\Delta-\tau_{ 0})+w(t) \tag{7}\]  
where \(*\) denotes linear convolution and \(\widehat{s}(t)=g_{s}(t)*\widehat{h}(t)\) is the received waveform corresponding to a single symbol waveform, where:  
\[\widehat{h}(t)=\sum_{l=0}^{L-1}\alpha_{l}\delta(t-\tau_{l,0}) \tag{8}\]  
is a pseudo-channel response by eliminating the first arrival of \(h(t)\). The disturbance term, \(w(t)\), is assumed as a wide sense stationary process characterized as the additive noise with zero-mean and complex Gaussian distribution. The symbol, \(\tau_{0}\), is the direct propagation delay between the transmitter and the receiver and can be expressed as \(\tau_{0}=n_{s}T_{s}+n_{f}T_{f}+\epsilon\), based on different time scales at the symbol-level, frame-level and pulse-level [19]. Here, \(n_{s}=\lfloor\tau_{0}/T_{s}\rfloor\) stands for the symbol level offset, \(n_{f}=\lfloor(\tau_{0}-n_{s}T_{s})/T_{f}\rfloor\) is the frame-level offset, where \(n_{f}\in[0,N_{f}-1]\), and the pulse level offset is \(\epsilon\in[0,T_{f})\). \(\lfloor x\rfloor\) represents the largest integer not greater than \(x\). Typically, a symbol-by-symbol sliding correlation filer is applied to generate samples, \(x[i]\), at the symbol rate by [19, 22, 32]:  
\[x[i]=\int_{0}^{T_{s}}r(t+iT_{s})p_{r}(t-\tau_{0})dt \tag{9}\]  
where \(p_{r}(t)\) is the correlation template. Under the ideal case, where the channel knowledge is known and the timing, \(\tau_{0}\), is accurate, we choose \(p_{r}(t)=g_{s}(t)\), which is equivalent to maximum ratio combining, and Equation (9) is the optimal detector for demodulation. Typically, \(\tau_{0}\) is unknown and needs to be estimated. The accuracy of the timing estimate and the choice of the template, \(p_{r}(t)\), would significantly affect the received energy of the sample, \(x[i]\), which, in turn, impacts directly the bit error rate. The timing acquisition problem for guided elastic wave communication is challenging in particular, due to the inherent propagation characteristics of the elastic channel. Elastic waves are dispersive, which implies that there exists severe multipath energy spreading; Elastic waves are also multi-modal, which means strong peaks of the received signals may appear in many locations of the time domain waveform trace. To overcome these problems in timing acquisition and symbol demodulation, we propose the time reversal PPM method in the next sub-section. For simplicity, we choose \(N_{c}=1\) and \(N_{f}=1\). Hence, \(T_{s}=T_{f}\) and the general model of PPM in Equations (1) and (2) is therefore simplified.
# A Hybrid Recommender System for HCI Design Pattern Recommendations
## 5 Experimental Evaluation
### Study Protocol  
In order to verify the previously mentioned hypotheses, participants were asked to carry out two main tasks. The first task was to fill the pre-study questionnaire, while the second was focused on answering the post-test questionnaire. More specifically, the pre-study questionnaire was oriented towards gathering participants' information regarding their knowledge about recommender systems and their level of expertise with HCI design patterns. Concerning the post-test questionnaire, it was mainly aimed at evaluating the quality of user experience with the IDEPAR system and the relevance of the recommended design patterns. This questionnaire was prepared based on the ResQue framework, which is a well-known user-centric evaluation recommender system for assessing user's experience and their acceptance [27]. The ResQue framework provided a wide variety of question statements that were categorized into the following four layers:  
* Perceived system quality: refers to questions that assess the participant's perception of the objective characteristic related to the recommender system.
* Belief: concerns a higher level of the participant's perception of the recommender system.
* Attitude: includes questions that assess the participant's overall feeling regarding the system.
* Behavioral intention: includes questions that assess the recommender system's capability to engage participants to use it regularly.  
Questions that belong to these layers mainly address participants' perceived experiences of the recommender system and accuracy of design patterns. Indeed, these questions answered the two hypotheses (H1 and H2). From the questions provided by the ResQue questionnaire, 13 questions were considered in the post-test questionnaire. In this questionnaire, the five-point Likert scale (ranging from 1 to 5) was considered as the measurement scale used to assess the degree of participants' answers, with 1 signifying \"strongly disagree\" and 5 signifying \"strongly agree\". The selected questions and their categories are presented in Figure 18. The full version of the post-test questionnaire is available in Table A1 in Appendix A.  
Figure 18: Representative Questions from each ResQue layer.
# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 4 Discussion
### Mobility
Overall, subway proximity emerges as a premium attribute in urban real estate, overshadowing the influence of bus stops and somewhat outpacing train station access. The findings from SHAP values reinforce the importance of transportation infrastructure in real estate valuation. Properties with better access to public transportation can command higher prices, and this factor can often outweigh other considerations like property features and amenities. Gleaned from SHAP values, these insights have implications for various real estate stakeholders. For urban planners, the data underscores the value of investing in rapid transportation infrastructure. Real estate investors and developers might focus on properties near subways for higher returns. Agents can leverage these insights in their sales strategies by emphasizing properties with advantageous transportation links. However, while SHAP values provide a nuanced view of feature importance, they represent the model's internal reasoning and are not causal explanations. External factors such as demographic shifts, urban development policies, and cultural trends must be considered when translating these findings into market strategies, emphasizing the need for a broader and more integrated approach to understanding and leveraging these dynamics in real estate valuations.
## 3 Established Connection Phase
### Channel Re-Estimation and Prediction
In this regard, we need to regulate the switch from channel prediction to a new channel estimation. Let us consider \(V_{k}\) as the length of the AR model, related to the \(k\)-th sub-channel. Please note that it is not assured that all the \(V_{k}\) coincide. Moreover, delay spread may change during re-estimation, being smaller or larger with respect to the previously measured one. The delay spread being (considerably) changed indicates that the re-estimation was performed too late. On the other hand, if the changes measured are not sensible, it means that the re-estimation was performed early with respect to the changes. Hence, we introduce the variable \(L_{e}\) to evaluate the measure of delay spread changes, initialized to a high value so that \(L_{e}>>V_{k}\). Then, in order to switch from prediction to estimation, we consider re-estimation as performed after a number of frames \(L_{adapt}\) given by:  
\[L_{adapt}=\min\biggl{\{}\min_{k}\{V_{k}\},L_{e}\biggr{\}} \tag{18}\]  
thus, implicitly meaning that estimation is performed if \(L_{e}\) frames have passed, or if the prediction becomes unreliable for at least one sub-channel (\(\min_{k}\{V_{k}\}\)). In other words, the event occurring first drives the need for a new estimation. It is important to note that, after a re-estimation is performed, if the channel delay spread has remained essentially the same, then \(L_{e}\) is increased by a factor that we set as corresponding to 10% of its previous value. Otherwise, having delay spread as already changed suggests that we needed to re-estimate the channel earlier. Hence, \(L_{e}\) is updated by decreasing it by 10%. Finally, we would highlight that the proposed hybrid mechanism for channel estimation and prediction can be fruitfully exploited to deal with cross-talk mitigation, which represents a challenging issue for MIMO-OFDM systems. However, in such a more complex scenario, the sole channel equalization does not suffice to achieve good performance, since spatial ISI must also be mitigated. As a consequence, the number of channels to consider grows as well as the complexity of frequency and space equalization.
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 4 Results  
### Coupled Coordination Analysis  
By the end of 2023, 47 cases of skywalk systems were evident in North America, spreading across the United States and Canada, and 33 cases were apparent in Asia, spanning China, Japan, Southeast Asia, and India. Relevant data from these regions were input into the coupled coordination model (see Section 3.2. Experimental design for specific steps), and 80 data points that reflect the level of consistency between skywalk systems and the urban spatial environment's development were obtained (Table 3).  
Figure 4: (a) North American case distribution; (b) Asian case distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  
From the calculation results, it is evident that the D-values of Minneapolis, Shanghai Hongqiao, Shanghai Luijiazui, and Osaka Umeda Hub are 0.815, 0.884, 0.873, and 0.857, respectively, and these skywalk systems are well coordinated with the urban spatial environment. The two systems in Toledo, Seattle, Oklahoma City, San Diego, Fargo, Kansas City, and Shanghai's North Bund showed severe dissonance.  
In terms of spatial distribution, the cases of harmonization exhibit a certain distribution pattern: the construction of skywalk systems in Asia is mainly driven by the three-dimensional development of cities, while in North America, they are more reflective of climate adaptation. In Asia, taking China as an example, pedestrian systems with higher consistency with urban development are mainly located in the Pearl River Delta and Yangtze River Delta regions, including economic and financial centers such as Hong Kong, China, Shanghai, Shenzhen, and Guangzhou. These cities, which attract significant populations and investments, are facing the challenges of \"big city disease\" associated with rapid urbanization, such as land scarcity, traffic congestion, and environmental pollution. Consequently, many cities have begun to explore and promote the public transportation-oriented development (TOD) mode. The skywalk system can improve the transportation facilities, increase the space utilization rate of the city, and shorten the travel time in the city. In North America, taking the United States as an example, the consistency between pedestrian systems and urban development is highest in the cold climate region around the Great Lakes and the hot climate region of southern Texas. Cities around the Great Lakes, such as Minneapolis, Chicago, and Detroit, experience harsh winters that pose significant inconvenience to pedestrian travel. Skywalk systems are constructed in these cities to ensure that pedestrians can easily reach various destinations regardless of the weather. In southern Texas, cities like Houston and Dallas require skywalk systems to provide cool and refreshing walking spaces during their hot and humid summers, which can greatly reduce walking comfort.
## 1 Introduction
### Operational Proficiency and Failures Type Factor  
In fields such as road transportation, studies have examined the impact of different types of system failures on monitoring behavior and takeover performance. It has been found that drivers exhibit different behavioral patterns depending on the type of system failure encountered [14; 15; 16]. For example, drivers tend to take control of the vehicle at a faster speed in the case of system-limit failures compared to system-malfunction failures [15]. The impact of system failure types on drivers has gained attention from researchers, as evidenced by existing studies on trust and other factors in different types of automation failures [17; 18; 19].  
In the aviation domain, researchers typically study pilot behavior when encountering system-limit failures or system-malfunction failures [5; 20; 21; 22; 23; 24]. Previous studies often focused on investigating a single type of system failure, and predominantly system-limit failures. There has been limited direct comparison research that specifically investigates the differences in monitoring behavior between different types of failures.  
Extensive research has demonstrated the significant impact of pilot experience and operational proficiency on behavioral performance. Pilots are commonly categorized as novices or experts, and studies have investigated the differences between these two groups in terms of their control behavior, eye-tracking characteristics, and more. The results consistently show that experts outperform novices in various aspects. For example, experts demonstrate more effective eye movement patterns compared to novices, higher perceptual efficiency (more fixations with shorter durations), balanced visual attention allocation, more complex and detailed visual scanning patterns, and faster and more accurate decision-making [23; 24; 25]. The majority of existing studies have only considered a two-level categorization of experience or operational proficiency (novice and expert), while in reality, pilot experience or operational proficiency encompasses levels beyond two categories.  
Previous research has obtained expert detection abilities by comparing the behaviors of novices and experts, which is often used as guidance for training novices. While this approach may be applicable in most cases, it overlooks an important issue: even experienced expert pilots may not have an advantage when facing events such as system-malfunction failures that even designers cannot anticipate. Multiple accident cases have highlighted the limitations of relying solely on experience in such situations, where experienced captains have made erroneous actions [1; 26; 27]. Therefore, it is necessary to further explore the effects of failure types and operational experience on monitoring behaviors to deepen our understanding of behavioral performance in unexpected events and find solutions.
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 4 Wideband LNA Design Strategy Using Modified SSNM Conditions
### ISMN1: OM1 \(\approx\) IM2  
To synthesise the first inter-stage matching network (ISMN1), we first ensure that the output mismatch of stage 1 equals the input mismatch of Stage 2. Then ISMN1 is designed in such a way that input reflection coefficients (\(\Gamma_{\text{L11}}\), \(\Gamma_{\text{L12}}\), \(\Gamma_{\text{L13}}\)) at \(f_{1}\),\(f_{2}\), and \(f_{3}\) at port1 of ISMN1 are equal to or in the vicinity of \(\Gamma_{\text{L1 ref}}\) (derived from the output of the first stage) as shown in Figure 8. Similarly, the output port of ISMN1 (\(\Gamma_{\text{S21}}\), \(\Gamma_{\text{S22}}\), \(\Gamma_{\text{S23}}\)) is simultaneously matched at these three frequencies to \(\Gamma_{\text{S2 ref}}\), specified at the input of the second stage. This ensures consistent and optimised mismatch transfer across the entire targeted frequency band. To further simplify ISMN1, we can match \(\Gamma_{\text{S23}}\) of ISMN1 to \(\Gamma_{\text{S2 ref}}\) value, as we obtain maximum NF at high frequencies and doing this would not increase the NF beyond the overall limit of the noise figure.