esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results of Qualitative and Quantitative (Morphological) Analysis
### Blocks
#### 4.3.2 Size of Blocks (Block Area)  
The examination of block sizes, specifically their area and perimeter, aims to identify transformations of the geometric properties. By analyzing these measurements, we can logically identify variations and consistencies. This analysis helps us understand how features adapt or respond to changes in block size, as transformations in elements do not occur uniformly. This non-uniformity represents variable behavior in the geometric properties of the blocks within an intermediate level of specificity detailed in the built morphological framework.  
The distribution of the block area (Table 6) ranges from 41.24 m\({}^{2}\) to 55,912.13 m\({}^{2}\) in 1857. The mean area is significantly higher than the median, and most blocks have smaller areas, resulting in a right-skewed distribution. A few significantly larger blocks influence the mean and the standard deviation, which are considered outliers compared to the rest of the distribution. The high standard deviation also confirms the large variability in the block areas. A significant number of blocks are below 2000 m\({}^{2}\), indicating that smaller blocks are common. The spatial distribution of these smaller blocks corresponds to marketplace concentration. The presence of some exceptionally larger blocks, mainly positioned around the Khan aggregate, pulls the mean values upwards.  
Compared to 1912, the distribution is quite similar; there is substantial overlap in the block areas between the two years. Most block areas are clustered towards the lower end, but 1912 shows greater variability (a broader distribution) with more extreme values than 1857. This is because some small blocks in 1857 combined to create larger blocks in 1912. The distribution remains right-skewed over time, while the frequency of smaller blocks has decreased, and the presence of larger blocks has increased. This is evident in the rising standard deviation, indicating the highest variability among the three periods.  
The block pattern size established during the initial formation was exposed to significant constraints, which were replaced within the patterns, resulting in transformations in urban tissue over time.
# Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status
## 2 Materials and Methods
### Classification Experiments  
Machine learning-based classification was used to predict a class (FEV\({}_{1}\)) given a set of data points (speech features). FEV\({}_{1}\) was split into intervals specified by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) according to the severity degree [29], as shown in Table 1.  
The classification experiments were carried out in two different scenarios:  
Scenario 1: To discern between non-COPD/COPD individuals (only in rest condition since non-COPD subjects are not taking medication).  
Scenario 2: To discern between different COPD degrees in patients. Due to the severity degree of patients with mMRC = 4 and FEV\({}_{1}\) < 30%, the most severe patients were excluded due to their low representation. Only subjects from GOLD 1 and 2 classes were analysed. Here, two different classification experiments were tested: (a) using only in-rest recording conditions; (b) using all recording conditions, in two different subcases: 1) experiments using only speech information, and 2) experiments providing additional information on the recording condition and subject medication.  
The experiments were performed with the Weka workbench [30]. Among several classification algorithms included in Weka, Random Forest was chosen for being the best performative one in terms of accuracy. The whole set of instances were used in a 10-fold cross-validation to get the maximum number of instances in the classification task [19], ensuring that subjects used in the training folds were not present in the testing fold, and using the following hyperparameters: number of iterations (-I) 100, batch size percentage (-P) 100, seed (-S) 1, and a maximum path length of 0.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Degree** & **Range** \\ \hline
1 & mild & FEV\({}_{1}\)\(\geq\) 80\% \\
2 & moderate & 50\% \(\leq\) FEV\({}_{1}\)\(<\) 80\% \\
3 & severe & 30\% \(\leq\) FEV\({}_{1}\)\(<\) 50\% \\
4 & very severe & FEV\({}_{1}\)\(<\) 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intervals of FEV\({}_{1}\) values according to their severity degree. GOLD classification.
## 5 Experimental Results and Analysis
### Experimental Results
In the period from 20:00 to 22:00, a decrease in the personnel occupancy rate and equipment utilization rate from 16:00 to 20:00 has a greater impact than an increase in lighting switch time, so the energy consumption of air conditioning and refrigeration still maintains a downward trend from 16:00 to 20:00, and from 20:00 to 21:00, the influence brought by the accumulated lighting switch time rise before began to appear, so that the cooling load of air conditioning increased, and 21:00 to 22:00 switching lighting time dropped sharply, resulting in the cooling load of air conditioning. The cooling load of the unit area of the inpatient building in summer is from 16:00 to 20:00, and the outdoor temperature continues to decrease, leading to the cooling load of the air conditioning. From 22:00 to 23:00, as the lighting switch time had been rising slowly before, the cumulative impact gradually appeared, resulting in a small rebound in the air conditioning cooling load. Figures 18 and 19 show the cooling load of different types of hospital buildings in summer. The top four outpatient buildings in summer cooling load in the Beijing area are archives warehouse, pharmacy, B-mode ultrasonography, and infusion, with loads of 357.71 kW\(\cdot\)h/m\({}^{2}\), 142.12 kW\(\cdot\)h/m\({}^{2}\), 138.11 kW\(\cdot\)h/m\({}^{2}\), and 137.15 kW\(\cdot\)h/m\({}^{2}\), respectively. The top four outpatient buildings in summer cooling load in the Shanghai area are archives warehouse, infusion, waiting room, and conference room, with loads of 496.86 kW\(\cdot\)h/m\({}^{2}\), 320.68 kW\(\cdot\)h/m\({}^{2}\), 308.18 kW\(\cdot\)h/m\({}^{2}\), and 160.27 kW\(\cdot\)h/m\({}^{2}\), respectively. In summer, the cooling load of the ward and nurse station is the largest for the air conditioning load of inpatient buildings. The load in the Beijing area is 998.44 kW\(\cdot\)h/m\({}^{2}\) and 117.39 kW\(\cdot\)h/m\({}^{2}\), respectively. The loads in Shanghai are 1905.03 kW\(\cdot\)h/m\({}^{2}\) and 180.32 kW\(\cdot\)h/m\({}^{2}\), respectively. The air conditioning cooling load of hospital buildings in the Shanghai area is generally higher than that in the Beijing area in summer. Figure 15: Summer design daily cooling load. Figure 16: The unit area of outpatient buildings in Beijing (**a**) and Shanghai (**b**) is subjected to hourly cooling load. Figure 17: The unit area of inpatient buildings in Beijing (**a**) and Shanghai (**b**) is subjected to hourly cooling load. Figure 18: Statistics of summer cooling load in Beijing (**a**) and Shanghai (**b**) outpatient buildings.
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 3 Administration of IoT Deployments
### Reasoning Capabilities & Conflict Resolution  
The artificial intelligence (AI) behavior of each agent is developed in the rule engine Jess [70; 71]. For knowledge representation and reasoning, the Jess-EC [72] is used. The latter is an Event Calculus (EC) [73] implementation in Jess, offering the required semantics modeling. SPD-Safe's software layers are illustrated in Figure 1.  
Each agents' AI procedure implements automated temporal, casual, and epistemic reasoning with real-time events, action preconditions, rule priorities, indirect effects, context-sensitive side-effects, as well as the common law of inertia. Moreover, the reasoning capabilities can cope with the requirements of dynamic and partially known or uncertain domains.  
However, as agents exchange information, contradictory reasoning results may occur due to the local viewpoint of each entity and the lack of global knowledge. Thus, for resolving conflicts, SPD-Safe introduces the epistemic mechanism of share theories [63]. The participating entities send the involved theory rules to a mediator agent, along with the recently sensed local events. The mediator combines these elements and performs a reasoning operation that determines the final outcome and the state of the conflicting assets.  
Nevertheless, if an agent utilizes protected data that must be maintained locally and not distributed (e.g., confidential information regarding user policies or system settings), it will not be able to contribute in the share theory with its full knowledge. For this occasion, an alternative relational grading mechanism, called certainty degree [63], resolves the affair quickly and efficiently. The mechanism utilizes subjective criteria as well as the agents' roles and hierarchy, marshaling the problem without constructing the related share theory and retaining the system's coherency. Thus, the certainty degree is applied in affairs where reasoning with locally protected data is involved, otherwise a share theory is constructed.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 2 Related Work
### DRX in LTE
#### 2.1.1 Active State  
A UE sends or receives the data packets during DRX active state. This state does not allow UE to save the energy because UE has to receive the data packets during this state. Moreover, a UE has to turn on the radio circuitry throughout the active state. The active state of DRX can be controlled by two parameters.  
* Inactivity timer
* Active time  
All the data packets are transmitted/received during the active time. The inactivity timer is a countdown timer in DRX active state. This timer re-starts every time a new data packet arrives at eNB and then eNB serves the received packets to UE. In case of no new packet arrival, the inactivity timer gets expired and the UE switches to sleep state for a certain time period.
## 6 Research Areas
### Motion Control System  
Inspections of building facades, ducts, pipes, and shafts require a different type of locomotion and navigation. [PERSON] et al. [101] studied the use of cable-suspended robots for the inspection of high-rise towers or storage tanks from the outside. [PERSON] [228] developed a remotely-operated submersible vehicle for underwater inspection. [PERSON] et al. [9] developed a worm-like microbot for the inspection of small-diameter pipes and used infrared sensors for navigation and defect detection. ALICIA3 [229] is a wall-climbing robot that uses pressure-controlled suction cups to stay affixed to the walls and is used for the inspection of building facades. Researchers have installed multi-joint arms and grippers, telescopic supports, and rotating heads [29; 35] to enable the robots to reach hard-to-reach places. These works improved the overall reach of the robots.  
Apart from the basic wheel or rotor-based locomotion, hybrid locomotive systems further improve the maneuverability of the robot in cluttered environments, such as construction sites. A drone equipped with wheels and a propeller can inspect a building's exterior, move in small spaces, and also climb stairs [20]. A legged robot with magnetic pads under its feet can walk on the ground and climb on steel columns and pipes for closer inspection [35]. A climbing-flying robot can climb on walls and poles for better quality inspection and can fly to pass obstacles and avoid falling [36; 37].  
Aerial robots deal with a high degree of freedom and are extremely prone to environmental disturbances. Images captured from an aerial robot or UAV are often characterized as \"noisy\" [230]. To perform an effective inspection, the robot (or drone) needs to keep adjusting to the external environment. [PERSON] et al. [231] equipped a UAV with Lidar to measure distance and orientation for maintaining a stable altitude and position with respect to the inspection target, which was important for the ultrasonic gauge sensor used to measure the thickness of metallic sheets. [PERSON] et al. [232] developed a fuzzy logic-based control system to control the camera and the thrust of the UAV to get more stable images.  
In wall-climbing robots, the pressure in the suction cups is controlled using pressure sensors installed inside the robotic systems. This adjusts the suction strength for different surface conditions [233]. [PERSON] et al. [101] developed a rope-climbing robot for climbing and inspecting walls of storage tanks and high-rise buildings. They designed the control system to counteract the oscillations of the rope caused by the climbing motion of the robot.
# HAL Training in Spinal Cord Injured Patients: A Narrative Review of 10 Years Experience
## 3 Results  
In their paper from 2005 [PERSON] et al. [7] proposed for the first time an algorithm to estimate human intentions related with walking in order to comfortably and safely support a paraplegia patient's walk with the HAL robot suit. This work presents mainly technical data of algorithm development and therefore was not considered further in this review. Nevertheless, it should be mentioned here, as it has set the stage for further application studies.  
A total of 1940 studies were found using the search criteria described above. After excluding studies that had a completely different topic but were still listed, either used exoskeletons other than the HAL system, or used the HAL device for other conditions, and after removing all duplicates, 27 published articles were identified. Studies only reporting technology data (3) and reviews on general HAL application (3) were excluded. Twenty-one relevant articles, as deemed so by the authors, were then studied in full text (Figure 2). Overall, the studies discuss results from 344 participants, with some of the same subjects included in different studies.  
Of these 21 articles, 13 are reports about clinical trials using HAL [8; 9; 10] and eight are single-case [2; 22; 23; 24; 25] or two-case reports [21; 26; 27]. Ten articles originate from Germany [9; 10; 11; 12; 13; 16; 17; 20; 21; 22], ten from Japan [2; 8; 14; 15; 18; 19; 23; 24; 26; 27], and one from the USA [25]. Years of publication of these articles range from 2013 to 2021. The underlying experimental series started in part as early as 2012, which means that we present a survey of the last ten years in this review. To the best of our knowledge this is the first ever review summarizing experiences with HAL in people living with SCI.  
Figure 2: Presentation of the results of the literature search.  
Study protocols differed in terms of training type (treadmill or walker), training time (20 to 40 min of net HAL walking training), time period over which training was performed (2 to 52 weeks), number of training sessions per week (2 to 5), resulting total training sessions (8 to approximately 180), and assessments performed. The patients included in these studies represent the full range of spinal cord injuries. There are traumatic and non-traumatic injuries, chronic and acute patients, complete and incomplete paralysis, lesion levels from C2 to L4, American spinal injury association impairment scale (AIS) A to D, and patients with and without spastic motor behavior. The age of the participants varies from 16 to 74 years. Some studies conducted by the same research groups (Ruhr-University Bochum Group [9; 10; 11; 12; 13; 16; 17; 20; 21; 22], Keio University School of Medicine Group [15; 18; 19], University of Tsukuba and University of Tsukuba Hospital Group [8; 14; 23; 24; 26; 27]) have partial overlap in their study populations.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 4 Learning to Optimize An RIS
### Reinforcement Learning  
Another major framework in AI is deep reinforcement learning (DRL). In this framework, the agent (i.e., decision maker) gradually derives its best action through trial-and-error interactions with the environment over time. There are a few basic elements characterizing the DRL process: the state, the action, the reward, and the state action value function.  
1. **State**: a set, denoted by \(S\), characterizing the environment. The state \(s^{(t)}\in S\) denotes the environment at the time step \(t\).
2. **Action**: a set of allowable actions, denoted by \(A\). Once the agent takes an action \(a^{(t)}\in A\) at time instant \(t\) (determined by the state action value function), the state of the environment will transit from the current state \(s^{(t)}\) to the next state \(s^{(t+1)}\).
3. **Reward**: the performance metric of a particular action, denoted by \(r^{(t)}\) at time instant \(t\).
4. **State action value function**\((\mathbf{Q-function})\): while the reward represents the immediate return from action \(a\) at state \(s\), the state action value function indicates cumulative rewards the agent may get from taking action \(a\) in the state \(s\), which is denoted by \(Q(s,a)\).  
Depending on the types of action spaces, two DRL methods are available: the deep Q-network (DQN) algorithm, which is designed for discrete action spaces, and the deep deterministic policy gradient (DDPG), which is designed for continuous action spaces. Hence, DQN fits the discrete phase shift model, while the DDPG is employed for continuous phase shift variables.  
In this subsection, we present a mapping of DQN in the context of resource allocation problems in RIS-empowered wireless networks. In this model, the central controller, which controls the RIS, acts as the agent. At each time slot \(t\), the agent observes a state, \(s^{(t)}\in S\), which consists of all channel state information from the wireless system. According to the current state and the \(Q\)-function, the agent takes an action, \(a^{(t)}=\operatorname*{argmax}_{a}Q\Big{(}s^{(t)},a\Big{)}\in A\), where \(A\) consists of discrete phase shifts that each reflecting element is allowed to take. After performing an action \(a^{(t)}\), the agent obtains a reward \(r^{(t)}\) determined from the negative objective function of (5) and observes the next state \(s^{(t+1)}\) generated by the wireless system. At each time slot, \(Q\Big{(}s^{(t)},a^{(t)}\Big{)}\) is updated by  
\[Q\Big{(}s^{(t)},a^{(t)}\Big{)}=Q\Big{(}s^{(t)},a^{(t)}\Big{)}+\alpha\Big{(}r^ {(t)}+\gamma\operatorname*{argmax}_{a}Q\Big{(}s^{(t+1)},a\Big{)}-Q\Big{(}s^{( t)},a^{(t)}\Big{)}\Big{)}, \tag{11}\]where \(\alpha\) is the learning rate and \(\gamma\) is the discount factor designed for DQN. The aim of the DQN model is to enable the agent to perform actions to maximize the long-term sum reward.
# Effect of Lentinan on Lipid Oxidation and Quality Change in Goose Meathalls during Cold Storage
## 3 Results and Discussion
### Tbars  
TBARS is often used to evaluate the degree of lipid oxidation and rancidity of meat products during cold and frozen storage [34]. The LNT content (\(p<0.001\)), storage time (\(p<0.001\)) and LNT content \(\times\) storage time (\(p<0.001\)) significantly influenced the TBARS in the tested goose meatball (Table S1). The effects of LNT content and storage time on TBARS of goose meatballs during cold storage (4 \({}^{\circ}\)C) are shown in Figure 1. In general, the TBARS of each sample showed an upward trend with the increase in the storage time, and the TBARS of each treatment group decreased gradually with the increase in the amount of LNT in goose meatballs. The TBARS of goose meatballs with LNT addition was always lower than that of the LNT-free group no matter at which storage time. The TBARS of the LNT sample group decreased by 19-30% compared to the LNT-free group and reached to the maximum reduction (30%) at the 4% LNT group stored for 3 days, indicating that the addition of LNT could inhibit the fat oxidation of goose meatballs. In the study regarding the effect of polysaccharides from _Porphyra vezoensis_ (8 g/L) on Pacific white shrimp during storage, a 28.7% reduction in TBA was observed [35]. There was no significant difference in TBARS content between 2% LNT group and 4% LNT group stored for 7 and 12 days. This result indicates that the addition of excessive LNT does not have a dose effect on the oxidation of the sample during the later stage of storage. [PERSON] et al. [36] reported that when the TBA exceeded 1 mg/kg, the sensory properties of the sample were unacceptable and the sample showed a rotten smell. The TBARS of control group, 0.5% LNT group, 1% LNT group, 2% LNT group and 4% LNT group were 2.66, 1.99, 1.65, 0.97, and 0.86 mg/kg during the 12 days of storage, respectively. It illustrates that in the later stage of storage, the addition of LNT can effectively inhibit the fat oxidation of goose meatballs and help to prevent the formation of off-flavor. This may because LNT is able to slow down the chain reactions initiated by free radicals [37], and inhibit catalytic oxidation as well as the reactions with lipid peroxides [38]. Meanwhile, [PERSON] & [PERSON] [39] found that adding 3% chicory polysaccharide significantly decreased the oxidation of lipids in silver carp. [PERSON] et al. [40] reported that the addition of green algae polysaccharide in beef sausage lowered the content of TBARS.  
Figure 1: Effects of LNT content and storage time on TBARS of goose meatballs during cold storage (4 \({}^{\circ}\)C). Capital letters indicate significant differences between different storage times for the same sample (\(p\leq 0.05\)). Lowercase letters indicate significant differences between different contents of LNT sample during the same storage time (\(p\leq 0.05\)).
## 2 System Description and Analysis
### ACV Model
#### 2.2.2 Aerodynamics  
We assume that the point of action of the aerodynamics forces on the ship is \((x_{a},y_{a},z_{a})\). Aerodynamics forces and moments vector variable is:  
\[\mathbf{\tau}_{a}=[X_{a},Y_{a},K_{a},N_{a}] \tag{10}\]  
\(X_{a},Y_{a},K_{a},andN_{a}\) denote the projection of aerodynamics in four DOF. They are:  
\[X_{a} =-\frac{1}{2}\rho_{a}V_{a}^{2}C_{xa}S_{a}\] \[Y_{a} =\frac{1}{2}\rho_{a}V_{a}^{2}C_{ya}S_{a}\] \[K_{a} =\frac{1}{2}\rho_{a}V_{a}^{2}C_{kh}S_{a}L_{a}-Y_{a}z_{a}\] \[N_{a} =\frac{1}{2}\rho_{a}V_{a}^{2}C_{na}S_{a}L_{a}+Y_{a}x_{a}-X_{a}y_{a}\]  
where \(V_{a}=\sqrt{(u+V_{u}cos(\beta_{w}-\psi))^{2}+(v+V_{u}sin(\beta_{w}-\psi))^{2}}\) is the relative wind speed, and \(\beta_{a}=tan^{-1}[\frac{v+V_{u}sin(\beta_{w}-\psi)}{u+V_{u}cos(\beta_{w}- \psi)}]\) is relative wind angle, where \(V_{u}and\beta_{w}\) are the wind speed and wind angle in the Earth coordinate frame, respectively. \(C_{xa},C_{ya},C_{ka},C_{na}\) are aerodynamic coefficients obtained by wind tunnel experiments. \(S_{a}\) is the area of the ACV's horizontal projection.  
Then we can get aerodynamics vector function:  
\[\mathbf{\tau}_{a}=\mathbf{D_{a}(\mathbf{v})\
u+D_{na}(V_{w})\
u+W_{a}}, \tag{11}\]  
where \(\mathbf{D_{a}}\) denotes the aerodynamic damping, which relates to the ship speed \(\mathbf{\
u}\), and \(\mathbf{D_{na}(V_{w})}\) is damping formed by the coupling of the speed \(\mathbf{\
u}\) and wind speed \(\mathbf{V_{w}}\). \(\mathbf{W_{a}}\) is the wind disturbance vector. They are given as follows:  
\[\mathbf{D_{a}(\mathbf{\
u})}=\left[\begin{array}{cccc}k_{axu}u&k_{axv} &0&0\\ k_{ayu}u&k_{ayv}v&0&0\\ k_{apu}u&k_{apv}v&0&0\\ k_{amu}u&k_{amv}v&0&0\end{array}\right]\]  
\[\mathbf{D_{ca}(V_{w})}=\left[\begin{array}{cccc}k_{axu}&k_{axv}&0&0\\ k_{ayu}&k_{ayv}&0&0\\ k_{apu}&k_{apv}&0&0\\ k_{amu}&k_{amv}&0&0\end{array}\right]\left[\begin{array}{cccc}u_{w}^{b}\\ v_{w}^{b}\\ 0\\ 0\end{array}\right]\]  
\[\mathbf{W_{a}}=[k_{axu},k_{ayu},k_{aku},k_{am}]^{T}\mathbf{V}_{w}^{2}\]  
where  
\[k_{axu}=k_{axv} =-\rho_{a}C_{xa}S_{a}\] \[k_{ayu}=k_{ayv} =-\rho_{a}C_{ya}S_{a}\] \[k_{apu}=k_{apv} =\rho_{a}C_{mxa}S_{a}L_{a}-\rho_{a}C_{ya}S_{a}z_{a}\] \[k_{amu}=k_{amv} =\rho_{a}C_{mxa}S_{a}L_{a}+\rho_{a}C_{ya}S_{a}x_{a}-\rho_{a}C_{xa} S_{a}y_{a}\]